R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- array(list(356,182,89,386,213,97,444,227,154,387,209,81,327,219,110,448,221,116,225,114,73,182,97,73,460,205,174,411,215,103,342,224,130,361,189,91,377,182,136,331,201,106,428,198,136,340,173,122,352,238,131,461,258,135,221,122,75,198,101,68,422,259,143,329,243,115,320,188,93,375,173,128,364,224,152,351,215,125,380,196,107,319,159,116,322,187,220,386,208,137,221,131,34,187,93,51,344,210,153,342,228,145,365,176,116,313,195,145,356,188,98,337,188,118,389,190,139,326,188,140,343,176,113,357,225,149,220,93,79,218,79,47,391,235,166,425,247,180,332,195,122,298,197,134,360,211,114,336,156,125,325,209,181,393,180,142,301,185,143,426,303,187,265,129,137,210,85,62,429,249,239,440,231,157,357,212,139,431,240,187,442,234,99,442,217,146,544,287,175,420,221,148,396,208,130,482,241,183,261,156,115,211,96,80,448,320,223,468,242,131,464,227,201,425,200,157),dim=c(3,72),dimnames=list(c('Vlaanderen','Wallonië','Brussel'),1:72))
>  y <- array(NA,dim=c(3,72),dimnames=list(c('Vlaanderen','Wallonië','Brussel'),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 = 'no'
> par3 = '3'
> par2 = 'quantiles'
> 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

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] "Vlaanderen"
> x[,par1]
 [1] 356 386 444 387 327 448 225 182 460 411 342 361 377 331 428 340 352 461 221
[20] 198 422 329 320 375 364 351 380 319 322 386 221 187 344 342 365 313 356 337
[39] 389 326 343 357 220 218 391 425 332 298 360 336 325 393 301 426 265 210 429
[58] 440 357 431 442 442 544 420 396 482 261 211 448 468 464 425
> 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])

[182,336) [336,391) [391,544] 
       24        24        24 
> colnames(x)
[1] "Vlaanderen" "Walloni.."  "Brussel"   
> colnames(x)[par1]
[1] "Vlaanderen"
> x[,par1]
 [1] [336,391) [336,391) [391,544] [336,391) [182,336) [391,544] [182,336)
 [8] [182,336) [391,544] [391,544] [336,391) [336,391) [336,391) [182,336)
[15] [391,544] [336,391) [336,391) [391,544] [182,336) [182,336) [391,544]
[22] [182,336) [182,336) [336,391) [336,391) [336,391) [336,391) [182,336)
[29] [182,336) [336,391) [182,336) [182,336) [336,391) [336,391) [336,391)
[36] [182,336) [336,391) [336,391) [336,391) [182,336) [336,391) [336,391)
[43] [182,336) [182,336) [391,544] [391,544] [182,336) [182,336) [336,391)
[50] [336,391) [182,336) [391,544] [182,336) [391,544] [182,336) [182,336)
[57] [391,544] [391,544] [336,391) [391,544] [391,544] [391,544] [391,544]
[64] [391,544] [391,544] [391,544] [182,336) [182,336) [391,544] [391,544]
[71] [391,544] [391,544]
Levels: [182,336) [336,391) [391,544]
> 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/1mxzx1292094501.tab") 
+ }
+ }
> m

	 Conditional inference tree with 3 terminal nodes

Response:  as.factor(Vlaanderen) 
Inputs:  Walloni.., Brussel 
Number of observations:  72 

1) Walloni.. <= 159; criterion = 1, statistic = 33.662
  2)*  weights = 14 
1) Walloni.. > 159
  3) Walloni.. <= 225; criterion = 1, statistic = 18.329
    4)*  weights = 40 
  3) Walloni.. > 225
    5)*  weights = 18 
> postscript(file="/var/www/html/rcomp/tmp/2mxzx1292094501.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/3mxzx1292094501.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,]    2    2
 [3,]    3    3
 [4,]    2    2
 [5,]    1    2
 [6,]    3    2
 [7,]    1    1
 [8,]    1    1
 [9,]    3    2
[10,]    3    2
[11,]    2    2
[12,]    2    2
[13,]    2    2
[14,]    1    2
[15,]    3    2
[16,]    2    2
[17,]    2    3
[18,]    3    3
[19,]    1    1
[20,]    1    1
[21,]    3    3
[22,]    1    3
[23,]    1    2
[24,]    2    2
[25,]    2    2
[26,]    2    2
[27,]    2    2
[28,]    1    1
[29,]    1    2
[30,]    2    2
[31,]    1    1
[32,]    1    1
[33,]    2    2
[34,]    2    3
[35,]    2    2
[36,]    1    2
[37,]    2    2
[38,]    2    2
[39,]    2    2
[40,]    1    2
[41,]    2    2
[42,]    2    2
[43,]    1    1
[44,]    1    1
[45,]    3    3
[46,]    3    3
[47,]    1    2
[48,]    1    2
[49,]    2    2
[50,]    2    1
[51,]    1    2
[52,]    3    2
[53,]    1    2
[54,]    3    3
[55,]    1    1
[56,]    1    1
[57,]    3    3
[58,]    3    3
[59,]    2    2
[60,]    3    3
[61,]    3    3
[62,]    3    2
[63,]    3    3
[64,]    3    2
[65,]    3    2
[66,]    3    3
[67,]    1    1
[68,]    1    1
[69,]    3    3
[70,]    3    3
[71,]    3    3
[72,]    3    2
           
            [182,336) [336,391) [391,544]
  [182,336)        13        10         1
  [336,391)         1        21         2
  [391,544]         0         9        15
> postscript(file="/var/www/html/rcomp/tmp/4f6y01292094501.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/5bywr1292094501.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/6wydf1292094501.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/7ppcz1292094501.tab") 
+ }
> 
> try(system("convert tmp/2mxzx1292094501.ps tmp/2mxzx1292094501.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mxzx1292094501.ps tmp/3mxzx1292094501.png",intern=TRUE))
character(0)
> try(system("convert tmp/4f6y01292094501.ps tmp/4f6y01292094501.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.965   0.493   5.266