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(1 + ,1 + ,4 + ,0 + ,2 + ,1 + ,1 + ,0 + ,0 + ,2 + ,0 + ,1 + ,4 + ,1 + ,1.5 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,1 + ,1 + ,1 + ,0 + ,1 + ,2 + ,1 + ,1 + ,0 + ,1 + ,2 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,1 + ,4 + ,1 + ,2 + ,1 + ,1 + ,1 + ,0 + ,2 + ,0 + ,0 + ,4 + ,0 + ,2 + ,0 + ,1 + ,0 + ,1 + ,0 + ,0 + ,1 + ,2 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,2 + ,0 + ,0 + ,0 + ,NA + ,NA + ,1 + ,1 + ,0 + ,1 + ,2 + ,1 + ,1 + ,1 + ,0 + ,2 + ,1 + ,1 + ,0 + ,1 + ,0.5 + ,0 + ,1 + ,0 + ,1 + ,2 + ,0 + ,0 + ,2 + ,1 + ,0 + ,1 + ,1 + ,2 + ,1 + ,2 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0 + ,2 + ,NA + ,NA + ,1 + ,0 + ,0 + ,NA + ,NA + ,1 + ,1 + ,3 + ,1 + ,2 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,0 + ,NA + ,NA + ,0 + ,0 + ,0 + ,NA + ,NA + ,0 + ,0 + ,1 + ,0 + ,2 + ,1 + ,1 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,0 + ,0.5 + ,1 + ,1 + ,4 + ,0 + ,2 + ,0 + ,0 + ,0 + ,1 + ,0.5 + ,0 + ,0 + ,1 + ,NA + ,NA + ,0 + ,0 + ,0 + ,1 + ,0.5 + ,1 + ,1 + ,0 + ,NA + ,NA + ,1 + ,1 + ,4 + ,0 + ,2 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,1 + ,1 + ,1 + ,4 + ,1 + ,2 + ,1 + ,1 + ,0 + ,1 + ,1 + ,1 + ,1 + ,4 + ,1 + ,2 + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,0.5 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,1 + ,4 + ,1 + ,2 + ,0 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,0 + ,1 + ,1 + ,1 + ,4 + ,1 + ,2 + ,0 + ,0 + ,4 + ,0 + ,0.5 + ,0 + ,1 + ,0 + ,1 + ,2 + ,1 + ,1 + ,1 + ,1 + ,2 + ,0 + ,1 + ,0 + ,1 + ,2 + ,0 + ,0 + ,4 + ,NA + ,NA + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,2 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0.5 + ,0 + ,1 + ,4 + ,NA + ,NA + ,0 + ,0 + ,4 + ,0 + ,2 + ,0 + ,0 + ,0 + ,NA + ,NA + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,1 + ,4 + ,1 + ,2 + ,1 + ,1 + ,0 + ,1 + ,1 + ,1 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,2 + ,1 + ,2 + ,0 + ,1 + ,0 + ,0 + ,1 + ,0 + ,1 + ,0 + ,1 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,4 + ,1 + ,1 + ,1 + ,1 + ,4 + ,1 + ,2 + ,0 + ,1 + ,2 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,0 + ,0 + ,0 + ,0 + ,1 + ,4 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,2 + ,1 + ,0 + ,0 + ,1 + ,2 + ,0 + ,0 + ,1 + ,1 + ,2 + ,1 + ,1 + ,2 + ,1 + ,2 + ,1 + ,0 + ,0 + ,1 + ,2 + ,1 + ,1 + ,2 + ,1 + ,2 + ,0 + ,0 + ,0 + ,1 + ,2 + ,0 + ,0 + ,4 + ,1 + ,2 + ,0 + ,0 + ,4 + ,1 + ,2 + ,1 + ,0 + ,0 + ,1 + ,2 + ,0 + ,0 + ,0 + ,NA + ,NA + ,0 + ,0 + ,4 + ,1 + ,2 + ,1 + ,0 + ,0 + ,NA + ,NA + ,1 + ,1 + ,4 + ,1 + ,2 + ,0 + ,0 + ,2 + ,1 + ,2 + ,0 + ,0 + ,2 + ,NA + ,NA + ,1 + ,1 + ,0 + ,0 + ,0 + ,1 + ,1 + ,0 + ,1 + ,2 + ,1 + ,1 + ,4 + ,NA + ,NA + ,0 + ,1 + ,0 + ,1 + ,2 + ,1 + ,1 + ,0 + ,1 + ,2 + ,1 + ,1 + ,0 + ,1 + ,2 + ,1 + ,1 + ,4 + ,1 + ,2 + ,1 + ,1 + ,4 + ,1 + ,2 + ,0 + ,0 + ,0 + ,NA + ,NA + ,0 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,2 + ,0 + ,0 + ,0 + ,0 + ,1 + ,1 + ,2 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,2 + ,1 + ,2 + ,0 + ,1 + ,1 + ,0 + ,0) + ,dim=c(5 + ,105) + ,dimnames=list(c('pre' + ,'post1' + ,'post2' + ,'post3' + ,'post4') + ,1:105)) > y <- array(NA,dim=c(5,105),dimnames=list(c('pre','post1','post2','post3','post4'),1:105)) > 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 = '' > par2 = 'none' > par1 = '5' > par4 <- 'no' > par3 <- '' > par2 <- 'none' > par1 <- '5' > #'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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_regression_trees.wasp/ > #Source of accompanying publication: > # > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Hmisc library by Frank E Harrell Jr Type library(help='Hmisc'), ?Overview, or ?Hmisc.Overview') to see overall documentation. NOTE:Hmisc no longer redefines [.factor to drop unused levels when subsetting. To get the old behavior of Hmisc type dropUnusedLevels(). Attaching package: 'Hmisc' The following object(s) are masked from 'package:survival': untangle.specials The following object(s) are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "post4" > x[,par1] [1] 2.0 2.0 1.5 0.0 1.0 2.0 2.0 1.0 2.0 2.0 2.0 0.0 0.0 2.0 2.0 2.0 0.5 2.0 0.0 [20] 2.0 0.0 2.0 0.0 2.0 1.0 0.5 2.0 0.5 0.5 2.0 0.0 1.0 2.0 1.0 2.0 0.0 0.5 0.0 [39] 2.0 0.0 1.0 2.0 0.5 2.0 2.0 2.0 0.0 0.0 0.5 2.0 0.0 2.0 1.0 0.0 2.0 1.0 2.0 [58] 0.0 1.0 2.0 0.0 0.0 0.0 0.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 [77] 2.0 0.0 2.0 2.0 2.0 2.0 2.0 2.0 0.0 0.0 2.0 0.0 2.0 0.0 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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 0.5 1 1.5 2 24 7 9 1 49 > colnames(x) [1] "pre" "post1" "post2" "post3" "post4" > colnames(x)[par1] [1] "post4" > x[,par1] [1] 2.0 2.0 1.5 0.0 1.0 2.0 2.0 1.0 2.0 2.0 2.0 0.0 0.0 2.0 2.0 2.0 0.5 2.0 0.0 [20] 2.0 0.0 2.0 0.0 2.0 1.0 0.5 2.0 0.5 0.5 2.0 0.0 1.0 2.0 1.0 2.0 0.0 0.5 0.0 [39] 2.0 0.0 1.0 2.0 0.5 2.0 2.0 2.0 0.0 0.0 0.5 2.0 0.0 2.0 1.0 0.0 2.0 1.0 2.0 [58] 0.0 1.0 2.0 0.0 0.0 0.0 0.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 [77] 2.0 0.0 2.0 2.0 2.0 2.0 2.0 2.0 0.0 0.0 2.0 0.0 2.0 0.0 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/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/fisher/rcomp/tmp/1bf9e1354811914.tab") + } + } > m Conditional inference tree with 3 terminal nodes Response: post4 Inputs: pre, post1, post2, post3 Number of observations: 90 1) post3 <= 0; criterion = 0.997, statistic = 11.142 2)* weights = 29 1) post3 > 0 3) post2 <= 2; criterion = 0.957, statistic = 6.481 4)* weights = 45 3) post2 > 2 5)* weights = 16 > postscript(file="/var/fisher/rcomp/tmp/2bj7z1354811914.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/fisher/rcomp/tmp/3z1nv1354811914.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 2.0 0.7931034 1.2068966 2 2.0 0.7931034 1.2068966 3 1.5 1.9062500 -0.4062500 4 0.0 0.7931034 -0.7931034 5 1.0 1.3000000 -0.3000000 6 2.0 1.3000000 0.7000000 7 2.0 1.3000000 0.7000000 8 1.0 1.3000000 -0.3000000 9 2.0 1.9062500 0.0937500 10 2.0 0.7931034 1.2068966 11 2.0 0.7931034 1.2068966 12 0.0 1.3000000 -1.3000000 13 0.0 1.3000000 -1.3000000 14 2.0 0.7931034 1.2068966 15 2.0 1.3000000 0.7000000 16 2.0 0.7931034 1.2068966 17 0.5 1.3000000 -0.8000000 18 2.0 1.3000000 0.7000000 19 0.0 1.3000000 -1.3000000 20 2.0 1.3000000 0.7000000 21 0.0 0.7931034 -0.7931034 22 2.0 1.9062500 0.0937500 23 0.0 1.3000000 -1.3000000 24 2.0 0.7931034 1.2068966 25 1.0 1.3000000 -0.3000000 26 0.5 0.7931034 -0.2931034 27 2.0 0.7931034 1.2068966 28 0.5 1.3000000 -0.8000000 29 0.5 1.3000000 -0.8000000 30 2.0 0.7931034 1.2068966 31 0.0 1.3000000 -1.3000000 32 1.0 1.3000000 -0.3000000 33 2.0 1.9062500 0.0937500 34 1.0 1.3000000 -0.3000000 35 2.0 1.9062500 0.0937500 36 0.0 0.7931034 -0.7931034 37 0.5 1.3000000 -0.8000000 38 0.0 1.3000000 -1.3000000 39 2.0 1.9062500 0.0937500 40 0.0 0.7931034 -0.7931034 41 1.0 0.7931034 0.2068966 42 2.0 1.9062500 0.0937500 43 0.5 0.7931034 -0.2931034 44 2.0 1.3000000 0.7000000 45 2.0 1.3000000 0.7000000 46 2.0 1.3000000 0.7000000 47 0.0 0.7931034 -0.7931034 48 0.0 1.3000000 -1.3000000 49 0.5 1.3000000 -0.8000000 50 2.0 0.7931034 1.2068966 51 0.0 1.3000000 -1.3000000 52 2.0 1.9062500 0.0937500 53 1.0 1.3000000 -0.3000000 54 0.0 1.3000000 -1.3000000 55 2.0 1.3000000 0.7000000 56 1.0 0.7931034 0.2068966 57 2.0 1.3000000 0.7000000 58 0.0 0.7931034 -0.7931034 59 1.0 1.9062500 -0.9062500 60 2.0 1.9062500 0.0937500 61 0.0 0.7931034 -0.7931034 62 0.0 0.7931034 -0.7931034 63 0.0 0.7931034 -0.7931034 64 0.0 0.7931034 -0.7931034 65 2.0 1.3000000 0.7000000 66 2.0 1.3000000 0.7000000 67 2.0 1.3000000 0.7000000 68 2.0 1.3000000 0.7000000 69 2.0 1.3000000 0.7000000 70 2.0 1.3000000 0.7000000 71 2.0 1.3000000 0.7000000 72 2.0 1.9062500 0.0937500 73 2.0 1.9062500 0.0937500 74 2.0 1.3000000 0.7000000 75 2.0 1.9062500 0.0937500 76 2.0 1.9062500 0.0937500 77 2.0 1.3000000 0.7000000 78 0.0 0.7931034 -0.7931034 79 2.0 1.3000000 0.7000000 80 2.0 1.3000000 0.7000000 81 2.0 1.3000000 0.7000000 82 2.0 1.3000000 0.7000000 83 2.0 1.9062500 0.0937500 84 2.0 1.9062500 0.0937500 85 0.0 0.7931034 -0.7931034 86 0.0 0.7931034 -0.7931034 87 2.0 1.3000000 0.7000000 88 0.0 0.7931034 -0.7931034 89 2.0 1.3000000 0.7000000 90 0.0 0.7931034 -0.7931034 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/fisher/rcomp/tmp/4zy6m1354811914.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/fisher/rcomp/tmp/5dmm81354811914.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/fisher/rcomp/tmp/6780c1354811914.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/fisher/rcomp/tmp/7h7ia1354811914.tab") + } > > try(system("convert tmp/2bj7z1354811914.ps tmp/2bj7z1354811914.png",intern=TRUE)) character(0) > try(system("convert tmp/3z1nv1354811914.ps tmp/3z1nv1354811914.png",intern=TRUE)) character(0) > try(system("convert tmp/4zy6m1354811914.ps tmp/4zy6m1354811914.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.998 0.584 4.583