R version 2.12.0 (2010-10-15)
Copyright (C) 2010 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(112.3
+ ,112.9
+ ,88.7
+ ,105.1
+ ,117.3
+ ,130.5
+ ,94.6
+ ,114.9
+ ,111.1
+ ,137.9
+ ,98.7
+ ,106.4
+ ,102.2
+ ,115
+ ,84.2
+ ,104.5
+ ,104.3
+ ,116.8
+ ,87.7
+ ,121.6
+ ,122.9
+ ,140.9
+ ,103.3
+ ,141.4
+ ,107.6
+ ,120.7
+ ,88.2
+ ,99
+ ,121.3
+ ,134.2
+ ,93.4
+ ,126.7
+ ,131.5
+ ,147.3
+ ,106.3
+ ,134.1
+ ,89
+ ,112.4
+ ,73.1
+ ,81.3
+ ,104.4
+ ,107.1
+ ,78.6
+ ,88.6
+ ,128.9
+ ,128.4
+ ,101.6
+ ,132.7
+ ,135.9
+ ,137.7
+ ,101.4
+ ,132.9
+ ,133.3
+ ,135
+ ,98.5
+ ,134.4
+ ,121.3
+ ,151
+ ,99
+ ,103.7
+ ,120.5
+ ,137.4
+ ,89.5
+ ,119.7
+ ,120.4
+ ,132.4
+ ,83.5
+ ,115
+ ,137.9
+ ,161.3
+ ,97.4
+ ,132.9
+ ,126.1
+ ,139.8
+ ,87.8
+ ,108.5
+ ,133.2
+ ,146
+ ,90.4
+ ,113.9
+ ,151.1
+ ,166.5
+ ,101.6
+ ,142
+ ,105
+ ,143.3
+ ,80
+ ,97.7
+ ,119
+ ,121
+ ,81.7
+ ,92.2
+ ,140.4
+ ,152.6
+ ,96.4
+ ,128.8
+ ,156.6
+ ,154.4
+ ,110.2
+ ,134.9
+ ,137.1
+ ,154.6
+ ,101.1
+ ,128.2
+ ,122.7
+ ,158
+ ,89.3
+ ,114.8
+ ,125.8
+ ,142.6
+ ,90
+ ,117.9
+ ,139.3
+ ,153.4
+ ,95.4
+ ,119.1
+ ,134.9
+ ,163.4
+ ,100.3
+ ,120.7
+ ,149.2
+ ,167.3
+ ,99.5
+ ,129.1
+ ,132.3
+ ,154.8
+ ,93.9
+ ,117.6
+ ,149
+ ,165.7
+ ,100.6
+ ,129.2
+ ,117.2
+ ,144.7
+ ,84.7
+ ,100
+ ,119.6
+ ,120.9
+ ,81.6
+ ,87
+ ,152
+ ,152.8
+ ,109
+ ,128
+ ,149.4
+ ,160.2
+ ,99
+ ,127.7
+ ,127.3
+ ,128.3
+ ,81.1
+ ,93.4
+ ,114.1
+ ,150.5
+ ,81.8
+ ,84.1
+ ,102.1
+ ,117
+ ,66.5
+ ,71.7
+ ,107.7
+ ,116
+ ,66.4
+ ,83.2
+ ,104.4
+ ,133.3
+ ,86.3
+ ,89.1
+ ,102.1
+ ,116.4
+ ,73.6
+ ,79.6
+ ,96
+ ,104
+ ,71.5
+ ,62.8
+ ,109.3
+ ,126.6
+ ,87.2
+ ,95.1
+ ,90
+ ,92.9
+ ,65.3
+ ,63.6
+ ,83.9
+ ,83.6
+ ,69.7
+ ,61.4
+ ,112
+ ,112.8
+ ,95.5
+ ,98.2
+ ,114.3
+ ,113.2
+ ,86.3
+ ,95.3
+ ,103.6
+ ,118.5
+ ,81
+ ,81.5
+ ,91.7
+ ,125.5
+ ,88.7
+ ,85.5
+ ,80.8
+ ,91.3
+ ,71.9
+ ,71.1
+ ,87.2
+ ,105.4
+ ,78.6
+ ,78.1
+ ,109.2
+ ,121.3
+ ,96
+ ,103
+ ,102.7
+ ,106.9
+ ,81.1
+ ,86
+ ,95.1
+ ,109.4
+ ,77.5
+ ,86.2
+ ,117.5
+ ,132.6
+ ,97.3
+ ,105.7
+ ,85.1
+ ,96.8
+ ,78.6
+ ,57.2
+ ,92.1
+ ,100.3
+ ,79
+ ,73.7
+ ,113.5
+ ,119.2
+ ,93.4
+ ,120.5)
+ ,dim=c(4
+ ,60)
+ ,dimnames=list(c('X1'
+ ,'X2'
+ ,'X3'
+ ,'X4')
+ ,1:60))
>  y <- array(NA,dim=c(4,60),dimnames=list(c('X1','X2','X3','X4'),1:60))
>  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 = '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
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] "X4"
> x[,par1]
 [1] 105.1 114.9 106.4 104.5 121.6 141.4  99.0 126.7 134.1  81.3  88.6 132.7
[13] 132.9 134.4 103.7 119.7 115.0 132.9 108.5 113.9 142.0  97.7  92.2 128.8
[25] 134.9 128.2 114.8 117.9 119.1 120.7 129.1 117.6 129.2 100.0  87.0 128.0
[37] 127.7  93.4  84.1  71.7  83.2  89.1  79.6  62.8  95.1  63.6  61.4  98.2
[49]  95.3  81.5  85.5  71.1  78.1 103.0  86.0  86.2 105.7  57.2  73.7 120.5
> 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])

 57.2  61.4  62.8  63.6  71.1  71.7  73.7  78.1  79.6  81.3  81.5  83.2  84.1 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
 85.5    86  86.2    87  88.6  89.1  92.2  93.4  95.1  95.3  97.7  98.2    99 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
  100   103 103.7 104.5 105.1 105.7 106.4 108.5 113.9 114.8 114.9   115 117.6 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
117.9 119.1 119.7 120.5 120.7 121.6 126.7 127.7   128 128.2 128.8 129.1 129.2 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
132.7 132.9 134.1 134.4 134.9 141.4   142 
    1     2     1     1     1     1     1 
> colnames(x)
[1] "X1" "X2" "X3" "X4"
> colnames(x)[par1]
[1] "X4"
> x[,par1]
 [1] 105.1 114.9 106.4 104.5 121.6 141.4  99.0 126.7 134.1  81.3  88.6 132.7
[13] 132.9 134.4 103.7 119.7 115.0 132.9 108.5 113.9 142.0  97.7  92.2 128.8
[25] 134.9 128.2 114.8 117.9 119.1 120.7 129.1 117.6 129.2 100.0  87.0 128.0
[37] 127.7  93.4  84.1  71.7  83.2  89.1  79.6  62.8  95.1  63.6  61.4  98.2
[49]  95.3  81.5  85.5  71.1  78.1 103.0  86.0  86.2 105.7  57.2  73.7 120.5
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/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/rcomp/tmp/181ep1292933848.tab") 
+ }
+ }
> m

	 Conditional inference tree with 5 terminal nodes

Response:  X4 
Inputs:  X1, X2, X3 
Number of observations:  60 

1) X3 <= 87.2; criterion = 1, statistic = 45.962
  2) X1 <= 102.1; criterion = 0.999, statistic = 13.575
    3)*  weights = 11 
  2) X1 > 102.1
    4)*  weights = 15 
1) X3 > 87.2
  5) X1 <= 122.7; criterion = 1, statistic = 18.136
    6)*  weights = 14 
  5) X1 > 122.7
    7) X3 <= 97.4; criterion = 0.997, statistic = 10.558
      8)*  weights = 7 
    7) X3 > 97.4
      9)*  weights = 13 
> postscript(file="/var/www/rcomp/tmp/2jaea1292933848.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/rcomp/tmp/3jaea1292933848.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    105.1 108.91429  -3.8142857
2    114.9 108.91429   5.9857143
3    106.4 108.91429  -2.5142857
4    104.5  92.84667  11.6533333
5    121.6 108.91429  12.6857143
6    141.4 131.94615   9.4538462
7     99.0 108.91429  -9.9142857
8    126.7 108.91429  17.7857143
9    134.1 131.94615   2.1538462
10    81.3  71.51818   9.7818182
11    88.6  92.84667  -4.2466667
12   132.7 131.94615   0.7538462
13   132.9 131.94615   0.9538462
14   134.4 131.94615   2.4538462
15   103.7 108.91429  -5.2142857
16   119.7 108.91429  10.7857143
17   115.0  92.84667  22.1533333
18   132.9 119.81429  13.0857143
19   108.5 119.81429 -11.3142857
20   113.9 119.81429  -5.9142857
21   142.0 131.94615  10.0538462
22    97.7  92.84667   4.8533333
23    92.2  92.84667  -0.6466667
24   128.8 119.81429   8.9857143
25   134.9 131.94615   2.9538462
26   128.2 131.94615  -3.7461538
27   114.8 108.91429   5.8857143
28   117.9 119.81429  -1.9142857
29   119.1 119.81429  -0.7142857
30   120.7 131.94615 -11.2461538
31   129.1 131.94615  -2.8461538
32   117.6 119.81429  -2.2142857
33   129.2 131.94615  -2.7461538
34   100.0  92.84667   7.1533333
35    87.0  92.84667  -5.8466667
36   128.0 131.94615  -3.9461538
37   127.7 131.94615  -4.2461538
38    93.4  92.84667   0.5533333
39    84.1  92.84667  -8.7466667
40    71.7  71.51818   0.1818182
41    83.2  92.84667  -9.6466667
42    89.1  92.84667  -3.7466667
43    79.6  71.51818   8.0818182
44    62.8  71.51818  -8.7181818
45    95.1  92.84667   2.2533333
46    63.6  71.51818  -7.9181818
47    61.4  71.51818 -10.1181818
48    98.2 108.91429 -10.7142857
49    95.3  92.84667   2.4533333
50    81.5  92.84667 -11.3466667
51    85.5 108.91429 -23.4142857
52    71.1  71.51818  -0.4181818
53    78.1  71.51818   6.5818182
54   103.0 108.91429  -5.9142857
55    86.0  92.84667  -6.8466667
56    86.2  71.51818  14.6818182
57   105.7 108.91429  -3.2142857
58    57.2  71.51818 -14.3181818
59    73.7  71.51818   2.1818182
60   120.5 108.91429  11.5857143
> 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/rcomp/tmp/4tkdv1292933848.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/rcomp/tmp/5ptt41292933848.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/rcomp/tmp/6ikap1292933848.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/rcomp/tmp/7l39v1292933848.tab") 
+ }
> 
> try(system("convert tmp/2jaea1292933848.ps tmp/2jaea1292933848.png",intern=TRUE))
character(0)
> try(system("convert tmp/3jaea1292933848.ps tmp/3jaea1292933848.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tkdv1292933848.ps tmp/4tkdv1292933848.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.400   0.740   3.103