R version 2.9.0 (2009-04-17) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 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. 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> y <- array(NA,dim=c(6,430),dimnames=list(c('Wealth','Costs','Orders','Dividends','Group','Gender '),1:430)) > 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 = '2' > par2 = 'none' > par1 = '5' > #'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] "Group" > x[,par1] [1] 1 1 0 1 1 0 1 1 1 0 0 1 0 1 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0 1 1 1 1 0 0 0 1 [38] 1 0 0 0 0 0 0 1 1 1 0 0 1 1 1 1 1 0 0 0 0 1 1 0 1 1 0 0 0 1 0 1 1 0 0 0 0 [75] 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 1 1 0 0 0 0 0 1 1 0 1 0 1 [112] 0 1 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 1 [186] 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 [223] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 0 0 0 0 0 0 1 0 [260] 1 1 0 0 1 0 1 0 1 0 1 1 1 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 0 [297] 1 0 0 1 0 0 1 0 0 1 1 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 [334] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 [371] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 [408] 0 0 0 1 1 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 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 1 311 119 > colnames(x) [1] "Wealth" "Costs" "Orders" "Dividends" "Group" "Gender." > colnames(x)[par1] [1] "Group" > x[,par1] [1] 1 1 0 1 1 0 1 1 1 0 0 1 0 1 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0 1 1 1 1 0 0 0 1 [38] 1 0 0 0 0 0 0 1 1 1 0 0 1 1 1 1 1 0 0 0 0 1 1 0 1 1 0 0 0 1 0 1 1 0 0 0 0 [75] 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 1 1 0 0 0 0 0 1 1 0 1 0 1 [112] 0 1 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 1 [186] 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 [223] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 0 0 0 0 0 0 1 0 [260] 1 1 0 0 1 0 1 0 1 0 1 1 1 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 0 [297] 1 0 0 1 0 0 1 0 0 1 1 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 [334] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 [371] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 [408] 0 0 0 1 1 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 > 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/19p601292934533.tab") + } + } > m Conditional inference tree with 4 terminal nodes Response: Group Inputs: Wealth, Costs, Orders, Dividends, Gender. Number of observations: 430 1) Gender. <= 0; criterion = 1, statistic = 28.196 2) Costs <= 2675; criterion = 1, statistic = 21.552 3)* weights = 161 2) Costs > 2675 4)* weights = 21 1) Gender. > 0 5) Wealth <= 469107; criterion = 0.986, statistic = 8.984 6)* weights = 219 5) Wealth > 469107 7)* weights = 29 > postscript(file="/var/www/html/rcomp/tmp/29p601292934533.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/39p601292934533.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 1 0.72413793 0.27586207 2 1 0.72413793 0.27586207 3 0 0.32876712 -0.32876712 4 1 0.72413793 0.27586207 5 1 0.47619048 0.52380952 6 0 0.72413793 -0.72413793 7 1 0.47619048 0.52380952 8 1 0.72413793 0.27586207 9 1 0.72413793 0.27586207 10 0 0.72413793 -0.72413793 11 0 0.72413793 -0.72413793 12 1 0.72413793 0.27586207 13 0 0.32876712 -0.32876712 14 1 0.72413793 0.27586207 15 0 0.47619048 -0.47619048 16 0 0.47619048 -0.47619048 17 1 0.72413793 0.27586207 18 1 0.72413793 0.27586207 19 0 0.32876712 -0.32876712 20 1 0.72413793 0.27586207 21 1 0.47619048 0.52380952 22 0 0.47619048 -0.47619048 23 0 0.32876712 -0.32876712 24 0 0.32876712 -0.32876712 25 0 0.72413793 -0.72413793 26 1 0.72413793 0.27586207 27 0 0.32876712 -0.32876712 28 0 0.72413793 -0.72413793 29 0 0.32876712 -0.32876712 30 1 0.72413793 0.27586207 31 1 0.72413793 0.27586207 32 1 0.47619048 0.52380952 33 1 0.72413793 0.27586207 34 0 0.72413793 -0.72413793 35 0 0.32876712 -0.32876712 36 0 0.72413793 -0.72413793 37 1 0.72413793 0.27586207 38 1 0.72413793 0.27586207 39 0 0.32876712 -0.32876712 40 0 0.47619048 -0.47619048 41 0 0.32876712 -0.32876712 42 0 0.47619048 -0.47619048 43 0 0.47619048 -0.47619048 44 0 0.47619048 -0.47619048 45 1 0.32876712 0.67123288 46 1 0.32876712 0.67123288 47 1 0.32876712 0.67123288 48 0 0.32876712 -0.32876712 49 0 0.32876712 -0.32876712 50 1 0.72413793 0.27586207 51 1 0.72413793 0.27586207 52 1 0.72413793 0.27586207 53 1 0.09937888 0.90062112 54 1 0.32876712 0.67123288 55 0 0.09937888 -0.09937888 56 0 0.72413793 -0.72413793 57 0 0.47619048 -0.47619048 58 0 0.32876712 -0.32876712 59 1 0.32876712 0.67123288 60 1 0.47619048 0.52380952 61 0 0.09937888 -0.09937888 62 1 0.32876712 0.67123288 63 1 0.47619048 0.52380952 64 0 0.09937888 -0.09937888 65 0 0.32876712 -0.32876712 66 0 0.32876712 -0.32876712 67 1 0.32876712 0.67123288 68 0 0.32876712 -0.32876712 69 1 0.32876712 0.67123288 70 1 0.32876712 0.67123288 71 0 0.47619048 -0.47619048 72 0 0.09937888 -0.09937888 73 0 0.32876712 -0.32876712 74 0 0.32876712 -0.32876712 75 1 0.32876712 0.67123288 76 0 0.09937888 -0.09937888 77 0 0.09937888 -0.09937888 78 0 0.32876712 -0.32876712 79 1 0.32876712 0.67123288 80 0 0.32876712 -0.32876712 81 1 0.47619048 0.52380952 82 0 0.32876712 -0.32876712 83 0 0.09937888 -0.09937888 84 0 0.32876712 -0.32876712 85 1 0.32876712 0.67123288 86 0 0.32876712 -0.32876712 87 0 0.09937888 -0.09937888 88 0 0.09937888 -0.09937888 89 0 0.09937888 -0.09937888 90 0 0.09937888 -0.09937888 91 1 0.32876712 0.67123288 92 0 0.32876712 -0.32876712 93 1 0.32876712 0.67123288 94 0 0.32876712 -0.32876712 95 1 0.47619048 0.52380952 96 0 0.32876712 -0.32876712 97 1 0.32876712 0.67123288 98 0 0.09937888 -0.09937888 99 1 0.32876712 0.67123288 100 1 0.32876712 0.67123288 101 0 0.32876712 -0.32876712 102 0 0.32876712 -0.32876712 103 0 0.32876712 -0.32876712 104 0 0.32876712 -0.32876712 105 0 0.32876712 -0.32876712 106 1 0.32876712 0.67123288 107 1 0.72413793 0.27586207 108 0 0.47619048 -0.47619048 109 1 0.32876712 0.67123288 110 0 0.32876712 -0.32876712 111 1 0.32876712 0.67123288 112 0 0.47619048 -0.47619048 113 1 0.32876712 0.67123288 114 1 0.32876712 0.67123288 115 0 0.09937888 -0.09937888 116 0 0.09937888 -0.09937888 117 0 0.09937888 -0.09937888 118 1 0.32876712 0.67123288 119 0 0.32876712 -0.32876712 120 0 0.32876712 -0.32876712 121 0 0.09937888 -0.09937888 122 0 0.32876712 -0.32876712 123 1 0.72413793 0.27586207 124 1 0.32876712 0.67123288 125 1 0.47619048 0.52380952 126 0 0.09937888 -0.09937888 127 0 0.09937888 -0.09937888 128 0 0.09937888 -0.09937888 129 0 0.32876712 -0.32876712 130 0 0.09937888 -0.09937888 131 0 0.32876712 -0.32876712 132 0 0.32876712 -0.32876712 133 0 0.09937888 -0.09937888 134 0 0.09937888 -0.09937888 135 0 0.09937888 -0.09937888 136 0 0.32876712 -0.32876712 137 0 0.09937888 -0.09937888 138 0 0.09937888 -0.09937888 139 0 0.09937888 -0.09937888 140 0 0.09937888 -0.09937888 141 0 0.32876712 -0.32876712 142 0 0.09937888 -0.09937888 143 0 0.09937888 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0.09937888 -0.09937888 318 0 0.09937888 -0.09937888 319 0 0.09937888 -0.09937888 320 0 0.32876712 -0.32876712 321 0 0.32876712 -0.32876712 322 0 0.32876712 -0.32876712 323 1 0.09937888 0.90062112 324 0 0.32876712 -0.32876712 325 0 0.32876712 -0.32876712 326 1 0.09937888 0.90062112 327 1 0.32876712 0.67123288 328 0 0.32876712 -0.32876712 329 0 0.32876712 -0.32876712 330 0 0.32876712 -0.32876712 331 0 0.09937888 -0.09937888 332 0 0.32876712 -0.32876712 333 0 0.32876712 -0.32876712 334 0 0.09937888 -0.09937888 335 0 0.09937888 -0.09937888 336 0 0.32876712 -0.32876712 337 0 0.09937888 -0.09937888 338 0 0.09937888 -0.09937888 339 0 0.32876712 -0.32876712 340 0 0.09937888 -0.09937888 341 0 0.09937888 -0.09937888 342 0 0.09937888 -0.09937888 343 0 0.32876712 -0.32876712 344 0 0.09937888 -0.09937888 345 0 0.09937888 -0.09937888 346 0 0.09937888 -0.09937888 347 0 0.09937888 -0.09937888 348 0 0.32876712 -0.32876712 349 0 0.09937888 -0.09937888 350 0 0.32876712 -0.32876712 351 0 0.09937888 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0 0.09937888 -0.09937888 387 0 0.32876712 -0.32876712 388 0 0.09937888 -0.09937888 389 0 0.32876712 -0.32876712 390 0 0.32876712 -0.32876712 391 0 0.09937888 -0.09937888 392 0 0.32876712 -0.32876712 393 0 0.09937888 -0.09937888 394 1 0.47619048 0.52380952 395 0 0.32876712 -0.32876712 396 0 0.32876712 -0.32876712 397 1 0.32876712 0.67123288 398 0 0.32876712 -0.32876712 399 1 0.32876712 0.67123288 400 0 0.09937888 -0.09937888 401 0 0.09937888 -0.09937888 402 0 0.09937888 -0.09937888 403 1 0.09937888 0.90062112 404 0 0.09937888 -0.09937888 405 0 0.09937888 -0.09937888 406 0 0.32876712 -0.32876712 407 0 0.32876712 -0.32876712 408 0 0.09937888 -0.09937888 409 0 0.32876712 -0.32876712 410 0 0.32876712 -0.32876712 411 1 0.32876712 0.67123288 412 1 0.32876712 0.67123288 413 1 0.32876712 0.67123288 414 0 0.32876712 -0.32876712 415 0 0.09937888 -0.09937888 416 0 0.32876712 -0.32876712 417 1 0.09937888 0.90062112 418 0 0.09937888 -0.09937888 419 0 0.09937888 -0.09937888 420 0 0.09937888 -0.09937888 421 0 0.32876712 -0.32876712 422 1 0.32876712 0.67123288 423 0 0.32876712 -0.32876712 424 0 0.32876712 -0.32876712 425 1 0.32876712 0.67123288 426 0 0.32876712 -0.32876712 427 0 0.32876712 -0.32876712 428 0 0.32876712 -0.32876712 429 0 0.32876712 -0.32876712 430 0 0.32876712 -0.32876712 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/www/html/rcomp/tmp/4kgol1292934533.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/58ilf1292934533.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/6f1ir1292934533.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/78szu1292934533.tab") + } > > try(system("convert tmp/29p601292934533.ps tmp/29p601292934533.png",intern=TRUE)) character(0) > try(system("convert tmp/39p601292934533.ps tmp/39p601292934533.png",intern=TRUE)) character(0) > try(system("convert tmp/4kgol1292934533.ps tmp/4kgol1292934533.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.467 0.676 17.169