R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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 = '4'
> #'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] "Persoonlijke_redenen"
> x[,par1]
  [1] 26 25 28 30 28 40 28 27 25 27 32 28 21 40 29 27 31 33 28 26 25 37 13 32 32
 [26] 38 30 33 22 29 33 31 23 42 35 31 31 38 34 33 23 18 33 26 29 23 18 36 21 31
 [51] 31 29 24 35 37 29 31 34 38 27 33 36 27 33 24 31 31 23 38 30 39 28 39 19 32
 [76] 32 35 42 25 11 31 30 30 31 28 34 32 30 27 36 32 27 35 34 32 28 29 18 34 35
[101] 34 26 30 35 17 34 30 31 25 16 35 28 42 30 37 26 28 33 29 21 38 18 38 30 35
[126] 34 21 30 32 23 31 26 29 28 29 36 36 31 30 29 35 26 25 25 20 27
> 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])

11 13 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 
 1  1  1  1  4  1  1  4  1  5  2  7  7  8 11 10 12 14  9  8  8  9  5  3  6  2 
40 42 
 2  3 
> colnames(x)
[1] "Carrièremogelijkheden" "Geen_Motivatie"        "Leermogelijkheden"    
[4] "Persoonlijke_redenen"  "Ouders"               
> colnames(x)[par1]
[1] "Persoonlijke_redenen"
> x[,par1]
  [1] 26 25 28 30 28 40 28 27 25 27 32 28 21 40 29 27 31 33 28 26 25 37 13 32 32
 [26] 38 30 33 22 29 33 31 23 42 35 31 31 38 34 33 23 18 33 26 29 23 18 36 21 31
 [51] 31 29 24 35 37 29 31 34 38 27 33 36 27 33 24 31 31 23 38 30 39 28 39 19 32
 [76] 32 35 42 25 11 31 30 30 31 28 34 32 30 27 36 32 27 35 34 32 28 29 18 34 35
[101] 34 26 30 35 17 34 30 31 25 16 35 28 42 30 37 26 28 33 29 21 38 18 38 30 35
[126] 34 21 30 32 23 31 26 29 28 29 36 36 31 30 29 35 26 25 25 20 27
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/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/freestat/rcomp/tmp/1nsvm1292920547.tab") 
+ }
+ }
> m

	 Conditional inference tree with 3 terminal nodes

Response:  Persoonlijke_redenen 
Inputs:  Carrièremogelijkheden, Geen_Motivatie, Leermogelijkheden, Ouders 
Number of observations:  146 

1) Leermogelijkheden <= 42; criterion = 1, statistic = 36.649
  2)*  weights = 97 
1) Leermogelijkheden > 42
  3) Leermogelijkheden <= 47; criterion = 0.972, statistic = 7.239
    4)*  weights = 30 
  3) Leermogelijkheden > 47
    5)*  weights = 19 
> postscript(file="/var/www/html/freestat/rcomp/tmp/2nsvm1292920547.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/freestat/rcomp/tmp/3nsvm1292920547.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        26  35.63158  -9.63157895
2        25  27.93814  -2.93814433
3        28  27.93814   0.06185567
4        30  27.93814   2.06185567
5        28  27.93814   0.06185567
6        40  35.63158   4.36842105
7        28  27.93814   0.06185567
8        27  31.90000  -4.90000000
9        25  31.90000  -6.90000000
10       27  27.93814  -0.93814433
11       32  27.93814   4.06185567
12       28  27.93814   0.06185567
13       21  27.93814  -6.93814433
14       40  35.63158   4.36842105
15       29  27.93814   1.06185567
16       27  27.93814  -0.93814433
17       31  35.63158  -4.63157895
18       33  27.93814   5.06185567
19       28  27.93814   0.06185567
20       26  27.93814  -1.93814433
21       25  27.93814  -2.93814433
22       37  31.90000   5.10000000
23       13  27.93814 -14.93814433
24       32  27.93814   4.06185567
25       32  31.90000   0.10000000
26       38  31.90000   6.10000000
27       30  31.90000  -1.90000000
28       33  31.90000   1.10000000
29       22  27.93814  -5.93814433
30       29  27.93814   1.06185567
31       33  27.93814   5.06185567
32       31  27.93814   3.06185567
33       23  27.93814  -4.93814433
34       42  35.63158   6.36842105
35       35  31.90000   3.10000000
36       31  27.93814   3.06185567
37       31  27.93814   3.06185567
38       38  27.93814  10.06185567
39       34  35.63158  -1.63157895
40       33  31.90000   1.10000000
41       23  27.93814  -4.93814433
42       18  27.93814  -9.93814433
43       33  27.93814   5.06185567
44       26  31.90000  -5.90000000
45       29  27.93814   1.06185567
46       23  27.93814  -4.93814433
47       18  27.93814  -9.93814433
48       36  31.90000   4.10000000
49       21  27.93814  -6.93814433
50       31  35.63158  -4.63157895
51       31  27.93814   3.06185567
52       29  27.93814   1.06185567
53       24  27.93814  -3.93814433
54       35  27.93814   7.06185567
55       37  31.90000   5.10000000
56       29  27.93814   1.06185567
57       31  27.93814   3.06185567
58       34  31.90000   2.10000000
59       38  27.93814  10.06185567
60       27  27.93814  -0.93814433
61       33  27.93814   5.06185567
62       36  35.63158   0.36842105
63       27  31.90000  -4.90000000
64       33  31.90000   1.10000000
65       24  27.93814  -3.93814433
66       31  31.90000  -0.90000000
67       31  31.90000  -0.90000000
68       23  27.93814  -4.93814433
69       38  35.63158   2.36842105
70       30  27.93814   2.06185567
71       39  35.63158   3.36842105
72       28  27.93814   0.06185567
73       39  27.93814  11.06185567
74       19  27.93814  -8.93814433
75       32  27.93814   4.06185567
76       32  27.93814   4.06185567
77       35  27.93814   7.06185567
78       42  35.63158   6.36842105
79       25  27.93814  -2.93814433
80       11  27.93814 -16.93814433
81       31  27.93814   3.06185567
82       30  27.93814   2.06185567
83       30  35.63158  -5.63157895
84       31  27.93814   3.06185567
85       28  31.90000  -3.90000000
86       34  27.93814   6.06185567
87       32  27.93814   4.06185567
88       30  31.90000  -1.90000000
89       27  27.93814  -0.93814433
90       36  35.63158   0.36842105
91       32  27.93814   4.06185567
92       27  27.93814  -0.93814433
93       35  31.90000   3.10000000
94       34  27.93814   6.06185567
95       32  27.93814   4.06185567
96       28  27.93814   0.06185567
97       29  31.90000  -2.90000000
98       18  27.93814  -9.93814433
99       34  31.90000   2.10000000
100      35  27.93814   7.06185567
101      34  27.93814   6.06185567
102      26  27.93814  -1.93814433
103      30  27.93814   2.06185567
104      35  35.63158  -0.63157895
105      17  27.93814 -10.93814433
106      34  27.93814   6.06185567
107      30  27.93814   2.06185567
108      31  27.93814   3.06185567
109      25  27.93814  -2.93814433
110      16  27.93814 -11.93814433
111      35  31.90000   3.10000000
112      28  27.93814   0.06185567
113      42  31.90000  10.10000000
114      30  31.90000  -1.90000000
115      37  35.63158   1.36842105
116      26  27.93814  -1.93814433
117      28  27.93814   0.06185567
118      33  31.90000   1.10000000
119      29  27.93814   1.06185567
120      21  27.93814  -6.93814433
121      38  35.63158   2.36842105
122      18  27.93814  -9.93814433
123      38  27.93814  10.06185567
124      30  27.93814   2.06185567
125      35  35.63158  -0.63157895
126      34  31.90000   2.10000000
127      21  27.93814  -6.93814433
128      30  27.93814   2.06185567
129      32  27.93814   4.06185567
130      23  31.90000  -8.90000000
131      31  35.63158  -4.63157895
132      26  27.93814  -1.93814433
133      29  27.93814   1.06185567
134      28  31.90000  -3.90000000
135      29  27.93814   1.06185567
136      36  35.63158   0.36842105
137      36  27.93814   8.06185567
138      31  31.90000  -0.90000000
139      30  27.93814   2.06185567
140      29  27.93814   1.06185567
141      35  27.93814   7.06185567
142      26  27.93814  -1.93814433
143      25  27.93814  -2.93814433
144      25  27.93814  -2.93814433
145      20  27.93814  -7.93814433
146      27  27.93814  -0.93814433
> 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/freestat/rcomp/tmp/4yjcp1292920547.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/freestat/rcomp/tmp/5cbsg1292920547.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/freestat/rcomp/tmp/6n2911292920547.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/freestat/rcomp/tmp/783qo1292920547.tab") 
+ }
> 
> try(system("convert tmp/2nsvm1292920547.ps tmp/2nsvm1292920547.png",intern=TRUE))
character(0)
> try(system("convert tmp/3nsvm1292920547.ps tmp/3nsvm1292920547.png",intern=TRUE))
character(0)
> try(system("convert tmp/4yjcp1292920547.ps tmp/4yjcp1292920547.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  4.168   0.753   4.315