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 + ,41 + ,38 + ,14 + ,12 + ,2 + ,39 + ,32 + ,18 + ,11 + ,2 + ,30 + ,35 + ,11 + ,14 + ,1 + ,31 + ,33 + ,12 + ,12 + ,2 + ,34 + ,37 + ,16 + ,21 + ,2 + ,35 + ,29 + ,18 + ,12 + ,2 + ,39 + ,31 + ,14 + ,22 + ,2 + ,34 + ,36 + ,14 + ,11 + ,2 + ,36 + ,35 + ,15 + ,10 + ,2 + ,37 + ,38 + ,15 + ,13 + ,1 + ,38 + ,31 + ,17 + ,10 + ,2 + ,36 + ,34 + ,19 + ,8 + ,1 + ,38 + ,35 + ,10 + ,15 + ,2 + ,39 + ,38 + ,16 + ,14 + ,2 + ,33 + ,37 + ,18 + ,10 + ,1 + ,32 + ,33 + ,14 + ,14 + ,1 + ,36 + ,32 + ,14 + ,14 + ,2 + ,38 + ,38 + ,17 + ,11 + ,1 + ,39 + ,38 + ,14 + ,10 + ,2 + ,32 + ,32 + ,16 + ,13 + ,1 + ,32 + ,33 + ,18 + ,7 + ,2 + ,31 + ,31 + ,11 + ,14 + ,2 + ,39 + ,38 + ,14 + ,12 + ,2 + ,37 + ,39 + ,12 + ,14 + ,1 + ,39 + ,32 + ,17 + ,11 + ,2 + ,41 + ,32 + ,9 + ,9 + ,1 + ,36 + ,35 + ,16 + ,11 + ,2 + ,33 + ,37 + ,14 + ,15 + ,2 + ,33 + ,33 + ,15 + ,14 + ,1 + ,34 + ,33 + ,11 + ,13 + ,2 + ,31 + ,28 + ,16 + ,9 + ,1 + ,27 + ,32 + ,13 + ,15 + ,2 + ,37 + ,31 + ,17 + ,10 + ,2 + ,34 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,16 + ,12 + ,1 + ,32 + ,28 + ,11 + ,11 + ,2 + ,33 + ,39 + ,15 + ,16 + ,1 + ,40 + ,32 + ,9 + ,19 + ,2 + ,38 + ,35 + ,16 + ,11 + ,1 + ,41 + ,39 + ,15 + ,16 + ,1 + ,36 + ,35 + ,10 + ,15 + ,2 + ,43 + ,42 + ,10 + ,24 + ,2 + ,30 + ,34 + ,15 + ,14 + ,2 + ,31 + ,33 + ,11 + ,15 + ,2 + ,32 + ,41 + ,13 + ,11 + ,1 + ,32 + ,33 + ,14 + ,15 + ,2 + ,37 + ,34 + ,18 + ,12 + ,1 + ,37 + ,32 + ,16 + ,10 + ,2 + ,33 + ,40 + ,14 + ,14 + ,2 + ,34 + ,40 + ,14 + ,13 + ,2 + ,33 + ,35 + ,14 + ,9 + ,2 + ,38 + ,36 + ,14 + ,15 + ,2 + ,33 + ,37 + ,12 + ,15 + ,2 + ,31 + ,27 + ,14 + ,14 + ,2 + ,38 + ,39 + ,15 + ,11 + ,2 + ,37 + ,38 + ,15 + ,8 + ,2 + ,33 + ,31 + ,15 + ,11 + ,2 + ,31 + ,33 + ,13 + ,11 + ,1 + ,39 + ,32 + ,17 + ,8 + ,2 + ,44 + ,39 + ,17 + ,10 + ,2 + ,33 + ,36 + ,19 + ,11 + ,2 + ,35 + ,33 + ,15 + ,13 + ,1 + ,32 + ,33 + ,13 + ,11 + ,1 + ,28 + ,32 + ,9 + ,20 + ,2 + ,40 + ,37 + ,15 + ,10 + ,1 + ,27 + ,30 + ,15 + ,15 + ,1 + ,37 + ,38 + ,15 + ,12 + ,2 + ,32 + ,29 + ,16 + ,14 + ,1 + ,28 + ,22 + ,11 + ,23 + ,1 + ,34 + ,35 + ,14 + ,14 + ,2 + ,30 + ,35 + ,11 + ,16 + ,2 + ,35 + ,34 + ,15 + ,11 + ,1 + ,31 + ,35 + ,13 + ,12 + ,2 + ,32 + ,34 + ,15 + ,10 + ,1 + ,30 + ,34 + ,16 + ,14 + ,2 + ,30 + ,35 + ,14 + ,12 + ,1 + ,31 + ,23 + ,15 + ,12 + ,2 + ,40 + ,31 + ,16 + ,11 + ,2 + ,32 + ,27 + ,16 + ,12 + ,1 + ,36 + ,36 + ,11 + ,13 + ,1 + ,32 + ,31 + ,12 + ,11 + ,1 + ,35 + ,32 + ,9 + ,19 + ,2 + ,38 + ,39 + ,16 + ,12 + ,2 + ,42 + ,37 + ,13 + ,17 + ,1 + ,34 + ,38 + ,16 + ,9 + ,2 + ,35 + ,39 + ,12 + ,12 + ,2 + ,35 + ,34 + ,9 + ,19 + ,2 + ,33 + ,31 + ,13 + ,18 + ,2 + ,36 + ,32 + ,13 + ,15 + ,2 + ,32 + ,37 + ,14 + ,14 + ,2 + ,33 + ,36 + ,19 + ,11 + ,2 + ,34 + ,32 + ,13 + ,9 + ,2 + ,32 + ,35 + ,12 + ,18 + ,2 + ,34 + ,36 + ,13 + ,16) + ,dim=c(5 + ,162) + ,dimnames=list(c('Gender' + ,'Connected' + ,'Separate' + ,'Happiness' + ,'Depression') + ,1:162)) > y <- array(NA,dim=c(5,162),dimnames=list(c('Gender','Connected','Separate','Happiness','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 = '5' > par2 = 'none' > par1 = '1' > #'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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from 'package:survival': untangle.specials The following object(s) are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "Gender" > x[,par1] [1] 2 2 2 1 2 2 2 2 2 2 1 2 1 2 2 1 1 2 1 2 1 2 2 2 1 2 1 2 2 1 2 1 2 2 1 1 1 [38] 1 2 1 1 2 1 1 2 2 2 2 2 1 2 1 1 2 1 2 2 2 1 2 1 1 2 2 2 1 2 1 2 1 1 2 2 1 [75] 1 2 2 2 1 1 1 1 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 1 2 1 2 1 2 1 2 1 1 2 2 [112] 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 1 2 1 1 2 1 1 2 2 1 2 1 2 1 2 2 1 [149] 1 1 2 2 1 2 2 2 2 2 2 2 2 2 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) 1 2 61 101 > colnames(x) [1] "Gender" "Connected" "Separate" "Happiness" "Depression" > colnames(x)[par1] [1] "Gender" > x[,par1] [1] 2 2 2 1 2 2 2 2 2 2 1 2 1 2 2 1 1 2 1 2 1 2 2 2 1 2 1 2 2 1 2 1 2 2 1 1 1 [38] 1 2 1 1 2 1 1 2 2 2 2 2 1 2 1 1 2 1 2 2 2 1 2 1 1 2 2 2 1 2 1 2 1 1 2 2 1 [75] 1 2 2 2 1 1 1 1 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 1 2 1 2 1 2 1 2 1 1 2 2 [112] 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 1 2 1 1 2 1 1 2 2 1 2 1 2 1 2 2 1 [149] 1 1 2 2 1 2 2 2 2 2 2 2 2 2 > 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/1gltg1323806194.tab") + } + } > m Conditional inference tree with 2 terminal nodes Response: Gender Inputs: Connected, Separate, Happiness, Depression Number of observations: 162 1) Separate <= 33; criterion = 0.991, statistic = 9.256 2)* weights = 72 1) Separate > 33 3)* weights = 90 > postscript(file="/var/wessaorg/rcomp/tmp/2u2ub1323806194.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/3vzsv1323806194.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 1.744444 0.2555556 2 2 1.472222 0.5277778 3 2 1.744444 0.2555556 4 1 1.472222 -0.4722222 5 2 1.744444 0.2555556 6 2 1.472222 0.5277778 7 2 1.472222 0.5277778 8 2 1.744444 0.2555556 9 2 1.744444 0.2555556 10 2 1.744444 0.2555556 11 1 1.472222 -0.4722222 12 2 1.744444 0.2555556 13 1 1.744444 -0.7444444 14 2 1.744444 0.2555556 15 2 1.744444 0.2555556 16 1 1.472222 -0.4722222 17 1 1.472222 -0.4722222 18 2 1.744444 0.2555556 19 1 1.744444 -0.7444444 20 2 1.472222 0.5277778 21 1 1.472222 -0.4722222 22 2 1.472222 0.5277778 23 2 1.744444 0.2555556 24 2 1.744444 0.2555556 25 1 1.472222 -0.4722222 26 2 1.472222 0.5277778 27 1 1.744444 -0.7444444 28 2 1.744444 0.2555556 29 2 1.472222 0.5277778 30 1 1.472222 -0.4722222 31 2 1.472222 0.5277778 32 1 1.472222 -0.4722222 33 2 1.472222 0.5277778 34 2 1.744444 0.2555556 35 1 1.472222 -0.4722222 36 1 1.472222 -0.4722222 37 1 1.472222 -0.4722222 38 1 1.472222 -0.4722222 39 2 1.472222 0.5277778 40 1 1.472222 -0.4722222 41 1 1.472222 -0.4722222 42 2 1.472222 0.5277778 43 1 1.472222 -0.4722222 44 1 1.472222 -0.4722222 45 2 1.472222 0.5277778 46 2 1.744444 0.2555556 47 2 1.744444 0.2555556 48 2 1.744444 0.2555556 49 2 1.744444 0.2555556 50 1 1.472222 -0.4722222 51 2 1.744444 0.2555556 52 1 1.472222 -0.4722222 53 1 1.472222 -0.4722222 54 2 1.744444 0.2555556 55 1 1.472222 -0.4722222 56 2 1.472222 0.5277778 57 2 1.744444 0.2555556 58 2 1.744444 0.2555556 59 1 1.744444 -0.7444444 60 2 1.744444 0.2555556 61 1 1.472222 -0.4722222 62 1 1.744444 -0.7444444 63 2 1.744444 0.2555556 64 2 1.744444 0.2555556 65 2 1.472222 0.5277778 66 1 1.744444 -0.7444444 67 2 1.744444 0.2555556 68 1 1.472222 -0.4722222 69 2 1.744444 0.2555556 70 1 1.744444 -0.7444444 71 1 1.744444 -0.7444444 72 2 1.744444 0.2555556 73 2 1.744444 0.2555556 74 1 1.744444 -0.7444444 75 1 1.472222 -0.4722222 76 2 1.472222 0.5277778 77 2 1.744444 0.2555556 78 2 1.472222 0.5277778 79 1 1.472222 -0.4722222 80 1 1.472222 -0.4722222 81 1 1.744444 -0.7444444 82 1 1.744444 -0.7444444 83 2 1.744444 0.2555556 84 1 1.472222 -0.4722222 85 2 1.744444 0.2555556 86 2 1.472222 0.5277778 87 1 1.744444 -0.7444444 88 2 1.744444 0.2555556 89 2 1.472222 0.5277778 90 2 1.472222 0.5277778 91 2 1.744444 0.2555556 92 2 1.472222 0.5277778 93 2 1.744444 0.2555556 94 2 1.472222 0.5277778 95 2 1.472222 0.5277778 96 2 1.744444 0.2555556 97 2 1.744444 0.2555556 98 2 1.472222 0.5277778 99 1 1.744444 -0.7444444 100 1 1.744444 -0.7444444 101 2 1.744444 0.2555556 102 1 1.744444 -0.7444444 103 2 1.744444 0.2555556 104 1 1.472222 -0.4722222 105 2 1.744444 0.2555556 106 1 1.472222 -0.4722222 107 2 1.744444 0.2555556 108 1 1.744444 -0.7444444 109 1 1.744444 -0.7444444 110 2 1.744444 0.2555556 111 2 1.744444 0.2555556 112 2 1.472222 0.5277778 113 2 1.744444 0.2555556 114 1 1.472222 -0.4722222 115 2 1.744444 0.2555556 116 1 1.472222 -0.4722222 117 2 1.744444 0.2555556 118 2 1.744444 0.2555556 119 2 1.744444 0.2555556 120 2 1.744444 0.2555556 121 2 1.744444 0.2555556 122 2 1.472222 0.5277778 123 2 1.744444 0.2555556 124 2 1.744444 0.2555556 125 2 1.472222 0.5277778 126 2 1.472222 0.5277778 127 1 1.472222 -0.4722222 128 2 1.744444 0.2555556 129 2 1.744444 0.2555556 130 2 1.472222 0.5277778 131 1 1.472222 -0.4722222 132 1 1.472222 -0.4722222 133 2 1.744444 0.2555556 134 1 1.472222 -0.4722222 135 1 1.744444 -0.7444444 136 2 1.472222 0.5277778 137 1 1.472222 -0.4722222 138 1 1.744444 -0.7444444 139 2 1.744444 0.2555556 140 2 1.744444 0.2555556 141 1 1.744444 -0.7444444 142 2 1.744444 0.2555556 143 1 1.744444 -0.7444444 144 2 1.744444 0.2555556 145 1 1.472222 -0.4722222 146 2 1.472222 0.5277778 147 2 1.472222 0.5277778 148 1 1.744444 -0.7444444 149 1 1.472222 -0.4722222 150 1 1.472222 -0.4722222 151 2 1.744444 0.2555556 152 2 1.744444 0.2555556 153 1 1.744444 -0.7444444 154 2 1.744444 0.2555556 155 2 1.744444 0.2555556 156 2 1.472222 0.5277778 157 2 1.472222 0.5277778 158 2 1.744444 0.2555556 159 2 1.744444 0.2555556 160 2 1.472222 0.5277778 161 2 1.744444 0.2555556 162 2 1.744444 0.2555556 > 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/47ify1323806194.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/58t3m1323806194.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/6isyb1323806194.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/77v2o1323806194.tab") + } > > try(system("convert tmp/2u2ub1323806194.ps tmp/2u2ub1323806194.png",intern=TRUE)) character(0) > try(system("convert tmp/3vzsv1323806194.ps tmp/3vzsv1323806194.png",intern=TRUE)) character(0) > try(system("convert tmp/47ify1323806194.ps tmp/47ify1323806194.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.075 0.329 3.813