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(178421 + ,1.23 + ,2.35 + ,2.50 + ,2.84 + ,2.54 + ,2.39 + ,0.28 + ,0.95 + ,1.31 + ,8890176 + ,139871 + ,1.22 + ,2.32 + ,2.59 + ,2.85 + ,2.58 + ,2.59 + ,0.28 + ,0.97 + ,1.33 + ,8194413 + ,118159 + ,1.21 + ,2.36 + ,2.56 + ,2.80 + ,2.55 + ,3.48 + ,0.26 + ,0.97 + ,1.32 + ,7722000 + ,109763 + ,1.22 + ,2.28 + ,2.59 + ,2.83 + ,2.56 + ,3.36 + ,0.24 + ,0.95 + ,1.34 + ,7769178 + ,97415 + ,1.21 + ,2.26 + ,2.58 + ,2.83 + ,2.59 + ,3.28 + ,0.25 + ,0.96 + ,1.34 + ,7449343 + ,119190 + ,1.22 + ,2.31 + ,2.62 + ,2.80 + ,2.57 + ,3.41 + ,0.26 + ,0.96 + ,1.28 + ,7929370 + ,97903 + ,1.21 + ,2.28 + ,2.59 + ,2.77 + ,2.60 + ,3.46 + ,0.27 + ,0.94 + ,1.33 + ,7473017 + ,96953 + ,1.20 + ,2.20 + ,2.58 + ,2.75 + ,2.57 + ,3.38 + ,0.26 + ,0.96 + ,1.33 + ,7472424 + ,87888 + ,1.18 + ,2.24 + ,2.57 + ,2.80 + ,2.48 + ,3.18 + ,0.28 + ,0.98 + ,1.37 + ,7292436 + ,84637 + ,1.19 + ,2.33 + ,2.57 + ,2.85 + ,2.51 + ,3.47 + ,0.27 + ,0.97 + ,1.37 + ,7215340 + ,90549 + ,1.20 + ,2.36 + ,2.55 + 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,7166769 + ,90781 + ,1.24 + ,2.32 + ,2.71 + ,2.92 + ,2.88 + ,3.50 + ,0.21 + ,1.06 + ,1.49 + ,7538708) + ,dim=c(11 + ,130) + ,dimnames=list(c('QBEFRU' + ,'PBEPIL' + ,'PBEABD' + ,'PBEFRU' + ,'PBEREG' + ,'PCHEXO' + ,'PAMMOGRA' + ,'PAMMULTI' + ,'PSOCOLA' + ,'PICET' + ,'BUDBEER') + ,1:130)) > y <- array(NA,dim=c(11,130),dimnames=list(c('QBEFRU','PBEPIL','PBEABD','PBEFRU','PBEREG','PCHEXO','PAMMOGRA','PAMMULTI','PSOCOLA','PICET','BUDBEER'),1:130)) > 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 = '1' > par4 <- 'no' > par3 <- '' > par2 <- 'none' > par1 <- '1' > #'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] "QBEFRU" > x[,par1] [1] 178421 139871 118159 109763 97415 119190 97903 96953 87888 84637 [11] 90549 95680 99371 79984 86752 85733 84906 78356 108895 101768 [21] 73285 65724 67457 67203 69273 80807 75129 74991 68157 73858 [31] 71349 85634 91624 116014 120033 108651 105378 138939 132974 135277 [41] 152741 158417 157460 193997 154089 147570 162924 153629 155907 197675 [51] 250708 266652 209842 165826 137152 150581 145973 126532 115437 119526 [61] 110856 97243 103876 116370 109616 98365 90440 88899 92358 88394 [71] 98219 113546 107168 77540 74944 75641 75910 87384 84615 80420 [81] 80784 79933 82118 91420 112426 114528 131025 116460 111258 155318 [91] 155078 134794 139985 198778 172436 169585 203702 282392 220658 194472 [101] 269246 215340 218319 195724 174614 172085 152347 189615 173804 145683 [111] 133550 121156 112040 120767 127019 136295 113425 107815 100298 97048 [121] 98750 98235 101254 139589 134921 80355 80396 82183 79709 90781 > 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]) 65724 67203 67457 68157 69273 71349 73285 73858 74944 74991 75129 1 1 1 1 1 1 1 1 1 1 1 75641 75910 77540 78356 79709 79933 79984 80355 80396 80420 80784 1 1 1 1 1 1 1 1 1 1 1 80807 82118 82183 84615 84637 84906 85634 85733 86752 87384 87888 1 1 1 1 1 1 1 1 1 1 1 88394 88899 90440 90549 90781 91420 91624 92358 95680 96953 97048 1 1 1 1 1 1 1 1 1 1 1 97243 97415 97903 98219 98235 98365 98750 99371 100298 101254 101768 1 1 1 1 1 1 1 1 1 1 1 103876 105378 107168 107815 108651 108895 109616 109763 110856 111258 112040 1 1 1 1 1 1 1 1 1 1 1 112426 113425 113546 114528 115437 116014 116370 116460 118159 119190 119526 1 1 1 1 1 1 1 1 1 1 1 120033 120767 121156 126532 127019 131025 132974 133550 134794 134921 135277 1 1 1 1 1 1 1 1 1 1 1 136295 137152 138939 139589 139871 139985 145683 145973 147570 150581 152347 1 1 1 1 1 1 1 1 1 1 1 152741 153629 154089 155078 155318 155907 157460 158417 162924 165826 169585 1 1 1 1 1 1 1 1 1 1 1 172085 172436 173804 174614 178421 189615 193997 194472 195724 197675 198778 1 1 1 1 1 1 1 1 1 1 1 203702 209842 215340 218319 220658 250708 266652 269246 282392 1 1 1 1 1 1 1 1 1 > colnames(x) [1] "QBEFRU" "PBEPIL" "PBEABD" "PBEFRU" "PBEREG" "PCHEXO" [7] "PAMMOGRA" "PAMMULTI" "PSOCOLA" "PICET" "BUDBEER" > colnames(x)[par1] [1] "QBEFRU" > x[,par1] [1] 178421 139871 118159 109763 97415 119190 97903 96953 87888 84637 [11] 90549 95680 99371 79984 86752 85733 84906 78356 108895 101768 [21] 73285 65724 67457 67203 69273 80807 75129 74991 68157 73858 [31] 71349 85634 91624 116014 120033 108651 105378 138939 132974 135277 [41] 152741 158417 157460 193997 154089 147570 162924 153629 155907 197675 [51] 250708 266652 209842 165826 137152 150581 145973 126532 115437 119526 [61] 110856 97243 103876 116370 109616 98365 90440 88899 92358 88394 [71] 98219 113546 107168 77540 74944 75641 75910 87384 84615 80420 [81] 80784 79933 82118 91420 112426 114528 131025 116460 111258 155318 [91] 155078 134794 139985 198778 172436 169585 203702 282392 220658 194472 [101] 269246 215340 218319 195724 174614 172085 152347 189615 173804 145683 [111] 133550 121156 112040 120767 127019 136295 113425 107815 100298 97048 [121] 98750 98235 101254 139589 134921 80355 80396 82183 79709 90781 > 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/15oyt1354993786.tab") + } + } > m Conditional inference tree with 10 terminal nodes Response: QBEFRU Inputs: PBEPIL, PBEABD, PBEFRU, PBEREG, PCHEXO, PAMMOGRA, PAMMULTI, PSOCOLA, PICET, BUDBEER Number of observations: 130 1) BUDBEER <= 8534307; criterion = 1, statistic = 110.586 2) BUDBEER <= 7703698; criterion = 1, statistic = 64.846 3) BUDBEER <= 7290803; criterion = 1, statistic = 31.449 4) BUDBEER <= 7105114; criterion = 1, statistic = 17.775 5)* weights = 16 4) BUDBEER > 7105114 6)* weights = 13 3) BUDBEER > 7290803 7) PBEREG <= 2.83; criterion = 0.986, statistic = 10.242 8)* weights = 10 7) PBEREG > 2.83 9)* weights = 12 2) BUDBEER > 7703698 10) PBEREG <= 2.93; criterion = 0.989, statistic = 10.684 11) BUDBEER <= 8136158; criterion = 0.998, statistic = 14.108 12)* weights = 17 11) BUDBEER > 8136158 13)* weights = 16 10) PBEREG > 2.93 14)* weights = 7 1) BUDBEER > 8534307 15) BUDBEER <= 9222117; criterion = 1, statistic = 24.226 16) PBEREG <= 2.86; criterion = 0.977, statistic = 9.298 17)* weights = 18 16) PBEREG > 2.86 18)* weights = 9 15) BUDBEER > 9222117 19)* weights = 12 > postscript(file="/var/wessaorg/rcomp/tmp/2n1hv1354993786.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/34bxb1354993786.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 178421 165099.94 13321.0556 2 139871 135773.31 4097.6875 3 118159 112972.29 5186.7059 4 109763 112972.29 -3209.2941 5 97415 101190.20 -3775.2000 6 119190 112972.29 6217.7059 7 97903 101190.20 -3287.2000 8 96953 101190.20 -4237.2000 9 87888 101190.20 -13302.2000 10 84637 83749.92 887.0769 11 90549 83749.92 6799.0769 12 95680 101190.20 -5510.2000 13 99371 112972.29 -13601.2941 14 79984 83749.92 -3765.9231 15 86752 83749.92 3002.0769 16 85733 83749.92 1983.0769 17 84906 88560.50 -3654.5000 18 78356 88560.50 -10204.5000 19 108895 147076.44 -38181.4444 20 101768 99678.86 2089.1429 21 73285 73200.75 84.2500 22 65724 73200.75 -7476.7500 23 67457 73200.75 -5743.7500 24 67203 73200.75 -5997.7500 25 69273 73200.75 -3927.7500 26 80807 83749.92 -2942.9231 27 75129 73200.75 1928.2500 28 74991 73200.75 1790.2500 29 68157 73200.75 -5043.7500 30 73858 73200.75 657.2500 31 71349 73200.75 -1851.7500 32 85634 83749.92 1884.0769 33 91624 83749.92 7874.0769 34 116014 112972.29 3041.7059 35 120033 135773.31 -15740.3125 36 108651 101190.20 7460.8000 37 105378 112972.29 -7594.2941 38 138939 165099.94 -26160.9444 39 132974 135773.31 -2799.3125 40 135277 135773.31 -496.3125 41 152741 135773.31 16967.6875 42 158417 165099.94 -6682.9444 43 157460 165099.94 -7639.9444 44 193997 165099.94 28897.0556 45 154089 135773.31 18315.6875 46 147570 135773.31 11796.6875 47 162924 165099.94 -2175.9444 48 153629 165099.94 -11470.9444 49 155907 165099.94 -9192.9444 50 197675 220598.00 -22923.0000 51 250708 220598.00 30110.0000 52 266652 220598.00 46054.0000 53 209842 220598.00 -10756.0000 54 165826 135773.31 30052.6875 55 137152 135773.31 1378.6875 56 150581 165099.94 -14518.9444 57 145973 165099.94 -19126.9444 58 126532 112972.29 13559.7059 59 115437 101190.20 14246.8000 60 119526 112972.29 6553.7059 61 110856 101190.20 9665.8000 62 97243 101190.20 -3947.2000 63 103876 101190.20 2685.8000 64 116370 135773.31 -19403.3125 65 109616 112972.29 -3356.2941 66 98365 88560.50 9804.5000 67 90440 88560.50 1879.5000 68 88899 88560.50 338.5000 69 92358 99678.86 -7320.8571 70 88394 88560.50 -166.5000 71 98219 99678.86 -1459.8571 72 113546 147076.44 -33530.4444 73 107168 99678.86 7489.1429 74 77540 73200.75 4339.2500 75 74944 73200.75 1743.2500 76 75641 73200.75 2440.2500 77 75910 73200.75 2709.2500 78 87384 88560.50 -1176.5000 79 84615 88560.50 -3945.5000 80 80420 83749.92 -3329.9231 81 80784 83749.92 -2965.9231 82 79933 83749.92 -3816.9231 83 82118 88560.50 -6442.5000 84 91420 88560.50 2859.5000 85 112426 135773.31 -23347.3125 86 114528 112972.29 1555.7059 87 131025 135773.31 -4748.3125 88 116460 112972.29 3487.7059 89 111258 112972.29 -1714.2941 90 155318 165099.94 -9781.9444 91 155078 165099.94 -10021.9444 92 134794 147076.44 -12282.4444 93 139985 165099.94 -25114.9444 94 198778 220598.00 -21820.0000 95 172436 165099.94 7336.0556 96 169585 165099.94 4485.0556 97 203702 220598.00 -16896.0000 98 282392 220598.00 61794.0000 99 220658 220598.00 60.0000 100 194472 147076.44 47395.5556 101 269246 220598.00 48648.0000 102 215340 165099.94 50240.0556 103 218319 220598.00 -2279.0000 104 195724 165099.94 30624.0556 105 174614 147076.44 27537.5556 106 172085 165099.94 6985.0556 107 152347 147076.44 5270.5556 108 189615 220598.00 -30983.0000 109 173804 147076.44 26727.5556 110 145683 135773.31 9909.6875 111 133550 135773.31 -2223.3125 112 121156 112972.29 8183.7059 113 112040 112972.29 -932.2941 114 120767 135773.31 -15006.3125 115 127019 135773.31 -8754.3125 116 136295 147076.44 -10781.4444 117 113425 112972.29 452.7059 118 107815 112972.29 -5157.2941 119 100298 112972.29 -12674.2941 120 97048 88560.50 8487.5000 121 98750 99678.86 -928.8571 122 98235 99678.86 -1443.8571 123 101254 99678.86 1575.1429 124 139589 220598.00 -81009.0000 125 134921 147076.44 -12155.4444 126 80355 73200.75 7154.2500 127 80396 73200.75 7195.2500 128 82183 83749.92 -1566.9231 129 79709 83749.92 -4040.9231 130 90781 88560.50 2220.5000 > 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/4uudj1354993786.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/5sy2s1354993786.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/6ki281354993786.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/71wd51354993786.tab") + } > > try(system("convert tmp/2n1hv1354993786.ps tmp/2n1hv1354993786.png",intern=TRUE)) character(0) > try(system("convert tmp/34bxb1354993786.ps tmp/34bxb1354993786.png",intern=TRUE)) character(0) > try(system("convert tmp/4uudj1354993786.ps tmp/4uudj1354993786.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.086 0.372 5.501