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(2
+ ,13
+ ,12
+ ,2
+ ,16
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+ ,2
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+ ,1
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+ ,2
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+ ,1
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+ ,1
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+ ,2
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+ ,1
+ ,16
+ ,12
+ ,2
+ ,10
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+ ,2
+ ,18
+ ,12
+ ,1
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+ ,13
+ ,1
+ ,16
+ ,11
+ ,1
+ ,13
+ ,19
+ ,2
+ ,10
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+ ,2
+ ,15
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+ ,1
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+ ,9
+ ,2
+ ,16
+ ,12
+ ,2
+ ,14
+ ,19
+ ,2
+ ,10
+ ,18
+ ,2
+ ,17
+ ,15
+ ,2
+ ,13
+ ,14
+ ,2
+ ,15
+ ,11
+ ,2
+ ,16
+ ,9
+ ,2
+ ,12
+ ,18
+ ,2
+ ,13
+ ,16)
+ ,dim=c(3
+ ,162)
+ ,dimnames=list(c('Gender'
+ ,'Learning'
+ ,'Depression')
+ ,1:162))
>  y <- array(NA,dim=c(3,162),dimnames=list(c('Gender','Learning','Depression'),1:162))
>  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 = 'quantiles'
> par1 = '2'
> 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] "Learning"
> x[,par1]
  [1] 13 16 19 15 14 13 19 15 14 15 16 16 16 16 17 15 15 20 18 16 16 16 19 16 17
 [26] 17 16 15 16 14 15 12 14 16 14  7 10 14 16 16 16 14 20 14 14 11 14 15 16 14
 [51] 16 14 12 16  9 14 16 16 15 16 12 16 16 14 16 17 18 18 12 16 10 14 18 18 16
 [76] 17 16 16 13 16 16 20 16 15 15 16 14 16 16 15 12 17 16 15 13 16 16 16 16 14
[101] 16 16 20 15 16 13 17 16 16 12 16 16 17 13 12 18 14 14 13 16 13 16 13 16 15
[126] 16 15 17 15 12 16 10 16 12 14 15 13 15 11 12  8 16 15 17 16 10 18 13 16 13
[151] 10 15 16 16 14 10 17 13 15 16 12 13
> 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])

[ 7,15) [15,17) [17,20] 
     57      80      25 
> colnames(x)
[1] "Gender"     "Learning"   "Depression"
> colnames(x)[par1]
[1] "Learning"
> x[,par1]
  [1] [ 7,15) [15,17) [17,20] [15,17) [ 7,15) [ 7,15) [17,20] [15,17) [ 7,15)
 [10] [15,17) [15,17) [15,17) [15,17) [15,17) [17,20] [15,17) [15,17) [17,20]
 [19] [17,20] [15,17) [15,17) [15,17) [17,20] [15,17) [17,20] [17,20] [15,17)
 [28] [15,17) [15,17) [ 7,15) [15,17) [ 7,15) [ 7,15) [15,17) [ 7,15) [ 7,15)
 [37] [ 7,15) [ 7,15) [15,17) [15,17) [15,17) [ 7,15) [17,20] [ 7,15) [ 7,15)
 [46] [ 7,15) [ 7,15) [15,17) [15,17) [ 7,15) [15,17) [ 7,15) [ 7,15) [15,17)
 [55] [ 7,15) [ 7,15) [15,17) [15,17) [15,17) [15,17) [ 7,15) [15,17) [15,17)
 [64] [ 7,15) [15,17) [17,20] [17,20] [17,20] [ 7,15) [15,17) [ 7,15) [ 7,15)
 [73] [17,20] [17,20] [15,17) [17,20] [15,17) [15,17) [ 7,15) [15,17) [15,17)
 [82] [17,20] [15,17) [15,17) [15,17) [15,17) [ 7,15) [15,17) [15,17) [15,17)
 [91] [ 7,15) [17,20] [15,17) [15,17) [ 7,15) [15,17) [15,17) [15,17) [15,17)
[100] [ 7,15) [15,17) [15,17) [17,20] [15,17) [15,17) [ 7,15) [17,20] [15,17)
[109] [15,17) [ 7,15) [15,17) [15,17) [17,20] [ 7,15) [ 7,15) [17,20] [ 7,15)
[118] [ 7,15) [ 7,15) [15,17) [ 7,15) [15,17) [ 7,15) [15,17) [15,17) [15,17)
[127] [15,17) [17,20] [15,17) [ 7,15) [15,17) [ 7,15) [15,17) [ 7,15) [ 7,15)
[136] [15,17) [ 7,15) [15,17) [ 7,15) [ 7,15) [ 7,15) [15,17) [15,17) [17,20]
[145] [15,17) [ 7,15) [17,20] [ 7,15) [15,17) [ 7,15) [ 7,15) [15,17) [15,17)
[154] [15,17) [ 7,15) [ 7,15) [17,20] [ 7,15) [15,17) [15,17) [ 7,15) [ 7,15)
Levels: [ 7,15) [15,17) [17,20]
> 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/1cw8d1323538905.tab") 
+ }
+ }
> m

	 Conditional inference tree with 2 terminal nodes

Response:  as.factor(Learning) 
Inputs:  Gender, Depression 
Number of observations:  162 

1) Depression <= 17; criterion = 0.993, statistic = 11.261
  2)*  weights = 147 
1) Depression > 17
  3)*  weights = 15 
> postscript(file="/var/wessaorg/rcomp/tmp/2mukb1323538905.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/3a6hd1323538905.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)
+ }
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
       [,1] [,2]
  [1,]    1    2
  [2,]    2    2
  [3,]    3    2
  [4,]    2    2
  [5,]    1    1
  [6,]    1    2
  [7,]    3    1
  [8,]    2    2
  [9,]    1    2
 [10,]    2    2
 [11,]    2    2
 [12,]    2    2
 [13,]    2    2
 [14,]    2    2
 [15,]    3    2
 [16,]    2    2
 [17,]    2    2
 [18,]    3    2
 [19,]    3    2
 [20,]    2    2
 [21,]    2    2
 [22,]    2    2
 [23,]    3    2
 [24,]    2    2
 [25,]    3    2
 [26,]    3    2
 [27,]    2    2
 [28,]    2    2
 [29,]    2    2
 [30,]    1    2
 [31,]    2    2
 [32,]    1    2
 [33,]    1    2
 [34,]    2    2
 [35,]    1    2
 [36,]    1    2
 [37,]    1    1
 [38,]    1    2
 [39,]    2    2
 [40,]    2    2
 [41,]    2    2
 [42,]    1    2
 [43,]    3    2
 [44,]    1    2
 [45,]    1    2
 [46,]    1    2
 [47,]    1    2
 [48,]    2    2
 [49,]    2    2
 [50,]    1    2
 [51,]    2    2
 [52,]    1    2
 [53,]    1    1
 [54,]    2    2
 [55,]    1    2
 [56,]    1    2
 [57,]    2    2
 [58,]    2    2
 [59,]    2    2
 [60,]    2    2
 [61,]    1    1
 [62,]    2    2
 [63,]    2    2
 [64,]    1    2
 [65,]    2    2
 [66,]    3    2
 [67,]    3    2
 [68,]    3    2
 [69,]    1    2
 [70,]    2    2
 [71,]    1    2
 [72,]    1    2
 [73,]    3    2
 [74,]    3    2
 [75,]    2    2
 [76,]    3    1
 [77,]    2    2
 [78,]    2    2
 [79,]    1    2
 [80,]    2    2
 [81,]    2    2
 [82,]    3    2
 [83,]    2    2
 [84,]    2    2
 [85,]    2    2
 [86,]    2    2
 [87,]    1    2
 [88,]    2    2
 [89,]    2    2
 [90,]    2    1
 [91,]    1    2
 [92,]    3    2
 [93,]    2    2
 [94,]    2    2
 [95,]    1    2
 [96,]    2    2
 [97,]    2    2
 [98,]    2    2
 [99,]    2    2
[100,]    1    2
[101,]    2    2
[102,]    2    2
[103,]    3    2
[104,]    2    2
[105,]    2    2
[106,]    1    1
[107,]    3    2
[108,]    2    2
[109,]    2    2
[110,]    1    1
[111,]    2    2
[112,]    2    2
[113,]    3    2
[114,]    1    2
[115,]    1    2
[116,]    3    2
[117,]    1    2
[118,]    1    2
[119,]    1    2
[120,]    2    2
[121,]    1    2
[122,]    2    2
[123,]    1    2
[124,]    2    2
[125,]    2    2
[126,]    2    2
[127,]    2    2
[128,]    3    2
[129,]    2    2
[130,]    1    2
[131,]    2    2
[132,]    1    1
[133,]    2    2
[134,]    1    2
[135,]    1    2
[136,]    2    2
[137,]    1    1
[138,]    2    2
[139,]    1    2
[140,]    1    2
[141,]    1    2
[142,]    2    2
[143,]    2    2
[144,]    3    2
[145,]    2    2
[146,]    1    2
[147,]    3    2
[148,]    1    2
[149,]    2    2
[150,]    1    1
[151,]    1    2
[152,]    2    2
[153,]    2    2
[154,]    2    2
[155,]    1    1
[156,]    1    1
[157,]    3    2
[158,]    1    2
[159,]    2    2
[160,]    2    2
[161,]    1    1
[162,]    1    2
         
          [ 7,15) [15,17) [17,20]
  [ 7,15)      12      45       0
  [15,17)       1      79       0
  [17,20]       2      23       0
> postscript(file="/var/wessaorg/rcomp/tmp/4jhqp1323538905.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/523cm1323538905.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/61qlj1323538905.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/7c9qt1323538905.tab") 
+ }
> 
> try(system("convert tmp/2mukb1323538905.ps tmp/2mukb1323538905.png",intern=TRUE))
character(0)
> try(system("convert tmp/3a6hd1323538905.ps tmp/3a6hd1323538905.png",intern=TRUE))
character(0)
> try(system("convert tmp/4jhqp1323538905.ps tmp/4jhqp1323538905.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.361   0.197   3.034