R version 2.12.0 (2010-10-15) Copyright (C) 2010 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(0.6000 + ,1.0800 + ,1.0100 + ,1.6100 + ,1.7700 + ,1.3900 + ,1.7700 + ,0.6000 + ,1.0900 + ,1.0000 + ,1.5800 + ,1.7700 + ,1.3500 + ,1.9800 + ,0.6000 + ,1.1000 + ,1.0000 + ,1.6900 + ,1.7700 + ,1.3900 + ,1.9400 + ,0.6000 + ,1.1000 + ,1.0000 + ,1.7800 + ,1.7700 + ,1.3700 + ,1.8500 + ,0.6000 + ,1.1100 + ,1.0600 + ,1.7600 + ,1.7400 + ,1.3800 + ,1.8400 + ,0.6000 + ,1.1000 + ,1.2200 + ,1.8300 + ,1.7800 + ,1.5100 + ,1.8200 + ,0.6000 + ,1.1000 + ,1.2400 + ,1.8000 + ,1.7800 + ,1.5100 + ,1.8300 + ,0.6000 + ,1.1100 + ,1.3400 + ,1.5700 + ,1.7800 + ,1.4500 + ,1.9100 + ,0.6100 + ,1.1100 + ,1.3000 + ,1.4500 + ,1.7800 + ,1.3000 + ,1.8500 + ,0.6100 + ,1.1100 + ,1.0500 + ,1.4000 + ,1.8100 + ,1.2900 + ,1.8100 + ,0.6100 + ,1.1100 + ,1.0000 + ,1.5500 + ,1.8400 + ,1.4400 + ,1.8300 + ,0.6100 + ,1.1100 + ,1.0000 + ,1.5800 + ,1.8000 + ,1.4600 + ,1.7900 + ,0.6100 + ,1.1200 + ,1.0100 + ,1.5800 + ,1.7800 + ,1.5000 + ,1.8000 + ,0.6100 + ,1.1100 + ,1.0200 + ,1.5900 + ,1.7600 + 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,0.6300 + ,1.1000 + ,1.4400 + ,2.0700 + ,1.8100 + ,1.6300 + ,2.1400 + ,0.6300 + ,1.1000 + ,1.4400 + ,2.1500 + ,1.7800 + ,1.6800 + ,2.1500 + ,0.6400 + ,1.1100 + ,1.4500 + ,2.1000 + ,1.8000 + ,1.5900 + ,2.0500 + ,0.6400 + ,1.1300 + ,1.3900 + ,1.6800 + ,1.7900 + ,1.5400 + ,2.0500 + ,0.6400 + ,1.1300 + ,1.4800 + ,1.6800 + ,1.8300 + ,1.5100 + ,1.9500 + ,0.6500 + ,1.1300 + ,1.3200 + ,1.6500 + ,1.8200 + ,1.5000 + ,2.0200 + ,0.6500 + ,1.1300 + ,1.2900 + ,1.7200 + ,1.8000 + ,1.7100 + ,2.0200 + ,0.6500 + ,1.1400 + ,1.3100 + ,1.7300 + ,1.8200 + ,1.6000 + ,1.8800 + ,0.6500 + ,1.1400 + ,1.2700 + ,1.7600 + ,1.8400 + ,1.5500 + ,1.9600 + ,0.6500 + ,1.1400 + ,1.3800 + ,1.8400 + ,1.8200 + ,1.6300 + ,1.9300 + ,0.6500 + ,1.1500 + ,1.3800 + ,1.9900 + ,1.8100 + ,1.6400 + ,2.0300 + ,0.6500 + ,1.1500 + ,1.4500 + ,2.0500 + ,1.7900 + ,1.6800 + ,2.1000 + ,0.6500 + ,1.1500 + ,1.5000 + ,2.1200 + ,1.8700 + ,1.7200 + ,1.9500 + ,0.6500 + ,1.1500 + ,1.6300 + ,2.1300 + ,1.8900 + ,1.7600 + ,2.0700 + ,0.6600 + ,1.1500 + 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,1.8800 + ,1.6000 + ,2.0000 + ,0.6600 + ,1.1100 + ,1.2900 + ,2.0400 + ,1.8900 + ,1.6800 + ,2.0500 + ,0.6600 + ,1.1100 + ,1.2000 + ,2.0600 + ,1.9200 + ,1.7700 + ,2.0800 + ,0.6600 + ,1.1100 + ,1.2000 + ,1.9300 + ,1.9100 + ,1.7500 + ,2.0700 + ,0.7100 + ,1.1400 + ,1.2100 + ,1.8600 + ,1.8900 + ,1.7600 + ,2.1200 + ,0.7400 + ,1.1500 + ,1.2100 + ,1.9400 + ,1.8900 + ,1.8900 + ,2.1300 + ,0.7500 + ,1.1500 + ,1.1900 + ,2.3500 + ,1.9800 + ,1.8800 + ,2.1600 + ,0.7500 + ,1.1600 + ,1.1800 + ,2.4600 + ,2.0200 + ,1.9000 + ,2.2500 + ,0.7500 + ,1.1500 + ,1.1700 + ,2.5900 + ,2.0200 + ,1.9100 + ,2.2600 + ,0.7500 + ,1.1600 + ,1.2200 + ,2.6600 + ,1.9900 + ,1.9100 + ,2.3900 + ,0.7000 + ,1.1300 + ,1.2500 + ,2.4100 + ,1.9700 + ,1.8400 + ,2.3600 + ,0.6900 + ,1.1300 + ,1.3000 + ,2.1800 + ,1.9600 + ,1.6900 + ,2.2600 + ,0.6900 + ,1.1200 + ,1.3300 + ,2.1300 + ,1.9500 + ,1.6100 + ,2.2600 + ,0.6800 + ,1.1200 + ,1.1800 + ,2.1100 + ,1.9800 + ,1.6700 + ,2.2700 + ,0.6800 + ,1.1100 + ,1.1800 + ,2.1200 + ,2.0000 + ,1.8400 + ,2.2900 + ,0.6800 + ,1.1100 + ,1.1900 + ,2.1600 + ,2.0000 + ,1.8400 + ,2.2100) + ,dim=c(7 + ,72) + ,dimnames=list(c('Mineraalwater' + ,'Vruchtesappen' + ,'Jonagold' + ,'Sinaasappelen' + ,'Citroenen' + ,'Pompelmoezen' + ,'Bananen') + ,1:72)) > y <- array(NA,dim=c(7,72),dimnames=list(c('Mineraalwater','Vruchtesappen','Jonagold','Sinaasappelen','Citroenen','Pompelmoezen','Bananen'),1:72)) > 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 = '3' > 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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from 'package:survival': untangle.specials The following object(s) are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "Vruchtesappen" > x[,par1] [1] 1.08 1.09 1.10 1.10 1.11 1.10 1.10 1.11 1.11 1.11 1.11 1.11 1.12 1.11 1.11 [16] 1.12 1.12 1.11 1.12 1.11 1.11 1.10 1.10 1.10 1.11 1.10 1.10 1.09 1.10 1.10 [31] 1.11 1.13 1.13 1.13 1.13 1.14 1.14 1.14 1.15 1.15 1.15 1.15 1.15 1.15 1.14 [46] 1.14 1.14 1.13 1.12 1.13 1.13 1.13 1.12 1.13 1.12 1.12 1.11 1.11 1.11 1.11 [61] 1.14 1.15 1.15 1.16 1.15 1.16 1.13 1.13 1.12 1.12 1.11 1.11 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) [1.08,1.12) [1.12,1.14) [1.14,1.16] 33 21 18 > colnames(x) [1] "Mineraalwater" "Vruchtesappen" "Jonagold" "Sinaasappelen" [5] "Citroenen" "Pompelmoezen" "Bananen" > colnames(x)[par1] [1] "Vruchtesappen" > x[,par1] [1] [1.08,1.12) [1.08,1.12) [1.08,1.12) [1.08,1.12) [1.08,1.12) [1.08,1.12) [7] [1.08,1.12) [1.08,1.12) [1.08,1.12) [1.08,1.12) [1.08,1.12) [1.08,1.12) [13] [1.12,1.14) [1.08,1.12) [1.08,1.12) [1.12,1.14) [1.12,1.14) [1.08,1.12) [19] [1.12,1.14) [1.08,1.12) [1.08,1.12) [1.08,1.12) [1.08,1.12) [1.08,1.12) [25] [1.08,1.12) [1.08,1.12) [1.08,1.12) [1.08,1.12) [1.08,1.12) [1.08,1.12) [31] [1.08,1.12) [1.12,1.14) [1.12,1.14) [1.12,1.14) [1.12,1.14) [1.14,1.16] [37] [1.14,1.16] [1.14,1.16] [1.14,1.16] [1.14,1.16] [1.14,1.16] [1.14,1.16] [43] [1.14,1.16] [1.14,1.16] [1.14,1.16] [1.14,1.16] [1.14,1.16] [1.12,1.14) [49] [1.12,1.14) [1.12,1.14) [1.12,1.14) [1.12,1.14) [1.12,1.14) [1.12,1.14) [55] [1.12,1.14) [1.12,1.14) [1.08,1.12) [1.08,1.12) [1.08,1.12) [1.08,1.12) [61] [1.14,1.16] [1.14,1.16] [1.14,1.16] [1.14,1.16] [1.14,1.16] [1.14,1.16] [67] [1.12,1.14) [1.12,1.14) [1.12,1.14) [1.12,1.14) [1.08,1.12) [1.08,1.12) Levels: [1.08,1.12) [1.12,1.14) [1.14,1.16] > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/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/rcomp/tmp/15aw81292344499.tab") + } + } m.ct.i.pred m.ct.i.actu 1 2 3 1 232 37 38 2 38 138 11 3 0 55 107 [1] 0.7557003 [1] 0.7379679 [1] 0.6604938 [1] 0.7271341 m.ct.x.pred m.ct.x.actu 1 2 3 1 20 3 0 2 4 18 1 3 0 8 10 [1] 0.8695652 [1] 0.7826087 [1] 0.5555556 [1] 0.75 > m Conditional inference tree with 3 terminal nodes Response: as.factor(Vruchtesappen) Inputs: Mineraalwater, Jonagold, Sinaasappelen, Citroenen, Pompelmoezen, Bananen Number of observations: 72 1) Mineraalwater <= 0.63; criterion = 1, statistic = 26.351 2)* weights = 29 1) Mineraalwater > 0.63 3) Pompelmoezen <= 1.72; criterion = 0.995, statistic = 14.317 4)* weights = 26 3) Pompelmoezen > 1.72 5)* weights = 17 > postscript(file="/var/www/rcomp/tmp/25aw81292344499.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/rcomp/tmp/35aw81292344499.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,] 1 1 [2,] 1 1 [3,] 1 1 [4,] 1 1 [5,] 1 1 [6,] 1 1 [7,] 1 1 [8,] 1 1 [9,] 1 1 [10,] 1 1 [11,] 1 1 [12,] 1 1 [13,] 2 1 [14,] 1 1 [15,] 1 1 [16,] 2 1 [17,] 2 1 [18,] 1 1 [19,] 2 1 [20,] 1 1 [21,] 1 1 [22,] 1 1 [23,] 1 1 [24,] 1 2 [25,] 1 1 [26,] 1 1 [27,] 1 1 [28,] 1 1 [29,] 1 1 [30,] 1 1 [31,] 1 2 [32,] 2 2 [33,] 2 2 [34,] 2 2 [35,] 2 2 [36,] 3 2 [37,] 3 2 [38,] 3 2 [39,] 3 2 [40,] 3 2 [41,] 3 2 [42,] 3 3 [43,] 3 3 [44,] 3 3 [45,] 3 3 [46,] 3 3 [47,] 3 3 [48,] 2 2 [49,] 2 2 [50,] 2 2 [51,] 2 2 [52,] 2 2 [53,] 2 2 [54,] 2 2 [55,] 2 2 [56,] 2 2 [57,] 1 2 [58,] 1 2 [59,] 1 3 [60,] 1 3 [61,] 3 3 [62,] 3 3 [63,] 3 3 [64,] 3 3 [65,] 3 3 [66,] 3 3 [67,] 2 3 [68,] 2 2 [69,] 2 2 [70,] 2 2 [71,] 1 3 [72,] 1 3 [1.08,1.12) [1.12,1.14) [1.14,1.16] [1.08,1.12) 25 4 4 [1.12,1.14) 4 16 1 [1.14,1.16] 0 6 12 > postscript(file="/var/www/rcomp/tmp/4ravw1292344499.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/rcomp/tmp/5ubck1292344499.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/rcomp/tmp/6xtsq1292344499.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/rcomp/tmp/7qk9t1292344499.tab") + } > > try(system("convert tmp/25aw81292344499.ps tmp/25aw81292344499.png",intern=TRUE)) character(0) > try(system("convert tmp/35aw81292344499.ps tmp/35aw81292344499.png",intern=TRUE)) character(0) > try(system("convert tmp/4ravw1292344499.ps tmp/4ravw1292344499.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.58 0.69 3.42