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| Package: limma |
| Version: 2.6.0 |
| Command: /home/biocbuild/arch/sparc/R-2.3.0/bin/R CMD check limma_2.6.0.tar.gz |
| RetCode: 0 |
| Time: 500.8 seconds |
| Status: OK |
| CheckDir: limma.Rcheck |
| Warnings: 0 |
* checking for working latex ... OK
* using log directory '/loc/biocbuild/1.8d/madman/Rpacks/limma.Rcheck'
* using Version 2.3.0 (2006-04-24)
* checking for file 'limma/DESCRIPTION' ... OK
* this is package 'limma' version '2.6.0'
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking whether package 'limma' can be installed ... OK
* checking package directory ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for syntax errors ... OK
* checking R files for library.dynam ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking Rd files ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* creating limma-Ex.R ... OK
* checking examples ... OK
* checking tests ...
make[1]: Entering directory `/loc/biocbuild/1.8d/madman/Rpacks/limma.Rcheck/tests'
Running 'limma-Tests.R'
Comparing 'limma-Tests.Rout' to 'limma-Tests.Rout.save' ...2,926d1
<
< > library(limma)
< >
< > set.seed(0); u <- runif(100)
< >
< > ### splitName
< >
< > x <- c("ab;cd;efg","abc;def","z","")
< > splitName(x)
< $Name
< [1] "ab;cd" "abc" "z" ""
<
< $Annotation
< [1] "efg" "def" "" ""
<
< >
< > ### 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 0 1 2
<
< $G
< [1] -1 0 1 2
<
< > backgroundCorrect(RG, method="half")
< An object of class "RGList"
< $R
< [1] 0.5 0.5 1.0 2.0
<
< $G
< [1] 0.5 0.5 1.0 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] 4 5 6 7
<
< $G
< [1] 4 5 6 7
<
< >
< > ### normalizeWithinArrays
< >
< > library(sma)
< > data(MouseArray)
< > MA <- normalizeWithinArrays(mouse.data, mouse.setup, method="robustspline")
< > MA$M[1:5,]
< [,1] [,2] [,3] [,4] [,5] [,6]
< [1,] -0.21539109 -0.79670669 -0.55011008 0.14243756 -0.3933328 0.86741957
< [2,] 0.06449435 0.16873653 0.26020426 0.92440874 0.6640048 1.30672583
< [3,] -0.23149571 -0.66662065 -0.68092134 -0.09651125 -0.4205728 -0.31124721
< [4,] -0.20090146 -0.09709476 -0.28354313 0.32830186 0.1916112 -0.09738907
< [5,] -0.86822005 -0.13192148 -0.08634807 -0.01017014 0.2763200 -0.22570480
< > MA <- normalizeWithinArrays(mouse.data, mouse.setup)
< > MA$M[1:5,]
< [,1] [,2] [,3] [,4] [,5] [,6]
< [1,] -0.22006681 -0.85229101 -0.61528102 0.07080387 -0.4017245 0.8790516
< [2,] 0.06720908 0.11711457 0.21083609 0.99616190 0.6494259 1.3351120
< [3,] -0.23069447 -0.71229077 -0.72631373 -0.12375213 -0.4262350 -0.3237170
< [4,] -0.17262990 -0.06186499 -0.28347377 0.27201473 0.2028371 -0.1018497
< [5,] -0.83900000 -0.09643457 -0.08877846 -0.06550247 0.2807478 -0.2229941
< >
< > ### normalizeBetweenArrays
< >
< > MA <- normalizeBetweenArrays(MA,method="scale")
< > MA$M[1:5,]
< [,1] [,2] [,3] [,4] [,5] [,6]
< [1,] -0.22060913 -0.97047013 -0.7132995 0.05299212 -0.4035381 0.8835727
< [2,] 0.06737471 0.13335374 0.2444237 0.74556284 0.6523577 1.3419787
< [3,] -0.23126298 -0.81105738 -0.8420205 -0.09262048 -0.4281592 -0.3253819
< [4,] -0.17305532 -0.07044322 -0.3286331 0.20358545 0.2037528 -0.1023735
< [5,] -0.84106756 -0.10980624 -0.1029215 -0.04902437 0.2820152 -0.2241410
< > MA$A[1:5,]
< [,1] [,2] [,3] [,4] [,5] [,6]
< [1,] 11.332980 11.198841 11.337353 9.693899 11.196822 10.506374
< [2,] 11.245664 11.074098 11.051345 10.931562 11.273305 10.008818
< [3,] 10.113995 10.923628 12.322088 9.875351 11.096463 10.829522
< [4,] 8.390963 9.019036 8.720987 9.774672 8.826249 9.113240
< [5,] 8.684837 9.017042 8.406961 9.477079 8.739632 8.557627
< > MA <- normalizeBetweenArrays(MA,method="quantile")
< > MA$M[1:5,]
< [,1] [,2] [,3] [,4] [,5] [,6]
< [1,] -0.31703694 -0.9938725 -0.5791881 0.03617137 -0.3769488 0.9820991
< [2,] 0.03923233 0.1066559 0.2312904 0.76612052 0.6368203 1.4728996
< [3,] -0.27566044 -0.8580353 -0.7504079 -0.08854074 -0.4200884 -0.2960210
< [4,] -0.11946685 -0.1095793 -0.2985336 0.15876207 0.2612499 -0.1006169
< [5,] -0.67628732 -0.1634459 -0.0938785 -0.05338925 0.3477450 -0.2227479
< > MA$A[1:5,]
< [,1] [,2] [,3] [,4] [,5] [,6]
< [1,] 11.478807 11.311915 11.142829 9.749722 11.137385 10.56415
< [2,] 11.369349 11.191410 10.896307 10.893490 11.205219 10.04138
< [3,] 10.124225 11.010219 12.026393 9.906701 11.045121 10.91363
< [4,] 8.521087 8.771148 8.810923 9.817860 8.681051 9.06633
< [5,] 8.772261 8.766051 8.538890 9.580934 8.567045 8.55471
< >
< > ### unwrapdups
< >
< > M <- matrix(1:12,6,2)
< > unwrapdups(M,ndups=1)
< [,1] [,2]
< [1,] 1 7
< [2,] 2 8
< [3,] 3 9
< [4,] 4 10
< [5,] 5 11
< [6,] 6 12
< > unwrapdups(M,ndups=2)
< [,1] [,2] [,3] [,4]
< [1,] 1 2 7 8
< [2,] 3 4 9 10
< [3,] 5 6 11 12
< > unwrapdups(M,ndups=3)
< [,1] [,2] [,3] [,4] [,5] [,6]
< [1,] 1 2 3 7 8 9
< [2,] 4 5 6 10 11 12
< > unwrapdups(M,ndups=2,spacing=3)
< [,1] [,2] [,3] [,4]
< [1,] 1 4 7 10
< [2,] 2 5 8 11
< [3,] 3 6 9 12
< >
< > ### trigammaInverse
< >
< > trigammaInverse(c(1e-6,NA,5,1e6))
< [1] 1.000000e+06 NA 4.961687e-01 1.000001e-03
< >
< > ### lm.series, contrasts.fit, ebayes
< >
< > M <- matrix(rnorm(10*6,sd=0.3),10,6)
< > M[1,1:3] <- M[1,1:3] + 2
< > design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
< > fit <- lm.series(M,design=design)
< > contrast.matrix <- cbind(First3=c(1,0),Last3=c(0,1),"Last3-First3"=c(-1,1))
< > fit2 <- contrasts.fit(fit,contrasts=contrast.matrix)
< > eb <- ebayes(fit2)
< >
< > eb$t
< First3 Last3 Last3-First3
< [1,] 13.01360810 0.8094614 -8.62963489
< [2,] -0.08220793 -0.2496031 -0.11836624
< [3,] 0.53689924 0.1037124 -0.30630936
< [4,] -0.64950290 -0.6643004 -0.01046340
< [5,] -0.12967606 -0.6044961 -0.33574846
< [6,] 1.00443329 0.1749033 -0.58656627
< [7,] -0.41799559 -0.3567558 0.04330306
< [8,] 0.04763415 1.7686344 1.21693097
< [9,] -1.82026162 0.6205108 1.72588671
< [10,] -1.66163020 2.0938216 2.65550546
< > eb$s2.prior
< [1] 0.07549435
< > eb$s2.post
< [1] 0.07549435 0.07549435 0.07549435 0.07549435 0.07549435 0.07549435
< [7] 0.07549435 0.07549435 0.07549435 0.07549435
< > eb$df.prior
< [1] Inf
< > eb$lods
< First3 Last3 Last3-First3
< [1,] 76.894615 -4.836703 29.863710
< [2,] -7.551544 -5.007910 -7.137158
< [3,] -7.411171 -5.022793 -7.097495
< [4,] -7.344554 -4.898476 -7.144066
< [5,] -7.546529 -4.920386 -7.088102
< [6,] -7.051826 -5.017066 -6.973142
< [7,] -7.467789 -4.989149 -7.143189
< [8,] -7.553783 -4.122674 -6.408184
< [9,] -5.902688 -4.914721 -5.663877
< [10,] -6.178115 -3.760000 -3.639805
< > eb$p.value
< First3 Last3 Last3-First3
< [1,] 1.023910e-38 0.41824980 6.154813e-18
< [2,] 9.344814e-01 0.80289433 9.057775e-01
< [3,] 5.913372e-01 0.91739759 7.593691e-01
< [4,] 5.160134e-01 0.50649808 9.916516e-01
< [5,] 8.968227e-01 0.54551387 7.370606e-01
< [6,] 3.151698e-01 0.86115561 5.574950e-01
< [7,] 6.759503e-01 0.72127462 9.654600e-01
< [8,] 9.620078e-01 0.07695490 2.236305e-01
< [9,] 6.871917e-02 0.53492156 8.436780e-02
< [10,] 9.658694e-02 0.03627587 7.918965e-03
< > eb$var.prior
< [1] 123.7528665 0.4556155 108.4630118
< >
< > ### toptable
< >
< > toptable(fit)
< M t P.Value adj.P.Val B
< 1 2.064402265 13.01360810 1.023910e-38 1.023910e-37 76.894615
< 9 -0.288755599 -1.82026162 6.871917e-02 3.219565e-01 -5.902688
< 10 -0.263591244 -1.66163020 9.658694e-02 3.219565e-01 -6.178115
< 6 0.159337391 1.00443329 3.151698e-01 7.879245e-01 -7.051826
< 4 -0.103033320 -0.64950290 5.160134e-01 9.620078e-01 -7.344554
< 3 0.085170539 0.53689924 5.913372e-01 9.620078e-01 -7.411171
< 7 -0.066308362 -0.41799559 6.759503e-01 9.620078e-01 -7.467789
< 5 -0.020571048 -0.12967606 8.968227e-01 9.620078e-01 -7.546529
< 2 -0.013040982 -0.08220793 9.344814e-01 9.620078e-01 -7.551544
< 8 0.007556402 0.04763415 9.620078e-01 9.620078e-01 -7.553783
< >
< > ### duplicateCorrelation
< >
< > cor.out <- duplicateCorrelation(M)
<
< Attaching package: 'statmod'
<
<
< The following object(s) are masked from package:limma :
<
< matvec vecmat
<
< > cor.out$consensus.correlation
< [1] -0.1300222
< > cor.out$atanh.correlations
< [1] -0.3496702 -0.3528761 0.1320187 -0.7957172 0.7124326
< >
< > ### gls.series
< >
< > fit <- gls.series(M,design,correlation=cor.out$cor)
< > fit$coefficients
< First3Arrays Last3Arrays
< [1,] 1.02568064 0.04440632
< [2,] -0.00893139 -0.04446419
< [3,] 0.06938317 -0.03407404
< [4,] -0.02937598 0.11198606
< [5,] -0.27617342 0.21529287
< > fit$stdev.unscaled
< First3Arrays Last3Arrays
< [1,] 0.3807838 0.3807838
< [2,] 0.3807838 0.3807838
< [3,] 0.3807838 0.3807838
< [4,] 0.3807838 0.3807838
< [5,] 0.3807838 0.3807838
< > fit$sigma
< [1] 0.7880432 0.2880540 0.1997484 0.2750895 0.2621346
< > fit$df.residual
< [1] 10 10 10 10 10
< >
< > ### mrlm
< >
< > fit <- mrlm(M,design)
< > fit$coef
< [,1] [,2]
< [1,] 2.064402265 0.23453509
< [2,] -0.013040982 -0.15267834
< [3,] -0.030835828 0.01645232
< [4,] -0.103033320 -0.10538070
< [5,] -0.020571048 -0.09589370
< [6,] 0.159337391 0.02774563
< [7,] -0.066308362 -0.05659364
< [8,] 0.007556402 0.38166839
< [9,] -0.288755599 0.09843418
< [10,] -0.263591244 0.33215155
< > fit$stdev.unscaled
< [,1] [,2]
< [1,] 0.5773503 0.7315593
< [2,] 0.5773503 0.6511403
< [3,] 0.6269590 0.5773503
< [4,] 0.5773503 0.5773503
< [5,] 0.5773503 0.5773503
< [6,] 0.5773503 0.5773503
< [7,] 0.5773503 0.5773503
< [8,] 0.5773503 0.6527609
< [9,] 0.5773503 0.5773503
< [10,] 0.5773503 0.5773503
< > fit$sigma
< [1] 0.0755165 0.1410025 0.3087025 0.1390960 0.3289335 0.1719261 0.4295126
< [8] 0.1197697 0.3906706 0.2267115
< > 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(1:3,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
<
< $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
<
< $Amean
< [1] 0.2034961 0.1954604 -0.2863347 0.1188659 0.1784593
<
< $contrasts
< alpha-beta mu+alpha mu+beta
< 1 0 1 1
< 2 1 1 0
< 3 -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
<
< $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
< >
OK
make[1]: Leaving directory `/loc/biocbuild/1.8d/madman/Rpacks/limma.Rcheck/tests'
OK
* checking package vignettes in 'inst/doc' ... OK
* creating limma-manual.tex ... OK
* checking limma-manual.tex ... OK