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.

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Type 'contributors()' for more information and
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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(293403
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+ ,dim=c(6
+ ,164)
+ ,dimnames=list(c('y'
+ ,'x1'
+ ,'x2'
+ ,'x3'
+ ,'x4'
+ ,'x5')
+ ,1:164))
>  y <- array(NA,dim=c(6,164),dimnames=list(c('y','x1','x2','x3','x4','x5'),1:164))
>  for (i in 1:dim(x)[1])
+  {
+  	for (j in 1:dim(x)[2])
+  	{
+  		y[i,j] <- as.numeric(x[i,j])
+  	}
+  }
> par4 = 'yes'
> par3 = '2'
> par2 = 'quantiles'
> par1 = '1'
> 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] "y"
> x[,par1]
  [1] 293403 277108 264020 260646 246100 244051 241329 234730 234509 233482
 [11] 233406 228548 223914 223696 223004 213765 210554 202204 199512 195304
 [21] 191467 191381 191276 190410 188967 188780 185139 185039 184217 181853
 [31] 181379 181344 179562 178863 178140 176789 176460 175877 175568 174107
 [41] 173587 173260 172684 167845 167131 167105 166790 164767 162810 162336
 [51] 161678 158980 157250 156833 155383 154991 154730 151503 146455 143937
 [61] 142339 142146 142141 142069 141933 139350 139144 137793 136911 136548
 [71] 135171 134043 131876 131122 130539 130533 130232 129100 128655 128066
 [81] 127619 127324 126683 126681 125971 125366 122433 121135 119291 118958
 [91] 118807 118372 116900 116775 115199 114928 114397 113337 111664 108715
[101] 107342 107335 106539 105615 105410 105324 103012 102531 101324 100885
[111] 100672  99946  99768  99246  98599  98030  94763  93340  93125  91185
[121]  90961  90938  89318  88817  84944  84572  84256  80953  78800  78776
[131]  75812  75426  74398  74112  73567  69471  68948  67746  67507  65029
[141]  64320  61857  61499  50999  46660  43287  38214  35523  32750  31414
[151]  24188  22938  21054  17547  14688   7199    969    455    203     98
[161]      0      0      0      0
> 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])

[     0,127324) [127324,293403] 
             82              82 
> colnames(x)
[1] "y"  "x1" "x2" "x3" "x4" "x5"
> colnames(x)[par1]
[1] "y"
> x[,par1]
  [1] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
  [5] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
  [9] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
 [13] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
 [17] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
 [21] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
 [25] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
 [29] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
 [33] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
 [37] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
 [41] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
 [45] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
 [49] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
 [53] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
 [57] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
 [61] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
 [65] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
 [69] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
 [73] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
 [77] [127324,293403] [127324,293403] [127324,293403] [127324,293403]
 [81] [127324,293403] [127324,293403] [     0,127324) [     0,127324)
 [85] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
 [89] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
 [93] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
 [97] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
[101] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
[105] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
[109] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
[113] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
[117] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
[121] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
[125] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
[129] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
[133] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
[137] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
[141] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
[145] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
[149] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
[153] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
[157] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
[161] [     0,127324) [     0,127324) [     0,127324) [     0,127324)
Levels: [     0,127324) [127324,293403]
> 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/1xtsb1356093715.tab") 
+ }
+ }
           m.ct.i.pred
m.ct.i.actu   1   2
          1 655 101
          2 141 610
[1] 0.8664021
[1] 0.8122503
[1] 0.8394161
           m.ct.x.pred
m.ct.x.actu  1  2
          1 55  9
          2 10 59
[1] 0.859375
[1] 0.8550725
[1] 0.8571429
> m

	 Conditional inference tree with 4 terminal nodes

Response:  as.factor(y) 
Inputs:  x1, x2, x3, x4, x5 
Number of observations:  164 

1) x1 <= 48; criterion = 1, statistic = 68.565
  2)*  weights = 60 
1) x1 > 48
  3) x4 <= 72; criterion = 0.998, statistic = 12.133
    4)*  weights = 20 
  3) x4 > 72
    5) x2 <= 54; criterion = 0.983, statistic = 8.562
      6)*  weights = 9 
    5) x2 > 54
      7)*  weights = 75 
> postscript(file="/var/fisher/rcomp/tmp/233vr1356093715.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/3u7z71356093715.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    2
  [2,]    2    1
  [3,]    2    2
  [4,]    2    2
  [5,]    2    2
  [6,]    2    2
  [7,]    2    2
  [8,]    2    2
  [9,]    2    2
 [10,]    2    2
 [11,]    2    2
 [12,]    2    2
 [13,]    2    2
 [14,]    2    2
 [15,]    2    2
 [16,]    2    2
 [17,]    2    2
 [18,]    2    1
 [19,]    2    2
 [20,]    2    1
 [21,]    2    2
 [22,]    2    2
 [23,]    2    2
 [24,]    2    2
 [25,]    2    2
 [26,]    2    2
 [27,]    2    2
 [28,]    2    2
 [29,]    2    1
 [30,]    2    2
 [31,]    2    2
 [32,]    2    2
 [33,]    2    2
 [34,]    2    2
 [35,]    2    2
 [36,]    2    2
 [37,]    2    2
 [38,]    2    2
 [39,]    2    2
 [40,]    2    2
 [41,]    2    2
 [42,]    2    1
 [43,]    2    2
 [44,]    2    2
 [45,]    2    1
 [46,]    2    1
 [47,]    2    1
 [48,]    2    2
 [49,]    2    2
 [50,]    2    2
 [51,]    2    2
 [52,]    2    2
 [53,]    2    2
 [54,]    2    2
 [55,]    2    2
 [56,]    2    2
 [57,]    2    2
 [58,]    2    2
 [59,]    2    2
 [60,]    2    2
 [61,]    2    2
 [62,]    2    2
 [63,]    2    1
 [64,]    2    2
 [65,]    2    2
 [66,]    2    2
 [67,]    2    2
 [68,]    2    2
 [69,]    2    2
 [70,]    2    2
 [71,]    2    2
 [72,]    2    1
 [73,]    2    1
 [74,]    2    1
 [75,]    2    1
 [76,]    2    2
 [77,]    2    2
 [78,]    2    1
 [79,]    2    2
 [80,]    2    1
 [81,]    2    1
 [82,]    2    2
 [83,]    1    1
 [84,]    1    1
 [85,]    1    1
 [86,]    1    1
 [87,]    1    1
 [88,]    1    1
 [89,]    1    1
 [90,]    1    1
 [91,]    1    1
 [92,]    1    1
 [93,]    1    1
 [94,]    1    1
 [95,]    1    1
 [96,]    1    1
 [97,]    1    2
 [98,]    1    1
 [99,]    1    2
[100,]    1    1
[101,]    1    1
[102,]    1    1
[103,]    1    1
[104,]    1    1
[105,]    1    2
[106,]    1    1
[107,]    1    1
[108,]    1    1
[109,]    1    2
[110,]    1    1
[111,]    1    1
[112,]    1    2
[113,]    1    1
[114,]    1    1
[115,]    1    1
[116,]    1    1
[117,]    1    2
[118,]    1    1
[119,]    1    1
[120,]    1    1
[121,]    1    1
[122,]    1    1
[123,]    1    2
[124,]    1    1
[125,]    1    2
[126,]    1    1
[127,]    1    2
[128,]    1    1
[129,]    1    1
[130,]    1    1
[131,]    1    1
[132,]    1    1
[133,]    1    1
[134,]    1    1
[135,]    1    1
[136,]    1    1
[137,]    1    1
[138,]    1    1
[139,]    1    1
[140,]    1    1
[141,]    1    1
[142,]    1    1
[143,]    1    1
[144,]    1    1
[145,]    1    1
[146,]    1    1
[147,]    1    1
[148,]    1    1
[149,]    1    1
[150,]    1    1
[151,]    1    1
[152,]    1    1
[153,]    1    1
[154,]    1    1
[155,]    1    1
[156,]    1    1
[157,]    1    1
[158,]    1    1
[159,]    1    1
[160,]    1    1
[161,]    1    1
[162,]    1    1
[163,]    1    1
[164,]    1    1
                 
                  [     0,127324) [127324,293403]
  [     0,127324)              73               9
  [127324,293403]              16              66
> postscript(file="/var/fisher/rcomp/tmp/4l8tj1356093715.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/59q2q1356093715.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/6xgf31356093715.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/7plnz1356093715.tab") 
+ }
> 
> try(system("convert tmp/233vr1356093715.ps tmp/233vr1356093715.png",intern=TRUE))
character(0)
> try(system("convert tmp/3u7z71356093715.ps tmp/3u7z71356093715.png",intern=TRUE))
character(0)
> try(system("convert tmp/4l8tj1356093715.ps tmp/4l8tj1356093715.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  5.241   0.694   5.922