Back to Multiple platform build/check report for BioC 3.13
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This page was generated on 2021-10-15 15:06:09 -0400 (Fri, 15 Oct 2021).

CHECK results for limma on tokay2

To the developers/maintainers of the limma package:
- Please allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/limma.git to
reflect on this report. See How and When does the builder pull? When will my changes propagate? here for more information.
- Make sure to use the following settings in order to reproduce any error or warning you see on this page.

raw results

Package 985/2041HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
limma 3.48.3  (landing page)
Gordon Smyth
Snapshot Date: 2021-10-14 04:50:12 -0400 (Thu, 14 Oct 2021)
git_url: https://git.bioconductor.org/packages/limma
git_branch: RELEASE_3_13
git_last_commit: 80282e9
git_last_commit_date: 2021-08-09 23:13:41 -0400 (Mon, 09 Aug 2021)
nebbiolo1Linux (Ubuntu 20.04.2 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
tokay2Windows Server 2012 R2 Standard / x64  OK    OK    OK    OK  UNNEEDED, same version is already published
machv2macOS 10.14.6 Mojave / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published

Summary

Package: limma
Version: 3.48.3
Command: C:\Users\biocbuild\bbs-3.13-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:limma.install-out.txt --library=C:\Users\biocbuild\bbs-3.13-bioc\R\library --no-vignettes --timings limma_3.48.3.tar.gz
StartedAt: 2021-10-15 01:07:30 -0400 (Fri, 15 Oct 2021)
EndedAt: 2021-10-15 01:09:34 -0400 (Fri, 15 Oct 2021)
EllapsedTime: 123.8 seconds
RetCode: 0
Status:   OK  
CheckDir: limma.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   C:\Users\biocbuild\bbs-3.13-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:limma.install-out.txt --library=C:\Users\biocbuild\bbs-3.13-bioc\R\library --no-vignettes --timings limma_3.48.3.tar.gz
###
##############################################################################
##############################################################################


* using log directory 'C:/Users/biocbuild/bbs-3.13-bioc/meat/limma.Rcheck'
* using R version 4.1.1 (2021-08-10)
* using platform: x86_64-w64-mingw32 (64-bit)
* using session charset: ISO8859-1
* using option '--no-vignettes'
* checking for file 'limma/DESCRIPTION' ... OK
* this is package 'limma' version '3.48.3'
* 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 whether package 'limma' can be installed ... OK
* checking installed package size ... NOTE
  installed size is  5.0Mb
  sub-directories of 1Mb or more:
    doc    1.4Mb
    help   1.2Mb
    html   1.2Mb
* checking package directory ... OK
* checking 'build' directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* loading checks for arch 'i386'
** 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
* loading checks for arch 'x64'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking compiled code ... NOTE
Note: information on .o files for i386 is not available
Note: information on .o files for x64 is not available
File 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/limma/libs/i386/limma.dll':
  Found 'abort', possibly from 'abort' (C), 'runtime' (Fortran)
File 'C:/Users/biocbuild/bbs-3.13-bioc/R/library/limma/libs/x64/limma.dll':
  Found 'abort', possibly from 'abort' (C), 'runtime' (Fortran)

Compiled code should not call entry points which might terminate R nor
write to stdout/stderr instead of to the console, nor use Fortran I/O
nor system RNGs. The detected symbols are linked into the code but
might come from libraries and not actually be called.

See 'Writing portable packages' in the 'Writing R Extensions' manual.
* checking installed files from 'inst/doc' ... OK
* checking files in 'vignettes' ... OK
* checking examples ...
** running examples for arch 'i386' ... OK
** running examples for arch 'x64' ... OK
* checking for unstated dependencies in 'tests' ... OK
* checking tests ...
** running tests for arch 'i386' ...
  Running 'limma-Tests.R'
  Comparing 'limma-Tests.Rout' to 'limma-Tests.Rout.save' ... OK
 OK
** running tests for arch 'x64' ...
  Running 'limma-Tests.R'
  Comparing 'limma-Tests.Rout' to 'limma-Tests.Rout.save' ... OK
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in 'inst/doc' ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 2 NOTEs
See
  'C:/Users/biocbuild/bbs-3.13-bioc/meat/limma.Rcheck/00check.log'
for details.



Installation output

limma.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   C:\cygwin\bin\curl.exe -O http://155.52.207.165/BBS/3.13/bioc/src/contrib/limma_3.48.3.tar.gz && rm -rf limma.buildbin-libdir && mkdir limma.buildbin-libdir && C:\Users\biocbuild\bbs-3.13-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=limma.buildbin-libdir limma_3.48.3.tar.gz && C:\Users\biocbuild\bbs-3.13-bioc\R\bin\R.exe CMD INSTALL limma_3.48.3.zip && rm limma_3.48.3.tar.gz limma_3.48.3.zip
###
##############################################################################
##############################################################################


  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed

  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
100 1461k  100 1461k    0     0  3432k      0 --:--:-- --:--:-- --:--:-- 3438k

install for i386

* installing *source* package 'limma' ...
** using staged installation
** libs
"C:/rtools40/mingw32/bin/"gcc  -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG     -I"c:/extsoft/include"     -O2 -Wall  -std=gnu99 -mfpmath=sse -msse2 -mstackrealign  -c init.c -o init.o
"C:/rtools40/mingw32/bin/"gcc  -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG     -I"c:/extsoft/include"     -O2 -Wall  -std=gnu99 -mfpmath=sse -msse2 -mstackrealign  -c normexp.c -o normexp.o
"C:/rtools40/mingw32/bin/"gcc  -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG     -I"c:/extsoft/include"     -O2 -Wall  -std=gnu99 -mfpmath=sse -msse2 -mstackrealign  -c weighted_lowess.c -o weighted_lowess.o
C:/rtools40/mingw32/bin/gcc -shared -s -static-libgcc -o limma.dll tmp.def init.o normexp.o weighted_lowess.o -Lc:/extsoft/lib/i386 -Lc:/extsoft/lib -LC:/Users/BIOCBU~1/BBS-3~1.13-/R/bin/i386 -lR
installing to C:/Users/biocbuild/bbs-3.13-bioc/meat/limma.buildbin-libdir/00LOCK-limma/00new/limma/libs/i386
** R
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
  converting help for package 'limma'
    finding HTML links ... done
    01Introduction                          html  
    02classes                               html  
    03reading                               html  
    04Background                            html  
    05Normalization                         html  
    06linearmodels                          html  
    07SingleChannel                         html  
    08Tests                                 html  
    09Diagnostics                           html  
    10GeneSetTests                          html  
    11RNAseq                                html  
    EList                                   html  
    LargeDataObject                         html  
    PrintLayout                             html  
    TestResults                             html  
    alias2Symbol                            html  
    anova-method                            html  
    arrayWeights                            html  
    arrayWeightsQuick                       html  
    asMatrixWeights                         html  
    asdataframe                             html  
    asmalist                                html  
    asmatrix                                html  
    auROC                                   html  
    avearrays                               html  
    avedups                                 html  
    avereps                                 html  
    backgroundcorrect                       html  
    barcodeplot                             html  
    beadCountWeights                        html  
    blockDiag                               html  
    bwss                                    html  
    bwss.matrix                             html  
    camera                                  html  
    cbind                                   html  
    changelog                               html  
    channel2M                               html  
    chooseLowessSpan                        html  
    classifytestsF                          html  
    contrastAsCoef                          html  
    contrasts.fit                           html  
    controlStatus                           html  
    coolmap                                 html  
    cumOverlap                              html  
    decideTests                             html  
    detectionPValue                         html  
    diffSplice                              html  
    dim                                     html  
    dimnames                                html  
    dupcor                                  html  
    ebayes                                  html  
    exprsMA                                 html  
    fitGammaIntercept                       html  
    fitfdist                                html  
    fitmixture                              html  
    fitted.MArrayLM                         html  
    genas                                   html  
    geneSetTest                             html  
    getEAWP                                 html  
    getSpacing                              html  
    getlayout                               html  
    gls.series                              html  
    goana                                   html  
    gridspotrc                              html  
    head                                    html  
    heatdiagram                             html  
REDIRECT:topic	 Previous alias or file overwritten by alias: C:/Users/biocbuild/bbs-3.13-bioc/meat/limma.buildbin-libdir/00LOCK-limma/00new/limma/help/heatDiagram.html
    helpMethods                             html  
    ids2indices                             html  
    imageplot                               html  
    imageplot3by2                           html  
    intraspotCorrelation                    html  
    isfullrank                              html  
    isnumeric                               html  
    kooperberg                              html  
    limmaUsersGuide                         html  
    lm.series                               html  
    lmFit                                   html  
    lmscFit                                 html  
    loessfit                                html  
    logcosh                                 html  
    logsumexp                               html  
    ma3x3                                   html  
    makeContrasts                           html  
    makeunique                              html  
    malist                                  html  
    marraylm                                html  
    mdplot                                  html  
    merge                                   html  
    mergeScansRG                            html  
    modelMatrix                             html  
    modifyWeights                           html  
    mrlm                                    html  
    nec                                     html  
    normalizeCyclicLoess                    html  
    normalizeMedianAbsValues                html  
    normalizeRobustSpline                   html  
    normalizeVSN                            html  
    normalizeWithinArrays                   html  
    normalizebetweenarrays                  html  
    normalizeprintorder                     html  
    normalizequantiles                      html  
    normexpfit                              html  
    normexpfitcontrol                       html  
    normexpfitdetectionp                    html  
    normexpsignal                           html  
    plotDensities                           html  
    plotExonJunc                            html  
    plotExons                               html  
    plotFB                                  html  
    plotMD                                  html  
    plotMDS                                 html  
    plotRLDF                                html  
    plotSA                                  html  
    plotSplice                              html  
    plotWithHighlights                      html  
    plotlines                               html  
    plotma                                  html  
    plotma3by2                              html  
    plotprinttiploess                       html  
    poolvar                                 html  
    predFCm                                 html  
    printHead                               html  
    printorder                              html  
    printtipWeights                         html  
    propTrueNull                            html  
    propexpr                                html  
    protectMetachar                         html  
    qqt                                     html  
    qualwt                                  html  
    rankSumTestwithCorrelation              html  
    read.columns                            html  
    read.idat                               html  
    read.ilmn                               html  
    read.ilmn.targets                       html  
    read.maimages                           html  
    readGPRHeader                           html  
    readImaGeneHeader                       html  
    readSpotTypes                           html  
    readTargets                             html  
    readgal                                 html  
    removeBatchEffect                       html  
    removeext                               html  
    residuals.MArrayLM                      html  
    rglist                                  html  
    roast                                   html  
    romer                                   html  
    selectmodel                             html  
    squeezeVar                              html  
    strsplit2                               html  
    subsetting                              html  
    summary                                 html  
    targetsA2C                              html  
    tmixture                                html  
    topGO                                   html  
    topRomer                                html  
    topSplice                               html  
    toptable                                html  
    tricubeMovingAverage                    html  
    trigammainverse                         html  
    trimWhiteSpace                          html  
    uniquegenelist                          html  
    unwrapdups                              html  
    venn                                    html  
    volcanoplot                             html  
    voom                                    html  
    voomWithQualityWeights                  html  
    vooma                                   html  
    weightedLowess                          html  
    weightedmedian                          html  
    writefit                                html  
    wsva                                    html  
    zscore                                  html  
    zscoreT                                 html  
** building package indices
** installing vignettes
   'intro.Rnw' 
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path

install for x64

* installing *source* package 'limma' ...
** libs
"C:/rtools40/mingw64/bin/"gcc  -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG     -I"C:/extsoft/include"     -O2 -Wall  -std=gnu99 -mfpmath=sse -msse2 -mstackrealign  -c init.c -o init.o
"C:/rtools40/mingw64/bin/"gcc  -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG     -I"C:/extsoft/include"     -O2 -Wall  -std=gnu99 -mfpmath=sse -msse2 -mstackrealign  -c normexp.c -o normexp.o
"C:/rtools40/mingw64/bin/"gcc  -I"C:/Users/BIOCBU~1/BBS-3~1.13-/R/include" -DNDEBUG     -I"C:/extsoft/include"     -O2 -Wall  -std=gnu99 -mfpmath=sse -msse2 -mstackrealign  -c weighted_lowess.c -o weighted_lowess.o
C:/rtools40/mingw64/bin/gcc -shared -s -static-libgcc -o limma.dll tmp.def init.o normexp.o weighted_lowess.o -LC:/extsoft/lib/x64 -LC:/extsoft/lib -LC:/Users/BIOCBU~1/BBS-3~1.13-/R/bin/x64 -lR
installing to C:/Users/biocbuild/bbs-3.13-bioc/meat/limma.buildbin-libdir/limma/libs/x64
** testing if installed package can be loaded
* MD5 sums
packaged installation of 'limma' as limma_3.48.3.zip
* DONE (limma)
* installing to library 'C:/Users/biocbuild/bbs-3.13-bioc/R/library'
package 'limma' successfully unpacked and MD5 sums checked

Tests output

limma.Rcheck/tests_i386/limma-Tests.Rout.save


R version 4.1.0 beta (2021-05-06 r80268) -- "Camp Pontanezen"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(limma)
> options(warnPartialMatchArgs=TRUE,warnPartialMatchAttr=TRUE,warnPartialMatchDollar=TRUE)
> 
> set.seed(0); u <- runif(100)
> 
> ### strsplit2
> 
> x <- c("ab;cd;efg","abc;def","z","")
> strsplit2(x,split=";")
     [,1]  [,2]  [,3] 
[1,] "ab"  "cd"  "efg"
[2,] "abc" "def" ""   
[3,] "z"   ""    ""   
[4,] ""    ""    ""   
> 
> ### removeext
> 
> removeExt(c("slide1.spot","slide.2.spot"))
[1] "slide1"  "slide.2"
> removeExt(c("slide1.spot","slide"))
[1] "slide1.spot" "slide"      
> 
> ### printorder
> 
> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6),ndups=2,start="topright",npins=4)
$printorder
  [1]   6   5   4   3   2   1  12  11  10   9   8   7  18  17  16  15  14  13
 [19]  24  23  22  21  20  19  30  29  28  27  26  25  36  35  34  33  32  31
 [37]  42  41  40  39  38  37  48  47  46  45  44  43   6   5   4   3   2   1
 [55]  12  11  10   9   8   7  18  17  16  15  14  13  24  23  22  21  20  19
 [73]  30  29  28  27  26  25  36  35  34  33  32  31  42  41  40  39  38  37
 [91]  48  47  46  45  44  43   6   5   4   3   2   1  12  11  10   9   8   7
[109]  18  17  16  15  14  13  24  23  22  21  20  19  30  29  28  27  26  25
[127]  36  35  34  33  32  31  42  41  40  39  38  37  48  47  46  45  44  43
[145]   6   5   4   3   2   1  12  11  10   9   8   7  18  17  16  15  14  13
[163]  24  23  22  21  20  19  30  29  28  27  26  25  36  35  34  33  32  31
[181]  42  41  40  39  38  37  48  47  46  45  44  43  54  53  52  51  50  49
[199]  60  59  58  57  56  55  66  65  64  63  62  61  72  71  70  69  68  67
[217]  78  77  76  75  74  73  84  83  82  81  80  79  90  89  88  87  86  85
[235]  96  95  94  93  92  91  54  53  52  51  50  49  60  59  58  57  56  55
[253]  66  65  64  63  62  61  72  71  70  69  68  67  78  77  76  75  74  73
[271]  84  83  82  81  80  79  90  89  88  87  86  85  96  95  94  93  92  91
[289]  54  53  52  51  50  49  60  59  58  57  56  55  66  65  64  63  62  61
[307]  72  71  70  69  68  67  78  77  76  75  74  73  84  83  82  81  80  79
[325]  90  89  88  87  86  85  96  95  94  93  92  91  54  53  52  51  50  49
[343]  60  59  58  57  56  55  66  65  64  63  62  61  72  71  70  69  68  67
[361]  78  77  76  75  74  73  84  83  82  81  80  79  90  89  88  87  86  85
[379]  96  95  94  93  92  91 102 101 100  99  98  97 108 107 106 105 104 103
[397] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[415] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[433] 102 101 100  99  98  97 108 107 106 105 104 103 114 113 112 111 110 109
[451] 120 119 118 117 116 115 126 125 124 123 122 121 132 131 130 129 128 127
[469] 138 137 136 135 134 133 144 143 142 141 140 139 102 101 100  99  98  97
[487] 108 107 106 105 104 103 114 113 112 111 110 109 120 119 118 117 116 115
[505] 126 125 124 123 122 121 132 131 130 129 128 127 138 137 136 135 134 133
[523] 144 143 142 141 140 139 102 101 100  99  98  97 108 107 106 105 104 103
[541] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[559] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[577] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[595] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[613] 186 185 184 183 182 181 192 191 190 189 188 187 150 149 148 147 146 145
[631] 156 155 154 153 152 151 162 161 160 159 158 157 168 167 166 165 164 163
[649] 174 173 172 171 170 169 180 179 178 177 176 175 186 185 184 183 182 181
[667] 192 191 190 189 188 187 150 149 148 147 146 145 156 155 154 153 152 151
[685] 162 161 160 159 158 157 168 167 166 165 164 163 174 173 172 171 170 169
[703] 180 179 178 177 176 175 186 185 184 183 182 181 192 191 190 189 188 187
[721] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[739] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[757] 186 185 184 183 182 181 192 191 190 189 188 187

$plate
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

$plate.r
  [1]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4
 [26]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  3  3
 [51]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
 [76]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  2  2  2  2
[101]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
[126]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  1  1  1  1  1  1
[151]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[176]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  8  8  8  8  8  8  8  8
[201]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8
[226]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  7  7  7  7  7  7  7  7  7  7
[251]  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7
[276]  7  7  7  7  7  7  7  7  7  7  7  7  7  6  6  6  6  6  6  6  6  6  6  6  6
[301]  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6
[326]  6  6  6  6  6  6  6  6  6  6  6  5  5  5  5  5  5  5  5  5  5  5  5  5  5
[351]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
[376]  5  5  5  5  5  5  5  5  5 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[401] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[426] 12 12 12 12 12 12 12 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[451] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[476] 11 11 11 11 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[501] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[526] 10 10 10  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[551]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[576]  9 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
[601] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 15
[626] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
[651] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 14 14 14
[676] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
[701] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 13 13 13 13 13
[726] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
[751] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13

$plate.c
  [1]  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15
 [26] 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3
 [51]  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14
 [76] 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2
[101]  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13
[126] 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1
[151]  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18
[176] 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6
[201]  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17
[226] 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5
[251]  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16
[276] 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4
[301]  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21
[326] 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9
[351]  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20
[376] 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8
[401]  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19
[426] 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7
[451] 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24
[476] 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12
[501] 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23
[526] 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11
[551] 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22
[576] 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10
[601] 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3
[626]  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15
[651] 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2
[676]  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14
[701] 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1
[726]  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13
[751] 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22

$plateposition
  [1] "p1D03" "p1D03" "p1D02" "p1D02" "p1D01" "p1D01" "p1D06" "p1D06" "p1D05"
 [10] "p1D05" "p1D04" "p1D04" "p1D09" "p1D09" "p1D08" "p1D08" "p1D07" "p1D07"
 [19] "p1D12" "p1D12" "p1D11" "p1D11" "p1D10" "p1D10" "p1D15" "p1D15" "p1D14"
 [28] "p1D14" "p1D13" "p1D13" "p1D18" "p1D18" "p1D17" "p1D17" "p1D16" "p1D16"
 [37] "p1D21" "p1D21" "p1D20" "p1D20" "p1D19" "p1D19" "p1D24" "p1D24" "p1D23"
 [46] "p1D23" "p1D22" "p1D22" "p1C03" "p1C03" "p1C02" "p1C02" "p1C01" "p1C01"
 [55] "p1C06" "p1C06" "p1C05" "p1C05" "p1C04" "p1C04" "p1C09" "p1C09" "p1C08"
 [64] "p1C08" "p1C07" "p1C07" "p1C12" "p1C12" "p1C11" "p1C11" "p1C10" "p1C10"
 [73] "p1C15" "p1C15" "p1C14" "p1C14" "p1C13" "p1C13" "p1C18" "p1C18" "p1C17"
 [82] "p1C17" "p1C16" "p1C16" "p1C21" "p1C21" "p1C20" "p1C20" "p1C19" "p1C19"
 [91] "p1C24" "p1C24" "p1C23" "p1C23" "p1C22" "p1C22" "p1B03" "p1B03" "p1B02"
[100] "p1B02" "p1B01" "p1B01" "p1B06" "p1B06" "p1B05" "p1B05" "p1B04" "p1B04"
[109] "p1B09" "p1B09" "p1B08" "p1B08" "p1B07" "p1B07" "p1B12" "p1B12" "p1B11"
[118] "p1B11" "p1B10" "p1B10" "p1B15" "p1B15" "p1B14" "p1B14" "p1B13" "p1B13"
[127] "p1B18" "p1B18" "p1B17" "p1B17" "p1B16" "p1B16" "p1B21" "p1B21" "p1B20"
[136] "p1B20" "p1B19" "p1B19" "p1B24" "p1B24" "p1B23" "p1B23" "p1B22" "p1B22"
[145] "p1A03" "p1A03" "p1A02" "p1A02" "p1A01" "p1A01" "p1A06" "p1A06" "p1A05"
[154] "p1A05" "p1A04" "p1A04" "p1A09" "p1A09" "p1A08" "p1A08" "p1A07" "p1A07"
[163] "p1A12" "p1A12" "p1A11" "p1A11" "p1A10" "p1A10" "p1A15" "p1A15" "p1A14"
[172] "p1A14" "p1A13" "p1A13" "p1A18" "p1A18" "p1A17" "p1A17" "p1A16" "p1A16"
[181] "p1A21" "p1A21" "p1A20" "p1A20" "p1A19" "p1A19" "p1A24" "p1A24" "p1A23"
[190] "p1A23" "p1A22" "p1A22" "p1H03" "p1H03" "p1H02" "p1H02" "p1H01" "p1H01"
[199] "p1H06" "p1H06" "p1H05" "p1H05" "p1H04" "p1H04" "p1H09" "p1H09" "p1H08"
[208] "p1H08" "p1H07" "p1H07" "p1H12" "p1H12" "p1H11" "p1H11" "p1H10" "p1H10"
[217] "p1H15" "p1H15" "p1H14" "p1H14" "p1H13" "p1H13" "p1H18" "p1H18" "p1H17"
[226] "p1H17" "p1H16" "p1H16" "p1H21" "p1H21" "p1H20" "p1H20" "p1H19" "p1H19"
[235] "p1H24" "p1H24" "p1H23" "p1H23" "p1H22" "p1H22" "p1G03" "p1G03" "p1G02"
[244] "p1G02" "p1G01" "p1G01" "p1G06" "p1G06" "p1G05" "p1G05" "p1G04" "p1G04"
[253] "p1G09" "p1G09" "p1G08" "p1G08" "p1G07" "p1G07" "p1G12" "p1G12" "p1G11"
[262] "p1G11" "p1G10" "p1G10" "p1G15" "p1G15" "p1G14" "p1G14" "p1G13" "p1G13"
[271] "p1G18" "p1G18" "p1G17" "p1G17" "p1G16" "p1G16" "p1G21" "p1G21" "p1G20"
[280] "p1G20" "p1G19" "p1G19" "p1G24" "p1G24" "p1G23" "p1G23" "p1G22" "p1G22"
[289] "p1F03" "p1F03" "p1F02" "p1F02" "p1F01" "p1F01" "p1F06" "p1F06" "p1F05"
[298] "p1F05" "p1F04" "p1F04" "p1F09" "p1F09" "p1F08" "p1F08" "p1F07" "p1F07"
[307] "p1F12" "p1F12" "p1F11" "p1F11" "p1F10" "p1F10" "p1F15" "p1F15" "p1F14"
[316] "p1F14" "p1F13" "p1F13" "p1F18" "p1F18" "p1F17" "p1F17" "p1F16" "p1F16"
[325] "p1F21" "p1F21" "p1F20" "p1F20" "p1F19" "p1F19" "p1F24" "p1F24" "p1F23"
[334] "p1F23" "p1F22" "p1F22" "p1E03" "p1E03" "p1E02" "p1E02" "p1E01" "p1E01"
[343] "p1E06" "p1E06" "p1E05" "p1E05" "p1E04" "p1E04" "p1E09" "p1E09" "p1E08"
[352] "p1E08" "p1E07" "p1E07" "p1E12" "p1E12" "p1E11" "p1E11" "p1E10" "p1E10"
[361] "p1E15" "p1E15" "p1E14" "p1E14" "p1E13" "p1E13" "p1E18" "p1E18" "p1E17"
[370] "p1E17" "p1E16" "p1E16" "p1E21" "p1E21" "p1E20" "p1E20" "p1E19" "p1E19"
[379] "p1E24" "p1E24" "p1E23" "p1E23" "p1E22" "p1E22" "p1L03" "p1L03" "p1L02"
[388] "p1L02" "p1L01" "p1L01" "p1L06" "p1L06" "p1L05" "p1L05" "p1L04" "p1L04"
[397] "p1L09" "p1L09" "p1L08" "p1L08" "p1L07" "p1L07" "p1L12" "p1L12" "p1L11"
[406] "p1L11" "p1L10" "p1L10" "p1L15" "p1L15" "p1L14" "p1L14" "p1L13" "p1L13"
[415] "p1L18" "p1L18" "p1L17" "p1L17" "p1L16" "p1L16" "p1L21" "p1L21" "p1L20"
[424] "p1L20" "p1L19" "p1L19" "p1L24" "p1L24" "p1L23" "p1L23" "p1L22" "p1L22"
[433] "p1K03" "p1K03" "p1K02" "p1K02" "p1K01" "p1K01" "p1K06" "p1K06" "p1K05"
[442] "p1K05" "p1K04" "p1K04" "p1K09" "p1K09" "p1K08" "p1K08" "p1K07" "p1K07"
[451] "p1K12" "p1K12" "p1K11" "p1K11" "p1K10" "p1K10" "p1K15" "p1K15" "p1K14"
[460] "p1K14" "p1K13" "p1K13" "p1K18" "p1K18" "p1K17" "p1K17" "p1K16" "p1K16"
[469] "p1K21" "p1K21" "p1K20" "p1K20" "p1K19" "p1K19" "p1K24" "p1K24" "p1K23"
[478] "p1K23" "p1K22" "p1K22" "p1J03" "p1J03" "p1J02" "p1J02" "p1J01" "p1J01"
[487] "p1J06" "p1J06" "p1J05" "p1J05" "p1J04" "p1J04" "p1J09" "p1J09" "p1J08"
[496] "p1J08" "p1J07" "p1J07" "p1J12" "p1J12" "p1J11" "p1J11" "p1J10" "p1J10"
[505] "p1J15" "p1J15" "p1J14" "p1J14" "p1J13" "p1J13" "p1J18" "p1J18" "p1J17"
[514] "p1J17" "p1J16" "p1J16" "p1J21" "p1J21" "p1J20" "p1J20" "p1J19" "p1J19"
[523] "p1J24" "p1J24" "p1J23" "p1J23" "p1J22" "p1J22" "p1I03" "p1I03" "p1I02"
[532] "p1I02" "p1I01" "p1I01" "p1I06" "p1I06" "p1I05" "p1I05" "p1I04" "p1I04"
[541] "p1I09" "p1I09" "p1I08" "p1I08" "p1I07" "p1I07" "p1I12" "p1I12" "p1I11"
[550] "p1I11" "p1I10" "p1I10" "p1I15" "p1I15" "p1I14" "p1I14" "p1I13" "p1I13"
[559] "p1I18" "p1I18" "p1I17" "p1I17" "p1I16" "p1I16" "p1I21" "p1I21" "p1I20"
[568] "p1I20" "p1I19" "p1I19" "p1I24" "p1I24" "p1I23" "p1I23" "p1I22" "p1I22"
[577] "p1P03" "p1P03" "p1P02" "p1P02" "p1P01" "p1P01" "p1P06" "p1P06" "p1P05"
[586] "p1P05" "p1P04" "p1P04" "p1P09" "p1P09" "p1P08" "p1P08" "p1P07" "p1P07"
[595] "p1P12" "p1P12" "p1P11" "p1P11" "p1P10" "p1P10" "p1P15" "p1P15" "p1P14"
[604] "p1P14" "p1P13" "p1P13" "p1P18" "p1P18" "p1P17" "p1P17" "p1P16" "p1P16"
[613] "p1P21" "p1P21" "p1P20" "p1P20" "p1P19" "p1P19" "p1P24" "p1P24" "p1P23"
[622] "p1P23" "p1P22" "p1P22" "p1O03" "p1O03" "p1O02" "p1O02" "p1O01" "p1O01"
[631] "p1O06" "p1O06" "p1O05" "p1O05" "p1O04" "p1O04" "p1O09" "p1O09" "p1O08"
[640] "p1O08" "p1O07" "p1O07" "p1O12" "p1O12" "p1O11" "p1O11" "p1O10" "p1O10"
[649] "p1O15" "p1O15" "p1O14" "p1O14" "p1O13" "p1O13" "p1O18" "p1O18" "p1O17"
[658] "p1O17" "p1O16" "p1O16" "p1O21" "p1O21" "p1O20" "p1O20" "p1O19" "p1O19"
[667] "p1O24" "p1O24" "p1O23" "p1O23" "p1O22" "p1O22" "p1N03" "p1N03" "p1N02"
[676] "p1N02" "p1N01" "p1N01" "p1N06" "p1N06" "p1N05" "p1N05" "p1N04" "p1N04"
[685] "p1N09" "p1N09" "p1N08" "p1N08" "p1N07" "p1N07" "p1N12" "p1N12" "p1N11"
[694] "p1N11" "p1N10" "p1N10" "p1N15" "p1N15" "p1N14" "p1N14" "p1N13" "p1N13"
[703] "p1N18" "p1N18" "p1N17" "p1N17" "p1N16" "p1N16" "p1N21" "p1N21" "p1N20"
[712] "p1N20" "p1N19" "p1N19" "p1N24" "p1N24" "p1N23" "p1N23" "p1N22" "p1N22"
[721] "p1M03" "p1M03" "p1M02" "p1M02" "p1M01" "p1M01" "p1M06" "p1M06" "p1M05"
[730] "p1M05" "p1M04" "p1M04" "p1M09" "p1M09" "p1M08" "p1M08" "p1M07" "p1M07"
[739] "p1M12" "p1M12" "p1M11" "p1M11" "p1M10" "p1M10" "p1M15" "p1M15" "p1M14"
[748] "p1M14" "p1M13" "p1M13" "p1M18" "p1M18" "p1M17" "p1M17" "p1M16" "p1M16"
[757] "p1M21" "p1M21" "p1M20" "p1M20" "p1M19" "p1M19" "p1M24" "p1M24" "p1M23"
[766] "p1M23" "p1M22" "p1M22"

> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6))
$printorder
  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
 [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2
 [51]  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
 [76] 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4
[101]  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
[126] 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6
[151]  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
[176] 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8
[201]  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
[226] 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10
[251] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
[276] 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12
[301] 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
[326] 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14
[351] 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
[376] 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16
[401] 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
[426] 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18
[451] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
[476] 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
[501] 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
[526] 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22
[551] 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
[576] 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
[601] 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1
[626]  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
[651] 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3
[676]  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
[701] 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5
[726]  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
[751] 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

$plate
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[334] 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2
[371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[519] 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2
[556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[667] 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2
[704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

$plate.r
  [1]  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  4
 [26]  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3  3
 [51]  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3  3
 [76]  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2  2
[101]  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2  2
[126]  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1  1
[151]  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1  5
[176]  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  4  4  4  4  4  4  8  8
[201]  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  4  4  4  4  4  4  8  8  8
[226]  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3  3  3  3  3  3  7  7  7  7
[251]  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3  3  3  3  3  7  7  7  7  7
[276]  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2  2  2  2  6  6  6  6  6  6
[301] 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2  2  2  6  6  6  6  6  6 10
[326] 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1  1  5  5  5  5  5  5  9  9
[351]  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9
[376]  9  9  9 13 13 13 13 13 13  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12
[401] 12 12 16 16 16 16 16 16  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12
[426] 12 16 16 16 16 16 16  3  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11
[451] 15 15 15 15 15 15  3  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15
[476] 15 15 15 15 15  2  2  2  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14
[501] 14 14 14 14  2  2  2  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14
[526] 14 14 14  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13
[551] 13 13  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13
[576] 13  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16
[601]  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3
[626]  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3
[651]  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2
[676]  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2
[701]  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1
[726]  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1
[751]  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13

$plate.c
  [1]  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1
 [26]  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5
 [51]  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9
 [76] 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13
[101] 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17
[126] 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21
[151]  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1
[176]  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  2  6 10 14 18 22  2  6
[201] 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10
[226] 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14
[251] 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18
[276] 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22
[301]  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2
[326]  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6
[351] 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10
[376] 14 18 22  2  6 10 14 18 22  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15
[401] 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19
[426] 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23
[451]  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3
[476]  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7
[501] 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11
[526] 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15
[551] 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19
[576] 23  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24
[601]  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4
[626]  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8
[651] 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12
[676] 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16
[701] 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20
[726] 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24
[751]  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24

$plateposition
  [1] "p1D01" "p1D05" "p1D09" "p1D13" "p1D17" "p1D21" "p1H01" "p1H05" "p1H09"
 [10] "p1H13" "p1H17" "p1H21" "p1L01" "p1L05" "p1L09" "p1L13" "p1L17" "p1L21"
 [19] "p1P01" "p1P05" "p1P09" "p1P13" "p1P17" "p1P21" "p2D01" "p2D05" "p2D09"
 [28] "p2D13" "p2D17" "p2D21" "p2H01" "p2H05" "p2H09" "p2H13" "p2H17" "p2H21"
 [37] "p2L01" "p2L05" "p2L09" "p2L13" "p2L17" "p2L21" "p2P01" "p2P05" "p2P09"
 [46] "p2P13" "p2P17" "p2P21" "p1C01" "p1C05" "p1C09" "p1C13" "p1C17" "p1C21"
 [55] "p1G01" "p1G05" "p1G09" "p1G13" "p1G17" "p1G21" "p1K01" "p1K05" "p1K09"
 [64] "p1K13" "p1K17" "p1K21" "p1O01" "p1O05" "p1O09" "p1O13" "p1O17" "p1O21"
 [73] "p2C01" "p2C05" "p2C09" "p2C13" "p2C17" "p2C21" "p2G01" "p2G05" "p2G09"
 [82] "p2G13" "p2G17" "p2G21" "p2K01" "p2K05" "p2K09" "p2K13" "p2K17" "p2K21"
 [91] "p2O01" "p2O05" "p2O09" "p2O13" "p2O17" "p2O21" "p1B01" "p1B05" "p1B09"
[100] "p1B13" "p1B17" "p1B21" "p1F01" "p1F05" "p1F09" "p1F13" "p1F17" "p1F21"
[109] "p1J01" "p1J05" "p1J09" "p1J13" "p1J17" "p1J21" "p1N01" "p1N05" "p1N09"
[118] "p1N13" "p1N17" "p1N21" "p2B01" "p2B05" "p2B09" "p2B13" "p2B17" "p2B21"
[127] "p2F01" "p2F05" "p2F09" "p2F13" "p2F17" "p2F21" "p2J01" "p2J05" "p2J09"
[136] "p2J13" "p2J17" "p2J21" "p2N01" "p2N05" "p2N09" "p2N13" "p2N17" "p2N21"
[145] "p1A01" "p1A05" "p1A09" "p1A13" "p1A17" "p1A21" "p1E01" "p1E05" "p1E09"
[154] "p1E13" "p1E17" "p1E21" "p1I01" "p1I05" "p1I09" "p1I13" "p1I17" "p1I21"
[163] "p1M01" "p1M05" "p1M09" "p1M13" "p1M17" "p1M21" "p2A01" "p2A05" "p2A09"
[172] "p2A13" "p2A17" "p2A21" "p2E01" "p2E05" "p2E09" "p2E13" "p2E17" "p2E21"
[181] "p2I01" "p2I05" "p2I09" "p2I13" "p2I17" "p2I21" "p2M01" "p2M05" "p2M09"
[190] "p2M13" "p2M17" "p2M21" "p1D02" "p1D06" "p1D10" "p1D14" "p1D18" "p1D22"
[199] "p1H02" "p1H06" "p1H10" "p1H14" "p1H18" "p1H22" "p1L02" "p1L06" "p1L10"
[208] "p1L14" "p1L18" "p1L22" "p1P02" "p1P06" "p1P10" "p1P14" "p1P18" "p1P22"
[217] "p2D02" "p2D06" "p2D10" "p2D14" "p2D18" "p2D22" "p2H02" "p2H06" "p2H10"
[226] "p2H14" "p2H18" "p2H22" "p2L02" "p2L06" "p2L10" "p2L14" "p2L18" "p2L22"
[235] "p2P02" "p2P06" "p2P10" "p2P14" "p2P18" "p2P22" "p1C02" "p1C06" "p1C10"
[244] "p1C14" "p1C18" "p1C22" "p1G02" "p1G06" "p1G10" "p1G14" "p1G18" "p1G22"
[253] "p1K02" "p1K06" "p1K10" "p1K14" "p1K18" "p1K22" "p1O02" "p1O06" "p1O10"
[262] "p1O14" "p1O18" "p1O22" "p2C02" "p2C06" "p2C10" "p2C14" "p2C18" "p2C22"
[271] "p2G02" "p2G06" "p2G10" "p2G14" "p2G18" "p2G22" "p2K02" "p2K06" "p2K10"
[280] "p2K14" "p2K18" "p2K22" "p2O02" "p2O06" "p2O10" "p2O14" "p2O18" "p2O22"
[289] "p1B02" "p1B06" "p1B10" "p1B14" "p1B18" "p1B22" "p1F02" "p1F06" "p1F10"
[298] "p1F14" "p1F18" "p1F22" "p1J02" "p1J06" "p1J10" "p1J14" "p1J18" "p1J22"
[307] "p1N02" "p1N06" "p1N10" "p1N14" "p1N18" "p1N22" "p2B02" "p2B06" "p2B10"
[316] "p2B14" "p2B18" "p2B22" "p2F02" "p2F06" "p2F10" "p2F14" "p2F18" "p2F22"
[325] "p2J02" "p2J06" "p2J10" "p2J14" "p2J18" "p2J22" "p2N02" "p2N06" "p2N10"
[334] "p2N14" "p2N18" "p2N22" "p1A02" "p1A06" "p1A10" "p1A14" "p1A18" "p1A22"
[343] "p1E02" "p1E06" "p1E10" "p1E14" "p1E18" "p1E22" "p1I02" "p1I06" "p1I10"
[352] "p1I14" "p1I18" "p1I22" "p1M02" "p1M06" "p1M10" "p1M14" "p1M18" "p1M22"
[361] "p2A02" "p2A06" "p2A10" "p2A14" "p2A18" "p2A22" "p2E02" "p2E06" "p2E10"
[370] "p2E14" "p2E18" "p2E22" "p2I02" "p2I06" "p2I10" "p2I14" "p2I18" "p2I22"
[379] "p2M02" "p2M06" "p2M10" "p2M14" "p2M18" "p2M22" "p1D03" "p1D07" "p1D11"
[388] "p1D15" "p1D19" "p1D23" "p1H03" "p1H07" "p1H11" "p1H15" "p1H19" "p1H23"
[397] "p1L03" "p1L07" "p1L11" "p1L15" "p1L19" "p1L23" "p1P03" "p1P07" "p1P11"
[406] "p1P15" "p1P19" "p1P23" "p2D03" "p2D07" "p2D11" "p2D15" "p2D19" "p2D23"
[415] "p2H03" "p2H07" "p2H11" "p2H15" "p2H19" "p2H23" "p2L03" "p2L07" "p2L11"
[424] "p2L15" "p2L19" "p2L23" "p2P03" "p2P07" "p2P11" "p2P15" "p2P19" "p2P23"
[433] "p1C03" "p1C07" "p1C11" "p1C15" "p1C19" "p1C23" "p1G03" "p1G07" "p1G11"
[442] "p1G15" "p1G19" "p1G23" "p1K03" "p1K07" "p1K11" "p1K15" "p1K19" "p1K23"
[451] "p1O03" "p1O07" "p1O11" "p1O15" "p1O19" "p1O23" "p2C03" "p2C07" "p2C11"
[460] "p2C15" "p2C19" "p2C23" "p2G03" "p2G07" "p2G11" "p2G15" "p2G19" "p2G23"
[469] "p2K03" "p2K07" "p2K11" "p2K15" "p2K19" "p2K23" "p2O03" "p2O07" "p2O11"
[478] "p2O15" "p2O19" "p2O23" "p1B03" "p1B07" "p1B11" "p1B15" "p1B19" "p1B23"
[487] "p1F03" "p1F07" "p1F11" "p1F15" "p1F19" "p1F23" "p1J03" "p1J07" "p1J11"
[496] "p1J15" "p1J19" "p1J23" "p1N03" "p1N07" "p1N11" "p1N15" "p1N19" "p1N23"
[505] "p2B03" "p2B07" "p2B11" "p2B15" "p2B19" "p2B23" "p2F03" "p2F07" "p2F11"
[514] "p2F15" "p2F19" "p2F23" "p2J03" "p2J07" "p2J11" "p2J15" "p2J19" "p2J23"
[523] "p2N03" "p2N07" "p2N11" "p2N15" "p2N19" "p2N23" "p1A03" "p1A07" "p1A11"
[532] "p1A15" "p1A19" "p1A23" "p1E03" "p1E07" "p1E11" "p1E15" "p1E19" "p1E23"
[541] "p1I03" "p1I07" "p1I11" "p1I15" "p1I19" "p1I23" "p1M03" "p1M07" "p1M11"
[550] "p1M15" "p1M19" "p1M23" "p2A03" "p2A07" "p2A11" "p2A15" "p2A19" "p2A23"
[559] "p2E03" "p2E07" "p2E11" "p2E15" "p2E19" "p2E23" "p2I03" "p2I07" "p2I11"
[568] "p2I15" "p2I19" "p2I23" "p2M03" "p2M07" "p2M11" "p2M15" "p2M19" "p2M23"
[577] "p1D04" "p1D08" "p1D12" "p1D16" "p1D20" "p1D24" "p1H04" "p1H08" "p1H12"
[586] "p1H16" "p1H20" "p1H24" "p1L04" "p1L08" "p1L12" "p1L16" "p1L20" "p1L24"
[595] "p1P04" "p1P08" "p1P12" "p1P16" "p1P20" "p1P24" "p2D04" "p2D08" "p2D12"
[604] "p2D16" "p2D20" "p2D24" "p2H04" "p2H08" "p2H12" "p2H16" "p2H20" "p2H24"
[613] "p2L04" "p2L08" "p2L12" "p2L16" "p2L20" "p2L24" "p2P04" "p2P08" "p2P12"
[622] "p2P16" "p2P20" "p2P24" "p1C04" "p1C08" "p1C12" "p1C16" "p1C20" "p1C24"
[631] "p1G04" "p1G08" "p1G12" "p1G16" "p1G20" "p1G24" "p1K04" "p1K08" "p1K12"
[640] "p1K16" "p1K20" "p1K24" "p1O04" "p1O08" "p1O12" "p1O16" "p1O20" "p1O24"
[649] "p2C04" "p2C08" "p2C12" "p2C16" "p2C20" "p2C24" "p2G04" "p2G08" "p2G12"
[658] "p2G16" "p2G20" "p2G24" "p2K04" "p2K08" "p2K12" "p2K16" "p2K20" "p2K24"
[667] "p2O04" "p2O08" "p2O12" "p2O16" "p2O20" "p2O24" "p1B04" "p1B08" "p1B12"
[676] "p1B16" "p1B20" "p1B24" "p1F04" "p1F08" "p1F12" "p1F16" "p1F20" "p1F24"
[685] "p1J04" "p1J08" "p1J12" "p1J16" "p1J20" "p1J24" "p1N04" "p1N08" "p1N12"
[694] "p1N16" "p1N20" "p1N24" "p2B04" "p2B08" "p2B12" "p2B16" "p2B20" "p2B24"
[703] "p2F04" "p2F08" "p2F12" "p2F16" "p2F20" "p2F24" "p2J04" "p2J08" "p2J12"
[712] "p2J16" "p2J20" "p2J24" "p2N04" "p2N08" "p2N12" "p2N16" "p2N20" "p2N24"
[721] "p1A04" "p1A08" "p1A12" "p1A16" "p1A20" "p1A24" "p1E04" "p1E08" "p1E12"
[730] "p1E16" "p1E20" "p1E24" "p1I04" "p1I08" "p1I12" "p1I16" "p1I20" "p1I24"
[739] "p1M04" "p1M08" "p1M12" "p1M16" "p1M20" "p1M24" "p2A04" "p2A08" "p2A12"
[748] "p2A16" "p2A20" "p2A24" "p2E04" "p2E08" "p2E12" "p2E16" "p2E20" "p2E24"
[757] "p2I04" "p2I08" "p2I12" "p2I16" "p2I20" "p2I24" "p2M04" "p2M08" "p2M12"
[766] "p2M16" "p2M20" "p2M24"

> 
> ### merge.rglist
> 
> R <- G <- matrix(11:14,4,2)
> rownames(R) <- rownames(G) <- c("a","a","b","c")
> RG1 <- new("RGList",list(R=R,G=G))
> R <- G <- matrix(21:24,4,2)
> rownames(R) <- rownames(G) <- c("b","a","a","c")
> RG2 <- new("RGList",list(R=R,G=G))
> merge(RG1,RG2)
An object of class "RGList"
$R
  [,1] [,2] [,3] [,4]
a   11   11   22   22
a   12   12   23   23
b   13   13   21   21
c   14   14   24   24

$G
  [,1] [,2] [,3] [,4]
a   11   11   22   22
a   12   12   23   23
b   13   13   21   21
c   14   14   24   24

> merge(RG2,RG1)
An object of class "RGList"
$R
  [,1] [,2] [,3] [,4]
b   21   21   13   13
a   22   22   11   11
a   23   23   12   12
c   24   24   14   14

$G
  [,1] [,2] [,3] [,4]
b   21   21   13   13
a   22   22   11   11
a   23   23   12   12
c   24   24   14   14

> 
> ### background correction
> 
> RG <- new("RGList", list(R=c(1,2,3,4),G=c(1,2,3,4),Rb=c(2,2,2,2),Gb=c(2,2,2,2)))
> backgroundCorrect(RG)
An object of class "RGList"
$R
     [,1]
[1,]   -1
[2,]    0
[3,]    1
[4,]    2

$G
     [,1]
[1,]   -1
[2,]    0
[3,]    1
[4,]    2

> backgroundCorrect(RG, method="half")
An object of class "RGList"
$R
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

$G
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

> backgroundCorrect(RG, method="minimum")
An object of class "RGList"
$R
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

$G
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

> backgroundCorrect(RG, offset=5)
An object of class "RGList"
$R
     [,1]
[1,]    4
[2,]    5
[3,]    6
[4,]    7

$G
     [,1]
[1,]    4
[2,]    5
[3,]    6
[4,]    7

> 
> ### loessFit
> 
> x <- 1:100
> y <- rnorm(100)
> out <- loessFit(y,x)
> f1 <- quantile(out$fitted)
> r1 <- quantile(out$residuals)
> w <- rep(1,100)
> w[1:50] <- 0.5
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f2 <- quantile(out$fitted)
> r2 <- quantile(out$residuals)
> out <- loessFit(y,x,weights=w,method="locfit")
> f3 <- quantile(out$fitted)
> r3 <- quantile(out$residuals)
> out <- loessFit(y,x,weights=w,method="loess")
> f4 <- quantile(out$fitted)
> r4 <- quantile(out$residuals)
> w <- rep(1,100)
> w[2*(1:50)] <- 0
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f5 <- quantile(out$fitted)
> r5 <- quantile(out$residuals)
> data.frame(f1,f2,f3,f4,f5)
              f1           f2          f3          f4          f5
0%   -0.78835384 -0.687432210 -0.78957137 -0.76756060 -0.63778292
25%  -0.18340154 -0.179683572 -0.18979269 -0.16773223 -0.38064318
50%  -0.11492924 -0.114796040 -0.12087983 -0.07185314 -0.15971879
75%   0.01507921 -0.008145125 -0.01857508  0.04030634  0.07839396
100%  0.21653837  0.145106033  0.19214597  0.21417361  0.51836274
> data.frame(r1,r2,r3,r4,r5)
              r1          r2          r3           r4          r5
0%   -2.04434053 -2.05132680 -2.02404318 -2.101242874 -2.22280633
25%  -0.59321065 -0.57200209 -0.58975649 -0.577887481 -0.71037756
50%   0.05874864  0.04514326  0.08335198 -0.001769806  0.06785517
75%   0.56010750  0.55124530  0.57618740  0.561454370  0.65383830
100%  2.57936026  2.64549799  2.57549257  2.402324533  2.28648835
> 
> ### normalizeWithinArrays
> 
> RG <- new("RGList",list())
> RG$R <- matrix(rexp(100*2),100,2)
> RG$G <- matrix(rexp(100*2),100,2)
> RG$Rb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RG$Gb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="saddle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
       V1                V2                V3               V4        
 Min.   :0.01626   Min.   :0.01213   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.35497   1st Qu.:0.29133   1st Qu.:0.2745   1st Qu.:0.3953  
 Median :0.71793   Median :0.70294   Median :0.6339   Median :0.8223  
 Mean   :0.90184   Mean   :1.00122   Mean   :0.9454   Mean   :1.1324  
 3rd Qu.:1.16891   3rd Qu.:1.33139   3rd Qu.:1.4059   3rd Qu.:1.4221  
 Max.   :4.56267   Max.   :6.37947   Max.   :5.0486   Max.   :6.6295  
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="mle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
       V1                V2                V3               V4        
 Min.   :0.01701   Min.   :0.01255   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.35423   1st Qu.:0.29118   1st Qu.:0.2745   1st Qu.:0.3953  
 Median :0.71719   Median :0.70280   Median :0.6339   Median :0.8223  
 Mean   :0.90118   Mean   :1.00110   Mean   :0.9454   Mean   :1.1324  
 3rd Qu.:1.16817   3rd Qu.:1.33124   3rd Qu.:1.4059   3rd Qu.:1.4221  
 Max.   :4.56193   Max.   :6.37932   Max.   :5.0486   Max.   :6.6295  
> MA <- normalizeWithinArrays(RGb,method="loess")
> summary(MA$M)
       V1                 V2          
 Min.   :-5.88044   Min.   :-5.66985  
 1st Qu.:-1.18483   1st Qu.:-1.57014  
 Median :-0.21632   Median : 0.04823  
 Mean   : 0.03487   Mean   :-0.05481  
 3rd Qu.: 1.49669   3rd Qu.: 1.45113  
 Max.   : 7.07324   Max.   : 6.19744  
> #MA <- normalizeWithinArrays(RG[,1:2], mouse.setup, method="robustspline")
> #MA$M[1:5,]
> #MA <- normalizeWithinArrays(mouse.data, mouse.setup)
> #MA$M[1:5,]
> 
> ### normalizeBetweenArrays
> 
> MA2 <- normalizeBetweenArrays(MA,method="scale")
> MA$M[1:5,]
           [,1]       [,2]
[1,] -1.1689588  4.5558123
[2,]  0.8971363  0.3296544
[3,]  2.8247439  1.4249960
[4,] -1.8533240  0.4804851
[5,]  1.9158459 -5.5087631
> MA$A[1:5,]
            [,1]       [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200  0.2071043
[4,]  0.09281027 -1.3880965
[5,]  0.22385828 -3.0855818
> MA2 <- normalizeBetweenArrays(MA,method="quantile")
> MA$M[1:5,]
           [,1]       [,2]
[1,] -1.1689588  4.5558123
[2,]  0.8971363  0.3296544
[3,]  2.8247439  1.4249960
[4,] -1.8533240  0.4804851
[5,]  1.9158459 -5.5087631
> MA$A[1:5,]
            [,1]       [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200  0.2071043
[4,]  0.09281027 -1.3880965
[5,]  0.22385828 -3.0855818
> 
> ### unwrapdups
> 
> M <- matrix(1:12,6,2)
> unwrapdups(M,ndups=1)
     [,1] [,2]
[1,]    1    7
[2,]    2    8
[3,]    3    9
[4,]    4   10
[5,]    5   11
[6,]    6   12
> unwrapdups(M,ndups=2)
     [,1] [,2] [,3] [,4]
[1,]    1    2    7    8
[2,]    3    4    9   10
[3,]    5    6   11   12
> unwrapdups(M,ndups=3)
     [,1] [,2] [,3] [,4] [,5] [,6]
[1,]    1    2    3    7    8    9
[2,]    4    5    6   10   11   12
> unwrapdups(M,ndups=2,spacing=3)
     [,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    2    5    8   11
[3,]    3    6    9   12
> 
> ### trigammaInverse
> 
> trigammaInverse(c(1e-6,NA,5,1e6))
[1] 1.000000e+06           NA 4.961687e-01 1.000001e-03
> 
> ### lmFit, eBayes, topTable
> 
> M <- matrix(rnorm(10*6,sd=0.3),10,6)
> rownames(M) <- LETTERS[1:10]
> M[1,1:3] <- M[1,1:3] + 2
> design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
> contrast.matrix <- cbind(First3=c(1,0),Last3=c(0,1),"Last3-First3"=c(-1,1))
> fit <- lmFit(M,design)
> fit2 <- eBayes(contrasts.fit(fit,contrasts=contrast.matrix))
> topTable(fit2)
       First3       Last3 Last3.First3      AveExpr           F      P.Value
A  1.77602021  0.06025114  -1.71576906  0.918135675 50.91471061 7.727200e-23
D -0.05454069  0.39127869   0.44581938  0.168369004  2.51638838 8.075072e-02
F -0.16249607 -0.33009728  -0.16760121 -0.246296671  2.18256779 1.127516e-01
G  0.30852468 -0.06873462  -0.37725930  0.119895035  1.61088775 1.997102e-01
H -0.16942269  0.20578118   0.37520387  0.018179245  1.14554368 3.180510e-01
J  0.21417623  0.07074940  -0.14342683  0.142462814  0.82029274 4.403027e-01
C -0.12236781  0.15095948   0.27332729  0.014295836  0.60885003 5.439761e-01
B -0.11982833  0.13529287   0.25512120  0.007732271  0.52662792 5.905931e-01
E  0.01897934  0.10434934   0.08536999  0.061664340  0.18136849 8.341279e-01
I -0.04720963  0.03996397   0.08717360 -0.003622829  0.06168476 9.401792e-01
     adj.P.Val
A 7.727200e-22
D 3.758388e-01
F 3.758388e-01
G 4.992756e-01
H 6.361019e-01
J 7.338379e-01
C 7.382414e-01
B 7.382414e-01
E 9.268088e-01
I 9.401792e-01
> topTable(fit2,coef=3,resort.by="logFC")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="p")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,sort.by="logFC",resort.by="t")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="B")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,lfc=1)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p.value=0.2)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p.value=0.2,lfc=0.5)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p.value=0.2,lfc=0.5,sort.by="none")
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> contrasts.fit(fit[1:3,],contrast.matrix[,0])
An object of class "MArrayLM"
$coefficients
 
A
B
C

$rank
[1] 2

$assign
NULL

$qr
$qr
     First3Arrays Last3Arrays
[1,]   -1.7320508   0.0000000
[2,]    0.5773503  -1.7320508
[3,]    0.5773503   0.0000000
[4,]    0.0000000   0.5773503
[5,]    0.0000000   0.5773503
[6,]    0.0000000   0.5773503

$qraux
[1] 1.57735 1.00000

$pivot
[1] 1 2

$tol
[1] 1e-07

$rank
[1] 2


$df.residual
[1] 4 4 4

$sigma
        A         B         C 
0.3299787 0.3323336 0.2315815 

$cov.coefficients
<0 x 0 matrix>

$stdev.unscaled
 
A
B
C

$pivot
[1] 1 2

$Amean
          A           B           C 
0.918135675 0.007732271 0.014295836 

$method
[1] "ls"

$design
     First3Arrays Last3Arrays
[1,]            1           0
[2,]            1           0
[3,]            1           0
[4,]            0           1
[5,]            0           1
[6,]            0           1

$contrasts
    
[1,]
[2,]

> fit$coefficients[1,1] <- NA
> contrasts.fit(fit[1:3,],contrast.matrix)$coefficients
      First3      Last3 Last3-First3
A         NA 0.06025114           NA
B -0.1198283 0.13529287    0.2551212
C -0.1223678 0.15095948    0.2733273
> 
> designlist <- list(Null=matrix(1,6,1),Two=design,Three=cbind(1,c(0,0,1,1,0,0),c(0,0,0,0,1,1)))
> out <- selectModel(M,designlist)
> table(out$pref)

 Null   Two Three 
    5     3     2 
> 
> ### marray object
> 
> #suppressMessages(suppressWarnings(gotmarray <- require(marray,quietly=TRUE)))
> #if(gotmarray) {
> #	data(swirl)
> #	snorm = maNorm(swirl)
> #	fit <- lmFit(snorm, design = c(1,-1,-1,1))
> #	fit <- eBayes(fit)
> #	topTable(fit,resort.by="AveExpr")
> #}
> 
> ### duplicateCorrelation
> 
> cor.out <- duplicateCorrelation(M)
> cor.out$consensus.correlation
[1] -0.09290714
> cor.out$atanh.correlations
[1] -0.4419130  0.4088967 -0.1964978 -0.6093769  0.3730118
> 
> ### gls.series
> 
> fit <- gls.series(M,design,correlation=cor.out$cor)
> fit$coefficients
     First3Arrays Last3Arrays
[1,]   0.82809594  0.09777201
[2,]  -0.08845425  0.27111909
[3,]  -0.07175836 -0.11287397
[4,]   0.06955100  0.06852328
[5,]   0.08348330  0.05535668
> fit$stdev.unscaled
     First3Arrays Last3Arrays
[1,]    0.3888215   0.3888215
[2,]    0.3888215   0.3888215
[3,]    0.3888215   0.3888215
[4,]    0.3888215   0.3888215
[5,]    0.3888215   0.3888215
> fit$sigma
[1] 0.7630059 0.2152728 0.3350370 0.3227781 0.3405473
> fit$df.residual
[1] 10 10 10 10 10
> 
> ### mrlm
> 
> fit <- mrlm(M,design)
Warning message:
In rlm.default(x = X, y = y, weights = w, ...) :
  'rlm' failed to converge in 20 steps
> fit$coefficients
  First3Arrays Last3Arrays
A   1.75138894  0.06025114
B  -0.11982833  0.10322039
C  -0.09302502  0.15095948
D  -0.05454069  0.33700045
E   0.07927938  0.10434934
F  -0.16249607 -0.34010852
G   0.30852468 -0.06873462
H  -0.16942269  0.24392984
I  -0.04720963  0.03996397
J   0.21417623 -0.05679272
> fit$stdev.unscaled
  First3Arrays Last3Arrays
A    0.5933418   0.5773503
B    0.5773503   0.6096497
C    0.6017444   0.5773503
D    0.5773503   0.6266021
E    0.6307703   0.5773503
F    0.5773503   0.5846707
G    0.5773503   0.5773503
H    0.5773503   0.6544564
I    0.5773503   0.5773503
J    0.5773503   0.6689776
> fit$sigma
 [1] 0.2894294 0.2679396 0.2090236 0.1461395 0.2309018 0.2827476 0.2285945
 [8] 0.2267556 0.3537469 0.2172409
> fit$df.residual
 [1] 4 4 4 4 4 4 4 4 4 4
> 
> # Similar to Mette Langaas 19 May 2004
> set.seed(123)
> narrays <- 9
> ngenes <- 5
> mu <- 0
> alpha <- 2
> beta <- -2
> epsilon <- matrix(rnorm(narrays*ngenes,0,1),ncol=narrays)
> X <- cbind(rep(1,9),c(0,0,0,1,1,1,0,0,0),c(0,0,0,0,0,0,1,1,1))
> dimnames(X) <- list(1:9,c("mu","alpha","beta"))
> yvec <- mu*X[,1]+alpha*X[,2]+beta*X[,3]
> ymat <- matrix(rep(yvec,ngenes),ncol=narrays,byrow=T)+epsilon
> ymat[5,1:2] <- NA
> fit <- lmFit(ymat,design=X)
> test.contr <- cbind(c(0,1,-1),c(1,1,0),c(1,0,1))
> dimnames(test.contr) <- list(c("mu","alpha","beta"),c("alpha-beta","mu+alpha","mu+beta"))
> fit2 <- contrasts.fit(fit,contrasts=test.contr)
> eBayes(fit2)
An object of class "MArrayLM"
$coefficients
     alpha-beta mu+alpha   mu+beta
[1,]   3.537333 1.677465 -1.859868
[2,]   4.355578 2.372554 -1.983024
[3,]   3.197645 1.053584 -2.144061
[4,]   2.697734 1.611443 -1.086291
[5,]   3.502304 2.051995 -1.450309

$stdev.unscaled
     alpha-beta  mu+alpha   mu+beta
[1,]  0.8164966 0.5773503 0.5773503
[2,]  0.8164966 0.5773503 0.5773503
[3,]  0.8164966 0.5773503 0.5773503
[4,]  0.8164966 0.5773503 0.5773503
[5,]  1.1547005 0.8368633 0.8368633

$sigma
[1] 1.3425032 0.4647155 1.1993444 0.9428569 0.9421509

$df.residual
[1] 6 6 6 6 4

$cov.coefficients
           alpha-beta     mu+alpha       mu+beta
alpha-beta  0.6666667 3.333333e-01 -3.333333e-01
mu+alpha    0.3333333 3.333333e-01  5.551115e-17
mu+beta    -0.3333333 5.551115e-17  3.333333e-01

$pivot
[1] 1 2 3

$rank
[1] 3

$Amean
[1]  0.2034961  0.1954604 -0.2863347  0.1188659  0.1784593

$method
[1] "ls"

$design
  mu alpha beta
1  1     0    0
2  1     0    0
3  1     0    0
4  1     1    0
5  1     1    0
6  1     1    0
7  1     0    1
8  1     0    1
9  1     0    1

$contrasts
      alpha-beta mu+alpha mu+beta
mu             0        1       1
alpha          1        1       0
beta          -1        0       1

$df.prior
[1] 9.306153

$s2.prior
[1] 0.923179

$var.prior
[1] 17.33142 17.33142 12.26855

$proportion
[1] 0.01

$s2.post
[1] 1.2677996 0.6459499 1.1251558 0.9097727 0.9124980

$t
     alpha-beta mu+alpha   mu+beta
[1,]   3.847656 2.580411 -2.860996
[2,]   6.637308 5.113018 -4.273553
[3,]   3.692066 1.720376 -3.500994
[4,]   3.464003 2.926234 -1.972606
[5,]   3.175181 2.566881 -1.814221

$df.total
[1] 15.30615 15.30615 15.30615 15.30615 13.30615

$p.value
       alpha-beta     mu+alpha      mu+beta
[1,] 1.529450e-03 0.0206493481 0.0117123495
[2,] 7.144893e-06 0.0001195844 0.0006385076
[3,] 2.109270e-03 0.1055117477 0.0031325769
[4,] 3.381970e-03 0.0102514264 0.0668844448
[5,] 7.124839e-03 0.0230888584 0.0922478630

$lods
     alpha-beta  mu+alpha    mu+beta
[1,]  -1.013417 -3.702133 -3.0332393
[2,]   3.981496  1.283349 -0.2615911
[3,]  -1.315036 -5.168621 -1.7864101
[4,]  -1.757103 -3.043209 -4.6191869
[5,]  -2.257358 -3.478267 -4.5683738

$F
[1]  7.421911 22.203107  7.608327  6.227010  5.060579

$F.p.value
[1] 5.581800e-03 2.988923e-05 5.080726e-03 1.050148e-02 2.320274e-02

> 
> ### uniquegenelist
> 
> uniquegenelist(letters[1:8],ndups=2)
[1] "a" "c" "e" "g"
> uniquegenelist(letters[1:8],ndups=2,spacing=2)
[1] "a" "b" "e" "f"
> 
> ### classifyTests
> 
> tstat <- matrix(c(0,5,0, 0,2.5,0, -2,-2,2, 1,1,1), 4, 3, byrow=TRUE)
> classifyTestsF(tstat)
TestResults matrix
     [,1] [,2] [,3]
[1,]    0    1    0
[2,]    0    0    0
[3,]   -1   -1    1
[4,]    0    0    0
> classifyTestsF(tstat,fstat.only=TRUE)
[1] 8.333333 2.083333 4.000000 1.000000
attr(,"df1")
[1] 3
attr(,"df2")
[1] Inf
> limma:::.classifyTestsP(tstat)
TestResults matrix
     [,1] [,2] [,3]
[1,]    0    1    0
[2,]    0    1    0
[3,]    0    0    0
[4,]    0    0    0
> 
> ### avereps
> 
> x <- matrix(rnorm(8*3),8,3)
> colnames(x) <- c("S1","S2","S3")
> rownames(x) <- c("b","a","a","c","c","b","b","b")
> avereps(x)
          S1         S2         S3
b -0.2353018  0.5220094  0.2302895
a -0.4347701  0.6453498 -0.6758914
c  0.3482980 -0.4820695 -0.3841313
> 
> ### roast
> 
> y <- matrix(rnorm(100*4),100,4)
> sigma <- sqrt(2/rchisq(100,df=7))
> y <- y*sigma
> design <- cbind(Intercept=1,Group=c(0,0,1,1))
> iset1 <- 1:5
> y[iset1,3:4] <- y[iset1,3:4]+3
> iset2 <- 6:10
> roast(y=y,iset1,design,contrast=2)
         Active.Prop     P.Value
Down               0 0.997999500
Up                 1 0.002250563
UpOrDown           1 0.004500000
Mixed              1 0.004500000
> roast(y=y,iset1,design,contrast=2,array.weights=c(0.5,1,0.5,1))
         Active.Prop     P.Value
Down               0 0.998749687
Up                 1 0.001500375
UpOrDown           1 0.003000000
Mixed              1 0.003000000
> w <- matrix(runif(100*4),100,4)
> roast(y=y,iset1,design,contrast=2,weights=w)
         Active.Prop     P.Value
Down               0 0.996999250
Up                 1 0.003250813
UpOrDown           1 0.006500000
Mixed              1 0.006500000
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,gene.weights=runif(100))
     NGenes PropDown PropUp Direction PValue    FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up 0.0055 0.0105       0.0055    0.0105
set2      5        0      0        Up 0.2025 0.2025       0.4715    0.4715
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,array.weights=c(0.5,1,0.5,1))
     NGenes PropDown PropUp Direction PValue    FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up 0.0050 0.0095        0.005    0.0095
set2      5        0      0        Up 0.6845 0.6845        0.642    0.6420
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w)
     NGenes PropDown PropUp Direction PValue    FDR PValue.Mixed FDR.Mixed
set1      5        0    1.0        Up 0.0030 0.0055        0.003    0.0055
set2      5        0    0.2      Down 0.9615 0.9615        0.496    0.4960
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes PropDown PropUp Direction PValue    FDR PValue.Mixed FDR.Mixed
set1      5        0    1.0        Up 0.0025 0.0045       0.0025    0.0045
set2      5        0    0.2      Down 0.8930 0.8930       0.4380    0.4380
> fry(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes Direction      PValue         FDR PValue.Mixed    FDR.Mixed
set1      5        Up 0.001568924 0.003137848 0.0001156464 0.0002312929
set2      5      Down 0.932105219 0.932105219 0.4315499569 0.4315499569
> rownames(y) <- paste0("Gene",1:100)
> iset1A <- rownames(y)[1:5]
> fry(y=y,index=iset1A,design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes Direction      PValue PValue.Mixed
set1      5        Up 0.001568924 0.0001156464
> 
> ### camera
> 
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1),allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction      PValue
set1      5  -0.2481655        Up 0.001050253
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction       PValue        FDR
set1      5  -0.2481655        Up 0.0009047749 0.00180955
set2      5   0.1719094      Down 0.9068364378 0.90683644
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1))
     NGenes Direction       PValue
set1      5        Up 1.105329e-10
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2)
     NGenes Direction       PValue          FDR
set1      5        Up 7.334400e-12 1.466880e-11
set2      5      Down 8.677115e-01 8.677115e-01
> camera(y=y,iset1A,design,contrast=2)
     NGenes Direction     PValue
set1      5        Up 7.3344e-12
> 
> ### with EList arg
> 
> y <- new("EList",list(E=y))
> roast(y=y,iset1,design,contrast=2)
         Active.Prop     P.Value
Down               0 0.996999250
Up                 1 0.003250813
UpOrDown           1 0.006500000
Mixed              1 0.006500000
> camera(y=y,iset1,design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction       PValue
set1      5  -0.2481655        Up 0.0009047749
> camera(y=y,iset1,design,contrast=2)
     NGenes Direction     PValue
set1      5        Up 7.3344e-12
> 
> ### eBayes with trend
> 
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene2   3.729512  1.73488969  4.865697 0.0004854886 0.02902331  0.1596831
Gene3   3.488703  1.03931081  4.754954 0.0005804663 0.02902331 -0.0144071
Gene4   2.696676  1.74060725  3.356468 0.0063282637 0.21094212 -2.3434702
Gene1   2.391846  1.72305203  3.107124 0.0098781268 0.24695317 -2.7738874
Gene33 -1.492317 -0.07525287 -2.783817 0.0176475742 0.29965463 -3.3300835
Gene5   2.387967  1.63066783  2.773444 0.0179792778 0.29965463 -3.3478204
Gene80 -1.839760 -0.32802306 -2.503584 0.0291489863 0.37972679 -3.8049642
Gene39  1.366141 -0.27360750  2.451133 0.0320042242 0.37972679 -3.8925860
Gene95 -1.907074  1.26297763 -2.414217 0.0341754107 0.37972679 -3.9539571
Gene50  1.034777  0.01608433  2.054690 0.0642289403 0.59978803 -4.5350317
> fit$df.prior
[1] 9.098442
> fit$s2.prior
    Gene1     Gene2     Gene3     Gene4     Gene5     Gene6     Gene7     Gene8 
0.6901845 0.6977354 0.3860494 0.7014122 0.6341068 0.2926337 0.3077620 0.3058098 
    Gene9    Gene10    Gene11    Gene12    Gene13    Gene14    Gene15    Gene16 
0.2985145 0.2832520 0.3232434 0.3279710 0.2816081 0.2943502 0.3127994 0.2894802 
   Gene17    Gene18    Gene19    Gene20    Gene21    Gene22    Gene23    Gene24 
0.2812758 0.2840051 0.2839124 0.2954261 0.2838592 0.2812704 0.3157029 0.2844541 
   Gene25    Gene26    Gene27    Gene28    Gene29    Gene30    Gene31    Gene32 
0.4778832 0.2818242 0.2930360 0.2940957 0.2941862 0.3234399 0.3164779 0.2853510 
   Gene33    Gene34    Gene35    Gene36    Gene37    Gene38    Gene39    Gene40 
0.2988244 0.3450090 0.3048596 0.3089086 0.3104534 0.4551549 0.3220008 0.2813286 
   Gene41    Gene42    Gene43    Gene44    Gene45    Gene46    Gene47    Gene48 
0.2826027 0.2822504 0.2823330 0.3170673 0.3146173 0.3146793 0.2916540 0.2975003 
   Gene49    Gene50    Gene51    Gene52    Gene53    Gene54    Gene55    Gene56 
0.3538946 0.2907240 0.3199596 0.2816641 0.2814293 0.2996822 0.2812885 0.2896157 
   Gene57    Gene58    Gene59    Gene60    Gene61    Gene62    Gene63    Gene64 
0.2955317 0.2815907 0.2919420 0.2849675 0.3540805 0.3491713 0.2975019 0.2939325 
   Gene65    Gene66    Gene67    Gene68    Gene69    Gene70    Gene71    Gene72 
0.2986943 0.3265466 0.3402343 0.3394927 0.2813283 0.2814440 0.3089669 0.3030850 
   Gene73    Gene74    Gene75    Gene76    Gene77    Gene78    Gene79    Gene80 
0.2859286 0.2813216 0.3475231 0.3334419 0.2949550 0.3108702 0.2959688 0.3295294 
   Gene81    Gene82    Gene83    Gene84    Gene85    Gene86    Gene87    Gene88 
0.3413700 0.2946268 0.3029565 0.2920284 0.2926205 0.2818046 0.3425116 0.2882936 
   Gene89    Gene90    Gene91    Gene92    Gene93    Gene94    Gene95    Gene96 
0.2945459 0.3077919 0.2892134 0.2823787 0.3048049 0.2961408 0.4590012 0.2812784 
   Gene97    Gene98    Gene99   Gene100 
0.2846345 0.2819651 0.3137551 0.2856081 
> summary(fit$s2.post)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2335  0.2603  0.2997  0.3375  0.3655  0.7812 
> 
> y$E[1,1] <- NA
> y$E[1,3] <- NA
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene3   3.488703  1.03931081  4.604490 0.0007644061 0.07644061 -0.2333915
Gene2   3.729512  1.73488969  4.158038 0.0016033158 0.08016579 -0.9438583
Gene4   2.696676  1.74060725  2.898102 0.0145292666 0.44537707 -3.0530813
Gene33 -1.492317 -0.07525287 -2.784004 0.0178150826 0.44537707 -3.2456324
Gene5   2.387967  1.63066783  2.495395 0.0297982959 0.46902627 -3.7272957
Gene80 -1.839760 -0.32802306 -2.491115 0.0300256116 0.46902627 -3.7343584
Gene39  1.366141 -0.27360750  2.440729 0.0328318388 0.46902627 -3.8172597
Gene1   2.638272  1.47993643  2.227507 0.0530016060 0.58890673 -3.9537576
Gene95 -1.907074  1.26297763 -2.288870 0.0429197808 0.53649726 -4.0642439
Gene50  1.034777  0.01608433  2.063663 0.0635275235 0.60439978 -4.4204731
> fit$df.residual[1]
[1] 0
> fit$df.prior
[1] 8.971891
> fit$s2.prior
  [1] 0.7014084 0.9646561 0.4276287 0.9716476 0.8458852 0.2910492 0.3097052
  [8] 0.3074225 0.2985517 0.2786374 0.3267121 0.3316013 0.2766404 0.2932679
 [15] 0.3154347 0.2869186 0.2761395 0.2799884 0.2795119 0.2946468 0.2794412
 [22] 0.2761282 0.3186442 0.2806092 0.4596465 0.2767847 0.2924541 0.2939204
 [29] 0.2930568 0.3269177 0.3194905 0.2814293 0.2989389 0.3483845 0.3062977
 [36] 0.3110287 0.3127934 0.4418052 0.3254067 0.2761732 0.2780422 0.2773311
 [43] 0.2776653 0.3201314 0.3174515 0.3175199 0.2897731 0.2972785 0.3567262
 [50] 0.2885556 0.3232426 0.2767207 0.2762915 0.3000062 0.2761306 0.2870975
 [57] 0.2947817 0.2766152 0.2901489 0.2813183 0.3568982 0.3724440 0.2972804
 [64] 0.2927300 0.2987764 0.3301406 0.3437962 0.3430762 0.2761729 0.2763094
 [71] 0.3110958 0.3041715 0.2822004 0.2761654 0.3507694 0.3371214 0.2940441
 [78] 0.3132660 0.2953388 0.3331880 0.3448949 0.2946558 0.3040162 0.2902616
 [85] 0.2910320 0.2769211 0.3459946 0.2859057 0.2935193 0.3097398 0.2865663
 [92] 0.2774968 0.3062327 0.2955576 0.5425422 0.2761214 0.2808585 0.2771484
 [99] 0.3164981 0.2817725
> summary(fit$s2.post)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2296  0.2581  0.3003  0.3453  0.3652  0.9158 
> 
> ### eBayes with robust
> 
> fitr <- lmFit(y,design)
> fitr <- eBayes(fitr,robust=TRUE)
> summary(fitr$df.prior)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  6.717   9.244   9.244   9.194   9.244   9.244 
> topTable(fitr,coef=2)
           logFC     AveExpr         t      P.Value   adj.P.Val          B
Gene2   3.729512  1.73488969  7.108463 1.752774e-05 0.001752774  3.3517310
Gene3   3.488703  1.03931081  5.041209 3.526138e-04 0.017630688  0.4056329
Gene4   2.696676  1.74060725  4.697690 6.150508e-04 0.020501693 -0.1463315
Gene5   2.387967  1.63066783  3.451807 5.245019e-03 0.131125480 -2.2678836
Gene1   2.638272  1.47993643  3.317593 8.651142e-03 0.173022847 -2.4400000
Gene33 -1.492317 -0.07525287 -2.716431 1.970991e-02 0.297950865 -3.5553166
Gene95 -1.907074  1.26297763 -2.685067 2.085656e-02 0.297950865 -3.6094982
Gene80 -1.839760 -0.32802306 -2.535926 2.727440e-02 0.340929958 -3.8653107
Gene39  1.366141 -0.27360750  2.469570 3.071854e-02 0.341317083 -3.9779817
Gene50  1.034777  0.01608433  1.973040 7.357960e-02 0.632875126 -4.7877548
> fitr <- eBayes(fitr,trend=TRUE,robust=TRUE)
> summary(fitr$df.prior)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  7.809   8.972   8.972   8.949   8.972   8.972 
> topTable(fitr,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene2   3.729512  1.73488969  4.754160 0.0005999064 0.05999064 -0.0218247
Gene3   3.488703  1.03931081  3.761219 0.0031618743 0.15809372 -1.6338257
Gene4   2.696676  1.74060725  3.292262 0.0071993347 0.23997782 -2.4295326
Gene33 -1.492317 -0.07525287 -3.063180 0.0108203134 0.27050784 -2.8211394
Gene50  1.034777  0.01608433  2.645717 0.0228036320 0.38815282 -3.5304767
Gene5   2.387967  1.63066783  2.633901 0.0232891695 0.38815282 -3.5503445
Gene1   2.638272  1.47993643  2.204116 0.0550613420 0.58959402 -4.0334169
Gene80 -1.839760 -0.32802306 -2.332729 0.0397331916 0.56761702 -4.0496640
Gene39  1.366141 -0.27360750  2.210665 0.0492211477 0.58959402 -4.2469578
Gene95 -1.907074  1.26297763 -2.106861 0.0589594023 0.58959402 -4.4117140
> 
> ### voom
> 
> y <- matrix(rpois(100*4,lambda=20),100,4)
> design <- cbind(Int=1,x=c(0,0,1,1))
> v <- voom(y,design)
> names(v)
[1] "E"       "weights" "design"  "targets"
> summary(v$E)
       V1              V2              V3              V4       
 Min.   :12.38   Min.   :12.32   Min.   :12.17   Min.   :12.08  
 1st Qu.:13.11   1st Qu.:13.05   1st Qu.:13.11   1st Qu.:13.03  
 Median :13.34   Median :13.28   Median :13.35   Median :13.35  
 Mean   :13.29   Mean   :13.29   Mean   :13.28   Mean   :13.28  
 3rd Qu.:13.48   3rd Qu.:13.54   3rd Qu.:13.48   3rd Qu.:13.50  
 Max.   :14.01   Max.   :13.95   Max.   :14.03   Max.   :14.05  
> summary(v$weights)
       V1               V2               V3               V4        
 Min.   : 7.729   Min.   : 7.729   Min.   : 7.729   Min.   : 7.729  
 1st Qu.:13.859   1st Qu.:15.067   1st Qu.:14.254   1st Qu.:13.592  
 Median :15.913   Median :16.621   Median :16.081   Median :16.028  
 Mean   :16.773   Mean   :18.525   Mean   :18.472   Mean   :17.112  
 3rd Qu.:18.214   3rd Qu.:20.002   3rd Qu.:18.475   3rd Qu.:18.398  
 Max.   :34.331   Max.   :34.331   Max.   :34.331   Max.   :34.331  
> 
> ### goana
> 
> EB <- c("133746","1339","134","1340","134083","134111","134147","134187","134218","134266",
+ "134353","134359","134391","134429","134430","1345","134510","134526","134549","1346",
+ "134637","1347","134701","134728","1348","134829","134860","134864","1349","134957",
+ "135","1350","1351","135112","135114","135138","135152","135154","1352","135228",
+ "135250","135293","135295","1353","135458","1355","1356","135644","135656","1357",
+ "1358","135892","1359","135924","135935","135941","135946","135948","136","1360",
+ "136051","1361","1362","136227","136242","136259","1363","136306","136319","136332",
+ "136371","1364","1365","136541","1366","136647","1368","136853","1369","136991",
+ "1370","137075","1371","137209","1373","137362","1374","137492","1375","1376",
+ "137682","137695","137735","1378","137814","137868","137872","137886","137902","137964")
> go <- goana(fit,FDR=0.8,geneid=EB)
> topGO(go,number=10,truncate.term=30)
                                     Term Ont  N Up Down        P.Up
GO:0070062          extracellular exosome  CC  8  0    4 1.000000000
GO:0043230        extracellular organelle  CC  8  0    4 1.000000000
GO:1903561          extracellular vesicle  CC  8  0    4 1.000000000
GO:0040011                     locomotion  BP  7  5    0 0.006651547
GO:0032502          developmental process  BP 23  4    6 0.844070086
GO:0032501 multicellular organismal pr...  BP 31  7    7 0.620372627
GO:0006915              apoptotic process  BP  5  4    1 0.009503355
GO:0012501          programmed cell death  BP  5  4    1 0.009503355
GO:0042981 regulation of apoptotic pro...  BP  5  4    1 0.009503355
GO:0040012       regulation of locomotion  BP  5  4    0 0.009503355
                P.Down
GO:0070062 0.003047199
GO:0043230 0.003047199
GO:1903561 0.003047199
GO:0040011 1.000000000
GO:0032502 0.009014340
GO:0032501 0.009111120
GO:0006915 0.416247633
GO:0012501 0.416247633
GO:0042981 0.416247633
GO:0040012 1.000000000
> topGO(go,number=10,truncate.term=30,sort="down")
                                     Term Ont  N Up Down      P.Up      P.Down
GO:0070062          extracellular exosome  CC  8  0    4 1.0000000 0.003047199
GO:0043230        extracellular organelle  CC  8  0    4 1.0000000 0.003047199
GO:1903561          extracellular vesicle  CC  8  0    4 1.0000000 0.003047199
GO:0032502          developmental process  BP 23  4    6 0.8440701 0.009014340
GO:0032501 multicellular organismal pr...  BP 31  7    7 0.6203726 0.009111120
GO:0031982                        vesicle  CC 18  1    5 0.9946677 0.015552466
GO:0051604             protein maturation  BP  7  1    3 0.8497705 0.020760307
GO:0016485             protein processing  BP  7  1    3 0.8497705 0.020760307
GO:0009887     animal organ morphogenesis  BP  3  0    2 1.0000000 0.025788497
GO:0055082 cellular chemical homeostas...  BP  3  1    2 0.5476190 0.025788497
> 
> proc.time()
   user  system elapsed 
   2.26    0.31    2.56 

limma.Rcheck/tests_x64/limma-Tests.Rout.save


R version 4.1.0 beta (2021-05-06 r80268) -- "Camp Pontanezen"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(limma)
> options(warnPartialMatchArgs=TRUE,warnPartialMatchAttr=TRUE,warnPartialMatchDollar=TRUE)
> 
> set.seed(0); u <- runif(100)
> 
> ### strsplit2
> 
> x <- c("ab;cd;efg","abc;def","z","")
> strsplit2(x,split=";")
     [,1]  [,2]  [,3] 
[1,] "ab"  "cd"  "efg"
[2,] "abc" "def" ""   
[3,] "z"   ""    ""   
[4,] ""    ""    ""   
> 
> ### removeext
> 
> removeExt(c("slide1.spot","slide.2.spot"))
[1] "slide1"  "slide.2"
> removeExt(c("slide1.spot","slide"))
[1] "slide1.spot" "slide"      
> 
> ### printorder
> 
> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6),ndups=2,start="topright",npins=4)
$printorder
  [1]   6   5   4   3   2   1  12  11  10   9   8   7  18  17  16  15  14  13
 [19]  24  23  22  21  20  19  30  29  28  27  26  25  36  35  34  33  32  31
 [37]  42  41  40  39  38  37  48  47  46  45  44  43   6   5   4   3   2   1
 [55]  12  11  10   9   8   7  18  17  16  15  14  13  24  23  22  21  20  19
 [73]  30  29  28  27  26  25  36  35  34  33  32  31  42  41  40  39  38  37
 [91]  48  47  46  45  44  43   6   5   4   3   2   1  12  11  10   9   8   7
[109]  18  17  16  15  14  13  24  23  22  21  20  19  30  29  28  27  26  25
[127]  36  35  34  33  32  31  42  41  40  39  38  37  48  47  46  45  44  43
[145]   6   5   4   3   2   1  12  11  10   9   8   7  18  17  16  15  14  13
[163]  24  23  22  21  20  19  30  29  28  27  26  25  36  35  34  33  32  31
[181]  42  41  40  39  38  37  48  47  46  45  44  43  54  53  52  51  50  49
[199]  60  59  58  57  56  55  66  65  64  63  62  61  72  71  70  69  68  67
[217]  78  77  76  75  74  73  84  83  82  81  80  79  90  89  88  87  86  85
[235]  96  95  94  93  92  91  54  53  52  51  50  49  60  59  58  57  56  55
[253]  66  65  64  63  62  61  72  71  70  69  68  67  78  77  76  75  74  73
[271]  84  83  82  81  80  79  90  89  88  87  86  85  96  95  94  93  92  91
[289]  54  53  52  51  50  49  60  59  58  57  56  55  66  65  64  63  62  61
[307]  72  71  70  69  68  67  78  77  76  75  74  73  84  83  82  81  80  79
[325]  90  89  88  87  86  85  96  95  94  93  92  91  54  53  52  51  50  49
[343]  60  59  58  57  56  55  66  65  64  63  62  61  72  71  70  69  68  67
[361]  78  77  76  75  74  73  84  83  82  81  80  79  90  89  88  87  86  85
[379]  96  95  94  93  92  91 102 101 100  99  98  97 108 107 106 105 104 103
[397] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[415] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[433] 102 101 100  99  98  97 108 107 106 105 104 103 114 113 112 111 110 109
[451] 120 119 118 117 116 115 126 125 124 123 122 121 132 131 130 129 128 127
[469] 138 137 136 135 134 133 144 143 142 141 140 139 102 101 100  99  98  97
[487] 108 107 106 105 104 103 114 113 112 111 110 109 120 119 118 117 116 115
[505] 126 125 124 123 122 121 132 131 130 129 128 127 138 137 136 135 134 133
[523] 144 143 142 141 140 139 102 101 100  99  98  97 108 107 106 105 104 103
[541] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[559] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[577] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[595] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[613] 186 185 184 183 182 181 192 191 190 189 188 187 150 149 148 147 146 145
[631] 156 155 154 153 152 151 162 161 160 159 158 157 168 167 166 165 164 163
[649] 174 173 172 171 170 169 180 179 178 177 176 175 186 185 184 183 182 181
[667] 192 191 190 189 188 187 150 149 148 147 146 145 156 155 154 153 152 151
[685] 162 161 160 159 158 157 168 167 166 165 164 163 174 173 172 171 170 169
[703] 180 179 178 177 176 175 186 185 184 183 182 181 192 191 190 189 188 187
[721] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[739] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[757] 186 185 184 183 182 181 192 191 190 189 188 187

$plate
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

$plate.r
  [1]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4
 [26]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  3  3
 [51]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
 [76]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  2  2  2  2
[101]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
[126]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  1  1  1  1  1  1
[151]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[176]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  8  8  8  8  8  8  8  8
[201]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8
[226]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  7  7  7  7  7  7  7  7  7  7
[251]  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7
[276]  7  7  7  7  7  7  7  7  7  7  7  7  7  6  6  6  6  6  6  6  6  6  6  6  6
[301]  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6
[326]  6  6  6  6  6  6  6  6  6  6  6  5  5  5  5  5  5  5  5  5  5  5  5  5  5
[351]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
[376]  5  5  5  5  5  5  5  5  5 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[401] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[426] 12 12 12 12 12 12 12 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[451] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[476] 11 11 11 11 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[501] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[526] 10 10 10  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[551]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[576]  9 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
[601] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 15
[626] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
[651] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 14 14 14
[676] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
[701] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 13 13 13 13 13
[726] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
[751] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13

$plate.c
  [1]  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15
 [26] 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3
 [51]  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14
 [76] 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2
[101]  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13
[126] 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1
[151]  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18
[176] 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6
[201]  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17
[226] 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5
[251]  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16
[276] 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4
[301]  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21
[326] 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9
[351]  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20
[376] 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8
[401]  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19
[426] 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7
[451] 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24
[476] 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12
[501] 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23
[526] 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11
[551] 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22
[576] 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10
[601] 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3
[626]  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15
[651] 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2
[676]  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14
[701] 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1
[726]  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13
[751] 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22

$plateposition
  [1] "p1D03" "p1D03" "p1D02" "p1D02" "p1D01" "p1D01" "p1D06" "p1D06" "p1D05"
 [10] "p1D05" "p1D04" "p1D04" "p1D09" "p1D09" "p1D08" "p1D08" "p1D07" "p1D07"
 [19] "p1D12" "p1D12" "p1D11" "p1D11" "p1D10" "p1D10" "p1D15" "p1D15" "p1D14"
 [28] "p1D14" "p1D13" "p1D13" "p1D18" "p1D18" "p1D17" "p1D17" "p1D16" "p1D16"
 [37] "p1D21" "p1D21" "p1D20" "p1D20" "p1D19" "p1D19" "p1D24" "p1D24" "p1D23"
 [46] "p1D23" "p1D22" "p1D22" "p1C03" "p1C03" "p1C02" "p1C02" "p1C01" "p1C01"
 [55] "p1C06" "p1C06" "p1C05" "p1C05" "p1C04" "p1C04" "p1C09" "p1C09" "p1C08"
 [64] "p1C08" "p1C07" "p1C07" "p1C12" "p1C12" "p1C11" "p1C11" "p1C10" "p1C10"
 [73] "p1C15" "p1C15" "p1C14" "p1C14" "p1C13" "p1C13" "p1C18" "p1C18" "p1C17"
 [82] "p1C17" "p1C16" "p1C16" "p1C21" "p1C21" "p1C20" "p1C20" "p1C19" "p1C19"
 [91] "p1C24" "p1C24" "p1C23" "p1C23" "p1C22" "p1C22" "p1B03" "p1B03" "p1B02"
[100] "p1B02" "p1B01" "p1B01" "p1B06" "p1B06" "p1B05" "p1B05" "p1B04" "p1B04"
[109] "p1B09" "p1B09" "p1B08" "p1B08" "p1B07" "p1B07" "p1B12" "p1B12" "p1B11"
[118] "p1B11" "p1B10" "p1B10" "p1B15" "p1B15" "p1B14" "p1B14" "p1B13" "p1B13"
[127] "p1B18" "p1B18" "p1B17" "p1B17" "p1B16" "p1B16" "p1B21" "p1B21" "p1B20"
[136] "p1B20" "p1B19" "p1B19" "p1B24" "p1B24" "p1B23" "p1B23" "p1B22" "p1B22"
[145] "p1A03" "p1A03" "p1A02" "p1A02" "p1A01" "p1A01" "p1A06" "p1A06" "p1A05"
[154] "p1A05" "p1A04" "p1A04" "p1A09" "p1A09" "p1A08" "p1A08" "p1A07" "p1A07"
[163] "p1A12" "p1A12" "p1A11" "p1A11" "p1A10" "p1A10" "p1A15" "p1A15" "p1A14"
[172] "p1A14" "p1A13" "p1A13" "p1A18" "p1A18" "p1A17" "p1A17" "p1A16" "p1A16"
[181] "p1A21" "p1A21" "p1A20" "p1A20" "p1A19" "p1A19" "p1A24" "p1A24" "p1A23"
[190] "p1A23" "p1A22" "p1A22" "p1H03" "p1H03" "p1H02" "p1H02" "p1H01" "p1H01"
[199] "p1H06" "p1H06" "p1H05" "p1H05" "p1H04" "p1H04" "p1H09" "p1H09" "p1H08"
[208] "p1H08" "p1H07" "p1H07" "p1H12" "p1H12" "p1H11" "p1H11" "p1H10" "p1H10"
[217] "p1H15" "p1H15" "p1H14" "p1H14" "p1H13" "p1H13" "p1H18" "p1H18" "p1H17"
[226] "p1H17" "p1H16" "p1H16" "p1H21" "p1H21" "p1H20" "p1H20" "p1H19" "p1H19"
[235] "p1H24" "p1H24" "p1H23" "p1H23" "p1H22" "p1H22" "p1G03" "p1G03" "p1G02"
[244] "p1G02" "p1G01" "p1G01" "p1G06" "p1G06" "p1G05" "p1G05" "p1G04" "p1G04"
[253] "p1G09" "p1G09" "p1G08" "p1G08" "p1G07" "p1G07" "p1G12" "p1G12" "p1G11"
[262] "p1G11" "p1G10" "p1G10" "p1G15" "p1G15" "p1G14" "p1G14" "p1G13" "p1G13"
[271] "p1G18" "p1G18" "p1G17" "p1G17" "p1G16" "p1G16" "p1G21" "p1G21" "p1G20"
[280] "p1G20" "p1G19" "p1G19" "p1G24" "p1G24" "p1G23" "p1G23" "p1G22" "p1G22"
[289] "p1F03" "p1F03" "p1F02" "p1F02" "p1F01" "p1F01" "p1F06" "p1F06" "p1F05"
[298] "p1F05" "p1F04" "p1F04" "p1F09" "p1F09" "p1F08" "p1F08" "p1F07" "p1F07"
[307] "p1F12" "p1F12" "p1F11" "p1F11" "p1F10" "p1F10" "p1F15" "p1F15" "p1F14"
[316] "p1F14" "p1F13" "p1F13" "p1F18" "p1F18" "p1F17" "p1F17" "p1F16" "p1F16"
[325] "p1F21" "p1F21" "p1F20" "p1F20" "p1F19" "p1F19" "p1F24" "p1F24" "p1F23"
[334] "p1F23" "p1F22" "p1F22" "p1E03" "p1E03" "p1E02" "p1E02" "p1E01" "p1E01"
[343] "p1E06" "p1E06" "p1E05" "p1E05" "p1E04" "p1E04" "p1E09" "p1E09" "p1E08"
[352] "p1E08" "p1E07" "p1E07" "p1E12" "p1E12" "p1E11" "p1E11" "p1E10" "p1E10"
[361] "p1E15" "p1E15" "p1E14" "p1E14" "p1E13" "p1E13" "p1E18" "p1E18" "p1E17"
[370] "p1E17" "p1E16" "p1E16" "p1E21" "p1E21" "p1E20" "p1E20" "p1E19" "p1E19"
[379] "p1E24" "p1E24" "p1E23" "p1E23" "p1E22" "p1E22" "p1L03" "p1L03" "p1L02"
[388] "p1L02" "p1L01" "p1L01" "p1L06" "p1L06" "p1L05" "p1L05" "p1L04" "p1L04"
[397] "p1L09" "p1L09" "p1L08" "p1L08" "p1L07" "p1L07" "p1L12" "p1L12" "p1L11"
[406] "p1L11" "p1L10" "p1L10" "p1L15" "p1L15" "p1L14" "p1L14" "p1L13" "p1L13"
[415] "p1L18" "p1L18" "p1L17" "p1L17" "p1L16" "p1L16" "p1L21" "p1L21" "p1L20"
[424] "p1L20" "p1L19" "p1L19" "p1L24" "p1L24" "p1L23" "p1L23" "p1L22" "p1L22"
[433] "p1K03" "p1K03" "p1K02" "p1K02" "p1K01" "p1K01" "p1K06" "p1K06" "p1K05"
[442] "p1K05" "p1K04" "p1K04" "p1K09" "p1K09" "p1K08" "p1K08" "p1K07" "p1K07"
[451] "p1K12" "p1K12" "p1K11" "p1K11" "p1K10" "p1K10" "p1K15" "p1K15" "p1K14"
[460] "p1K14" "p1K13" "p1K13" "p1K18" "p1K18" "p1K17" "p1K17" "p1K16" "p1K16"
[469] "p1K21" "p1K21" "p1K20" "p1K20" "p1K19" "p1K19" "p1K24" "p1K24" "p1K23"
[478] "p1K23" "p1K22" "p1K22" "p1J03" "p1J03" "p1J02" "p1J02" "p1J01" "p1J01"
[487] "p1J06" "p1J06" "p1J05" "p1J05" "p1J04" "p1J04" "p1J09" "p1J09" "p1J08"
[496] "p1J08" "p1J07" "p1J07" "p1J12" "p1J12" "p1J11" "p1J11" "p1J10" "p1J10"
[505] "p1J15" "p1J15" "p1J14" "p1J14" "p1J13" "p1J13" "p1J18" "p1J18" "p1J17"
[514] "p1J17" "p1J16" "p1J16" "p1J21" "p1J21" "p1J20" "p1J20" "p1J19" "p1J19"
[523] "p1J24" "p1J24" "p1J23" "p1J23" "p1J22" "p1J22" "p1I03" "p1I03" "p1I02"
[532] "p1I02" "p1I01" "p1I01" "p1I06" "p1I06" "p1I05" "p1I05" "p1I04" "p1I04"
[541] "p1I09" "p1I09" "p1I08" "p1I08" "p1I07" "p1I07" "p1I12" "p1I12" "p1I11"
[550] "p1I11" "p1I10" "p1I10" "p1I15" "p1I15" "p1I14" "p1I14" "p1I13" "p1I13"
[559] "p1I18" "p1I18" "p1I17" "p1I17" "p1I16" "p1I16" "p1I21" "p1I21" "p1I20"
[568] "p1I20" "p1I19" "p1I19" "p1I24" "p1I24" "p1I23" "p1I23" "p1I22" "p1I22"
[577] "p1P03" "p1P03" "p1P02" "p1P02" "p1P01" "p1P01" "p1P06" "p1P06" "p1P05"
[586] "p1P05" "p1P04" "p1P04" "p1P09" "p1P09" "p1P08" "p1P08" "p1P07" "p1P07"
[595] "p1P12" "p1P12" "p1P11" "p1P11" "p1P10" "p1P10" "p1P15" "p1P15" "p1P14"
[604] "p1P14" "p1P13" "p1P13" "p1P18" "p1P18" "p1P17" "p1P17" "p1P16" "p1P16"
[613] "p1P21" "p1P21" "p1P20" "p1P20" "p1P19" "p1P19" "p1P24" "p1P24" "p1P23"
[622] "p1P23" "p1P22" "p1P22" "p1O03" "p1O03" "p1O02" "p1O02" "p1O01" "p1O01"
[631] "p1O06" "p1O06" "p1O05" "p1O05" "p1O04" "p1O04" "p1O09" "p1O09" "p1O08"
[640] "p1O08" "p1O07" "p1O07" "p1O12" "p1O12" "p1O11" "p1O11" "p1O10" "p1O10"
[649] "p1O15" "p1O15" "p1O14" "p1O14" "p1O13" "p1O13" "p1O18" "p1O18" "p1O17"
[658] "p1O17" "p1O16" "p1O16" "p1O21" "p1O21" "p1O20" "p1O20" "p1O19" "p1O19"
[667] "p1O24" "p1O24" "p1O23" "p1O23" "p1O22" "p1O22" "p1N03" "p1N03" "p1N02"
[676] "p1N02" "p1N01" "p1N01" "p1N06" "p1N06" "p1N05" "p1N05" "p1N04" "p1N04"
[685] "p1N09" "p1N09" "p1N08" "p1N08" "p1N07" "p1N07" "p1N12" "p1N12" "p1N11"
[694] "p1N11" "p1N10" "p1N10" "p1N15" "p1N15" "p1N14" "p1N14" "p1N13" "p1N13"
[703] "p1N18" "p1N18" "p1N17" "p1N17" "p1N16" "p1N16" "p1N21" "p1N21" "p1N20"
[712] "p1N20" "p1N19" "p1N19" "p1N24" "p1N24" "p1N23" "p1N23" "p1N22" "p1N22"
[721] "p1M03" "p1M03" "p1M02" "p1M02" "p1M01" "p1M01" "p1M06" "p1M06" "p1M05"
[730] "p1M05" "p1M04" "p1M04" "p1M09" "p1M09" "p1M08" "p1M08" "p1M07" "p1M07"
[739] "p1M12" "p1M12" "p1M11" "p1M11" "p1M10" "p1M10" "p1M15" "p1M15" "p1M14"
[748] "p1M14" "p1M13" "p1M13" "p1M18" "p1M18" "p1M17" "p1M17" "p1M16" "p1M16"
[757] "p1M21" "p1M21" "p1M20" "p1M20" "p1M19" "p1M19" "p1M24" "p1M24" "p1M23"
[766] "p1M23" "p1M22" "p1M22"

> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6))
$printorder
  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
 [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2
 [51]  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
 [76] 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4
[101]  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
[126] 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6
[151]  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
[176] 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8
[201]  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
[226] 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10
[251] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
[276] 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12
[301] 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
[326] 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14
[351] 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
[376] 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16
[401] 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
[426] 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18
[451] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
[476] 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
[501] 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
[526] 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22
[551] 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
[576] 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
[601] 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1
[626]  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
[651] 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3
[676]  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
[701] 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5
[726]  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
[751] 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

$plate
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[334] 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2
[371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[519] 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2
[556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[667] 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2
[704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

$plate.r
  [1]  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  4
 [26]  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3  3
 [51]  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3  3
 [76]  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2  2
[101]  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2  2
[126]  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1  1
[151]  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1  5
[176]  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  4  4  4  4  4  4  8  8
[201]  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  4  4  4  4  4  4  8  8  8
[226]  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3  3  3  3  3  3  7  7  7  7
[251]  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3  3  3  3  3  7  7  7  7  7
[276]  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2  2  2  2  6  6  6  6  6  6
[301] 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2  2  2  6  6  6  6  6  6 10
[326] 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1  1  5  5  5  5  5  5  9  9
[351]  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9
[376]  9  9  9 13 13 13 13 13 13  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12
[401] 12 12 16 16 16 16 16 16  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12
[426] 12 16 16 16 16 16 16  3  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11
[451] 15 15 15 15 15 15  3  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15
[476] 15 15 15 15 15  2  2  2  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14
[501] 14 14 14 14  2  2  2  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14
[526] 14 14 14  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13
[551] 13 13  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13
[576] 13  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16
[601]  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3
[626]  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3
[651]  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2
[676]  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2
[701]  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1
[726]  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1
[751]  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13

$plate.c
  [1]  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1
 [26]  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5
 [51]  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9
 [76] 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13
[101] 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17
[126] 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21
[151]  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1
[176]  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  2  6 10 14 18 22  2  6
[201] 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10
[226] 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14
[251] 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18
[276] 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22
[301]  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2
[326]  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6
[351] 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10
[376] 14 18 22  2  6 10 14 18 22  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15
[401] 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19
[426] 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23
[451]  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3
[476]  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7
[501] 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11
[526] 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15
[551] 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19
[576] 23  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24
[601]  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4
[626]  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8
[651] 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12
[676] 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16
[701] 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20
[726] 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24
[751]  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24

$plateposition
  [1] "p1D01" "p1D05" "p1D09" "p1D13" "p1D17" "p1D21" "p1H01" "p1H05" "p1H09"
 [10] "p1H13" "p1H17" "p1H21" "p1L01" "p1L05" "p1L09" "p1L13" "p1L17" "p1L21"
 [19] "p1P01" "p1P05" "p1P09" "p1P13" "p1P17" "p1P21" "p2D01" "p2D05" "p2D09"
 [28] "p2D13" "p2D17" "p2D21" "p2H01" "p2H05" "p2H09" "p2H13" "p2H17" "p2H21"
 [37] "p2L01" "p2L05" "p2L09" "p2L13" "p2L17" "p2L21" "p2P01" "p2P05" "p2P09"
 [46] "p2P13" "p2P17" "p2P21" "p1C01" "p1C05" "p1C09" "p1C13" "p1C17" "p1C21"
 [55] "p1G01" "p1G05" "p1G09" "p1G13" "p1G17" "p1G21" "p1K01" "p1K05" "p1K09"
 [64] "p1K13" "p1K17" "p1K21" "p1O01" "p1O05" "p1O09" "p1O13" "p1O17" "p1O21"
 [73] "p2C01" "p2C05" "p2C09" "p2C13" "p2C17" "p2C21" "p2G01" "p2G05" "p2G09"
 [82] "p2G13" "p2G17" "p2G21" "p2K01" "p2K05" "p2K09" "p2K13" "p2K17" "p2K21"
 [91] "p2O01" "p2O05" "p2O09" "p2O13" "p2O17" "p2O21" "p1B01" "p1B05" "p1B09"
[100] "p1B13" "p1B17" "p1B21" "p1F01" "p1F05" "p1F09" "p1F13" "p1F17" "p1F21"
[109] "p1J01" "p1J05" "p1J09" "p1J13" "p1J17" "p1J21" "p1N01" "p1N05" "p1N09"
[118] "p1N13" "p1N17" "p1N21" "p2B01" "p2B05" "p2B09" "p2B13" "p2B17" "p2B21"
[127] "p2F01" "p2F05" "p2F09" "p2F13" "p2F17" "p2F21" "p2J01" "p2J05" "p2J09"
[136] "p2J13" "p2J17" "p2J21" "p2N01" "p2N05" "p2N09" "p2N13" "p2N17" "p2N21"
[145] "p1A01" "p1A05" "p1A09" "p1A13" "p1A17" "p1A21" "p1E01" "p1E05" "p1E09"
[154] "p1E13" "p1E17" "p1E21" "p1I01" "p1I05" "p1I09" "p1I13" "p1I17" "p1I21"
[163] "p1M01" "p1M05" "p1M09" "p1M13" "p1M17" "p1M21" "p2A01" "p2A05" "p2A09"
[172] "p2A13" "p2A17" "p2A21" "p2E01" "p2E05" "p2E09" "p2E13" "p2E17" "p2E21"
[181] "p2I01" "p2I05" "p2I09" "p2I13" "p2I17" "p2I21" "p2M01" "p2M05" "p2M09"
[190] "p2M13" "p2M17" "p2M21" "p1D02" "p1D06" "p1D10" "p1D14" "p1D18" "p1D22"
[199] "p1H02" "p1H06" "p1H10" "p1H14" "p1H18" "p1H22" "p1L02" "p1L06" "p1L10"
[208] "p1L14" "p1L18" "p1L22" "p1P02" "p1P06" "p1P10" "p1P14" "p1P18" "p1P22"
[217] "p2D02" "p2D06" "p2D10" "p2D14" "p2D18" "p2D22" "p2H02" "p2H06" "p2H10"
[226] "p2H14" "p2H18" "p2H22" "p2L02" "p2L06" "p2L10" "p2L14" "p2L18" "p2L22"
[235] "p2P02" "p2P06" "p2P10" "p2P14" "p2P18" "p2P22" "p1C02" "p1C06" "p1C10"
[244] "p1C14" "p1C18" "p1C22" "p1G02" "p1G06" "p1G10" "p1G14" "p1G18" "p1G22"
[253] "p1K02" "p1K06" "p1K10" "p1K14" "p1K18" "p1K22" "p1O02" "p1O06" "p1O10"
[262] "p1O14" "p1O18" "p1O22" "p2C02" "p2C06" "p2C10" "p2C14" "p2C18" "p2C22"
[271] "p2G02" "p2G06" "p2G10" "p2G14" "p2G18" "p2G22" "p2K02" "p2K06" "p2K10"
[280] "p2K14" "p2K18" "p2K22" "p2O02" "p2O06" "p2O10" "p2O14" "p2O18" "p2O22"
[289] "p1B02" "p1B06" "p1B10" "p1B14" "p1B18" "p1B22" "p1F02" "p1F06" "p1F10"
[298] "p1F14" "p1F18" "p1F22" "p1J02" "p1J06" "p1J10" "p1J14" "p1J18" "p1J22"
[307] "p1N02" "p1N06" "p1N10" "p1N14" "p1N18" "p1N22" "p2B02" "p2B06" "p2B10"
[316] "p2B14" "p2B18" "p2B22" "p2F02" "p2F06" "p2F10" "p2F14" "p2F18" "p2F22"
[325] "p2J02" "p2J06" "p2J10" "p2J14" "p2J18" "p2J22" "p2N02" "p2N06" "p2N10"
[334] "p2N14" "p2N18" "p2N22" "p1A02" "p1A06" "p1A10" "p1A14" "p1A18" "p1A22"
[343] "p1E02" "p1E06" "p1E10" "p1E14" "p1E18" "p1E22" "p1I02" "p1I06" "p1I10"
[352] "p1I14" "p1I18" "p1I22" "p1M02" "p1M06" "p1M10" "p1M14" "p1M18" "p1M22"
[361] "p2A02" "p2A06" "p2A10" "p2A14" "p2A18" "p2A22" "p2E02" "p2E06" "p2E10"
[370] "p2E14" "p2E18" "p2E22" "p2I02" "p2I06" "p2I10" "p2I14" "p2I18" "p2I22"
[379] "p2M02" "p2M06" "p2M10" "p2M14" "p2M18" "p2M22" "p1D03" "p1D07" "p1D11"
[388] "p1D15" "p1D19" "p1D23" "p1H03" "p1H07" "p1H11" "p1H15" "p1H19" "p1H23"
[397] "p1L03" "p1L07" "p1L11" "p1L15" "p1L19" "p1L23" "p1P03" "p1P07" "p1P11"
[406] "p1P15" "p1P19" "p1P23" "p2D03" "p2D07" "p2D11" "p2D15" "p2D19" "p2D23"
[415] "p2H03" "p2H07" "p2H11" "p2H15" "p2H19" "p2H23" "p2L03" "p2L07" "p2L11"
[424] "p2L15" "p2L19" "p2L23" "p2P03" "p2P07" "p2P11" "p2P15" "p2P19" "p2P23"
[433] "p1C03" "p1C07" "p1C11" "p1C15" "p1C19" "p1C23" "p1G03" "p1G07" "p1G11"
[442] "p1G15" "p1G19" "p1G23" "p1K03" "p1K07" "p1K11" "p1K15" "p1K19" "p1K23"
[451] "p1O03" "p1O07" "p1O11" "p1O15" "p1O19" "p1O23" "p2C03" "p2C07" "p2C11"
[460] "p2C15" "p2C19" "p2C23" "p2G03" "p2G07" "p2G11" "p2G15" "p2G19" "p2G23"
[469] "p2K03" "p2K07" "p2K11" "p2K15" "p2K19" "p2K23" "p2O03" "p2O07" "p2O11"
[478] "p2O15" "p2O19" "p2O23" "p1B03" "p1B07" "p1B11" "p1B15" "p1B19" "p1B23"
[487] "p1F03" "p1F07" "p1F11" "p1F15" "p1F19" "p1F23" "p1J03" "p1J07" "p1J11"
[496] "p1J15" "p1J19" "p1J23" "p1N03" "p1N07" "p1N11" "p1N15" "p1N19" "p1N23"
[505] "p2B03" "p2B07" "p2B11" "p2B15" "p2B19" "p2B23" "p2F03" "p2F07" "p2F11"
[514] "p2F15" "p2F19" "p2F23" "p2J03" "p2J07" "p2J11" "p2J15" "p2J19" "p2J23"
[523] "p2N03" "p2N07" "p2N11" "p2N15" "p2N19" "p2N23" "p1A03" "p1A07" "p1A11"
[532] "p1A15" "p1A19" "p1A23" "p1E03" "p1E07" "p1E11" "p1E15" "p1E19" "p1E23"
[541] "p1I03" "p1I07" "p1I11" "p1I15" "p1I19" "p1I23" "p1M03" "p1M07" "p1M11"
[550] "p1M15" "p1M19" "p1M23" "p2A03" "p2A07" "p2A11" "p2A15" "p2A19" "p2A23"
[559] "p2E03" "p2E07" "p2E11" "p2E15" "p2E19" "p2E23" "p2I03" "p2I07" "p2I11"
[568] "p2I15" "p2I19" "p2I23" "p2M03" "p2M07" "p2M11" "p2M15" "p2M19" "p2M23"
[577] "p1D04" "p1D08" "p1D12" "p1D16" "p1D20" "p1D24" "p1H04" "p1H08" "p1H12"
[586] "p1H16" "p1H20" "p1H24" "p1L04" "p1L08" "p1L12" "p1L16" "p1L20" "p1L24"
[595] "p1P04" "p1P08" "p1P12" "p1P16" "p1P20" "p1P24" "p2D04" "p2D08" "p2D12"
[604] "p2D16" "p2D20" "p2D24" "p2H04" "p2H08" "p2H12" "p2H16" "p2H20" "p2H24"
[613] "p2L04" "p2L08" "p2L12" "p2L16" "p2L20" "p2L24" "p2P04" "p2P08" "p2P12"
[622] "p2P16" "p2P20" "p2P24" "p1C04" "p1C08" "p1C12" "p1C16" "p1C20" "p1C24"
[631] "p1G04" "p1G08" "p1G12" "p1G16" "p1G20" "p1G24" "p1K04" "p1K08" "p1K12"
[640] "p1K16" "p1K20" "p1K24" "p1O04" "p1O08" "p1O12" "p1O16" "p1O20" "p1O24"
[649] "p2C04" "p2C08" "p2C12" "p2C16" "p2C20" "p2C24" "p2G04" "p2G08" "p2G12"
[658] "p2G16" "p2G20" "p2G24" "p2K04" "p2K08" "p2K12" "p2K16" "p2K20" "p2K24"
[667] "p2O04" "p2O08" "p2O12" "p2O16" "p2O20" "p2O24" "p1B04" "p1B08" "p1B12"
[676] "p1B16" "p1B20" "p1B24" "p1F04" "p1F08" "p1F12" "p1F16" "p1F20" "p1F24"
[685] "p1J04" "p1J08" "p1J12" "p1J16" "p1J20" "p1J24" "p1N04" "p1N08" "p1N12"
[694] "p1N16" "p1N20" "p1N24" "p2B04" "p2B08" "p2B12" "p2B16" "p2B20" "p2B24"
[703] "p2F04" "p2F08" "p2F12" "p2F16" "p2F20" "p2F24" "p2J04" "p2J08" "p2J12"
[712] "p2J16" "p2J20" "p2J24" "p2N04" "p2N08" "p2N12" "p2N16" "p2N20" "p2N24"
[721] "p1A04" "p1A08" "p1A12" "p1A16" "p1A20" "p1A24" "p1E04" "p1E08" "p1E12"
[730] "p1E16" "p1E20" "p1E24" "p1I04" "p1I08" "p1I12" "p1I16" "p1I20" "p1I24"
[739] "p1M04" "p1M08" "p1M12" "p1M16" "p1M20" "p1M24" "p2A04" "p2A08" "p2A12"
[748] "p2A16" "p2A20" "p2A24" "p2E04" "p2E08" "p2E12" "p2E16" "p2E20" "p2E24"
[757] "p2I04" "p2I08" "p2I12" "p2I16" "p2I20" "p2I24" "p2M04" "p2M08" "p2M12"
[766] "p2M16" "p2M20" "p2M24"

> 
> ### merge.rglist
> 
> R <- G <- matrix(11:14,4,2)
> rownames(R) <- rownames(G) <- c("a","a","b","c")
> RG1 <- new("RGList",list(R=R,G=G))
> R <- G <- matrix(21:24,4,2)
> rownames(R) <- rownames(G) <- c("b","a","a","c")
> RG2 <- new("RGList",list(R=R,G=G))
> merge(RG1,RG2)
An object of class "RGList"
$R
  [,1] [,2] [,3] [,4]
a   11   11   22   22
a   12   12   23   23
b   13   13   21   21
c   14   14   24   24

$G
  [,1] [,2] [,3] [,4]
a   11   11   22   22
a   12   12   23   23
b   13   13   21   21
c   14   14   24   24

> merge(RG2,RG1)
An object of class "RGList"
$R
  [,1] [,2] [,3] [,4]
b   21   21   13   13
a   22   22   11   11
a   23   23   12   12
c   24   24   14   14

$G
  [,1] [,2] [,3] [,4]
b   21   21   13   13
a   22   22   11   11
a   23   23   12   12
c   24   24   14   14

> 
> ### background correction
> 
> RG <- new("RGList", list(R=c(1,2,3,4),G=c(1,2,3,4),Rb=c(2,2,2,2),Gb=c(2,2,2,2)))
> backgroundCorrect(RG)
An object of class "RGList"
$R
     [,1]
[1,]   -1
[2,]    0
[3,]    1
[4,]    2

$G
     [,1]
[1,]   -1
[2,]    0
[3,]    1
[4,]    2

> backgroundCorrect(RG, method="half")
An object of class "RGList"
$R
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

$G
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

> backgroundCorrect(RG, method="minimum")
An object of class "RGList"
$R
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

$G
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

> backgroundCorrect(RG, offset=5)
An object of class "RGList"
$R
     [,1]
[1,]    4
[2,]    5
[3,]    6
[4,]    7

$G
     [,1]
[1,]    4
[2,]    5
[3,]    6
[4,]    7

> 
> ### loessFit
> 
> x <- 1:100
> y <- rnorm(100)
> out <- loessFit(y,x)
> f1 <- quantile(out$fitted)
> r1 <- quantile(out$residuals)
> w <- rep(1,100)
> w[1:50] <- 0.5
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f2 <- quantile(out$fitted)
> r2 <- quantile(out$residuals)
> out <- loessFit(y,x,weights=w,method="locfit")
> f3 <- quantile(out$fitted)
> r3 <- quantile(out$residuals)
> out <- loessFit(y,x,weights=w,method="loess")
> f4 <- quantile(out$fitted)
> r4 <- quantile(out$residuals)
> w <- rep(1,100)
> w[2*(1:50)] <- 0
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f5 <- quantile(out$fitted)
> r5 <- quantile(out$residuals)
> data.frame(f1,f2,f3,f4,f5)
              f1           f2          f3          f4          f5
0%   -0.78835384 -0.687432210 -0.78957137 -0.76756060 -0.63778292
25%  -0.18340154 -0.179683572 -0.18979269 -0.16773223 -0.38064318
50%  -0.11492924 -0.114796040 -0.12087983 -0.07185314 -0.15971879
75%   0.01507921 -0.008145125 -0.01857508  0.04030634  0.07839396
100%  0.21653837  0.145106033  0.19214597  0.21417361  0.51836274
> data.frame(r1,r2,r3,r4,r5)
              r1          r2          r3           r4          r5
0%   -2.04434053 -2.05132680 -2.02404318 -2.101242874 -2.22280633
25%  -0.59321065 -0.57200209 -0.58975649 -0.577887481 -0.71037756
50%   0.05874864  0.04514326  0.08335198 -0.001769806  0.06785517
75%   0.56010750  0.55124530  0.57618740  0.561454370  0.65383830
100%  2.57936026  2.64549799  2.57549257  2.402324533  2.28648835
> 
> ### normalizeWithinArrays
> 
> RG <- new("RGList",list())
> RG$R <- matrix(rexp(100*2),100,2)
> RG$G <- matrix(rexp(100*2),100,2)
> RG$Rb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RG$Gb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="saddle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
       V1                V2                V3               V4        
 Min.   :0.01626   Min.   :0.01213   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.35497   1st Qu.:0.29133   1st Qu.:0.2745   1st Qu.:0.3953  
 Median :0.71793   Median :0.70294   Median :0.6339   Median :0.8223  
 Mean   :0.90184   Mean   :1.00122   Mean   :0.9454   Mean   :1.1324  
 3rd Qu.:1.16891   3rd Qu.:1.33139   3rd Qu.:1.4059   3rd Qu.:1.4221  
 Max.   :4.56267   Max.   :6.37947   Max.   :5.0486   Max.   :6.6295  
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="mle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
       V1                V2                V3               V4        
 Min.   :0.01701   Min.   :0.01255   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.35423   1st Qu.:0.29118   1st Qu.:0.2745   1st Qu.:0.3953  
 Median :0.71719   Median :0.70280   Median :0.6339   Median :0.8223  
 Mean   :0.90118   Mean   :1.00110   Mean   :0.9454   Mean   :1.1324  
 3rd Qu.:1.16817   3rd Qu.:1.33124   3rd Qu.:1.4059   3rd Qu.:1.4221  
 Max.   :4.56193   Max.   :6.37932   Max.   :5.0486   Max.   :6.6295  
> MA <- normalizeWithinArrays(RGb,method="loess")
> summary(MA$M)
       V1                 V2          
 Min.   :-5.88044   Min.   :-5.66985  
 1st Qu.:-1.18483   1st Qu.:-1.57014  
 Median :-0.21632   Median : 0.04823  
 Mean   : 0.03487   Mean   :-0.05481  
 3rd Qu.: 1.49669   3rd Qu.: 1.45113  
 Max.   : 7.07324   Max.   : 6.19744  
> #MA <- normalizeWithinArrays(RG[,1:2], mouse.setup, method="robustspline")
> #MA$M[1:5,]
> #MA <- normalizeWithinArrays(mouse.data, mouse.setup)
> #MA$M[1:5,]
> 
> ### normalizeBetweenArrays
> 
> MA2 <- normalizeBetweenArrays(MA,method="scale")
> MA$M[1:5,]
           [,1]       [,2]
[1,] -1.1689588  4.5558123
[2,]  0.8971363  0.3296544
[3,]  2.8247439  1.4249960
[4,] -1.8533240  0.4804851
[5,]  1.9158459 -5.5087631
> MA$A[1:5,]
            [,1]       [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200  0.2071043
[4,]  0.09281027 -1.3880965
[5,]  0.22385828 -3.0855818
> MA2 <- normalizeBetweenArrays(MA,method="quantile")
> MA$M[1:5,]
           [,1]       [,2]
[1,] -1.1689588  4.5558123
[2,]  0.8971363  0.3296544
[3,]  2.8247439  1.4249960
[4,] -1.8533240  0.4804851
[5,]  1.9158459 -5.5087631
> MA$A[1:5,]
            [,1]       [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200  0.2071043
[4,]  0.09281027 -1.3880965
[5,]  0.22385828 -3.0855818
> 
> ### unwrapdups
> 
> M <- matrix(1:12,6,2)
> unwrapdups(M,ndups=1)
     [,1] [,2]
[1,]    1    7
[2,]    2    8
[3,]    3    9
[4,]    4   10
[5,]    5   11
[6,]    6   12
> unwrapdups(M,ndups=2)
     [,1] [,2] [,3] [,4]
[1,]    1    2    7    8
[2,]    3    4    9   10
[3,]    5    6   11   12
> unwrapdups(M,ndups=3)
     [,1] [,2] [,3] [,4] [,5] [,6]
[1,]    1    2    3    7    8    9
[2,]    4    5    6   10   11   12
> unwrapdups(M,ndups=2,spacing=3)
     [,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    2    5    8   11
[3,]    3    6    9   12
> 
> ### trigammaInverse
> 
> trigammaInverse(c(1e-6,NA,5,1e6))
[1] 1.000000e+06           NA 4.961687e-01 1.000001e-03
> 
> ### lmFit, eBayes, topTable
> 
> M <- matrix(rnorm(10*6,sd=0.3),10,6)
> rownames(M) <- LETTERS[1:10]
> M[1,1:3] <- M[1,1:3] + 2
> design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
> contrast.matrix <- cbind(First3=c(1,0),Last3=c(0,1),"Last3-First3"=c(-1,1))
> fit <- lmFit(M,design)
> fit2 <- eBayes(contrasts.fit(fit,contrasts=contrast.matrix))
> topTable(fit2)
       First3       Last3 Last3.First3      AveExpr           F      P.Value
A  1.77602021  0.06025114  -1.71576906  0.918135675 50.91471061 7.727200e-23
D -0.05454069  0.39127869   0.44581938  0.168369004  2.51638838 8.075072e-02
F -0.16249607 -0.33009728  -0.16760121 -0.246296671  2.18256779 1.127516e-01
G  0.30852468 -0.06873462  -0.37725930  0.119895035  1.61088775 1.997102e-01
H -0.16942269  0.20578118   0.37520387  0.018179245  1.14554368 3.180510e-01
J  0.21417623  0.07074940  -0.14342683  0.142462814  0.82029274 4.403027e-01
C -0.12236781  0.15095948   0.27332729  0.014295836  0.60885003 5.439761e-01
B -0.11982833  0.13529287   0.25512120  0.007732271  0.52662792 5.905931e-01
E  0.01897934  0.10434934   0.08536999  0.061664340  0.18136849 8.341279e-01
I -0.04720963  0.03996397   0.08717360 -0.003622829  0.06168476 9.401792e-01
     adj.P.Val
A 7.727200e-22
D 3.758388e-01
F 3.758388e-01
G 4.992756e-01
H 6.361019e-01
J 7.338379e-01
C 7.382414e-01
B 7.382414e-01
E 9.268088e-01
I 9.401792e-01
> topTable(fit2,coef=3,resort.by="logFC")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="p")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,sort.by="logFC",resort.by="t")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="B")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,lfc=1)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p.value=0.2)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p.value=0.2,lfc=0.5)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p.value=0.2,lfc=0.5,sort.by="none")
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> contrasts.fit(fit[1:3,],contrast.matrix[,0])
An object of class "MArrayLM"
$coefficients
 
A
B
C

$rank
[1] 2

$assign
NULL

$qr
$qr
     First3Arrays Last3Arrays
[1,]   -1.7320508   0.0000000
[2,]    0.5773503  -1.7320508
[3,]    0.5773503   0.0000000
[4,]    0.0000000   0.5773503
[5,]    0.0000000   0.5773503
[6,]    0.0000000   0.5773503

$qraux
[1] 1.57735 1.00000

$pivot
[1] 1 2

$tol
[1] 1e-07

$rank
[1] 2


$df.residual
[1] 4 4 4

$sigma
        A         B         C 
0.3299787 0.3323336 0.2315815 

$cov.coefficients
<0 x 0 matrix>

$stdev.unscaled
 
A
B
C

$pivot
[1] 1 2

$Amean
          A           B           C 
0.918135675 0.007732271 0.014295836 

$method
[1] "ls"

$design
     First3Arrays Last3Arrays
[1,]            1           0
[2,]            1           0
[3,]            1           0
[4,]            0           1
[5,]            0           1
[6,]            0           1

$contrasts
    
[1,]
[2,]

> fit$coefficients[1,1] <- NA
> contrasts.fit(fit[1:3,],contrast.matrix)$coefficients
      First3      Last3 Last3-First3
A         NA 0.06025114           NA
B -0.1198283 0.13529287    0.2551212
C -0.1223678 0.15095948    0.2733273
> 
> designlist <- list(Null=matrix(1,6,1),Two=design,Three=cbind(1,c(0,0,1,1,0,0),c(0,0,0,0,1,1)))
> out <- selectModel(M,designlist)
> table(out$pref)

 Null   Two Three 
    5     3     2 
> 
> ### marray object
> 
> #suppressMessages(suppressWarnings(gotmarray <- require(marray,quietly=TRUE)))
> #if(gotmarray) {
> #	data(swirl)
> #	snorm = maNorm(swirl)
> #	fit <- lmFit(snorm, design = c(1,-1,-1,1))
> #	fit <- eBayes(fit)
> #	topTable(fit,resort.by="AveExpr")
> #}
> 
> ### duplicateCorrelation
> 
> cor.out <- duplicateCorrelation(M)
> cor.out$consensus.correlation
[1] -0.09290714
> cor.out$atanh.correlations
[1] -0.4419130  0.4088967 -0.1964978 -0.6093769  0.3730118
> 
> ### gls.series
> 
> fit <- gls.series(M,design,correlation=cor.out$cor)
> fit$coefficients
     First3Arrays Last3Arrays
[1,]   0.82809594  0.09777201
[2,]  -0.08845425  0.27111909
[3,]  -0.07175836 -0.11287397
[4,]   0.06955100  0.06852328
[5,]   0.08348330  0.05535668
> fit$stdev.unscaled
     First3Arrays Last3Arrays
[1,]    0.3888215   0.3888215
[2,]    0.3888215   0.3888215
[3,]    0.3888215   0.3888215
[4,]    0.3888215   0.3888215
[5,]    0.3888215   0.3888215
> fit$sigma
[1] 0.7630059 0.2152728 0.3350370 0.3227781 0.3405473
> fit$df.residual
[1] 10 10 10 10 10
> 
> ### mrlm
> 
> fit <- mrlm(M,design)
Warning message:
In rlm.default(x = X, y = y, weights = w, ...) :
  'rlm' failed to converge in 20 steps
> fit$coefficients
  First3Arrays Last3Arrays
A   1.75138894  0.06025114
B  -0.11982833  0.10322039
C  -0.09302502  0.15095948
D  -0.05454069  0.33700045
E   0.07927938  0.10434934
F  -0.16249607 -0.34010852
G   0.30852468 -0.06873462
H  -0.16942269  0.24392984
I  -0.04720963  0.03996397
J   0.21417623 -0.05679272
> fit$stdev.unscaled
  First3Arrays Last3Arrays
A    0.5933418   0.5773503
B    0.5773503   0.6096497
C    0.6017444   0.5773503
D    0.5773503   0.6266021
E    0.6307703   0.5773503
F    0.5773503   0.5846707
G    0.5773503   0.5773503
H    0.5773503   0.6544564
I    0.5773503   0.5773503
J    0.5773503   0.6689776
> fit$sigma
 [1] 0.2894294 0.2679396 0.2090236 0.1461395 0.2309018 0.2827476 0.2285945
 [8] 0.2267556 0.3537469 0.2172409
> fit$df.residual
 [1] 4 4 4 4 4 4 4 4 4 4
> 
> # Similar to Mette Langaas 19 May 2004
> set.seed(123)
> narrays <- 9
> ngenes <- 5
> mu <- 0
> alpha <- 2
> beta <- -2
> epsilon <- matrix(rnorm(narrays*ngenes,0,1),ncol=narrays)
> X <- cbind(rep(1,9),c(0,0,0,1,1,1,0,0,0),c(0,0,0,0,0,0,1,1,1))
> dimnames(X) <- list(1:9,c("mu","alpha","beta"))
> yvec <- mu*X[,1]+alpha*X[,2]+beta*X[,3]
> ymat <- matrix(rep(yvec,ngenes),ncol=narrays,byrow=T)+epsilon
> ymat[5,1:2] <- NA
> fit <- lmFit(ymat,design=X)
> test.contr <- cbind(c(0,1,-1),c(1,1,0),c(1,0,1))
> dimnames(test.contr) <- list(c("mu","alpha","beta"),c("alpha-beta","mu+alpha","mu+beta"))
> fit2 <- contrasts.fit(fit,contrasts=test.contr)
> eBayes(fit2)
An object of class "MArrayLM"
$coefficients
     alpha-beta mu+alpha   mu+beta
[1,]   3.537333 1.677465 -1.859868
[2,]   4.355578 2.372554 -1.983024
[3,]   3.197645 1.053584 -2.144061
[4,]   2.697734 1.611443 -1.086291
[5,]   3.502304 2.051995 -1.450309

$stdev.unscaled
     alpha-beta  mu+alpha   mu+beta
[1,]  0.8164966 0.5773503 0.5773503
[2,]  0.8164966 0.5773503 0.5773503
[3,]  0.8164966 0.5773503 0.5773503
[4,]  0.8164966 0.5773503 0.5773503
[5,]  1.1547005 0.8368633 0.8368633

$sigma
[1] 1.3425032 0.4647155 1.1993444 0.9428569 0.9421509

$df.residual
[1] 6 6 6 6 4

$cov.coefficients
           alpha-beta     mu+alpha       mu+beta
alpha-beta  0.6666667 3.333333e-01 -3.333333e-01
mu+alpha    0.3333333 3.333333e-01  5.551115e-17
mu+beta    -0.3333333 5.551115e-17  3.333333e-01

$pivot
[1] 1 2 3

$rank
[1] 3

$Amean
[1]  0.2034961  0.1954604 -0.2863347  0.1188659  0.1784593

$method
[1] "ls"

$design
  mu alpha beta
1  1     0    0
2  1     0    0
3  1     0    0
4  1     1    0
5  1     1    0
6  1     1    0
7  1     0    1
8  1     0    1
9  1     0    1

$contrasts
      alpha-beta mu+alpha mu+beta
mu             0        1       1
alpha          1        1       0
beta          -1        0       1

$df.prior
[1] 9.306153

$s2.prior
[1] 0.923179

$var.prior
[1] 17.33142 17.33142 12.26855

$proportion
[1] 0.01

$s2.post
[1] 1.2677996 0.6459499 1.1251558 0.9097727 0.9124980

$t
     alpha-beta mu+alpha   mu+beta
[1,]   3.847656 2.580411 -2.860996
[2,]   6.637308 5.113018 -4.273553
[3,]   3.692066 1.720376 -3.500994
[4,]   3.464003 2.926234 -1.972606
[5,]   3.175181 2.566881 -1.814221

$df.total
[1] 15.30615 15.30615 15.30615 15.30615 13.30615

$p.value
       alpha-beta     mu+alpha      mu+beta
[1,] 1.529450e-03 0.0206493481 0.0117123495
[2,] 7.144893e-06 0.0001195844 0.0006385076
[3,] 2.109270e-03 0.1055117477 0.0031325769
[4,] 3.381970e-03 0.0102514264 0.0668844448
[5,] 7.124839e-03 0.0230888584 0.0922478630

$lods
     alpha-beta  mu+alpha    mu+beta
[1,]  -1.013417 -3.702133 -3.0332393
[2,]   3.981496  1.283349 -0.2615911
[3,]  -1.315036 -5.168621 -1.7864101
[4,]  -1.757103 -3.043209 -4.6191869
[5,]  -2.257358 -3.478267 -4.5683738

$F
[1]  7.421911 22.203107  7.608327  6.227010  5.060579

$F.p.value
[1] 5.581800e-03 2.988923e-05 5.080726e-03 1.050148e-02 2.320274e-02

> 
> ### uniquegenelist
> 
> uniquegenelist(letters[1:8],ndups=2)
[1] "a" "c" "e" "g"
> uniquegenelist(letters[1:8],ndups=2,spacing=2)
[1] "a" "b" "e" "f"
> 
> ### classifyTests
> 
> tstat <- matrix(c(0,5,0, 0,2.5,0, -2,-2,2, 1,1,1), 4, 3, byrow=TRUE)
> classifyTestsF(tstat)
TestResults matrix
     [,1] [,2] [,3]
[1,]    0    1    0
[2,]    0    0    0
[3,]   -1   -1    1
[4,]    0    0    0
> classifyTestsF(tstat,fstat.only=TRUE)
[1] 8.333333 2.083333 4.000000 1.000000
attr(,"df1")
[1] 3
attr(,"df2")
[1] Inf
> limma:::.classifyTestsP(tstat)
TestResults matrix
     [,1] [,2] [,3]
[1,]    0    1    0
[2,]    0    1    0
[3,]    0    0    0
[4,]    0    0    0
> 
> ### avereps
> 
> x <- matrix(rnorm(8*3),8,3)
> colnames(x) <- c("S1","S2","S3")
> rownames(x) <- c("b","a","a","c","c","b","b","b")
> avereps(x)
          S1         S2         S3
b -0.2353018  0.5220094  0.2302895
a -0.4347701  0.6453498 -0.6758914
c  0.3482980 -0.4820695 -0.3841313
> 
> ### roast
> 
> y <- matrix(rnorm(100*4),100,4)
> sigma <- sqrt(2/rchisq(100,df=7))
> y <- y*sigma
> design <- cbind(Intercept=1,Group=c(0,0,1,1))
> iset1 <- 1:5
> y[iset1,3:4] <- y[iset1,3:4]+3
> iset2 <- 6:10
> roast(y=y,iset1,design,contrast=2)
         Active.Prop     P.Value
Down               0 0.997999500
Up                 1 0.002250563
UpOrDown           1 0.004500000
Mixed              1 0.004500000
> roast(y=y,iset1,design,contrast=2,array.weights=c(0.5,1,0.5,1))
         Active.Prop     P.Value
Down               0 0.998749687
Up                 1 0.001500375
UpOrDown           1 0.003000000
Mixed              1 0.003000000
> w <- matrix(runif(100*4),100,4)
> roast(y=y,iset1,design,contrast=2,weights=w)
         Active.Prop     P.Value
Down               0 0.996999250
Up                 1 0.003250813
UpOrDown           1 0.006500000
Mixed              1 0.006500000
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,gene.weights=runif(100))
     NGenes PropDown PropUp Direction PValue    FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up 0.0055 0.0105       0.0055    0.0105
set2      5        0      0        Up 0.2025 0.2025       0.4715    0.4715
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,array.weights=c(0.5,1,0.5,1))
     NGenes PropDown PropUp Direction PValue    FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up 0.0050 0.0095        0.005    0.0095
set2      5        0      0        Up 0.6845 0.6845        0.642    0.6420
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w)
     NGenes PropDown PropUp Direction PValue    FDR PValue.Mixed FDR.Mixed
set1      5        0    1.0        Up 0.0030 0.0055        0.003    0.0055
set2      5        0    0.2      Down 0.9615 0.9615        0.496    0.4960
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes PropDown PropUp Direction PValue    FDR PValue.Mixed FDR.Mixed
set1      5        0    1.0        Up 0.0025 0.0045       0.0025    0.0045
set2      5        0    0.2      Down 0.8930 0.8930       0.4380    0.4380
> fry(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes Direction      PValue         FDR PValue.Mixed    FDR.Mixed
set1      5        Up 0.001568924 0.003137848 0.0001156464 0.0002312929
set2      5      Down 0.932105219 0.932105219 0.4315499569 0.4315499569
> rownames(y) <- paste0("Gene",1:100)
> iset1A <- rownames(y)[1:5]
> fry(y=y,index=iset1A,design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes Direction      PValue PValue.Mixed
set1      5        Up 0.001568924 0.0001156464
> 
> ### camera
> 
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1),allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction      PValue
set1      5  -0.2481655        Up 0.001050253
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction       PValue        FDR
set1      5  -0.2481655        Up 0.0009047749 0.00180955
set2      5   0.1719094      Down 0.9068364378 0.90683644
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1))
     NGenes Direction       PValue
set1      5        Up 1.105329e-10
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2)
     NGenes Direction       PValue          FDR
set1      5        Up 7.334400e-12 1.466880e-11
set2      5      Down 8.677115e-01 8.677115e-01
> camera(y=y,iset1A,design,contrast=2)
     NGenes Direction     PValue
set1      5        Up 7.3344e-12
> 
> ### with EList arg
> 
> y <- new("EList",list(E=y))
> roast(y=y,iset1,design,contrast=2)
         Active.Prop     P.Value
Down               0 0.996999250
Up                 1 0.003250813
UpOrDown           1 0.006500000
Mixed              1 0.006500000
> camera(y=y,iset1,design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction       PValue
set1      5  -0.2481655        Up 0.0009047749
> camera(y=y,iset1,design,contrast=2)
     NGenes Direction     PValue
set1      5        Up 7.3344e-12
> 
> ### eBayes with trend
> 
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene2   3.729512  1.73488969  4.865697 0.0004854886 0.02902331  0.1596831
Gene3   3.488703  1.03931081  4.754954 0.0005804663 0.02902331 -0.0144071
Gene4   2.696676  1.74060725  3.356468 0.0063282637 0.21094212 -2.3434702
Gene1   2.391846  1.72305203  3.107124 0.0098781268 0.24695317 -2.7738874
Gene33 -1.492317 -0.07525287 -2.783817 0.0176475742 0.29965463 -3.3300835
Gene5   2.387967  1.63066783  2.773444 0.0179792778 0.29965463 -3.3478204
Gene80 -1.839760 -0.32802306 -2.503584 0.0291489863 0.37972679 -3.8049642
Gene39  1.366141 -0.27360750  2.451133 0.0320042242 0.37972679 -3.8925860
Gene95 -1.907074  1.26297763 -2.414217 0.0341754107 0.37972679 -3.9539571
Gene50  1.034777  0.01608433  2.054690 0.0642289403 0.59978803 -4.5350317
> fit$df.prior
[1] 9.098442
> fit$s2.prior
    Gene1     Gene2     Gene3     Gene4     Gene5     Gene6     Gene7     Gene8 
0.6901845 0.6977354 0.3860494 0.7014122 0.6341068 0.2926337 0.3077620 0.3058098 
    Gene9    Gene10    Gene11    Gene12    Gene13    Gene14    Gene15    Gene16 
0.2985145 0.2832520 0.3232434 0.3279710 0.2816081 0.2943502 0.3127994 0.2894802 
   Gene17    Gene18    Gene19    Gene20    Gene21    Gene22    Gene23    Gene24 
0.2812758 0.2840051 0.2839124 0.2954261 0.2838592 0.2812704 0.3157029 0.2844541 
   Gene25    Gene26    Gene27    Gene28    Gene29    Gene30    Gene31    Gene32 
0.4778832 0.2818242 0.2930360 0.2940957 0.2941862 0.3234399 0.3164779 0.2853510 
   Gene33    Gene34    Gene35    Gene36    Gene37    Gene38    Gene39    Gene40 
0.2988244 0.3450090 0.3048596 0.3089086 0.3104534 0.4551549 0.3220008 0.2813286 
   Gene41    Gene42    Gene43    Gene44    Gene45    Gene46    Gene47    Gene48 
0.2826027 0.2822504 0.2823330 0.3170673 0.3146173 0.3146793 0.2916540 0.2975003 
   Gene49    Gene50    Gene51    Gene52    Gene53    Gene54    Gene55    Gene56 
0.3538946 0.2907240 0.3199596 0.2816641 0.2814293 0.2996822 0.2812885 0.2896157 
   Gene57    Gene58    Gene59    Gene60    Gene61    Gene62    Gene63    Gene64 
0.2955317 0.2815907 0.2919420 0.2849675 0.3540805 0.3491713 0.2975019 0.2939325 
   Gene65    Gene66    Gene67    Gene68    Gene69    Gene70    Gene71    Gene72 
0.2986943 0.3265466 0.3402343 0.3394927 0.2813283 0.2814440 0.3089669 0.3030850 
   Gene73    Gene74    Gene75    Gene76    Gene77    Gene78    Gene79    Gene80 
0.2859286 0.2813216 0.3475231 0.3334419 0.2949550 0.3108702 0.2959688 0.3295294 
   Gene81    Gene82    Gene83    Gene84    Gene85    Gene86    Gene87    Gene88 
0.3413700 0.2946268 0.3029565 0.2920284 0.2926205 0.2818046 0.3425116 0.2882936 
   Gene89    Gene90    Gene91    Gene92    Gene93    Gene94    Gene95    Gene96 
0.2945459 0.3077919 0.2892134 0.2823787 0.3048049 0.2961408 0.4590012 0.2812784 
   Gene97    Gene98    Gene99   Gene100 
0.2846345 0.2819651 0.3137551 0.2856081 
> summary(fit$s2.post)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2335  0.2603  0.2997  0.3375  0.3655  0.7812 
> 
> y$E[1,1] <- NA
> y$E[1,3] <- NA
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene3   3.488703  1.03931081  4.604490 0.0007644061 0.07644061 -0.2333915
Gene2   3.729512  1.73488969  4.158038 0.0016033158 0.08016579 -0.9438583
Gene4   2.696676  1.74060725  2.898102 0.0145292666 0.44537707 -3.0530813
Gene33 -1.492317 -0.07525287 -2.784004 0.0178150826 0.44537707 -3.2456324
Gene5   2.387967  1.63066783  2.495395 0.0297982959 0.46902627 -3.7272957
Gene80 -1.839760 -0.32802306 -2.491115 0.0300256116 0.46902627 -3.7343584
Gene39  1.366141 -0.27360750  2.440729 0.0328318388 0.46902627 -3.8172597
Gene1   2.638272  1.47993643  2.227507 0.0530016060 0.58890673 -3.9537576
Gene95 -1.907074  1.26297763 -2.288870 0.0429197808 0.53649726 -4.0642439
Gene50  1.034777  0.01608433  2.063663 0.0635275235 0.60439978 -4.4204731
> fit$df.residual[1]
[1] 0
> fit$df.prior
[1] 8.971891
> fit$s2.prior
  [1] 0.7014084 0.9646561 0.4276287 0.9716476 0.8458852 0.2910492 0.3097052
  [8] 0.3074225 0.2985517 0.2786374 0.3267121 0.3316013 0.2766404 0.2932679
 [15] 0.3154347 0.2869186 0.2761395 0.2799884 0.2795119 0.2946468 0.2794412
 [22] 0.2761282 0.3186442 0.2806092 0.4596465 0.2767847 0.2924541 0.2939204
 [29] 0.2930568 0.3269177 0.3194905 0.2814293 0.2989389 0.3483845 0.3062977
 [36] 0.3110287 0.3127934 0.4418052 0.3254067 0.2761732 0.2780422 0.2773311
 [43] 0.2776653 0.3201314 0.3174515 0.3175199 0.2897731 0.2972785 0.3567262
 [50] 0.2885556 0.3232426 0.2767207 0.2762915 0.3000062 0.2761306 0.2870975
 [57] 0.2947817 0.2766152 0.2901489 0.2813183 0.3568982 0.3724440 0.2972804
 [64] 0.2927300 0.2987764 0.3301406 0.3437962 0.3430762 0.2761729 0.2763094
 [71] 0.3110958 0.3041715 0.2822004 0.2761654 0.3507694 0.3371214 0.2940441
 [78] 0.3132660 0.2953388 0.3331880 0.3448949 0.2946558 0.3040162 0.2902616
 [85] 0.2910320 0.2769211 0.3459946 0.2859057 0.2935193 0.3097398 0.2865663
 [92] 0.2774968 0.3062327 0.2955576 0.5425422 0.2761214 0.2808585 0.2771484
 [99] 0.3164981 0.2817725
> summary(fit$s2.post)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2296  0.2581  0.3003  0.3453  0.3652  0.9158 
> 
> ### eBayes with robust
> 
> fitr <- lmFit(y,design)
> fitr <- eBayes(fitr,robust=TRUE)
> summary(fitr$df.prior)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  6.717   9.244   9.244   9.194   9.244   9.244 
> topTable(fitr,coef=2)
           logFC     AveExpr         t      P.Value   adj.P.Val          B
Gene2   3.729512  1.73488969  7.108463 1.752774e-05 0.001752774  3.3517310
Gene3   3.488703  1.03931081  5.041209 3.526138e-04 0.017630688  0.4056329
Gene4   2.696676  1.74060725  4.697690 6.150508e-04 0.020501693 -0.1463315
Gene5   2.387967  1.63066783  3.451807 5.245019e-03 0.131125480 -2.2678836
Gene1   2.638272  1.47993643  3.317593 8.651142e-03 0.173022847 -2.4400000
Gene33 -1.492317 -0.07525287 -2.716431 1.970991e-02 0.297950865 -3.5553166
Gene95 -1.907074  1.26297763 -2.685067 2.085656e-02 0.297950865 -3.6094982
Gene80 -1.839760 -0.32802306 -2.535926 2.727440e-02 0.340929958 -3.8653107
Gene39  1.366141 -0.27360750  2.469570 3.071854e-02 0.341317083 -3.9779817
Gene50  1.034777  0.01608433  1.973040 7.357960e-02 0.632875126 -4.7877548
> fitr <- eBayes(fitr,trend=TRUE,robust=TRUE)
> summary(fitr$df.prior)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  7.809   8.972   8.972   8.949   8.972   8.972 
> topTable(fitr,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene2   3.729512  1.73488969  4.754160 0.0005999064 0.05999064 -0.0218247
Gene3   3.488703  1.03931081  3.761219 0.0031618743 0.15809372 -1.6338257
Gene4   2.696676  1.74060725  3.292262 0.0071993347 0.23997782 -2.4295326
Gene33 -1.492317 -0.07525287 -3.063180 0.0108203134 0.27050784 -2.8211394
Gene50  1.034777  0.01608433  2.645717 0.0228036320 0.38815282 -3.5304767
Gene5   2.387967  1.63066783  2.633901 0.0232891695 0.38815282 -3.5503445
Gene1   2.638272  1.47993643  2.204116 0.0550613420 0.58959402 -4.0334169
Gene80 -1.839760 -0.32802306 -2.332729 0.0397331916 0.56761702 -4.0496640
Gene39  1.366141 -0.27360750  2.210665 0.0492211477 0.58959402 -4.2469578
Gene95 -1.907074  1.26297763 -2.106861 0.0589594023 0.58959402 -4.4117140
> 
> ### voom
> 
> y <- matrix(rpois(100*4,lambda=20),100,4)
> design <- cbind(Int=1,x=c(0,0,1,1))
> v <- voom(y,design)
> names(v)
[1] "E"       "weights" "design"  "targets"
> summary(v$E)
       V1              V2              V3              V4       
 Min.   :12.38   Min.   :12.32   Min.   :12.17   Min.   :12.08  
 1st Qu.:13.11   1st Qu.:13.05   1st Qu.:13.11   1st Qu.:13.03  
 Median :13.34   Median :13.28   Median :13.35   Median :13.35  
 Mean   :13.29   Mean   :13.29   Mean   :13.28   Mean   :13.28  
 3rd Qu.:13.48   3rd Qu.:13.54   3rd Qu.:13.48   3rd Qu.:13.50  
 Max.   :14.01   Max.   :13.95   Max.   :14.03   Max.   :14.05  
> summary(v$weights)
       V1               V2               V3               V4        
 Min.   : 7.729   Min.   : 7.729   Min.   : 7.729   Min.   : 7.729  
 1st Qu.:13.859   1st Qu.:15.067   1st Qu.:14.254   1st Qu.:13.592  
 Median :15.913   Median :16.621   Median :16.081   Median :16.028  
 Mean   :16.773   Mean   :18.525   Mean   :18.472   Mean   :17.112  
 3rd Qu.:18.214   3rd Qu.:20.002   3rd Qu.:18.475   3rd Qu.:18.398  
 Max.   :34.331   Max.   :34.331   Max.   :34.331   Max.   :34.331  
> 
> ### goana
> 
> EB <- c("133746","1339","134","1340","134083","134111","134147","134187","134218","134266",
+ "134353","134359","134391","134429","134430","1345","134510","134526","134549","1346",
+ "134637","1347","134701","134728","1348","134829","134860","134864","1349","134957",
+ "135","1350","1351","135112","135114","135138","135152","135154","1352","135228",
+ "135250","135293","135295","1353","135458","1355","1356","135644","135656","1357",
+ "1358","135892","1359","135924","135935","135941","135946","135948","136","1360",
+ "136051","1361","1362","136227","136242","136259","1363","136306","136319","136332",
+ "136371","1364","1365","136541","1366","136647","1368","136853","1369","136991",
+ "1370","137075","1371","137209","1373","137362","1374","137492","1375","1376",
+ "137682","137695","137735","1378","137814","137868","137872","137886","137902","137964")
> go <- goana(fit,FDR=0.8,geneid=EB)
> topGO(go,number=10,truncate.term=30)
                                     Term Ont  N Up Down        P.Up
GO:0070062          extracellular exosome  CC  8  0    4 1.000000000
GO:0043230        extracellular organelle  CC  8  0    4 1.000000000
GO:1903561          extracellular vesicle  CC  8  0    4 1.000000000
GO:0040011                     locomotion  BP  7  5    0 0.006651547
GO:0032502          developmental process  BP 23  4    6 0.844070086
GO:0032501 multicellular organismal pr...  BP 31  7    7 0.620372627
GO:0006915              apoptotic process  BP  5  4    1 0.009503355
GO:0012501          programmed cell death  BP  5  4    1 0.009503355
GO:0042981 regulation of apoptotic pro...  BP  5  4    1 0.009503355
GO:0040012       regulation of locomotion  BP  5  4    0 0.009503355
                P.Down
GO:0070062 0.003047199
GO:0043230 0.003047199
GO:1903561 0.003047199
GO:0040011 1.000000000
GO:0032502 0.009014340
GO:0032501 0.009111120
GO:0006915 0.416247633
GO:0012501 0.416247633
GO:0042981 0.416247633
GO:0040012 1.000000000
> topGO(go,number=10,truncate.term=30,sort="down")
                                     Term Ont  N Up Down      P.Up      P.Down
GO:0070062          extracellular exosome  CC  8  0    4 1.0000000 0.003047199
GO:0043230        extracellular organelle  CC  8  0    4 1.0000000 0.003047199
GO:1903561          extracellular vesicle  CC  8  0    4 1.0000000 0.003047199
GO:0032502          developmental process  BP 23  4    6 0.8440701 0.009014340
GO:0032501 multicellular organismal pr...  BP 31  7    7 0.6203726 0.009111120
GO:0031982                        vesicle  CC 18  1    5 0.9946677 0.015552466
GO:0051604             protein maturation  BP  7  1    3 0.8497705 0.020760307
GO:0016485             protein processing  BP  7  1    3 0.8497705 0.020760307
GO:0009887     animal organ morphogenesis  BP  3  0    2 1.0000000 0.025788497
GO:0055082 cellular chemical homeostas...  BP  3  1    2 0.5476190 0.025788497
> 
> proc.time()
   user  system elapsed 
   2.26    0.31    2.56 

limma.Rcheck/tests_i386/limma-Tests.Rout


R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

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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.

> library(limma)
> options(warnPartialMatchArgs=TRUE,warnPartialMatchAttr=TRUE,warnPartialMatchDollar=TRUE)
> 
> set.seed(0); u <- runif(100)
> 
> ### strsplit2
> 
> x <- c("ab;cd;efg","abc;def","z","")
> strsplit2(x,split=";")
     [,1]  [,2]  [,3] 
[1,] "ab"  "cd"  "efg"
[2,] "abc" "def" ""   
[3,] "z"   ""    ""   
[4,] ""    ""    ""   
> 
> ### removeext
> 
> removeExt(c("slide1.spot","slide.2.spot"))
[1] "slide1"  "slide.2"
> removeExt(c("slide1.spot","slide"))
[1] "slide1.spot" "slide"      
> 
> ### printorder
> 
> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6),ndups=2,start="topright",npins=4)
$printorder
  [1]   6   5   4   3   2   1  12  11  10   9   8   7  18  17  16  15  14  13
 [19]  24  23  22  21  20  19  30  29  28  27  26  25  36  35  34  33  32  31
 [37]  42  41  40  39  38  37  48  47  46  45  44  43   6   5   4   3   2   1
 [55]  12  11  10   9   8   7  18  17  16  15  14  13  24  23  22  21  20  19
 [73]  30  29  28  27  26  25  36  35  34  33  32  31  42  41  40  39  38  37
 [91]  48  47  46  45  44  43   6   5   4   3   2   1  12  11  10   9   8   7
[109]  18  17  16  15  14  13  24  23  22  21  20  19  30  29  28  27  26  25
[127]  36  35  34  33  32  31  42  41  40  39  38  37  48  47  46  45  44  43
[145]   6   5   4   3   2   1  12  11  10   9   8   7  18  17  16  15  14  13
[163]  24  23  22  21  20  19  30  29  28  27  26  25  36  35  34  33  32  31
[181]  42  41  40  39  38  37  48  47  46  45  44  43  54  53  52  51  50  49
[199]  60  59  58  57  56  55  66  65  64  63  62  61  72  71  70  69  68  67
[217]  78  77  76  75  74  73  84  83  82  81  80  79  90  89  88  87  86  85
[235]  96  95  94  93  92  91  54  53  52  51  50  49  60  59  58  57  56  55
[253]  66  65  64  63  62  61  72  71  70  69  68  67  78  77  76  75  74  73
[271]  84  83  82  81  80  79  90  89  88  87  86  85  96  95  94  93  92  91
[289]  54  53  52  51  50  49  60  59  58  57  56  55  66  65  64  63  62  61
[307]  72  71  70  69  68  67  78  77  76  75  74  73  84  83  82  81  80  79
[325]  90  89  88  87  86  85  96  95  94  93  92  91  54  53  52  51  50  49
[343]  60  59  58  57  56  55  66  65  64  63  62  61  72  71  70  69  68  67
[361]  78  77  76  75  74  73  84  83  82  81  80  79  90  89  88  87  86  85
[379]  96  95  94  93  92  91 102 101 100  99  98  97 108 107 106 105 104 103
[397] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[415] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[433] 102 101 100  99  98  97 108 107 106 105 104 103 114 113 112 111 110 109
[451] 120 119 118 117 116 115 126 125 124 123 122 121 132 131 130 129 128 127
[469] 138 137 136 135 134 133 144 143 142 141 140 139 102 101 100  99  98  97
[487] 108 107 106 105 104 103 114 113 112 111 110 109 120 119 118 117 116 115
[505] 126 125 124 123 122 121 132 131 130 129 128 127 138 137 136 135 134 133
[523] 144 143 142 141 140 139 102 101 100  99  98  97 108 107 106 105 104 103
[541] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[559] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[577] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[595] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[613] 186 185 184 183 182 181 192 191 190 189 188 187 150 149 148 147 146 145
[631] 156 155 154 153 152 151 162 161 160 159 158 157 168 167 166 165 164 163
[649] 174 173 172 171 170 169 180 179 178 177 176 175 186 185 184 183 182 181
[667] 192 191 190 189 188 187 150 149 148 147 146 145 156 155 154 153 152 151
[685] 162 161 160 159 158 157 168 167 166 165 164 163 174 173 172 171 170 169
[703] 180 179 178 177 176 175 186 185 184 183 182 181 192 191 190 189 188 187
[721] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[739] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[757] 186 185 184 183 182 181 192 191 190 189 188 187

$plate
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

$plate.r
  [1]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4
 [26]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  3  3
 [51]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
 [76]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  2  2  2  2
[101]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
[126]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  1  1  1  1  1  1
[151]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[176]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  8  8  8  8  8  8  8  8
[201]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8
[226]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  7  7  7  7  7  7  7  7  7  7
[251]  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7
[276]  7  7  7  7  7  7  7  7  7  7  7  7  7  6  6  6  6  6  6  6  6  6  6  6  6
[301]  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6
[326]  6  6  6  6  6  6  6  6  6  6  6  5  5  5  5  5  5  5  5  5  5  5  5  5  5
[351]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
[376]  5  5  5  5  5  5  5  5  5 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[401] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[426] 12 12 12 12 12 12 12 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[451] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[476] 11 11 11 11 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[501] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[526] 10 10 10  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[551]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[576]  9 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
[601] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 15
[626] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
[651] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 14 14 14
[676] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
[701] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 13 13 13 13 13
[726] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
[751] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13

$plate.c
  [1]  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15
 [26] 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3
 [51]  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14
 [76] 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2
[101]  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13
[126] 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1
[151]  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18
[176] 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6
[201]  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17
[226] 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5
[251]  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16
[276] 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4
[301]  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21
[326] 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9
[351]  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20
[376] 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8
[401]  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19
[426] 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7
[451] 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24
[476] 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12
[501] 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23
[526] 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11
[551] 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22
[576] 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10
[601] 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3
[626]  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15
[651] 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2
[676]  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14
[701] 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1
[726]  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13
[751] 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22

$plateposition
  [1] "p1D03" "p1D03" "p1D02" "p1D02" "p1D01" "p1D01" "p1D06" "p1D06" "p1D05"
 [10] "p1D05" "p1D04" "p1D04" "p1D09" "p1D09" "p1D08" "p1D08" "p1D07" "p1D07"
 [19] "p1D12" "p1D12" "p1D11" "p1D11" "p1D10" "p1D10" "p1D15" "p1D15" "p1D14"
 [28] "p1D14" "p1D13" "p1D13" "p1D18" "p1D18" "p1D17" "p1D17" "p1D16" "p1D16"
 [37] "p1D21" "p1D21" "p1D20" "p1D20" "p1D19" "p1D19" "p1D24" "p1D24" "p1D23"
 [46] "p1D23" "p1D22" "p1D22" "p1C03" "p1C03" "p1C02" "p1C02" "p1C01" "p1C01"
 [55] "p1C06" "p1C06" "p1C05" "p1C05" "p1C04" "p1C04" "p1C09" "p1C09" "p1C08"
 [64] "p1C08" "p1C07" "p1C07" "p1C12" "p1C12" "p1C11" "p1C11" "p1C10" "p1C10"
 [73] "p1C15" "p1C15" "p1C14" "p1C14" "p1C13" "p1C13" "p1C18" "p1C18" "p1C17"
 [82] "p1C17" "p1C16" "p1C16" "p1C21" "p1C21" "p1C20" "p1C20" "p1C19" "p1C19"
 [91] "p1C24" "p1C24" "p1C23" "p1C23" "p1C22" "p1C22" "p1B03" "p1B03" "p1B02"
[100] "p1B02" "p1B01" "p1B01" "p1B06" "p1B06" "p1B05" "p1B05" "p1B04" "p1B04"
[109] "p1B09" "p1B09" "p1B08" "p1B08" "p1B07" "p1B07" "p1B12" "p1B12" "p1B11"
[118] "p1B11" "p1B10" "p1B10" "p1B15" "p1B15" "p1B14" "p1B14" "p1B13" "p1B13"
[127] "p1B18" "p1B18" "p1B17" "p1B17" "p1B16" "p1B16" "p1B21" "p1B21" "p1B20"
[136] "p1B20" "p1B19" "p1B19" "p1B24" "p1B24" "p1B23" "p1B23" "p1B22" "p1B22"
[145] "p1A03" "p1A03" "p1A02" "p1A02" "p1A01" "p1A01" "p1A06" "p1A06" "p1A05"
[154] "p1A05" "p1A04" "p1A04" "p1A09" "p1A09" "p1A08" "p1A08" "p1A07" "p1A07"
[163] "p1A12" "p1A12" "p1A11" "p1A11" "p1A10" "p1A10" "p1A15" "p1A15" "p1A14"
[172] "p1A14" "p1A13" "p1A13" "p1A18" "p1A18" "p1A17" "p1A17" "p1A16" "p1A16"
[181] "p1A21" "p1A21" "p1A20" "p1A20" "p1A19" "p1A19" "p1A24" "p1A24" "p1A23"
[190] "p1A23" "p1A22" "p1A22" "p1H03" "p1H03" "p1H02" "p1H02" "p1H01" "p1H01"
[199] "p1H06" "p1H06" "p1H05" "p1H05" "p1H04" "p1H04" "p1H09" "p1H09" "p1H08"
[208] "p1H08" "p1H07" "p1H07" "p1H12" "p1H12" "p1H11" "p1H11" "p1H10" "p1H10"
[217] "p1H15" "p1H15" "p1H14" "p1H14" "p1H13" "p1H13" "p1H18" "p1H18" "p1H17"
[226] "p1H17" "p1H16" "p1H16" "p1H21" "p1H21" "p1H20" "p1H20" "p1H19" "p1H19"
[235] "p1H24" "p1H24" "p1H23" "p1H23" "p1H22" "p1H22" "p1G03" "p1G03" "p1G02"
[244] "p1G02" "p1G01" "p1G01" "p1G06" "p1G06" "p1G05" "p1G05" "p1G04" "p1G04"
[253] "p1G09" "p1G09" "p1G08" "p1G08" "p1G07" "p1G07" "p1G12" "p1G12" "p1G11"
[262] "p1G11" "p1G10" "p1G10" "p1G15" "p1G15" "p1G14" "p1G14" "p1G13" "p1G13"
[271] "p1G18" "p1G18" "p1G17" "p1G17" "p1G16" "p1G16" "p1G21" "p1G21" "p1G20"
[280] "p1G20" "p1G19" "p1G19" "p1G24" "p1G24" "p1G23" "p1G23" "p1G22" "p1G22"
[289] "p1F03" "p1F03" "p1F02" "p1F02" "p1F01" "p1F01" "p1F06" "p1F06" "p1F05"
[298] "p1F05" "p1F04" "p1F04" "p1F09" "p1F09" "p1F08" "p1F08" "p1F07" "p1F07"
[307] "p1F12" "p1F12" "p1F11" "p1F11" "p1F10" "p1F10" "p1F15" "p1F15" "p1F14"
[316] "p1F14" "p1F13" "p1F13" "p1F18" "p1F18" "p1F17" "p1F17" "p1F16" "p1F16"
[325] "p1F21" "p1F21" "p1F20" "p1F20" "p1F19" "p1F19" "p1F24" "p1F24" "p1F23"
[334] "p1F23" "p1F22" "p1F22" "p1E03" "p1E03" "p1E02" "p1E02" "p1E01" "p1E01"
[343] "p1E06" "p1E06" "p1E05" "p1E05" "p1E04" "p1E04" "p1E09" "p1E09" "p1E08"
[352] "p1E08" "p1E07" "p1E07" "p1E12" "p1E12" "p1E11" "p1E11" "p1E10" "p1E10"
[361] "p1E15" "p1E15" "p1E14" "p1E14" "p1E13" "p1E13" "p1E18" "p1E18" "p1E17"
[370] "p1E17" "p1E16" "p1E16" "p1E21" "p1E21" "p1E20" "p1E20" "p1E19" "p1E19"
[379] "p1E24" "p1E24" "p1E23" "p1E23" "p1E22" "p1E22" "p1L03" "p1L03" "p1L02"
[388] "p1L02" "p1L01" "p1L01" "p1L06" "p1L06" "p1L05" "p1L05" "p1L04" "p1L04"
[397] "p1L09" "p1L09" "p1L08" "p1L08" "p1L07" "p1L07" "p1L12" "p1L12" "p1L11"
[406] "p1L11" "p1L10" "p1L10" "p1L15" "p1L15" "p1L14" "p1L14" "p1L13" "p1L13"
[415] "p1L18" "p1L18" "p1L17" "p1L17" "p1L16" "p1L16" "p1L21" "p1L21" "p1L20"
[424] "p1L20" "p1L19" "p1L19" "p1L24" "p1L24" "p1L23" "p1L23" "p1L22" "p1L22"
[433] "p1K03" "p1K03" "p1K02" "p1K02" "p1K01" "p1K01" "p1K06" "p1K06" "p1K05"
[442] "p1K05" "p1K04" "p1K04" "p1K09" "p1K09" "p1K08" "p1K08" "p1K07" "p1K07"
[451] "p1K12" "p1K12" "p1K11" "p1K11" "p1K10" "p1K10" "p1K15" "p1K15" "p1K14"
[460] "p1K14" "p1K13" "p1K13" "p1K18" "p1K18" "p1K17" "p1K17" "p1K16" "p1K16"
[469] "p1K21" "p1K21" "p1K20" "p1K20" "p1K19" "p1K19" "p1K24" "p1K24" "p1K23"
[478] "p1K23" "p1K22" "p1K22" "p1J03" "p1J03" "p1J02" "p1J02" "p1J01" "p1J01"
[487] "p1J06" "p1J06" "p1J05" "p1J05" "p1J04" "p1J04" "p1J09" "p1J09" "p1J08"
[496] "p1J08" "p1J07" "p1J07" "p1J12" "p1J12" "p1J11" "p1J11" "p1J10" "p1J10"
[505] "p1J15" "p1J15" "p1J14" "p1J14" "p1J13" "p1J13" "p1J18" "p1J18" "p1J17"
[514] "p1J17" "p1J16" "p1J16" "p1J21" "p1J21" "p1J20" "p1J20" "p1J19" "p1J19"
[523] "p1J24" "p1J24" "p1J23" "p1J23" "p1J22" "p1J22" "p1I03" "p1I03" "p1I02"
[532] "p1I02" "p1I01" "p1I01" "p1I06" "p1I06" "p1I05" "p1I05" "p1I04" "p1I04"
[541] "p1I09" "p1I09" "p1I08" "p1I08" "p1I07" "p1I07" "p1I12" "p1I12" "p1I11"
[550] "p1I11" "p1I10" "p1I10" "p1I15" "p1I15" "p1I14" "p1I14" "p1I13" "p1I13"
[559] "p1I18" "p1I18" "p1I17" "p1I17" "p1I16" "p1I16" "p1I21" "p1I21" "p1I20"
[568] "p1I20" "p1I19" "p1I19" "p1I24" "p1I24" "p1I23" "p1I23" "p1I22" "p1I22"
[577] "p1P03" "p1P03" "p1P02" "p1P02" "p1P01" "p1P01" "p1P06" "p1P06" "p1P05"
[586] "p1P05" "p1P04" "p1P04" "p1P09" "p1P09" "p1P08" "p1P08" "p1P07" "p1P07"
[595] "p1P12" "p1P12" "p1P11" "p1P11" "p1P10" "p1P10" "p1P15" "p1P15" "p1P14"
[604] "p1P14" "p1P13" "p1P13" "p1P18" "p1P18" "p1P17" "p1P17" "p1P16" "p1P16"
[613] "p1P21" "p1P21" "p1P20" "p1P20" "p1P19" "p1P19" "p1P24" "p1P24" "p1P23"
[622] "p1P23" "p1P22" "p1P22" "p1O03" "p1O03" "p1O02" "p1O02" "p1O01" "p1O01"
[631] "p1O06" "p1O06" "p1O05" "p1O05" "p1O04" "p1O04" "p1O09" "p1O09" "p1O08"
[640] "p1O08" "p1O07" "p1O07" "p1O12" "p1O12" "p1O11" "p1O11" "p1O10" "p1O10"
[649] "p1O15" "p1O15" "p1O14" "p1O14" "p1O13" "p1O13" "p1O18" "p1O18" "p1O17"
[658] "p1O17" "p1O16" "p1O16" "p1O21" "p1O21" "p1O20" "p1O20" "p1O19" "p1O19"
[667] "p1O24" "p1O24" "p1O23" "p1O23" "p1O22" "p1O22" "p1N03" "p1N03" "p1N02"
[676] "p1N02" "p1N01" "p1N01" "p1N06" "p1N06" "p1N05" "p1N05" "p1N04" "p1N04"
[685] "p1N09" "p1N09" "p1N08" "p1N08" "p1N07" "p1N07" "p1N12" "p1N12" "p1N11"
[694] "p1N11" "p1N10" "p1N10" "p1N15" "p1N15" "p1N14" "p1N14" "p1N13" "p1N13"
[703] "p1N18" "p1N18" "p1N17" "p1N17" "p1N16" "p1N16" "p1N21" "p1N21" "p1N20"
[712] "p1N20" "p1N19" "p1N19" "p1N24" "p1N24" "p1N23" "p1N23" "p1N22" "p1N22"
[721] "p1M03" "p1M03" "p1M02" "p1M02" "p1M01" "p1M01" "p1M06" "p1M06" "p1M05"
[730] "p1M05" "p1M04" "p1M04" "p1M09" "p1M09" "p1M08" "p1M08" "p1M07" "p1M07"
[739] "p1M12" "p1M12" "p1M11" "p1M11" "p1M10" "p1M10" "p1M15" "p1M15" "p1M14"
[748] "p1M14" "p1M13" "p1M13" "p1M18" "p1M18" "p1M17" "p1M17" "p1M16" "p1M16"
[757] "p1M21" "p1M21" "p1M20" "p1M20" "p1M19" "p1M19" "p1M24" "p1M24" "p1M23"
[766] "p1M23" "p1M22" "p1M22"

> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6))
$printorder
  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
 [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2
 [51]  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
 [76] 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4
[101]  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
[126] 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6
[151]  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
[176] 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8
[201]  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
[226] 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10
[251] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
[276] 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12
[301] 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
[326] 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14
[351] 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
[376] 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16
[401] 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
[426] 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18
[451] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
[476] 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
[501] 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
[526] 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22
[551] 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
[576] 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
[601] 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1
[626]  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
[651] 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3
[676]  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
[701] 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5
[726]  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
[751] 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

$plate
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[334] 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2
[371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[519] 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2
[556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[667] 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2
[704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

$plate.r
  [1]  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  4
 [26]  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3  3
 [51]  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3  3
 [76]  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2  2
[101]  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2  2
[126]  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1  1
[151]  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1  5
[176]  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  4  4  4  4  4  4  8  8
[201]  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  4  4  4  4  4  4  8  8  8
[226]  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3  3  3  3  3  3  7  7  7  7
[251]  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3  3  3  3  3  7  7  7  7  7
[276]  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2  2  2  2  6  6  6  6  6  6
[301] 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2  2  2  6  6  6  6  6  6 10
[326] 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1  1  5  5  5  5  5  5  9  9
[351]  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9
[376]  9  9  9 13 13 13 13 13 13  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12
[401] 12 12 16 16 16 16 16 16  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12
[426] 12 16 16 16 16 16 16  3  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11
[451] 15 15 15 15 15 15  3  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15
[476] 15 15 15 15 15  2  2  2  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14
[501] 14 14 14 14  2  2  2  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14
[526] 14 14 14  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13
[551] 13 13  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13
[576] 13  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16
[601]  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3
[626]  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3
[651]  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2
[676]  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2
[701]  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1
[726]  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1
[751]  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13

$plate.c
  [1]  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1
 [26]  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5
 [51]  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9
 [76] 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13
[101] 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17
[126] 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21
[151]  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1
[176]  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  2  6 10 14 18 22  2  6
[201] 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10
[226] 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14
[251] 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18
[276] 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22
[301]  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2
[326]  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6
[351] 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10
[376] 14 18 22  2  6 10 14 18 22  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15
[401] 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19
[426] 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23
[451]  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3
[476]  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7
[501] 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11
[526] 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15
[551] 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19
[576] 23  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24
[601]  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4
[626]  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8
[651] 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12
[676] 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16
[701] 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20
[726] 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24
[751]  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24

$plateposition
  [1] "p1D01" "p1D05" "p1D09" "p1D13" "p1D17" "p1D21" "p1H01" "p1H05" "p1H09"
 [10] "p1H13" "p1H17" "p1H21" "p1L01" "p1L05" "p1L09" "p1L13" "p1L17" "p1L21"
 [19] "p1P01" "p1P05" "p1P09" "p1P13" "p1P17" "p1P21" "p2D01" "p2D05" "p2D09"
 [28] "p2D13" "p2D17" "p2D21" "p2H01" "p2H05" "p2H09" "p2H13" "p2H17" "p2H21"
 [37] "p2L01" "p2L05" "p2L09" "p2L13" "p2L17" "p2L21" "p2P01" "p2P05" "p2P09"
 [46] "p2P13" "p2P17" "p2P21" "p1C01" "p1C05" "p1C09" "p1C13" "p1C17" "p1C21"
 [55] "p1G01" "p1G05" "p1G09" "p1G13" "p1G17" "p1G21" "p1K01" "p1K05" "p1K09"
 [64] "p1K13" "p1K17" "p1K21" "p1O01" "p1O05" "p1O09" "p1O13" "p1O17" "p1O21"
 [73] "p2C01" "p2C05" "p2C09" "p2C13" "p2C17" "p2C21" "p2G01" "p2G05" "p2G09"
 [82] "p2G13" "p2G17" "p2G21" "p2K01" "p2K05" "p2K09" "p2K13" "p2K17" "p2K21"
 [91] "p2O01" "p2O05" "p2O09" "p2O13" "p2O17" "p2O21" "p1B01" "p1B05" "p1B09"
[100] "p1B13" "p1B17" "p1B21" "p1F01" "p1F05" "p1F09" "p1F13" "p1F17" "p1F21"
[109] "p1J01" "p1J05" "p1J09" "p1J13" "p1J17" "p1J21" "p1N01" "p1N05" "p1N09"
[118] "p1N13" "p1N17" "p1N21" "p2B01" "p2B05" "p2B09" "p2B13" "p2B17" "p2B21"
[127] "p2F01" "p2F05" "p2F09" "p2F13" "p2F17" "p2F21" "p2J01" "p2J05" "p2J09"
[136] "p2J13" "p2J17" "p2J21" "p2N01" "p2N05" "p2N09" "p2N13" "p2N17" "p2N21"
[145] "p1A01" "p1A05" "p1A09" "p1A13" "p1A17" "p1A21" "p1E01" "p1E05" "p1E09"
[154] "p1E13" "p1E17" "p1E21" "p1I01" "p1I05" "p1I09" "p1I13" "p1I17" "p1I21"
[163] "p1M01" "p1M05" "p1M09" "p1M13" "p1M17" "p1M21" "p2A01" "p2A05" "p2A09"
[172] "p2A13" "p2A17" "p2A21" "p2E01" "p2E05" "p2E09" "p2E13" "p2E17" "p2E21"
[181] "p2I01" "p2I05" "p2I09" "p2I13" "p2I17" "p2I21" "p2M01" "p2M05" "p2M09"
[190] "p2M13" "p2M17" "p2M21" "p1D02" "p1D06" "p1D10" "p1D14" "p1D18" "p1D22"
[199] "p1H02" "p1H06" "p1H10" "p1H14" "p1H18" "p1H22" "p1L02" "p1L06" "p1L10"
[208] "p1L14" "p1L18" "p1L22" "p1P02" "p1P06" "p1P10" "p1P14" "p1P18" "p1P22"
[217] "p2D02" "p2D06" "p2D10" "p2D14" "p2D18" "p2D22" "p2H02" "p2H06" "p2H10"
[226] "p2H14" "p2H18" "p2H22" "p2L02" "p2L06" "p2L10" "p2L14" "p2L18" "p2L22"
[235] "p2P02" "p2P06" "p2P10" "p2P14" "p2P18" "p2P22" "p1C02" "p1C06" "p1C10"
[244] "p1C14" "p1C18" "p1C22" "p1G02" "p1G06" "p1G10" "p1G14" "p1G18" "p1G22"
[253] "p1K02" "p1K06" "p1K10" "p1K14" "p1K18" "p1K22" "p1O02" "p1O06" "p1O10"
[262] "p1O14" "p1O18" "p1O22" "p2C02" "p2C06" "p2C10" "p2C14" "p2C18" "p2C22"
[271] "p2G02" "p2G06" "p2G10" "p2G14" "p2G18" "p2G22" "p2K02" "p2K06" "p2K10"
[280] "p2K14" "p2K18" "p2K22" "p2O02" "p2O06" "p2O10" "p2O14" "p2O18" "p2O22"
[289] "p1B02" "p1B06" "p1B10" "p1B14" "p1B18" "p1B22" "p1F02" "p1F06" "p1F10"
[298] "p1F14" "p1F18" "p1F22" "p1J02" "p1J06" "p1J10" "p1J14" "p1J18" "p1J22"
[307] "p1N02" "p1N06" "p1N10" "p1N14" "p1N18" "p1N22" "p2B02" "p2B06" "p2B10"
[316] "p2B14" "p2B18" "p2B22" "p2F02" "p2F06" "p2F10" "p2F14" "p2F18" "p2F22"
[325] "p2J02" "p2J06" "p2J10" "p2J14" "p2J18" "p2J22" "p2N02" "p2N06" "p2N10"
[334] "p2N14" "p2N18" "p2N22" "p1A02" "p1A06" "p1A10" "p1A14" "p1A18" "p1A22"
[343] "p1E02" "p1E06" "p1E10" "p1E14" "p1E18" "p1E22" "p1I02" "p1I06" "p1I10"
[352] "p1I14" "p1I18" "p1I22" "p1M02" "p1M06" "p1M10" "p1M14" "p1M18" "p1M22"
[361] "p2A02" "p2A06" "p2A10" "p2A14" "p2A18" "p2A22" "p2E02" "p2E06" "p2E10"
[370] "p2E14" "p2E18" "p2E22" "p2I02" "p2I06" "p2I10" "p2I14" "p2I18" "p2I22"
[379] "p2M02" "p2M06" "p2M10" "p2M14" "p2M18" "p2M22" "p1D03" "p1D07" "p1D11"
[388] "p1D15" "p1D19" "p1D23" "p1H03" "p1H07" "p1H11" "p1H15" "p1H19" "p1H23"
[397] "p1L03" "p1L07" "p1L11" "p1L15" "p1L19" "p1L23" "p1P03" "p1P07" "p1P11"
[406] "p1P15" "p1P19" "p1P23" "p2D03" "p2D07" "p2D11" "p2D15" "p2D19" "p2D23"
[415] "p2H03" "p2H07" "p2H11" "p2H15" "p2H19" "p2H23" "p2L03" "p2L07" "p2L11"
[424] "p2L15" "p2L19" "p2L23" "p2P03" "p2P07" "p2P11" "p2P15" "p2P19" "p2P23"
[433] "p1C03" "p1C07" "p1C11" "p1C15" "p1C19" "p1C23" "p1G03" "p1G07" "p1G11"
[442] "p1G15" "p1G19" "p1G23" "p1K03" "p1K07" "p1K11" "p1K15" "p1K19" "p1K23"
[451] "p1O03" "p1O07" "p1O11" "p1O15" "p1O19" "p1O23" "p2C03" "p2C07" "p2C11"
[460] "p2C15" "p2C19" "p2C23" "p2G03" "p2G07" "p2G11" "p2G15" "p2G19" "p2G23"
[469] "p2K03" "p2K07" "p2K11" "p2K15" "p2K19" "p2K23" "p2O03" "p2O07" "p2O11"
[478] "p2O15" "p2O19" "p2O23" "p1B03" "p1B07" "p1B11" "p1B15" "p1B19" "p1B23"
[487] "p1F03" "p1F07" "p1F11" "p1F15" "p1F19" "p1F23" "p1J03" "p1J07" "p1J11"
[496] "p1J15" "p1J19" "p1J23" "p1N03" "p1N07" "p1N11" "p1N15" "p1N19" "p1N23"
[505] "p2B03" "p2B07" "p2B11" "p2B15" "p2B19" "p2B23" "p2F03" "p2F07" "p2F11"
[514] "p2F15" "p2F19" "p2F23" "p2J03" "p2J07" "p2J11" "p2J15" "p2J19" "p2J23"
[523] "p2N03" "p2N07" "p2N11" "p2N15" "p2N19" "p2N23" "p1A03" "p1A07" "p1A11"
[532] "p1A15" "p1A19" "p1A23" "p1E03" "p1E07" "p1E11" "p1E15" "p1E19" "p1E23"
[541] "p1I03" "p1I07" "p1I11" "p1I15" "p1I19" "p1I23" "p1M03" "p1M07" "p1M11"
[550] "p1M15" "p1M19" "p1M23" "p2A03" "p2A07" "p2A11" "p2A15" "p2A19" "p2A23"
[559] "p2E03" "p2E07" "p2E11" "p2E15" "p2E19" "p2E23" "p2I03" "p2I07" "p2I11"
[568] "p2I15" "p2I19" "p2I23" "p2M03" "p2M07" "p2M11" "p2M15" "p2M19" "p2M23"
[577] "p1D04" "p1D08" "p1D12" "p1D16" "p1D20" "p1D24" "p1H04" "p1H08" "p1H12"
[586] "p1H16" "p1H20" "p1H24" "p1L04" "p1L08" "p1L12" "p1L16" "p1L20" "p1L24"
[595] "p1P04" "p1P08" "p1P12" "p1P16" "p1P20" "p1P24" "p2D04" "p2D08" "p2D12"
[604] "p2D16" "p2D20" "p2D24" "p2H04" "p2H08" "p2H12" "p2H16" "p2H20" "p2H24"
[613] "p2L04" "p2L08" "p2L12" "p2L16" "p2L20" "p2L24" "p2P04" "p2P08" "p2P12"
[622] "p2P16" "p2P20" "p2P24" "p1C04" "p1C08" "p1C12" "p1C16" "p1C20" "p1C24"
[631] "p1G04" "p1G08" "p1G12" "p1G16" "p1G20" "p1G24" "p1K04" "p1K08" "p1K12"
[640] "p1K16" "p1K20" "p1K24" "p1O04" "p1O08" "p1O12" "p1O16" "p1O20" "p1O24"
[649] "p2C04" "p2C08" "p2C12" "p2C16" "p2C20" "p2C24" "p2G04" "p2G08" "p2G12"
[658] "p2G16" "p2G20" "p2G24" "p2K04" "p2K08" "p2K12" "p2K16" "p2K20" "p2K24"
[667] "p2O04" "p2O08" "p2O12" "p2O16" "p2O20" "p2O24" "p1B04" "p1B08" "p1B12"
[676] "p1B16" "p1B20" "p1B24" "p1F04" "p1F08" "p1F12" "p1F16" "p1F20" "p1F24"
[685] "p1J04" "p1J08" "p1J12" "p1J16" "p1J20" "p1J24" "p1N04" "p1N08" "p1N12"
[694] "p1N16" "p1N20" "p1N24" "p2B04" "p2B08" "p2B12" "p2B16" "p2B20" "p2B24"
[703] "p2F04" "p2F08" "p2F12" "p2F16" "p2F20" "p2F24" "p2J04" "p2J08" "p2J12"
[712] "p2J16" "p2J20" "p2J24" "p2N04" "p2N08" "p2N12" "p2N16" "p2N20" "p2N24"
[721] "p1A04" "p1A08" "p1A12" "p1A16" "p1A20" "p1A24" "p1E04" "p1E08" "p1E12"
[730] "p1E16" "p1E20" "p1E24" "p1I04" "p1I08" "p1I12" "p1I16" "p1I20" "p1I24"
[739] "p1M04" "p1M08" "p1M12" "p1M16" "p1M20" "p1M24" "p2A04" "p2A08" "p2A12"
[748] "p2A16" "p2A20" "p2A24" "p2E04" "p2E08" "p2E12" "p2E16" "p2E20" "p2E24"
[757] "p2I04" "p2I08" "p2I12" "p2I16" "p2I20" "p2I24" "p2M04" "p2M08" "p2M12"
[766] "p2M16" "p2M20" "p2M24"

> 
> ### merge.rglist
> 
> R <- G <- matrix(11:14,4,2)
> rownames(R) <- rownames(G) <- c("a","a","b","c")
> RG1 <- new("RGList",list(R=R,G=G))
> R <- G <- matrix(21:24,4,2)
> rownames(R) <- rownames(G) <- c("b","a","a","c")
> RG2 <- new("RGList",list(R=R,G=G))
> merge(RG1,RG2)
An object of class "RGList"
$R
  [,1] [,2] [,3] [,4]
a   11   11   22   22
a   12   12   23   23
b   13   13   21   21
c   14   14   24   24

$G
  [,1] [,2] [,3] [,4]
a   11   11   22   22
a   12   12   23   23
b   13   13   21   21
c   14   14   24   24

> merge(RG2,RG1)
An object of class "RGList"
$R
  [,1] [,2] [,3] [,4]
b   21   21   13   13
a   22   22   11   11
a   23   23   12   12
c   24   24   14   14

$G
  [,1] [,2] [,3] [,4]
b   21   21   13   13
a   22   22   11   11
a   23   23   12   12
c   24   24   14   14

> 
> ### background correction
> 
> RG <- new("RGList", list(R=c(1,2,3,4),G=c(1,2,3,4),Rb=c(2,2,2,2),Gb=c(2,2,2,2)))
> backgroundCorrect(RG)
An object of class "RGList"
$R
     [,1]
[1,]   -1
[2,]    0
[3,]    1
[4,]    2

$G
     [,1]
[1,]   -1
[2,]    0
[3,]    1
[4,]    2

> backgroundCorrect(RG, method="half")
An object of class "RGList"
$R
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

$G
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

> backgroundCorrect(RG, method="minimum")
An object of class "RGList"
$R
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

$G
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

> backgroundCorrect(RG, offset=5)
An object of class "RGList"
$R
     [,1]
[1,]    4
[2,]    5
[3,]    6
[4,]    7

$G
     [,1]
[1,]    4
[2,]    5
[3,]    6
[4,]    7

> 
> ### loessFit
> 
> x <- 1:100
> y <- rnorm(100)
> out <- loessFit(y,x)
> f1 <- quantile(out$fitted)
> r1 <- quantile(out$residuals)
> w <- rep(1,100)
> w[1:50] <- 0.5
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f2 <- quantile(out$fitted)
> r2 <- quantile(out$residuals)
> out <- loessFit(y,x,weights=w,method="locfit")
> f3 <- quantile(out$fitted)
> r3 <- quantile(out$residuals)
> out <- loessFit(y,x,weights=w,method="loess")
> f4 <- quantile(out$fitted)
> r4 <- quantile(out$residuals)
> w <- rep(1,100)
> w[2*(1:50)] <- 0
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f5 <- quantile(out$fitted)
> r5 <- quantile(out$residuals)
> data.frame(f1,f2,f3,f4,f5)
              f1           f2          f3          f4          f5
0%   -0.78835384 -0.687432210 -0.78957137 -0.76756060 -0.63778292
25%  -0.18340154 -0.179683572 -0.18979269 -0.16773223 -0.38064318
50%  -0.11492924 -0.114796040 -0.12087983 -0.07185314 -0.15971879
75%   0.01507921 -0.008145125 -0.01857508  0.04030634  0.07839396
100%  0.21653837  0.145106033  0.19214597  0.21417361  0.51836274
> data.frame(r1,r2,r3,r4,r5)
              r1          r2          r3           r4          r5
0%   -2.04434053 -2.05132680 -2.02404318 -2.101242874 -2.22280633
25%  -0.59321065 -0.57200209 -0.58975649 -0.577887481 -0.71037756
50%   0.05874864  0.04514326  0.08335198 -0.001769806  0.06785517
75%   0.56010750  0.55124530  0.57618740  0.561454370  0.65383830
100%  2.57936026  2.64549799  2.57549257  2.402324533  2.28648835
> 
> ### normalizeWithinArrays
> 
> RG <- new("RGList",list())
> RG$R <- matrix(rexp(100*2),100,2)
> RG$G <- matrix(rexp(100*2),100,2)
> RG$Rb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RG$Gb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="saddle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
       V1                V2                V3               V4        
 Min.   :0.01626   Min.   :0.01213   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.35497   1st Qu.:0.29133   1st Qu.:0.2745   1st Qu.:0.3953  
 Median :0.71793   Median :0.70294   Median :0.6339   Median :0.8223  
 Mean   :0.90184   Mean   :1.00122   Mean   :0.9454   Mean   :1.1324  
 3rd Qu.:1.16891   3rd Qu.:1.33139   3rd Qu.:1.4059   3rd Qu.:1.4221  
 Max.   :4.56267   Max.   :6.37947   Max.   :5.0486   Max.   :6.6295  
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="mle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
       V1                V2                V3               V4        
 Min.   :0.01701   Min.   :0.01255   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.35423   1st Qu.:0.29118   1st Qu.:0.2745   1st Qu.:0.3953  
 Median :0.71719   Median :0.70280   Median :0.6339   Median :0.8223  
 Mean   :0.90118   Mean   :1.00110   Mean   :0.9454   Mean   :1.1324  
 3rd Qu.:1.16817   3rd Qu.:1.33124   3rd Qu.:1.4059   3rd Qu.:1.4221  
 Max.   :4.56193   Max.   :6.37932   Max.   :5.0486   Max.   :6.6295  
> MA <- normalizeWithinArrays(RGb,method="loess")
> summary(MA$M)
       V1                 V2          
 Min.   :-5.88044   Min.   :-5.66985  
 1st Qu.:-1.18483   1st Qu.:-1.57014  
 Median :-0.21632   Median : 0.04823  
 Mean   : 0.03487   Mean   :-0.05481  
 3rd Qu.: 1.49669   3rd Qu.: 1.45113  
 Max.   : 7.07324   Max.   : 6.19744  
> #MA <- normalizeWithinArrays(RG[,1:2], mouse.setup, method="robustspline")
> #MA$M[1:5,]
> #MA <- normalizeWithinArrays(mouse.data, mouse.setup)
> #MA$M[1:5,]
> 
> ### normalizeBetweenArrays
> 
> MA2 <- normalizeBetweenArrays(MA,method="scale")
> MA$M[1:5,]
           [,1]       [,2]
[1,] -1.1689588  4.5558123
[2,]  0.8971363  0.3296544
[3,]  2.8247439  1.4249960
[4,] -1.8533240  0.4804851
[5,]  1.9158459 -5.5087631
> MA$A[1:5,]
            [,1]       [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200  0.2071043
[4,]  0.09281027 -1.3880965
[5,]  0.22385828 -3.0855818
> MA2 <- normalizeBetweenArrays(MA,method="quantile")
> MA$M[1:5,]
           [,1]       [,2]
[1,] -1.1689588  4.5558123
[2,]  0.8971363  0.3296544
[3,]  2.8247439  1.4249960
[4,] -1.8533240  0.4804851
[5,]  1.9158459 -5.5087631
> MA$A[1:5,]
            [,1]       [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200  0.2071043
[4,]  0.09281027 -1.3880965
[5,]  0.22385828 -3.0855818
> 
> ### unwrapdups
> 
> M <- matrix(1:12,6,2)
> unwrapdups(M,ndups=1)
     [,1] [,2]
[1,]    1    7
[2,]    2    8
[3,]    3    9
[4,]    4   10
[5,]    5   11
[6,]    6   12
> unwrapdups(M,ndups=2)
     [,1] [,2] [,3] [,4]
[1,]    1    2    7    8
[2,]    3    4    9   10
[3,]    5    6   11   12
> unwrapdups(M,ndups=3)
     [,1] [,2] [,3] [,4] [,5] [,6]
[1,]    1    2    3    7    8    9
[2,]    4    5    6   10   11   12
> unwrapdups(M,ndups=2,spacing=3)
     [,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    2    5    8   11
[3,]    3    6    9   12
> 
> ### trigammaInverse
> 
> trigammaInverse(c(1e-6,NA,5,1e6))
[1] 1.000000e+06           NA 4.961687e-01 1.000001e-03
> 
> ### lmFit, eBayes, topTable
> 
> M <- matrix(rnorm(10*6,sd=0.3),10,6)
> rownames(M) <- LETTERS[1:10]
> M[1,1:3] <- M[1,1:3] + 2
> design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
> contrast.matrix <- cbind(First3=c(1,0),Last3=c(0,1),"Last3-First3"=c(-1,1))
> fit <- lmFit(M,design)
> fit2 <- eBayes(contrasts.fit(fit,contrasts=contrast.matrix))
> topTable(fit2)
       First3       Last3 Last3.First3      AveExpr           F      P.Value
A  1.77602021  0.06025114  -1.71576906  0.918135675 50.91471061 7.727200e-23
D -0.05454069  0.39127869   0.44581938  0.168369004  2.51638838 8.075072e-02
F -0.16249607 -0.33009728  -0.16760121 -0.246296671  2.18256779 1.127516e-01
G  0.30852468 -0.06873462  -0.37725930  0.119895035  1.61088775 1.997102e-01
H -0.16942269  0.20578118   0.37520387  0.018179245  1.14554368 3.180510e-01
J  0.21417623  0.07074940  -0.14342683  0.142462814  0.82029274 4.403027e-01
C -0.12236781  0.15095948   0.27332729  0.014295836  0.60885003 5.439761e-01
B -0.11982833  0.13529287   0.25512120  0.007732271  0.52662792 5.905931e-01
E  0.01897934  0.10434934   0.08536999  0.061664340  0.18136849 8.341279e-01
I -0.04720963  0.03996397   0.08717360 -0.003622829  0.06168476 9.401792e-01
     adj.P.Val
A 7.727200e-22
D 3.758388e-01
F 3.758388e-01
G 4.992756e-01
H 6.361019e-01
J 7.338379e-01
C 7.382414e-01
B 7.382414e-01
E 9.268088e-01
I 9.401792e-01
> topTable(fit2,coef=3,resort.by="logFC")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="p")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,sort.by="logFC",resort.by="t")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="B")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,lfc=1)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p.value=0.2)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p.value=0.2,lfc=0.5)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p.value=0.2,lfc=0.5,sort.by="none")
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> contrasts.fit(fit[1:3,],contrast.matrix[,0])
An object of class "MArrayLM"
$coefficients
 
A
B
C

$rank
[1] 2

$assign
NULL

$qr
$qr
     First3Arrays Last3Arrays
[1,]   -1.7320508   0.0000000
[2,]    0.5773503  -1.7320508
[3,]    0.5773503   0.0000000
[4,]    0.0000000   0.5773503
[5,]    0.0000000   0.5773503
[6,]    0.0000000   0.5773503

$qraux
[1] 1.57735 1.00000

$pivot
[1] 1 2

$tol
[1] 1e-07

$rank
[1] 2


$df.residual
[1] 4 4 4

$sigma
        A         B         C 
0.3299787 0.3323336 0.2315815 

$cov.coefficients
<0 x 0 matrix>

$stdev.unscaled
 
A
B
C

$pivot
[1] 1 2

$Amean
          A           B           C 
0.918135675 0.007732271 0.014295836 

$method
[1] "ls"

$design
     First3Arrays Last3Arrays
[1,]            1           0
[2,]            1           0
[3,]            1           0
[4,]            0           1
[5,]            0           1
[6,]            0           1

$contrasts
    
[1,]
[2,]

> fit$coefficients[1,1] <- NA
> contrasts.fit(fit[1:3,],contrast.matrix)$coefficients
      First3      Last3 Last3-First3
A         NA 0.06025114           NA
B -0.1198283 0.13529287    0.2551212
C -0.1223678 0.15095948    0.2733273
> 
> designlist <- list(Null=matrix(1,6,1),Two=design,Three=cbind(1,c(0,0,1,1,0,0),c(0,0,0,0,1,1)))
> out <- selectModel(M,designlist)
> table(out$pref)

 Null   Two Three 
    5     3     2 
> 
> ### marray object
> 
> #suppressMessages(suppressWarnings(gotmarray <- require(marray,quietly=TRUE)))
> #if(gotmarray) {
> #	data(swirl)
> #	snorm = maNorm(swirl)
> #	fit <- lmFit(snorm, design = c(1,-1,-1,1))
> #	fit <- eBayes(fit)
> #	topTable(fit,resort.by="AveExpr")
> #}
> 
> ### duplicateCorrelation
> 
> cor.out <- duplicateCorrelation(M)
> cor.out$consensus.correlation
[1] -0.09290714
> cor.out$atanh.correlations
[1] -0.4419130  0.4088967 -0.1964978 -0.6093769  0.3730118
> 
> ### gls.series
> 
> fit <- gls.series(M,design,correlation=cor.out$cor)
> fit$coefficients
     First3Arrays Last3Arrays
[1,]   0.82809594  0.09777201
[2,]  -0.08845425  0.27111909
[3,]  -0.07175836 -0.11287397
[4,]   0.06955100  0.06852328
[5,]   0.08348330  0.05535668
> fit$stdev.unscaled
     First3Arrays Last3Arrays
[1,]    0.3888215   0.3888215
[2,]    0.3888215   0.3888215
[3,]    0.3888215   0.3888215
[4,]    0.3888215   0.3888215
[5,]    0.3888215   0.3888215
> fit$sigma
[1] 0.7630059 0.2152728 0.3350370 0.3227781 0.3405473
> fit$df.residual
[1] 10 10 10 10 10
> 
> ### mrlm
> 
> fit <- mrlm(M,design)
Warning message:
In rlm.default(x = X, y = y, weights = w, ...) :
  'rlm' failed to converge in 20 steps
> fit$coefficients
  First3Arrays Last3Arrays
A   1.75138894  0.06025114
B  -0.11982833  0.10322039
C  -0.09302502  0.15095948
D  -0.05454069  0.33700045
E   0.07927938  0.10434934
F  -0.16249607 -0.34010852
G   0.30852468 -0.06873462
H  -0.16942269  0.24392984
I  -0.04720963  0.03996397
J   0.21417623 -0.05679272
> fit$stdev.unscaled
  First3Arrays Last3Arrays
A    0.5933418   0.5773503
B    0.5773503   0.6096497
C    0.6017444   0.5773503
D    0.5773503   0.6266021
E    0.6307703   0.5773503
F    0.5773503   0.5846707
G    0.5773503   0.5773503
H    0.5773503   0.6544564
I    0.5773503   0.5773503
J    0.5773503   0.6689776
> fit$sigma
 [1] 0.2894294 0.2679396 0.2090236 0.1461395 0.2309018 0.2827476 0.2285945
 [8] 0.2267556 0.3537469 0.2172409
> fit$df.residual
 [1] 4 4 4 4 4 4 4 4 4 4
> 
> # Similar to Mette Langaas 19 May 2004
> set.seed(123)
> narrays <- 9
> ngenes <- 5
> mu <- 0
> alpha <- 2
> beta <- -2
> epsilon <- matrix(rnorm(narrays*ngenes,0,1),ncol=narrays)
> X <- cbind(rep(1,9),c(0,0,0,1,1,1,0,0,0),c(0,0,0,0,0,0,1,1,1))
> dimnames(X) <- list(1:9,c("mu","alpha","beta"))
> yvec <- mu*X[,1]+alpha*X[,2]+beta*X[,3]
> ymat <- matrix(rep(yvec,ngenes),ncol=narrays,byrow=T)+epsilon
> ymat[5,1:2] <- NA
> fit <- lmFit(ymat,design=X)
> test.contr <- cbind(c(0,1,-1),c(1,1,0),c(1,0,1))
> dimnames(test.contr) <- list(c("mu","alpha","beta"),c("alpha-beta","mu+alpha","mu+beta"))
> fit2 <- contrasts.fit(fit,contrasts=test.contr)
> eBayes(fit2)
An object of class "MArrayLM"
$coefficients
     alpha-beta mu+alpha   mu+beta
[1,]   3.537333 1.677465 -1.859868
[2,]   4.355578 2.372554 -1.983024
[3,]   3.197645 1.053584 -2.144061
[4,]   2.697734 1.611443 -1.086291
[5,]   3.502304 2.051995 -1.450309

$stdev.unscaled
     alpha-beta  mu+alpha   mu+beta
[1,]  0.8164966 0.5773503 0.5773503
[2,]  0.8164966 0.5773503 0.5773503
[3,]  0.8164966 0.5773503 0.5773503
[4,]  0.8164966 0.5773503 0.5773503
[5,]  1.1547005 0.8368633 0.8368633

$sigma
[1] 1.3425032 0.4647155 1.1993444 0.9428569 0.9421509

$df.residual
[1] 6 6 6 6 4

$cov.coefficients
           alpha-beta     mu+alpha       mu+beta
alpha-beta  0.6666667 3.333333e-01 -3.333333e-01
mu+alpha    0.3333333 3.333333e-01  5.551115e-17
mu+beta    -0.3333333 5.551115e-17  3.333333e-01

$pivot
[1] 1 2 3

$rank
[1] 3

$Amean
[1]  0.2034961  0.1954604 -0.2863347  0.1188659  0.1784593

$method
[1] "ls"

$design
  mu alpha beta
1  1     0    0
2  1     0    0
3  1     0    0
4  1     1    0
5  1     1    0
6  1     1    0
7  1     0    1
8  1     0    1
9  1     0    1

$contrasts
      alpha-beta mu+alpha mu+beta
mu             0        1       1
alpha          1        1       0
beta          -1        0       1

$df.prior
[1] 9.306153

$s2.prior
[1] 0.923179

$var.prior
[1] 17.33142 17.33142 12.26855

$proportion
[1] 0.01

$s2.post
[1] 1.2677996 0.6459499 1.1251558 0.9097727 0.9124980

$t
     alpha-beta mu+alpha   mu+beta
[1,]   3.847656 2.580411 -2.860996
[2,]   6.637308 5.113018 -4.273553
[3,]   3.692066 1.720376 -3.500994
[4,]   3.464003 2.926234 -1.972606
[5,]   3.175181 2.566881 -1.814221

$df.total
[1] 15.30615 15.30615 15.30615 15.30615 13.30615

$p.value
       alpha-beta     mu+alpha      mu+beta
[1,] 1.529450e-03 0.0206493481 0.0117123495
[2,] 7.144893e-06 0.0001195844 0.0006385076
[3,] 2.109270e-03 0.1055117477 0.0031325769
[4,] 3.381970e-03 0.0102514264 0.0668844448
[5,] 7.124839e-03 0.0230888584 0.0922478630

$lods
     alpha-beta  mu+alpha    mu+beta
[1,]  -1.013417 -3.702133 -3.0332393
[2,]   3.981496  1.283349 -0.2615911
[3,]  -1.315036 -5.168621 -1.7864101
[4,]  -1.757103 -3.043209 -4.6191869
[5,]  -2.257358 -3.478267 -4.5683738

$F
[1]  7.421911 22.203107  7.608327  6.227010  5.060579

$F.p.value
[1] 5.581800e-03 2.988923e-05 5.080726e-03 1.050148e-02 2.320274e-02

> 
> ### uniquegenelist
> 
> uniquegenelist(letters[1:8],ndups=2)
[1] "a" "c" "e" "g"
> uniquegenelist(letters[1:8],ndups=2,spacing=2)
[1] "a" "b" "e" "f"
> 
> ### classifyTests
> 
> tstat <- matrix(c(0,5,0, 0,2.5,0, -2,-2,2, 1,1,1), 4, 3, byrow=TRUE)
> classifyTestsF(tstat)
TestResults matrix
     [,1] [,2] [,3]
[1,]    0    1    0
[2,]    0    0    0
[3,]   -1   -1    1
[4,]    0    0    0
> classifyTestsF(tstat,fstat.only=TRUE)
[1] 8.333333 2.083333 4.000000 1.000000
attr(,"df1")
[1] 3
attr(,"df2")
[1] Inf
> limma:::.classifyTestsP(tstat)
TestResults matrix
     [,1] [,2] [,3]
[1,]    0    1    0
[2,]    0    1    0
[3,]    0    0    0
[4,]    0    0    0
> 
> ### avereps
> 
> x <- matrix(rnorm(8*3),8,3)
> colnames(x) <- c("S1","S2","S3")
> rownames(x) <- c("b","a","a","c","c","b","b","b")
> avereps(x)
          S1         S2         S3
b -0.2353018  0.5220094  0.2302895
a -0.4347701  0.6453498 -0.6758914
c  0.3482980 -0.4820695 -0.3841313
> 
> ### roast
> 
> y <- matrix(rnorm(100*4),100,4)
> sigma <- sqrt(2/rchisq(100,df=7))
> y <- y*sigma
> design <- cbind(Intercept=1,Group=c(0,0,1,1))
> iset1 <- 1:5
> y[iset1,3:4] <- y[iset1,3:4]+3
> iset2 <- 6:10
> roast(y=y,iset1,design,contrast=2)
         Active.Prop     P.Value
Down               0 0.997999500
Up                 1 0.002250563
UpOrDown           1 0.004500000
Mixed              1 0.004500000
> roast(y=y,iset1,design,contrast=2,array.weights=c(0.5,1,0.5,1))
         Active.Prop     P.Value
Down               0 0.998749687
Up                 1 0.001500375
UpOrDown           1 0.003000000
Mixed              1 0.003000000
> w <- matrix(runif(100*4),100,4)
> roast(y=y,iset1,design,contrast=2,weights=w)
         Active.Prop     P.Value
Down               0 0.996999250
Up                 1 0.003250813
UpOrDown           1 0.006500000
Mixed              1 0.006500000
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,gene.weights=runif(100))
     NGenes PropDown PropUp Direction PValue    FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up 0.0055 0.0105       0.0055    0.0105
set2      5        0      0        Up 0.2025 0.2025       0.4715    0.4715
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,array.weights=c(0.5,1,0.5,1))
     NGenes PropDown PropUp Direction PValue    FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up 0.0050 0.0095        0.005    0.0095
set2      5        0      0        Up 0.6845 0.6845        0.642    0.6420
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w)
     NGenes PropDown PropUp Direction PValue    FDR PValue.Mixed FDR.Mixed
set1      5        0    1.0        Up 0.0030 0.0055        0.003    0.0055
set2      5        0    0.2      Down 0.9615 0.9615        0.496    0.4960
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes PropDown PropUp Direction PValue    FDR PValue.Mixed FDR.Mixed
set1      5        0    1.0        Up 0.0025 0.0045       0.0025    0.0045
set2      5        0    0.2      Down 0.8930 0.8930       0.4380    0.4380
> fry(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes Direction      PValue         FDR PValue.Mixed    FDR.Mixed
set1      5        Up 0.001568924 0.003137848 0.0001156464 0.0002312929
set2      5      Down 0.932105219 0.932105219 0.4315499569 0.4315499569
> rownames(y) <- paste0("Gene",1:100)
> iset1A <- rownames(y)[1:5]
> fry(y=y,index=iset1A,design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes Direction      PValue PValue.Mixed
set1      5        Up 0.001568924 0.0001156464
> 
> ### camera
> 
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1),allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction      PValue
set1      5  -0.2481655        Up 0.001050253
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction       PValue        FDR
set1      5  -0.2481655        Up 0.0009047749 0.00180955
set2      5   0.1719094      Down 0.9068364378 0.90683644
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1))
     NGenes Direction       PValue
set1      5        Up 1.105329e-10
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2)
     NGenes Direction       PValue          FDR
set1      5        Up 7.334400e-12 1.466880e-11
set2      5      Down 8.677115e-01 8.677115e-01
> camera(y=y,iset1A,design,contrast=2)
     NGenes Direction     PValue
set1      5        Up 7.3344e-12
> 
> ### with EList arg
> 
> y <- new("EList",list(E=y))
> roast(y=y,iset1,design,contrast=2)
         Active.Prop     P.Value
Down               0 0.996999250
Up                 1 0.003250813
UpOrDown           1 0.006500000
Mixed              1 0.006500000
> camera(y=y,iset1,design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction       PValue
set1      5  -0.2481655        Up 0.0009047749
> camera(y=y,iset1,design,contrast=2)
     NGenes Direction     PValue
set1      5        Up 7.3344e-12
> 
> ### eBayes with trend
> 
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene2   3.729512  1.73488969  4.865697 0.0004854886 0.02902331  0.1596831
Gene3   3.488703  1.03931081  4.754954 0.0005804663 0.02902331 -0.0144071
Gene4   2.696676  1.74060725  3.356468 0.0063282637 0.21094212 -2.3434702
Gene1   2.391846  1.72305203  3.107124 0.0098781268 0.24695317 -2.7738874
Gene33 -1.492317 -0.07525287 -2.783817 0.0176475742 0.29965463 -3.3300835
Gene5   2.387967  1.63066783  2.773444 0.0179792778 0.29965463 -3.3478204
Gene80 -1.839760 -0.32802306 -2.503584 0.0291489863 0.37972679 -3.8049642
Gene39  1.366141 -0.27360750  2.451133 0.0320042242 0.37972679 -3.8925860
Gene95 -1.907074  1.26297763 -2.414217 0.0341754107 0.37972679 -3.9539571
Gene50  1.034777  0.01608433  2.054690 0.0642289403 0.59978803 -4.5350317
> fit$df.prior
[1] 9.098442
> fit$s2.prior
    Gene1     Gene2     Gene3     Gene4     Gene5     Gene6     Gene7     Gene8 
0.6901845 0.6977354 0.3860494 0.7014122 0.6341068 0.2926337 0.3077620 0.3058098 
    Gene9    Gene10    Gene11    Gene12    Gene13    Gene14    Gene15    Gene16 
0.2985145 0.2832520 0.3232434 0.3279710 0.2816081 0.2943502 0.3127994 0.2894802 
   Gene17    Gene18    Gene19    Gene20    Gene21    Gene22    Gene23    Gene24 
0.2812758 0.2840051 0.2839124 0.2954261 0.2838592 0.2812704 0.3157029 0.2844541 
   Gene25    Gene26    Gene27    Gene28    Gene29    Gene30    Gene31    Gene32 
0.4778832 0.2818242 0.2930360 0.2940957 0.2941862 0.3234399 0.3164779 0.2853510 
   Gene33    Gene34    Gene35    Gene36    Gene37    Gene38    Gene39    Gene40 
0.2988244 0.3450090 0.3048596 0.3089086 0.3104534 0.4551549 0.3220008 0.2813286 
   Gene41    Gene42    Gene43    Gene44    Gene45    Gene46    Gene47    Gene48 
0.2826027 0.2822504 0.2823330 0.3170673 0.3146173 0.3146793 0.2916540 0.2975003 
   Gene49    Gene50    Gene51    Gene52    Gene53    Gene54    Gene55    Gene56 
0.3538946 0.2907240 0.3199596 0.2816641 0.2814293 0.2996822 0.2812885 0.2896157 
   Gene57    Gene58    Gene59    Gene60    Gene61    Gene62    Gene63    Gene64 
0.2955317 0.2815907 0.2919420 0.2849675 0.3540805 0.3491713 0.2975019 0.2939325 
   Gene65    Gene66    Gene67    Gene68    Gene69    Gene70    Gene71    Gene72 
0.2986943 0.3265466 0.3402343 0.3394927 0.2813283 0.2814440 0.3089669 0.3030850 
   Gene73    Gene74    Gene75    Gene76    Gene77    Gene78    Gene79    Gene80 
0.2859286 0.2813216 0.3475231 0.3334419 0.2949550 0.3108702 0.2959688 0.3295294 
   Gene81    Gene82    Gene83    Gene84    Gene85    Gene86    Gene87    Gene88 
0.3413700 0.2946268 0.3029565 0.2920284 0.2926205 0.2818046 0.3425116 0.2882936 
   Gene89    Gene90    Gene91    Gene92    Gene93    Gene94    Gene95    Gene96 
0.2945459 0.3077919 0.2892134 0.2823787 0.3048049 0.2961408 0.4590012 0.2812784 
   Gene97    Gene98    Gene99   Gene100 
0.2846345 0.2819651 0.3137551 0.2856081 
> summary(fit$s2.post)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2335  0.2603  0.2997  0.3375  0.3655  0.7812 
> 
> y$E[1,1] <- NA
> y$E[1,3] <- NA
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene3   3.488703  1.03931081  4.604490 0.0007644061 0.07644061 -0.2333915
Gene2   3.729512  1.73488969  4.158038 0.0016033158 0.08016579 -0.9438583
Gene4   2.696676  1.74060725  2.898102 0.0145292666 0.44537707 -3.0530813
Gene33 -1.492317 -0.07525287 -2.784004 0.0178150826 0.44537707 -3.2456324
Gene5   2.387967  1.63066783  2.495395 0.0297982959 0.46902627 -3.7272957
Gene80 -1.839760 -0.32802306 -2.491115 0.0300256116 0.46902627 -3.7343584
Gene39  1.366141 -0.27360750  2.440729 0.0328318388 0.46902627 -3.8172597
Gene1   2.638272  1.47993643  2.227507 0.0530016060 0.58890673 -3.9537576
Gene95 -1.907074  1.26297763 -2.288870 0.0429197808 0.53649726 -4.0642439
Gene50  1.034777  0.01608433  2.063663 0.0635275235 0.60439978 -4.4204731
> fit$df.residual[1]
[1] 0
> fit$df.prior
[1] 8.971891
> fit$s2.prior
  [1] 0.7014084 0.9646561 0.4276287 0.9716476 0.8458852 0.2910492 0.3097052
  [8] 0.3074225 0.2985517 0.2786374 0.3267121 0.3316013 0.2766404 0.2932679
 [15] 0.3154347 0.2869186 0.2761395 0.2799884 0.2795119 0.2946468 0.2794412
 [22] 0.2761282 0.3186442 0.2806092 0.4596465 0.2767847 0.2924541 0.2939204
 [29] 0.2930568 0.3269177 0.3194905 0.2814293 0.2989389 0.3483845 0.3062977
 [36] 0.3110287 0.3127934 0.4418052 0.3254067 0.2761732 0.2780422 0.2773311
 [43] 0.2776653 0.3201314 0.3174515 0.3175199 0.2897731 0.2972785 0.3567262
 [50] 0.2885556 0.3232426 0.2767207 0.2762915 0.3000062 0.2761306 0.2870975
 [57] 0.2947817 0.2766152 0.2901489 0.2813183 0.3568982 0.3724440 0.2972804
 [64] 0.2927300 0.2987764 0.3301406 0.3437962 0.3430762 0.2761729 0.2763094
 [71] 0.3110958 0.3041715 0.2822004 0.2761654 0.3507694 0.3371214 0.2940441
 [78] 0.3132660 0.2953388 0.3331880 0.3448949 0.2946558 0.3040162 0.2902616
 [85] 0.2910320 0.2769211 0.3459946 0.2859057 0.2935193 0.3097398 0.2865663
 [92] 0.2774968 0.3062327 0.2955576 0.5425422 0.2761214 0.2808585 0.2771484
 [99] 0.3164981 0.2817725
> summary(fit$s2.post)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2296  0.2581  0.3003  0.3453  0.3652  0.9158 
> 
> ### eBayes with robust
> 
> fitr <- lmFit(y,design)
> fitr <- eBayes(fitr,robust=TRUE)
> summary(fitr$df.prior)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  6.717   9.244   9.244   9.194   9.244   9.244 
> topTable(fitr,coef=2)
           logFC     AveExpr         t      P.Value   adj.P.Val          B
Gene2   3.729512  1.73488969  7.108463 1.752774e-05 0.001752774  3.3517310
Gene3   3.488703  1.03931081  5.041209 3.526138e-04 0.017630688  0.4056329
Gene4   2.696676  1.74060725  4.697690 6.150508e-04 0.020501693 -0.1463315
Gene5   2.387967  1.63066783  3.451807 5.245019e-03 0.131125480 -2.2678836
Gene1   2.638272  1.47993643  3.317593 8.651142e-03 0.173022847 -2.4400000
Gene33 -1.492317 -0.07525287 -2.716431 1.970991e-02 0.297950865 -3.5553166
Gene95 -1.907074  1.26297763 -2.685067 2.085656e-02 0.297950865 -3.6094982
Gene80 -1.839760 -0.32802306 -2.535926 2.727440e-02 0.340929958 -3.8653107
Gene39  1.366141 -0.27360750  2.469570 3.071854e-02 0.341317083 -3.9779817
Gene50  1.034777  0.01608433  1.973040 7.357960e-02 0.632875126 -4.7877548
> fitr <- eBayes(fitr,trend=TRUE,robust=TRUE)
> summary(fitr$df.prior)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  7.809   8.972   8.972   8.949   8.972   8.972 
> topTable(fitr,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene2   3.729512  1.73488969  4.754160 0.0005999064 0.05999064 -0.0218247
Gene3   3.488703  1.03931081  3.761219 0.0031618743 0.15809372 -1.6338257
Gene4   2.696676  1.74060725  3.292262 0.0071993347 0.23997782 -2.4295326
Gene33 -1.492317 -0.07525287 -3.063180 0.0108203134 0.27050784 -2.8211394
Gene50  1.034777  0.01608433  2.645717 0.0228036320 0.38815282 -3.5304767
Gene5   2.387967  1.63066783  2.633901 0.0232891695 0.38815282 -3.5503445
Gene1   2.638272  1.47993643  2.204116 0.0550613420 0.58959402 -4.0334169
Gene80 -1.839760 -0.32802306 -2.332729 0.0397331916 0.56761702 -4.0496640
Gene39  1.366141 -0.27360750  2.210665 0.0492211477 0.58959402 -4.2469578
Gene95 -1.907074  1.26297763 -2.106861 0.0589594023 0.58959402 -4.4117140
> 
> ### voom
> 
> y <- matrix(rpois(100*4,lambda=20),100,4)
> design <- cbind(Int=1,x=c(0,0,1,1))
> v <- voom(y,design)
> names(v)
[1] "E"       "weights" "design"  "targets"
> summary(v$E)
       V1              V2              V3              V4       
 Min.   :12.38   Min.   :12.32   Min.   :12.17   Min.   :12.08  
 1st Qu.:13.11   1st Qu.:13.05   1st Qu.:13.11   1st Qu.:13.03  
 Median :13.34   Median :13.28   Median :13.35   Median :13.35  
 Mean   :13.29   Mean   :13.29   Mean   :13.28   Mean   :13.28  
 3rd Qu.:13.48   3rd Qu.:13.54   3rd Qu.:13.48   3rd Qu.:13.50  
 Max.   :14.01   Max.   :13.95   Max.   :14.03   Max.   :14.05  
> summary(v$weights)
       V1               V2               V3               V4        
 Min.   : 7.729   Min.   : 7.729   Min.   : 7.729   Min.   : 7.729  
 1st Qu.:13.859   1st Qu.:15.067   1st Qu.:14.254   1st Qu.:13.592  
 Median :15.913   Median :16.621   Median :16.081   Median :16.028  
 Mean   :16.773   Mean   :18.525   Mean   :18.472   Mean   :17.112  
 3rd Qu.:18.214   3rd Qu.:20.002   3rd Qu.:18.475   3rd Qu.:18.398  
 Max.   :34.331   Max.   :34.331   Max.   :34.331   Max.   :34.331  
> 
> ### goana
> 
> EB <- c("133746","1339","134","1340","134083","134111","134147","134187","134218","134266",
+ "134353","134359","134391","134429","134430","1345","134510","134526","134549","1346",
+ "134637","1347","134701","134728","1348","134829","134860","134864","1349","134957",
+ "135","1350","1351","135112","135114","135138","135152","135154","1352","135228",
+ "135250","135293","135295","1353","135458","1355","1356","135644","135656","1357",
+ "1358","135892","1359","135924","135935","135941","135946","135948","136","1360",
+ "136051","1361","1362","136227","136242","136259","1363","136306","136319","136332",
+ "136371","1364","1365","136541","1366","136647","1368","136853","1369","136991",
+ "1370","137075","1371","137209","1373","137362","1374","137492","1375","1376",
+ "137682","137695","137735","1378","137814","137868","137872","137886","137902","137964")
> go <- goana(fit,FDR=0.8,geneid=EB)
> topGO(go,number=10,truncate.term=30)
                                     Term Ont  N Up Down        P.Up
GO:0070062          extracellular exosome  CC  8  0    4 1.000000000
GO:0043230        extracellular organelle  CC  8  0    4 1.000000000
GO:1903561          extracellular vesicle  CC  8  0    4 1.000000000
GO:0040011                     locomotion  BP  7  5    0 0.006651547
GO:0032502          developmental process  BP 23  4    6 0.844070086
GO:0032501 multicellular organismal pr...  BP 31  7    7 0.620372627
GO:0006915              apoptotic process  BP  5  4    1 0.009503355
GO:0012501          programmed cell death  BP  5  4    1 0.009503355
GO:0042981 regulation of apoptotic pro...  BP  5  4    1 0.009503355
GO:0040012       regulation of locomotion  BP  5  4    0 0.009503355
                P.Down
GO:0070062 0.003047199
GO:0043230 0.003047199
GO:1903561 0.003047199
GO:0040011 1.000000000
GO:0032502 0.009014340
GO:0032501 0.009111120
GO:0006915 0.416247633
GO:0012501 0.416247633
GO:0042981 0.416247633
GO:0040012 1.000000000
> topGO(go,number=10,truncate.term=30,sort="down")
                                     Term Ont  N Up Down      P.Up      P.Down
GO:0070062          extracellular exosome  CC  8  0    4 1.0000000 0.003047199
GO:0043230        extracellular organelle  CC  8  0    4 1.0000000 0.003047199
GO:1903561          extracellular vesicle  CC  8  0    4 1.0000000 0.003047199
GO:0032502          developmental process  BP 23  4    6 0.8440701 0.009014340
GO:0032501 multicellular organismal pr...  BP 31  7    7 0.6203726 0.009111120
GO:0031982                        vesicle  CC 18  1    5 0.9946677 0.015552466
GO:0051604             protein maturation  BP  7  1    3 0.8497705 0.020760307
GO:0016485             protein processing  BP  7  1    3 0.8497705 0.020760307
GO:0009887     animal organ morphogenesis  BP  3  0    2 1.0000000 0.025788497
GO:0055082 cellular chemical homeostas...  BP  3  1    2 0.5476190 0.025788497
> 
> proc.time()
   user  system elapsed 
   5.21    0.39    5.62 

limma.Rcheck/tests_x64/limma-Tests.Rout


R version 4.1.1 (2021-08-10) -- "Kick Things"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(limma)
> options(warnPartialMatchArgs=TRUE,warnPartialMatchAttr=TRUE,warnPartialMatchDollar=TRUE)
> 
> set.seed(0); u <- runif(100)
> 
> ### strsplit2
> 
> x <- c("ab;cd;efg","abc;def","z","")
> strsplit2(x,split=";")
     [,1]  [,2]  [,3] 
[1,] "ab"  "cd"  "efg"
[2,] "abc" "def" ""   
[3,] "z"   ""    ""   
[4,] ""    ""    ""   
> 
> ### removeext
> 
> removeExt(c("slide1.spot","slide.2.spot"))
[1] "slide1"  "slide.2"
> removeExt(c("slide1.spot","slide"))
[1] "slide1.spot" "slide"      
> 
> ### printorder
> 
> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6),ndups=2,start="topright",npins=4)
$printorder
  [1]   6   5   4   3   2   1  12  11  10   9   8   7  18  17  16  15  14  13
 [19]  24  23  22  21  20  19  30  29  28  27  26  25  36  35  34  33  32  31
 [37]  42  41  40  39  38  37  48  47  46  45  44  43   6   5   4   3   2   1
 [55]  12  11  10   9   8   7  18  17  16  15  14  13  24  23  22  21  20  19
 [73]  30  29  28  27  26  25  36  35  34  33  32  31  42  41  40  39  38  37
 [91]  48  47  46  45  44  43   6   5   4   3   2   1  12  11  10   9   8   7
[109]  18  17  16  15  14  13  24  23  22  21  20  19  30  29  28  27  26  25
[127]  36  35  34  33  32  31  42  41  40  39  38  37  48  47  46  45  44  43
[145]   6   5   4   3   2   1  12  11  10   9   8   7  18  17  16  15  14  13
[163]  24  23  22  21  20  19  30  29  28  27  26  25  36  35  34  33  32  31
[181]  42  41  40  39  38  37  48  47  46  45  44  43  54  53  52  51  50  49
[199]  60  59  58  57  56  55  66  65  64  63  62  61  72  71  70  69  68  67
[217]  78  77  76  75  74  73  84  83  82  81  80  79  90  89  88  87  86  85
[235]  96  95  94  93  92  91  54  53  52  51  50  49  60  59  58  57  56  55
[253]  66  65  64  63  62  61  72  71  70  69  68  67  78  77  76  75  74  73
[271]  84  83  82  81  80  79  90  89  88  87  86  85  96  95  94  93  92  91
[289]  54  53  52  51  50  49  60  59  58  57  56  55  66  65  64  63  62  61
[307]  72  71  70  69  68  67  78  77  76  75  74  73  84  83  82  81  80  79
[325]  90  89  88  87  86  85  96  95  94  93  92  91  54  53  52  51  50  49
[343]  60  59  58  57  56  55  66  65  64  63  62  61  72  71  70  69  68  67
[361]  78  77  76  75  74  73  84  83  82  81  80  79  90  89  88  87  86  85
[379]  96  95  94  93  92  91 102 101 100  99  98  97 108 107 106 105 104 103
[397] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[415] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[433] 102 101 100  99  98  97 108 107 106 105 104 103 114 113 112 111 110 109
[451] 120 119 118 117 116 115 126 125 124 123 122 121 132 131 130 129 128 127
[469] 138 137 136 135 134 133 144 143 142 141 140 139 102 101 100  99  98  97
[487] 108 107 106 105 104 103 114 113 112 111 110 109 120 119 118 117 116 115
[505] 126 125 124 123 122 121 132 131 130 129 128 127 138 137 136 135 134 133
[523] 144 143 142 141 140 139 102 101 100  99  98  97 108 107 106 105 104 103
[541] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[559] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[577] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[595] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[613] 186 185 184 183 182 181 192 191 190 189 188 187 150 149 148 147 146 145
[631] 156 155 154 153 152 151 162 161 160 159 158 157 168 167 166 165 164 163
[649] 174 173 172 171 170 169 180 179 178 177 176 175 186 185 184 183 182 181
[667] 192 191 190 189 188 187 150 149 148 147 146 145 156 155 154 153 152 151
[685] 162 161 160 159 158 157 168 167 166 165 164 163 174 173 172 171 170 169
[703] 180 179 178 177 176 175 186 185 184 183 182 181 192 191 190 189 188 187
[721] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[739] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[757] 186 185 184 183 182 181 192 191 190 189 188 187

$plate
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

$plate.r
  [1]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4
 [26]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  3  3
 [51]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
 [76]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  2  2  2  2
[101]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
[126]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  1  1  1  1  1  1
[151]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[176]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  8  8  8  8  8  8  8  8
[201]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8
[226]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  7  7  7  7  7  7  7  7  7  7
[251]  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7
[276]  7  7  7  7  7  7  7  7  7  7  7  7  7  6  6  6  6  6  6  6  6  6  6  6  6
[301]  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6
[326]  6  6  6  6  6  6  6  6  6  6  6  5  5  5  5  5  5  5  5  5  5  5  5  5  5
[351]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
[376]  5  5  5  5  5  5  5  5  5 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[401] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[426] 12 12 12 12 12 12 12 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[451] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[476] 11 11 11 11 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[501] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[526] 10 10 10  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[551]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[576]  9 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
[601] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 15
[626] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
[651] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 14 14 14
[676] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
[701] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 13 13 13 13 13
[726] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
[751] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13

$plate.c
  [1]  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15
 [26] 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3
 [51]  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14
 [76] 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2
[101]  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13
[126] 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1
[151]  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18
[176] 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6
[201]  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17
[226] 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5
[251]  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16
[276] 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4
[301]  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21
[326] 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9
[351]  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20
[376] 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8
[401]  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19
[426] 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7
[451] 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24
[476] 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12
[501] 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23
[526] 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11
[551] 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22
[576] 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10
[601] 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3
[626]  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15
[651] 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2
[676]  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14
[701] 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1
[726]  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13
[751] 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22

$plateposition
  [1] "p1D03" "p1D03" "p1D02" "p1D02" "p1D01" "p1D01" "p1D06" "p1D06" "p1D05"
 [10] "p1D05" "p1D04" "p1D04" "p1D09" "p1D09" "p1D08" "p1D08" "p1D07" "p1D07"
 [19] "p1D12" "p1D12" "p1D11" "p1D11" "p1D10" "p1D10" "p1D15" "p1D15" "p1D14"
 [28] "p1D14" "p1D13" "p1D13" "p1D18" "p1D18" "p1D17" "p1D17" "p1D16" "p1D16"
 [37] "p1D21" "p1D21" "p1D20" "p1D20" "p1D19" "p1D19" "p1D24" "p1D24" "p1D23"
 [46] "p1D23" "p1D22" "p1D22" "p1C03" "p1C03" "p1C02" "p1C02" "p1C01" "p1C01"
 [55] "p1C06" "p1C06" "p1C05" "p1C05" "p1C04" "p1C04" "p1C09" "p1C09" "p1C08"
 [64] "p1C08" "p1C07" "p1C07" "p1C12" "p1C12" "p1C11" "p1C11" "p1C10" "p1C10"
 [73] "p1C15" "p1C15" "p1C14" "p1C14" "p1C13" "p1C13" "p1C18" "p1C18" "p1C17"
 [82] "p1C17" "p1C16" "p1C16" "p1C21" "p1C21" "p1C20" "p1C20" "p1C19" "p1C19"
 [91] "p1C24" "p1C24" "p1C23" "p1C23" "p1C22" "p1C22" "p1B03" "p1B03" "p1B02"
[100] "p1B02" "p1B01" "p1B01" "p1B06" "p1B06" "p1B05" "p1B05" "p1B04" "p1B04"
[109] "p1B09" "p1B09" "p1B08" "p1B08" "p1B07" "p1B07" "p1B12" "p1B12" "p1B11"
[118] "p1B11" "p1B10" "p1B10" "p1B15" "p1B15" "p1B14" "p1B14" "p1B13" "p1B13"
[127] "p1B18" "p1B18" "p1B17" "p1B17" "p1B16" "p1B16" "p1B21" "p1B21" "p1B20"
[136] "p1B20" "p1B19" "p1B19" "p1B24" "p1B24" "p1B23" "p1B23" "p1B22" "p1B22"
[145] "p1A03" "p1A03" "p1A02" "p1A02" "p1A01" "p1A01" "p1A06" "p1A06" "p1A05"
[154] "p1A05" "p1A04" "p1A04" "p1A09" "p1A09" "p1A08" "p1A08" "p1A07" "p1A07"
[163] "p1A12" "p1A12" "p1A11" "p1A11" "p1A10" "p1A10" "p1A15" "p1A15" "p1A14"
[172] "p1A14" "p1A13" "p1A13" "p1A18" "p1A18" "p1A17" "p1A17" "p1A16" "p1A16"
[181] "p1A21" "p1A21" "p1A20" "p1A20" "p1A19" "p1A19" "p1A24" "p1A24" "p1A23"
[190] "p1A23" "p1A22" "p1A22" "p1H03" "p1H03" "p1H02" "p1H02" "p1H01" "p1H01"
[199] "p1H06" "p1H06" "p1H05" "p1H05" "p1H04" "p1H04" "p1H09" "p1H09" "p1H08"
[208] "p1H08" "p1H07" "p1H07" "p1H12" "p1H12" "p1H11" "p1H11" "p1H10" "p1H10"
[217] "p1H15" "p1H15" "p1H14" "p1H14" "p1H13" "p1H13" "p1H18" "p1H18" "p1H17"
[226] "p1H17" "p1H16" "p1H16" "p1H21" "p1H21" "p1H20" "p1H20" "p1H19" "p1H19"
[235] "p1H24" "p1H24" "p1H23" "p1H23" "p1H22" "p1H22" "p1G03" "p1G03" "p1G02"
[244] "p1G02" "p1G01" "p1G01" "p1G06" "p1G06" "p1G05" "p1G05" "p1G04" "p1G04"
[253] "p1G09" "p1G09" "p1G08" "p1G08" "p1G07" "p1G07" "p1G12" "p1G12" "p1G11"
[262] "p1G11" "p1G10" "p1G10" "p1G15" "p1G15" "p1G14" "p1G14" "p1G13" "p1G13"
[271] "p1G18" "p1G18" "p1G17" "p1G17" "p1G16" "p1G16" "p1G21" "p1G21" "p1G20"
[280] "p1G20" "p1G19" "p1G19" "p1G24" "p1G24" "p1G23" "p1G23" "p1G22" "p1G22"
[289] "p1F03" "p1F03" "p1F02" "p1F02" "p1F01" "p1F01" "p1F06" "p1F06" "p1F05"
[298] "p1F05" "p1F04" "p1F04" "p1F09" "p1F09" "p1F08" "p1F08" "p1F07" "p1F07"
[307] "p1F12" "p1F12" "p1F11" "p1F11" "p1F10" "p1F10" "p1F15" "p1F15" "p1F14"
[316] "p1F14" "p1F13" "p1F13" "p1F18" "p1F18" "p1F17" "p1F17" "p1F16" "p1F16"
[325] "p1F21" "p1F21" "p1F20" "p1F20" "p1F19" "p1F19" "p1F24" "p1F24" "p1F23"
[334] "p1F23" "p1F22" "p1F22" "p1E03" "p1E03" "p1E02" "p1E02" "p1E01" "p1E01"
[343] "p1E06" "p1E06" "p1E05" "p1E05" "p1E04" "p1E04" "p1E09" "p1E09" "p1E08"
[352] "p1E08" "p1E07" "p1E07" "p1E12" "p1E12" "p1E11" "p1E11" "p1E10" "p1E10"
[361] "p1E15" "p1E15" "p1E14" "p1E14" "p1E13" "p1E13" "p1E18" "p1E18" "p1E17"
[370] "p1E17" "p1E16" "p1E16" "p1E21" "p1E21" "p1E20" "p1E20" "p1E19" "p1E19"
[379] "p1E24" "p1E24" "p1E23" "p1E23" "p1E22" "p1E22" "p1L03" "p1L03" "p1L02"
[388] "p1L02" "p1L01" "p1L01" "p1L06" "p1L06" "p1L05" "p1L05" "p1L04" "p1L04"
[397] "p1L09" "p1L09" "p1L08" "p1L08" "p1L07" "p1L07" "p1L12" "p1L12" "p1L11"
[406] "p1L11" "p1L10" "p1L10" "p1L15" "p1L15" "p1L14" "p1L14" "p1L13" "p1L13"
[415] "p1L18" "p1L18" "p1L17" "p1L17" "p1L16" "p1L16" "p1L21" "p1L21" "p1L20"
[424] "p1L20" "p1L19" "p1L19" "p1L24" "p1L24" "p1L23" "p1L23" "p1L22" "p1L22"
[433] "p1K03" "p1K03" "p1K02" "p1K02" "p1K01" "p1K01" "p1K06" "p1K06" "p1K05"
[442] "p1K05" "p1K04" "p1K04" "p1K09" "p1K09" "p1K08" "p1K08" "p1K07" "p1K07"
[451] "p1K12" "p1K12" "p1K11" "p1K11" "p1K10" "p1K10" "p1K15" "p1K15" "p1K14"
[460] "p1K14" "p1K13" "p1K13" "p1K18" "p1K18" "p1K17" "p1K17" "p1K16" "p1K16"
[469] "p1K21" "p1K21" "p1K20" "p1K20" "p1K19" "p1K19" "p1K24" "p1K24" "p1K23"
[478] "p1K23" "p1K22" "p1K22" "p1J03" "p1J03" "p1J02" "p1J02" "p1J01" "p1J01"
[487] "p1J06" "p1J06" "p1J05" "p1J05" "p1J04" "p1J04" "p1J09" "p1J09" "p1J08"
[496] "p1J08" "p1J07" "p1J07" "p1J12" "p1J12" "p1J11" "p1J11" "p1J10" "p1J10"
[505] "p1J15" "p1J15" "p1J14" "p1J14" "p1J13" "p1J13" "p1J18" "p1J18" "p1J17"
[514] "p1J17" "p1J16" "p1J16" "p1J21" "p1J21" "p1J20" "p1J20" "p1J19" "p1J19"
[523] "p1J24" "p1J24" "p1J23" "p1J23" "p1J22" "p1J22" "p1I03" "p1I03" "p1I02"
[532] "p1I02" "p1I01" "p1I01" "p1I06" "p1I06" "p1I05" "p1I05" "p1I04" "p1I04"
[541] "p1I09" "p1I09" "p1I08" "p1I08" "p1I07" "p1I07" "p1I12" "p1I12" "p1I11"
[550] "p1I11" "p1I10" "p1I10" "p1I15" "p1I15" "p1I14" "p1I14" "p1I13" "p1I13"
[559] "p1I18" "p1I18" "p1I17" "p1I17" "p1I16" "p1I16" "p1I21" "p1I21" "p1I20"
[568] "p1I20" "p1I19" "p1I19" "p1I24" "p1I24" "p1I23" "p1I23" "p1I22" "p1I22"
[577] "p1P03" "p1P03" "p1P02" "p1P02" "p1P01" "p1P01" "p1P06" "p1P06" "p1P05"
[586] "p1P05" "p1P04" "p1P04" "p1P09" "p1P09" "p1P08" "p1P08" "p1P07" "p1P07"
[595] "p1P12" "p1P12" "p1P11" "p1P11" "p1P10" "p1P10" "p1P15" "p1P15" "p1P14"
[604] "p1P14" "p1P13" "p1P13" "p1P18" "p1P18" "p1P17" "p1P17" "p1P16" "p1P16"
[613] "p1P21" "p1P21" "p1P20" "p1P20" "p1P19" "p1P19" "p1P24" "p1P24" "p1P23"
[622] "p1P23" "p1P22" "p1P22" "p1O03" "p1O03" "p1O02" "p1O02" "p1O01" "p1O01"
[631] "p1O06" "p1O06" "p1O05" "p1O05" "p1O04" "p1O04" "p1O09" "p1O09" "p1O08"
[640] "p1O08" "p1O07" "p1O07" "p1O12" "p1O12" "p1O11" "p1O11" "p1O10" "p1O10"
[649] "p1O15" "p1O15" "p1O14" "p1O14" "p1O13" "p1O13" "p1O18" "p1O18" "p1O17"
[658] "p1O17" "p1O16" "p1O16" "p1O21" "p1O21" "p1O20" "p1O20" "p1O19" "p1O19"
[667] "p1O24" "p1O24" "p1O23" "p1O23" "p1O22" "p1O22" "p1N03" "p1N03" "p1N02"
[676] "p1N02" "p1N01" "p1N01" "p1N06" "p1N06" "p1N05" "p1N05" "p1N04" "p1N04"
[685] "p1N09" "p1N09" "p1N08" "p1N08" "p1N07" "p1N07" "p1N12" "p1N12" "p1N11"
[694] "p1N11" "p1N10" "p1N10" "p1N15" "p1N15" "p1N14" "p1N14" "p1N13" "p1N13"
[703] "p1N18" "p1N18" "p1N17" "p1N17" "p1N16" "p1N16" "p1N21" "p1N21" "p1N20"
[712] "p1N20" "p1N19" "p1N19" "p1N24" "p1N24" "p1N23" "p1N23" "p1N22" "p1N22"
[721] "p1M03" "p1M03" "p1M02" "p1M02" "p1M01" "p1M01" "p1M06" "p1M06" "p1M05"
[730] "p1M05" "p1M04" "p1M04" "p1M09" "p1M09" "p1M08" "p1M08" "p1M07" "p1M07"
[739] "p1M12" "p1M12" "p1M11" "p1M11" "p1M10" "p1M10" "p1M15" "p1M15" "p1M14"
[748] "p1M14" "p1M13" "p1M13" "p1M18" "p1M18" "p1M17" "p1M17" "p1M16" "p1M16"
[757] "p1M21" "p1M21" "p1M20" "p1M20" "p1M19" "p1M19" "p1M24" "p1M24" "p1M23"
[766] "p1M23" "p1M22" "p1M22"

> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6))
$printorder
  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
 [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2
 [51]  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
 [76] 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4
[101]  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
[126] 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6
[151]  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
[176] 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8
[201]  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
[226] 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10
[251] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
[276] 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12
[301] 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
[326] 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14
[351] 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
[376] 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16
[401] 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
[426] 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18
[451] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
[476] 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
[501] 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
[526] 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22
[551] 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
[576] 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
[601] 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1
[626]  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
[651] 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3
[676]  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
[701] 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5
[726]  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
[751] 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

$plate
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[334] 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2
[371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[519] 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2
[556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[667] 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2
[704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

$plate.r
  [1]  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  4
 [26]  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3  3
 [51]  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3  3
 [76]  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2  2
[101]  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2  2
[126]  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1  1
[151]  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1  5
[176]  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  4  4  4  4  4  4  8  8
[201]  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  4  4  4  4  4  4  8  8  8
[226]  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3  3  3  3  3  3  7  7  7  7
[251]  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3  3  3  3  3  7  7  7  7  7
[276]  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2  2  2  2  6  6  6  6  6  6
[301] 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2  2  2  6  6  6  6  6  6 10
[326] 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1  1  5  5  5  5  5  5  9  9
[351]  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9
[376]  9  9  9 13 13 13 13 13 13  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12
[401] 12 12 16 16 16 16 16 16  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12
[426] 12 16 16 16 16 16 16  3  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11
[451] 15 15 15 15 15 15  3  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15
[476] 15 15 15 15 15  2  2  2  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14
[501] 14 14 14 14  2  2  2  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14
[526] 14 14 14  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13
[551] 13 13  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13
[576] 13  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16
[601]  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3
[626]  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3
[651]  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2
[676]  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2
[701]  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1
[726]  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1
[751]  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13

$plate.c
  [1]  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1
 [26]  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5
 [51]  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9
 [76] 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13
[101] 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17
[126] 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21
[151]  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1
[176]  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  2  6 10 14 18 22  2  6
[201] 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10
[226] 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14
[251] 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18
[276] 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22
[301]  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2
[326]  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6
[351] 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10
[376] 14 18 22  2  6 10 14 18 22  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15
[401] 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19
[426] 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23
[451]  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3
[476]  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7
[501] 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11
[526] 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15
[551] 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19
[576] 23  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24
[601]  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4
[626]  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8
[651] 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12
[676] 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16
[701] 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20
[726] 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24
[751]  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24

$plateposition
  [1] "p1D01" "p1D05" "p1D09" "p1D13" "p1D17" "p1D21" "p1H01" "p1H05" "p1H09"
 [10] "p1H13" "p1H17" "p1H21" "p1L01" "p1L05" "p1L09" "p1L13" "p1L17" "p1L21"
 [19] "p1P01" "p1P05" "p1P09" "p1P13" "p1P17" "p1P21" "p2D01" "p2D05" "p2D09"
 [28] "p2D13" "p2D17" "p2D21" "p2H01" "p2H05" "p2H09" "p2H13" "p2H17" "p2H21"
 [37] "p2L01" "p2L05" "p2L09" "p2L13" "p2L17" "p2L21" "p2P01" "p2P05" "p2P09"
 [46] "p2P13" "p2P17" "p2P21" "p1C01" "p1C05" "p1C09" "p1C13" "p1C17" "p1C21"
 [55] "p1G01" "p1G05" "p1G09" "p1G13" "p1G17" "p1G21" "p1K01" "p1K05" "p1K09"
 [64] "p1K13" "p1K17" "p1K21" "p1O01" "p1O05" "p1O09" "p1O13" "p1O17" "p1O21"
 [73] "p2C01" "p2C05" "p2C09" "p2C13" "p2C17" "p2C21" "p2G01" "p2G05" "p2G09"
 [82] "p2G13" "p2G17" "p2G21" "p2K01" "p2K05" "p2K09" "p2K13" "p2K17" "p2K21"
 [91] "p2O01" "p2O05" "p2O09" "p2O13" "p2O17" "p2O21" "p1B01" "p1B05" "p1B09"
[100] "p1B13" "p1B17" "p1B21" "p1F01" "p1F05" "p1F09" "p1F13" "p1F17" "p1F21"
[109] "p1J01" "p1J05" "p1J09" "p1J13" "p1J17" "p1J21" "p1N01" "p1N05" "p1N09"
[118] "p1N13" "p1N17" "p1N21" "p2B01" "p2B05" "p2B09" "p2B13" "p2B17" "p2B21"
[127] "p2F01" "p2F05" "p2F09" "p2F13" "p2F17" "p2F21" "p2J01" "p2J05" "p2J09"
[136] "p2J13" "p2J17" "p2J21" "p2N01" "p2N05" "p2N09" "p2N13" "p2N17" "p2N21"
[145] "p1A01" "p1A05" "p1A09" "p1A13" "p1A17" "p1A21" "p1E01" "p1E05" "p1E09"
[154] "p1E13" "p1E17" "p1E21" "p1I01" "p1I05" "p1I09" "p1I13" "p1I17" "p1I21"
[163] "p1M01" "p1M05" "p1M09" "p1M13" "p1M17" "p1M21" "p2A01" "p2A05" "p2A09"
[172] "p2A13" "p2A17" "p2A21" "p2E01" "p2E05" "p2E09" "p2E13" "p2E17" "p2E21"
[181] "p2I01" "p2I05" "p2I09" "p2I13" "p2I17" "p2I21" "p2M01" "p2M05" "p2M09"
[190] "p2M13" "p2M17" "p2M21" "p1D02" "p1D06" "p1D10" "p1D14" "p1D18" "p1D22"
[199] "p1H02" "p1H06" "p1H10" "p1H14" "p1H18" "p1H22" "p1L02" "p1L06" "p1L10"
[208] "p1L14" "p1L18" "p1L22" "p1P02" "p1P06" "p1P10" "p1P14" "p1P18" "p1P22"
[217] "p2D02" "p2D06" "p2D10" "p2D14" "p2D18" "p2D22" "p2H02" "p2H06" "p2H10"
[226] "p2H14" "p2H18" "p2H22" "p2L02" "p2L06" "p2L10" "p2L14" "p2L18" "p2L22"
[235] "p2P02" "p2P06" "p2P10" "p2P14" "p2P18" "p2P22" "p1C02" "p1C06" "p1C10"
[244] "p1C14" "p1C18" "p1C22" "p1G02" "p1G06" "p1G10" "p1G14" "p1G18" "p1G22"
[253] "p1K02" "p1K06" "p1K10" "p1K14" "p1K18" "p1K22" "p1O02" "p1O06" "p1O10"
[262] "p1O14" "p1O18" "p1O22" "p2C02" "p2C06" "p2C10" "p2C14" "p2C18" "p2C22"
[271] "p2G02" "p2G06" "p2G10" "p2G14" "p2G18" "p2G22" "p2K02" "p2K06" "p2K10"
[280] "p2K14" "p2K18" "p2K22" "p2O02" "p2O06" "p2O10" "p2O14" "p2O18" "p2O22"
[289] "p1B02" "p1B06" "p1B10" "p1B14" "p1B18" "p1B22" "p1F02" "p1F06" "p1F10"
[298] "p1F14" "p1F18" "p1F22" "p1J02" "p1J06" "p1J10" "p1J14" "p1J18" "p1J22"
[307] "p1N02" "p1N06" "p1N10" "p1N14" "p1N18" "p1N22" "p2B02" "p2B06" "p2B10"
[316] "p2B14" "p2B18" "p2B22" "p2F02" "p2F06" "p2F10" "p2F14" "p2F18" "p2F22"
[325] "p2J02" "p2J06" "p2J10" "p2J14" "p2J18" "p2J22" "p2N02" "p2N06" "p2N10"
[334] "p2N14" "p2N18" "p2N22" "p1A02" "p1A06" "p1A10" "p1A14" "p1A18" "p1A22"
[343] "p1E02" "p1E06" "p1E10" "p1E14" "p1E18" "p1E22" "p1I02" "p1I06" "p1I10"
[352] "p1I14" "p1I18" "p1I22" "p1M02" "p1M06" "p1M10" "p1M14" "p1M18" "p1M22"
[361] "p2A02" "p2A06" "p2A10" "p2A14" "p2A18" "p2A22" "p2E02" "p2E06" "p2E10"
[370] "p2E14" "p2E18" "p2E22" "p2I02" "p2I06" "p2I10" "p2I14" "p2I18" "p2I22"
[379] "p2M02" "p2M06" "p2M10" "p2M14" "p2M18" "p2M22" "p1D03" "p1D07" "p1D11"
[388] "p1D15" "p1D19" "p1D23" "p1H03" "p1H07" "p1H11" "p1H15" "p1H19" "p1H23"
[397] "p1L03" "p1L07" "p1L11" "p1L15" "p1L19" "p1L23" "p1P03" "p1P07" "p1P11"
[406] "p1P15" "p1P19" "p1P23" "p2D03" "p2D07" "p2D11" "p2D15" "p2D19" "p2D23"
[415] "p2H03" "p2H07" "p2H11" "p2H15" "p2H19" "p2H23" "p2L03" "p2L07" "p2L11"
[424] "p2L15" "p2L19" "p2L23" "p2P03" "p2P07" "p2P11" "p2P15" "p2P19" "p2P23"
[433] "p1C03" "p1C07" "p1C11" "p1C15" "p1C19" "p1C23" "p1G03" "p1G07" "p1G11"
[442] "p1G15" "p1G19" "p1G23" "p1K03" "p1K07" "p1K11" "p1K15" "p1K19" "p1K23"
[451] "p1O03" "p1O07" "p1O11" "p1O15" "p1O19" "p1O23" "p2C03" "p2C07" "p2C11"
[460] "p2C15" "p2C19" "p2C23" "p2G03" "p2G07" "p2G11" "p2G15" "p2G19" "p2G23"
[469] "p2K03" "p2K07" "p2K11" "p2K15" "p2K19" "p2K23" "p2O03" "p2O07" "p2O11"
[478] "p2O15" "p2O19" "p2O23" "p1B03" "p1B07" "p1B11" "p1B15" "p1B19" "p1B23"
[487] "p1F03" "p1F07" "p1F11" "p1F15" "p1F19" "p1F23" "p1J03" "p1J07" "p1J11"
[496] "p1J15" "p1J19" "p1J23" "p1N03" "p1N07" "p1N11" "p1N15" "p1N19" "p1N23"
[505] "p2B03" "p2B07" "p2B11" "p2B15" "p2B19" "p2B23" "p2F03" "p2F07" "p2F11"
[514] "p2F15" "p2F19" "p2F23" "p2J03" "p2J07" "p2J11" "p2J15" "p2J19" "p2J23"
[523] "p2N03" "p2N07" "p2N11" "p2N15" "p2N19" "p2N23" "p1A03" "p1A07" "p1A11"
[532] "p1A15" "p1A19" "p1A23" "p1E03" "p1E07" "p1E11" "p1E15" "p1E19" "p1E23"
[541] "p1I03" "p1I07" "p1I11" "p1I15" "p1I19" "p1I23" "p1M03" "p1M07" "p1M11"
[550] "p1M15" "p1M19" "p1M23" "p2A03" "p2A07" "p2A11" "p2A15" "p2A19" "p2A23"
[559] "p2E03" "p2E07" "p2E11" "p2E15" "p2E19" "p2E23" "p2I03" "p2I07" "p2I11"
[568] "p2I15" "p2I19" "p2I23" "p2M03" "p2M07" "p2M11" "p2M15" "p2M19" "p2M23"
[577] "p1D04" "p1D08" "p1D12" "p1D16" "p1D20" "p1D24" "p1H04" "p1H08" "p1H12"
[586] "p1H16" "p1H20" "p1H24" "p1L04" "p1L08" "p1L12" "p1L16" "p1L20" "p1L24"
[595] "p1P04" "p1P08" "p1P12" "p1P16" "p1P20" "p1P24" "p2D04" "p2D08" "p2D12"
[604] "p2D16" "p2D20" "p2D24" "p2H04" "p2H08" "p2H12" "p2H16" "p2H20" "p2H24"
[613] "p2L04" "p2L08" "p2L12" "p2L16" "p2L20" "p2L24" "p2P04" "p2P08" "p2P12"
[622] "p2P16" "p2P20" "p2P24" "p1C04" "p1C08" "p1C12" "p1C16" "p1C20" "p1C24"
[631] "p1G04" "p1G08" "p1G12" "p1G16" "p1G20" "p1G24" "p1K04" "p1K08" "p1K12"
[640] "p1K16" "p1K20" "p1K24" "p1O04" "p1O08" "p1O12" "p1O16" "p1O20" "p1O24"
[649] "p2C04" "p2C08" "p2C12" "p2C16" "p2C20" "p2C24" "p2G04" "p2G08" "p2G12"
[658] "p2G16" "p2G20" "p2G24" "p2K04" "p2K08" "p2K12" "p2K16" "p2K20" "p2K24"
[667] "p2O04" "p2O08" "p2O12" "p2O16" "p2O20" "p2O24" "p1B04" "p1B08" "p1B12"
[676] "p1B16" "p1B20" "p1B24" "p1F04" "p1F08" "p1F12" "p1F16" "p1F20" "p1F24"
[685] "p1J04" "p1J08" "p1J12" "p1J16" "p1J20" "p1J24" "p1N04" "p1N08" "p1N12"
[694] "p1N16" "p1N20" "p1N24" "p2B04" "p2B08" "p2B12" "p2B16" "p2B20" "p2B24"
[703] "p2F04" "p2F08" "p2F12" "p2F16" "p2F20" "p2F24" "p2J04" "p2J08" "p2J12"
[712] "p2J16" "p2J20" "p2J24" "p2N04" "p2N08" "p2N12" "p2N16" "p2N20" "p2N24"
[721] "p1A04" "p1A08" "p1A12" "p1A16" "p1A20" "p1A24" "p1E04" "p1E08" "p1E12"
[730] "p1E16" "p1E20" "p1E24" "p1I04" "p1I08" "p1I12" "p1I16" "p1I20" "p1I24"
[739] "p1M04" "p1M08" "p1M12" "p1M16" "p1M20" "p1M24" "p2A04" "p2A08" "p2A12"
[748] "p2A16" "p2A20" "p2A24" "p2E04" "p2E08" "p2E12" "p2E16" "p2E20" "p2E24"
[757] "p2I04" "p2I08" "p2I12" "p2I16" "p2I20" "p2I24" "p2M04" "p2M08" "p2M12"
[766] "p2M16" "p2M20" "p2M24"

> 
> ### merge.rglist
> 
> R <- G <- matrix(11:14,4,2)
> rownames(R) <- rownames(G) <- c("a","a","b","c")
> RG1 <- new("RGList",list(R=R,G=G))
> R <- G <- matrix(21:24,4,2)
> rownames(R) <- rownames(G) <- c("b","a","a","c")
> RG2 <- new("RGList",list(R=R,G=G))
> merge(RG1,RG2)
An object of class "RGList"
$R
  [,1] [,2] [,3] [,4]
a   11   11   22   22
a   12   12   23   23
b   13   13   21   21
c   14   14   24   24

$G
  [,1] [,2] [,3] [,4]
a   11   11   22   22
a   12   12   23   23
b   13   13   21   21
c   14   14   24   24

> merge(RG2,RG1)
An object of class "RGList"
$R
  [,1] [,2] [,3] [,4]
b   21   21   13   13
a   22   22   11   11
a   23   23   12   12
c   24   24   14   14

$G
  [,1] [,2] [,3] [,4]
b   21   21   13   13
a   22   22   11   11
a   23   23   12   12
c   24   24   14   14

> 
> ### background correction
> 
> RG <- new("RGList", list(R=c(1,2,3,4),G=c(1,2,3,4),Rb=c(2,2,2,2),Gb=c(2,2,2,2)))
> backgroundCorrect(RG)
An object of class "RGList"
$R
     [,1]
[1,]   -1
[2,]    0
[3,]    1
[4,]    2

$G
     [,1]
[1,]   -1
[2,]    0
[3,]    1
[4,]    2

> backgroundCorrect(RG, method="half")
An object of class "RGList"
$R
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

$G
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

> backgroundCorrect(RG, method="minimum")
An object of class "RGList"
$R
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

$G
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

> backgroundCorrect(RG, offset=5)
An object of class "RGList"
$R
     [,1]
[1,]    4
[2,]    5
[3,]    6
[4,]    7

$G
     [,1]
[1,]    4
[2,]    5
[3,]    6
[4,]    7

> 
> ### loessFit
> 
> x <- 1:100
> y <- rnorm(100)
> out <- loessFit(y,x)
> f1 <- quantile(out$fitted)
> r1 <- quantile(out$residuals)
> w <- rep(1,100)
> w[1:50] <- 0.5
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f2 <- quantile(out$fitted)
> r2 <- quantile(out$residuals)
> out <- loessFit(y,x,weights=w,method="locfit")
> f3 <- quantile(out$fitted)
> r3 <- quantile(out$residuals)
> out <- loessFit(y,x,weights=w,method="loess")
> f4 <- quantile(out$fitted)
> r4 <- quantile(out$residuals)
> w <- rep(1,100)
> w[2*(1:50)] <- 0
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f5 <- quantile(out$fitted)
> r5 <- quantile(out$residuals)
> data.frame(f1,f2,f3,f4,f5)
              f1           f2          f3          f4          f5
0%   -0.78835384 -0.687432210 -0.78957137 -0.76756060 -0.63778292
25%  -0.18340154 -0.179683572 -0.18979269 -0.16773223 -0.38064318
50%  -0.11492924 -0.114796040 -0.12087983 -0.07185314 -0.15971879
75%   0.01507921 -0.008145125 -0.01857508  0.04030634  0.07839396
100%  0.21653837  0.145106033  0.19214597  0.21417361  0.51836274
> data.frame(r1,r2,r3,r4,r5)
              r1          r2          r3           r4          r5
0%   -2.04434053 -2.05132680 -2.02404318 -2.101242874 -2.22280633
25%  -0.59321065 -0.57200209 -0.58975649 -0.577887481 -0.71037756
50%   0.05874864  0.04514326  0.08335198 -0.001769806  0.06785517
75%   0.56010750  0.55124530  0.57618740  0.561454370  0.65383830
100%  2.57936026  2.64549799  2.57549257  2.402324533  2.28648835
> 
> ### normalizeWithinArrays
> 
> RG <- new("RGList",list())
> RG$R <- matrix(rexp(100*2),100,2)
> RG$G <- matrix(rexp(100*2),100,2)
> RG$Rb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RG$Gb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="saddle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
       V1                V2                V3               V4        
 Min.   :0.01626   Min.   :0.01213   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.35497   1st Qu.:0.29133   1st Qu.:0.2745   1st Qu.:0.3953  
 Median :0.71793   Median :0.70294   Median :0.6339   Median :0.8223  
 Mean   :0.90184   Mean   :1.00122   Mean   :0.9454   Mean   :1.1324  
 3rd Qu.:1.16891   3rd Qu.:1.33139   3rd Qu.:1.4059   3rd Qu.:1.4221  
 Max.   :4.56267   Max.   :6.37947   Max.   :5.0486   Max.   :6.6295  
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="mle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
       V1                V2                V3               V4        
 Min.   :0.01701   Min.   :0.01255   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.35423   1st Qu.:0.29118   1st Qu.:0.2745   1st Qu.:0.3953  
 Median :0.71719   Median :0.70280   Median :0.6339   Median :0.8223  
 Mean   :0.90118   Mean   :1.00110   Mean   :0.9454   Mean   :1.1324  
 3rd Qu.:1.16817   3rd Qu.:1.33124   3rd Qu.:1.4059   3rd Qu.:1.4221  
 Max.   :4.56193   Max.   :6.37932   Max.   :5.0486   Max.   :6.6295  
> MA <- normalizeWithinArrays(RGb,method="loess")
> summary(MA$M)
       V1                 V2          
 Min.   :-5.88044   Min.   :-5.66985  
 1st Qu.:-1.18483   1st Qu.:-1.57014  
 Median :-0.21632   Median : 0.04823  
 Mean   : 0.03487   Mean   :-0.05481  
 3rd Qu.: 1.49669   3rd Qu.: 1.45113  
 Max.   : 7.07324   Max.   : 6.19744  
> #MA <- normalizeWithinArrays(RG[,1:2], mouse.setup, method="robustspline")
> #MA$M[1:5,]
> #MA <- normalizeWithinArrays(mouse.data, mouse.setup)
> #MA$M[1:5,]
> 
> ### normalizeBetweenArrays
> 
> MA2 <- normalizeBetweenArrays(MA,method="scale")
> MA$M[1:5,]
           [,1]       [,2]
[1,] -1.1689588  4.5558123
[2,]  0.8971363  0.3296544
[3,]  2.8247439  1.4249960
[4,] -1.8533240  0.4804851
[5,]  1.9158459 -5.5087631
> MA$A[1:5,]
            [,1]       [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200  0.2071043
[4,]  0.09281027 -1.3880965
[5,]  0.22385828 -3.0855818
> MA2 <- normalizeBetweenArrays(MA,method="quantile")
> MA$M[1:5,]
           [,1]       [,2]
[1,] -1.1689588  4.5558123
[2,]  0.8971363  0.3296544
[3,]  2.8247439  1.4249960
[4,] -1.8533240  0.4804851
[5,]  1.9158459 -5.5087631
> MA$A[1:5,]
            [,1]       [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200  0.2071043
[4,]  0.09281027 -1.3880965
[5,]  0.22385828 -3.0855818
> 
> ### unwrapdups
> 
> M <- matrix(1:12,6,2)
> unwrapdups(M,ndups=1)
     [,1] [,2]
[1,]    1    7
[2,]    2    8
[3,]    3    9
[4,]    4   10
[5,]    5   11
[6,]    6   12
> unwrapdups(M,ndups=2)
     [,1] [,2] [,3] [,4]
[1,]    1    2    7    8
[2,]    3    4    9   10
[3,]    5    6   11   12
> unwrapdups(M,ndups=3)
     [,1] [,2] [,3] [,4] [,5] [,6]
[1,]    1    2    3    7    8    9
[2,]    4    5    6   10   11   12
> unwrapdups(M,ndups=2,spacing=3)
     [,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    2    5    8   11
[3,]    3    6    9   12
> 
> ### trigammaInverse
> 
> trigammaInverse(c(1e-6,NA,5,1e6))
[1] 1.000000e+06           NA 4.961687e-01 1.000001e-03
> 
> ### lmFit, eBayes, topTable
> 
> M <- matrix(rnorm(10*6,sd=0.3),10,6)
> rownames(M) <- LETTERS[1:10]
> M[1,1:3] <- M[1,1:3] + 2
> design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
> contrast.matrix <- cbind(First3=c(1,0),Last3=c(0,1),"Last3-First3"=c(-1,1))
> fit <- lmFit(M,design)
> fit2 <- eBayes(contrasts.fit(fit,contrasts=contrast.matrix))
> topTable(fit2)
       First3       Last3 Last3.First3      AveExpr           F      P.Value
A  1.77602021  0.06025114  -1.71576906  0.918135675 50.91471061 7.727200e-23
D -0.05454069  0.39127869   0.44581938  0.168369004  2.51638838 8.075072e-02
F -0.16249607 -0.33009728  -0.16760121 -0.246296671  2.18256779 1.127516e-01
G  0.30852468 -0.06873462  -0.37725930  0.119895035  1.61088775 1.997102e-01
H -0.16942269  0.20578118   0.37520387  0.018179245  1.14554368 3.180510e-01
J  0.21417623  0.07074940  -0.14342683  0.142462814  0.82029274 4.403027e-01
C -0.12236781  0.15095948   0.27332729  0.014295836  0.60885003 5.439761e-01
B -0.11982833  0.13529287   0.25512120  0.007732271  0.52662792 5.905931e-01
E  0.01897934  0.10434934   0.08536999  0.061664340  0.18136849 8.341279e-01
I -0.04720963  0.03996397   0.08717360 -0.003622829  0.06168476 9.401792e-01
     adj.P.Val
A 7.727200e-22
D 3.758388e-01
F 3.758388e-01
G 4.992756e-01
H 6.361019e-01
J 7.338379e-01
C 7.382414e-01
B 7.382414e-01
E 9.268088e-01
I 9.401792e-01
> topTable(fit2,coef=3,resort.by="logFC")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="p")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,sort.by="logFC",resort.by="t")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="B")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,lfc=1)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p.value=0.2)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p.value=0.2,lfc=0.5)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p.value=0.2,lfc=0.5,sort.by="none")
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> contrasts.fit(fit[1:3,],contrast.matrix[,0])
An object of class "MArrayLM"
$coefficients
 
A
B
C

$rank
[1] 2

$assign
NULL

$qr
$qr
     First3Arrays Last3Arrays
[1,]   -1.7320508   0.0000000
[2,]    0.5773503  -1.7320508
[3,]    0.5773503   0.0000000
[4,]    0.0000000   0.5773503
[5,]    0.0000000   0.5773503
[6,]    0.0000000   0.5773503

$qraux
[1] 1.57735 1.00000

$pivot
[1] 1 2

$tol
[1] 1e-07

$rank
[1] 2


$df.residual
[1] 4 4 4

$sigma
        A         B         C 
0.3299787 0.3323336 0.2315815 

$cov.coefficients
<0 x 0 matrix>

$stdev.unscaled
 
A
B
C

$pivot
[1] 1 2

$Amean
          A           B           C 
0.918135675 0.007732271 0.014295836 

$method
[1] "ls"

$design
     First3Arrays Last3Arrays
[1,]            1           0
[2,]            1           0
[3,]            1           0
[4,]            0           1
[5,]            0           1
[6,]            0           1

$contrasts
    
[1,]
[2,]

> fit$coefficients[1,1] <- NA
> contrasts.fit(fit[1:3,],contrast.matrix)$coefficients
      First3      Last3 Last3-First3
A         NA 0.06025114           NA
B -0.1198283 0.13529287    0.2551212
C -0.1223678 0.15095948    0.2733273
> 
> designlist <- list(Null=matrix(1,6,1),Two=design,Three=cbind(1,c(0,0,1,1,0,0),c(0,0,0,0,1,1)))
> out <- selectModel(M,designlist)
> table(out$pref)

 Null   Two Three 
    5     3     2 
> 
> ### marray object
> 
> #suppressMessages(suppressWarnings(gotmarray <- require(marray,quietly=TRUE)))
> #if(gotmarray) {
> #	data(swirl)
> #	snorm = maNorm(swirl)
> #	fit <- lmFit(snorm, design = c(1,-1,-1,1))
> #	fit <- eBayes(fit)
> #	topTable(fit,resort.by="AveExpr")
> #}
> 
> ### duplicateCorrelation
> 
> cor.out <- duplicateCorrelation(M)
> cor.out$consensus.correlation
[1] -0.09290714
> cor.out$atanh.correlations
[1] -0.4419130  0.4088967 -0.1964978 -0.6093769  0.3730118
> 
> ### gls.series
> 
> fit <- gls.series(M,design,correlation=cor.out$cor)
> fit$coefficients
     First3Arrays Last3Arrays
[1,]   0.82809594  0.09777201
[2,]  -0.08845425  0.27111909
[3,]  -0.07175836 -0.11287397
[4,]   0.06955100  0.06852328
[5,]   0.08348330  0.05535668
> fit$stdev.unscaled
     First3Arrays Last3Arrays
[1,]    0.3888215   0.3888215
[2,]    0.3888215   0.3888215
[3,]    0.3888215   0.3888215
[4,]    0.3888215   0.3888215
[5,]    0.3888215   0.3888215
> fit$sigma
[1] 0.7630059 0.2152728 0.3350370 0.3227781 0.3405473
> fit$df.residual
[1] 10 10 10 10 10
> 
> ### mrlm
> 
> fit <- mrlm(M,design)
Warning message:
In rlm.default(x = X, y = y, weights = w, ...) :
  'rlm' failed to converge in 20 steps
> fit$coefficients
  First3Arrays Last3Arrays
A   1.75138894  0.06025114
B  -0.11982833  0.10322039
C  -0.09302502  0.15095948
D  -0.05454069  0.33700045
E   0.07927938  0.10434934
F  -0.16249607 -0.34010852
G   0.30852468 -0.06873462
H  -0.16942269  0.24392984
I  -0.04720963  0.03996397
J   0.21417623 -0.05679272
> fit$stdev.unscaled
  First3Arrays Last3Arrays
A    0.5933418   0.5773503
B    0.5773503   0.6096497
C    0.6017444   0.5773503
D    0.5773503   0.6266021
E    0.6307703   0.5773503
F    0.5773503   0.5846707
G    0.5773503   0.5773503
H    0.5773503   0.6544564
I    0.5773503   0.5773503
J    0.5773503   0.6689776
> fit$sigma
 [1] 0.2894294 0.2679396 0.2090236 0.1461395 0.2309018 0.2827476 0.2285945
 [8] 0.2267556 0.3537469 0.2172409
> fit$df.residual
 [1] 4 4 4 4 4 4 4 4 4 4
> 
> # Similar to Mette Langaas 19 May 2004
> set.seed(123)
> narrays <- 9
> ngenes <- 5
> mu <- 0
> alpha <- 2
> beta <- -2
> epsilon <- matrix(rnorm(narrays*ngenes,0,1),ncol=narrays)
> X <- cbind(rep(1,9),c(0,0,0,1,1,1,0,0,0),c(0,0,0,0,0,0,1,1,1))
> dimnames(X) <- list(1:9,c("mu","alpha","beta"))
> yvec <- mu*X[,1]+alpha*X[,2]+beta*X[,3]
> ymat <- matrix(rep(yvec,ngenes),ncol=narrays,byrow=T)+epsilon
> ymat[5,1:2] <- NA
> fit <- lmFit(ymat,design=X)
> test.contr <- cbind(c(0,1,-1),c(1,1,0),c(1,0,1))
> dimnames(test.contr) <- list(c("mu","alpha","beta"),c("alpha-beta","mu+alpha","mu+beta"))
> fit2 <- contrasts.fit(fit,contrasts=test.contr)
> eBayes(fit2)
An object of class "MArrayLM"
$coefficients
     alpha-beta mu+alpha   mu+beta
[1,]   3.537333 1.677465 -1.859868
[2,]   4.355578 2.372554 -1.983024
[3,]   3.197645 1.053584 -2.144061
[4,]   2.697734 1.611443 -1.086291
[5,]   3.502304 2.051995 -1.450309

$stdev.unscaled
     alpha-beta  mu+alpha   mu+beta
[1,]  0.8164966 0.5773503 0.5773503
[2,]  0.8164966 0.5773503 0.5773503
[3,]  0.8164966 0.5773503 0.5773503
[4,]  0.8164966 0.5773503 0.5773503
[5,]  1.1547005 0.8368633 0.8368633

$sigma
[1] 1.3425032 0.4647155 1.1993444 0.9428569 0.9421509

$df.residual
[1] 6 6 6 6 4

$cov.coefficients
           alpha-beta     mu+alpha       mu+beta
alpha-beta  0.6666667 3.333333e-01 -3.333333e-01
mu+alpha    0.3333333 3.333333e-01  5.551115e-17
mu+beta    -0.3333333 5.551115e-17  3.333333e-01

$pivot
[1] 1 2 3

$rank
[1] 3

$Amean
[1]  0.2034961  0.1954604 -0.2863347  0.1188659  0.1784593

$method
[1] "ls"

$design
  mu alpha beta
1  1     0    0
2  1     0    0
3  1     0    0
4  1     1    0
5  1     1    0
6  1     1    0
7  1     0    1
8  1     0    1
9  1     0    1

$contrasts
      alpha-beta mu+alpha mu+beta
mu             0        1       1
alpha          1        1       0
beta          -1        0       1

$df.prior
[1] 9.306153

$s2.prior
[1] 0.923179

$var.prior
[1] 17.33142 17.33142 12.26855

$proportion
[1] 0.01

$s2.post
[1] 1.2677996 0.6459499 1.1251558 0.9097727 0.9124980

$t
     alpha-beta mu+alpha   mu+beta
[1,]   3.847656 2.580411 -2.860996
[2,]   6.637308 5.113018 -4.273553
[3,]   3.692066 1.720376 -3.500994
[4,]   3.464003 2.926234 -1.972606
[5,]   3.175181 2.566881 -1.814221

$df.total
[1] 15.30615 15.30615 15.30615 15.30615 13.30615

$p.value
       alpha-beta     mu+alpha      mu+beta
[1,] 1.529450e-03 0.0206493481 0.0117123495
[2,] 7.144893e-06 0.0001195844 0.0006385076
[3,] 2.109270e-03 0.1055117477 0.0031325769
[4,] 3.381970e-03 0.0102514264 0.0668844448
[5,] 7.124839e-03 0.0230888584 0.0922478630

$lods
     alpha-beta  mu+alpha    mu+beta
[1,]  -1.013417 -3.702133 -3.0332393
[2,]   3.981496  1.283349 -0.2615911
[3,]  -1.315036 -5.168621 -1.7864101
[4,]  -1.757103 -3.043209 -4.6191869
[5,]  -2.257358 -3.478267 -4.5683738

$F
[1]  7.421911 22.203107  7.608327  6.227010  5.060579

$F.p.value
[1] 5.581800e-03 2.988923e-05 5.080726e-03 1.050148e-02 2.320274e-02

> 
> ### uniquegenelist
> 
> uniquegenelist(letters[1:8],ndups=2)
[1] "a" "c" "e" "g"
> uniquegenelist(letters[1:8],ndups=2,spacing=2)
[1] "a" "b" "e" "f"
> 
> ### classifyTests
> 
> tstat <- matrix(c(0,5,0, 0,2.5,0, -2,-2,2, 1,1,1), 4, 3, byrow=TRUE)
> classifyTestsF(tstat)
TestResults matrix
     [,1] [,2] [,3]
[1,]    0    1    0
[2,]    0    0    0
[3,]   -1   -1    1
[4,]    0    0    0
> classifyTestsF(tstat,fstat.only=TRUE)
[1] 8.333333 2.083333 4.000000 1.000000
attr(,"df1")
[1] 3
attr(,"df2")
[1] Inf
> limma:::.classifyTestsP(tstat)
TestResults matrix
     [,1] [,2] [,3]
[1,]    0    1    0
[2,]    0    1    0
[3,]    0    0    0
[4,]    0    0    0
> 
> ### avereps
> 
> x <- matrix(rnorm(8*3),8,3)
> colnames(x) <- c("S1","S2","S3")
> rownames(x) <- c("b","a","a","c","c","b","b","b")
> avereps(x)
          S1         S2         S3
b -0.2353018  0.5220094  0.2302895
a -0.4347701  0.6453498 -0.6758914
c  0.3482980 -0.4820695 -0.3841313
> 
> ### roast
> 
> y <- matrix(rnorm(100*4),100,4)
> sigma <- sqrt(2/rchisq(100,df=7))
> y <- y*sigma
> design <- cbind(Intercept=1,Group=c(0,0,1,1))
> iset1 <- 1:5
> y[iset1,3:4] <- y[iset1,3:4]+3
> iset2 <- 6:10
> roast(y=y,iset1,design,contrast=2)
         Active.Prop     P.Value
Down               0 0.997999500
Up                 1 0.002250563
UpOrDown           1 0.004500000
Mixed              1 0.004500000
> roast(y=y,iset1,design,contrast=2,array.weights=c(0.5,1,0.5,1))
         Active.Prop     P.Value
Down               0 0.998749687
Up                 1 0.001500375
UpOrDown           1 0.003000000
Mixed              1 0.003000000
> w <- matrix(runif(100*4),100,4)
> roast(y=y,iset1,design,contrast=2,weights=w)
         Active.Prop     P.Value
Down               0 0.996999250
Up                 1 0.003250813
UpOrDown           1 0.006500000
Mixed              1 0.006500000
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,gene.weights=runif(100))
     NGenes PropDown PropUp Direction PValue    FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up 0.0055 0.0105       0.0055    0.0105
set2      5        0      0        Up 0.2025 0.2025       0.4715    0.4715
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,array.weights=c(0.5,1,0.5,1))
     NGenes PropDown PropUp Direction PValue    FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up 0.0050 0.0095        0.005    0.0095
set2      5        0      0        Up 0.6845 0.6845        0.642    0.6420
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w)
     NGenes PropDown PropUp Direction PValue    FDR PValue.Mixed FDR.Mixed
set1      5        0    1.0        Up 0.0030 0.0055        0.003    0.0055
set2      5        0    0.2      Down 0.9615 0.9615        0.496    0.4960
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes PropDown PropUp Direction PValue    FDR PValue.Mixed FDR.Mixed
set1      5        0    1.0        Up 0.0025 0.0045       0.0025    0.0045
set2      5        0    0.2      Down 0.8930 0.8930       0.4380    0.4380
> fry(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes Direction      PValue         FDR PValue.Mixed    FDR.Mixed
set1      5        Up 0.001568924 0.003137848 0.0001156464 0.0002312929
set2      5      Down 0.932105219 0.932105219 0.4315499569 0.4315499569
> rownames(y) <- paste0("Gene",1:100)
> iset1A <- rownames(y)[1:5]
> fry(y=y,index=iset1A,design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes Direction      PValue PValue.Mixed
set1      5        Up 0.001568924 0.0001156464
> 
> ### camera
> 
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1),allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction      PValue
set1      5  -0.2481655        Up 0.001050253
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction       PValue        FDR
set1      5  -0.2481655        Up 0.0009047749 0.00180955
set2      5   0.1719094      Down 0.9068364378 0.90683644
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1))
     NGenes Direction       PValue
set1      5        Up 1.105329e-10
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2)
     NGenes Direction       PValue          FDR
set1      5        Up 7.334400e-12 1.466880e-11
set2      5      Down 8.677115e-01 8.677115e-01
> camera(y=y,iset1A,design,contrast=2)
     NGenes Direction     PValue
set1      5        Up 7.3344e-12
> 
> ### with EList arg
> 
> y <- new("EList",list(E=y))
> roast(y=y,iset1,design,contrast=2)
         Active.Prop     P.Value
Down               0 0.996999250
Up                 1 0.003250813
UpOrDown           1 0.006500000
Mixed              1 0.006500000
> camera(y=y,iset1,design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction       PValue
set1      5  -0.2481655        Up 0.0009047749
> camera(y=y,iset1,design,contrast=2)
     NGenes Direction     PValue
set1      5        Up 7.3344e-12
> 
> ### eBayes with trend
> 
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene2   3.729512  1.73488969  4.865697 0.0004854886 0.02902331  0.1596831
Gene3   3.488703  1.03931081  4.754954 0.0005804663 0.02902331 -0.0144071
Gene4   2.696676  1.74060725  3.356468 0.0063282637 0.21094212 -2.3434702
Gene1   2.391846  1.72305203  3.107124 0.0098781268 0.24695317 -2.7738874
Gene33 -1.492317 -0.07525287 -2.783817 0.0176475742 0.29965463 -3.3300835
Gene5   2.387967  1.63066783  2.773444 0.0179792778 0.29965463 -3.3478204
Gene80 -1.839760 -0.32802306 -2.503584 0.0291489863 0.37972679 -3.8049642
Gene39  1.366141 -0.27360750  2.451133 0.0320042242 0.37972679 -3.8925860
Gene95 -1.907074  1.26297763 -2.414217 0.0341754107 0.37972679 -3.9539571
Gene50  1.034777  0.01608433  2.054690 0.0642289403 0.59978803 -4.5350317
> fit$df.prior
[1] 9.098442
> fit$s2.prior
    Gene1     Gene2     Gene3     Gene4     Gene5     Gene6     Gene7     Gene8 
0.6901845 0.6977354 0.3860494 0.7014122 0.6341068 0.2926337 0.3077620 0.3058098 
    Gene9    Gene10    Gene11    Gene12    Gene13    Gene14    Gene15    Gene16 
0.2985145 0.2832520 0.3232434 0.3279710 0.2816081 0.2943502 0.3127994 0.2894802 
   Gene17    Gene18    Gene19    Gene20    Gene21    Gene22    Gene23    Gene24 
0.2812758 0.2840051 0.2839124 0.2954261 0.2838592 0.2812704 0.3157029 0.2844541 
   Gene25    Gene26    Gene27    Gene28    Gene29    Gene30    Gene31    Gene32 
0.4778832 0.2818242 0.2930360 0.2940957 0.2941862 0.3234399 0.3164779 0.2853510 
   Gene33    Gene34    Gene35    Gene36    Gene37    Gene38    Gene39    Gene40 
0.2988244 0.3450090 0.3048596 0.3089086 0.3104534 0.4551549 0.3220008 0.2813286 
   Gene41    Gene42    Gene43    Gene44    Gene45    Gene46    Gene47    Gene48 
0.2826027 0.2822504 0.2823330 0.3170673 0.3146173 0.3146793 0.2916540 0.2975003 
   Gene49    Gene50    Gene51    Gene52    Gene53    Gene54    Gene55    Gene56 
0.3538946 0.2907240 0.3199596 0.2816641 0.2814293 0.2996822 0.2812885 0.2896157 
   Gene57    Gene58    Gene59    Gene60    Gene61    Gene62    Gene63    Gene64 
0.2955317 0.2815907 0.2919420 0.2849675 0.3540805 0.3491713 0.2975019 0.2939325 
   Gene65    Gene66    Gene67    Gene68    Gene69    Gene70    Gene71    Gene72 
0.2986943 0.3265466 0.3402343 0.3394927 0.2813283 0.2814440 0.3089669 0.3030850 
   Gene73    Gene74    Gene75    Gene76    Gene77    Gene78    Gene79    Gene80 
0.2859286 0.2813216 0.3475231 0.3334419 0.2949550 0.3108702 0.2959688 0.3295294 
   Gene81    Gene82    Gene83    Gene84    Gene85    Gene86    Gene87    Gene88 
0.3413700 0.2946268 0.3029565 0.2920284 0.2926205 0.2818046 0.3425116 0.2882936 
   Gene89    Gene90    Gene91    Gene92    Gene93    Gene94    Gene95    Gene96 
0.2945459 0.3077919 0.2892134 0.2823787 0.3048049 0.2961408 0.4590012 0.2812784 
   Gene97    Gene98    Gene99   Gene100 
0.2846345 0.2819651 0.3137551 0.2856081 
> summary(fit$s2.post)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2335  0.2603  0.2997  0.3375  0.3655  0.7812 
> 
> y$E[1,1] <- NA
> y$E[1,3] <- NA
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene3   3.488703  1.03931081  4.604490 0.0007644061 0.07644061 -0.2333915
Gene2   3.729512  1.73488969  4.158038 0.0016033158 0.08016579 -0.9438583
Gene4   2.696676  1.74060725  2.898102 0.0145292666 0.44537707 -3.0530813
Gene33 -1.492317 -0.07525287 -2.784004 0.0178150826 0.44537707 -3.2456324
Gene5   2.387967  1.63066783  2.495395 0.0297982959 0.46902627 -3.7272957
Gene80 -1.839760 -0.32802306 -2.491115 0.0300256116 0.46902627 -3.7343584
Gene39  1.366141 -0.27360750  2.440729 0.0328318388 0.46902627 -3.8172597
Gene1   2.638272  1.47993643  2.227507 0.0530016060 0.58890673 -3.9537576
Gene95 -1.907074  1.26297763 -2.288870 0.0429197808 0.53649726 -4.0642439
Gene50  1.034777  0.01608433  2.063663 0.0635275235 0.60439978 -4.4204731
> fit$df.residual[1]
[1] 0
> fit$df.prior
[1] 8.971891
> fit$s2.prior
  [1] 0.7014084 0.9646561 0.4276287 0.9716476 0.8458852 0.2910492 0.3097052
  [8] 0.3074225 0.2985517 0.2786374 0.3267121 0.3316013 0.2766404 0.2932679
 [15] 0.3154347 0.2869186 0.2761395 0.2799884 0.2795119 0.2946468 0.2794412
 [22] 0.2761282 0.3186442 0.2806092 0.4596465 0.2767847 0.2924541 0.2939204
 [29] 0.2930568 0.3269177 0.3194905 0.2814293 0.2989389 0.3483845 0.3062977
 [36] 0.3110287 0.3127934 0.4418052 0.3254067 0.2761732 0.2780422 0.2773311
 [43] 0.2776653 0.3201314 0.3174515 0.3175199 0.2897731 0.2972785 0.3567262
 [50] 0.2885556 0.3232426 0.2767207 0.2762915 0.3000062 0.2761306 0.2870975
 [57] 0.2947817 0.2766152 0.2901489 0.2813183 0.3568982 0.3724440 0.2972804
 [64] 0.2927300 0.2987764 0.3301406 0.3437962 0.3430762 0.2761729 0.2763094
 [71] 0.3110958 0.3041715 0.2822004 0.2761654 0.3507694 0.3371214 0.2940441
 [78] 0.3132660 0.2953388 0.3331880 0.3448949 0.2946558 0.3040162 0.2902616
 [85] 0.2910320 0.2769211 0.3459946 0.2859057 0.2935193 0.3097398 0.2865663
 [92] 0.2774968 0.3062327 0.2955576 0.5425422 0.2761214 0.2808585 0.2771484
 [99] 0.3164981 0.2817725
> summary(fit$s2.post)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2296  0.2581  0.3003  0.3453  0.3652  0.9158 
> 
> ### eBayes with robust
> 
> fitr <- lmFit(y,design)
> fitr <- eBayes(fitr,robust=TRUE)
> summary(fitr$df.prior)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  6.717   9.244   9.244   9.194   9.244   9.244 
> topTable(fitr,coef=2)
           logFC     AveExpr         t      P.Value   adj.P.Val          B
Gene2   3.729512  1.73488969  7.108463 1.752774e-05 0.001752774  3.3517310
Gene3   3.488703  1.03931081  5.041209 3.526138e-04 0.017630688  0.4056329
Gene4   2.696676  1.74060725  4.697690 6.150508e-04 0.020501693 -0.1463315
Gene5   2.387967  1.63066783  3.451807 5.245019e-03 0.131125480 -2.2678836
Gene1   2.638272  1.47993643  3.317593 8.651142e-03 0.173022847 -2.4400000
Gene33 -1.492317 -0.07525287 -2.716431 1.970991e-02 0.297950865 -3.5553166
Gene95 -1.907074  1.26297763 -2.685067 2.085656e-02 0.297950865 -3.6094982
Gene80 -1.839760 -0.32802306 -2.535926 2.727440e-02 0.340929958 -3.8653107
Gene39  1.366141 -0.27360750  2.469570 3.071854e-02 0.341317083 -3.9779817
Gene50  1.034777  0.01608433  1.973040 7.357960e-02 0.632875126 -4.7877548
> fitr <- eBayes(fitr,trend=TRUE,robust=TRUE)
> summary(fitr$df.prior)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  7.809   8.972   8.972   8.949   8.972   8.972 
> topTable(fitr,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene2   3.729512  1.73488969  4.754160 0.0005999064 0.05999064 -0.0218247
Gene3   3.488703  1.03931081  3.761219 0.0031618743 0.15809372 -1.6338257
Gene4   2.696676  1.74060725  3.292262 0.0071993347 0.23997782 -2.4295326
Gene33 -1.492317 -0.07525287 -3.063180 0.0108203134 0.27050784 -2.8211394
Gene50  1.034777  0.01608433  2.645717 0.0228036320 0.38815282 -3.5304767
Gene5   2.387967  1.63066783  2.633901 0.0232891695 0.38815282 -3.5503445
Gene1   2.638272  1.47993643  2.204116 0.0550613420 0.58959402 -4.0334169
Gene80 -1.839760 -0.32802306 -2.332729 0.0397331916 0.56761702 -4.0496640
Gene39  1.366141 -0.27360750  2.210665 0.0492211477 0.58959402 -4.2469578
Gene95 -1.907074  1.26297763 -2.106861 0.0589594023 0.58959402 -4.4117140
> 
> ### voom
> 
> y <- matrix(rpois(100*4,lambda=20),100,4)
> design <- cbind(Int=1,x=c(0,0,1,1))
> v <- voom(y,design)
> names(v)
[1] "E"       "weights" "design"  "targets"
> summary(v$E)
       V1              V2              V3              V4       
 Min.   :12.38   Min.   :12.32   Min.   :12.17   Min.   :12.08  
 1st Qu.:13.11   1st Qu.:13.05   1st Qu.:13.11   1st Qu.:13.03  
 Median :13.34   Median :13.28   Median :13.35   Median :13.35  
 Mean   :13.29   Mean   :13.29   Mean   :13.28   Mean   :13.28  
 3rd Qu.:13.48   3rd Qu.:13.54   3rd Qu.:13.48   3rd Qu.:13.50  
 Max.   :14.01   Max.   :13.95   Max.   :14.03   Max.   :14.05  
> summary(v$weights)
       V1               V2               V3               V4        
 Min.   : 7.729   Min.   : 7.729   Min.   : 7.729   Min.   : 7.729  
 1st Qu.:13.859   1st Qu.:15.067   1st Qu.:14.254   1st Qu.:13.592  
 Median :15.913   Median :16.621   Median :16.081   Median :16.028  
 Mean   :16.773   Mean   :18.525   Mean   :18.472   Mean   :17.112  
 3rd Qu.:18.214   3rd Qu.:20.002   3rd Qu.:18.475   3rd Qu.:18.398  
 Max.   :34.331   Max.   :34.331   Max.   :34.331   Max.   :34.331  
> 
> ### goana
> 
> EB <- c("133746","1339","134","1340","134083","134111","134147","134187","134218","134266",
+ "134353","134359","134391","134429","134430","1345","134510","134526","134549","1346",
+ "134637","1347","134701","134728","1348","134829","134860","134864","1349","134957",
+ "135","1350","1351","135112","135114","135138","135152","135154","1352","135228",
+ "135250","135293","135295","1353","135458","1355","1356","135644","135656","1357",
+ "1358","135892","1359","135924","135935","135941","135946","135948","136","1360",
+ "136051","1361","1362","136227","136242","136259","1363","136306","136319","136332",
+ "136371","1364","1365","136541","1366","136647","1368","136853","1369","136991",
+ "1370","137075","1371","137209","1373","137362","1374","137492","1375","1376",
+ "137682","137695","137735","1378","137814","137868","137872","137886","137902","137964")
> go <- goana(fit,FDR=0.8,geneid=EB)
> topGO(go,number=10,truncate.term=30)
                                     Term Ont  N Up Down        P.Up
GO:0070062          extracellular exosome  CC  8  0    4 1.000000000
GO:0043230        extracellular organelle  CC  8  0    4 1.000000000
GO:1903561          extracellular vesicle  CC  8  0    4 1.000000000
GO:0040011                     locomotion  BP  7  5    0 0.006651547
GO:0032502          developmental process  BP 23  4    6 0.844070086
GO:0032501 multicellular organismal pr...  BP 31  7    7 0.620372627
GO:0006915              apoptotic process  BP  5  4    1 0.009503355
GO:0012501          programmed cell death  BP  5  4    1 0.009503355
GO:0042981 regulation of apoptotic pro...  BP  5  4    1 0.009503355
GO:0040012       regulation of locomotion  BP  5  4    0 0.009503355
                P.Down
GO:0070062 0.003047199
GO:0043230 0.003047199
GO:1903561 0.003047199
GO:0040011 1.000000000
GO:0032502 0.009014340
GO:0032501 0.009111120
GO:0006915 0.416247633
GO:0012501 0.416247633
GO:0042981 0.416247633
GO:0040012 1.000000000
> topGO(go,number=10,truncate.term=30,sort="down")
                                     Term Ont  N Up Down      P.Up      P.Down
GO:0070062          extracellular exosome  CC  8  0    4 1.0000000 0.003047199
GO:0043230        extracellular organelle  CC  8  0    4 1.0000000 0.003047199
GO:1903561          extracellular vesicle  CC  8  0    4 1.0000000 0.003047199
GO:0032502          developmental process  BP 23  4    6 0.8440701 0.009014340
GO:0032501 multicellular organismal pr...  BP 31  7    7 0.6203726 0.009111120
GO:0031982                        vesicle  CC 18  1    5 0.9946677 0.015552466
GO:0051604             protein maturation  BP  7  1    3 0.8497705 0.020760307
GO:0016485             protein processing  BP  7  1    3 0.8497705 0.020760307
GO:0009887     animal organ morphogenesis  BP  3  0    2 1.0000000 0.025788497
GO:0055082 cellular chemical homeostas...  BP  3  1    2 0.5476190 0.025788497
> 
> proc.time()
   user  system elapsed 
   5.06    0.23    5.28 

Example timings

limma.Rcheck/examples_i386/limma-Ex.timings

nameusersystemelapsed
EList0.080.000.08
LargeDataObject000
PrintLayout000
TestResults000
alias2Symbol3.750.274.01
arrayWeights000
arrayWeightsQuick000
asMatrixWeights000
auROC000
avearrays000
avereps000
backgroundcorrect000
barcodeplot0.030.000.03
beadCountWeights000
blockDiag000
camera0.010.000.01
cbind0.030.000.03
changelog0.130.170.41
channel2M000
chooseLowessSpan0.030.000.03
classifytestsF000
contrastAsCoef0.010.000.02
contrasts.fit0.020.000.01
controlStatus0.020.000.02
coolmap0.250.030.28
cumOverlap000
detectionPValue000
diffSplice000
dim000
dupcor0.170.000.17
ebayes0.010.000.02
fitGammaIntercept000
fitfdist000
fitmixture0.030.000.03
genas0.130.000.12
geneSetTest000
getSpacing000
getlayout000
goana000
head000
heatdiagram000
helpMethods000
ids2indices000
imageplot0.110.000.11
intraspotCorrelation000
isfullrank000
isnumeric000
kooperberg000
limmaUsersGuide000
lm.series000
lmFit0.600.000.59
lmscFit000
loessfit0.010.000.02
logcosh0.000.000.01
logsumexp000
ma3x3000
makeContrasts000
makeunique000
mdplot0.020.000.02
merge000
mergeScansRG000
modelMatrix0.010.000.01
modifyWeights000
nec000
normalizeMedianAbsValues000
normalizeRobustSpline0.000.010.02
normalizeVSN0.720.150.86
normalizebetweenarrays000
normalizeprintorder000
normexpfit000
normexpfitcontrol000
normexpfitdetectionp000
normexpsignal000
plotDensities000
plotExonJunc000
plotExons000
plotMD0.110.030.14
plotMDS0.010.010.04
plotRLDF000
plotSplice000
plotWithHighlights0.040.000.03
plotma0.140.000.14
poolvar000
predFCm0.020.000.02
printorder0.010.000.01
printtipWeights000
propTrueNull000
propexpr000
protectMetachar000
qqt0.020.000.02
qualwt000
rankSumTestwithCorrelation0.010.000.02
read.idat000
read.ilmn000
read.maimages000
readImaGeneHeader000
readgal000
removeBatchEffect0.020.000.01
removeext000
roast0.060.030.09
romer0.050.000.05
selectmodel0.010.000.02
squeezeVar000
strsplit2000
subsetting000
targetsA2C000
topGO000
topRomer000
topSplice000
toptable000
tricubeMovingAverage000
trigammainverse000
trimWhiteSpace000
uniquegenelist000
unwrapdups000
venn0.020.020.03
volcanoplot000
voom000
weightedLowess000
weightedmedian000
writefit000
zscore000
zscoreT000

limma.Rcheck/examples_x64/limma-Ex.timings

nameusersystemelapsed
EList0.080.000.08
LargeDataObject000
PrintLayout000
TestResults000
alias2Symbol4.250.114.36
arrayWeights0.020.000.01
arrayWeightsQuick000
asMatrixWeights000
auROC000
avearrays000
avereps000
backgroundcorrect000
barcodeplot0.060.000.06
beadCountWeights000
blockDiag000
camera0.030.000.04
cbind000
changelog0.140.140.28
channel2M000
chooseLowessSpan000
classifytestsF0.010.000.02
contrastAsCoef000
contrasts.fit0.020.000.01
controlStatus000
coolmap0.140.000.14
cumOverlap000
detectionPValue000
diffSplice000
dim000
dupcor0.120.020.15
ebayes0.020.000.01
fitGammaIntercept000
fitfdist000
fitmixture0.020.000.02
genas0.090.000.09
geneSetTest000
getSpacing0.010.000.02
getlayout000
goana000
head000
heatdiagram000
helpMethods000
ids2indices000
imageplot0.100.000.09
intraspotCorrelation000
isfullrank000
isnumeric000
kooperberg000
limmaUsersGuide000
lm.series000
lmFit0.640.000.64
lmscFit000
loessfit000
logcosh000
logsumexp000
ma3x3000
makeContrasts0.020.000.02
makeunique000
mdplot000
merge000
mergeScansRG000
modelMatrix0.010.000.01
modifyWeights000
nec000
normalizeMedianAbsValues000
normalizeRobustSpline0.000.010.02
normalizeVSN0.490.100.58
normalizebetweenarrays0.030.000.03
normalizeprintorder000
normexpfit000
normexpfitcontrol000
normexpfitdetectionp000
normexpsignal000
plotDensities000
plotExonJunc000
plotExons000
plotMD0.080.010.09
plotMDS0.010.000.02
plotRLDF0.020.000.01
plotSplice000
plotWithHighlights0.010.000.02
plotma0.110.000.11
poolvar000
predFCm0.020.000.01
printorder000
printtipWeights000
propTrueNull000
propexpr000
protectMetachar000
qqt000
qualwt000
rankSumTestwithCorrelation000
read.idat000
read.ilmn000
read.maimages000
readImaGeneHeader000
readgal000
removeBatchEffect0.010.000.02
removeext000
roast0.050.000.05
romer0.020.000.01
selectmodel0.010.000.02
squeezeVar000
strsplit2000
subsetting000
targetsA2C000
topGO0.020.000.01
topRomer000
topSplice000
toptable000
tricubeMovingAverage000
trigammainverse000
trimWhiteSpace000
uniquegenelist000
unwrapdups000
venn0.030.000.04
volcanoplot000
voom000
weightedLowess000
weightedmedian000
writefit000
zscore000
zscoreT000