group ws10

*The author of this computation has been verified*

R Software Module: /rwasp_regression_trees1.wasp (opens new window with default values)

Title produced by software: Recursive Partitioning (Regression Trees)

Date of computation: Tue, 21 Dec 2010 12:29:12 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/21/t1292934444tazj7a3mvaqhjp1.htm/, Retrieved Tue, 21 Dec 2010 13:27:28 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/21/t1292934444tazj7a3mvaqhjp1.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

4321023 29790 444 81767 1 1 4111912 87550 412 153198 1 1 223193 84738 428 -26007 0 1 1491348 54660 315 126942 1 1 1629616 42634 168 157214 1 0 1398893 40949 263 129352 0 1 1926517 45187 267 234817 1 0 983660 37704 228 60448 1 1 1443586 16275 129 47818 1 1 1073089 25830 104 245546 0 1 984885 12679 122 48020 0 1 1405225 18014 393 -1710 1 1 227132 43556 190 32648 0 1 929118 24811 280 95350 1 1 1071292 6575 63 151352 0 0 638830 7123 102 288170 0 0 856956 21950 265 114337 1 1 992426 37597 234 37884 1 1 444477 17821 277 122844 0 1 857217 12988 73 82340 1 1 711969 22330 67 79801 1 0 702380 13326 103 165548 0 0 358589 16189 290 116384 0 1 297978 7146 83 134028 0 1 585715 15824 56 63838 0 1 657954 27664 236 74996 1 1 209458 11920 73 31080 0 1 786690 8568 34 32168 0 1 439798 14416 139 49857 0 1 688779 3369 26 87161 1 1 574339 11819 70 106113 1 1 741409 6984 40 80570 1 0 597793 4519 42 102129 1 1 644190 2220 12 301670 0 1 377934 18562 211 102313 0 1 640273 10327 74 88577 0 1 697458 53 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 18 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Goodness of Fit Correlation 0.378 R-squared 0.1429 RMSE 0.4142 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 1 0.724137931034483 0.275862068965517 2 1 0.724137931034483 0.275862068965517 3 0 0.328767123287671 -0.328767123287671 4 1 0.724137931034483 0.275862068965517 5 1 0.476190476190476 0.523809523809524 6 0 0.724137931034483 -0.724137931034483 7 1 0.476190476190476 0.523809523809524 8 1 0.724137931034483 0.275862068965517 9 1 0.724137931034483 0.275862068965517 10 0 0.724137931034483 -0.724137931034483 11 0 0.724137931034483 -0.724137931034483 12 1 0.724137931034483 0.275862068965517 13 0 0.328767123287671 -0.328767123287671 14 1 0.724137931034483 0.275862068965517 15 0 0.476190476190476 -0.476190476190476 16 0 0.476190476190476 -0.476190476190476 17 1 0.724137931034483 0.275862068965517 18 1 0.724137931034483 0.275862068965517 19 0 0.328767123287671 -0.328767123287671 20 1 0.724137931034483 0.275862068965517 21 1 0.476190476190476 0.523809523809524 22 0 0.476190476190476 -0.476190476190476 23 0 0.328767123287671 -0.328767123287671 24 0 0.328767123287671 -0.328767123287671 25 0 0.724137931034483 -0.724137931034483 26 1 0.724137931034483 0.275862068965517 27 0 0.328767123287671 -0.328767123287671 28 0 0.724137931034483 -0.724137931034483 29 0 0.328767123287671 -0.328767123287671 30 1 0.724137931034483 0.275862068965517 31 1 0.724137931034483 0.275862068965517 32 1 0.476190476190476 0.523809523809524 33 1 0.724137931034483 0.275862068965517 34 0 0.724137931034483 -0.724137931034483 35 0 0.328767123287671 -0.328767123287671 36 0 0.724137931034483 -0.724137931034483 37 1 0.724137931034483 0.275862068965517 38 1 0.724137931034483 0.275862068965517 39 0 0.328767123287671 -0.328767123287671 40 0 0.476190476190476 -0.476190476190476 41 0 0.328767123287671 -0.328767123287671 42 0 0.476190476190476 -0.476190476190476 43 0 0.476190476190476 -0.476190476190476 44 0 0.476190476190476 -0.476190476190476 45 1 0.328767123287671 0.671232876712329 46 1 0.328767123287671 0.671232876712329 47 1 0.328767123287671 0.671232876712329 48 0 0.328767123287671 -0.328767123287671 49 0 0.328767123287671 -0.328767123287671 50 1 0.724137931034483 0.275862068965517 51 1 0.724137931034483 0.275862068965517 52 1 0.724137931034483 0.275862068965517 53 1 0.0993788819875776 0.900621118012422 54 1 0.328767123287671 0.671232876712329 55 0 0.0993788819875776 -0.0993788819875776 56 0 0.724137931034483 -0.724137931034483 57 0 0.476190476190476 -0.476190476190476 58 0 0.328767123287671 -0.328767123287671 59 1 0.328767123287671 0.671232876712329 60 1 0.476190476190476 0.523809523809524 61 0 0.0993788819875776 -0.0993788819875776 62 1 0.328767123287671 0.671232876712329 63 1 0.476190476190476 0.523809523809524 64 0 0.0993788819875776 -0.0993788819875776 65 0 0.328767123287671 -0.328767123287671 66 0 0.328767123287671 -0.328767123287671 67 1 0.328767123287671 0.671232876712329 68 0 0.328767123287671 -0.328767123287671 69 1 0.328767123287671 0.671232876712329 70 1 0.328767123287671 0.671232876712329 71 0 0.476190476190476 -0.476190476190476 72 0 0.0993788819875776 -0.0993788819875776 73 0 0.328767123287671 -0.328767123287671 74 0 0.328767123287671 -0.328767123287671 75 1 0.328767123287671 0.671232876712329 76 0 0.0993788819875776 -0.0993788819875776 77 0 0.0993788819875776 -0.0993788819875776 78 0 0.328767123287671 -0.328767123287671 79 1 0.328767123287671 0.671232876712329 80 0 0.328767123287671 -0.328767123287671 81 1 0.476190476190476 0.523809523809524 82 0 0.328767123287671 -0.328767123287671 83 0 0.0993788819875776 -0.0993788819875776 84 0 0.328767123287671 -0.328767123287671 85 1 0.328767123287671 0.671232876712329 86 0 0.328767123287671 -0.328767123287671 87 0 0.0993788819875776 -0.0993788819875776 88 0 0.0993788819875776 -0.0993788819875776 89 0 0.0993788819875776 -0.0993788819875776 90 0 0.0993788819875776 -0.0993788819875776 91 1 0.328767123287671 0.671232876712329 92 0 0.328767123287671 -0.328767123287671 93 1 0.328767123287671 0.671232876712329 94 0 0.328767123287671 -0.328767123287671 95 1 0.476190476190476 0.523809523809524 96 0 0.328767123287671 -0.328767123287671 97 1 0.328767123287671 0.671232876712329 98 0 0.0993788819875776 -0.0993788819875776 99 1 0.328767123287671 0.671232876712329 100 1 0.328767123287671 0.671232876712329 101 0 0.328767123287671 -0.328767123287671 102 0 0.328767123287671 -0.328767123287671 103 0 0.328767123287671 -0.328767123287671 104 0 0.328767123287671 -0.328767123287671 105 0 0.328767123287671 -0.328767123287671 106 1 0.328767123287671 0.671232876712329 107 1 0.724137931034483 0.275862068965517 108 0 0.476190476190476 -0.476190476190476 109 1 0.328767123287671 0.671232876712329 110 0 0.328767123287671 -0.328767123287671 111 1 0.328767123287671 0.671232876712329 112 0 0.476190476190476 -0.476190476190476 113 1 0.328767123287671 0.671232876712329 114 1 0.328767123287671 0.671232876712329 115 0 0.0993788819875776 -0.0993788819875776 116 0 0.0993788819875776 -0.0993788819875776 117 0 0.0993788819875776 -0.0993788819875776 118 1 0.328767123287671 0.671232876712329 119 0 0.328767123287671 -0.328767123287671 120 0 0.328767123287671 -0.328767123287671 121 0 0.0993788819875776 -0.0993788819875776 122 0 0.328767123287671 -0.328767123287671 123 1 0.724137931034483 0.275862068965517 124 1 0.328767123287671 0.671232876712329 125 1 0.476190476190476 0.523809523809524 126 0 0.0993788819875776 -0.0993788819875776 127 0 0.0993788819875776 -0.0993788819875776 128 0 0.0993788819875776 -0.0993788819875776 129 0 0.328767123287671 -0.328767123287671 130 0 0.0993788819875776 -0.0993788819875776 131 0 0.328767123287671 -0.328767123287671 132 0 0.328767123287671 -0.328767123287671 133 0 0.0993788819875776 -0.0993788819875776 134 0 0.0993788819875776 -0.0993788819875776 135 0 0.0993788819875776 -0.0993788819875776 136 0 0.328767123287671 -0.328767123287671 137 0 0.0993788819875776 -0.0993788819875776 138 0 0.0993788819875776 -0.0993788819875776 139 0 0.0993788819875776 -0.0993788819875776 140 0 0.0993788819875776 -0.0993788819875776 141 0 0.328767123287671 -0.328767123287671 142 0 0.0993788819875776 -0.0993788819875776 143 0 0.0993788819875776 -0.0993788819875776 144 0 0.328767123287671 -0.328767123287671 145 0 0.0993788819875776 -0.0993788819875776 146 0 0.328767123287671 -0.328767123287671 147 0 0.0993788819875776 -0.0993788819875776 148 0 0.328767123287671 -0.328767123287671 149 0 0.328767123287671 -0.328767123287671 150 0 0.0993788819875776 -0.0993788819875776 151 0 0.0993788819875776 -0.0993788819875776 152 0 0.0993788819875776 -0.0993788819875776 153 0 0.328767123287671 -0.328767123287671 154 0 0.0993788819875776 -0.0993788819875776 155 0 0.0993788819875776 -0.0993788819875776 156 0 0.0993788819875776 -0.0993788819875776 157 0 0.0993788819875776 -0.0993788819875776 158 0 0.0993788819875776 -0.0993788819875776 159 0 0.0993788819875776 -0.0993788819875776 160 0 0.0993788819875776 -0.0993788819875776 161 0 0.328767123287671 -0.328767123287671 162 0 0.0993788819875776 -0.0993788819875776 163 0 0.0993788819875776 -0.0993788819875776 164 0 0.328767123287671 -0.328767123287671 165 0 0.0993788819875776 -0.0993788819875776 166 0 0.328767123287671 -0.328767123287671 167 0 0.328767123287671 -0.328767123287671 168 0 0.328767123287671 -0.328767123287671 169 0 0.0993788819875776 -0.0993788819875776 170 0 0.0993788819875776 -0.0993788819875776 171 0 0.328767123287671 -0.328767123287671 172 0 0.328767123287671 -0.328767123287671 173 0 0.328767123287671 -0.328767123287671 174 0 0.328767123287671 -0.328767123287671 175 0 0.0993788819875776 -0.0993788819875776 176 0 0.0993788819875776 -0.0993788819875776 177 1 0.328767123287671 0.671232876712329 178 0 0.0993788819875776 -0.0993788819875776 179 0 0.0993788819875776 -0.0993788819875776 180 0 0.328767123287671 -0.328767123287671 181 1 0.0993788819875776 0.900621118012422 182 0 0.0993788819875776 -0.0993788819875776 183 1 0.0993788819875776 0.900621118012422 184 1 0.328767123287671 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0.328767123287671 -0.328767123287671 231 0 0.328767123287671 -0.328767123287671 232 0 0.328767123287671 -0.328767123287671 233 0 0.0993788819875776 -0.0993788819875776 234 0 0.0993788819875776 -0.0993788819875776 235 0 0.0993788819875776 -0.0993788819875776 236 0 0.0993788819875776 -0.0993788819875776 237 0 0.0993788819875776 -0.0993788819875776 238 0 0.328767123287671 -0.328767123287671 239 0 0.328767123287671 -0.328767123287671 240 0 0.0993788819875776 -0.0993788819875776 241 0 0.328767123287671 -0.328767123287671 242 0 0.0993788819875776 -0.0993788819875776 243 0 0.328767123287671 -0.328767123287671 244 0 0.328767123287671 -0.328767123287671 245 1 0.328767123287671 0.671232876712329 246 1 0.328767123287671 0.671232876712329 247 0 0.0993788819875776 -0.0993788819875776 248 1 0.328767123287671 0.671232876712329 249 1 0.0993788819875776 0.900621118012422 250 0 0.328767123287671 -0.328767123287671 251 1 0.328767123287671 0.671232876712329 252 0 0.328767123287671 -0.328767123287671 253 0 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0.671232876712329 413 1 0.328767123287671 0.671232876712329 414 0 0.328767123287671 -0.328767123287671 415 0 0.0993788819875776 -0.0993788819875776 416 0 0.328767123287671 -0.328767123287671 417 1 0.0993788819875776 0.900621118012422 418 0 0.0993788819875776 -0.0993788819875776 419 0 0.0993788819875776 -0.0993788819875776 420 0 0.0993788819875776 -0.0993788819875776 421 0 0.328767123287671 -0.328767123287671 422 1 0.328767123287671 0.671232876712329 423 0 0.328767123287671 -0.328767123287671 424 0 0.328767123287671 -0.328767123287671 425 1 0.328767123287671 0.671232876712329 426 0 0.328767123287671 -0.328767123287671 427 0 0.328767123287671 -0.328767123287671 428 0 0.328767123287671 -0.328767123287671 429 0 0.328767123287671 -0.328767123287671 430 0 0.328767123287671 -0.328767123287671

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292934444tazj7a3mvaqhjp1/29p601292934533.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292934444tazj7a3mvaqhjp1/29p601292934533.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292934444tazj7a3mvaqhjp1/39p601292934533.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292934444tazj7a3mvaqhjp1/39p601292934533.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292934444tazj7a3mvaqhjp1/4kgol1292934533.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292934444tazj7a3mvaqhjp1/4kgol1292934533.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = quantiles ; par3 = 2 ; par4 = no ;

Parameters (R input):

par1 = 5 ; par2 = none ; par3 = 2 ; par4 = no ;

R code (references can be found in the software module):

library(party) library(Hmisc) par1 <- as.numeric(par1) par3 <- as.numeric(par3) x <- data.frame(t(y)) is.data.frame(x) x <- x[!is.na(x[,par1]),] k <- length(x[1,]) n <- length(x[,1]) colnames(x)[par1] x[,par1] if (par2 == 'kmeans') { cl <- kmeans(x[,par1], par3) print(cl) clm <- matrix(cbind(cl$centers,1:par3),ncol=2) clm <- clm[sort.list(clm[,1]),] for (i in 1:par3) { cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='') } cl$cluster <- as.factor(cl$cluster) print(cl$cluster) x[,par1] <- cl$cluster } if (par2 == 'quantiles') { x[,par1] <- cut2(x[,par1],g=par3) } if (par2 == 'hclust') { hc <- hclust(dist(x[,par1])^2, 'cen') print(hc) memb <- cutree(hc, k = par3) dum <- c(mean(x[memb==1,par1])) for (i in 2:par3) { dum <- c(dum, mean(x[memb==i,par1])) } hcm <- matrix(cbind(dum,1:par3),ncol=2) hcm <- hcm[sort.list(hcm[,1]),] for (i in 1:par3) { memb[memb==hcm[i,2]] <- paste('C',i,sep='') } memb <- as.factor(memb) print(memb) x[,par1] <- memb } if (par2=='equal') { ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep='')) x[,par1] <- as.factor(ed) } table(x[,par1]) colnames(x) colnames(x)[par1] x[,par1] if (par2 == 'none') { m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) } load(file='createtable') if (par2 != 'none') { m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x) if (par4=='yes') { a<-table.start() a<-table.row.start(a) a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) a<-table.element(a,'Prediction (training)',par3+1,TRUE) a<-table.element(a,'Prediction (testing)',par3+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Actual',1,TRUE) for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) a<-table.element(a,'CV',1,TRUE) for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) a<-table.element(a,'CV',1,TRUE) a<-table.row.end(a) for (i in 1:10) { ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1)) m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,]) if (i==1) { m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,]) m.ct.i.actu <- x[ind==1,par1] m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,]) m.ct.x.actu <- x[ind==2,par1] } else { m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,])) m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1]) m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,])) m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1]) } } print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred)) numer <- 0 for (i in 1:par3) { print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,])) numer <- numer + m.ct.i.tab[i,i] } print(m.ct.i.cp <- numer / sum(m.ct.i.tab)) print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred)) numer <- 0 for (i in 1:par3) { print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,])) numer <- numer + m.ct.x.tab[i,i] } print(m.ct.x.cp <- numer / sum(m.ct.x.tab)) for (i in 1:par3) { a<-table.row.start(a) a<-table.element(a,paste('C',i,sep=''),1,TRUE) for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj]) a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4)) for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj]) a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4)) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,'Overall',1,TRUE) for (jjj in 1:par3) a<-table.element(a,'-') a<-table.element(a,round(m.ct.i.cp,4)) for (jjj in 1:par3) a<-table.element(a,'-') a<-table.element(a,round(m.ct.x.cp,4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') } } m bitmap(file='test1.png') plot(m) dev.off() bitmap(file='test1a.png') plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response') dev.off() if (par2 == 'none') { forec <- predict(m) result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec)) colnames(result) <- c('Actuals','Forecasts','Residuals') print(result) } if (par2 != 'none') { print(cbind(as.factor(x[,par1]),predict(m))) myt <- table(as.factor(x[,par1]),predict(m)) print(myt) } bitmap(file='test2.png') if(par2=='none') { op <- par(mfrow=c(2,2)) plot(density(result$Actuals),main='Kernel Density Plot of Actuals') plot(density(result$Residuals),main='Kernel Density Plot of Residuals') plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals') plot(density(result$Forecasts),main='Kernel Density Plot of Predictions') par(op) } if(par2!='none') { plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted') } dev.off() if (par2 == 'none') { detcoef <- cor(result$Forecasts,result$Actuals) a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goodness of Fit',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Correlation',1,TRUE) a<-table.element(a,round(detcoef,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'R-squared',1,TRUE) a<-table.element(a,round(detcoef*detcoef,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'RMSE',1,TRUE) a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'#',header=TRUE) a<-table.element(a,'Actuals',header=TRUE) a<-table.element(a,'Forecasts',header=TRUE) a<-table.element(a,'Residuals',header=TRUE) a<-table.row.end(a) for (i in 1:length(result$Actuals)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,result$Actuals[i]) a<-table.element(a,result$Forecasts[i]) a<-table.element(a,result$Residuals[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') } if (par2 != 'none') { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) for (i in 1:par3) { a<-table.element(a,paste('C',i,sep=''),1,TRUE) } a<-table.row.end(a) for (i in 1:par3) { a<-table.row.start(a) a<-table.element(a,paste('C',i,sep=''),1,TRUE) for (j in 1:par3) { a<-table.element(a,myt[i,j]) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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