Back to Multiple platform build/check report for BioC 3.9 |
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This page was generated on 2019-10-16 11:55:12 -0400 (Wed, 16 Oct 2019).
Package 34/1741 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||
affyPLM 1.60.0 Ben Bolstad
| malbec2 | Linux (Ubuntu 18.04.2 LTS) / x86_64 | OK | OK | [ OK ] | |||||||
tokay2 | Windows Server 2012 R2 Standard / x64 | OK | OK | OK | OK | |||||||
celaya2 | OS X 10.11.6 El Capitan / x86_64 | OK | OK | OK | OK |
Package: affyPLM |
Version: 1.60.0 |
Command: /home/biocbuild/bbs-3.9-bioc/R/bin/R CMD check --install=check:affyPLM.install-out.txt --library=/home/biocbuild/bbs-3.9-bioc/R/library --no-vignettes --timings affyPLM_1.60.0.tar.gz |
StartedAt: 2019-10-15 23:40:40 -0400 (Tue, 15 Oct 2019) |
EndedAt: 2019-10-15 23:44:31 -0400 (Tue, 15 Oct 2019) |
EllapsedTime: 231.6 seconds |
RetCode: 0 |
Status: OK |
CheckDir: affyPLM.Rcheck |
Warnings: 0 |
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/bbs-3.9-bioc/R/bin/R CMD check --install=check:affyPLM.install-out.txt --library=/home/biocbuild/bbs-3.9-bioc/R/library --no-vignettes --timings affyPLM_1.60.0.tar.gz ### ############################################################################## ############################################################################## * using log directory ‘/home/biocbuild/bbs-3.9-bioc/meat/affyPLM.Rcheck’ * using R version 3.6.1 (2019-07-05) * using platform: x86_64-pc-linux-gnu (64-bit) * using session charset: UTF-8 * using option ‘--no-vignettes’ * checking for file ‘affyPLM/DESCRIPTION’ ... OK * this is package ‘affyPLM’ version ‘1.60.0’ * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking for sufficient/correct file permissions ... OK * checking whether package ‘affyPLM’ can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking ‘build’ directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking R files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the package can be unloaded cleanly ... OK * checking whether the namespace can be loaded with stated dependencies ... OK * checking whether the namespace can be unloaded cleanly ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking line endings in C/C++/Fortran sources/headers ... OK * checking line endings in Makefiles ... OK * checking compilation flags in Makevars ... OK * checking for GNU extensions in Makefiles ... OK * checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK * checking compiled code ... NOTE Note: information on .o files is not available * checking files in ‘vignettes’ ... OK * checking examples ... OK Examples with CPU or elapsed time > 5s user system elapsed threestep 21.288 0.020 21.309 fitPLM 12.275 0.196 12.478 PLMset2exprSet 6.435 0.140 6.576 * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... Running ‘C_code_tests.R’ Running ‘PLM_tests.R’ Running ‘preprocess_tests.R’ Running ‘threestepPLM_tests.R’ OK * checking for unstated dependencies in vignettes ... OK * checking package vignettes in ‘inst/doc’ ... OK * checking running R code from vignettes ... SKIPPED * checking re-building of vignette outputs ... SKIPPED * checking PDF version of manual ... OK * DONE Status: 1 NOTE See ‘/home/biocbuild/bbs-3.9-bioc/meat/affyPLM.Rcheck/00check.log’ for details.
affyPLM.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/bbs-3.9-bioc/R/bin/R CMD INSTALL affyPLM ### ############################################################################## ############################################################################## * installing to library ‘/home/biocbuild/bbs-3.9-bioc/R/library’ * installing *source* package ‘affyPLM’ ... ** using staged installation ** libs gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c LESN.c -o LESN.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c PLM_avg_log.c -o PLM_avg_log.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c PLM_biweight.c -o PLM_biweight.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c PLM_log_avg.c -o PLM_log_avg.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c PLM_medianPM.c -o PLM_medianPM.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c PLM_median_logPM.c -o PLM_median_logPM.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c PLM_medianpolish.c -o PLM_medianpolish.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c PLM_modelmatrix.c -o PLM_modelmatrix.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c SCAB.c -o SCAB.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c chipbackground.c -o chipbackground.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c common_types.c -o common_types.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c do_PLMrlm.c -o do_PLMrlm.o do_PLMrlm.c: In function ‘do_PLM_rlm’: do_PLMrlm.c:620:7: warning: variable ‘first_ind’ set but not used [-Wunused-but-set-variable] int first_ind; ^~~~~~~~~ gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c do_PLMrma.c -o do_PLMrma.o do_PLMrma.c: In function ‘do_PLMrma’: do_PLMrma.c:209:7: warning: variable ‘first_ind’ set but not used [-Wunused-but-set-variable] int first_ind; ^~~~~~~~~ gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c do_PLMthreestep.c -o do_PLMthreestep.o do_PLMthreestep.c: In function ‘do_PLMthreestep’: do_PLMthreestep.c:118:7: warning: variable ‘first_ind’ set but not used [-Wunused-but-set-variable] int first_ind; ^~~~~~~~~ gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c idealmismatch.c -o idealmismatch.o idealmismatch.c: In function ‘IdealMM_correct_single’: idealmismatch.c:71:7: warning: variable ‘first_ind’ set but not used [-Wunused-but-set-variable] int first_ind; ^~~~~~~~~ idealmismatch.c: In function ‘SpecificBiweightCorrect_single’: idealmismatch.c:183:7: warning: variable ‘first_ind’ set but not used [-Wunused-but-set-variable] int first_ind; ^~~~~~~~~ gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c lm_threestep.c -o lm_threestep.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c matrix_functions.c -o matrix_functions.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c nthLargestPM.c -o nthLargestPM.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c preprocess.c -o preprocess.o preprocess.c: In function ‘pp_background’: preprocess.c:158:7: warning: variable ‘which_lesn’ set but not used [-Wunused-but-set-variable] int which_lesn; ^~~~~~~~~~ preprocess.c: In function ‘pp_bothstages’: preprocess.c:677:12: warning: variable ‘cols’ set but not used [-Wunused-but-set-variable] int rows,cols; ^~~~ preprocess.c:677:7: warning: variable ‘rows’ set but not used [-Wunused-but-set-variable] int rows,cols; ^~~~ gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c psi_fns.c -o psi_fns.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c qnorm_probeset.c -o qnorm_probeset.o qnorm_probeset.c: In function ‘qnorm_probeset_c’: qnorm_probeset.c:110:7: warning: variable ‘first_ind’ set but not used [-Wunused-but-set-variable] int first_ind; ^~~~~~~~~ gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c read_rmaexpress.c -o read_rmaexpress.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c rlm_PLM.c -o rlm_PLM.o rlm_PLM.c: In function ‘R_rlm_PLMset_c’: rlm_PLM.c:1481:12: warning: variable ‘cols’ set but not used [-Wunused-but-set-variable] int rows,cols; ^~~~ rlm_PLM.c:1481:7: warning: variable ‘rows’ set but not used [-Wunused-but-set-variable] int rows,cols; ^~~~ gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c rlm_threestep.c -o rlm_threestep.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c rmaPLM_pseudo.c -o rmaPLM_pseudo.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c rma_PLM.c -o rma_PLM.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c rma_common.c -o rma_common.o rma_common.c: In function ‘median’: rma_common.c:60:7: warning: unused variable ‘i’ [-Wunused-variable] int i; ^ gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c scaling.c -o scaling.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c threestep.c -o threestep.o threestep.c: In function ‘threestep_summary’: threestep.c:82:15: warning: variable ‘MM’ set but not used [-Wunused-but-set-variable] double *PM,*MM; ^~ threestep.c: In function ‘R_threestep_c’: threestep.c:193:12: warning: variable ‘cols’ set but not used [-Wunused-but-set-variable] int rows,cols; ^~~~ threestep.c:193:7: warning: variable ‘rows’ set but not used [-Wunused-but-set-variable] int rows,cols; ^~~~ gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c threestep_PLM.c -o threestep_PLM.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c threestep_common.c -o threestep_common.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c threestep_summary.c -o threestep_summary.o threestep_summary.c: In function ‘do_3summary’: threestep_summary.c:73:7: warning: variable ‘first_ind’ set but not used [-Wunused-but-set-variable] int first_ind; ^~~~~~~~~ gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c threestep_summary_methods.c -o threestep_summary_methods.o gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I"/home/biocbuild/bbs-3.9-bioc/R/library/preprocessCore/include" -I/usr/local/include -fpic -g -O2 -Wall -c transfns.c -o transfns.o gcc -shared -L/home/biocbuild/bbs-3.9-bioc/R/lib -L/usr/local/lib -o affyPLM.so LESN.o PLM_avg_log.o PLM_biweight.o PLM_log_avg.o PLM_medianPM.o PLM_median_logPM.o PLM_medianpolish.o PLM_modelmatrix.o SCAB.o chipbackground.o common_types.o do_PLMrlm.o do_PLMrma.o do_PLMthreestep.o idealmismatch.o lm_threestep.o matrix_functions.o nthLargestPM.o preprocess.o psi_fns.o qnorm_probeset.o read_rmaexpress.o rlm_PLM.o rlm_threestep.o rmaPLM_pseudo.o rma_PLM.o rma_common.o scaling.o threestep.o threestep_PLM.o threestep_common.o threestep_summary.o threestep_summary_methods.o transfns.o -L/home/biocbuild/bbs-3.9-bioc/R/lib -lRlapack -L/home/biocbuild/bbs-3.9-bioc/R/lib -lRblas -lgfortran -lm -lquadmath -lz -L/home/biocbuild/bbs-3.9-bioc/R/lib -lR installing to /home/biocbuild/bbs-3.9-bioc/R/library/00LOCK-affyPLM/00new/affyPLM/libs ** R ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** checking absolute paths in shared objects and dynamic libraries ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (affyPLM)
affyPLM.Rcheck/tests/C_code_tests.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes" Copyright (C) 2019 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > #### > #### This code is messy, possibly incomplete and only for > #### the use of developers. > #### > #### > > test.c.code <- FALSE > test.PLM.modelmatrix <- FALSE > test.rlm <- FALSE > > if (test.c.code){ + + library(affyPLM) + narrays <- 10 + nprobes <- 11 + nprobetypes <- 2 + ncols <- 10 + + MMs <- rnorm(narrays*nprobes*nprobetypes) + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + + #test making intercept column + matrix(.C("R_PLM_matrix_intercept",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),0)[[1]],ncol=10) + + #test making an MM covariate column + matrix(.C("R_PLM_matrix_MM",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(1),as.double(MMs))[[1]],ncol=10) + + # sample effect aka chip effect, aka expression values + matrix(.C("R_PLM_matrix_sample_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(0))[[1]],ncol=10) + matrix(.C("R_PLM_matrix_sample_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(1))[[1]],ncol=10) + matrix(.C("R_PLM_matrix_sample_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(-1))[[1]],ncol=10) + + + + #probe-type parameter overall + matrix(.C("R_PLM_matrix_probe_type_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(0),as.integer(0),integer(narrays),as.integer(1))[[1]],ncol=10) + + #probe-type parameter within sample + matrix(.C("R_PLM_matrix_probe_type_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(1),as.integer(1),integer(narrays),as.integer(1))[[1]],ncol=10) + matrix(.C("R_PLM_matrix_probe_type_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(-1),as.integer(1),integer(narrays),as.integer(1))[[1]],ncol=10) + ncols <- 20 + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + matrix(.C("R_PLM_matrix_probe_type_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(0),as.integer(1),integer(narrays),as.integer(0))[[1]],ncol=20) + + + #probe-type-parameter within a chip-level factor (eg treatment, or genotype variable) + trt.cov <- rep(0:1,5) + ncols <- 10 + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + matrix(.C("R_PLM_matrix_probe_type_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(0),as.integer(2),as.integer(trt.cov),as.integer(1))[[1]],ncol=10) + matrix(.C("R_PLM_matrix_probe_type_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(1),as.integer(2),as.integer(trt.cov),as.integer(1))[[1]],ncol=10) + matrix(.C("R_PLM_matrix_probe_type_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(-1),as.integer(2),as.integer(trt.cov),as.integer(1))[[1]],ncol=10) + + trt.cov <- rep(0:4,2) + matrix(.C("R_PLM_matrix_probe_type_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(-1),as.integer(2),as.integer(trt.cov),as.integer(4))[[1]],ncol=10) + + + + + #probe effects - overall + ncols <- 11 + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + + + matrix(.C("R_PLM_matrix_probe_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(0),as.integer(0),as.integer(trt.cov),as.integer(4))[[1]],ncol=11) + matrix(.C("R_PLM_matrix_probe_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(1),as.integer(0),as.integer(trt.cov),as.integer(4))[[1]],ncol=11) + matrix(.C("R_PLM_matrix_probe_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(-1),as.integer(0),as.integer(trt.cov),as.integer(4))[[1]],ncol=11) + + + + #probe effects within treatment or genotype factor + trt.cov <- rep(0:1,5) + ncols <- 22 + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + matrix(.C("R_PLM_matrix_probe_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(0),as.integer(2),as.integer(trt.cov),as.integer(1))[[1]],ncol=22) + matrix(.C("R_PLM_matrix_probe_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(1),as.integer(2),as.integer(trt.cov),as.integer(1))[[1]],ncol=22) + matrix(.C("R_PLM_matrix_probe_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(-1),as.integer(2),as.integer(trt.cov),as.integer(1))[[1]],ncol=22) + + + #probe effects within probetype + matrix(.C("R_PLM_matrix_probe_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(0),as.integer(3),as.integer(trt.cov),as.integer(1))[[1]],ncol=22) + matrix(.C("R_PLM_matrix_probe_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(1),as.integer(3),as.integer(trt.cov),as.integer(1))[[1]],ncol=22) + matrix(.C("R_PLM_matrix_probe_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(-1),as.integer(3),as.integer(trt.cov),as.integer(1))[[1]],ncol=22) + + + #probe effects within probetype within treatment or genotype factor variable + trt.cov <- rep(0:1,5) + ncols <- 44 + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + matrix(.C("R_PLM_matrix_probe_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(0),as.integer(4),as.integer(trt.cov),as.integer(1))[[1]],ncol=44) + matrix(.C("R_PLM_matrix_probe_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(1),as.integer(4),as.integer(trt.cov),as.integer(1))[[1]],ncol=44) + matrix(.C("R_PLM_matrix_probe_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(-1),as.integer(4),as.integer(trt.cov),as.integer(1))[[1]],ncol=44) + + nprobetypes <- 1 + trt.cov <- rep(0:1,5) + ncols <- 44 + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + matrix(.C("R_PLM_matrix_probe_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(0),as.integer(4),as.integer(trt.cov),as.integer(1))[[1]],ncol=44) + matrix(.C("R_PLM_matrix_probe_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(1),as.integer(4),as.integer(trt.cov),as.integer(1))[[1]],ncol=44) + matrix(.C("R_PLM_matrix_probe_effect",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(-1),as.integer(4),as.integer(trt.cov),as.integer(1))[[1]],ncol=44) + + + # copy across chip level variables into model matrix + nprobetypes <- 1 + trt.cov <- rep(0:1,5) + ncols <- 10 + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + trt.variables <- rnorm(10) + + matrix(.C("R_PLM_matrix_chiplevel",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.double(trt.variables),as.integer(1))[[1]],ncol=10) + + + ### + ### Build a few design matrices and compare with R model.matrix + ### + + + for (nprobetypes in 1:2){ + for (narrays in 2:15){ + for (nprobes in 2:20){ + for (constraint.type in c("contr.sum","contr.treatment")){ + if (constraint.type == "contr.sum"){ + ct.type <- -1 + } else { + ct.type <- 1 + } + + + ncols <- nprobes -1 + narrays + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + + X <- matrix(.C("R_PLM_Matrix_constructtest",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(1),as.integer(1),as.integer(0),as.integer(1),as.integer(ct.type))[[1]],ncol=ncols) + + probe.effect <- factor(rep(1:nprobes,narrays*nprobetypes)) + sample.effect <- factor(rep(rep(c(1:narrays),rep(nprobes,narrays)),nprobetypes)) + if (nprobetypes == 2){ + probe.type.effect <- factor(rep(1:2,c(narrays*nprobes,narrays*nprobes))) + } else { + probe.type.effect <- factor(rep(1,narrays*nprobes)) + } + + if (any(X!=model.matrix(~ C(sample.effect,constraint.type) + C(probe.effect,constraint.type)))){ + stop("Model matrix function problem ",narrays," ", nprobes, " ", nprobetypes) + } + + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + X <- matrix(.C("R_PLM_Matrix_constructtest",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(1),as.integer(0),as.integer(1),as.integer(ct.type))[[1]],ncol=ncols) + + if (any(X!=model.matrix(~ -1 + C(sample.effect,constraint.type) + C(probe.effect,constraint.type)))){ + stop("Model matrix function problem ",narrays," ", nprobes," ", nprobetypes) + } + + ncols <- nprobes + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + X <- matrix(.C("R_PLM_Matrix_constructtest",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(1),as.integer(0),as.integer(0),as.integer(1),as.integer(ct.type))[[1]],ncol=ncols) + + if (any(X!=model.matrix(~ C(probe.effect,constraint.type)))){ + stop("Model matrix function problem ",narrays," ", nprobes," ", nprobetypes) + } + + + ncols <- nprobes + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + X <- matrix(.C("R_PLM_Matrix_constructtest",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(0),as.integer(0),as.integer(1),as.integer(ct.type))[[1]],ncol=ncols) + + if (any(X!=model.matrix(~-1+ C(probe.effect,constraint.type)))){ + stop("Model matrix function problem ",narrays," ", nprobes," ", nprobetypes) + } + + } + } + } + } + + ### + ### Build a few more design matrices and compare with R model.matrix + ### + + + for (narrays in 2:15){ + for (nprobes in 2:20){ + for (constraint.type in c("contr.sum","contr.treatment")){ + probe.effect <- factor(rep(1:nprobes,narrays*nprobetypes)) + sample.effect <- factor(rep(rep(c(1:narrays),rep(nprobes,narrays)),nprobetypes)) + if (constraint.type == "contr.sum"){ + ct.type <- -1 + } else { + ct.type <- 1 + } + + + if (nprobetypes == 2){ + probe.type.effect <- factor(rep(1:2,c(narrays*nprobes,narrays*nprobes))) + } else { + probe.type.effect <- factor(rep(1,narrays*nprobes)) + } + ncols <- nprobetypes + nprobes -1 + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + X <- matrix(.C("R_PLM_Matrix_constructtest",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(0),as.integer(1),as.integer(1),as.integer(ct.type))[[1]],ncol=ncols) + + if (any(X!=model.matrix(~-1+ C(probe.type.effect,constraint.type) + C(probe.effect,constraint.type)))){ + stop("Model matrix function problem ",narrays," ", nprobes," ", nprobetypes) + } + + ncols <- nprobetypes + nprobes -1 + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + X <- matrix(.C("R_PLM_Matrix_constructtest",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(1),as.integer(0),as.integer(1),as.integer(1),as.integer(ct.type))[[1]],ncol=ncols) + + if (any(X!=model.matrix(~ C(probe.type.effect,constraint.type) + C(probe.effect,constraint.type)))){ + stop("Model matrix function problem ",narrays," ", nprobes," ", nprobetypes) + } + + ncols <- narrays + nprobetypes + nprobes -2 + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + X <- matrix(.C("R_PLM_Matrix_constructtest",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(1),as.integer(1),as.integer(1),as.integer(ct.type))[[1]],ncol=ncols) + + if (any(X!=model.matrix(~ -1 + C(sample.effect,constraint.type) + C(probe.type.effect,constraint.type) + C(probe.effect,constraint.type)))){ + stop("Model matrix function problem ",narrays," ", nprobes," ", nprobetypes) + } + + ncols <- narrays + nprobetypes + nprobes -2 + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + X <- matrix(.C("R_PLM_Matrix_constructtest",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(1),as.integer(1),as.integer(1),as.integer(1),as.integer(ct.type))[[1]],ncol=ncols) + + if (any(X!=model.matrix(~ + C(sample.effect,constraint.type) + C(probe.type.effect,constraint.type) + C(probe.effect,constraint.type)))){ + stop("Model matrix function problem ",narrays," ", nprobes," ", nprobetypes) + } + + ncols <- narrays + nprobetypes -1 + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + X <- matrix(.C("R_PLM_Matrix_constructtest",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(1),as.integer(1),as.integer(1),as.integer(0),as.integer(ct.type))[[1]],ncol=ncols) + + if (any(X!=model.matrix(~ C(sample.effect,constraint.type) + C(probe.type.effect,constraint.type)))){ + stop("Model matrix function problem ",narrays," ", nprobes," ", nprobetypes) + } + + ncols <- narrays + nprobetypes -1 + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + X <- matrix(.C("R_PLM_Matrix_constructtest",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(1),as.integer(1),as.integer(0),as.integer(ct.type))[[1]],ncol=ncols) + + if (any(X!=model.matrix(~ -1 + C(sample.effect,constraint.type) + C(probe.type.effect,constraint.type)))){ + stop("Model matrix function problem ",narrays," ", nprobes," ", nprobetypes) + } + + ncols <- nprobetypes + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + X <- matrix(.C("R_PLM_Matrix_constructtest",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(1),as.integer(0),as.integer(1),as.integer(0),as.integer(ct.type))[[1]],ncol=ncols) + + if (any(X!=model.matrix(~ C(probe.type.effect,constraint.type)))){ + stop("Model matrix function problem ",narrays," ", nprobes," ", nprobetypes) + } + + ncols <- nprobetypes + X <- matrix(0,narrays*nprobes*nprobetypes,ncols) + X <- matrix(.C("R_PLM_Matrix_constructtest",as.double(as.vector(X)), as.integer(narrays),as.integer(nprobes),as.integer(nprobetypes),as.integer(0),as.integer(0),as.integer(1),as.integer(0),as.integer(ct.type))[[1]],ncol=ncols) + + if (any(X!=model.matrix(~-1 + C(probe.type.effect,constraint.type)))){ + stop("Model matrix function problem ",narrays," ", nprobes," ", nprobetypes) + } + + } + + + } + } + + + narrays <- 2 + nprobes <- 7 + nprobetypes <- 2 + + probe.effect <- factor(rep(1:nprobes,narrays*nprobetypes)) + sample.effect <- factor(rep(rep(c(1:narrays),rep(nprobes,narrays)),nprobetypes)) + if (constraint.type == "contr.sum"){ + ct.type <- -1 + } else { + ct.type <- 1 + } + + + if (nprobetypes == 2){ + probe.type.effect <- factor(rep(1:2,c(narrays*nprobes,narrays*nprobes))) + } else { + probe.type.effect <- factor(rep(1,narrays*nprobes)) + } + + + model.matrix(~-1 +probe.effect/probe.type.effect) + + + library(affyPLM) + output <- verify.output.param(list(weights = FALSE, residuals = FALSE, varcov = "none", resid.SE = TRUE)) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + library(affydata) + data(Dilution) + + # fit a PM ~ samples model + R.model <- list(mmorpm.covariate=0,response.variable=1,which.parameter.types=as.integer(c(0,0,1,0,0)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,0,0,0)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=NULL,max.probe.trt.factor=0,chipcovariates =matrix(0,0,0), probe.type.levels=list(),probe.trt.levels=list()) + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + sample.effect <- rep(1:4,c(16,16,16,16)) + probe.effect <- rep(1:16,4) + + library(MASS) + fit <- rlm(as.vector(log2(pm(Dilution)[1:16,])) ~ -1 + factor(sample.effect)) + + if (any(Fitresults[[1]][1,] != coef(fit))){ + stop("Problem in model fitting procedure") + } + + sample.effect <- rep(1:4,c(20,20,20,20)) + probe.effect <- rep(1:20,4) + fit <- rlm(as.vector(log2(pm(Dilution)[201781:201800,])) ~ -1 + factor(sample.effect)) + if (any(Fitresults[[1]][12625,] != coef(fit))){ + stop("Problem in model fitting procedure") + } + + + # fit a samples + probes model + R.model <- list(mmorpm.covariate=0,response.variable=1,which.parameter.types=as.integer(c(0,0,1,0,1)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,0,0,-1)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=NULL,max.probe.trt.factor=0,chipcovariates =matrix(0,0,0), probe.type.levels=list(),probe.trt.levels=list()) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + sample.effect <- rep(1:4,c(16,16,16,16)) + probe.effect <- rep(1:16,4) + + fit <- rlm(as.vector(log2(pm(Dilution)[1:16,])) ~ -1 + factor(sample.effect)+C(factor(probe.effect),"contr.sum")) + + if (any(abs(Fitresults[[1]][1,] -coef(fit)[1:4]) > 1e-13)){ + stop("Problem in model fitting procedure") + } + + if (any(abs(as.vector(Fitresults[[2]][[1]]) - coef(fit)[5:19]) > 1e-13)){ + stop("Problem in model fitting procedure") + } + + + + + sample.effect <- rep(1:4,c(20,20,20,20)) + probe.effect <- rep(1:20,4) + fit <- rlm(as.vector(log2(pm(Dilution)[201781:201800,])) ~ -1 + factor(sample.effect)+C(factor(probe.effect),"contr.sum")) + + if (any(abs(Fitresults[[1]][12625,] -coef(fit)[1:4])> 1e-13)){ + stop("Problem in model fitting procedure") + } + + if (any(abs(as.vector(Fitresults[[2]][[12625]])- coef(fit)[5:23])>1e-13)){ + stop("Problem in model fitting procedure") + } + + # fit an MM ~ samples model + R.model <- list(mmorpm.covariate=0,response.variable=-1,which.parameter.types=as.integer(c(0,0,1,0,0)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,0,0,0)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=NULL,max.probe.trt.factor=0,chipcovariates =matrix(0,0,0), probe.type.levels=list(),probe.trt.levels=list()) + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + sample.effect <- rep(1:4,c(16,16,16,16)) + probe.effect <- rep(1:16,4) + + library(MASS) + fit <- rlm(as.vector(log2(mm(Dilution)[1:16,])) ~ -1 + factor(sample.effect)) + + if (any(abs(Fitresults[[1]][1,] - coef(fit)) > 1e-13)){ + stop("Problem in model fitting procedure") + } + + sample.effect <- rep(1:4,c(20,20,20,20)) + probe.effect <- rep(1:20,4) + fit <- rlm(as.vector(log2(mm(Dilution)[201781:201800,])) ~ -1 + factor(sample.effect)) + if (any(abs(Fitresults[[1]][12625,] - coef(fit))>1e-13)){ + stop("Problem in model fitting procedure") + } + + # fit a MM ~ samples + probes model + R.model <- list(mmorpm.covariate=0,response.variable=-1,which.parameter.types=as.integer(c(0,0,1,0,1)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,0,0,-1)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=NULL,max.probe.trt.factor=0,chipcovariates =matrix(0,0,0), probe.type.levels=list(),probe.trt.levels=list()) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + sample.effect <- rep(1:4,c(16,16,16,16)) + probe.effect <- rep(1:16,4) + + fit <- rlm(as.vector(log2(mm(Dilution)[1:16,])) ~ -1 + factor(sample.effect)+C(factor(probe.effect),"contr.sum")) + + if (any(abs(Fitresults[[1]][1,]-coef(fit)[1:4]) > 1e-13)){ + stop("Problem in model fitting procedure") + } + + if (any(abs(as.vector(Fitresults[[2]][[1]])- coef(fit)[5:19])>1e-13)){ + stop("Problem in model fitting procedure") + } + + + + + sample.effect <- rep(1:4,c(20,20,20,20)) + probe.effect <- rep(1:20,4) + fit <- rlm(as.vector(log2(mm(Dilution)[201781:201800,])) ~ -1 + factor(sample.effect)+C(factor(probe.effect),"contr.sum")) + + if (any(abs(Fitresults[[1]][12625,]- coef(fit)[1:4])>1e-13)){ + stop("Problem in model fitting procedure") + } + + if (any(abs(as.vector(Fitresults[[2]][[12625]])-coef(fit)[5:23])>1e14)){ + stop("Problem in model fitting procedure") + } + + + # a treatment model + treatment.effect <- c(1,1,2,2) + + covariates <- model.matrix(~ -1 + as.factor(treatment.effect)) + + R.model <- list(mmorpm.covariate=0,response.variable=-1,which.parameter.types=as.integer(c(0,1,0,0,0)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,0,0,0)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=NULL,max.probe.trt.factor=0,chipcovariates =covariates, probe.type.levels=list(),probe.trt.levels=list()) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + treatment.effect <- rep(c(1,1,2,2),c(16,16,16,16)) + fit <- rlm(as.vector(log2(mm(Dilution)[1:16,])) ~ -1 + factor(treatment.effect)) + + if (any(abs(Fitresults[[1]][1,]-coef(fit)[1:2]) > 1e-13)){ + stop("Problem in model fitting procedure") + } + + output <- verify.output.param(list(weights = FALSE, residuals = FALSE, varcov = "none", resid.SE = TRUE)) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + + # a treatment + probes model with contr.treatment constraint + R.model <- list(mmorpm.covariate=0,response.variable=-1,which.parameter.types=as.integer(c(0,1,0,0,1)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,0,0,1)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=NULL,max.probe.trt.factor=0,chipcovariates =covariates, probe.type.levels=list(),probe.trt.levels=list()) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + treatment.effect <- rep(c(1,1,2,2),c(20,20,20,20)) + probe.effect <- rep(1:20,4) + fit <- rlm(as.vector(log2(mm(Dilution)[201761:201780,])) ~ -1 + factor(treatment.effect)+C(factor(probe.effect),"contr.treatment")) + + if (any(abs(Fitresults[[1]][12624,]-coef(fit)[1:2]) > 1e-13)){ + stop("Problem in model fitting procedure") + } + + if (any(abs(as.vector(Fitresults[[2]][[12624]])-coef(fit)[3:21])>1e14)){ + stop("Problem in model fitting procedure") + } + + + + + # MM + samples + probes + R.model <- list(mmorpm.covariate=1,response.variable=1,which.parameter.types=as.integer(c(0,0,0,0,0)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,0,0,0)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=NULL,max.probe.trt.factor=0,chipcovariates =matrix(0,0,0), probe.type.levels=list(),probe.trt.levels=list()) + + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + sample.effect <- rep(1:4,c(16,16,16,16)) + probe.effect <- rep(1:16,4) + + fit <- rlm(as.vector(log2(pm(Dilution)[1:16,])) ~ -1 + as.vector(log2(mm(Dilution)[1:16,]))) + + + if (any(abs(as.vector(Fitresults[[6]][[1]]) - coef(fit)) > 1e-13)){ + stop("Problem in model fitting procedure") + } + + R.model <- list(mmorpm.covariate=1,response.variable=1,which.parameter.types=as.integer(c(0,0,1,0,0)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,0,0,0)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=NULL,max.probe.trt.factor=0,chipcovariates =matrix(0,0,0),probe.type.levels=list(),probe.trt.levels=list()) + + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + fit <- rlm(as.vector(log2(pm(Dilution)[1:16,])) ~ -1 + as.vector(log2(mm(Dilution)[1:16,]))+ as.factor(sample.effect)) + if (any(abs(as.vector(Fitresults[[6]][[1]]) - coef(fit)[1]) > 1e-13)){ + stop("Problem in model fitting procedure") + } + + if (any(abs(as.vector(Fitresults[[1]][1,]) - coef(fit)[2:5]) > 1e-13)){ + stop("Problem in model fitting procedure") + } + + R.model <- list(mmorpm.covariate=1,response.variable=1,which.parameter.types=as.integer(c(0,0,1,0,1)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,0,0,-1)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=NULL,max.probe.trt.factor=0,chipcovariates =matrix(0,0,0),probe.type.levels=list(),probe.trt.levels=list()) + + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + fit <- rlm(as.vector(log2(pm(Dilution)[1:16,])) ~ -1 + as.vector(log2(mm(Dilution)[1:16,]))+ as.factor(sample.effect) + C(as.factor(probe.effect),"contr.sum")) + if (any(abs(as.vector(Fitresults[[6]][[1]]) - coef(fit)[1]) > 1e-13)){ + stop("Problem in model fitting procedure") + } + + if (any(abs(as.vector(Fitresults[[1]][1,]) - coef(fit)[2:5]) > 1e-13)){ + stop("Problem in model fitting procedure") + } + + + ## PM and MM are response + + + sample.effect <- rep(1:4,c(32,32,32,32)) + probe.effect <- rep(1:16,8) + + + + # PMMM ~ -1 + samples + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,0,1,0,0)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,0,0,0)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=NULL,max.probe.trt.factor=0,chipcovariates =matrix(0,0,0),probe.type.levels=list(),probe.trt.levels=list()) + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + fit <- rlm(as.vector(rbind(log2(pm(Dilution)[1:16,]),log2(mm(Dilution)[1:16,]))) ~ -1 + as.factor(sample.effect)) + if (any(abs(as.vector(Fitresults[[1]][1,]) - coef(fit)) > 1e-13)){ + stop("Problem in model fitting procedure") + } + + # PMMM ~ -1 + samples +PROBES + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,0,1,0,1)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,0,0,-1)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=NULL,max.probe.trt.factor=0,chipcovariates =matrix(0,0,0),probe.type.levels=list(),probe.trt.levels=list()) + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + fit <- rlm(as.vector(rbind(log2(pm(Dilution)[1:16,]),log2(mm(Dilution)[1:16,]))) ~ -1 + as.factor(sample.effect)+C(as.factor(probe.effect),"contr.sum") ) + if (any(abs(as.vector(Fitresults[[1]][1,]) - coef(fit)[1:4]) > 1e-13)){ + stop("Problem in model fitting procedure") + } + + # a probe.type effect + probe.type.effect <- rep(rep(1:2,c(16,16)),4) + + # PMMM ~ -1 + samples + probe.type + PROBES + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,0,1,1,1)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,0,-1,-1)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=NULL,max.probe.trt.factor=0,chipcovariates =matrix(0,0,0),probe.type.levels=list(),probe.trt.levels=list()) + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + fit <- rlm(as.vector(rbind(log2(pm(Dilution)[1:16,]),log2(mm(Dilution)[1:16,]))) ~ -1 + as.factor(sample.effect)+ C(as.factor(probe.type.effect),"contr.sum")+ C(as.factor(probe.effect),"contr.sum") ) + + if (any(abs(as.vector(Fitresults[[1]][1,]) - coef(fit)[1:4]) > 1e-13)){ + stop("Problem in model fitting procedure") + } + + if (any(abs(as.vector(Fitresults[[6]][1,]) - coef(fit)[5]) > 1e-13)){ + stop("Problem in model fitting procedure") + } + + + + #### store weights PM ~ -1 + samples + + output <- verify.output.param(list(weights = TRUE, residuals = TRUE, varcov = "none", resid.SE = TRUE)) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + + R.model <- list(mmorpm.covariate=0,response.variable=1,which.parameter.types=as.integer(c(0,0,1,0,0)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,0,0,0)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=NULL,max.probe.trt.factor=0,chipcovariates =matrix(0,0,0),probe.type.levels=list(),probe.trt.levels=list()) + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + #### store weights PMMM ~ -1 + samples + + output <- verify.output.param(list(weights = TRUE, residuals = TRUE, varcov = "none", resid.SE = TRUE)) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,0,1,0,0)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,0,0,0)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=NULL,max.probe.trt.factor=0,chipcovariates =matrix(0,0,0),probe.type.levels=list(),probe.trt.levels=list()) + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + #### store weights PMMM ~ -1 + samples + probe.type + probes + + output <- verify.output.param(list(weights = TRUE, residuals = TRUE, varcov ="none", resid.SE = TRUE)) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,0,1,1,1)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,0,1,-1)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=NULL,max.probe.trt.factor=0,chipcovariates =matrix(0,0,0),probe.type.levels=list(),probe.trt.levels=list()) + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + ## PM ~ -1 + treatment + probes in treatment + output <- verify.output.param(list(weights = TRUE, residuals = TRUE, varcov = "none", resid.SE = TRUE)) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + treatment.effect <- c(1,1,2,2) + + covariates <- model.matrix(~ -1 + as.factor(treatment.effect)) + + R.model <- list(mmorpm.covariate=0,response.variable=1,which.parameter.types=as.integer(c(0,1,0,0,1)),strata=as.integer(c(0,0,0,0,2)),constraints=as.integer(c(0,0,0,0,-1)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=1,chipcovariates =covariates,probe.type.levels=list(),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + ## PMMM ~ -1 + treatment + probes in treatment + output <- list(weights = TRUE, residuals = TRUE, varcov = c("none","chiplevel", "all"), resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + treatment.effect <- c(1,1,2,2) + + covariates <- model.matrix(~ -1 + as.factor(treatment.effect)) + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,1,0,0,1)),strata=as.integer(c(0,0,0,0,2)),constraints=as.integer(c(0,0,0,0,-1)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=1,chipcovariates =covariates,probe.type.levels=list(),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + ## PMMM ~ -1 + treatment + probe.effect in treatment + output <- list(weights = TRUE, residuals = TRUE, varcov = c("none","chiplevel", "all"), resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + treatment.effect <- c(1,1,2,2) + + covariates <- model.matrix(~ -1 + as.factor(treatment.effect)) + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,1,0,1,0)),strata=as.integer(c(0,0,0,2,0)),constraints=as.integer(c(0,0,0,-1,0)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=0,chipcovariates =covariates,probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + ## PMMM ~ -1+ probes + output <- list(weights = TRUE, residuals = TRUE, varcov = c("none","chiplevel", "all"), resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,0,0,0,1)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,0,0,0)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=NULL,max.probe.trt.factor=0,chipcovariates =matrix(0,0,0),probe.type.levels=list(),probe.trt.levels=list()) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + ## PMMM ~ -1+ probes.type + output <- list(weights = TRUE, residuals = TRUE, varcov = c("none","chiplevel", "all"), resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,0,0,1,0)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,0,0,0)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=NULL,max.probe.trt.factor=0,chipcovariates =matrix(0,0,0),probe.type.levels=list(),probe.trt.levels=list()) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + ## PMMM ~ -1+ probes.type + probes + output <- list(weights = TRUE, residuals = TRUE, varcov = c("none","chiplevel", "all"), resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,0,0,1,1)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,0,0,-1)),probe.type.trt.factor=NULL,max.probe.type.trt.factor=0,probe.trt.factor=NULL,max.probe.trt.factor=0,chipcovariates =matrix(0,0,0),probe.type.levels=list(),probe.trt.levels=list()) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + ## PMMM ~ -1+ probes.type + probes with both within treatment factor + output <- list(weights = TRUE, residuals = TRUE, varcov = c("none","chiplevel", "all"), resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,0,0,1,1)),strata=as.integer(c(0,0,0,2,2)),constraints=as.integer(c(0,0,0,0,-1)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=1,chipcovariates =matrix(0,0,0),probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + ## PMMM ~ -1+ probes.type + probes with both within treatment factor and probes also within probe.type + output <- list(weights = TRUE, residuals = TRUE, varcov = c("none","chiplevel", "all"), resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,0,0,1,1)),strata=as.integer(c(0,0,0,2,4)),constraints=as.integer(c(0,0,0,0,-1)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=1,chipcovariates =matrix(0,0,0),probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + ## PMMM ~ -1+ probes.type + probes probe.types within treatment factor and probes also within probe.type + output <- list(weights = TRUE, residuals = TRUE, varcov = c("none","chiplevel", "all"), resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,0,0,1,1)),strata=as.integer(c(0,0,0,2,3)),constraints=as.integer(c(0,0,0,0,-1)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=1,chipcovariates =matrix(0,0,0),probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + ## PMMM ~ -1+ probes.type + probes probe.types within samples and probes also within probe.type + output <- list(weights = TRUE, residuals = TRUE, varcov = c("none","chiplevel", "all"), resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,0,0,1,1)),strata=as.integer(c(0,0,0,1,3)),constraints=as.integer(c(0,0,0,0,-1)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=1,chipcovariates =matrix(0,0,0),probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + + ## PMMM ~ intercept + probes.type + probes probe.types within samples and probes also within probe.type + output <- list(weights = TRUE, residuals = TRUE, varcov = c("none","chiplevel", "all"), resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(1,0,0,1,1)),strata=as.integer(c(0,0,0,1,3)),constraints=as.integer(c(0,0,0,-1,-1)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=1,chipcovariates =matrix(0,0,0),probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + ## PMMM ~ intercept + probes probes also within probe.type + output <- list(weights = TRUE, residuals = TRUE, varcov = c("none","chiplevel", "all"), resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(1,0,0,0,1)),strata=as.integer(c(0,0,0,0,3)),constraints=as.integer(c(0,0,0,0,-1)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=1,chipcovariates =matrix(0,0,0),probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + ## PMMM ~ -1+ probes probes also within probe.type + output <- list(weights = TRUE, residuals = TRUE, varcov = c("none","chiplevel", "all"), resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,0,0,0,1)),strata=as.integer(c(0,0,0,0,3)),constraints=as.integer(c(0,0,0,0,0)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=1,chipcovariates =matrix(0,0,0),probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + # now play with varcov output + output <- list(weights = TRUE, residuals = TRUE, varcov ="chiplevel", resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,0,1,0,1)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,0,0,-1)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=1,chipcovariates =matrix(0,0,0),probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + # now play with varcov output and treatment + output <- list(weights = TRUE, residuals = TRUE, varcov ="chiplevel", resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + treatment.effect <- c(1,1,2,2) + + covariates <- model.matrix(~ -1 + as.factor(treatment.effect)) + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,1,0,0,0)),strata=as.integer(c(0,0,0,2,0)),constraints=as.integer(c(0,0,0,0,0)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=0,chipcovariates =covariates,probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + + # now play with varcov output and an intercept + output <- list(weights = TRUE, residuals = TRUE, varcov ="chiplevel", resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(1,0,1,0,1)),strata=as.integer(c(0,0,0,0,0)),constraints=as.integer(c(0,0,-1,0,-1)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=1,chipcovariates =matrix(0,0,0),probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + + # now play with varcov output and treatment and intercept + output <- list(weights = TRUE, residuals = TRUE, varcov ="chiplevel", resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + treatment.effect <- c(1,1,2,2) + + covariates <- matrix(model.matrix(~ as.factor(treatment.effect))[,2]) + colnames(covariates) <- "trt_2" + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(1,1,0,0,0)),strata=as.integer(c(0,0,0,2,0)),constraints=as.integer(c(0,0,0,0,0)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=0,chipcovariates =covariates,probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + + # now play with varcov all option output and treatment and intercept + output <- list(weights = TRUE, residuals = TRUE, varcov ="all", resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + treatment.effect <- c(1,1,2,2) + + covariates <- matrix(model.matrix(~ as.factor(treatment.effect))[,2]) + colnames(covariates) <- "trt_2" + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(1,1,0,0,0)),strata=as.integer(c(0,0,0,2,0)),constraints=as.integer(c(0,0,0,0,0)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=0,chipcovariates =covariates,probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + # now play with varcov all option output and samples and intercept + output <- list(weights = TRUE, residuals = TRUE, varcov ="all", resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(1,0,1,0,1)),strata=as.integer(c(0,0,0,2,0)),constraints=as.integer(c(0,0,-1,0,-1)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=0,chipcovariates =matrix(0,0,0),probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + # now play with varcov all option output and samples and intercept, MM covarite + output <- list(weights = TRUE, residuals = TRUE, varcov ="all", resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + R.model <- list(mmorpm.covariate=1,response.variable=1,which.parameter.types=as.integer(c(1,0,1,0,1)),strata=as.integer(c(0,0,0,2,0)),constraints=as.integer(c(0,0,-1,0,-1)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=0,chipcovariates =matrix(0,0,0),probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + + + + + + ## now play with varcov all option output and samples and intercept, MM covariate and input chip weights + output <- verify.output.param(list(weights = TRUE, residuals = TRUE, varcov ="all", resid.SE = TRUE)) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=c(1,1,0.5,0.5),weights.probe=NULL) + + R.model <- list(mmorpm.covariate=0,response.variable=1,which.parameter.types=as.integer(c(0,0,1,0,1)),strata=as.integer(c(0,0,0,2,0)),constraints=as.integer(c(0,0,0,0,-1)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=0,chipcovariates =matrix(0,0,0),probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + ## now play with varcov all option output and samples and intercept, MM covariate and input chip weights + output <- list(weights = TRUE, residuals = TRUE, varcov ="all", resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=runif(201800)) + + R.model <- list(mmorpm.covariate=0,response.variable=1,which.parameter.types=as.integer(c(0,0,1,0,1)),strata=as.integer(c(0,0,0,2,0)),constraints=as.integer(c(0,0,0,0,-1)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=0,chipcovariates =matrix(0,0,0),probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + + output <- list(weights = TRUE, residuals = TRUE, varcov ="all", resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=c(rep(c(1,0.5),c(201800,201800)))) + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,0,1,0,1)),strata=as.integer(c(0,0,0,2,0)),constraints=as.integer(c(0,0,0,0,-1)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=0,chipcovariates =matrix(0,0,0),probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + + output <- list(weights = TRUE, residuals = TRUE, varcov ="all", resid.SE = TRUE) + modelparam <- list(trans.fn="cuberoot", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,0,1,0,1)),strata=as.integer(c(0,0,0,2,0)),constraints=as.integer(c(0,0,0,0,-1)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=0,chipcovariates =matrix(0,0,0),probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + + output <- list(weights = TRUE, residuals = TRUE, varcov ="all", resid.SE = TRUE) + modelparam <- list(trans.fn="log10", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + + R.model <- list(mmorpm.covariate=0,response.variable=0,which.parameter.types=as.integer(c(0,0,1,0,1)),strata=as.integer(c(0,0,0,2,0)),constraints=as.integer(c(0,0,0,0,-1)),probe.type.trt.factor=as.integer(c(0,0,1,1)),max.probe.type.trt.factor=1,probe.trt.factor=as.integer(c(0,0,1,1)),max.probe.trt.factor=0,chipcovariates =matrix(0,0,0),probe.type.levels=list("blah"=c("A","B")),probe.trt.levels=list("blah"=c("A","B"))) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + } > > > > if (test.PLM.modelmatrix){ + + library(affyPLM);data(Dilution) + + #PLM.designmatrix3(Dilution) + + #PLM.designmatrix3(Dilution,model=MM ~ PM -1 + samples +probe.type:probes) + + #PLM.designmatrix3(Dilution,model=MM ~ PM -1 + samples:probe.type + liver:probe.type:probes + liver:samples) + #PLM.designmatrix3(Dilution,model=MM ~ PM + samples:probe.type + liver:probe.type:probes + liver + samples) + + + + + output <- list(weights = TRUE, residuals = TRUE, varcov ="all", resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + #blah <- c(1,5,5,1) + #R.model <- PLM.designmatrix3(Dilution,model=PMMM ~ probes + blah,constraint.type=c(probes="contr.sum")) + #R.model <- PLM.designmatrix3(Dilution,model=PMMM ~ -1 + blah:probe.type) + #R.model <- PLM.designmatrix3(Dilution,model=PMMM ~ -1 +probes:probe.type) + #R.model <- PLM.designmatrix3(Dilution,model=PMMM ~ -1 +probes:blah) + #R.model <- PLM.designmatrix3(Dilution,model=PMMM ~ -1 +probes:probe.type:blah) + #output <- list(weights = TRUE, residuals = TRUE, varcov ="chiplevel", resid.SE = TRUE) + # R.model <- PLM.designmatrix3(Dilution,model=PMMM ~ samples,constraint.type=c(samples="contr.sum")) + # R.model <- PLM.designmatrix3(Dilution,model=PMMM ~ blah) + # R.model <- PLM.designmatrix3(Dilution,model=PMMM ~ -1 + samples) + #R.model <- PLM.designmatrix3(Dilution,model=PMMM ~ probes + blah) + #R.model <- PLM.designmatrix3(Dilution,model=PMMM ~ probes + blah) + #Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + library(affyPLM);data(Dilution) + output <- list(weights = TRUE, residuals = TRUE, varcov ="chiplevel", resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + R.model <- PLM.designmatrix3(Dilution,model=PMMM ~ probe.type + probe.type:probes + samples) + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + library(affyPLM);data(Dilution) + output <- list(weights = TRUE, residuals = TRUE, varcov ="chiplevel", resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + R.model <- PLM.designmatrix3(Dilution,model=PMMM ~ samples:probe.type + probe.type:probes + samples) + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + output <- list(weights = TRUE, residuals = TRUE, varcov ="chiplevel", resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + blah <- c(1,2,2) + R.model <- PLM.designmatrix3(Dilution,model=PMMM ~ blah:probe.type + probe.type:probes + samples) + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + output <- list(weights = TRUE, residuals = TRUE, varcov ="all", resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + blah <- c(1,2,2) + R.model <- PLM.designmatrix3(Dilution,model=PM ~ -1 + probes + MM + blah) + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + output <- list(weights = TRUE, residuals = TRUE, varcov ="all", resid.SE = TRUE) + modelparam <- list(trans.fn="log2", se.type = 4, psi.type = 0, psi.k =1.345,max.its = 20, init.method = "ls",weights.chip=NULL,weights.probe=NULL) + blah <- c(1,2,2) + R.model <- PLM.designmatrix3(Dilution,model=PM ~ -1 + probes + MM + samples) + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + + + + #test some of the verification functions + + + output <- verify.output.param() + modelparam <- verify.model.param(Dilution,PM ~ -1 + probes + MM + samples) + R.model <- PLM.designmatrix3(Dilution,model=PM ~ -1 + probes + MM + samples) + + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + ##verify.model.param(Dilution,PM ~ -1 + probes + MM + samples,model.param=list(weights.probe=rep(1,10))) + + modelparam <- verify.model.param(Dilution,PMMM ~ -1 + probes + samples,model.param=list(weights.chip=c(1,2,3),weights.probe=rep(1,2400*2))) + R.model <- PLM.designmatrix3(Dilution,model=PMMM ~ -1 + probes + samples) + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + + modelparam <- verify.model.param(Dilution,PM ~ -1 + probes + samples,model.param=list()) + R.model <- PLM.designmatrix3(Dilution,model=PM ~ -1 + probes + samples) + Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + ## probes <- rep(1:16,3) + ## chips <- rep(1:3,c(16,16,16)) + + ## library(MASS) + + ##fit <- rlm(log2(as.vector(pm(Dilution,"HG2188-HT2258_at"))) ~ -1 + as.factor(chips) + C(as.factor(probes),"contr.sum")) + + + #test creating a PLMset based on the output from rlm_PLMset + + ### x <- new("PLMset") + ### x@chip.coefs=Fitresults[[1]] + ### x@probe.coefs= Fitresults[[2]] + ### x@weights=Fitresults[[3]] + ### x@se.chip.coefs=Fitresults[[4]] + ### x@se.probe.coefs=Fitresults[[5]] + ### x@exprs=Fitresults[[6]] + ### x@se.exprs=Fitresults[[7]] + ### x@residuals=Fitresults[[8]] + ### x@residualSE=Fitresults[[9]] + ### x@varcov = Fitresults[[10]] + ### x@cdfName = Dilution@cdfName + ### x@phenoData = Dilution@phenoData + ### x@annotation = Dilution@annotation + ### x@description = Dilution@description + ### x@notes = Dilution@notes + ### x@nrow= Dilution@nrow + ### x@ncol= Dilution@ncol + ### x@model.description = c(x@model.description, list(R.model=R.model)) + ### image(x) + + + + + ### data(Dilution) + ### output <- verify.output.param() + ### modelparam <- verify.model.param(Dilution,PMMM ~ -1 + probe.type:probes + samples + samples:probe.type,model.param=list()) + ### R.model <- PLM.designmatrix3(Dilution,model=PMMM ~ -1 + probe.type:probes + samples+ samples:probe.type) + ### Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + ### output <- verify.output.param() + ### modelparam <- verify.model.param(Dilution,MM ~ -1 + probes + samples,model.param=list()) + ### R.model <- PLM.designmatrix3(Dilution,model=MM ~ -1 + probes + samples) + ### Fitresults <- .Call("rlm_PLMset",pm(Dilution),mm(Dilution),probeNames(Dilution),length(geneNames(Dilution)),R.model,output,modelparam) + + + + ### x <- new("PLMset") + ### x@chip.coefs=Fitresults[[1]] + ### x@probe.coefs= Fitresults[[2]] + ### x@weights=Fitresults[[3]] + ### x@se.chip.coefs=Fitresults[[4]] + ### x@se.probe.coefs=Fitresults[[5]] + ### x@exprs=Fitresults[[6]] + ### x@se.exprs=Fitresults[[7]] + ### x@residuals=Fitresults[[8]] + ### x@residualSE=Fitresults[[9]] + ### x@varcov = Fitresults[[10]] + ### x@cdfName = Dilution@cdfName + ### x@phenoData = Dilution@phenoData + ### x@annotation = Dilution@annotation + ### x@description = Dilution@description + ### x@notes = Dilution@notes + ### x@nrow= Dilution@nrow + ### x@ncol= Dilution@ncol + ### x@model.description = c(x@model.description, list(R.model=R.model)) + ### image(x) + ### image(x,type="pos.resids") + ### image(x,type="neg.resids") + ### image(x,type="sign.resids") + + ### resid(x,"1091_at") + + + + ### weights(x,c("1091_at","1092_at")) + + + ### image(x,type="resids",standardize=TRUE) + + + + + + + } > > > > > > if (test.rlm){ + + + library(affyPLM);data(Dilution) + + y <- as.vector(log2(pm(Dilution)[1:16,])) + + w <- runif(64) + + probes <- rep(1:16,4) + samples <- rep(1:4,c(16,16,16,16)) + + x <- model.matrix( ~ -1 + as.factor(samples) + C(as.factor(probes),"contr.sum")) + x <- as.vector(x) + + cols <- 19 + rows <- 64 + + + # rlm_wfit_R(double *x, double *y, double *w, int *rows, int *cols, double *out_beta, double *out_resids, double *out_weights) + + fit1 <-.C("rlm_wfit_R",as.double(x),as.double(y),as.double(w),as.integer(rows),as.integer(cols),double(cols),double(rows),double(rows)) + + + library(MASS) + + fit2 <- rlm(y ~ -1 + as.factor(samples) + C(as.factor(probes),"contr.sum"),weights=w,wt.method="case") + + if (any(abs(coef(fit2) - fit1[[6]]) > 10e-14)){ + stop("Weighted RLM did not work") + } + + + + + + y <- as.vector(log2(pm(Dilution,"1001_at"))) + x <- as.vector(log2(mm(Dilution,"1001_at"))) + + rlm(y ~ -1 + x + as.factor(samples) + C(as.factor(probes),"contr.sum")) + + + + + + + + + + + + } > > > > > proc.time() user system elapsed 4.243 0.049 4.278
affyPLM.Rcheck/tests/PLM_tests.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes" Copyright (C) 2019 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > do.all.tests <- FALSE > if (do.all.tests){ + + # this file tests fitPLM and the PLMset object + + library(affyPLM) + + library(affydata) + data(Dilution) + + + Pset <- fitPLM(Dilution) + + #check accessors for parameters and se + + coefs(Pset)[1:5,] + se(Pset)[1:5,] + coefs.probe(Pset)[1:5] + se.probe(Pset)[1:5] + coefs.const(Pset) + se.const(Pset) + + #accessors for weights and residuals + + weights(Pset)[[1]][1:5,] + resid(Pset)[[1]][1:5,] + + + #test varcov + + Pset <- fitPLM(Dilution,background=FALSE,normalize=FALSE,output.param=list(varcov="chiplevel")) + varcov(Pset)[1:3] + + + #test each of the possible weight functions + Pset <- fitPLM(Dilution,background=FALSE,normalize=FALSE,model.param=list(psi.type="Huber")) + Pset <- fitPLM(Dilution,background=FALSE,normalize=FALSE,model.param=list(psi.type="fair")) + Pset <- fitPLM(Dilution,background=FALSE,normalize=FALSE,model.param=list(psi.type="Cauchy")) + Pset <- fitPLM(Dilution,background=FALSE,normalize=FALSE,model.param=list(psi.type="Geman-McClure")) + Pset <- fitPLM(Dilution,background=FALSE,normalize=FALSE,model.param=list(psi.type="Welsch")) + Pset <- fitPLM(Dilution,background=FALSE,normalize=FALSE,model.param=list(psi.type="Tukey")) + Pset <- fitPLM(Dilution,background=FALSE,normalize=FALSE,model.param=list(psi.type="Andrews")) + + # a larger example to do some testing of the graphical functions + + data(Dilution) + + Pset <- fitPLM(Dilution) + + #testing the image capabilities + + image(Pset,which=2) + image(Pset,which=2,type="resids") + image(Pset,which=2,type="pos.resids") + image(Pset,which=2,type="neg.resids") + image(Pset,which=2,type="resids",use.log=FALSE,add.legend=TRUE) + + boxplot(Pset) + Mbox(Pset) + + + #test some non-default models functions + # no preprocessing for speed + + Pset <- fitPLM(Dilution, PM ~ -1 + probes + liver,background=FALSE,normalize=FALSE) + coefs(Pset)[1:5,] + se(Pset)[1:5,] + + + Pset <- fitPLM(Dilution, PM ~ -1 + probes + liver + scanner,background=FALSE,normalize=FALSE) + coefs(Pset)[1:5,] + se(Pset)[1:5,] + + #checking the constraints + Pset <- fitPLM(Dilution, PM ~ -1 + probes + liver + scanner,constraint.type=c(default="contr.sum"),background=FALSE,normalize=FALSE) + coefs(Pset)[1:5,] + se(Pset)[1:5,] + + Pset <- fitPLM(Dilution, PM ~ -1 + liver + scanner,constraint.type=c(default="contr.sum"),background=FALSE,normalize=FALSE) + coefs(Pset)[1:5,] + se(Pset)[1:5,] + coefs.probe(Pset) # should be empty + + Pset <- fitPLM(Dilution, PM ~ -1 + liver + scanner,constraint.type=c(probes="contr.treatment"),background=FALSE,normalize=FALSE) + coefs(Pset)[1:5,] + se(Pset)[1:5,] + coefs.probe(Pset) # should be empty + + + Pset <- fitPLM(Dilution, PM ~ -1 + probes + liver + scanner,constraint.type=c(probes="contr.treatment"),background=FALSE,normalize=FALSE) + coefs(Pset)[1:5,] + se(Pset)[1:5,] + coefs.probe(Pset)[1:16] + + + scanner2 <- c(1,2,1,2) + Pset <- fitPLM(Dilution, PM ~ -1 + probes + liver + scanner2,constraint.type=c(probes="contr.sum"),background=FALSE,normalize=FALSE) + coefs(Pset)[1:5,] + se(Pset)[1:5,] + coefs.probe(Pset)[1:16] + + # + #Pset <- fitPLM(Dilution,model=PM~-1+probes+scanner,normalize=FALSE,background=FALSE,model.param=list(se.type=3)) + #se(Pset)[1:10,] + + #check that fitPLM rlm agrees with threestep rlm and threestepPLM rlm + + + Pset <- fitPLM(Dilution) + eset <- threestep(Dilution,summary.method="rlm") + Pset2 <- threestepPLM(Dilution,summary.method="rlm") + + if (any(abs(coefs(Pset) - exprs(eset)) > 1e-14)){ + stop("no agreement between fitPLM and threestep") + } + + if (any(abs(coefs(Pset) - coefs(Pset2)) > 1e-14)){ + stop("no agreement between fitPLM and threestep") + } + } > > proc.time() user system elapsed 0.275 0.040 0.302
affyPLM.Rcheck/tests/preprocess_tests.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes" Copyright (C) 2019 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > #test the preprocessing functionality > > library(affyPLM) Loading required package: BiocGenerics Loading required package: parallel Attaching package: 'BiocGenerics' The following objects are masked from 'package:parallel': clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply, parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB The following objects are masked from 'package:stats': IQR, mad, sd, var, xtabs The following objects are masked from 'package:base': Filter, Find, Map, Position, Reduce, anyDuplicated, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted, lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table, tapply, union, unique, unsplit, which, which.max, which.min Loading required package: affy Loading required package: Biobase Welcome to Bioconductor Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'. Loading required package: gcrma Loading required package: preprocessCore > library(affydata) Package LibPath Item [1,] "affydata" "/home/biocbuild/bbs-3.9-bioc/R/library" "Dilution" Title [1,] "AffyBatch instance Dilution" > data(Dilution) > > > ### NO LONGER SUPPORTED eset <- threestep(Dilution,background.method="RMA.1") > eset <- threestep(Dilution,background.method="RMA.2") Warning messages: 1: replacing previous import 'AnnotationDbi::tail' by 'utils::tail' when loading 'hgu95av2cdf' 2: replacing previous import 'AnnotationDbi::head' by 'utils::head' when loading 'hgu95av2cdf' > eset <- threestep(Dilution,background.method="IdealMM") > eset <- threestep(Dilution,background.method="MAS") > eset <- threestep(Dilution,background.method="MASIM") > eset <- threestep(Dilution,background.method="LESN2") > eset <- threestep(Dilution,background.method="LESN1") > eset <- threestep(Dilution,background.method="LESN0") > > eset <- threestep(Dilution,normalize.method="quantile",background=FALSE) > eset <- threestep(Dilution,normalize.method="quantile.probeset",background=FALSE) > eset <- threestep(Dilution,normalize.method="scaling",background=FALSE) > > > > proc.time() user system elapsed 24.364 0.436 24.789
affyPLM.Rcheck/tests/threestepPLM_tests.Rout
R version 3.6.1 (2019-07-05) -- "Action of the Toes" Copyright (C) 2019 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > if (.Platform$OS.type != "windows"){ + library(affyPLM) + + # test threestep and threestepPLM to see if they agree + + + check.coefs <- function(Pset,Pset2){ + if (any(abs(coefs(Pset) - exprs(Pset2)) > 1e-14)){ + stop("No agreement between threestepPLM and threestep in coefs") + } + } + + check.resids <- function(Pset,Pset2){ + if (any(resid(Pset) != resid(Pset2))){ + stop("No agreement between threestepPLM and rmaPLM/threestep in residuals") + } + } + + + library(affydata) + data(Dilution) + + Pset <- threestepPLM(Dilution) + Pset2 <- threestep(Dilution) + check.coefs(Pset,Pset2) + + Pset <- threestepPLM(Dilution,summary.method="tukey.biweight") + Pset2 <- threestep(Dilution,summary.method="tukey.biweight") + check.coefs(Pset,Pset2) + + Pset <- threestepPLM(Dilution,summary.method="average.log") + Pset2 <- threestep(Dilution,summary.method="average.log") + check.coefs(Pset,Pset2) + + Pset <- threestepPLM(Dilution,summary.method="rlm") + Pset2 <- threestep(Dilution,summary.method="rlm") + check.coefs(Pset,Pset2) + + Pset <- threestepPLM(Dilution,summary.method="log.average") + Pset2 <- threestep(Dilution,summary.method="log.average") + check.coefs(Pset,Pset2) + + Pset <- threestepPLM(Dilution,summary.method="log.median") + Pset2 <- threestep(Dilution,summary.method="log.median") + check.coefs(Pset,Pset2) + + Pset <- threestepPLM(Dilution,summary.method="median.log") + Pset2 <- threestep(Dilution,summary.method="median.log") + check.coefs(Pset,Pset2) + + Pset <- threestepPLM(Dilution,summary.method="log.2nd.largest") + Pset2 <- threestep(Dilution,summary.method="log.2nd.largest") + check.coefs(Pset,Pset2) + + Pset <- threestepPLM(Dilution,summary.method="lm") + Pset2 <- threestep(Dilution,summary.method="lm") + check.coefs(Pset,Pset2) + + #check if threestepPLM agrees with rmaPLM + Pset <- threestepPLM(Dilution) + Pset2 <- rmaPLM(Dilution) + + if (any(coefs(Pset) != coefs(Pset2))){ + stop("No agreement between threestepPLM and rmaPLM in coefs") + } + + + if (any(resid(Pset)[[1]] != resid(Pset2)[[1]])){ + stop("No agreement between threestepPLM and rmaPLM in residuals") + } + } Loading required package: BiocGenerics Loading required package: parallel Attaching package: 'BiocGenerics' The following objects are masked from 'package:parallel': clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply, parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB The following objects are masked from 'package:stats': IQR, mad, sd, var, xtabs The following objects are masked from 'package:base': Filter, Find, Map, Position, Reduce, anyDuplicated, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted, lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table, tapply, union, unique, unsplit, which, which.max, which.min Loading required package: affy Loading required package: Biobase Welcome to Bioconductor Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'. Loading required package: gcrma Loading required package: preprocessCore Package LibPath Item [1,] "affydata" "/home/biocbuild/bbs-3.9-bioc/R/library" "Dilution" Title [1,] "AffyBatch instance Dilution" Warning messages: 1: replacing previous import 'AnnotationDbi::tail' by 'utils::tail' when loading 'hgu95av2cdf' 2: replacing previous import 'AnnotationDbi::head' by 'utils::head' when loading 'hgu95av2cdf' > > proc.time() user system elapsed 66.218 0.410 66.656
affyPLM.Rcheck/affyPLM-Ex.timings
name | user | system | elapsed | |
PLMset2exprSet | 6.435 | 0.140 | 6.576 | |
bg.correct.LESN | 1.267 | 0.020 | 1.287 | |
fitPLM | 12.275 | 0.196 | 12.478 | |
normalize.exprSet | 1.389 | 0.024 | 1.413 | |
normalize.scaling | 2.168 | 0.012 | 2.179 | |
preprocess | 2.446 | 0.016 | 2.462 | |
rmaPLM | 0.524 | 0.000 | 0.523 | |
threestep | 21.288 | 0.020 | 21.309 | |
threestepPLM | 0.415 | 0.008 | 0.424 | |