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(1818
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+ ,125
+ ,71
+ ,100350
+ ,17372
+ ,75882
+ ,80
+ ,72)
+ ,dim=c(15
+ ,164)
+ ,dimnames=list(c('Pageviews'
+ ,'TimeRFC'
+ ,'Logins'
+ ,'CCviews'
+ ,'PR'
+ ,'Shared'
+ ,'BloggedC'
+ ,'ReviewedC'
+ ,'Feedbackm'
+ ,'+120C'
+ ,'#Characters'
+ ,'#Revisions'
+ ,'#secondsC'
+ ,'Hyperlinks'
+ ,'Blogs')
+ ,1:164))
>  y <- array(NA,dim=c(15,164),dimnames=list(c('Pageviews','TimeRFC','Logins','CCviews','PR','Shared','BloggedC','ReviewedC','Feedbackm','+120C','#Characters','#Revisions','#secondsC','Hyperlinks','Blogs'),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 = '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] "TimeRFC"
> x[,par1]
  [1] 279055 212450 233939 222117 189911  70849 605767  33186 227332 267925
 [11] 372083 279589 212638 368577 269455 402600 335735 432711 185822 267365
 [21] 279452 527853 227252 200004 257239 271341 324969 349753 200723 399591
 [31] 327660 269239 397689 130446 430118 273950 429837 254312 120351 395658
 [41] 349119 220517 234780 191784 186112 459665  78800 259887 368086 230299
 [51] 256033  24188 400109  65029 101097 319020 375638 375011 387748 280106
 [61] 400971 322780 291391 295075 280018 267432 217181 258166 277891 192894
 [71] 271853  73566 276269 242619 230030 371391 398698 243355 233519 219936
 [81] 206169 483429 146100 295224  80953 217384 179344 416097 395041 180679
 [91] 311447 292260 199481 282361 329281 234577 310685 352078 416463 429565
[101] 297080 331792 242507  43287 238089 285479 310383 321797 193926 175737
[111] 354041 303566  23668 196743  61857 217543 440711  21054 252805  31961
[121] 360436 251948 187320 180842  38214 289296 358276 211775 447335 348017
[131] 441946 215177 140328 318092 466139 162406 417354 178322 292443 283913
[141] 253950 389698 246963 173260 346748 188437 279125 314070      1  14688
[151]     98    455      0      0 291847 415839      0    203   7199  46660
[161]  17547 121550    969 242774
> 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])

[     0,259887) [259887,605767] 
             82              82 
> colnames(x)
 [1] "Pageviews"    "TimeRFC"      "Logins"       "CCviews"      "PR"          
 [6] "Shared"       "BloggedC"     "ReviewedC"    "Feedbackm"    "X.120C"      
[11] "X.Characters" "X.Revisions"  "X.secondsC"   "Hyperlinks"   "Blogs"       
> colnames(x)[par1]
[1] "TimeRFC"
> x[,par1]
  [1] [259887,605767] [     0,259887) [     0,259887) [     0,259887)
  [5] [     0,259887) [     0,259887) [259887,605767] [     0,259887)
  [9] [     0,259887) [259887,605767] [259887,605767] [259887,605767]
 [13] [     0,259887) [259887,605767] [259887,605767] [259887,605767]
 [17] [259887,605767] [259887,605767] [     0,259887) [259887,605767]
 [21] [259887,605767] [259887,605767] [     0,259887) [     0,259887)
 [25] [     0,259887) [259887,605767] [259887,605767] [259887,605767]
 [29] [     0,259887) [259887,605767] [259887,605767] [259887,605767]
 [33] [259887,605767] [     0,259887) [259887,605767] [259887,605767]
 [37] [259887,605767] [     0,259887) [     0,259887) [259887,605767]
 [41] [259887,605767] [     0,259887) [     0,259887) [     0,259887)
 [45] [     0,259887) [259887,605767] [     0,259887) [259887,605767]
 [49] [259887,605767] [     0,259887) [     0,259887) [     0,259887)
 [53] [259887,605767] [     0,259887) [     0,259887) [259887,605767]
 [57] [259887,605767] [259887,605767] [259887,605767] [259887,605767]
 [61] [259887,605767] [259887,605767] [259887,605767] [259887,605767]
 [65] [259887,605767] [259887,605767] [     0,259887) [     0,259887)
 [69] [259887,605767] [     0,259887) [259887,605767] [     0,259887)
 [73] [259887,605767] [     0,259887) [     0,259887) [259887,605767]
 [77] [259887,605767] [     0,259887) [     0,259887) [     0,259887)
 [81] [     0,259887) [259887,605767] [     0,259887) [259887,605767]
 [85] [     0,259887) [     0,259887) [     0,259887) [259887,605767]
 [89] [259887,605767] [     0,259887) [259887,605767] [259887,605767]
 [93] [     0,259887) [259887,605767] [259887,605767] [     0,259887)
 [97] [259887,605767] [259887,605767] [259887,605767] [259887,605767]
[101] [259887,605767] [259887,605767] [     0,259887) [     0,259887)
[105] [     0,259887) [259887,605767] [259887,605767] [259887,605767]
[109] [     0,259887) [     0,259887) [259887,605767] [259887,605767]
[113] [     0,259887) [     0,259887) [     0,259887) [     0,259887)
[117] [259887,605767] [     0,259887) [     0,259887) [     0,259887)
[121] [259887,605767] [     0,259887) [     0,259887) [     0,259887)
[125] [     0,259887) [259887,605767] [259887,605767] [     0,259887)
[129] [259887,605767] [259887,605767] [259887,605767] [     0,259887)
[133] [     0,259887) [259887,605767] [259887,605767] [     0,259887)
[137] [259887,605767] [     0,259887) [259887,605767] [259887,605767]
[141] [     0,259887) [259887,605767] [     0,259887) [     0,259887)
[145] [259887,605767] [     0,259887) [259887,605767] [259887,605767]
[149] [     0,259887) [     0,259887) [     0,259887) [     0,259887)
[153] [     0,259887) [     0,259887) [259887,605767] [259887,605767]
[157] [     0,259887) [     0,259887) [     0,259887) [     0,259887)
[161] [     0,259887) [     0,259887) [     0,259887) [     0,259887)
Levels: [     0,259887) [259887,605767]
> 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/1xlfy1324680293.tab") 
+ }
+ }
> m

	 Conditional inference tree with 4 terminal nodes

Response:  as.factor(TimeRFC) 
Inputs:  Pageviews, Logins, CCviews, PR, Shared, BloggedC, ReviewedC, Feedbackm, X.120C, X.Characters, X.Revisions, X.secondsC, Hyperlinks, Blogs 
Number of observations:  164 

1) X.secondsC <= 113963; criterion = 1, statistic = 75.615
  2) Pageviews <= 2235; criterion = 1, statistic = 22.083
    3)*  weights = 61 
  2) Pageviews > 2235
    4)*  weights = 26 
1) X.secondsC > 113963
  5) BloggedC <= 107; criterion = 1, statistic = 21.45
    6)*  weights = 22 
  5) BloggedC > 107
    7)*  weights = 55 
> postscript(file="/var/wessaorg/rcomp/tmp/2zp471324680293.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/3x07n1324680293.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,]    1    1
  [4,]    1    2
  [5,]    1    1
  [6,]    1    1
  [7,]    2    2
  [8,]    1    1
  [9,]    1    1
 [10,]    2    1
 [11,]    2    2
 [12,]    2    2
 [13,]    1    1
 [14,]    2    2
 [15,]    2    2
 [16,]    2    2
 [17,]    2    2
 [18,]    2    2
 [19,]    1    1
 [20,]    2    2
 [21,]    2    2
 [22,]    2    2
 [23,]    1    1
 [24,]    1    1
 [25,]    1    2
 [26,]    2    2
 [27,]    2    2
 [28,]    2    2
 [29,]    1    1
 [30,]    2    2
 [31,]    2    2
 [32,]    2    1
 [33,]    2    2
 [34,]    1    1
 [35,]    2    2
 [36,]    2    2
 [37,]    2    2
 [38,]    1    1
 [39,]    1    1
 [40,]    2    2
 [41,]    2    2
 [42,]    1    1
 [43,]    1    1
 [44,]    1    1
 [45,]    1    1
 [46,]    2    2
 [47,]    1    1
 [48,]    2    2
 [49,]    2    2
 [50,]    1    2
 [51,]    1    1
 [52,]    1    1
 [53,]    2    1
 [54,]    1    1
 [55,]    1    1
 [56,]    2    1
 [57,]    2    2
 [58,]    2    2
 [59,]    2    2
 [60,]    2    2
 [61,]    2    2
 [62,]    2    2
 [63,]    2    2
 [64,]    2    1
 [65,]    2    1
 [66,]    2    2
 [67,]    1    1
 [68,]    1    1
 [69,]    2    2
 [70,]    1    1
 [71,]    2    2
 [72,]    1    1
 [73,]    2    2
 [74,]    1    2
 [75,]    1    1
 [76,]    2    2
 [77,]    2    2
 [78,]    1    1
 [79,]    1    2
 [80,]    1    1
 [81,]    1    1
 [82,]    2    2
 [83,]    1    1
 [84,]    2    2
 [85,]    1    1
 [86,]    1    2
 [87,]    1    1
 [88,]    2    1
 [89,]    2    2
 [90,]    1    2
 [91,]    2    1
 [92,]    2    2
 [93,]    1    1
 [94,]    2    2
 [95,]    2    1
 [96,]    1    1
 [97,]    2    2
 [98,]    2    2
 [99,]    2    2
[100,]    2    2
[101,]    2    2
[102,]    2    2
[103,]    1    2
[104,]    1    1
[105,]    1    2
[106,]    2    2
[107,]    2    2
[108,]    2    2
[109,]    1    1
[110,]    1    2
[111,]    2    1
[112,]    2    2
[113,]    1    1
[114,]    1    1
[115,]    1    1
[116,]    1    1
[117,]    2    2
[118,]    1    1
[119,]    1    1
[120,]    1    1
[121,]    2    2
[122,]    1    1
[123,]    1    1
[124,]    1    1
[125,]    1    1
[126,]    2    2
[127,]    2    2
[128,]    1    1
[129,]    2    2
[130,]    2    2
[131,]    2    2
[132,]    1    1
[133,]    1    1
[134,]    2    2
[135,]    2    2
[136,]    1    1
[137,]    2    2
[138,]    1    1
[139,]    2    2
[140,]    2    2
[141,]    1    1
[142,]    2    1
[143,]    1    2
[144,]    1    1
[145,]    2    2
[146,]    1    1
[147,]    2    2
[148,]    2    2
[149,]    1    1
[150,]    1    1
[151,]    1    1
[152,]    1    1
[153,]    1    1
[154,]    1    1
[155,]    2    2
[156,]    2    2
[157,]    1    1
[158,]    1    1
[159,]    1    1
[160,]    1    1
[161,]    1    1
[162,]    1    1
[163,]    1    1
[164,]    1    1
                 
                  [     0,259887) [259887,605767]
  [     0,259887)              71              11
  [259887,605767]              12              70
> postscript(file="/var/wessaorg/rcomp/tmp/4y86e1324680293.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/5zb7t1324680293.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/6rnm11324680293.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/7fczy1324680293.tab") 
+ }
> 
> try(system("convert tmp/2zp471324680293.ps tmp/2zp471324680293.png",intern=TRUE))
character(0)
> try(system("convert tmp/3x07n1324680293.ps tmp/3x07n1324680293.png",intern=TRUE))
character(0)
> try(system("convert tmp/4y86e1324680293.ps tmp/4y86e1324680293.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  4.015   0.244   4.254