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. > x <- array(list(2 + ,13 + ,12 + ,2 + ,16 + ,11 + ,2 + ,19 + ,14 + ,1 + ,15 + ,12 + ,2 + ,14 + ,21 + ,2 + ,13 + ,12 + ,2 + ,19 + ,22 + ,2 + ,15 + ,11 + ,2 + ,14 + ,10 + ,2 + ,15 + ,13 + ,1 + ,16 + ,10 + ,2 + ,16 + ,8 + ,1 + ,16 + ,15 + ,2 + ,16 + ,14 + ,2 + ,17 + ,10 + ,1 + ,15 + ,14 + ,1 + ,15 + ,14 + ,2 + ,20 + ,11 + ,1 + ,18 + ,10 + ,2 + ,16 + ,13 + ,1 + ,16 + ,7 + ,2 + ,16 + ,14 + ,2 + ,19 + ,12 + ,2 + ,16 + ,14 + ,1 + ,17 + ,11 + ,2 + ,17 + ,9 + ,1 + ,16 + ,11 + ,2 + ,15 + ,15 + ,2 + ,16 + ,14 + ,1 + ,14 + ,13 + ,2 + ,15 + ,9 + ,1 + ,12 + ,15 + ,2 + ,14 + ,10 + ,2 + ,16 + ,11 + ,1 + ,14 + ,13 + ,1 + ,7 + ,8 + ,1 + ,10 + ,20 + ,1 + ,14 + ,12 + ,2 + ,16 + ,10 + ,1 + ,16 + ,10 + ,1 + ,16 + ,9 + ,2 + ,14 + ,14 + ,1 + ,20 + ,8 + ,1 + ,14 + ,14 + ,2 + ,14 + ,11 + ,2 + ,11 + ,13 + ,2 + ,14 + ,9 + ,2 + ,15 + ,11 + ,2 + ,16 + ,15 + ,1 + ,14 + ,11 + ,2 + ,16 + ,10 + ,1 + ,14 + ,14 + ,1 + ,12 + ,18 + ,2 + ,16 + ,14 + ,1 + ,9 + ,11 + ,2 + ,14 + ,12 + ,2 + ,16 + ,13 + ,2 + ,16 + ,9 + ,1 + ,15 + ,10 + ,2 + ,16 + ,15 + ,1 + ,12 + ,20 + ,1 + ,16 + ,12 + ,2 + ,16 + ,12 + ,2 + ,14 + ,14 + ,2 + ,16 + ,13 + ,1 + ,17 + ,11 + ,2 + ,18 + ,17 + ,1 + ,18 + ,12 + ,2 + ,12 + ,13 + ,1 + ,16 + ,14 + ,1 + ,10 + ,13 + ,2 + ,14 + ,15 + ,2 + ,18 + ,13 + ,1 + ,18 + ,10 + ,1 + ,16 + ,11 + ,2 + ,17 + ,19 + ,2 + ,16 + ,13 + ,2 + ,16 + ,17 + ,1 + ,13 + ,13 + ,1 + ,16 + ,9 + ,1 + ,16 + ,11 + ,1 + ,20 + ,10 + ,2 + ,16 + ,9 + ,1 + ,15 + ,12 + ,2 + ,15 + ,12 + ,2 + ,16 + ,13 + ,1 + ,14 + ,13 + ,2 + ,16 + ,12 + ,2 + ,16 + ,15 + ,2 + ,15 + ,22 + ,2 + ,12 + ,13 + ,2 + ,17 + ,15 + ,2 + ,16 + ,13 + ,2 + ,15 + ,15 + ,2 + ,13 + ,10 + ,2 + ,16 + ,11 + ,2 + ,16 + ,16 + ,2 + ,16 + ,11 + ,1 + ,16 + ,11 + ,1 + ,14 + ,10 + ,2 + ,16 + ,10 + ,1 + ,16 + ,16 + ,2 + ,20 + ,12 + ,1 + ,15 + ,11 + ,2 + ,16 + ,16 + ,1 + ,13 + ,19 + ,2 + ,17 + ,11 + ,1 + ,16 + ,16 + ,1 + ,16 + ,15 + ,2 + ,12 + ,24 + ,2 + ,16 + ,14 + ,2 + ,16 + ,15 + ,2 + ,17 + ,11 + ,1 + ,13 + ,15 + ,2 + ,12 + ,12 + ,1 + ,18 + ,10 + ,2 + ,14 + ,14 + ,2 + ,14 + ,13 + ,2 + ,13 + ,9 + ,2 + ,16 + ,15 + ,2 + ,13 + ,15 + ,2 + ,16 + ,14 + ,2 + ,13 + ,11 + ,2 + ,16 + ,8 + ,2 + ,15 + ,11 + ,2 + ,16 + ,11 + ,1 + ,15 + ,8 + ,2 + ,17 + ,10 + ,2 + ,15 + ,11 + ,2 + ,12 + ,13 + ,1 + ,16 + ,11 + ,1 + ,10 + ,20 + ,2 + ,16 + ,10 + ,1 + ,12 + ,15 + ,1 + ,14 + ,12 + ,2 + ,15 + ,14 + ,1 + ,13 + ,23 + ,1 + ,15 + ,14 + ,2 + ,11 + ,16 + ,2 + ,12 + ,11 + ,1 + ,8 + ,12 + ,2 + ,16 + ,10 + ,1 + ,15 + ,14 + ,2 + ,17 + ,12 + ,1 + ,16 + ,12 + ,2 + ,10 + ,11 + ,2 + ,18 + ,12 + ,1 + ,13 + ,13 + ,1 + ,16 + ,11 + ,1 + ,13 + ,19 + ,2 + ,10 + ,12 + ,2 + ,15 + ,17 + ,1 + ,16 + ,9 + ,2 + ,16 + ,12 + ,2 + ,14 + ,19 + ,2 + ,10 + ,18 + ,2 + ,17 + ,15 + ,2 + ,13 + ,14 + ,2 + ,15 + ,11 + ,2 + ,16 + ,9 + ,2 + ,12 + ,18 + ,2 + ,13 + ,16) + ,dim=c(3 + ,162) + ,dimnames=list(c('Gender' + ,'Learning' + ,'Depression') + ,1:162)) > y <- array(NA,dim=c(3,162),dimnames=list(c('Gender','Learning','Depression'),1:162)) > 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 = 'quantiles' > par1 = '2' > 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 <- 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] "Learning" > x[,par1] [1] 13 16 19 15 14 13 19 15 14 15 16 16 16 16 17 15 15 20 18 16 16 16 19 16 17 [26] 17 16 15 16 14 15 12 14 16 14 7 10 14 16 16 16 14 20 14 14 11 14 15 16 14 [51] 16 14 12 16 9 14 16 16 15 16 12 16 16 14 16 17 18 18 12 16 10 14 18 18 16 [76] 17 16 16 13 16 16 20 16 15 15 16 14 16 16 15 12 17 16 15 13 16 16 16 16 14 [101] 16 16 20 15 16 13 17 16 16 12 16 16 17 13 12 18 14 14 13 16 13 16 13 16 15 [126] 16 15 17 15 12 16 10 16 12 14 15 13 15 11 12 8 16 15 17 16 10 18 13 16 13 [151] 10 15 16 16 14 10 17 13 15 16 12 13 > 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]) [ 7,15) [15,17) [17,20] 57 80 25 > colnames(x) [1] "Gender" "Learning" "Depression" > colnames(x)[par1] [1] "Learning" > x[,par1] [1] [ 7,15) [15,17) [17,20] [15,17) [ 7,15) [ 7,15) [17,20] [15,17) [ 7,15) [10] [15,17) [15,17) [15,17) [15,17) [15,17) [17,20] [15,17) [15,17) [17,20] [19] [17,20] [15,17) [15,17) [15,17) [17,20] [15,17) [17,20] [17,20] [15,17) [28] [15,17) [15,17) [ 7,15) [15,17) [ 7,15) [ 7,15) [15,17) [ 7,15) [ 7,15) [37] [ 7,15) [ 7,15) [15,17) [15,17) [15,17) [ 7,15) [17,20] [ 7,15) [ 7,15) [46] [ 7,15) [ 7,15) [15,17) [15,17) [ 7,15) [15,17) [ 7,15) [ 7,15) [15,17) [55] [ 7,15) [ 7,15) [15,17) [15,17) [15,17) [15,17) [ 7,15) [15,17) [15,17) [64] [ 7,15) [15,17) [17,20] [17,20] [17,20] [ 7,15) [15,17) [ 7,15) [ 7,15) [73] [17,20] [17,20] [15,17) [17,20] [15,17) [15,17) [ 7,15) [15,17) [15,17) [82] [17,20] [15,17) [15,17) [15,17) [15,17) [ 7,15) [15,17) [15,17) [15,17) [91] [ 7,15) [17,20] [15,17) [15,17) [ 7,15) [15,17) [15,17) [15,17) [15,17) [100] [ 7,15) [15,17) [15,17) [17,20] [15,17) [15,17) [ 7,15) [17,20] [15,17) [109] [15,17) [ 7,15) [15,17) [15,17) [17,20] [ 7,15) [ 7,15) [17,20] [ 7,15) [118] [ 7,15) [ 7,15) [15,17) [ 7,15) [15,17) [ 7,15) [15,17) [15,17) [15,17) [127] [15,17) [17,20] [15,17) [ 7,15) [15,17) [ 7,15) [15,17) [ 7,15) [ 7,15) [136] [15,17) [ 7,15) [15,17) [ 7,15) [ 7,15) [ 7,15) [15,17) [15,17) [17,20] [145] [15,17) [ 7,15) [17,20] [ 7,15) [15,17) [ 7,15) [ 7,15) [15,17) [15,17) [154] [15,17) [ 7,15) [ 7,15) [17,20] [ 7,15) [15,17) [15,17) [ 7,15) [ 7,15) Levels: [ 7,15) [15,17) [17,20] > 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/1cw8d1323538905.tab") + } + } > m Conditional inference tree with 2 terminal nodes Response: as.factor(Learning) Inputs: Gender, Depression Number of observations: 162 1) Depression <= 17; criterion = 0.993, statistic = 11.261 2)* weights = 147 1) Depression > 17 3)* weights = 15 > postscript(file="/var/wessaorg/rcomp/tmp/2mukb1323538905.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/3a6hd1323538905.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) + } > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } [,1] [,2] [1,] 1 2 [2,] 2 2 [3,] 3 2 [4,] 2 2 [5,] 1 1 [6,] 1 2 [7,] 3 1 [8,] 2 2 [9,] 1 2 [10,] 2 2 [11,] 2 2 [12,] 2 2 [13,] 2 2 [14,] 2 2 [15,] 3 2 [16,] 2 2 [17,] 2 2 [18,] 3 2 [19,] 3 2 [20,] 2 2 [21,] 2 2 [22,] 2 2 [23,] 3 2 [24,] 2 2 [25,] 3 2 [26,] 3 2 [27,] 2 2 [28,] 2 2 [29,] 2 2 [30,] 1 2 [31,] 2 2 [32,] 1 2 [33,] 1 2 [34,] 2 2 [35,] 1 2 [36,] 1 2 [37,] 1 1 [38,] 1 2 [39,] 2 2 [40,] 2 2 [41,] 2 2 [42,] 1 2 [43,] 3 2 [44,] 1 2 [45,] 1 2 [46,] 1 2 [47,] 1 2 [48,] 2 2 [49,] 2 2 [50,] 1 2 [51,] 2 2 [52,] 1 2 [53,] 1 1 [54,] 2 2 [55,] 1 2 [56,] 1 2 [57,] 2 2 [58,] 2 2 [59,] 2 2 [60,] 2 2 [61,] 1 1 [62,] 2 2 [63,] 2 2 [64,] 1 2 [65,] 2 2 [66,] 3 2 [67,] 3 2 [68,] 3 2 [69,] 1 2 [70,] 2 2 [71,] 1 2 [72,] 1 2 [73,] 3 2 [74,] 3 2 [75,] 2 2 [76,] 3 1 [77,] 2 2 [78,] 2 2 [79,] 1 2 [80,] 2 2 [81,] 2 2 [82,] 3 2 [83,] 2 2 [84,] 2 2 [85,] 2 2 [86,] 2 2 [87,] 1 2 [88,] 2 2 [89,] 2 2 [90,] 2 1 [91,] 1 2 [92,] 3 2 [93,] 2 2 [94,] 2 2 [95,] 1 2 [96,] 2 2 [97,] 2 2 [98,] 2 2 [99,] 2 2 [100,] 1 2 [101,] 2 2 [102,] 2 2 [103,] 3 2 [104,] 2 2 [105,] 2 2 [106,] 1 1 [107,] 3 2 [108,] 2 2 [109,] 2 2 [110,] 1 1 [111,] 2 2 [112,] 2 2 [113,] 3 2 [114,] 1 2 [115,] 1 2 [116,] 3 2 [117,] 1 2 [118,] 1 2 [119,] 1 2 [120,] 2 2 [121,] 1 2 [122,] 2 2 [123,] 1 2 [124,] 2 2 [125,] 2 2 [126,] 2 2 [127,] 2 2 [128,] 3 2 [129,] 2 2 [130,] 1 2 [131,] 2 2 [132,] 1 1 [133,] 2 2 [134,] 1 2 [135,] 1 2 [136,] 2 2 [137,] 1 1 [138,] 2 2 [139,] 1 2 [140,] 1 2 [141,] 1 2 [142,] 2 2 [143,] 2 2 [144,] 3 2 [145,] 2 2 [146,] 1 2 [147,] 3 2 [148,] 1 2 [149,] 2 2 [150,] 1 1 [151,] 1 2 [152,] 2 2 [153,] 2 2 [154,] 2 2 [155,] 1 1 [156,] 1 1 [157,] 3 2 [158,] 1 2 [159,] 2 2 [160,] 2 2 [161,] 1 1 [162,] 1 2 [ 7,15) [15,17) [17,20] [ 7,15) 12 45 0 [15,17) 1 79 0 [17,20] 2 23 0 > postscript(file="/var/wessaorg/rcomp/tmp/4jhqp1323538905.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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/523cm1323538905.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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/61qlj1323538905.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/7c9qt1323538905.tab") + } > > try(system("convert tmp/2mukb1323538905.ps tmp/2mukb1323538905.png",intern=TRUE)) character(0) > try(system("convert tmp/3a6hd1323538905.ps tmp/3a6hd1323538905.png",intern=TRUE)) character(0) > try(system("convert tmp/4jhqp1323538905.ps tmp/4jhqp1323538905.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.361 0.197 3.034