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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+ ,9
+ ,35
+ ,20
+ ,33
+ ,48
+ ,18
+ ,32
+ ,27
+ ,19)
+ ,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 = '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] "Carri..remogelijkheden"
> x[,par1]
  [1] 46 44 42 41 48 49 51 47 49 46 51 54 52 52 45 52 56 54 50 35 48 37 47 31 45
 [26] 47 44 30 40 44 43 51 48 55 48 53 49 44 45 40 44 41 46 47 48 43 46 53 33 47
 [51] 43 45 49 45 37 42 43 44 39 37 53 48 47 49 47 56 51 43 51 36 55 33 42 43 44
 [76] 47 43 47 41 53 47 23 43 47 47 49 50 43 44 49 47 39 49 41 40 38 43 55 46 54
[101] 47 35 41 53 44 48 49 39 45 34 46 45 53 51 45 50 41 44 43 42 48 45 48 48 53
[126] 45 45 50 48 41 53 40 49 46 48 43 53 51 41 45 44 43 34 38 40 48
> 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])

23 30 31 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 
 1  1  1  2  2  2  1  3  2  3  5  8  4 13 11 12  7 14 13 10  4  7  3  9  3  3 
56 
 2 
> colnames(x)
[1] "Carri..remogelijkheden" "Geen_Motivatie"         "Leermogelijkheden"     
[4] "Persoonlijke_redenen"   "Ouders"                
> colnames(x)[par1]
[1] "Carri..remogelijkheden"
> x[,par1]
  [1] 46 44 42 41 48 49 51 47 49 46 51 54 52 52 45 52 56 54 50 35 48 37 47 31 45
 [26] 47 44 30 40 44 43 51 48 55 48 53 49 44 45 40 44 41 46 47 48 43 46 53 33 47
 [51] 43 45 49 45 37 42 43 44 39 37 53 48 47 49 47 56 51 43 51 36 55 33 42 43 44
 [76] 47 43 47 41 53 47 23 43 47 47 49 50 43 44 49 47 39 49 41 40 38 43 55 46 54
[101] 47 35 41 53 44 48 49 39 45 34 46 45 53 51 45 50 41 44 43 42 48 45 48 48 53
[126] 45 45 50 48 41 53 40 49 46 48 43 53 51 41 45 44 43 34 38 40 48
> 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/1ekb81292847415.tab") 
+ }
+ }
> m

	 Conditional inference tree with 2 terminal nodes

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

1) Leermogelijkheden <= 47; criterion = 0.999, statistic = 13.023
  2)*  weights = 127 
1) Leermogelijkheden > 47
  3)*  weights = 19 
> postscript(file="/var/www/html/rcomp/tmp/2ekb81292847415.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/3ekb81292847415.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        46  49.36842  -3.3684211
2        44  44.81890  -0.8188976
3        42  44.81890  -2.8188976
4        41  44.81890  -3.8188976
5        48  44.81890   3.1811024
6        49  49.36842  -0.3684211
7        51  44.81890   6.1811024
8        47  44.81890   2.1811024
9        49  44.81890   4.1811024
10       46  44.81890   1.1811024
11       51  44.81890   6.1811024
12       54  44.81890   9.1811024
13       52  44.81890   7.1811024
14       52  49.36842   2.6315789
15       45  44.81890   0.1811024
16       52  44.81890   7.1811024
17       56  49.36842   6.6315789
18       54  44.81890   9.1811024
19       50  44.81890   5.1811024
20       35  44.81890  -9.8188976
21       48  44.81890   3.1811024
22       37  44.81890  -7.8188976
23       47  44.81890   2.1811024
24       31  44.81890 -13.8188976
25       45  44.81890   0.1811024
26       47  44.81890   2.1811024
27       44  44.81890  -0.8188976
28       30  44.81890 -14.8188976
29       40  44.81890  -4.8188976
30       44  44.81890  -0.8188976
31       43  44.81890  -1.8188976
32       51  44.81890   6.1811024
33       48  44.81890   3.1811024
34       55  49.36842   5.6315789
35       48  44.81890   3.1811024
36       53  44.81890   8.1811024
37       49  44.81890   4.1811024
38       44  44.81890  -0.8188976
39       45  49.36842  -4.3684211
40       40  44.81890  -4.8188976
41       44  44.81890  -0.8188976
42       41  44.81890  -3.8188976
43       46  44.81890   1.1811024
44       47  44.81890   2.1811024
45       48  44.81890   3.1811024
46       43  44.81890  -1.8188976
47       46  44.81890   1.1811024
48       53  44.81890   8.1811024
49       33  44.81890 -11.8188976
50       47  49.36842  -2.3684211
51       43  44.81890  -1.8188976
52       45  44.81890   0.1811024
53       49  44.81890   4.1811024
54       45  44.81890   0.1811024
55       37  44.81890  -7.8188976
56       42  44.81890  -2.8188976
57       43  44.81890  -1.8188976
58       44  44.81890  -0.8188976
59       39  44.81890  -5.8188976
60       37  44.81890  -7.8188976
61       53  44.81890   8.1811024
62       48  49.36842  -1.3684211
63       47  44.81890   2.1811024
64       49  44.81890   4.1811024
65       47  44.81890   2.1811024
66       56  44.81890  11.1811024
67       51  44.81890   6.1811024
68       43  44.81890  -1.8188976
69       51  49.36842   1.6315789
70       36  44.81890  -8.8188976
71       55  49.36842   5.6315789
72       33  44.81890 -11.8188976
73       42  44.81890  -2.8188976
74       43  44.81890  -1.8188976
75       44  44.81890  -0.8188976
76       47  44.81890   2.1811024
77       43  44.81890  -1.8188976
78       47  49.36842  -2.3684211
79       41  44.81890  -3.8188976
80       53  44.81890   8.1811024
81       47  44.81890   2.1811024
82       23  44.81890 -21.8188976
83       43  49.36842  -6.3684211
84       47  44.81890   2.1811024
85       47  44.81890   2.1811024
86       49  44.81890   4.1811024
87       50  44.81890   5.1811024
88       43  44.81890  -1.8188976
89       44  44.81890  -0.8188976
90       49  49.36842  -0.3684211
91       47  44.81890   2.1811024
92       39  44.81890  -5.8188976
93       49  44.81890   4.1811024
94       41  44.81890  -3.8188976
95       40  44.81890  -4.8188976
96       38  44.81890  -6.8188976
97       43  44.81890  -1.8188976
98       55  44.81890  10.1811024
99       46  44.81890   1.1811024
100      54  44.81890   9.1811024
101      47  44.81890   2.1811024
102      35  44.81890  -9.8188976
103      41  44.81890  -3.8188976
104      53  49.36842   3.6315789
105      44  44.81890  -0.8188976
106      48  44.81890   3.1811024
107      49  44.81890   4.1811024
108      39  44.81890  -5.8188976
109      45  44.81890   0.1811024
110      34  44.81890 -10.8188976
111      46  44.81890   1.1811024
112      45  44.81890   0.1811024
113      53  44.81890   8.1811024
114      51  44.81890   6.1811024
115      45  49.36842  -4.3684211
116      50  44.81890   5.1811024
117      41  44.81890  -3.8188976
118      44  44.81890  -0.8188976
119      43  44.81890  -1.8188976
120      42  44.81890  -2.8188976
121      48  49.36842  -1.3684211
122      45  44.81890   0.1811024
123      48  44.81890   3.1811024
124      48  44.81890   3.1811024
125      53  49.36842   3.6315789
126      45  44.81890   0.1811024
127      45  44.81890   0.1811024
128      50  44.81890   5.1811024
129      48  44.81890   3.1811024
130      41  44.81890  -3.8188976
131      53  49.36842   3.6315789
132      40  44.81890  -4.8188976
133      49  44.81890   4.1811024
134      46  44.81890   1.1811024
135      48  44.81890   3.1811024
136      43  49.36842  -6.3684211
137      53  44.81890   8.1811024
138      51  44.81890   6.1811024
139      41  44.81890  -3.8188976
140      45  44.81890   0.1811024
141      44  44.81890  -0.8188976
142      43  44.81890  -1.8188976
143      34  44.81890 -10.8188976
144      38  44.81890  -6.8188976
145      40  44.81890  -4.8188976
146      48  44.81890   3.1811024
> 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/4ptaa1292847415.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/53l811292847415.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/6o4771292847415.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/7zd6a1292847415.tab") 
+ }
> 
> try(system("convert tmp/2ekb81292847415.ps tmp/2ekb81292847415.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ekb81292847415.ps tmp/3ekb81292847415.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ptaa1292847415.ps tmp/4ptaa1292847415.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.688   0.607   6.156