R version 2.13.0 (2011-04-13)
Copyright (C) 2011 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(18
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+ ,dim=c(5
+ ,144)
+ ,dimnames=list(c('TNORC'
+ ,'TNOLI'
+ ,'NOBC'
+ ,'NOSFBM'
+ ,'TNOPV')
+ ,1:144))
>  y <- array(NA,dim=c(5,144),dimnames=list(c('TNORC','TNOLI','NOBC','NOSFBM','TNOPV'),1:144))
>  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 = '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
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] "TNORC"
> x[,par1]
  [1] 18 20  0 26 31 36 23 30 30 26 24 30 21 25 18 19 33 15 34 18 15 30 25 34 21
 [26] 21 25 31 31 20 28 20 17 25 24  0 27 14 32 31 21 34 23 24 26 22 35 21 31 26
 [51] 22 21 27 30 33 11 26 26 23 38 29 19 19 26 26 29 36 25 24 21 19 12 30 21 34
 [76] 32 28 28 21 31 26 29 23 25 22 26 33 24 24 21 28 27 25 15 13 36 24  1 24 31
[101]  4 20 23 23 12 16 29 10  0 25 21 23 21 21  0  0 23 29 28 23  1 29 17 29 12
[126]  2 21 25 29  2  0 18  1 21  0  4  0 25 26  0  4 17 21 22
> 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])

 0  1  2  4 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 
 9  3  2  3  1  1  3  1  1  3  1  3  4  4  4 16  4  9  8 10 11  3  5  8  6  7 
32 33 34 35 36 38 
 2  3  4  1  3  1 
> colnames(x)
[1] "TNORC"  "TNOLI"  "NOBC"   "NOSFBM" "TNOPV" 
> colnames(x)[par1]
[1] "TNORC"
> x[,par1]
  [1] 18 20  0 26 31 36 23 30 30 26 24 30 21 25 18 19 33 15 34 18 15 30 25 34 21
 [26] 21 25 31 31 20 28 20 17 25 24  0 27 14 32 31 21 34 23 24 26 22 35 21 31 26
 [51] 22 21 27 30 33 11 26 26 23 38 29 19 19 26 26 29 36 25 24 21 19 12 30 21 34
 [76] 32 28 28 21 31 26 29 23 25 22 26 33 24 24 21 28 27 25 15 13 36 24  1 24 31
[101]  4 20 23 23 12 16 29 10  0 25 21 23 21 21  0  0 23 29 28 23  1 29 17 29 12
[126]  2 21 25 29  2  0 18  1 21  0  4  0 25 26  0  4 17 21 22
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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/wessaorg/rcomp/tmp/1sk7y1324374095.tab") 
+ }
+ }
> m

	 Conditional inference tree with 6 terminal nodes

Response:  TNORC 
Inputs:  TNOLI, NOBC, NOSFBM, TNOPV 
Number of observations:  144 

1) NOSFBM <= 32; criterion = 1, statistic = 113.698
  2) NOSFBM <= 7; criterion = 1, statistic = 15.789
    3)*  weights = 17 
  2) NOSFBM > 7
    4)*  weights = 8 
1) NOSFBM > 32
  5) NOSFBM <= 84; criterion = 1, statistic = 66.132
    6) NOSFBM <= 49; criterion = 1, statistic = 21.192
      7)*  weights = 16 
    6) NOSFBM > 49
      8)*  weights = 66 
  5) NOSFBM > 84
    9) NOSFBM <= 107; criterion = 0.999, statistic = 14.087
      10)*  weights = 25 
    9) NOSFBM > 107
      11)*  weights = 12 
> postscript(file="/var/wessaorg/rcomp/tmp/2bu0b1324374095.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/wessaorg/rcomp/tmp/3slzv1324374095.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        18 24.181818 -6.1818182
2        20 24.181818 -4.1818182
3         0  1.117647 -1.1176471
4        26 24.181818  1.8181818
5        31 33.500000 -2.5000000
6        36 33.500000  2.5000000
7        23 24.181818 -1.1818182
8        30 28.320000  1.6800000
9        30 24.181818  5.8181818
10       26 24.181818  1.8181818
11       24 24.181818 -0.1818182
12       30 28.320000  1.6800000
13       21 18.625000  2.3750000
14       25 28.320000 -3.3200000
15       18 24.181818 -6.1818182
16       19 24.181818 -5.1818182
17       33 33.500000 -0.5000000
18       15 18.625000 -3.6250000
19       34 33.500000  0.5000000
20       18 18.625000 -0.6250000
21       15 18.625000 -3.6250000
22       30 24.181818  5.8181818
23       25 24.181818  0.8181818
24       34 24.181818  9.8181818
25       21 24.181818 -3.1818182
26       21 24.181818 -3.1818182
27       25 28.320000 -3.3200000
28       31 24.181818  6.8181818
29       31 28.320000  2.6800000
30       20 24.181818 -4.1818182
31       28 24.181818  3.8181818
32       20 24.181818 -4.1818182
33       17 18.625000 -1.6250000
34       25 24.181818  0.8181818
35       24 28.320000 -4.3200000
36        0  1.117647 -1.1176471
37       27 24.181818  2.8181818
38       14 18.625000 -4.6250000
39       32 28.320000  3.6800000
40       31 33.500000 -2.5000000
41       21 24.181818 -3.1818182
42       34 33.500000  0.5000000
43       23 24.181818 -1.1818182
44       24 24.181818 -0.1818182
45       26 28.320000 -2.3200000
46       22 24.181818 -2.1818182
47       35 24.181818 10.8181818
48       21 24.181818 -3.1818182
49       31 33.500000 -2.5000000
50       26 24.181818  1.8181818
51       22 18.625000  3.3750000
52       21 24.181818 -3.1818182
53       27 28.320000 -1.3200000
54       30 24.181818  5.8181818
55       33 33.500000 -0.5000000
56       11 13.625000 -2.6250000
57       26 24.181818  1.8181818
58       26 24.181818  1.8181818
59       23 28.320000 -5.3200000
60       38 28.320000  9.6800000
61       29 28.320000  0.6800000
62       19 24.181818 -5.1818182
63       19 24.181818 -5.1818182
64       26 24.181818  1.8181818
65       26 28.320000 -2.3200000
66       29 28.320000  0.6800000
67       36 33.500000  2.5000000
68       25 24.181818  0.8181818
69       24 24.181818 -0.1818182
70       21 18.625000  2.3750000
71       19 18.625000  0.3750000
72       12 13.625000 -1.6250000
73       30 28.320000  1.6800000
74       21 18.625000  2.3750000
75       34 33.500000  0.5000000
76       32 28.320000  3.6800000
77       28 28.320000 -0.3200000
78       28 28.320000 -0.3200000
79       21 18.625000  2.3750000
80       31 24.181818  6.8181818
81       26 24.181818  1.8181818
82       29 28.320000  0.6800000
83       23 13.625000  9.3750000
84       25 24.181818  0.8181818
85       22 24.181818 -2.1818182
86       26 24.181818  1.8181818
87       33 33.500000 -0.5000000
88       24 24.181818 -0.1818182
89       24 24.181818 -0.1818182
90       21 24.181818 -3.1818182
91       28 24.181818  3.8181818
92       27 24.181818  2.8181818
93       25 24.181818  0.8181818
94       15 24.181818 -9.1818182
95       13 13.625000 -0.6250000
96       36 33.500000  2.5000000
97       24 24.181818 -0.1818182
98        1  1.117647 -0.1176471
99       24 24.181818 -0.1818182
100      31 28.320000  2.6800000
101       4  1.117647  2.8823529
102      20 24.181818 -4.1818182
103      23 24.181818 -1.1818182
104      23 24.181818 -1.1818182
105      12 13.625000 -1.6250000
106      16 13.625000  2.3750000
107      29 28.320000  0.6800000
108      10 13.625000 -3.6250000
109       0  1.117647 -1.1176471
110      25 24.181818  0.8181818
111      21 24.181818 -3.1818182
112      23 24.181818 -1.1818182
113      21 24.181818 -3.1818182
114      21 18.625000  2.3750000
115       0  1.117647 -1.1176471
116       0  1.117647 -1.1176471
117      23 24.181818 -1.1818182
118      29 24.181818  4.8181818
119      28 24.181818  3.8181818
120      23 28.320000 -5.3200000
121       1  1.117647 -0.1176471
122      29 28.320000  0.6800000
123      17 18.625000 -1.6250000
124      29 24.181818  4.8181818
125      12 13.625000 -1.6250000
126       2  1.117647  0.8823529
127      21 24.181818 -3.1818182
128      25 28.320000 -3.3200000
129      29 28.320000  0.6800000
130       2  1.117647  0.8823529
131       0  1.117647 -1.1176471
132      18 18.625000 -0.6250000
133       1  1.117647 -0.1176471
134      21 18.625000  2.3750000
135       0  1.117647 -1.1176471
136       4  1.117647  2.8823529
137       0  1.117647 -1.1176471
138      25 24.181818  0.8181818
139      26 24.181818  1.8181818
140       0  1.117647 -1.1176471
141       4  1.117647  2.8823529
142      17 18.625000 -1.6250000
143      21 24.181818 -3.1818182
144      22 24.181818 -2.1818182
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
> postscript(file="/var/wessaorg/rcomp/tmp/4mv5d1324374095.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/wessaorg/rcomp/tmp/5yly21324374095.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/wessaorg/rcomp/tmp/6r2v01324374095.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/wessaorg/rcomp/tmp/7jlui1324374095.tab") 
+ }
> 
> try(system("convert tmp/2bu0b1324374095.ps tmp/2bu0b1324374095.png",intern=TRUE))
character(0)
> try(system("convert tmp/3slzv1324374095.ps tmp/3slzv1324374095.png",intern=TRUE))
character(0)
> try(system("convert tmp/4mv5d1324374095.ps tmp/4mv5d1324374095.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.334   0.255   3.601