R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (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. > x <- array(list(317 + ,310232.86 + ,0.939 + ,0.923 + ,0.869 + ,267 + ,1330141.29 + ,0.623 + ,0.843 + ,0.618 + ,204 + ,139390.2 + ,0.784 + ,0.77 + ,0.713 + ,198 + ,62348.45 + ,0.815 + ,0.949 + ,0.832 + ,107 + ,81644.45 + ,0.928 + ,0.953 + ,0.838 + ,89 + ,64768.39 + ,0.87 + ,0.971 + ,0.819 + ,88 + ,48636.07 + ,0.934 + ,0.956 + ,0.808 + ,80 + ,126804.43 + ,0.883 + ,1 + ,0.827 + ,79 + ,21515.75 + ,0.981 + ,0.976 + ,0.837 + ,69 + ,60748.96 + ,0.856 + ,0.976 + ,0.799 + ,53 + ,9992.34 + ,0.866 + ,0.858 + ,0.732 + ,50 + ,16783.09 + ,0.931 + ,0.958 + ,0.845 + ,49 + ,45415.6 + ,0.858 + ,0.765 + ,0.591 + ,42 + ,15460.48 + ,0.834 + ,0.742 + ,0.668 + ,39 + ,4252.28 + ,1 + ,0.957 + ,0.783 + ,39 + ,46505.96 + ,0.874 + ,0.969 + ,0.799 + ,34 + ,201103.33 + ,0.663 + ,0.844 + ,0.662 + ,33 + ,76923.3 + ,0.64 + ,0.836 + ,0.662 + ,32 + ,2847.23 + ,0.768 + ,0.838 + ,0.598 + ,29 + ,10201.71 + ,0.924 + ,0.91 + ,0.769 + ,27 + ,33759.74 + ,0.927 + ,0.962 + ,0.84 + ,25 + ,9612.63 + ,0.776 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,'GNI/cap2011') + ,1:182)) > y <- array(NA,dim=c(5,182),dimnames=list(c('TotalPoints','POP','Education2011','LifeExpectancy2011','GNI/cap2011'),1:182)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par4 = 'no' > par3 = '3' > par2 = 'none' > par1 = '1' > par4 <- 'no' > par3 <- '3' > par2 <- 'none' > par1 <- '1' > #'GNU S' R Code compiled by R2WASP v. 1.2.291 () > #Author: root > #To cite this work: Wessa P., 2012, Recursive Partitioning (Regression Trees) (v1.0.3) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_regression_trees.wasp/ > #Source of accompanying publication: > # > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Hmisc library by Frank E Harrell Jr Type library(help='Hmisc'), ?Overview, or ?Hmisc.Overview') to see overall documentation. NOTE:Hmisc no longer redefines [.factor to drop unused levels when subsetting. To get the old behavior of Hmisc type dropUnusedLevels(). Attaching package: 'Hmisc' The following object(s) are masked from 'package:survival': untangle.specials The following object(s) are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "TotalPoints" > x[,par1] [1] 317 267 204 198 107 89 88 80 79 69 53 50 49 42 39 39 34 33 [19] 32 29 27 25 23 22 21 20 20 20 20 19 16 15 15 14 14 14 [37] 14 13 10 9 9 9 8 8 8 8 7 7 6 5 5 5 5 5 [55] 5 4 4 4 4 3 3 3 3 2 2 2 2 2 2 2 2 2 [73] 2 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 [91] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [109] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [127] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [145] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [163] 0 0 0 0 0 0 0 0 0 0 5 1 0 0 37 1 0 0 [181] 0 0 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) 0 1 2 3 4 5 6 7 8 9 10 13 14 15 16 19 20 21 22 23 100 7 10 4 4 7 1 2 4 3 1 1 4 2 1 1 4 1 1 1 25 27 29 32 33 34 37 39 42 49 50 53 69 79 80 88 89 107 198 204 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 267 317 1 1 > colnames(x) [1] "TotalPoints" "POP" "Education2011" [4] "LifeExpectancy2011" "GNI.cap2011" > colnames(x)[par1] [1] "TotalPoints" > x[,par1] [1] 317 267 204 198 107 89 88 80 79 69 53 50 49 42 39 39 34 33 [19] 32 29 27 25 23 22 21 20 20 20 20 19 16 15 15 14 14 14 [37] 14 13 10 9 9 9 8 8 8 8 7 7 6 5 5 5 5 5 [55] 5 4 4 4 4 3 3 3 3 2 2 2 2 2 2 2 2 2 [73] 2 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 [91] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [109] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [127] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [145] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [163] 0 0 0 0 0 0 0 0 0 0 5 1 0 0 37 1 0 0 [181] 0 0 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/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="/var/wessaorg/rcomp/tmp/1wgxz1356878060.tab") + } + } > m Conditional inference tree with 7 terminal nodes Response: TotalPoints Inputs: POP, Education2011, LifeExpectancy2011, GNI.cap2011 Number of observations: 182 1) POP <= 44205.29; criterion = 1, statistic = 42.784 2) Education2011 <= 0.806; criterion = 1, statistic = 32.562 3) Education2011 <= 0.763; criterion = 0.995, statistic = 10.422 4) POP <= 29671.6; criterion = 0.998, statistic = 12.412 5) Education2011 <= 0.578; criterion = 0.966, statistic = 6.901 6)* weights = 53 5) Education2011 > 0.578 7)* weights = 52 4) POP > 29671.6 8)* weights = 7 3) Education2011 > 0.763 9)* weights = 14 2) Education2011 > 0.806 10)* weights = 29 1) POP > 44205.29 11) GNI.cap2011 <= 0.7; criterion = 0.98, statistic = 7.877 12)* weights = 18 11) GNI.cap2011 > 0.7 13)* weights = 9 > postscript(file="/var/wessaorg/rcomp/tmp/2oijx1356878060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(m) > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/32lzx1356878060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response') > dev.off() null device 1 > 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) + } Actuals Forecasts Residuals 1 317 132.3333333 184.6666667 2 267 26.2222222 240.7777778 3 204 132.3333333 71.6666667 4 198 132.3333333 65.6666667 5 107 132.3333333 -25.3333333 6 89 132.3333333 -43.3333333 7 88 132.3333333 -44.3333333 8 80 132.3333333 -52.3333333 9 79 18.4137931 60.5862069 10 69 132.3333333 -63.3333333 11 53 18.4137931 34.5862069 12 50 18.4137931 31.5862069 13 49 26.2222222 22.7777778 14 42 18.4137931 23.5862069 15 39 18.4137931 20.5862069 16 39 132.3333333 -93.3333333 17 34 26.2222222 7.7777778 18 33 26.2222222 6.7777778 19 32 7.5714286 24.4285714 20 29 18.4137931 10.5862069 21 27 18.4137931 8.5862069 22 25 7.5714286 17.4285714 23 23 7.0000000 16.0000000 24 22 18.4137931 3.5862069 25 21 18.4137931 2.5862069 26 20 1.7307692 18.2692308 27 20 26.2222222 -6.2222222 28 20 18.4137931 1.5862069 29 20 26.2222222 -6.2222222 30 19 7.5714286 11.4285714 31 16 18.4137931 -2.4137931 32 15 7.0000000 8.0000000 33 15 26.2222222 -11.2222222 34 14 18.4137931 -4.4137931 35 14 18.4137931 -4.4137931 36 14 26.2222222 -12.2222222 37 14 18.4137931 -4.4137931 38 13 18.4137931 -5.4137931 39 10 18.4137931 -8.4137931 40 9 7.5714286 1.4285714 41 9 7.5714286 1.4285714 42 9 18.4137931 -9.4137931 43 8 26.2222222 -18.2222222 44 8 1.7307692 6.2692308 45 8 1.7307692 6.2692308 46 8 1.7307692 6.2692308 47 7 1.7307692 5.2692308 48 7 1.7307692 5.2692308 49 6 18.4137931 -12.4137931 50 5 7.0000000 -2.0000000 51 5 7.5714286 -2.5714286 52 5 18.4137931 -13.4137931 53 5 26.2222222 -21.2222222 54 5 7.0000000 -2.0000000 55 5 1.7307692 3.2692308 56 4 1.7307692 2.2692308 57 4 18.4137931 -14.4137931 58 4 26.2222222 -22.2222222 59 4 18.4137931 -14.4137931 60 3 7.5714286 -4.5714286 61 3 18.4137931 -15.4137931 62 3 26.2222222 -23.2222222 63 3 1.7307692 1.2692308 64 2 1.7307692 0.2692308 65 2 7.5714286 -5.5714286 66 2 1.7307692 0.2692308 67 2 18.4137931 -16.4137931 68 2 0.0754717 1.9245283 69 2 7.5714286 -5.5714286 70 2 1.7307692 0.2692308 71 2 1.7307692 0.2692308 72 2 1.7307692 0.2692308 73 2 1.7307692 0.2692308 74 1 0.0754717 0.9245283 75 1 18.4137931 -17.4137931 76 1 7.0000000 -6.0000000 77 1 1.7307692 -0.7307692 78 1 1.7307692 -0.7307692 79 0 1.7307692 -1.7307692 80 0 0.0754717 -0.0754717 81 0 1.7307692 -1.7307692 82 0 18.4137931 -18.4137931 83 0 26.2222222 -26.2222222 84 0 1.7307692 -1.7307692 85 0 0.0754717 -0.0754717 86 0 0.0754717 -0.0754717 87 0 1.7307692 -1.7307692 88 0 1.7307692 -1.7307692 89 0 0.0754717 -0.0754717 90 0 0.0754717 -0.0754717 91 0 0.0754717 -0.0754717 92 0 0.0754717 -0.0754717 93 0 0.0754717 -0.0754717 94 0 0.0754717 -0.0754717 95 0 0.0754717 -0.0754717 96 0 7.5714286 -7.5714286 97 0 0.0754717 -0.0754717 98 0 0.0754717 -0.0754717 99 0 1.7307692 -1.7307692 100 0 0.0754717 -0.0754717 101 0 0.0754717 -0.0754717 102 0 1.7307692 -1.7307692 103 0 26.2222222 -26.2222222 104 0 1.7307692 -1.7307692 105 0 1.7307692 -1.7307692 106 0 0.0754717 -0.0754717 107 0 0.0754717 -0.0754717 108 0 7.5714286 -7.5714286 109 0 0.0754717 -0.0754717 110 0 0.0754717 -0.0754717 111 0 0.0754717 -0.0754717 112 0 0.0754717 -0.0754717 113 0 1.7307692 -1.7307692 114 0 0.0754717 -0.0754717 115 0 0.0754717 -0.0754717 116 0 18.4137931 -18.4137931 117 0 0.0754717 -0.0754717 118 0 18.4137931 -18.4137931 119 0 1.7307692 -1.7307692 120 0 1.7307692 -1.7307692 121 0 1.7307692 -1.7307692 122 0 0.0754717 -0.0754717 123 0 1.7307692 -1.7307692 124 0 0.0754717 -0.0754717 125 0 0.0754717 -0.0754717 126 0 1.7307692 -1.7307692 127 0 7.5714286 -7.5714286 128 0 0.0754717 -0.0754717 129 0 0.0754717 -0.0754717 130 0 0.0754717 -0.0754717 131 0 0.0754717 -0.0754717 132 0 7.5714286 -7.5714286 133 0 0.0754717 -0.0754717 134 0 1.7307692 -1.7307692 135 0 1.7307692 -1.7307692 136 0 0.0754717 -0.0754717 137 0 1.7307692 -1.7307692 138 0 0.0754717 -0.0754717 139 0 0.0754717 -0.0754717 140 0 0.0754717 -0.0754717 141 0 26.2222222 -26.2222222 142 0 26.2222222 -26.2222222 143 0 1.7307692 -1.7307692 144 0 0.0754717 -0.0754717 145 0 1.7307692 -1.7307692 146 0 1.7307692 -1.7307692 147 0 26.2222222 -26.2222222 148 0 0.0754717 -0.0754717 149 0 0.0754717 -0.0754717 150 0 1.7307692 -1.7307692 151 0 1.7307692 -1.7307692 152 0 0.0754717 -0.0754717 153 0 1.7307692 -1.7307692 154 0 0.0754717 -0.0754717 155 0 0.0754717 -0.0754717 156 0 1.7307692 -1.7307692 157 0 1.7307692 -1.7307692 158 0 1.7307692 -1.7307692 159 0 7.0000000 -7.0000000 160 0 0.0754717 -0.0754717 161 0 0.0754717 -0.0754717 162 0 7.0000000 -7.0000000 163 0 0.0754717 -0.0754717 164 0 0.0754717 -0.0754717 165 0 7.5714286 -7.5714286 166 0 1.7307692 -1.7307692 167 0 1.7307692 -1.7307692 168 0 1.7307692 -1.7307692 169 0 0.0754717 -0.0754717 170 0 26.2222222 -26.2222222 171 0 0.0754717 -0.0754717 172 0 0.0754717 -0.0754717 173 5 1.7307692 3.2692308 174 1 1.7307692 -0.7307692 175 0 1.7307692 -1.7307692 176 0 1.7307692 -1.7307692 177 37 18.4137931 18.5862069 178 1 0.0754717 0.9245283 179 0 0.0754717 -0.0754717 180 0 18.4137931 -18.4137931 181 0 1.7307692 -1.7307692 182 0 0.0754717 -0.0754717 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/wessaorg/rcomp/tmp/4yj9l1356878060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > 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$Forecasts),main='Kernel Density Plot of Predictions') + par(op) + } > if(par2!='none') { + plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted') + } > dev.off() null device 1 > if (par2 == 'none') { + detcoef <- cor(result$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="/var/wessaorg/rcomp/tmp/5y9b01356878060.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="/var/wessaorg/rcomp/tmp/6c9d11356878060.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="/var/wessaorg/rcomp/tmp/7h1t41356878060.tab") + } > > try(system("convert tmp/2oijx1356878060.ps tmp/2oijx1356878060.png",intern=TRUE)) character(0) > try(system("convert tmp/32lzx1356878060.ps tmp/32lzx1356878060.png",intern=TRUE)) character(0) > try(system("convert tmp/4yj9l1356878060.ps tmp/4yj9l1356878060.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.310 0.385 5.727