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Type 'q()' to quit R. > x <- array(list(2 + ,73 + ,69 + ,1 + ,58 + ,53 + ,1 + ,68 + ,43 + ,2 + ,69 + ,60 + ,1 + ,62 + ,49 + ,1 + ,68 + ,62 + ,1 + ,65 + ,45 + ,1 + ,65 + ,50 + ,1 + ,81 + ,75 + ,1 + ,73 + ,82 + ,2 + ,64 + ,60 + ,1 + ,68 + ,59 + ,1 + ,51 + ,21 + ,1 + ,68 + ,40 + ,1 + ,61 + ,62 + ,2 + ,77 + ,54 + ,1 + ,69 + ,47 + ,1 + ,73 + ,59 + ,2 + ,61 + ,37 + ,2 + ,62 + ,43 + ,1 + ,63 + ,48 + ,1 + ,69 + ,59 + ,2 + ,47 + ,79 + ,2 + ,66 + ,62 + ,1 + ,58 + ,16 + ,2 + ,63 + ,38 + ,1 + ,69 + ,58 + ,2 + ,59 + ,60 + ,1 + ,59 + ,72 + ,2 + ,63 + ,67 + ,2 + ,65 + ,55 + ,1 + ,65 + ,47 + ,2 + ,71 + ,59 + ,1 + ,60 + ,49 + ,2 + ,66 + ,47 + ,1 + ,67 + ,57 + ,2 + ,81 + ,39 + ,1 + ,62 + ,49 + ,1 + ,63 + ,26 + ,2 + ,73 + ,53 + ,2 + ,55 + ,75 + ,1 + ,59 + ,65 + ,1 + ,64 + ,49 + ,2 + ,63 + ,48 + ,2 + ,74 + ,45 + ,2 + ,67 + ,31 + ,1 + ,64 + ,67 + ,1 + ,73 + ,61 + ,1 + ,54 + ,49 + ,1 + ,76 + ,69 + ,2 + ,74 + ,54 + ,2 + ,63 + ,80 + ,2 + ,73 + ,57 + ,2 + ,67 + ,34 + ,2 + ,68 + ,69 + ,1 + ,66 + ,44 + ,2 + ,62 + ,70 + ,2 + ,71 + ,51 + ,1 + ,68 + ,66 + ,1 + ,63 + ,18 + ,1 + ,75 + ,74 + ,1 + ,77 + ,59 + ,2 + ,62 + ,48 + ,1 + ,74 + ,55 + ,2 + ,67 + ,44 + ,2 + ,56 + ,56 + ,2 + ,60 + ,65 + ,2 + ,58 + ,77 + ,1 + ,65 + ,46 + ,2 + ,49 + ,70 + ,1 + ,61 + ,39 + ,2 + ,66 + ,55 + ,2 + ,64 + ,44 + ,2 + ,65 + ,45 + ,1 + ,46 + ,45 + ,2 + ,81 + ,25 + ,2 + ,65 + ,49 + ,1 + ,72 + ,65 + ,2 + ,65 + ,45 + ,2 + ,74 + ,71 + ,1 + ,69 + ,48 + ,1 + ,59 + ,41 + ,2 + ,58 + ,40 + ,1 + ,71 + ,64 + ,2 + ,79 + ,56 + ,2 + ,68 + ,52 + ,1 + ,66 + ,41 + ,2 + ,62 + ,45 + ,1 + ,69 + ,49 + ,1 + ,60 + ,42 + ,2 + ,63 + ,54 + ,1 + ,62 + ,40 + ,1 + ,61 + ,40 + ,2 + ,65 + ,51 + ,1 + ,64 + ,48 + ,2 + ,67 + ,80 + ,2 + ,56 + ,38 + ,2 + ,56 + ,57 + ,1 + ,48 + ,28 + ,1 + ,74 + ,51 + ,1 + ,69 + ,46 + ,1 + ,62 + ,58 + ,1 + ,73 + ,67 + ,1 + ,64 + ,72 + ,1 + ,57 + ,26 + ,1 + ,57 + ,54 + ,2 + ,60 + ,53 + ,2 + ,61 + ,69 + ,1 + ,72 + ,64 + ,1 + ,57 + ,47 + ,1 + ,51 + ,43 + ,1 + ,63 + ,66 + ,1 + ,54 + ,54 + ,1 + ,72 + ,62 + ,1 + ,62 + ,52 + ,1 + ,68 + ,64 + ,1 + ,62 + ,55 + ,2 + ,63 + ,57 + ,1 + ,77 + ,74 + ,1 + ,57 + ,32 + ,1 + ,57 + ,38 + ,1 + ,61 + ,66 + ,2 + ,66 + ,37 + ,1 + ,65 + ,26 + ,1 + ,63 + ,64 + ,1 + ,59 + ,28 + ,2 + ,66 + ,66 + ,1 + ,68 + ,65 + ,1 + ,72 + ,48 + ,1 + ,68 + ,44 + ,2 + ,68 + ,64 + ,1 + ,67 + ,39 + ,1 + ,59 + ,50 + ,1 + ,56 + ,52 + ,1 + ,62 + ,66 + ,2 + ,55 + ,48 + ,2 + ,72 + ,70 + ,2 + ,68 + ,66 + ,1 + ,67 + ,61 + ,1 + ,54 + ,31 + ,2 + ,69 + ,61 + ,1 + ,61 + ,54 + ,1 + ,55 + ,34 + ,2 + ,75 + ,62 + ,1 + ,55 + ,47 + ,1 + ,49 + ,52 + ,2 + ,54 + ,37 + ,1 + ,51 + ,46 + ,1 + ,66 + ,50 + ,1 + ,73 + ,61 + ,2 + ,63 + ,70 + ,2 + ,61 + ,38 + ,1 + ,74 + ,63 + ,2 + ,81 + ,34 + ,1 + ,58 + ,46 + ,1 + ,62 + ,40 + ,1 + ,64 + ,30 + ,1 + ,62 + ,35 + ,1 + ,85 + ,51 + ,1 + ,74 + ,56 + ,1 + ,51 + ,68 + ,1 + ,66 + ,39 + ,2 + ,61 + ,44 + ,1 + ,72 + ,58) + ,dim=c(3 + ,164) + ,dimnames=list(c('Gender' + ,'Nonverbal' + ,'Anxiety') + ,1:164)) > y <- array(NA,dim=c(3,164),dimnames=list(c('Gender','Nonverbal','Anxiety'),1:164)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par4 = 'yes' > par3 = '2' > par2 = 'quantiles' > par1 = '2' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Dr. Ian E. Holliday > #To cite this work: Ian E. Holliday, 2009, YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from package:survival : untangle.specials The following object(s) are masked from package:base : format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "Nonverbal" > x[,par1] [1] 73 58 68 69 62 68 65 65 81 73 64 68 51 68 61 77 69 73 61 62 63 69 47 66 58 [26] 63 69 59 59 63 65 65 71 60 66 67 81 62 63 73 55 59 64 63 74 67 64 73 54 76 [51] 74 63 73 67 68 66 62 71 68 63 75 77 62 74 67 56 60 58 65 49 61 66 64 65 46 [76] 81 65 72 65 74 69 59 58 71 79 68 66 62 69 60 63 62 61 65 64 67 56 56 48 74 [101] 69 62 73 64 57 57 60 61 72 57 51 63 54 72 62 68 62 63 77 57 57 61 66 65 63 [126] 59 66 68 72 68 68 67 59 56 62 55 72 68 67 54 69 61 55 75 55 49 54 51 66 73 [151] 63 61 74 81 58 62 64 62 85 74 51 66 61 72 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) [46,65) [65,85] 82 82 > colnames(x) [1] "Gender" "Nonverbal" "Anxiety" > colnames(x)[par1] [1] "Nonverbal" > x[,par1] [1] [65,85] [46,65) [65,85] [65,85] [46,65) [65,85] [65,85] [65,85] [65,85] [10] [65,85] [46,65) [65,85] [46,65) [65,85] [46,65) [65,85] [65,85] [65,85] [19] [46,65) [46,65) [46,65) [65,85] [46,65) [65,85] [46,65) [46,65) [65,85] [28] [46,65) [46,65) [46,65) [65,85] [65,85] [65,85] [46,65) [65,85] [65,85] [37] [65,85] [46,65) [46,65) [65,85] [46,65) [46,65) [46,65) [46,65) [65,85] [46] [65,85] [46,65) [65,85] [46,65) [65,85] [65,85] [46,65) [65,85] [65,85] [55] [65,85] [65,85] [46,65) [65,85] [65,85] [46,65) [65,85] [65,85] [46,65) [64] [65,85] [65,85] [46,65) [46,65) [46,65) [65,85] [46,65) [46,65) [65,85] [73] [46,65) [65,85] [46,65) [65,85] [65,85] [65,85] [65,85] [65,85] [65,85] [82] [46,65) [46,65) [65,85] [65,85] [65,85] [65,85] [46,65) [65,85] [46,65) [91] [46,65) [46,65) [46,65) [65,85] [46,65) [65,85] [46,65) [46,65) [46,65) [100] [65,85] [65,85] [46,65) [65,85] [46,65) [46,65) [46,65) [46,65) [46,65) [109] [65,85] [46,65) [46,65) [46,65) [46,65) [65,85] [46,65) [65,85] [46,65) [118] [46,65) [65,85] [46,65) [46,65) [46,65) [65,85] [65,85] [46,65) [46,65) [127] [65,85] [65,85] [65,85] [65,85] [65,85] [65,85] [46,65) [46,65) [46,65) [136] [46,65) [65,85] [65,85] [65,85] [46,65) [65,85] [46,65) [46,65) [65,85] [145] [46,65) [46,65) [46,65) [46,65) [65,85] [65,85] [46,65) [46,65) [65,85] [154] [65,85] [46,65) [46,65) [46,65) [46,65) [65,85] [65,85] [46,65) [65,85] [163] [46,65) [65,85] Levels: [46,65) [65,85] > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/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/www/html/rcomp/tmp/11w9f1292349430.tab") + } + } m.ct.i.pred m.ct.i.actu 1 2 1 271 475 2 177 564 [1] 0.3632708 [1] 0.7611336 [1] 0.5615333 m.ct.x.pred m.ct.x.actu 1 2 1 19 55 2 23 56 [1] 0.2567568 [1] 0.7088608 [1] 0.4901961 > m Conditional inference tree with 2 terminal nodes Response: as.factor(Nonverbal) Inputs: Gender, Anxiety Number of observations: 164 1) Anxiety <= 43; criterion = 0.957, statistic = 5.281 2)* weights = 39 1) Anxiety > 43 3)* weights = 125 > postscript(file="/var/www/html/rcomp/tmp/21w9f1292349430.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/www/html/rcomp/tmp/31w9f1292349430.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,] 2 2 [2,] 1 2 [3,] 2 1 [4,] 2 2 [5,] 1 2 [6,] 2 2 [7,] 2 2 [8,] 2 2 [9,] 2 2 [10,] 2 2 [11,] 1 2 [12,] 2 2 [13,] 1 1 [14,] 2 1 [15,] 1 2 [16,] 2 2 [17,] 2 2 [18,] 2 2 [19,] 1 1 [20,] 1 1 [21,] 1 2 [22,] 2 2 [23,] 1 2 [24,] 2 2 [25,] 1 1 [26,] 1 1 [27,] 2 2 [28,] 1 2 [29,] 1 2 [30,] 1 2 [31,] 2 2 [32,] 2 2 [33,] 2 2 [34,] 1 2 [35,] 2 2 [36,] 2 2 [37,] 2 1 [38,] 1 2 [39,] 1 1 [40,] 2 2 [41,] 1 2 [42,] 1 2 [43,] 1 2 [44,] 1 2 [45,] 2 2 [46,] 2 1 [47,] 1 2 [48,] 2 2 [49,] 1 2 [50,] 2 2 [51,] 2 2 [52,] 1 2 [53,] 2 2 [54,] 2 1 [55,] 2 2 [56,] 2 2 [57,] 1 2 [58,] 2 2 [59,] 2 2 [60,] 1 1 [61,] 2 2 [62,] 2 2 [63,] 1 2 [64,] 2 2 [65,] 2 2 [66,] 1 2 [67,] 1 2 [68,] 1 2 [69,] 2 2 [70,] 1 2 [71,] 1 1 [72,] 2 2 [73,] 1 2 [74,] 2 2 [75,] 1 2 [76,] 2 1 [77,] 2 2 [78,] 2 2 [79,] 2 2 [80,] 2 2 [81,] 2 2 [82,] 1 1 [83,] 1 1 [84,] 2 2 [85,] 2 2 [86,] 2 2 [87,] 2 1 [88,] 1 2 [89,] 2 2 [90,] 1 1 [91,] 1 2 [92,] 1 1 [93,] 1 1 [94,] 2 2 [95,] 1 2 [96,] 2 2 [97,] 1 1 [98,] 1 2 [99,] 1 1 [100,] 2 2 [101,] 2 2 [102,] 1 2 [103,] 2 2 [104,] 1 2 [105,] 1 1 [106,] 1 2 [107,] 1 2 [108,] 1 2 [109,] 2 2 [110,] 1 2 [111,] 1 1 [112,] 1 2 [113,] 1 2 [114,] 2 2 [115,] 1 2 [116,] 2 2 [117,] 1 2 [118,] 1 2 [119,] 2 2 [120,] 1 1 [121,] 1 1 [122,] 1 2 [123,] 2 1 [124,] 2 1 [125,] 1 2 [126,] 1 1 [127,] 2 2 [128,] 2 2 [129,] 2 2 [130,] 2 2 [131,] 2 2 [132,] 2 1 [133,] 1 2 [134,] 1 2 [135,] 1 2 [136,] 1 2 [137,] 2 2 [138,] 2 2 [139,] 2 2 [140,] 1 1 [141,] 2 2 [142,] 1 2 [143,] 1 1 [144,] 2 2 [145,] 1 2 [146,] 1 2 [147,] 1 1 [148,] 1 2 [149,] 2 2 [150,] 2 2 [151,] 1 2 [152,] 1 1 [153,] 2 2 [154,] 2 1 [155,] 1 2 [156,] 1 1 [157,] 1 1 [158,] 1 1 [159,] 2 2 [160,] 2 2 [161,] 1 2 [162,] 2 1 [163,] 1 2 [164,] 2 2 [46,65) [65,85] [46,65) 27 55 [65,85] 12 70 > postscript(file="/var/www/html/rcomp/tmp/4un901292349430.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/www/html/rcomp/tmp/5qf691292349430.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/www/html/rcomp/tmp/6i6ou1292349430.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/www/html/rcomp/tmp/7m7401292349430.tab") + } > > try(system("convert tmp/21w9f1292349430.ps tmp/21w9f1292349430.png",intern=TRUE)) character(0) > try(system("convert tmp/31w9f1292349430.ps tmp/31w9f1292349430.png",intern=TRUE)) character(0) > try(system("convert tmp/4un901292349430.ps tmp/4un901292349430.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.428 0.507 5.463