R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> x <- array(list(6.80
+ ,225.00
+ ,0.44
+ ,0.67
+ ,9.20
+ ,6.30
+ ,180.00
+ ,0.44
+ ,0.80
+ ,11.70
+ ,6.40
+ ,190.00
+ ,0.46
+ ,0.76
+ ,15.80
+ ,6.20
+ ,180.00
+ ,0.42
+ ,0.65
+ ,8.60
+ ,6.90
+ ,205.00
+ ,0.45
+ ,0.90
+ ,23.20
+ ,6.40
+ ,225.00
+ ,0.43
+ ,0.78
+ ,27.40
+ ,6.30
+ ,185.00
+ ,0.49
+ ,0.77
+ ,9.30
+ ,6.80
+ ,235.00
+ ,0.47
+ ,0.75
+ ,16.00
+ ,6.90
+ ,235.00
+ ,0.44
+ ,0.82
+ ,4.70
+ ,6.70
+ ,210.00
+ ,0.48
+ ,0.83
+ ,12.50
+ ,6.90
+ ,245.00
+ ,0.52
+ ,0.63
+ ,20.10
+ ,6.90
+ ,245.00
+ ,0.49
+ ,0.76
+ ,9.10
+ ,6.30
+ ,185.00
+ ,0.37
+ ,0.71
+ ,8.10
+ ,6.10
+ ,185.00
+ ,0.42
+ ,0.78
+ ,8.60
+ ,6.20
+ ,180.00
+ ,0.44
+ ,0.78
+ ,20.30
+ ,6.80
+ ,220.00
+ ,0.50
+ ,0.88
+ ,25.00
+ ,6.50
+ ,194.00
+ ,0.50
+ ,0.83
+ ,19.20
+ ,7.60
+ ,225.00
+ ,0.43
+ ,0.57
+ ,3.30
+ ,6.30
+ ,210.00
+ ,0.37
+ ,0.82
+ ,11.20
+ ,7.10
+ ,240.00
+ ,0.50
+ ,0.71
+ ,10.50
+ ,6.80
+ ,225.00
+ ,0.40
+ ,0.77
+ ,10.10
+ ,7.30
+ ,263.00
+ ,0.48
+ ,0.66
+ ,7.20
+ ,6.40
+ ,210.00
+ ,0.48
+ ,0.24
+ ,13.60
+ ,6.80
+ ,235.00
+ ,0.43
+ ,0.73
+ ,9.00
+ ,7.20
+ ,230.00
+ ,0.56
+ ,0.72
+ ,24.60
+ ,6.40
+ ,190.00
+ ,0.44
+ ,0.76
+ ,12.60
+ ,6.60
+ ,220.00
+ ,0.49
+ ,0.75
+ ,5.60
+ ,6.80
+ ,210.00
+ ,0.40
+ ,0.74
+ ,8.70
+ ,6.10
+ ,180.00
+ ,0.42
+ ,0.71
+ ,7.70
+ ,6.50
+ ,235.00
+ ,0.49
+ ,0.74
+ ,24.10
+ ,6.40
+ ,185.00
+ ,0.48
+ ,0.86
+ ,11.70
+ ,6.00
+ ,175.00
+ ,0.39
+ ,0.72
+ ,7.70
+ ,6.00
+ ,192.00
+ ,0.44
+ ,0.79
+ ,9.60
+ ,7.30
+ ,263.00
+ ,0.48
+ ,0.66
+ ,7.20
+ ,6.10
+ ,180.00
+ ,0.34
+ ,0.82
+ ,12.30
+ ,6.70
+ ,240.00
+ ,0.52
+ ,0.73
+ ,8.90
+ ,6.40
+ ,210.00
+ ,0.48
+ ,0.85
+ ,13.60
+ ,5.80
+ ,160.00
+ ,0.41
+ ,0.81
+ ,11.20
+ ,6.90
+ ,230.00
+ ,0.41
+ ,0.60
+ ,2.80
+ ,7.00
+ ,245.00
+ ,0.41
+ ,0.57
+ ,3.20
+ ,7.30
+ ,228.00
+ ,0.45
+ ,0.73
+ ,9.40
+ ,5.90
+ ,155.00
+ ,0.29
+ ,0.71
+ ,11.90
+ ,6.20
+ ,200.00
+ ,0.45
+ ,0.80
+ ,15.40
+ ,6.80
+ ,235.00
+ ,0.55
+ ,0.78
+ ,7.40
+ ,7.00
+ ,235.00
+ ,0.48
+ ,0.74
+ ,18.90
+ ,5.90
+ ,105.00
+ ,0.36
+ ,0.84
+ ,7.90
+ ,6.10
+ ,180.00
+ ,0.53
+ ,0.79
+ ,12.20
+ ,5.70
+ ,185.00
+ ,0.35
+ ,0.70
+ ,11.00
+ ,7.10
+ ,245.00
+ ,0.41
+ ,0.78
+ ,2.80
+ ,5.80
+ ,180.00
+ ,0.43
+ ,0.87
+ ,11.80
+ ,7.40
+ ,240.00
+ ,0.60
+ ,0.71
+ ,17.10
+ ,6.80
+ ,225.00
+ ,0.48
+ ,0.70
+ ,11.60
+ ,6.80
+ ,215.00
+ ,0.46
+ ,0.73
+ ,5.80
+ ,7.00
+ ,230.00
+ ,0.44
+ ,0.76
+ ,8.30)
+ ,dim=c(5
+ ,54)
+ ,dimnames=list(c('X1'
+ ,'X2'
+ ,'X3'
+ ,'X4'
+ ,'X5
')
+ ,1:54))
>  y <- array(NA,dim=c(5,54),dimnames=list(c('X1','X2','X3','X4','X5
'),1:54))
>  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 = '0'
> par2 = 'none'
> par1 = '3'
> par4 <- 'no'
> par3 <- '0'
> par2 <- 'none'
> par1 <- '3'
> #'GNU S' R Code compiled by R2WASP v. 1.2.291 ()
> #Author: root
> #To cite this work: Wessa P., 2012, Recursive Partitioning (Regression Trees) (v1.0.3) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_regression_trees.wasp/
> #Source of accompanying publication: 
> #
> 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, 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)
Hmisc library by Frank E Harrell Jr

Type library(help='Hmisc'), ?Overview, or ?Hmisc.Overview')
to see overall documentation.

NOTE:Hmisc no longer redefines [.factor to drop unused levels when
subsetting.  To get the old behavior of Hmisc type dropUnusedLevels().


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] "X3"
> x[,par1]
 [1] 0.44 0.44 0.46 0.42 0.45 0.43 0.49 0.47 0.44 0.48 0.52 0.49 0.37 0.42 0.44
[16] 0.50 0.50 0.43 0.37 0.50 0.40 0.48 0.48 0.43 0.56 0.44 0.49 0.40 0.42 0.49
[31] 0.48 0.39 0.44 0.48 0.34 0.52 0.48 0.41 0.41 0.41 0.45 0.29 0.45 0.55 0.48
[46] 0.36 0.53 0.35 0.41 0.43 0.60 0.48 0.46 0.44
> 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])

0.29 0.34 0.35 0.36 0.37 0.39  0.4 0.41 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 
   1    1    1    1    2    1    2    4    3    4    7    3    2    1    8    4 
 0.5 0.52 0.53 0.55 0.56  0.6 
   3    2    1    1    1    1 
> colnames(x)
[1] "X1"  "X2"  "X3"  "X4"  "X5."
> colnames(x)[par1]
[1] "X3"
> x[,par1]
 [1] 0.44 0.44 0.46 0.42 0.45 0.43 0.49 0.47 0.44 0.48 0.52 0.49 0.37 0.42 0.44
[16] 0.50 0.50 0.43 0.37 0.50 0.40 0.48 0.48 0.43 0.56 0.44 0.49 0.40 0.42 0.49
[31] 0.48 0.39 0.44 0.48 0.34 0.52 0.48 0.41 0.41 0.41 0.45 0.29 0.45 0.55 0.48
[46] 0.36 0.53 0.35 0.41 0.43 0.60 0.48 0.46 0.44
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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/fisher/rcomp/tmp/1nttf1355172982.tab") 
+ }
+ }
> m

	 Conditional inference tree with 2 terminal nodes

Response:  X3 
Inputs:  X1, X2, X4, X5. 
Number of observations:  54 

1) X2 <= 185; criterion = 0.999, statistic = 13.832
  2)*  weights = 16 
1) X2 > 185
  3)*  weights = 38 
> postscript(file="/var/fisher/rcomp/tmp/25gz61355172982.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/fisher/rcomp/tmp/3nld01355172982.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     0.44 0.4660526 -0.026052632
2     0.44 0.4112500  0.028750000
3     0.46 0.4660526 -0.006052632
4     0.42 0.4112500  0.008750000
5     0.45 0.4660526 -0.016052632
6     0.43 0.4660526 -0.036052632
7     0.49 0.4112500  0.078750000
8     0.47 0.4660526  0.003947368
9     0.44 0.4660526 -0.026052632
10    0.48 0.4660526  0.013947368
11    0.52 0.4660526  0.053947368
12    0.49 0.4660526  0.023947368
13    0.37 0.4112500 -0.041250000
14    0.42 0.4112500  0.008750000
15    0.44 0.4112500  0.028750000
16    0.50 0.4660526  0.033947368
17    0.50 0.4660526  0.033947368
18    0.43 0.4660526 -0.036052632
19    0.37 0.4660526 -0.096052632
20    0.50 0.4660526  0.033947368
21    0.40 0.4660526 -0.066052632
22    0.48 0.4660526  0.013947368
23    0.48 0.4660526  0.013947368
24    0.43 0.4660526 -0.036052632
25    0.56 0.4660526  0.093947368
26    0.44 0.4660526 -0.026052632
27    0.49 0.4660526  0.023947368
28    0.40 0.4660526 -0.066052632
29    0.42 0.4112500  0.008750000
30    0.49 0.4660526  0.023947368
31    0.48 0.4112500  0.068750000
32    0.39 0.4112500 -0.021250000
33    0.44 0.4660526 -0.026052632
34    0.48 0.4660526  0.013947368
35    0.34 0.4112500 -0.071250000
36    0.52 0.4660526  0.053947368
37    0.48 0.4660526  0.013947368
38    0.41 0.4112500 -0.001250000
39    0.41 0.4660526 -0.056052632
40    0.41 0.4660526 -0.056052632
41    0.45 0.4660526 -0.016052632
42    0.29 0.4112500 -0.121250000
43    0.45 0.4660526 -0.016052632
44    0.55 0.4660526  0.083947368
45    0.48 0.4660526  0.013947368
46    0.36 0.4112500 -0.051250000
47    0.53 0.4112500  0.118750000
48    0.35 0.4112500 -0.061250000
49    0.41 0.4660526 -0.056052632
50    0.43 0.4112500  0.018750000
51    0.60 0.4660526  0.133947368
52    0.48 0.4660526  0.013947368
53    0.46 0.4660526 -0.006052632
54    0.44 0.4660526 -0.026052632
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
> postscript(file="/var/fisher/rcomp/tmp/4lda91355172982.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/fisher/rcomp/tmp/5f5s91355172982.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/fisher/rcomp/tmp/6fd8t1355172982.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/fisher/rcomp/tmp/7u9dd1355172982.tab") 
+ }
> 
> try(system("convert tmp/25gz61355172982.ps tmp/25gz61355172982.png",intern=TRUE))
character(0)
> try(system("convert tmp/3nld01355172982.ps tmp/3nld01355172982.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lda91355172982.ps tmp/4lda91355172982.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.799   0.591   4.377