R version 2.13.0 (2011-04-13) Copyright (C) 2011 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-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. > par9 = 'COLLES all' > par8 = 'ATTLES all' > par7 = 'all' > par6 = 'bachelor' > par5 = 'male' > par4 = 'no' > par3 = '3' > par2 = 'none' > par1 = '0' > par9 <- 'COLLES all' > par8 <- 'ATTLES all' > par7 <- 'all' > par6 <- 'bachelor' > par5 <- 'male' > par4 <- 'no' > par3 <- '3' > par2 <- 'none' > par1 <- '0' > #'GNU S' R Code compiled by R2WASP v. 1.2.291 () > #Author: root > #To cite this work: Wessa P., 2012, Recursive Partitioning (Regression Trees) in Information Management (v1.0.8) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_regression_trees.wasp/ > #Source of accompanying publication: > # > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from 'package:survival': untangle.specials The following object(s) are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- as.data.frame(read.table(file='http://www.wessa.net/download/utaut.csv',sep=',',header=T)) > x$U25 <- 6-x$U25 > if(par5 == 'female') x <- x[x$Gender==0,] > if(par5 == 'male') x <- x[x$Gender==1,] > if(par6 == 'prep') x <- x[x$Pop==1,] > if(par6 == 'bachelor') x <- x[x$Pop==0,] > if(par7 != 'all') { + x <- x[x$Year==as.numeric(par7),] + } > cAc <- with(x,cbind( A1, A2, A3, A4, A5, A6, A7, A8, A9,A10)) > cAs <- with(x,cbind(A11,A12,A13,A14,A15,A16,A17,A18,A19,A20)) > cA <- cbind(cAc,cAs) > cCa <- with(x,cbind(C1,C3,C5,C7, C9,C11,C13,C15,C17,C19,C21,C23,C25,C27,C29,C31,C33,C35,C37,C39,C41,C43,C45,C47)) > cCp <- with(x,cbind(C2,C4,C6,C8,C10,C12,C14,C16,C18,C20,C22,C24,C26,C28,C30,C32,C34,C36,C38,C40,C42,C44,C46,C48)) > cC <- cbind(cCa,cCp) > cU <- with(x,cbind(U1,U2,U3,U4,U5,U6,U7,U8,U9,U10,U11,U12,U13,U14,U15,U16,U17,U18,U19,U20,U21,U22,U23,U24,U25,U26,U27,U28,U29,U30,U31,U32,U33)) > cE <- with(x,cbind(BC,NNZFG,MRT,AFL,LPM,LPC,W,WPA)) > cX <- with(x,cbind(X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11,X12,X13,X14,X15,X16,X17,X18)) > if (par8=='ATTLES connected') x <- cAc > if (par8=='ATTLES separate') x <- cAs > if (par8=='ATTLES all') x <- cA > if (par8=='COLLES actuals') x <- cCa > if (par8=='COLLES preferred') x <- cCp > if (par8=='COLLES all') x <- cC > if (par8=='CSUQ') x <- cU > if (par8=='Learning Activities') x <- cE > if (par8=='Exam Items') x <- cX > if (par9=='ATTLES connected') y <- cAc > if (par9=='ATTLES separate') y <- cAs > if (par9=='ATTLES all') y <- cA > if (par9=='COLLES actuals') y <- cCa > if (par9=='COLLES preferred') y <- cCp > if (par9=='COLLES all') y <- cC > if (par9=='CSUQ') y <- cU > if (par9=='Learning Activities') y <- cE > if (par9=='Exam Items') y <- cX > if (par1==0) { + nr <- length(y[,1]) + nc <- length(y[1,]) + mysum <- array(0,dim=nr) + for(jjj in 1:nr) { + for(iii in 1:nc) { + mysum[jjj] = mysum[jjj] + y[jjj,iii] + } + } + y <- mysum + } else { + y <- y[,par1] + } > nx <- cbind(y,x) > colnames(nx) <- c('endo',colnames(x)) > x <- nx > par1=1 > ncol <- length(x[1,]) > for (jjj in 1:ncol) { + x <- x[!is.na(x[,jjj]),] + } > x <- as.data.frame(x) > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "endo" > x[,par1] [1] 200 169 154 162 195 175 139 164 143 157 197 162 181 132 174 156 155 151 [19] 142 152 147 184 157 162 200 166 135 179 192 176 162 161 168 137 216 196 [37] 208 163 149 161 207 151 188 118 161 155 164 163 181 171 162 193 160 187 [55] 194 168 123 154 181 172 189 170 193 170 188 193 178 160 149 145 188 181 [73] 161 197 172 169 201 179 180 177 175 201 171 197 208 169 190 199 199 164 [91] 144 163 172 155 167 166 192 202 191 194 171 164 145 146 181 202 156 179 [109] 177 169 182 142 212 177 186 156 185 220 196 190 169 174 193 182 190 222 [127] 157 197 197 141 182 206 164 175 144 160 168 193 215 203 130 177 188 166 [145] 186 207 214 186 180 99 205 174 172 172 163 158 146 172 185 159 197 164 [163] 179 137 177 157 165 200 172 141 157 163 143 165 167 164 202 143 148 173 [181] 170 169 175 > 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]) 99 118 123 130 132 135 137 139 141 142 143 144 145 146 147 148 149 151 152 154 1 1 1 1 1 1 2 1 2 2 3 2 2 2 1 1 2 2 1 2 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 3 3 5 1 1 3 4 5 5 7 2 3 2 3 6 3 3 7 1 3 175 176 177 178 179 180 181 182 184 185 186 187 188 189 190 191 192 193 194 195 4 1 5 1 4 2 5 3 1 2 3 1 4 1 3 1 2 5 2 1 196 197 199 200 201 202 203 205 206 207 208 212 214 215 216 220 222 2 6 2 3 2 3 1 1 1 2 2 1 1 1 1 1 1 > colnames(x) [1] "endo" "A1" "A2" "A3" "A4" "A5" "A6" "A7" "A8" "A9" [11] "A10" "A11" "A12" "A13" "A14" "A15" "A16" "A17" "A18" "A19" [21] "A20" > colnames(x)[par1] [1] "endo" > x[,par1] [1] 200 169 154 162 195 175 139 164 143 157 197 162 181 132 174 156 155 151 [19] 142 152 147 184 157 162 200 166 135 179 192 176 162 161 168 137 216 196 [37] 208 163 149 161 207 151 188 118 161 155 164 163 181 171 162 193 160 187 [55] 194 168 123 154 181 172 189 170 193 170 188 193 178 160 149 145 188 181 [73] 161 197 172 169 201 179 180 177 175 201 171 197 208 169 190 199 199 164 [91] 144 163 172 155 167 166 192 202 191 194 171 164 145 146 181 202 156 179 [109] 177 169 182 142 212 177 186 156 185 220 196 190 169 174 193 182 190 222 [127] 157 197 197 141 182 206 164 175 144 160 168 193 215 203 130 177 188 166 [145] 186 207 214 186 180 99 205 174 172 172 163 158 146 172 185 159 197 164 [163] 179 137 177 157 165 200 172 141 157 163 143 165 167 164 202 143 148 173 [181] 170 169 175 > 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/1bmz41335773896.tab") + } + } > m Conditional inference tree with 2 terminal nodes Response: endo Inputs: A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20 Number of observations: 183 1) A15 <= 3; criterion = 0.999, statistic = 16.71 2)* weights = 104 1) A15 > 3 3)* weights = 79 > postscript(file="/var/wessaorg/rcomp/tmp/2qm321335773896.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/3kzil1335773896.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 1 200 179.3038 20.6962025 2 169 167.8942 1.1057692 3 154 167.8942 -13.8942308 4 162 179.3038 -17.3037975 5 195 179.3038 15.6962025 6 175 179.3038 -4.3037975 7 139 179.3038 -40.3037975 8 164 167.8942 -3.8942308 9 143 167.8942 -24.8942308 10 157 167.8942 -10.8942308 11 197 179.3038 17.6962025 12 162 167.8942 -5.8942308 13 181 167.8942 13.1057692 14 132 167.8942 -35.8942308 15 174 167.8942 6.1057692 16 156 167.8942 -11.8942308 17 155 167.8942 -12.8942308 18 151 167.8942 -16.8942308 19 142 167.8942 -25.8942308 20 152 167.8942 -15.8942308 21 147 167.8942 -20.8942308 22 184 167.8942 16.1057692 23 157 179.3038 -22.3037975 24 162 179.3038 -17.3037975 25 200 179.3038 20.6962025 26 166 179.3038 -13.3037975 27 135 167.8942 -32.8942308 28 179 179.3038 -0.3037975 29 192 179.3038 12.6962025 30 176 179.3038 -3.3037975 31 162 167.8942 -5.8942308 32 161 167.8942 -6.8942308 33 168 179.3038 -11.3037975 34 137 179.3038 -42.3037975 35 216 179.3038 36.6962025 36 196 179.3038 16.6962025 37 208 179.3038 28.6962025 38 163 167.8942 -4.8942308 39 149 167.8942 -18.8942308 40 161 167.8942 -6.8942308 41 207 167.8942 39.1057692 42 151 167.8942 -16.8942308 43 188 179.3038 8.6962025 44 118 179.3038 -61.3037975 45 161 179.3038 -18.3037975 46 155 167.8942 -12.8942308 47 164 167.8942 -3.8942308 48 163 179.3038 -16.3037975 49 181 167.8942 13.1057692 50 171 167.8942 3.1057692 51 162 179.3038 -17.3037975 52 193 179.3038 13.6962025 53 160 167.8942 -7.8942308 54 187 179.3038 7.6962025 55 194 179.3038 14.6962025 56 168 179.3038 -11.3037975 57 123 179.3038 -56.3037975 58 154 167.8942 -13.8942308 59 181 167.8942 13.1057692 60 172 167.8942 4.1057692 61 189 179.3038 9.6962025 62 170 179.3038 -9.3037975 63 193 167.8942 25.1057692 64 170 167.8942 2.1057692 65 188 179.3038 8.6962025 66 193 179.3038 13.6962025 67 178 179.3038 -1.3037975 68 160 167.8942 -7.8942308 69 149 167.8942 -18.8942308 70 145 167.8942 -22.8942308 71 188 179.3038 8.6962025 72 181 179.3038 1.6962025 73 161 179.3038 -18.3037975 74 197 167.8942 29.1057692 75 172 179.3038 -7.3037975 76 169 167.8942 1.1057692 77 201 167.8942 33.1057692 78 179 179.3038 -0.3037975 79 180 179.3038 0.6962025 80 177 179.3038 -2.3037975 81 175 179.3038 -4.3037975 82 201 179.3038 21.6962025 83 171 167.8942 3.1057692 84 197 167.8942 29.1057692 85 208 167.8942 40.1057692 86 169 167.8942 1.1057692 87 190 179.3038 10.6962025 88 199 179.3038 19.6962025 89 199 179.3038 19.6962025 90 164 167.8942 -3.8942308 91 144 179.3038 -35.3037975 92 163 179.3038 -16.3037975 93 172 179.3038 -7.3037975 94 155 179.3038 -24.3037975 95 167 167.8942 -0.8942308 96 166 167.8942 -1.8942308 97 192 167.8942 24.1057692 98 202 179.3038 22.6962025 99 191 167.8942 23.1057692 100 194 167.8942 26.1057692 101 171 167.8942 3.1057692 102 164 167.8942 -3.8942308 103 145 167.8942 -22.8942308 104 146 179.3038 -33.3037975 105 181 167.8942 13.1057692 106 202 167.8942 34.1057692 107 156 167.8942 -11.8942308 108 179 167.8942 11.1057692 109 177 167.8942 9.1057692 110 169 179.3038 -10.3037975 111 182 167.8942 14.1057692 112 142 167.8942 -25.8942308 113 212 179.3038 32.6962025 114 177 167.8942 9.1057692 115 186 179.3038 6.6962025 116 156 167.8942 -11.8942308 117 185 167.8942 17.1057692 118 220 167.8942 52.1057692 119 196 179.3038 16.6962025 120 190 179.3038 10.6962025 121 169 167.8942 1.1057692 122 174 179.3038 -5.3037975 123 193 179.3038 13.6962025 124 182 179.3038 2.6962025 125 190 167.8942 22.1057692 126 222 179.3038 42.6962025 127 157 167.8942 -10.8942308 128 197 167.8942 29.1057692 129 197 179.3038 17.6962025 130 141 167.8942 -26.8942308 131 182 167.8942 14.1057692 132 206 179.3038 26.6962025 133 164 179.3038 -15.3037975 134 175 167.8942 7.1057692 135 144 167.8942 -23.8942308 136 160 167.8942 -7.8942308 137 168 179.3038 -11.3037975 138 193 167.8942 25.1057692 139 215 179.3038 35.6962025 140 203 167.8942 35.1057692 141 130 167.8942 -37.8942308 142 177 167.8942 9.1057692 143 188 167.8942 20.1057692 144 166 167.8942 -1.8942308 145 186 179.3038 6.6962025 146 207 179.3038 27.6962025 147 214 179.3038 34.6962025 148 186 179.3038 6.6962025 149 180 167.8942 12.1057692 150 99 179.3038 -80.3037975 151 205 167.8942 37.1057692 152 174 167.8942 6.1057692 153 172 167.8942 4.1057692 154 172 167.8942 4.1057692 155 163 167.8942 -4.8942308 156 158 167.8942 -9.8942308 157 146 167.8942 -21.8942308 158 172 167.8942 4.1057692 159 185 179.3038 5.6962025 160 159 167.8942 -8.8942308 161 197 167.8942 29.1057692 162 164 167.8942 -3.8942308 163 179 167.8942 11.1057692 164 137 167.8942 -30.8942308 165 177 179.3038 -2.3037975 166 157 167.8942 -10.8942308 167 165 167.8942 -2.8942308 168 200 179.3038 20.6962025 169 172 179.3038 -7.3037975 170 141 167.8942 -26.8942308 171 157 167.8942 -10.8942308 172 163 167.8942 -4.8942308 173 143 167.8942 -24.8942308 174 165 179.3038 -14.3037975 175 167 167.8942 -0.8942308 176 164 167.8942 -3.8942308 177 202 179.3038 22.6962025 178 143 167.8942 -24.8942308 179 148 167.8942 -19.8942308 180 173 167.8942 5.1057692 181 170 179.3038 -9.3037975 182 169 179.3038 -10.3037975 183 175 179.3038 -4.3037975 > 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/4uwv71335773896.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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/51wr31335773896.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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/6nsfj1335773896.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/7x3x91335773896.tab") + } > > try(system("convert tmp/2qm321335773896.ps tmp/2qm321335773896.png",intern=TRUE)) character(0) > try(system("convert tmp/3kzil1335773896.ps tmp/3kzil1335773896.png",intern=TRUE)) character(0) > try(system("convert tmp/4uwv71335773896.ps tmp/4uwv71335773896.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.359 0.311 3.671