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 '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(176508
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+ ,dim=c(5
+ ,172)
+ ,dimnames=list(c('Tijd'
+ ,'Gedeelde_compendia'
+ ,'Aantal_blogs'
+ ,'Reviews'
+ ,'Reviews+120')
+ ,1:172))
>  y <- array(NA,dim=c(5,172),dimnames=list(c('Tijd','Gedeelde_compendia','Aantal_blogs','Reviews','Reviews+120'),1:172))
>  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 = '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] "Tijd"
> x[,par1]
  [1] 176508 165446 237213 133131 324799 236785 344297 174724 174415 223632
 [11] 124817 325107   7176 265769 175824 111665 362301 168809  24188 329267
 [21] 218946 244052 341570 256462 196553 174184 187559  73566 182999 152299
 [31] 346485 193339 122774 112611 286468 148446 140344 220516 243060 162765
 [41] 232138  85574 232317 164709 220801  92661 133328  61361 100750 101523
 [51] 243511  22938 152474 132487  21054 209641  46698 131698 244749 272458
 [61] 108043 351067 229242  84207 250047 299775 173260  92499  74408 181633
 [71] 271856  95227 139942  72880  65475  71965 181528 134019  56375  65490
 [81]  76302 104011  30989  63123  74914  81437  65745  56653 158399  73624
 [91]  91899 139526  86678 150580  99611  31706  89806  19764  64187  72535
[101] 210907 120982 385534 149061 230964 135473 215147 153935 225548 210767
[111] 170266 294424 106408  96560 149112 152871 183167 103597 235800 143246
[121] 187681 167488 143756 243199 130585 182079 265318 310839 225060 144966
[131]  99466 102010  99923 317394  22648  31414 128423  97839 328107 158015
[141] 120445 324598 131069 204271 116048 195838 254488 224330 135781  81240
[151]  98146  59194 118612 135131 108446 121848  81872  58981  53515  98104
[161] 135458  31774  51567 102538  99373  86230  30837  64175  59382 119308
[171]  76702  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])

[  7176,135781) [135781,385534] 
             86              86 
> colnames(x)
[1] "Tijd"               "Gedeelde_compendia" "Aantal_blogs"      
[4] "Reviews"            "Reviews.120"       
> colnames(x)[par1]
[1] "Tijd"
> x[,par1]
  [1] [135781,385534] [135781,385534] [135781,385534] [  7176,135781)
  [5] [135781,385534] [135781,385534] [135781,385534] [135781,385534]
  [9] [135781,385534] [135781,385534] [  7176,135781) [135781,385534]
 [13] [  7176,135781) [135781,385534] [135781,385534] [  7176,135781)
 [17] [135781,385534] [135781,385534] [  7176,135781) [135781,385534]
 [21] [135781,385534] [135781,385534] [135781,385534] [135781,385534]
 [25] [135781,385534] [135781,385534] [135781,385534] [  7176,135781)
 [29] [135781,385534] [135781,385534] [135781,385534] [135781,385534]
 [33] [  7176,135781) [  7176,135781) [135781,385534] [135781,385534]
 [37] [135781,385534] [135781,385534] [135781,385534] [135781,385534]
 [41] [135781,385534] [  7176,135781) [135781,385534] [135781,385534]
 [45] [135781,385534] [  7176,135781) [  7176,135781) [  7176,135781)
 [49] [  7176,135781) [  7176,135781) [135781,385534] [  7176,135781)
 [53] [135781,385534] [  7176,135781) [  7176,135781) [135781,385534]
 [57] [  7176,135781) [  7176,135781) [135781,385534] [135781,385534]
 [61] [  7176,135781) [135781,385534] [135781,385534] [  7176,135781)
 [65] [135781,385534] [135781,385534] [135781,385534] [  7176,135781)
 [69] [  7176,135781) [135781,385534] [135781,385534] [  7176,135781)
 [73] [135781,385534] [  7176,135781) [  7176,135781) [  7176,135781)
 [77] [135781,385534] [  7176,135781) [  7176,135781) [  7176,135781)
 [81] [  7176,135781) [  7176,135781) [  7176,135781) [  7176,135781)
 [85] [  7176,135781) [  7176,135781) [  7176,135781) [  7176,135781)
 [89] [135781,385534] [  7176,135781) [  7176,135781) [135781,385534]
 [93] [  7176,135781) [135781,385534] [  7176,135781) [  7176,135781)
 [97] [  7176,135781) [  7176,135781) [  7176,135781) [  7176,135781)
[101] [135781,385534] [  7176,135781) [135781,385534] [135781,385534]
[105] [135781,385534] [  7176,135781) [135781,385534] [135781,385534]
[109] [135781,385534] [135781,385534] [135781,385534] [135781,385534]
[113] [  7176,135781) [  7176,135781) [135781,385534] [135781,385534]
[117] [135781,385534] [  7176,135781) [135781,385534] [135781,385534]
[121] [135781,385534] [135781,385534] [135781,385534] [135781,385534]
[125] [  7176,135781) [135781,385534] [135781,385534] [135781,385534]
[129] [135781,385534] [135781,385534] [  7176,135781) [  7176,135781)
[133] [  7176,135781) [135781,385534] [  7176,135781) [  7176,135781)
[137] [  7176,135781) [  7176,135781) [135781,385534] [135781,385534]
[141] [  7176,135781) [135781,385534] [  7176,135781) [135781,385534]
[145] [  7176,135781) [135781,385534] [135781,385534] [135781,385534]
[149] [135781,385534] [  7176,135781) [  7176,135781) [  7176,135781)
[153] [  7176,135781) [  7176,135781) [  7176,135781) [  7176,135781)
[157] [  7176,135781) [  7176,135781) [  7176,135781) [  7176,135781)
[161] [  7176,135781) [  7176,135781) [  7176,135781) [  7176,135781)
[165] [  7176,135781) [  7176,135781) [  7176,135781) [  7176,135781)
[169] [  7176,135781) [  7176,135781) [  7176,135781) [  7176,135781)
Levels: [  7176,135781) [135781,385534]
> 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/1kb0a1323773622.tab") 
+ }
+ }
> m

	 Conditional inference tree with 4 terminal nodes

Response:  as.factor(Tijd) 
Inputs:  Gedeelde_compendia, Aantal_blogs, Reviews, Reviews.120 
Number of observations:  172 

1) Aantal_blogs <= 56; criterion = 1, statistic = 90.313
  2) Reviews.120 <= 68; criterion = 0.999, statistic = 13.523
    3)*  weights = 52 
  2) Reviews.120 > 68
    4)*  weights = 43 
1) Aantal_blogs > 56
  5) Reviews <= 23; criterion = 0.993, statistic = 9.77
    6)*  weights = 7 
  5) Reviews > 23
    7)*  weights = 70 
> postscript(file="/var/wessaorg/rcomp/tmp/29k321323773622.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/3owra1323773622.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    2
  [3,]    2    2
  [4,]    1    1
  [5,]    2    2
  [6,]    2    2
  [7,]    2    2
  [8,]    2    2
  [9,]    2    2
 [10,]    2    2
 [11,]    1    1
 [12,]    2    2
 [13,]    1    1
 [14,]    2    2
 [15,]    2    1
 [16,]    1    1
 [17,]    2    2
 [18,]    2    2
 [19,]    1    1
 [20,]    2    2
 [21,]    2    2
 [22,]    2    2
 [23,]    2    1
 [24,]    2    2
 [25,]    2    1
 [26,]    2    2
 [27,]    2    2
 [28,]    1    1
 [29,]    2    2
 [30,]    2    2
 [31,]    2    2
 [32,]    2    2
 [33,]    1    1
 [34,]    1    1
 [35,]    2    2
 [36,]    2    2
 [37,]    2    1
 [38,]    2    2
 [39,]    2    2
 [40,]    2    2
 [41,]    2    2
 [42,]    1    1
 [43,]    2    2
 [44,]    2    2
 [45,]    2    1
 [46,]    1    1
 [47,]    1    1
 [48,]    1    1
 [49,]    1    2
 [50,]    1    1
 [51,]    2    2
 [52,]    1    1
 [53,]    2    2
 [54,]    1    2
 [55,]    1    1
 [56,]    2    2
 [57,]    1    1
 [58,]    1    1
 [59,]    2    2
 [60,]    2    2
 [61,]    1    1
 [62,]    2    2
 [63,]    2    2
 [64,]    1    1
 [65,]    2    1
 [66,]    2    2
 [67,]    2    1
 [68,]    1    1
 [69,]    1    1
 [70,]    2    1
 [71,]    2    2
 [72,]    1    1
 [73,]    2    1
 [74,]    1    1
 [75,]    1    1
 [76,]    1    1
 [77,]    2    1
 [78,]    1    1
 [79,]    1    1
 [80,]    1    1
 [81,]    1    1
 [82,]    1    1
 [83,]    1    1
 [84,]    1    1
 [85,]    1    1
 [86,]    1    1
 [87,]    1    1
 [88,]    1    1
 [89,]    2    1
 [90,]    1    1
 [91,]    1    1
 [92,]    2    1
 [93,]    1    1
 [94,]    2    1
 [95,]    1    1
 [96,]    1    1
 [97,]    1    1
 [98,]    1    1
 [99,]    1    1
[100,]    1    1
[101,]    2    2
[102,]    1    2
[103,]    2    2
[104,]    2    1
[105,]    2    2
[106,]    1    1
[107,]    2    2
[108,]    2    1
[109,]    2    2
[110,]    2    2
[111,]    2    1
[112,]    2    2
[113,]    1    1
[114,]    1    1
[115,]    2    1
[116,]    2    2
[117,]    2    2
[118,]    1    1
[119,]    2    1
[120,]    2    2
[121,]    2    2
[122,]    2    2
[123,]    2    2
[124,]    2    2
[125,]    1    2
[126,]    2    2
[127,]    2    2
[128,]    2    2
[129,]    2    2
[130,]    2    1
[131,]    1    1
[132,]    1    1
[133,]    1    1
[134,]    2    2
[135,]    1    1
[136,]    1    1
[137,]    1    1
[138,]    1    1
[139,]    2    2
[140,]    2    2
[141,]    1    1
[142,]    2    2
[143,]    1    1
[144,]    2    2
[145,]    1    1
[146,]    2    2
[147,]    2    2
[148,]    2    2
[149,]    2    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
[165,]    1    1
[166,]    1    1
[167,]    1    1
[168,]    1    1
[169,]    1    1
[170,]    1    1
[171,]    1    1
[172,]    1    1
                 
                  [  7176,135781) [135781,385534]
  [  7176,135781)              82               4
  [135781,385534]              20              66
> postscript(file="/var/wessaorg/rcomp/tmp/4vfqv1323773622.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/5jgff1323773622.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/6w3dw1323773622.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/7prt41323773622.tab") 
+ }
> 
> try(system("convert tmp/29k321323773622.ps tmp/29k321323773622.png",intern=TRUE))
character(0)
> try(system("convert tmp/3owra1323773622.ps tmp/3owra1323773622.png",intern=TRUE))
character(0)
> try(system("convert tmp/4vfqv1323773622.ps tmp/4vfqv1323773622.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.619   0.240   2.853