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)

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> x <- array(list(194.9,1.79,195.5,1.95,196,2.26,196.2,2.04,196.2,2.16,196.2,2.75,196.2,2.79,197,2.88,197.7,3.36,198,2.97,198.2,3.1,198.5,2.49,198.6,2.2,199.5,2.25,200,2.09,201.3,2.79,202.2,3.14,202.9,2.93,203.5,2.65,203.5,2.67,204,2.26,204.1,2.35,204.3,2.13,204.5,2.18,204.8,2.9,205.1,2.63,205.7,2.67,206.5,1.81,206.9,1.33,207.1,0.88,207.8,1.28,208,1.26,208.5,1.26,208.6,1.29,209,1.1,209.1,1.37,209.7,1.21,209.8,1.74,209.9,1.76,210,1.48,210.8,1.04,211.4,1.62,211.7,1.49,212,1.79,212.2,1.8,212.4,1.58,212.9,1.86,213.4,1.74,213.7,1.59,214,1.26,214.3,1.13,214.8,1.92,215,2.61,215.9,2.26,216.4,2.41,216.9,2.26,217.2,2.03,217.5,2.86,217.9,2.55,218.1,2.27,218.6,2.26,218.9,2.57,219.3,3.07,220.4,2.76,220.9,2.51,221,2.87,221.8,3.14,222,3.11,222.2,3.16,222.5,2.47,222.9,2.57,223.1,2.89),dim=c(2,72),dimnames=list(c('Uurloon','Inflatie'),1:72))
>  y <- array(NA,dim=c(2,72),dimnames=list(c('Uurloon','Inflatie'),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 = '2'
> par2 = 'quantiles'
> par1 = '1'
> 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] "Uurloon"
> x[,par1]
 [1] 194.9 195.5 196.0 196.2 196.2 196.2 196.2 197.0 197.7 198.0 198.2 198.5
[13] 198.6 199.5 200.0 201.3 202.2 202.9 203.5 203.5 204.0 204.1 204.3 204.5
[25] 204.8 205.1 205.7 206.5 206.9 207.1 207.8 208.0 208.5 208.6 209.0 209.1
[37] 209.7 209.8 209.9 210.0 210.8 211.4 211.7 212.0 212.2 212.4 212.9 213.4
[49] 213.7 214.0 214.3 214.8 215.0 215.9 216.4 216.9 217.2 217.5 217.9 218.1
[61] 218.6 218.9 219.3 220.4 220.9 221.0 221.8 222.0 222.2 222.5 222.9 223.1
> 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])

[195,210) [210,223] 
       36        36 
> colnames(x)
[1] "Uurloon"  "Inflatie"
> colnames(x)[par1]
[1] "Uurloon"
> x[,par1]
 [1] [195,210) [195,210) [195,210) [195,210) [195,210) [195,210) [195,210)
 [8] [195,210) [195,210) [195,210) [195,210) [195,210) [195,210) [195,210)
[15] [195,210) [195,210) [195,210) [195,210) [195,210) [195,210) [195,210)
[22] [195,210) [195,210) [195,210) [195,210) [195,210) [195,210) [195,210)
[29] [195,210) [195,210) [195,210) [195,210) [195,210) [195,210) [195,210)
[36] [195,210) [210,223] [210,223] [210,223] [210,223] [210,223] [210,223]
[43] [210,223] [210,223] [210,223] [210,223] [210,223] [210,223] [210,223]
[50] [210,223] [210,223] [210,223] [210,223] [210,223] [210,223] [210,223]
[57] [210,223] [210,223] [210,223] [210,223] [210,223] [210,223] [210,223]
[64] [210,223] [210,223] [210,223] [210,223] [210,223] [210,223] [210,223]
[71] [210,223] [210,223]
Levels: [195,210) [210,223]
> 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/1859j1323793157.tab") 
+ }
+ }
           m.ct.i.pred
m.ct.i.actu   1   2
          1 167 158
          2 157 169
[1] 0.5138462
[1] 0.5184049
[1] 0.516129
           m.ct.x.pred
m.ct.x.actu  1  2
          1 13 22
          2 23 11
[1] 0.3714286
[1] 0.3235294
[1] 0.3478261
> m

	 Conditional inference tree with 1 terminal nodes

Response:  as.factor(Uurloon) 
Input:  Inflatie 
Number of observations:  72 

1)*  weights = 72 
> postscript(file="/var/wessaorg/rcomp/tmp/2okwy1323793157.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/3kmmf1323793157.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,]    1    1
[14,]    1    1
[15,]    1    1
[16,]    1    1
[17,]    1    1
[18,]    1    1
[19,]    1    1
[20,]    1    1
[21,]    1    1
[22,]    1    1
[23,]    1    1
[24,]    1    1
[25,]    1    1
[26,]    1    1
[27,]    1    1
[28,]    1    1
[29,]    1    1
[30,]    1    1
[31,]    1    1
[32,]    1    1
[33,]    1    1
[34,]    1    1
[35,]    1    1
[36,]    1    1
[37,]    2    1
[38,]    2    1
[39,]    2    1
[40,]    2    1
[41,]    2    1
[42,]    2    1
[43,]    2    1
[44,]    2    1
[45,]    2    1
[46,]    2    1
[47,]    2    1
[48,]    2    1
[49,]    2    1
[50,]    2    1
[51,]    2    1
[52,]    2    1
[53,]    2    1
[54,]    2    1
[55,]    2    1
[56,]    2    1
[57,]    2    1
[58,]    2    1
[59,]    2    1
[60,]    2    1
[61,]    2    1
[62,]    2    1
[63,]    2    1
[64,]    2    1
[65,]    2    1
[66,]    2    1
[67,]    2    1
[68,]    2    1
[69,]    2    1
[70,]    2    1
[71,]    2    1
[72,]    2    1
           
            [195,210) [210,223]
  [195,210)        36         0
  [210,223]        36         0
> postscript(file="/var/wessaorg/rcomp/tmp/4bsff1323793157.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/5k10q1323793157.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/6vlxn1323793157.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/7yn0k1323793157.tab") 
+ }
> 
> try(system("convert tmp/2okwy1323793157.ps tmp/2okwy1323793157.png",intern=TRUE))
character(0)
> try(system("convert tmp/3kmmf1323793157.ps tmp/3kmmf1323793157.png",intern=TRUE))
character(0)
> try(system("convert tmp/4bsff1323793157.ps tmp/4bsff1323793157.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.217   0.323   3.966