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(0,210907,0,2,0,149061,0,0,0,237213,1,0,0,133131,1,4,0,324799,1,0,0,230964,0,-1,0,236785,1,0,0,344297,1,1,0,174724,1,0,0,174415,1,3,0,223632,1,-1,0,294424,0,4,0,325107,1,3,0,106408,0,1,0,96560,0,0,0,265769,1,-2,0,149112,0,-4,0,152871,0,2,0,362301,1,2,0,183167,0,-4,0,218946,1,2,0,244052,1,2,0,341570,1,0,0,196553,1,-3,0,143246,0,2,0,143756,0,4,0,152299,1,2,0,193339,1,2,0,130585,0,-4,0,112611,1,3,0,148446,1,3,0,182079,0,2,0,243060,1,-1,0,162765,1,-3,0,85574,1,0,0,225060,0,1,0,133328,1,-3,0,100750,1,3,0,101523,1,0,0,243511,1,0,0,152474,1,0,0,132487,1,3,0,317394,0,-3,0,244749,1,0,0,128423,0,2,0,97839,0,-1,1,229242,1,2,1,324598,0,2,1,195838,0,-2,1,254488,0,0,1,92499,1,-2,1,224330,0,0,1,181633,1,6,1,271856,1,-3,1,95227,1,3,1,98146,0,0,1,118612,0,-2,1,65475,1,1,1,108446,0,0,1,121848,0,2,1,76302,1,2,1,98104,0,-3,1,30989,1,-2,1,31774,0,1,1,150580,1,-4,1,59382,0,1,1,84105,0,0),dim=c(4,67),dimnames=list(c('pop','time_in_rfc','gender','total_tests'),1:67))
>  y <- array(NA,dim=c(4,67),dimnames=list(c('pop','time_in_rfc','gender','total_tests'),1:67))
>  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 = '2'
> 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] "time_in_rfc"
> x[,par1]
 [1] 210907 149061 237213 133131 324799 230964 236785 344297 174724 174415
[11] 223632 294424 325107 106408  96560 265769 149112 152871 362301 183167
[21] 218946 244052 341570 196553 143246 143756 152299 193339 130585 112611
[31] 148446 182079 243060 162765  85574 225060 133328 100750 101523 243511
[41] 152474 132487 317394 244749 128423  97839 229242 324598 195838 254488
[51]  92499 224330 181633 271856  95227  98146 118612  65475 108446 121848
[61]  76302  98104  30989  31774 150580  59382  84105
> 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])

[ 30989,162765) [162765,362301] 
             34              33 
> colnames(x)
[1] "pop"         "time_in_rfc" "gender"      "total_tests"
> colnames(x)[par1]
[1] "time_in_rfc"
> x[,par1]
 [1] [162765,362301] [ 30989,162765) [162765,362301] [ 30989,162765)
 [5] [162765,362301] [162765,362301] [162765,362301] [162765,362301]
 [9] [162765,362301] [162765,362301] [162765,362301] [162765,362301]
[13] [162765,362301] [ 30989,162765) [ 30989,162765) [162765,362301]
[17] [ 30989,162765) [ 30989,162765) [162765,362301] [162765,362301]
[21] [162765,362301] [162765,362301] [162765,362301] [162765,362301]
[25] [ 30989,162765) [ 30989,162765) [ 30989,162765) [162765,362301]
[29] [ 30989,162765) [ 30989,162765) [ 30989,162765) [162765,362301]
[33] [162765,362301] [162765,362301] [ 30989,162765) [162765,362301]
[37] [ 30989,162765) [ 30989,162765) [ 30989,162765) [162765,362301]
[41] [ 30989,162765) [ 30989,162765) [162765,362301] [162765,362301]
[45] [ 30989,162765) [ 30989,162765) [162765,362301] [162765,362301]
[49] [162765,362301] [162765,362301] [ 30989,162765) [162765,362301]
[53] [162765,362301] [162765,362301] [ 30989,162765) [ 30989,162765)
[57] [ 30989,162765) [ 30989,162765) [ 30989,162765) [ 30989,162765)
[61] [ 30989,162765) [ 30989,162765) [ 30989,162765) [ 30989,162765)
[65] [ 30989,162765) [ 30989,162765) [ 30989,162765)
Levels: [ 30989,162765) [162765,362301]
> 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/116981323615602.tab") 
+ }
+ }
> m

	 Conditional inference tree with 1 terminal nodes

Response:  as.factor(time_in_rfc) 
Inputs:  pop, gender, total_tests 
Number of observations:  67 

1)*  weights = 67 
> postscript(file="/var/www/rcomp/tmp/27wqs1323615602.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/3xa2r1323615602.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,]    2    1
 [2,]    1    1
 [3,]    2    1
 [4,]    1    1
 [5,]    2    1
 [6,]    2    1
 [7,]    2    1
 [8,]    2    1
 [9,]    2    1
[10,]    2    1
[11,]    2    1
[12,]    2    1
[13,]    2    1
[14,]    1    1
[15,]    1    1
[16,]    2    1
[17,]    1    1
[18,]    1    1
[19,]    2    1
[20,]    2    1
[21,]    2    1
[22,]    2    1
[23,]    2    1
[24,]    2    1
[25,]    1    1
[26,]    1    1
[27,]    1    1
[28,]    2    1
[29,]    1    1
[30,]    1    1
[31,]    1    1
[32,]    2    1
[33,]    2    1
[34,]    2    1
[35,]    1    1
[36,]    2    1
[37,]    1    1
[38,]    1    1
[39,]    1    1
[40,]    2    1
[41,]    1    1
[42,]    1    1
[43,]    2    1
[44,]    2    1
[45,]    1    1
[46,]    1    1
[47,]    2    1
[48,]    2    1
[49,]    2    1
[50,]    2    1
[51,]    1    1
[52,]    2    1
[53,]    2    1
[54,]    2    1
[55,]    1    1
[56,]    1    1
[57,]    1    1
[58,]    1    1
[59,]    1    1
[60,]    1    1
[61,]    1    1
[62,]    1    1
[63,]    1    1
[64,]    1    1
[65,]    1    1
[66,]    1    1
[67,]    1    1
                 
                  [ 30989,162765) [162765,362301]
  [ 30989,162765)              34               0
  [162765,362301]              33               0
> postscript(file="/var/www/rcomp/tmp/4ilu71323615602.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/5uarj1323615602.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/6l0i31323615602.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/7czlp1323615602.tab") 
+ }
> 
> try(system("convert tmp/27wqs1323615602.ps tmp/27wqs1323615602.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xa2r1323615602.ps tmp/3xa2r1323615602.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ilu71323615602.ps tmp/4ilu71323615602.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.920   0.110   2.012