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)

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> x <- array(list(235.1,9700,280.7,9081,264.6,9084,240.7,9743,201.4,8587,240.8,9731,241.1,9563,223.8,9998,206.1,9437,174.7,10038,203.3,9918,220.5,9252,299.5,9737,347.4,9035,338.3,9133,327.7,9487,351.6,8700,396.6,9627,438.8,8947,395.6,9283,363.5,8829,378.8,9947,357,9628,369,9318,464.8,9605,479.1,8640,431.3,9214,366.5,9567,326.3,8547,355.1,9185,331.6,9470,261.3,9123,249,9278,205.5,10170,235.6,9434,240.9,9655,264.9,9429,253.8,8739,232.3,9552,193.8,9687,177,9019,213.2,9672,207.2,9206,180.6,9069,188.6,9788,175.4,10312,199,10105,179.6,9863,225.8,9656,234,9295,200.2,9946,183.6,9701,178.2,9049,203.2,10190,208.5,9706,191.8,9765,172.8,9893,148,9994,159.4,10433,154.5,10073,213.2,10112,196.4,9266,182.8,9820,176.4,10097,153.6,9115,173.2,10411,171,9678,151.2,10408,161.9,10153,157.2,10368,201.7,10581,236.4,10597,356.1,10680,398.3,9738,403.7,9556),dim=c(2,75),dimnames=list(c('Werkloosheid','Geboortes'),1:75))
>  y <- array(NA,dim=c(2,75),dimnames=list(c('Werkloosheid','Geboortes'),1:75))
>  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 = '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] "Geboortes"
> x[,par1]
 [1]  9700  9081  9084  9743  8587  9731  9563  9998  9437 10038  9918  9252
[13]  9737  9035  9133  9487  8700  9627  8947  9283  8829  9947  9628  9318
[25]  9605  8640  9214  9567  8547  9185  9470  9123  9278 10170  9434  9655
[37]  9429  8739  9552  9687  9019  9672  9206  9069  9788 10312 10105  9863
[49]  9656  9295  9946  9701  9049 10190  9706  9765  9893  9994 10433 10073
[61] 10112  9266  9820 10097  9115 10411  9678 10408 10153 10368 10581 10597
[73] 10680  9738  9556
> 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])

[8547, 9656) [9656,10680] 
          38           37 
> colnames(x)
[1] "Werkloosheid" "Geboortes"   
> colnames(x)[par1]
[1] "Geboortes"
> x[,par1]
 [1] [9656,10680] [8547, 9656) [8547, 9656) [9656,10680] [8547, 9656)
 [6] [9656,10680] [8547, 9656) [9656,10680] [8547, 9656) [9656,10680]
[11] [9656,10680] [8547, 9656) [9656,10680] [8547, 9656) [8547, 9656)
[16] [8547, 9656) [8547, 9656) [8547, 9656) [8547, 9656) [8547, 9656)
[21] [8547, 9656) [9656,10680] [8547, 9656) [8547, 9656) [8547, 9656)
[26] [8547, 9656) [8547, 9656) [8547, 9656) [8547, 9656) [8547, 9656)
[31] [8547, 9656) [8547, 9656) [8547, 9656) [9656,10680] [8547, 9656)
[36] [8547, 9656) [8547, 9656) [8547, 9656) [8547, 9656) [9656,10680]
[41] [8547, 9656) [9656,10680] [8547, 9656) [8547, 9656) [9656,10680]
[46] [9656,10680] [9656,10680] [9656,10680] [9656,10680] [8547, 9656)
[51] [9656,10680] [9656,10680] [8547, 9656) [9656,10680] [9656,10680]
[56] [9656,10680] [9656,10680] [9656,10680] [9656,10680] [9656,10680]
[61] [9656,10680] [8547, 9656) [9656,10680] [9656,10680] [8547, 9656)
[66] [9656,10680] [9656,10680] [9656,10680] [9656,10680] [9656,10680]
[71] [9656,10680] [9656,10680] [9656,10680] [9656,10680] [8547, 9656)
Levels: [8547, 9656) [9656,10680]
> 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/1c5kq1293029507.tab") 
+ }
+ }
> m

	 Conditional inference tree with 2 terminal nodes

Response:  as.factor(Geboortes) 
Input:  Werkloosheid 
Number of observations:  75 

1) Werkloosheid <= 240.8; criterion = 1, statistic = 18.962
  2)*  weights = 45 
1) Werkloosheid > 240.8
  3)*  weights = 30 
> postscript(file="/var/www/rcomp/tmp/2c5kq1293029507.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/3c5kq1293029507.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,]    2    2
 [2,]    1    1
 [3,]    1    1
 [4,]    2    2
 [5,]    1    2
 [6,]    2    2
 [7,]    1    1
 [8,]    2    2
 [9,]    1    2
[10,]    2    2
[11,]    2    2
[12,]    1    2
[13,]    2    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,]    2    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,]    2    2
[35,]    1    2
[36,]    1    1
[37,]    1    1
[38,]    1    1
[39,]    1    2
[40,]    2    2
[41,]    1    2
[42,]    2    2
[43,]    1    2
[44,]    1    2
[45,]    2    2
[46,]    2    2
[47,]    2    2
[48,]    2    2
[49,]    2    2
[50,]    1    2
[51,]    2    2
[52,]    2    2
[53,]    1    2
[54,]    2    2
[55,]    2    2
[56,]    2    2
[57,]    2    2
[58,]    2    2
[59,]    2    2
[60,]    2    2
[61,]    2    2
[62,]    1    2
[63,]    2    2
[64,]    2    2
[65,]    1    2
[66,]    2    2
[67,]    2    2
[68,]    2    2
[69,]    2    2
[70,]    2    2
[71,]    2    2
[72,]    2    2
[73,]    2    1
[74,]    2    1
[75,]    1    1
              
               [8547, 9656) [9656,10680]
  [8547, 9656)           26           12
  [9656,10680]            4           33
> postscript(file="/var/www/rcomp/tmp/4nwjt1293029507.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/5johk1293029507.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/6bxyn1293029507.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/7xyfb1293029507.tab") 
+ }
> 
> try(system("convert tmp/2c5kq1293029507.ps tmp/2c5kq1293029507.png",intern=TRUE))
character(0)
> try(system("convert tmp/3c5kq1293029507.ps tmp/3c5kq1293029507.png",intern=TRUE))
character(0)
> try(system("convert tmp/4nwjt1293029507.ps tmp/4nwjt1293029507.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
   1.87    0.65    2.51