Gordon Smyth
| Snapshot Date: 2019-04-12 17:01:30 -0400 (Fri, 12 Apr 2019) |
| URL: https://git.bioconductor.org/packages/limma |
| Branch: RELEASE_3_8 |
| Last Commit: 77b292e |
| Last Changed Date: 2018-12-01 21:04:26 -0400 (Sat, 01 Dec 2018) |
| Back to Multiple platform build/check report for BioC 3.8 |
|
This page was generated on 2019-04-13 11:19:14 -0400 (Sat, 13 Apr 2019).
| Package 832/1649 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||
| limma 3.38.3 Gordon Smyth
| malbec1 | Linux (Ubuntu 16.04.6 LTS) / x86_64 | OK | OK | OK | | ||||||
| tokay1 | Windows Server 2012 R2 Standard / x64 | OK | OK | [ OK ] | OK | | ||||||
| merida1 | OS X 10.11.6 El Capitan / x86_64 | OK | OK | OK | OK | |
| Package: limma |
| Version: 3.38.3 |
| Command: C:\Users\biocbuild\bbs-3.8-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:limma.install-out.txt --library=C:\Users\biocbuild\bbs-3.8-bioc\R\library --no-vignettes --timings limma_3.38.3.tar.gz |
| StartedAt: 2019-04-13 03:23:16 -0400 (Sat, 13 Apr 2019) |
| EndedAt: 2019-04-13 03:25:54 -0400 (Sat, 13 Apr 2019) |
| EllapsedTime: 158.7 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: limma.Rcheck |
| Warnings: 0 |
############################################################################## ############################################################################## ### ### Running command: ### ### C:\Users\biocbuild\bbs-3.8-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:limma.install-out.txt --library=C:\Users\biocbuild\bbs-3.8-bioc\R\library --no-vignettes --timings limma_3.38.3.tar.gz ### ############################################################################## ############################################################################## * using log directory 'C:/Users/biocbuild/bbs-3.8-bioc/meat/limma.Rcheck' * using R version 3.5.3 (2019-03-11) * using platform: x86_64-w64-mingw32 (64-bit) * using session charset: ISO8859-1 * using option '--no-vignettes' * checking for file 'limma/DESCRIPTION' ... OK * this is package 'limma' version '3.38.3' * checking package namespace information ... OK * checking package dependencies ...Warning: unable to access index for repository https://CRAN.R-project.org/src/contrib: cannot open URL 'https://CRAN.R-project.org/src/contrib/PACKAGES' OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking whether package 'limma' can be installed ... OK * checking installed package size ... 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 * loading checks for arch 'i386' ** checking whether the package can be loaded ... OK ** checking whether the package can be loaded with stated dependencies ... OK ** checking whether the package can be unloaded cleanly ... OK ** checking whether the namespace can be loaded with stated dependencies ... OK ** checking whether the namespace can be unloaded cleanly ... OK * loading checks for arch 'x64' ** checking whether the package can be loaded ... OK ** checking whether the package can be loaded with stated dependencies ... OK ** checking whether the package can be unloaded cleanly ... OK ** checking whether the namespace can be loaded with stated dependencies ... OK ** checking whether the namespace can be unloaded cleanly ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking line endings in C/C++/Fortran sources/headers ... OK * checking compiled code ... NOTE Note: information on .o files for i386 is not available Note: information on .o files for x64 is not available File 'C:/Users/biocbuild/bbs-3.8-bioc/R/library/limma/libs/i386/limma.dll': Found 'abort', possibly from 'abort' (C), 'runtime' (Fortran) Compiled code should not call entry points which might terminate R nor write to stdout/stderr instead of to the console, nor use Fortran I/O nor system RNGs. The detected symbols are linked into the code but might come from libraries and not actually be called. See 'Writing portable packages' in the 'Writing R Extensions' manual. * checking installed files from 'inst/doc' ... OK * checking files in 'vignettes' ... OK * checking examples ... ** running examples for arch 'i386' ... OK ** running examples for arch 'x64' ... OK * checking for unstated dependencies in 'tests' ... OK * checking tests ... ** running tests for arch 'i386' ... Running 'limma-Tests.R' Comparing 'limma-Tests.Rout' to 'limma-Tests.Rout.save' ...595,599c595,599 < 0% -0.78835384 -0.687432210 -0.78957137 -0.75758558 -0.63778292 < 25% -0.18340154 -0.179683572 -0.18979269 -0.16363329 -0.38064318 < 50% -0.11492924 -0.114796040 -0.12087983 -0.07318718 -0.15971879 < 75% 0.01507921 -0.008145125 -0.01857508 0.03656491 0.07839396 < 100% 0.21653837 0.145106033 0.19214597 0.23710498 0.51836274 --- > 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 602,606c602,606 < 0% -2.04434053 -2.05132680 -2.02404318 -2.09602100 -2.22280633 < 25% -0.59321065 -0.57200209 -0.58975649 -0.58142533 -0.71037756 < 50% 0.05874864 0.04514326 0.08335198 -0.01037007 0.06785517 < 75% 0.56010750 0.55124530 0.57618740 0.55704748 0.65383830 < 100% 2.57936026 2.64549799 2.57549257 2.38180448 2.28648835 --- > 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 644,649c644,649 < Min. :-5.82498 Min. :-5.69877 < 1st Qu.:-1.19140 1st Qu.:-1.55421 < Median :-0.19318 Median : 0.06267 < Mean : 0.08691 Mean :-0.05369 < 3rd Qu.: 1.48646 3rd Qu.: 1.41900 < Max. : 7.16195 Max. : 6.28902 --- > 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 660,664c660,664 < [1,] -1.0618269 4.5343276 < [2,] 0.8507603 0.3495635 < [3,] 2.7703696 1.4459533 < [4,] -1.8511286 0.4894799 < [5,] 1.9180276 -5.5363732 --- > [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 675,679c675,679 < [1,] -1.0618269 4.5343276 < [2,] 0.8507603 0.3495635 < [3,] 2.7703696 1.4459533 < [4,] -1.8511286 0.4894799 < [5,] 1.9180276 -5.5363732 --- > [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 940,941c940,941 < mu+alpha 0.3333333 3.333333e-01 -1.464215e-16 < mu+beta -0.3333333 -1.464215e-16 3.333333e-01 --- > mu+alpha 0.3333333 3.333333e-01 5.551115e-17 > mu+beta -0.3333333 5.551115e-17 3.333333e-01 OK ** running tests for arch 'x64' ... Running 'limma-Tests.R' Comparing 'limma-Tests.Rout' to 'limma-Tests.Rout.save' ... OK OK * checking for unstated dependencies in vignettes ... OK * checking package vignettes in 'inst/doc' ... OK * checking running R code from vignettes ... SKIPPED * checking re-building of vignette outputs ... SKIPPED * checking PDF version of manual ... OK * DONE Status: 1 NOTE See 'C:/Users/biocbuild/bbs-3.8-bioc/meat/limma.Rcheck/00check.log' for details.
limma.Rcheck/00install.out
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###
### Running command:
###
### C:\cygwin\bin\curl.exe -O https://malbec1.bioconductor.org/BBS/3.8/bioc/src/contrib/limma_3.38.3.tar.gz && rm -rf limma.buildbin-libdir && mkdir limma.buildbin-libdir && C:\Users\biocbuild\bbs-3.8-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=limma.buildbin-libdir limma_3.38.3.tar.gz && C:\Users\biocbuild\bbs-3.8-bioc\R\bin\R.exe CMD INSTALL limma_3.38.3.zip && rm limma_3.38.3.tar.gz limma_3.38.3.zip
###
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% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
100 1410k 100 1410k 0 0 9.9M 0 --:--:-- --:--:-- --:--:-- 10.2M
install for i386
* installing *source* package 'limma' ...
** libs
C:/Rtools/mingw_32/bin/gcc -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/extsoft/include" -O3 -Wall -std=gnu99 -mtune=generic -c init.c -o init.o
C:/Rtools/mingw_32/bin/gcc -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/extsoft/include" -O3 -Wall -std=gnu99 -mtune=generic -c normexp.c -o normexp.o
C:/Rtools/mingw_32/bin/gcc -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/extsoft/include" -O3 -Wall -std=gnu99 -mtune=generic -c weighted_lowess.c -o weighted_lowess.o
C:/Rtools/mingw_32/bin/gcc -shared -s -static-libgcc -o limma.dll tmp.def init.o normexp.o weighted_lowess.o -LC:/extsoft/lib/i386 -LC:/extsoft/lib -LC:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/bin/i386 -lR
installing to C:/Users/biocbuild/bbs-3.8-bioc/meat/limma.buildbin-libdir/limma/libs/i386
** R
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
converting help for package 'limma'
finding HTML links ... done
01Introduction html
02classes html
03reading html
04Background html
05Normalization html
06linearmodels html
07SingleChannel html
08Tests html
09Diagnostics html
10GeneSetTests html
11RNAseq html
EList html
LargeDataObject html
PrintLayout html
TestResults html
alias2Symbol html
anova-method html
arrayWeights html
arrayWeightsQuick html
asMatrixWeights html
asdataframe html
asmalist html
asmatrix html
auROC html
avearrays html
avedups html
avereps html
backgroundcorrect html
barcodeplot html
beadCountWeights html
blockDiag html
bwss html
bwss.matrix html
camera html
cbind html
changelog html
channel2M html
classifytests html
contrastAsCoef html
contrasts.fit html
controlStatus html
coolmap html
cumOverlap html
decideTests html
detectionPValue html
diffSplice html
dim html
dimnames html
dupcor html
ebayes-deprecated html
ebayes html
exprsMA html
fitGammaIntercept html
fitfdist html
fitmixture html
fitted.MArrayLM html
genas html
geneSetTest html
getEAWP html
getSpacing html
getlayout html
gls.series html
goana html
gridspotrc html
heatdiagram html
helpMethods html
ids2indices html
imageplot html
imageplot3by2 html
intraspotCorrelation html
isfullrank html
isnumeric html
kooperberg html
limmaUsersGuide html
lm.series html
lmFit html
lmscFit html
loessfit html
logcosh html
logsumexp html
ma3x3 html
makeContrasts html
makeunique html
malist html
marraylm html
mdplot html
merge html
mergeScansRG html
modelMatrix html
modifyWeights html
mrlm html
nec html
normalizeCyclicLoess html
normalizeMedianAbsValues html
normalizeRobustSpline html
normalizeVSN html
normalizeWithinArrays html
normalizebetweenarrays html
normalizeprintorder html
normalizequantiles html
normexpfit html
normexpfitcontrol html
normexpfitdetectionp html
normexpsignal html
plotDensities html
plotExonJunc html
plotExons html
plotFB html
plotMD html
plotMDS html
plotRLDF html
plotSA html
plotSplice html
plotWithHighlights html
plotlines html
plotma html
plotma3by2 html
plotprinttiploess html
poolvar html
predFCm html
printHead html
printorder html
printtipWeights html
propTrueNull html
propexpr html
protectMetachar html
qqt html
qualwt html
rankSumTestwithCorrelation html
read.columns html
read.idat html
read.ilmn html
read.ilmn.targets html
read.maimages html
readGPRHeader html
readImaGeneHeader html
readSpotTypes html
readTargets html
readgal html
removeBatchEffect html
removeext html
residuals.MArrayLM html
rglist html
roast html
romer html
selectmodel html
squeezeVar html
strsplit2 html
subsetting html
summary html
targetsA2C html
tmixture html
topGO html
topRomer html
topSplice html
toptable html
tricubeMovingAverage html
trigammainverse html
trimWhiteSpace html
uniquegenelist html
unwrapdups html
venn html
volcanoplot html
voom html
voomWithQualityWeights html
vooma html
weightedLowess html
weightedmedian html
writefit html
wsva html
zscore html
** building package indices
** installing vignettes
'intro.Rnw'
** testing if installed package can be loaded
In R CMD INSTALL
install for x64
* installing *source* package 'limma' ...
** libs
C:/Rtools/mingw_64/bin/gcc -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/extsoft/include" -O2 -Wall -std=gnu99 -mtune=generic -c init.c -o init.o
C:/Rtools/mingw_64/bin/gcc -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/extsoft/include" -O2 -Wall -std=gnu99 -mtune=generic -c normexp.c -o normexp.o
C:/Rtools/mingw_64/bin/gcc -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG -I"C:/extsoft/include" -O2 -Wall -std=gnu99 -mtune=generic -c weighted_lowess.c -o weighted_lowess.o
C:/Rtools/mingw_64/bin/gcc -shared -s -static-libgcc -o limma.dll tmp.def init.o normexp.o weighted_lowess.o -LC:/extsoft/lib/x64 -LC:/extsoft/lib -LC:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/bin/x64 -lR
installing to C:/Users/biocbuild/bbs-3.8-bioc/meat/limma.buildbin-libdir/limma/libs/x64
** testing if installed package can be loaded
* MD5 sums
packaged installation of 'limma' as limma_3.38.3.zip
* DONE (limma)
In R CMD INSTALL
In R CMD INSTALL
* installing to library 'C:/Users/biocbuild/bbs-3.8-bioc/R/library'
package 'limma' successfully unpacked and MD5 sums checked
In R CMD INSTALL
|
limma.Rcheck/tests_i386/limma-Tests.Rout.save
R version 3.5.1 (2018-07-02) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(limma)
>
> 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
>
> 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
> FStat(tstat)
[1] 8.333333 2.083333 4.000000 1.000000
attr(,"df1")
[1] 3
attr(,"df2")
[1] Inf
> classifyTestsT(tstat)
TestResults matrix
[,1] [,2] [,3]
[1,] 0 1 0
[2,] 0 0 0
[3,] 0 0 0
[4,] 0 0 0
> 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 0.996498249
Up 1 0.004002001
UpOrDown 1 0.008000000
Mixed 1 0.008000000
> roast(y=y,iset1,design,contrast=2,array.weights=c(0.5,1,0.5,1))
Active.Prop P.Value
Down 0 0.99899950
Up 1 0.00150075
UpOrDown 1 0.00300000
Mixed 1 0.00300000
> w <- matrix(runif(100*4),100,4)
> roast(y=y,iset1,design,contrast=2,weights=w)
Active.Prop P.Value
Down 0 0.9994997
Up 1 0.0010005
UpOrDown 1 0.0020000
Mixed 1 0.0020000
> 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 0.008 0.015 0.008 0.015
set2 5 0 0 Up 0.959 0.959 0.687 0.687
> 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 0.004 0.007 0.004 0.007
set2 5 0 0 Up 0.679 0.679 0.658 0.658
> 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 0.0 1 Up 0.003 0.005 0.003 0.005
set2 5 0.2 0 Down 0.950 0.950 0.250 0.250
> 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 Up 0.001 0.001 0.001 0.001
set2 5 0 0 Down 0.791 0.791 0.146 0.146
> 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 0.0007432594 0.001486519 1.820548e-05 3.641096e-05
set2 5 Down 0.8208140511 0.820814051 2.211837e-01 2.211837e-01
> 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 0.0007432594 1.820548e-05
>
> ### 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 0.997498749
Up 1 0.003001501
UpOrDown 1 0.006000000
Mixed 1 0.006000000
> 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
>
> ### 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$E)
V1 V2 V3 V4
Min. :12.25 Min. :12.58 Min. :12.19 Min. :12.24
1st Qu.:13.13 1st Qu.:13.07 1st Qu.:13.15 1st Qu.:13.03
Median :13.29 Median :13.30 Median :13.30 Median :13.27
Mean :13.28 Mean :13.29 Mean :13.29 Mean :13.28
3rd Qu.:13.49 3rd Qu.:13.51 3rd Qu.:13.50 3rd Qu.:13.50
Max. :14.23 Max. :14.28 Max. :13.97 Max. :13.96
> summary(v$weights)
V1 V2 V3 V4
Min. : 5.935 Min. : 5.935 Min. : 5.935 Min. : 5.935
1st Qu.: 6.788 1st Qu.: 7.049 1st Qu.: 7.207 1st Qu.: 6.825
Median :11.066 Median :10.443 Median :10.606 Median :10.414
Mean :10.421 Mean :10.485 Mean :10.571 Mean :10.532
3rd Qu.:13.485 3rd Qu.:14.155 3rd Qu.:13.859 3rd Qu.:14.121
Max. :15.083 Max. :15.101 Max. :15.095 Max. :15.063
>
> ### 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: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:0072359 circulatory system developm... BP 2 0 2 1.000000000
GO:0007507 heart development BP 2 0 2 1.000000000
GO:0032501 multicellular organismal pr... BP 31 6 7 0.796992878
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
P.Down
GO:0070062 0.003047199
GO:0043230 0.003047199
GO:1903561 0.003047199
GO:0072359 0.009090909
GO:0007507 0.009090909
GO:0032501 0.009111120
GO:0098609 1.000000000
GO:0097190 1.000000000
GO:0031252 1.000000000
GO:0006897 1.000000000
> topGO(go,n=10,truncate.term=30,sort="down")
Term Ont N Up Down P.Up P.Down
GO:0070062 extracellular exosome CC 8 0 4 1.0000000 0.003047199
GO:0043230 extracellular organelle CC 8 0 4 1.0000000 0.003047199
GO:1903561 extracellular vesicle CC 8 0 4 1.0000000 0.003047199
GO:0072359 circulatory system developm... BP 2 0 2 1.0000000 0.009090909
GO:0007507 heart development BP 2 0 2 1.0000000 0.009090909
GO:0032501 multicellular organismal pr... BP 31 6 7 0.7969929 0.009111120
GO:0032502 developmental process BP 25 4 6 0.8946593 0.014492712
GO:0031982 vesicle CC 18 1 5 0.9946677 0.015552466
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
2.37 0.26 2.93
|
limma.Rcheck/tests_x64/limma-Tests.Rout.save
R version 3.5.1 (2018-07-02) -- "Feather Spray"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(limma)
>
> 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
>
> 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
> FStat(tstat)
[1] 8.333333 2.083333 4.000000 1.000000
attr(,"df1")
[1] 3
attr(,"df2")
[1] Inf
> classifyTestsT(tstat)
TestResults matrix
[,1] [,2] [,3]
[1,] 0 1 0
[2,] 0 0 0
[3,] 0 0 0
[4,] 0 0 0
> 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 0.996498249
Up 1 0.004002001
UpOrDown 1 0.008000000
Mixed 1 0.008000000
> roast(y=y,iset1,design,contrast=2,array.weights=c(0.5,1,0.5,1))
Active.Prop P.Value
Down 0 0.99899950
Up 1 0.00150075
UpOrDown 1 0.00300000
Mixed 1 0.00300000
> w <- matrix(runif(100*4),100,4)
> roast(y=y,iset1,design,contrast=2,weights=w)
Active.Prop P.Value
Down 0 0.9994997
Up 1 0.0010005
UpOrDown 1 0.0020000
Mixed 1 0.0020000
> 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 0.008 0.015 0.008 0.015
set2 5 0 0 Up 0.959 0.959 0.687 0.687
> 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 0.004 0.007 0.004 0.007
set2 5 0 0 Up 0.679 0.679 0.658 0.658
> 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 0.0 1 Up 0.003 0.005 0.003 0.005
set2 5 0.2 0 Down 0.950 0.950 0.250 0.250
> 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 Up 0.001 0.001 0.001 0.001
set2 5 0 0 Down 0.791 0.791 0.146 0.146
> 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 0.0007432594 0.001486519 1.820548e-05 3.641096e-05
set2 5 Down 0.8208140511 0.820814051 2.211837e-01 2.211837e-01
> 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 0.0007432594 1.820548e-05
>
> ### 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 0.997498749
Up 1 0.003001501
UpOrDown 1 0.006000000
Mixed 1 0.006000000
> 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
>
> ### 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$E)
V1 V2 V3 V4
Min. :12.25 Min. :12.58 Min. :12.19 Min. :12.24
1st Qu.:13.13 1st Qu.:13.07 1st Qu.:13.15 1st Qu.:13.03
Median :13.29 Median :13.30 Median :13.30 Median :13.27
Mean :13.28 Mean :13.29 Mean :13.29 Mean :13.28
3rd Qu.:13.49 3rd Qu.:13.51 3rd Qu.:13.50 3rd Qu.:13.50
Max. :14.23 Max. :14.28 Max. :13.97 Max. :13.96
> summary(v$weights)
V1 V2 V3 V4
Min. : 5.935 Min. : 5.935 Min. : 5.935 Min. : 5.935
1st Qu.: 6.788 1st Qu.: 7.049 1st Qu.: 7.207 1st Qu.: 6.825
Median :11.066 Median :10.443 Median :10.606 Median :10.414
Mean :10.421 Mean :10.485 Mean :10.571 Mean :10.532
3rd Qu.:13.485 3rd Qu.:14.155 3rd Qu.:13.859 3rd Qu.:14.121
Max. :15.083 Max. :15.101 Max. :15.095 Max. :15.063
>
> ### 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: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:0072359 circulatory system developm... BP 2 0 2 1.000000000
GO:0007507 heart development BP 2 0 2 1.000000000
GO:0032501 multicellular organismal pr... BP 31 6 7 0.796992878
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
P.Down
GO:0070062 0.003047199
GO:0043230 0.003047199
GO:1903561 0.003047199
GO:0072359 0.009090909
GO:0007507 0.009090909
GO:0032501 0.009111120
GO:0098609 1.000000000
GO:0097190 1.000000000
GO:0031252 1.000000000
GO:0006897 1.000000000
> topGO(go,n=10,truncate.term=30,sort="down")
Term Ont N Up Down P.Up P.Down
GO:0070062 extracellular exosome CC 8 0 4 1.0000000 0.003047199
GO:0043230 extracellular organelle CC 8 0 4 1.0000000 0.003047199
GO:1903561 extracellular vesicle CC 8 0 4 1.0000000 0.003047199
GO:0072359 circulatory system developm... BP 2 0 2 1.0000000 0.009090909
GO:0007507 heart development BP 2 0 2 1.0000000 0.009090909
GO:0032501 multicellular organismal pr... BP 31 6 7 0.7969929 0.009111120
GO:0032502 developmental process BP 25 4 6 0.8946593 0.014492712
GO:0031982 vesicle CC 18 1 5 0.9946677 0.015552466
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
2.37 0.26 2.93
|
|
limma.Rcheck/tests_i386/limma-Tests.Rout
R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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$residual)
> data.frame(f1,f2,f3,f4,f5)
f1 f2 f3 f4 f5
0% -0.78835384 -0.687432210 -0.78957137 -0.75758558 -0.63778292
25% -0.18340154 -0.179683572 -0.18979269 -0.16363329 -0.38064318
50% -0.11492924 -0.114796040 -0.12087983 -0.07318718 -0.15971879
75% 0.01507921 -0.008145125 -0.01857508 0.03656491 0.07839396
100% 0.21653837 0.145106033 0.19214597 0.23710498 0.51836274
> data.frame(r1,r2,r3,r4,r5)
r1 r2 r3 r4 r5
0% -2.04434053 -2.05132680 -2.02404318 -2.09602100 -2.22280633
25% -0.59321065 -0.57200209 -0.58975649 -0.58142533 -0.71037756
50% 0.05874864 0.04514326 0.08335198 -0.01037007 0.06785517
75% 0.56010750 0.55124530 0.57618740 0.55704748 0.65383830
100% 2.57936026 2.64549799 2.57549257 2.38180448 2.28648835
>
> ### normalizeWithinArrays
>
> RG <- new("RGList",list())
> RG$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. :-5.82498 Min. :-5.69877
1st Qu.:-1.19140 1st Qu.:-1.55421
Median :-0.19318 Median : 0.06267
Mean : 0.08691 Mean :-0.05369
3rd Qu.: 1.48646 3rd Qu.: 1.41900
Max. : 7.16195 Max. : 6.28902
> #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$M[1:5,]
[,1] [,2]
[1,] -1.0618269 4.5343276
[2,] 0.8507603 0.3495635
[3,] 2.7703696 1.4459533
[4,] -1.8511286 0.4894799
[5,] 1.9180276 -5.5363732
> MA$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$M[1:5,]
[,1] [,2]
[1,] -1.0618269 4.5343276
[2,] 0.8507603 0.3495635
[3,] 2.7703696 1.4459533
[4,] -1.8511286 0.4894799
[5,] 1.9180276 -5.5363732
> MA$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
>
> 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 -1.464215e-16
mu+beta -0.3333333 -1.464215e-16 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
> FStat(tstat)
[1] 8.333333 2.083333 4.000000 1.000000
attr(,"df1")
[1] 3
attr(,"df2")
[1] Inf
> classifyTestsT(tstat)
TestResults matrix
[,1] [,2] [,3]
[1,] 0 1 0
[2,] 0 0 0
[3,] 0 0 0
[4,] 0 0 0
> 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 0.996498249
Up 1 0.004002001
UpOrDown 1 0.008000000
Mixed 1 0.008000000
> roast(y=y,iset1,design,contrast=2,array.weights=c(0.5,1,0.5,1))
Active.Prop P.Value
Down 0 0.99899950
Up 1 0.00150075
UpOrDown 1 0.00300000
Mixed 1 0.00300000
> w <- matrix(runif(100*4),100,4)
> roast(y=y,iset1,design,contrast=2,weights=w)
Active.Prop P.Value
Down 0 0.9994997
Up 1 0.0010005
UpOrDown 1 0.0020000
Mixed 1 0.0020000
> 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 0.008 0.015 0.008 0.015
set2 5 0 0 Up 0.959 0.959 0.687 0.687
> 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 0.004 0.007 0.004 0.007
set2 5 0 0 Up 0.679 0.679 0.658 0.658
> 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 0.0 1 Up 0.003 0.005 0.003 0.005
set2 5 0.2 0 Down 0.950 0.950 0.250 0.250
> 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 Up 0.001 0.001 0.001 0.001
set2 5 0 0 Down 0.791 0.791 0.146 0.146
> 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 0.0007432594 0.001486519 1.820548e-05 3.641096e-05
set2 5 Down 0.8208140511 0.820814051 2.211837e-01 2.211837e-01
> 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 0.0007432594 1.820548e-05
>
> ### 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 0.997498749
Up 1 0.003001501
UpOrDown 1 0.006000000
Mixed 1 0.006000000
> 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
>
> ### 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$E)
V1 V2 V3 V4
Min. :12.25 Min. :12.58 Min. :12.19 Min. :12.24
1st Qu.:13.13 1st Qu.:13.07 1st Qu.:13.15 1st Qu.:13.03
Median :13.29 Median :13.30 Median :13.30 Median :13.27
Mean :13.28 Mean :13.29 Mean :13.29 Mean :13.28
3rd Qu.:13.49 3rd Qu.:13.51 3rd Qu.:13.50 3rd Qu.:13.50
Max. :14.23 Max. :14.28 Max. :13.97 Max. :13.96
> summary(v$weights)
V1 V2 V3 V4
Min. : 5.935 Min. : 5.935 Min. : 5.935 Min. : 5.935
1st Qu.: 6.788 1st Qu.: 7.049 1st Qu.: 7.207 1st Qu.: 6.825
Median :11.066 Median :10.443 Median :10.606 Median :10.414
Mean :10.421 Mean :10.485 Mean :10.571 Mean :10.532
3rd Qu.:13.485 3rd Qu.:14.155 3rd Qu.:13.859 3rd Qu.:14.121
Max. :15.083 Max. :15.101 Max. :15.095 Max. :15.063
>
> ### 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: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:0072359 circulatory system developm... BP 2 0 2 1.000000000
GO:0007507 heart development BP 2 0 2 1.000000000
GO:0032501 multicellular organismal pr... BP 31 6 7 0.796992878
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
P.Down
GO:0070062 0.003047199
GO:0043230 0.003047199
GO:1903561 0.003047199
GO:0072359 0.009090909
GO:0007507 0.009090909
GO:0032501 0.009111120
GO:0098609 1.000000000
GO:0097190 1.000000000
GO:0031252 1.000000000
GO:0006897 1.000000000
> topGO(go,n=10,truncate.term=30,sort="down")
Term Ont N Up Down P.Up P.Down
GO:0070062 extracellular exosome CC 8 0 4 1.0000000 0.003047199
GO:0043230 extracellular organelle CC 8 0 4 1.0000000 0.003047199
GO:1903561 extracellular vesicle CC 8 0 4 1.0000000 0.003047199
GO:0072359 circulatory system developm... BP 2 0 2 1.0000000 0.009090909
GO:0007507 heart development BP 2 0 2 1.0000000 0.009090909
GO:0032501 multicellular organismal pr... BP 31 6 7 0.7969929 0.009111120
GO:0032502 developmental process BP 25 4 6 0.8946593 0.014492712
GO:0031982 vesicle CC 18 1 5 0.9946677 0.015552466
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
3.37 0.26 3.67
|
|
|
limma.Rcheck/tests_x64/limma-Tests.Rout
R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(limma)
>
> 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
>
> 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
> FStat(tstat)
[1] 8.333333 2.083333 4.000000 1.000000
attr(,"df1")
[1] 3
attr(,"df2")
[1] Inf
> classifyTestsT(tstat)
TestResults matrix
[,1] [,2] [,3]
[1,] 0 1 0
[2,] 0 0 0
[3,] 0 0 0
[4,] 0 0 0
> 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 0.996498249
Up 1 0.004002001
UpOrDown 1 0.008000000
Mixed 1 0.008000000
> roast(y=y,iset1,design,contrast=2,array.weights=c(0.5,1,0.5,1))
Active.Prop P.Value
Down 0 0.99899950
Up 1 0.00150075
UpOrDown 1 0.00300000
Mixed 1 0.00300000
> w <- matrix(runif(100*4),100,4)
> roast(y=y,iset1,design,contrast=2,weights=w)
Active.Prop P.Value
Down 0 0.9994997
Up 1 0.0010005
UpOrDown 1 0.0020000
Mixed 1 0.0020000
> 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 0.008 0.015 0.008 0.015
set2 5 0 0 Up 0.959 0.959 0.687 0.687
> 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 0.004 0.007 0.004 0.007
set2 5 0 0 Up 0.679 0.679 0.658 0.658
> 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 0.0 1 Up 0.003 0.005 0.003 0.005
set2 5 0.2 0 Down 0.950 0.950 0.250 0.250
> 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 Up 0.001 0.001 0.001 0.001
set2 5 0 0 Down 0.791 0.791 0.146 0.146
> 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 0.0007432594 0.001486519 1.820548e-05 3.641096e-05
set2 5 Down 0.8208140511 0.820814051 2.211837e-01 2.211837e-01
> 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 0.0007432594 1.820548e-05
>
> ### 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 0.997498749
Up 1 0.003001501
UpOrDown 1 0.006000000
Mixed 1 0.006000000
> 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
>
> ### 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$E)
V1 V2 V3 V4
Min. :12.25 Min. :12.58 Min. :12.19 Min. :12.24
1st Qu.:13.13 1st Qu.:13.07 1st Qu.:13.15 1st Qu.:13.03
Median :13.29 Median :13.30 Median :13.30 Median :13.27
Mean :13.28 Mean :13.29 Mean :13.29 Mean :13.28
3rd Qu.:13.49 3rd Qu.:13.51 3rd Qu.:13.50 3rd Qu.:13.50
Max. :14.23 Max. :14.28 Max. :13.97 Max. :13.96
> summary(v$weights)
V1 V2 V3 V4
Min. : 5.935 Min. : 5.935 Min. : 5.935 Min. : 5.935
1st Qu.: 6.788 1st Qu.: 7.049 1st Qu.: 7.207 1st Qu.: 6.825
Median :11.066 Median :10.443 Median :10.606 Median :10.414
Mean :10.421 Mean :10.485 Mean :10.571 Mean :10.532
3rd Qu.:13.485 3rd Qu.:14.155 3rd Qu.:13.859 3rd Qu.:14.121
Max. :15.083 Max. :15.101 Max. :15.095 Max. :15.063
>
> ### 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: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:0072359 circulatory system developm... BP 2 0 2 1.000000000
GO:0007507 heart development BP 2 0 2 1.000000000
GO:0032501 multicellular organismal pr... BP 31 6 7 0.796992878
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
P.Down
GO:0070062 0.003047199
GO:0043230 0.003047199
GO:1903561 0.003047199
GO:0072359 0.009090909
GO:0007507 0.009090909
GO:0032501 0.009111120
GO:0098609 1.000000000
GO:0097190 1.000000000
GO:0031252 1.000000000
GO:0006897 1.000000000
> topGO(go,n=10,truncate.term=30,sort="down")
Term Ont N Up Down P.Up P.Down
GO:0070062 extracellular exosome CC 8 0 4 1.0000000 0.003047199
GO:0043230 extracellular organelle CC 8 0 4 1.0000000 0.003047199
GO:1903561 extracellular vesicle CC 8 0 4 1.0000000 0.003047199
GO:0072359 circulatory system developm... BP 2 0 2 1.0000000 0.009090909
GO:0007507 heart development BP 2 0 2 1.0000000 0.009090909
GO:0032501 multicellular organismal pr... BP 31 6 7 0.7969929 0.009111120
GO:0032502 developmental process BP 25 4 6 0.8946593 0.014492712
GO:0031982 vesicle CC 18 1 5 0.9946677 0.015552466
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
4.00 0.17 4.17
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limma.Rcheck/examples_i386/limma-Ex.timings
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limma.Rcheck/examples_x64/limma-Ex.timings
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