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 = 'female' > par4 = 'no' > par3 = '3' > par2 = 'none' > par1 = '0' > par9 <- 'COLLES all' > par8 <- 'ATTLES all' > par7 <- 'all' > par6 <- 'bachelor' > par5 <- 'female' > 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] 176 150 156 172 177 208 165 192 174 140 183 173 152 161 144 163 159 170 [19] 129 170 174 181 144 164 165 162 163 188 149 165 161 158 170 180 176 159 [37] 172 162 180 198 124 159 178 180 176 183 149 168 186 159 163 155 167 199 [55] 182 192 144 218 184 162 227 153 193 206 144 170 169 169 179 154 205 144 [73] 185 174 169 185 176 184 177 178 182 168 189 179 167 176 188 115 222 172 [91] 168 219 197 158 169 193 169 179 170 160 180 183 166 191 181 180 158 159 [109] 175 178 182 184 189 181 193 175 167 160 163 202 176 181 146 194 202 185 [127] 216 141 190 176 207 163 210 196 163 197 215 171 211 189 169 189 141 194 [145] 171 141 207 170 187 188 188 204 163 214 172 173 188 202 211 173 186 192 [163] 219 180 210 164 175 198 214 180 210 197 146 203 211 165 169 163 176 181 [181] 148 167 144 191 > 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]) 115 124 129 140 141 144 146 148 149 150 152 153 154 155 156 158 159 160 161 162 1 1 1 1 3 6 2 1 2 1 1 1 1 1 1 3 5 2 2 3 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 8 2 4 1 4 3 7 6 2 4 3 3 3 8 2 3 3 7 5 3 183 184 185 186 187 188 189 190 191 192 193 194 196 197 198 199 202 203 204 205 3 3 3 2 1 5 4 1 2 3 3 2 1 3 2 1 3 1 1 1 206 207 208 210 211 214 215 216 218 219 222 227 1 2 1 3 3 2 1 1 1 2 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] 176 150 156 172 177 208 165 192 174 140 183 173 152 161 144 163 159 170 [19] 129 170 174 181 144 164 165 162 163 188 149 165 161 158 170 180 176 159 [37] 172 162 180 198 124 159 178 180 176 183 149 168 186 159 163 155 167 199 [55] 182 192 144 218 184 162 227 153 193 206 144 170 169 169 179 154 205 144 [73] 185 174 169 185 176 184 177 178 182 168 189 179 167 176 188 115 222 172 [91] 168 219 197 158 169 193 169 179 170 160 180 183 166 191 181 180 158 159 [109] 175 178 182 184 189 181 193 175 167 160 163 202 176 181 146 194 202 185 [127] 216 141 190 176 207 163 210 196 163 197 215 171 211 189 169 189 141 194 [145] 171 141 207 170 187 188 188 204 163 214 172 173 188 202 211 173 186 192 [163] 219 180 210 164 175 198 214 180 210 197 146 203 211 165 169 163 176 181 [181] 148 167 144 191 > 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/13uig1335773778.tab") + } + } > m Conditional inference tree with 3 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: 184 1) A12 <= 4; criterion = 1, statistic = 18.185 2) A8 <= 4; criterion = 0.996, statistic = 13.83 3)* weights = 122 2) A8 > 4 4)* weights = 32 1) A12 > 4 5)* weights = 30 > postscript(file="/var/wessaorg/rcomp/tmp/26mpx1335773778.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/3akeh1335773778.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 1 176 189.1000 -13.1000000 2 150 171.4262 -21.4262295 3 156 171.4262 -15.4262295 4 172 171.4262 0.5737705 5 177 171.4262 5.5737705 6 208 171.4262 36.5737705 7 165 171.4262 -6.4262295 8 192 184.9062 7.0937500 9 174 171.4262 2.5737705 10 140 171.4262 -31.4262295 11 183 171.4262 11.5737705 12 173 171.4262 1.5737705 13 152 171.4262 -19.4262295 14 161 171.4262 -10.4262295 15 144 171.4262 -27.4262295 16 163 171.4262 -8.4262295 17 159 171.4262 -12.4262295 18 170 171.4262 -1.4262295 19 129 171.4262 -42.4262295 20 170 171.4262 -1.4262295 21 174 171.4262 2.5737705 22 181 184.9062 -3.9062500 23 144 171.4262 -27.4262295 24 164 184.9062 -20.9062500 25 165 189.1000 -24.1000000 26 162 171.4262 -9.4262295 27 163 171.4262 -8.4262295 28 188 171.4262 16.5737705 29 149 171.4262 -22.4262295 30 165 171.4262 -6.4262295 31 161 171.4262 -10.4262295 32 158 184.9062 -26.9062500 33 170 171.4262 -1.4262295 34 180 171.4262 8.5737705 35 176 171.4262 4.5737705 36 159 171.4262 -12.4262295 37 172 171.4262 0.5737705 38 162 171.4262 -9.4262295 39 180 184.9062 -4.9062500 40 198 184.9062 13.0937500 41 124 171.4262 -47.4262295 42 159 171.4262 -12.4262295 43 178 171.4262 6.5737705 44 180 171.4262 8.5737705 45 176 171.4262 4.5737705 46 183 171.4262 11.5737705 47 149 171.4262 -22.4262295 48 168 184.9062 -16.9062500 49 186 171.4262 14.5737705 50 159 171.4262 -12.4262295 51 163 171.4262 -8.4262295 52 155 171.4262 -16.4262295 53 167 171.4262 -4.4262295 54 199 171.4262 27.5737705 55 182 171.4262 10.5737705 56 192 171.4262 20.5737705 57 144 184.9062 -40.9062500 58 218 184.9062 33.0937500 59 184 184.9062 -0.9062500 60 162 171.4262 -9.4262295 61 227 184.9062 42.0937500 62 153 171.4262 -18.4262295 63 193 189.1000 3.9000000 64 206 184.9062 21.0937500 65 144 171.4262 -27.4262295 66 170 171.4262 -1.4262295 67 169 171.4262 -2.4262295 68 169 171.4262 -2.4262295 69 179 184.9062 -5.9062500 70 154 171.4262 -17.4262295 71 205 171.4262 33.5737705 72 144 171.4262 -27.4262295 73 185 171.4262 13.5737705 74 174 171.4262 2.5737705 75 169 184.9062 -15.9062500 76 185 171.4262 13.5737705 77 176 171.4262 4.5737705 78 184 184.9062 -0.9062500 79 177 171.4262 5.5737705 80 178 171.4262 6.5737705 81 182 171.4262 10.5737705 82 168 171.4262 -3.4262295 83 189 171.4262 17.5737705 84 179 184.9062 -5.9062500 85 167 171.4262 -4.4262295 86 176 171.4262 4.5737705 87 188 171.4262 16.5737705 88 115 171.4262 -56.4262295 89 222 189.1000 32.9000000 90 172 171.4262 0.5737705 91 168 171.4262 -3.4262295 92 219 189.1000 29.9000000 93 197 184.9062 12.0937500 94 158 171.4262 -13.4262295 95 169 171.4262 -2.4262295 96 193 189.1000 3.9000000 97 169 189.1000 -20.1000000 98 179 189.1000 -10.1000000 99 170 184.9062 -14.9062500 100 160 171.4262 -11.4262295 101 180 171.4262 8.5737705 102 183 189.1000 -6.1000000 103 166 171.4262 -5.4262295 104 191 189.1000 1.9000000 105 181 171.4262 9.5737705 106 180 184.9062 -4.9062500 107 158 171.4262 -13.4262295 108 159 189.1000 -30.1000000 109 175 189.1000 -14.1000000 110 178 171.4262 6.5737705 111 182 184.9062 -2.9062500 112 184 171.4262 12.5737705 113 189 189.1000 -0.1000000 114 181 171.4262 9.5737705 115 193 171.4262 21.5737705 116 175 171.4262 3.5737705 117 167 189.1000 -22.1000000 118 160 171.4262 -11.4262295 119 163 184.9062 -21.9062500 120 202 171.4262 30.5737705 121 176 171.4262 4.5737705 122 181 171.4262 9.5737705 123 146 171.4262 -25.4262295 124 194 189.1000 4.9000000 125 202 184.9062 17.0937500 126 185 189.1000 -4.1000000 127 216 189.1000 26.9000000 128 141 171.4262 -30.4262295 129 190 189.1000 0.9000000 130 176 189.1000 -13.1000000 131 207 171.4262 35.5737705 132 163 171.4262 -8.4262295 133 210 189.1000 20.9000000 134 196 184.9062 11.0937500 135 163 189.1000 -26.1000000 136 197 171.4262 25.5737705 137 215 171.4262 43.5737705 138 171 184.9062 -13.9062500 139 211 171.4262 39.5737705 140 189 189.1000 -0.1000000 141 169 171.4262 -2.4262295 142 189 171.4262 17.5737705 143 141 171.4262 -30.4262295 144 194 189.1000 4.9000000 145 171 184.9062 -13.9062500 146 141 171.4262 -30.4262295 147 207 171.4262 35.5737705 148 170 171.4262 -1.4262295 149 187 171.4262 15.5737705 150 188 189.1000 -1.1000000 151 188 189.1000 -1.1000000 152 204 171.4262 32.5737705 153 163 184.9062 -21.9062500 154 214 184.9062 29.0937500 155 172 171.4262 0.5737705 156 173 171.4262 1.5737705 157 188 171.4262 16.5737705 158 202 184.9062 17.0937500 159 211 184.9062 26.0937500 160 173 171.4262 1.5737705 161 186 184.9062 1.0937500 162 192 189.1000 2.9000000 163 219 171.4262 47.5737705 164 180 171.4262 8.5737705 165 210 189.1000 20.9000000 166 164 184.9062 -20.9062500 167 175 171.4262 3.5737705 168 198 189.1000 8.9000000 169 214 184.9062 29.0937500 170 180 171.4262 8.5737705 171 210 171.4262 38.5737705 172 197 189.1000 7.9000000 173 146 171.4262 -25.4262295 174 203 189.1000 13.9000000 175 211 171.4262 39.5737705 176 165 171.4262 -6.4262295 177 169 171.4262 -2.4262295 178 163 171.4262 -8.4262295 179 176 171.4262 4.5737705 180 181 171.4262 9.5737705 181 148 171.4262 -23.4262295 182 167 171.4262 -4.4262295 183 144 171.4262 -27.4262295 184 191 171.4262 19.5737705 > 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/4saig1335773778.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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/58mji1335773778.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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/6rep61335773778.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/7v0ca1335773778.tab") + } > > try(system("convert tmp/26mpx1335773778.ps tmp/26mpx1335773778.png",intern=TRUE)) character(0) > try(system("convert tmp/3akeh1335773778.ps tmp/3akeh1335773778.png",intern=TRUE)) character(0) > try(system("convert tmp/4saig1335773778.ps tmp/4saig1335773778.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.438 0.339 3.855