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CHECK report for limma on merida1

This page was generated on 2020-04-15 12:33:49 -0400 (Wed, 15 Apr 2020).

Package 903/1823HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
limma 3.42.2
Gordon Smyth
Snapshot Date: 2020-04-14 16:46:13 -0400 (Tue, 14 Apr 2020)
URL: https://git.bioconductor.org/packages/limma
Branch: RELEASE_3_10
Last Commit: 4b763ee
Last Changed Date: 2020-02-02 20:58:28 -0400 (Sun, 02 Feb 2020)
malbec1 Linux (Ubuntu 18.04.4 LTS) / x86_64  OK  OK  OK UNNEEDED, same version exists in internal repository
tokay1 Windows Server 2012 R2 Standard / x64  OK  OK  OK  OK UNNEEDED, same version exists in internal repository
merida1 OS X 10.11.6 El Capitan / x86_64  OK  OK [ OK ] OK UNNEEDED, same version exists in internal repository

Summary

Package: limma
Version: 3.42.2
Command: /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD check --install=check:limma.install-out.txt --library=/Library/Frameworks/R.framework/Versions/Current/Resources/library --no-vignettes --timings limma_3.42.2.tar.gz
StartedAt: 2020-04-15 03:24:31 -0400 (Wed, 15 Apr 2020)
EndedAt: 2020-04-15 03:26:20 -0400 (Wed, 15 Apr 2020)
EllapsedTime: 109.3 seconds
RetCode: 0
Status:  OK 
CheckDir: limma.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD check --install=check:limma.install-out.txt --library=/Library/Frameworks/R.framework/Versions/Current/Resources/library --no-vignettes --timings limma_3.42.2.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.10-bioc/meat/limma.Rcheck’
* using R version 3.6.3 (2020-02-29)
* using platform: x86_64-apple-darwin15.6.0 (64-bit)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘limma/DESCRIPTION’ ... OK
* this is package ‘limma’ version ‘3.42.2’
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘limma’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking compiled code ... NOTE
Note: information on .o files is not available
* checking sizes of PDF files under ‘inst/doc’ ... OK
* checking installed files from ‘inst/doc’ ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  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: 1 NOTE
See
  ‘/Users/biocbuild/bbs-3.10-bioc/meat/limma.Rcheck/00check.log’
for details.

Installation output

limma.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD INSTALL limma
###
##############################################################################
##############################################################################


* installing to library ‘/Library/Frameworks/R.framework/Versions/3.6/Resources/library’
* installing *source* package ‘limma’ ...
** using staged installation
** libs
clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX.sdk -I/usr/local/include  -fPIC  -Wall -g -O2  -c init.c -o init.o
clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX.sdk -I/usr/local/include  -fPIC  -Wall -g -O2  -c normexp.c -o normexp.o
clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX.sdk -I/usr/local/include  -fPIC  -Wall -g -O2  -c weighted_lowess.c -o weighted_lowess.o
clang -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -single_module -multiply_defined suppress -L/Library/Frameworks/R.framework/Resources/lib -L/usr/local/lib -o limma.so init.o normexp.o weighted_lowess.o -F/Library/Frameworks/R.framework/.. -framework R -Wl,-framework -Wl,CoreFoundation
installing to /Library/Frameworks/R.framework/Versions/3.6/Resources/library/00LOCK-limma/00new/limma/libs
** R
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
   ‘intro.Rnw’ 
** testing if installed package can be loaded from temporary location
** checking absolute paths in shared objects and dynamic libraries
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (limma)

Tests output

limma.Rcheck/tests/limma-Tests.Rout


R version 3.6.3 (2020-02-29) -- "Holding the Windsock"
Copyright (C) 2020 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin15.6.0 (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)
> 
> 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$fitted)
> r1 <- quantile(out$residual)
> w <- rep(1,100)
> w[1:50] <- 0.5
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f2 <- quantile(out$fitted)
> r2 <- quantile(out$residual)
> out <- loessFit(y,x,weights=w,method="locfit")
> f3 <- quantile(out$fitted)
> r3 <- quantile(out$residual)
> out <- loessFit(y,x,weights=w,method="loess")
> f4 <- quantile(out$fitted)
> r4 <- quantile(out$residual)
> w <- rep(1,100)
> w[2*(1:50)] <- 0
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f5 <- quantile(out$fitted)
> r5 <- quantile(out$residual)
> 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="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=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=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=0.2,lfc=0.5,sort="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

$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$coefficients[1,1] <- NA
> contrasts.fit(fit[1:3,],contrast.matrix)$coef
      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$df.residual
[1] 10 10 10 10 10
> 
> ### mrlm
> 
> fit <- mrlm(M,design)
Warning message:
In rlm.default(x = X, y = y, weights = w, ...) :
  'rlm' failed to converge in 20 steps
> fit$coef
  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

$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,n=10,truncate.term=30)
                                     Term Ont  N Up Down        P.Up
GO:0032502          developmental process  BP 26  4    7 0.914470437
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:0032501 multicellular organismal pr...  BP 30  6    7 0.762808313
GO:0098609             cell-cell adhesion  BP  5  4    0 0.009503355
GO:0097190    apoptotic signaling pathway  BP  3  3    0 0.010952381
GO:0031252              cell leading edge  CC  3  3    0 0.010952381
GO:0006897                    endocytosis  BP  3  3    0 0.010952381
GO:0098657               import into cell  BP  3  3    0 0.010952381
                P.Down
GO:0032502 0.002720775
GO:0070062 0.003047199
GO:0043230 0.003047199
GO:1903561 0.003047199
GO:0032501 0.007313910
GO:0098609 1.000000000
GO:0097190 1.000000000
GO:0031252 1.000000000
GO:0006897 1.000000000
GO:0098657 1.000000000
> topGO(go,n=10,truncate.term=30,sort="down")
                                     Term Ont  N Up Down      P.Up      P.Down
GO:0032502          developmental process  BP 26  4    7 0.9144704 0.002720775
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:0032501 multicellular organismal pr...  BP 30  6    7 0.7628083 0.007313910
GO:0048856 anatomical structure develo...  BP 25  4    6 0.8946593 0.014492712
GO:0031982                        vesicle  CC 18  1    5 0.9946677 0.015552466
GO:0007275 multicellular organism deve...  BP 20  3    5 0.8982815 0.025464546
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 
  4.890   0.368   5.258

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


R Under development (unstable) (2020-01-25 r77715) -- "Unsuffered Consequences"
Copyright (C) 2020 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.

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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)
> 
> 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$fitted)
> r1 <- quantile(out$residual)
> w <- rep(1,100)
> w[1:50] <- 0.5
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f2 <- quantile(out$fitted)
> r2 <- quantile(out$residual)
> out <- loessFit(y,x,weights=w,method="locfit")
> f3 <- quantile(out$fitted)
> r3 <- quantile(out$residual)
> out <- loessFit(y,x,weights=w,method="loess")
> f4 <- quantile(out$fitted)
> r4 <- quantile(out$residual)
> w <- rep(1,100)
> w[2*(1:50)] <- 0
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f5 <- quantile(out$fitted)
> r5 <- quantile(out$residual)
> 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="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=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=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=0.2,lfc=0.5,sort="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

$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$coefficients[1,1] <- NA
> contrasts.fit(fit[1:3,],contrast.matrix)$coef
      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$df.residual
[1] 10 10 10 10 10
> 
> ### mrlm
> 
> fit <- mrlm(M,design)
Warning message:
In rlm.default(x = X, y = y, weights = w, ...) :
  'rlm' failed to converge in 20 steps
> fit$coef
  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

$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,n=10,truncate.term=30)
                                     Term Ont  N Up Down        P.Up
GO:0032502          developmental process  BP 26  4    7 0.914470437
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:0032501 multicellular organismal pr...  BP 30  6    7 0.762808313
GO:0098609             cell-cell adhesion  BP  5  4    0 0.009503355
GO:0097190    apoptotic signaling pathway  BP  3  3    0 0.010952381
GO:0031252              cell leading edge  CC  3  3    0 0.010952381
GO:0006897                    endocytosis  BP  3  3    0 0.010952381
GO:0098657               import into cell  BP  3  3    0 0.010952381
                P.Down
GO:0032502 0.002720775
GO:0070062 0.003047199
GO:0043230 0.003047199
GO:1903561 0.003047199
GO:0032501 0.007313910
GO:0098609 1.000000000
GO:0097190 1.000000000
GO:0031252 1.000000000
GO:0006897 1.000000000
GO:0098657 1.000000000
> topGO(go,n=10,truncate.term=30,sort="down")
                                     Term Ont  N Up Down      P.Up      P.Down
GO:0032502          developmental process  BP 26  4    7 0.9144704 0.002720775
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:0032501 multicellular organismal pr...  BP 30  6    7 0.7628083 0.007313910
GO:0048856 anatomical structure develo...  BP 25  4    6 0.8946593 0.014492712
GO:0031982                        vesicle  CC 18  1    5 0.9946677 0.015552466
GO:0007275 multicellular organism deve...  BP 20  3    5 0.8982815 0.025464546
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 
   2.48    0.21    2.71

Example timings

limma.Rcheck/limma-Ex.timings

nameusersystemelapsed
LargeDataObject0.0000.0010.000
PrintLayout0.0000.0000.001
TestResults000
alias2Symbol4.1690.1984.381
arrayWeights0.0620.0040.066
arrayWeightsQuick000
asMatrixWeights0.0010.0000.001
auROC0.0010.0000.002
avearrays0.0020.0000.002
avereps0.0000.0000.001
backgroundcorrect0.0080.0010.008
barcodeplot0.0490.0030.052
beadCountWeights000
blockDiag0.0010.0000.000
camera0.0320.0020.034
cbind0.0040.0010.005
changelog0.0010.0000.001
channel2M0.0010.0010.002
classifytestsF0.0040.0000.004
contrastAsCoef0.0040.0000.004
contrasts.fit0.0130.0000.013
controlStatus0.0050.0000.006
coolmap0.1530.0120.166
cumOverlap0.0010.0000.001
detectionPValue0.0000.0000.001
diffSplice000
dim0.0010.0000.001
dupcor0.4370.0080.445
ebayes0.0120.0010.013
fitGammaIntercept0.0000.0000.001
fitfdist0.0010.0000.001
fitmixture0.0180.0010.020
genas0.0910.0040.096
geneSetTest0.0010.0000.002
getSpacing0.0010.0000.001
getlayout000
goana0.0000.0010.001
head0.0060.0010.006
heatdiagram0.0000.0000.001
helpMethods000
ids2indices0.0000.0010.000
imageplot0.0300.0020.031
intraspotCorrelation0.0000.0000.001
isfullrank0.0000.0000.001
isnumeric0.0010.0000.001
kooperberg0.0000.0010.001
limmaUsersGuide0.0010.0000.001
lm.series000
lmFit0.3660.0040.372
lmscFit0.0000.0000.001
loessfit0.0060.0010.006
logcosh0.0010.0000.000
logsumexp0.0010.0010.001
ma3x30.0010.0000.001
makeContrasts0.0010.0000.001
makeunique0.0010.0000.001
mdplot0.0030.0000.003
merge0.0050.0000.006
mergeScansRG0.0000.0010.000
modelMatrix0.0020.0000.001
modifyWeights0.0000.0000.001
nec0.0000.0010.000
normalizeMedianAbsValues0.0010.0000.001
normalizeRobustSpline0.0210.0020.024
normalizeVSN0.8040.0320.838
normalizebetweenarrays0.0030.0000.003
normalizeprintorder000
normexpfit0.0020.0010.002
normexpfitcontrol0.0010.0000.000
normexpfitdetectionp000
normexpsignal0.0000.0000.001
plotDensities000
plotExonJunc0.0010.0010.000
plotExons0.0000.0000.001
plotMD0.0280.0030.031
plotMDS0.0150.0020.018
plotRLDF0.0070.0010.008
plotSplice0.0000.0000.001
plotWithHighlights0.0080.0010.009
plotma0.0240.0020.027
poolvar0.0010.0000.002
predFCm0.0200.0020.021
printorder0.0100.0070.016
printtipWeights0.0000.0000.001
propTrueNull0.0020.0010.002
propexpr000
protectMetachar0.0010.0000.000
qqt0.0020.0010.002
qualwt000
rankSumTestwithCorrelation0.0090.0010.010
read.idat0.0000.0000.001
read.ilmn000
read.maimages000
readImaGeneHeader000
readgal000
removeBatchEffect0.0160.0010.017
removeext0.0010.0000.001
roast0.1090.0060.116
romer0.0190.0010.021
selectmodel0.0070.0010.008
squeezeVar0.0010.0000.001
strsplit20.0010.0000.001
subsetting0.0030.0010.003
targetsA2C0.0050.0000.005
topGO0.0010.0010.000
topRomer000
topSplice000
toptable000
tricubeMovingAverage0.0020.0010.003
trigammainverse000
trimWhiteSpace0.0010.0000.001
uniquegenelist0.0000.0000.001
unwrapdups0.0010.0000.001
venn0.0200.0000.021
volcanoplot000
voom000
weightedLowess0.0060.0010.007
weightedmedian000
zscore0.0010.0000.002
zscoreT0.0010.0000.000