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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Type 'q()' to quit R.

> x <- array(list(112285
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+ ,61
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+ ,53608
+ ,24)
+ ,dim=c(6
+ ,145)
+ ,dimnames=list(c('totsize'
+ ,'logins'
+ ,'tothyperlinks'
+ ,'feedback_messages_p120'
+ ,'totseconds(tekstverwerker)'
+ ,'compendiums_reviewed')
+ ,1:145))
>  y <- array(NA,dim=c(6,145),dimnames=list(c('totsize','logins','tothyperlinks','feedback_messages_p120','totseconds(tekstverwerker)','compendiums_reviewed'),1:145))
>  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
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] "totsize"
> x[,par1]
  [1] 112285  84786  83123 101193  38361  68504 119182  22807 116174  57635
 [11]  66198  71701  57793  80444  53855  97668 133824 101481  99645 114789
 [21]  99052  67654  65553  97500  69112  82753  85323  72654  30727  77873
 [31] 117478  74007  90183  61542 101494  55813  79215  55461  31081  83122
 [41]  70106  60578  79892  49810  71570 100708  33032  82875 139077  71595
 [51]  72260 115762  32551  31701  80670 143558 120733 105195  73107 132068
 [61] 149193  46821  87011  95260  55183 106671  73511  92945  78664  70054
 [71]  22618  74011  83737  69094  93133  95536  62133  61370  43836 106117
 [81]  38692  84651  56622  15986  95364  89691  67267 126846  41140 102860
 [91]  51715  55801 111813 120293 138599 161647 115929 162901 109825 129838
[101]  37510  43750  40652  87771  85872  89275 140867 120662 101338  65567
[111]  25162  40735  91413  97068  14116  76643 110681  92696  94785  83209
[121]  93815  86687 105547 103487  71220  56926  91721 115168 111194 135777
[131]  51513  74163  51633  75345  98952 102372  37238 103772 123969 135400
[141] 130115  64466  54990  34777  73224
> 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])

[14116, 83123) [83123,162901] 
            73             72 
> colnames(x)
[1] "totsize"                    "logins"                    
[3] "tothyperlinks"              "feedback_messages_p120"    
[5] "totseconds.tekstverwerker." "compendiums_reviewed"      
> colnames(x)[par1]
[1] "totsize"
> x[,par1]
  [1] [83123,162901] [83123,162901] [83123,162901] [83123,162901] [14116, 83123)
  [6] [14116, 83123) [83123,162901] [14116, 83123) [83123,162901] [14116, 83123)
 [11] [14116, 83123) [14116, 83123) [14116, 83123) [14116, 83123) [14116, 83123)
 [16] [83123,162901] [83123,162901] [83123,162901] [83123,162901] [83123,162901]
 [21] [83123,162901] [14116, 83123) [14116, 83123) [83123,162901] [14116, 83123)
 [26] [14116, 83123) [83123,162901] [14116, 83123) [14116, 83123) [14116, 83123)
 [31] [83123,162901] [14116, 83123) [83123,162901] [14116, 83123) [83123,162901]
 [36] [14116, 83123) [14116, 83123) [14116, 83123) [14116, 83123) [14116, 83123)
 [41] [14116, 83123) [14116, 83123) [14116, 83123) [14116, 83123) [14116, 83123)
 [46] [83123,162901] [14116, 83123) [14116, 83123) [83123,162901] [14116, 83123)
 [51] [14116, 83123) [83123,162901] [14116, 83123) [14116, 83123) [14116, 83123)
 [56] [83123,162901] [83123,162901] [83123,162901] [14116, 83123) [83123,162901]
 [61] [83123,162901] [14116, 83123) [83123,162901] [83123,162901] [14116, 83123)
 [66] [83123,162901] [14116, 83123) [83123,162901] [14116, 83123) [14116, 83123)
 [71] [14116, 83123) [14116, 83123) [83123,162901] [14116, 83123) [83123,162901]
 [76] [83123,162901] [14116, 83123) [14116, 83123) [14116, 83123) [83123,162901]
 [81] [14116, 83123) [83123,162901] [14116, 83123) [14116, 83123) [83123,162901]
 [86] [83123,162901] [14116, 83123) [83123,162901] [14116, 83123) [83123,162901]
 [91] [14116, 83123) [14116, 83123) [83123,162901] [83123,162901] [83123,162901]
 [96] [83123,162901] [83123,162901] [83123,162901] [83123,162901] [83123,162901]
[101] [14116, 83123) [14116, 83123) [14116, 83123) [83123,162901] [83123,162901]
[106] [83123,162901] [83123,162901] [83123,162901] [83123,162901] [14116, 83123)
[111] [14116, 83123) [14116, 83123) [83123,162901] [83123,162901] [14116, 83123)
[116] [14116, 83123) [83123,162901] [83123,162901] [83123,162901] [83123,162901]
[121] [83123,162901] [83123,162901] [83123,162901] [83123,162901] [14116, 83123)
[126] [14116, 83123) [83123,162901] [83123,162901] [83123,162901] [83123,162901]
[131] [14116, 83123) [14116, 83123) [14116, 83123) [14116, 83123) [83123,162901]
[136] [83123,162901] [14116, 83123) [83123,162901] [83123,162901] [83123,162901]
[141] [83123,162901] [14116, 83123) [14116, 83123) [14116, 83123) [14116, 83123)
Levels: [14116, 83123) [83123,162901]
> 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/1e04w1324655702.tab") 
+ }
+ }
           m.ct.i.pred
m.ct.i.actu   1   2
          1 565  94
          2 230 412
[1] 0.8573596
[1] 0.6417445
[1] 0.7509608
           m.ct.x.pred
m.ct.x.actu  1  2
          1 56 15
          2 34 44
[1] 0.7887324
[1] 0.5641026
[1] 0.6711409
> m

	 Conditional inference tree with 3 terminal nodes

Response:  as.factor(totsize) 
Inputs:  logins, tothyperlinks, feedback_messages_p120, totseconds.tekstverwerker., compendiums_reviewed 
Number of observations:  145 

1) tothyperlinks <= 131; criterion = 1, statistic = 44.728
  2) feedback_messages_p120 <= 90; criterion = 0.998, statistic = 13.026
    3)*  weights = 43 
  2) feedback_messages_p120 > 90
    4)*  weights = 65 
1) tothyperlinks > 131
  5)*  weights = 37 
> postscript(file="/var/wessaorg/rcomp/tmp/22y7h1324655702.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/38ukm1324655702.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    1
  [4,]    2    2
  [5,]    1    1
  [6,]    1    1
  [7,]    2    2
  [8,]    1    1
  [9,]    2    1
 [10,]    1    1
 [11,]    1    1
 [12,]    1    1
 [13,]    1    1
 [14,]    1    1
 [15,]    1    1
 [16,]    2    2
 [17,]    2    2
 [18,]    2    1
 [19,]    2    2
 [20,]    2    2
 [21,]    2    1
 [22,]    1    1
 [23,]    1    1
 [24,]    2    1
 [25,]    1    1
 [26,]    1    1
 [27,]    2    1
 [28,]    1    1
 [29,]    1    1
 [30,]    1    1
 [31,]    2    1
 [32,]    1    1
 [33,]    2    1
 [34,]    1    1
 [35,]    2    2
 [36,]    1    1
 [37,]    1    1
 [38,]    1    1
 [39,]    1    1
 [40,]    1    1
 [41,]    1    1
 [42,]    1    1
 [43,]    1    1
 [44,]    1    1
 [45,]    1    1
 [46,]    2    1
 [47,]    1    1
 [48,]    1    1
 [49,]    2    2
 [50,]    1    1
 [51,]    1    1
 [52,]    2    2
 [53,]    1    1
 [54,]    1    1
 [55,]    1    1
 [56,]    2    2
 [57,]    2    2
 [58,]    2    2
 [59,]    1    1
 [60,]    2    2
 [61,]    2    2
 [62,]    1    1
 [63,]    2    1
 [64,]    2    1
 [65,]    1    1
 [66,]    2    1
 [67,]    1    1
 [68,]    2    2
 [69,]    1    1
 [70,]    1    1
 [71,]    1    1
 [72,]    1    1
 [73,]    2    1
 [74,]    1    1
 [75,]    2    1
 [76,]    2    2
 [77,]    1    1
 [78,]    1    1
 [79,]    1    1
 [80,]    2    1
 [81,]    1    1
 [82,]    2    2
 [83,]    1    1
 [84,]    1    1
 [85,]    2    1
 [86,]    2    1
 [87,]    1    1
 [88,]    2    2
 [89,]    1    1
 [90,]    2    2
 [91,]    1    1
 [92,]    1    1
 [93,]    2    1
 [94,]    2    1
 [95,]    2    2
 [96,]    2    2
 [97,]    2    2
 [98,]    2    2
 [99,]    2    1
[100,]    2    2
[101,]    1    1
[102,]    1    1
[103,]    1    1
[104,]    2    2
[105,]    2    1
[106,]    2    1
[107,]    2    1
[108,]    2    1
[109,]    2    1
[110,]    1    1
[111,]    1    1
[112,]    1    1
[113,]    2    2
[114,]    2    1
[115,]    1    1
[116,]    1    1
[117,]    2    1
[118,]    2    1
[119,]    2    1
[120,]    2    1
[121,]    2    2
[122,]    2    2
[123,]    2    2
[124,]    2    2
[125,]    1    1
[126,]    1    1
[127,]    2    1
[128,]    2    2
[129,]    2    1
[130,]    2    2
[131,]    1    1
[132,]    1    1
[133,]    1    1
[134,]    1    1
[135,]    2    1
[136,]    2    2
[137,]    1    1
[138,]    2    2
[139,]    2    1
[140,]    2    1
[141,]    2    2
[142,]    1    1
[143,]    1    2
[144,]    1    1
[145,]    1    1
                
                 [14116, 83123) [83123,162901]
  [14116, 83123)             72              1
  [83123,162901]             36             36
> postscript(file="/var/wessaorg/rcomp/tmp/46jyv1324655702.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/5waom1324655702.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/6dnpa1324655702.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/7in1y1324655702.tab") 
+ }
> 
> try(system("convert tmp/22y7h1324655702.ps tmp/22y7h1324655702.png",intern=TRUE))
character(0)
> try(system("convert tmp/38ukm1324655702.ps tmp/38ukm1324655702.png",intern=TRUE))
character(0)
> try(system("convert tmp/46jyv1324655702.ps tmp/46jyv1324655702.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.466   0.242   3.707