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###
### Running command:
###
###   /home/rapidbuild/bbs-3.23-bioc-rapid/R/bin/R CMD check --install=check:affyPLM.install-out.txt --library=/home/rapidbuild/bbs-3.23-bioc-rapid/R/site-library --timings affyPLM_1.87.0.tar.gz
###
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* using log directory ‘/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/meat/affyPLM.Rcheck’
* using R Under development (unstable) (2025-10-28 r88973)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
    GNU Fortran (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
* running under: Ubuntu 24.04.3 LTS
* using session charset: UTF-8
* checking for file ‘affyPLM/DESCRIPTION’ ... OK
* this is package ‘affyPLM’ version ‘1.87.0’
* checking CRAN incoming feasibility ... NOTE
Maintainer: ‘Ben Bolstad <bmb@bmbolstad.com>’

Unknown, possibly misspelled, fields in DESCRIPTION:
  ‘git_url’ ‘git_branch’ ‘git_last_commit’ ‘git_last_commit_date’

No Authors@R field in DESCRIPTION.
Please add one, modifying
  Authors@R: person(given = "Ben",
                    family = "Bolstad",
                    role = c("aut", "cre"),
                    email = "bmb@bmbolstad.com")
as necessary.

Problems when reading CITATION file:
  It is recommended to use ‘given’ instead of ‘first’.
  It is recommended to use ‘given’ instead of ‘middle’.
  It is recommended to use ‘family’ instead of ‘last’.
Package CITATION file contains call(s) to old-style personList() or
as.personList().  Please use c() on person objects instead.
Package CITATION file contains call(s) to old-style citEntry().  Please
use bibentry() instead.

The Title field should be in title case. Current version is:
  ‘Methods for fitting probe-level models’
In title case that is:
  ‘Methods for Fitting Probe-Level Models’

The Description field should not start with the package name,
  'This package' or similar.
* 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 ... WARNING
Found the following significant warnings:
  rlm_PLM.c:870:32: warning: ‘%d’ directive output may be truncated writing between 1 and 10 bytes into a region of size 9 [-Wformat-truncation=]
  rlm_PLM.c:868:32: warning: ‘%d’ directive output may be truncated writing between 1 and 10 bytes into a region of size 9 [-Wformat-truncation=]
See ‘/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/meat/affyPLM.Rcheck/00install.out’ for details.
* used C compiler: ‘gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0’
* 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 code 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 loading without being on the library search path ... OK
* checking whether startup messages can be suppressed ... OK
* checking use of S3 registration ... 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 ... NOTE
Non-topic package-anchored link(s) in Rd file 'PLMset2exprSet.Rd':
  ‘[Biobase:class.ExpressionSet]{ExpressionSet}’

Non-topic package-anchored link(s) in Rd file 'ReadRMAExpress.Rd':
  ‘[Biobase:class.ExpressionSet]{ExpressionSet}’

Non-topic package-anchored link(s) in Rd file 'normalize.exprSet.Rd':
  ‘[Biobase:class.ExpressionSet]{ExpressionSet}’

Non-topic package-anchored link(s) in Rd file 'normalize.quantiles.probeset.Rd':
  ‘[affy:normalize.quantiles]{quantile}’
  ‘[affy:normalize.quantiles]{normalize.quantiles}’

Non-topic package-anchored link(s) in Rd file 'normalize.scaling.Rd':
  ‘[Biobase:class.ExpressionSet]{ExpressionSet}’

Non-topic package-anchored link(s) in Rd file 'threestep.Rd':
  ‘[Biobase:class.ExpressionSet]{ExpressionSet}’

See section 'Cross-references' in the 'Writing R Extensions' manual.
* 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 use of PKG_*FLAGS in Makefiles ... OK
* checking compiled code ... INFO
Note: information on .o files is not available
* checking sizes of PDF files under ‘inst/doc’ ...* checking files in ‘vignettes’ ... OK
* checking examples ... NOTE
Examples with CPU (user + system) or elapsed time > 5s
           user system elapsed
threestep 8.444  0.032   8.479
fitPLM    5.632  0.224   5.858
* 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 ... OK
* checking re-building of vignette outputs ... OK
* checking PDF version of manual ... OK
* DONE

Status: 1 WARNING, 3 NOTEs
See
  ‘/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/meat/affyPLM.Rcheck/00check.log’
for details.

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###
### Running command:
###
###   /home/rapidbuild/bbs-3.23-bioc-rapid/R/bin/R CMD INSTALL affyPLM
###
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* installing to library ‘/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library’
* installing *source* package ‘affyPLM’ ...
** this is package ‘affyPLM’ version ‘1.87.0’
** using staged installation
** libs
using C compiler: ‘gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0’
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c LESN.c -o LESN.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c PLM_avg_log.c -o PLM_avg_log.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c PLM_biweight.c -o PLM_biweight.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c PLM_log_avg.c -o PLM_log_avg.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c PLM_medianPM.c -o PLM_medianPM.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c PLM_median_logPM.c -o PLM_median_logPM.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c PLM_medianpolish.c -o PLM_medianpolish.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c PLM_modelmatrix.c -o PLM_modelmatrix.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c SCAB.c -o SCAB.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c chipbackground.c -o chipbackground.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c common_types.c -o common_types.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -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]
  620 |   int first_ind;
      |       ^~~~~~~~~
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -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]
  209 |   int first_ind;
      |       ^~~~~~~~~
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -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]
  118 |   int first_ind;
      |       ^~~~~~~~~
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -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]
   71 |   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]
  183 |   int first_ind;
      |       ^~~~~~~~~
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c lm_threestep.c -o lm_threestep.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c matrix_functions.c -o matrix_functions.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c nthLargestPM.c -o nthLargestPM.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -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]
  158 |   int which_lesn;
      |       ^~~~~~~~~~
preprocess.c: In function ‘pp_bothstages’:
preprocess.c:677:12: warning: variable ‘cols’ set but not used [-Wunused-but-set-variable]
  677 |   int rows,cols;
      |            ^~~~
preprocess.c:677:7: warning: variable ‘rows’ set but not used [-Wunused-but-set-variable]
  677 |   int rows,cols;
      |       ^~~~
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c psi_fns.c -o psi_fns.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -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]
  110 |   int first_ind;
      |       ^~~~~~~~~
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c read_rmaexpress.c -o read_rmaexpress.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -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]
 1481 |   int rows,cols;
      |            ^~~~
rlm_PLM.c:1481:7: warning: variable ‘rows’ set but not used [-Wunused-but-set-variable]
 1481 |   int rows,cols;
      |       ^~~~
rlm_PLM.c: In function ‘rlm_PLMset_nameoutput’:
rlm_PLM.c:870:32: warning: ‘%d’ directive output may be truncated writing between 1 and 10 bytes into a region of size 9 [-Wformat-truncation=]
  870 |           snprintf(tmp_str2,9,"%d",j+1);
      |                                ^~
rlm_PLM.c:870:31: note: directive argument in the range [1, 2147483647]
  870 |           snprintf(tmp_str2,9,"%d",j+1);
      |                               ^~~~
In file included from /usr/include/stdio.h:980,
                 from /home/rapidbuild/bbs-3.23-bioc-rapid/R/include/R.h:44,
                 from preprocess.h:4,
                 from rlm_PLM.c:71:
In function ‘snprintf’,
    inlined from ‘rlm_PLMset_nameoutput’ at rlm_PLM.c:870:4:
/usr/include/x86_64-linux-gnu/bits/stdio2.h:54:10: note: ‘__builtin___snprintf_chk’ output between 2 and 11 bytes into a destination of size 9
   54 |   return __builtin___snprintf_chk (__s, __n, __USE_FORTIFY_LEVEL - 1,
      |          ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
   55 |                                    __glibc_objsize (__s), __fmt,
      |                                    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
   56 |                                    __va_arg_pack ());
      |                                    ~~~~~~~~~~~~~~~~~
rlm_PLM.c: In function ‘rlm_PLMset_nameoutput’:
rlm_PLM.c:868:32: warning: ‘%d’ directive output may be truncated writing between 1 and 10 bytes into a region of size 9 [-Wformat-truncation=]
  868 |           snprintf(tmp_str2,9,"%d",j+2);
      |                                ^~
rlm_PLM.c:868:31: note: directive argument in the range [2, 2147483647]
  868 |           snprintf(tmp_str2,9,"%d",j+2);
      |                               ^~~~
In function ‘snprintf’,
    inlined from ‘rlm_PLMset_nameoutput’ at rlm_PLM.c:868:4:
/usr/include/x86_64-linux-gnu/bits/stdio2.h:54:10: note: ‘__builtin___snprintf_chk’ output between 2 and 11 bytes into a destination of size 9
   54 |   return __builtin___snprintf_chk (__s, __n, __USE_FORTIFY_LEVEL - 1,
      |          ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
   55 |                                    __glibc_objsize (__s), __fmt,
      |                                    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
   56 |                                    __va_arg_pack ());
      |                                    ~~~~~~~~~~~~~~~~~
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c rlm_threestep.c -o rlm_threestep.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c rmaPLM_pseudo.c -o rmaPLM_pseudo.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c rma_PLM.c -o rma_PLM.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -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]
   60 |   int i;
      |       ^
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c scaling.c -o scaling.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -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]
   82 |   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]
  193 |   int rows,cols;
      |            ^~~~
threestep.c:193:7: warning: variable ‘rows’ set but not used [-Wunused-but-set-variable]
  193 |   int rows,cols;
      |       ^~~~
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c threestep_PLM.c -o threestep_PLM.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c threestep_common.c -o threestep_common.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -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]
   73 |   int first_ind;
      |       ^~~~~~~~~
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c threestep_summary_methods.c -o threestep_summary_methods.o
gcc -std=gnu2x -I"/home/rapidbuild/bbs-3.23-bioc-rapid/R/include" -DNDEBUG  -I'/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library/preprocessCore/include' -I/usr/local/include    -fpic  -g -O2  -Wall -Werror=format-security -c transfns.c -o transfns.o
gcc -std=gnu2x -shared -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 -llapack -L/home/rapidbuild/bbs-3.23-bioc-rapid/R/lib -lRblas -lgfortran -lm -lquadmath -lz
installing to /media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-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)

R Under development (unstable) (2025-10-28 r88973) -- "Unsuffered Consequences"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu

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 
  0.807   0.047   0.841 

R Under development (unstable) (2025-10-28 r88973) -- "Unsuffered Consequences"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu

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 
  0.109   0.044   0.140 

R Under development (unstable) (2025-10-28 r88973) -- "Unsuffered Consequences"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu

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: generics

Attaching package: 'generics'

The following objects are masked from 'package:base':

    as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
    setequal, union


Attaching package: 'BiocGenerics'

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, aperm, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
    mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
    rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
    unsplit, 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   
[1,] "affydata"
     LibPath                                                                  
[1,] "/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library"
     Item       Title                        
[1,] "Dilution" "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 
 11.408   0.487  11.884 

R Under development (unstable) (2025-10-28 r88973) -- "Unsuffered Consequences"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu

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: generics

Attaching package: 'generics'

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    as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
    setequal, union


Attaching package: 'BiocGenerics'

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, aperm, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
    mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
    rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
    unsplit, 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   
[1,] "affydata"
     LibPath                                                                  
[1,] "/media/volume/teran2_disk/rapidbuild/bbs-3.23-bioc-rapid/R/site-library"
     Item       Title                        
[1,] "Dilution" "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 
 27.884   0.441  28.344