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(46
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+ ,dim=c(5
+ ,146)
+ ,dimnames=list(c('Carrièremogelijkheden'
+ ,'Geen_Motivatie'
+ ,'Leermogelijkheden'
+ ,'Persoonlijke_redenen'
+ ,'Ouders')
+ ,1:146))
>  y <- array(NA,dim=c(5,146),dimnames=list(c('Carrièremogelijkheden','Geen_Motivatie','Leermogelijkheden','Persoonlijke_redenen','Ouders'),1:146))
>  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 = ''
> par2 = 'none'
> par1 = '3'
> #'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] "Leermogelijkheden"
> x[,par1]
  [1] 52 39 42 35 32 49 33 47 46 40 33 39 37 56 36 24 56 32 41 24 42 47 25 33 43
 [26] 45 44 46 31 31 42 28 38 59 43 29 38 39 50 44 29 29 36 43 28 39 35 43 28 49
 [51] 33 39 36 24 47 34 33 43 41 40 39 54 43 45 29 45 47 38 52 34 56 26 42 32 39
 [76] 37 37 52 31 34 38 29 52 40 47 34 37 43 37 55 36 28 47 38 37 32 47 40 45 37
[101] 38 37 35 50 32 32 38 31 27 34 43 28 44 43 53 33 36 46 36 24 50 40 40 32 49
[126] 47 28 41 25 46 53 34 40 46 38 51 38 45 41 42 36 41 35 42 35 32
> 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])

24 25 26 27 28 29 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 49 50 51 
 4  2  1  1  6  5  4  8  6  6  5  7  8  9  7  7  5  6  9  3  5  5  8  3  3  1 
52 53 54 55 56 59 
 4  2  1  1  3  1 
> colnames(x)
[1] "Carri..remogelijkheden" "Geen_Motivatie"         "Leermogelijkheden"     
[4] "Persoonlijke_redenen"   "Ouders"                
> colnames(x)[par1]
[1] "Leermogelijkheden"
> x[,par1]
  [1] 52 39 42 35 32 49 33 47 46 40 33 39 37 56 36 24 56 32 41 24 42 47 25 33 43
 [26] 45 44 46 31 31 42 28 38 59 43 29 38 39 50 44 29 29 36 43 28 39 35 43 28 49
 [51] 33 39 36 24 47 34 33 43 41 40 39 54 43 45 29 45 47 38 52 34 56 26 42 32 39
 [76] 37 37 52 31 34 38 29 52 40 47 34 37 43 37 55 36 28 47 38 37 32 47 40 45 37
[101] 38 37 35 50 32 32 38 31 27 34 43 28 44 43 53 33 36 46 36 24 50 40 40 32 49
[126] 47 28 41 25 46 53 34 40 46 38 51 38 45 41 42 36 41 35 42 35 32
> 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/1rt2s1292920288.tab") 
+ }
+ }
> m

	 Conditional inference tree with 3 terminal nodes

Response:  Leermogelijkheden 
Inputs:  Carri..remogelijkheden, Geen_Motivatie, Persoonlijke_redenen, Ouders 
Number of observations:  146 

1) Persoonlijke_redenen <= 35; criterion = 1, statistic = 36.649
  2) Persoonlijke_redenen <= 22; criterion = 0.998, statistic = 12.535
    3)*  weights = 15 
  2) Persoonlijke_redenen > 22
    4)*  weights = 110 
1) Persoonlijke_redenen > 35
  5)*  weights = 21 
> postscript(file="/var/www/html/rcomp/tmp/2rt2s1292920288.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/3rt2s1292920288.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        52  38.36364  13.6363636
2        39  38.36364   0.6363636
3        42  38.36364   3.6363636
4        35  38.36364  -3.3636364
5        32  38.36364  -6.3636364
6        49  48.23810   0.7619048
7        33  38.36364  -5.3636364
8        47  38.36364   8.6363636
9        46  38.36364   7.6363636
10       40  38.36364   1.6363636
11       33  38.36364  -5.3636364
12       39  38.36364   0.6363636
13       37  32.26667   4.7333333
14       56  48.23810   7.7619048
15       36  38.36364  -2.3636364
16       24  38.36364 -14.3636364
17       56  38.36364  17.6363636
18       32  38.36364  -6.3636364
19       41  38.36364   2.6363636
20       24  38.36364 -14.3636364
21       42  38.36364   3.6363636
22       47  48.23810  -1.2380952
23       25  32.26667  -7.2666667
24       33  38.36364  -5.3636364
25       43  38.36364   4.6363636
26       45  48.23810  -3.2380952
27       44  38.36364   5.6363636
28       46  38.36364   7.6363636
29       31  32.26667  -1.2666667
30       31  38.36364  -7.3636364
31       42  38.36364   3.6363636
32       28  38.36364 -10.3636364
33       38  38.36364  -0.3636364
34       59  48.23810  10.7619048
35       43  38.36364   4.6363636
36       29  38.36364  -9.3636364
37       38  38.36364  -0.3636364
38       39  48.23810  -9.2380952
39       50  38.36364  11.6363636
40       44  38.36364   5.6363636
41       29  38.36364  -9.3636364
42       29  32.26667  -3.2666667
43       36  38.36364  -2.3636364
44       43  38.36364   4.6363636
45       28  38.36364 -10.3636364
46       39  38.36364   0.6363636
47       35  32.26667   2.7333333
48       43  48.23810  -5.2380952
49       28  32.26667  -4.2666667
50       49  38.36364  10.6363636
51       33  38.36364  -5.3636364
52       39  38.36364   0.6363636
53       36  38.36364  -2.3636364
54       24  38.36364 -14.3636364
55       47  48.23810  -1.2380952
56       34  38.36364  -4.3636364
57       33  38.36364  -5.3636364
58       43  38.36364   4.6363636
59       41  48.23810  -7.2380952
60       40  38.36364   1.6363636
61       39  38.36364   0.6363636
62       54  48.23810   5.7619048
63       43  38.36364   4.6363636
64       45  38.36364   6.6363636
65       29  38.36364  -9.3636364
66       45  38.36364   6.6363636
67       47  38.36364   8.6363636
68       38  38.36364  -0.3636364
69       52  48.23810   3.7619048
70       34  38.36364  -4.3636364
71       56  48.23810   7.7619048
72       26  38.36364 -12.3636364
73       42  48.23810  -6.2380952
74       32  32.26667  -0.2666667
75       39  38.36364   0.6363636
76       37  38.36364  -1.3636364
77       37  38.36364  -1.3636364
78       52  48.23810   3.7619048
79       31  38.36364  -7.3636364
80       34  32.26667   1.7333333
81       38  38.36364  -0.3636364
82       29  38.36364  -9.3636364
83       52  38.36364  13.6363636
84       40  38.36364   1.6363636
85       47  38.36364   8.6363636
86       34  38.36364  -4.3636364
87       37  38.36364  -1.3636364
88       43  38.36364   4.6363636
89       37  38.36364  -1.3636364
90       55  48.23810   6.7619048
91       36  38.36364  -2.3636364
92       28  38.36364 -10.3636364
93       47  38.36364   8.6363636
94       38  38.36364  -0.3636364
95       37  38.36364  -1.3636364
96       32  38.36364  -6.3636364
97       47  38.36364   8.6363636
98       40  32.26667   7.7333333
99       45  38.36364   6.6363636
100      37  38.36364  -1.3636364
101      38  38.36364  -0.3636364
102      37  38.36364  -1.3636364
103      35  38.36364  -3.3636364
104      50  38.36364  11.6363636
105      32  32.26667  -0.2666667
106      32  38.36364  -6.3636364
107      38  38.36364  -0.3636364
108      31  38.36364  -7.3636364
109      27  38.36364 -11.3636364
110      34  32.26667   1.7333333
111      43  38.36364   4.6363636
112      28  38.36364 -10.3636364
113      44  48.23810  -4.2380952
114      43  38.36364   4.6363636
115      53  48.23810   4.7619048
116      33  38.36364  -5.3636364
117      36  38.36364  -2.3636364
118      46  38.36364   7.6363636
119      36  38.36364  -2.3636364
120      24  32.26667  -8.2666667
121      50  48.23810   1.7619048
122      40  32.26667   7.7333333
123      40  48.23810  -8.2380952
124      32  38.36364  -6.3636364
125      49  38.36364  10.6363636
126      47  38.36364   8.6363636
127      28  32.26667  -4.2666667
128      41  38.36364   2.6363636
129      25  38.36364 -13.3636364
130      46  38.36364   7.6363636
131      53  38.36364  14.6363636
132      34  38.36364  -4.3636364
133      40  38.36364   1.6363636
134      46  38.36364   7.6363636
135      38  38.36364  -0.3636364
136      51  48.23810   2.7619048
137      38  48.23810 -10.2380952
138      45  38.36364   6.6363636
139      41  38.36364   2.6363636
140      42  38.36364   3.6363636
141      36  38.36364  -2.3636364
142      41  38.36364   2.6363636
143      35  38.36364  -3.3636364
144      42  38.36364   3.6363636
145      35  32.26667   2.7333333
146      32  38.36364  -6.3636364
> 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/41kjd1292920288.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/5xczm1292920288.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/6q3gp1292920288.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/7bmxd1292920288.tab") 
+ }
> 
> try(system("convert tmp/2rt2s1292920288.ps tmp/2rt2s1292920288.png",intern=TRUE))
character(0)
> try(system("convert tmp/3rt2s1292920288.ps tmp/3rt2s1292920288.png",intern=TRUE))
character(0)
> try(system("convert tmp/41kjd1292920288.ps tmp/41kjd1292920288.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.650   0.587   6.034