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(252101
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+ ,dim=c(7
+ ,164)
+ ,dimnames=list(c('Time'
+ ,'Logins'
+ ,'Views'
+ ,'Blogs'
+ ,'Reviews'
+ ,'LFM'
+ ,'Compendia_time
')
+ ,1:164))
>  y <- array(NA,dim=c(7,164),dimnames=list(c('Time','Logins','Views','Blogs','Reviews','LFM','Compendia_time
'),1:164))
>  for (i in 1:dim(x)[1])
+  {
+  	for (j in 1:dim(x)[2])
+  	{
+  		y[i,j] <- as.numeric(x[i,j])
+  	}
+  }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> 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] "Time"
> x[,par1]
  [1] 252101 134577 198520 189326 137449  65295 439387  33186 178368 186657
 [11] 261949 191051 138866 296878 192648 333462 243571 263451 155679 227053
 [21] 240028 388549 156540 148421 177732 191441 249893 236812 142329 259667
 [31] 231625 176062 286683  87485 322865 247082 344092 191653 114673 284224
 [41] 284195 155363 177306 144571 140319 405267  78800 201970 302674 164733
 [51] 194221  24188 342263  65029 101097 246088 273108 282220 273495 214872
 [61] 335121 267171 187938 229512 209798 201345 163833 204250 197813 132955
 [71] 216092  73566 213198 181713 148698 300103 251437 197295 158163 155529
 [81] 132672 377205 145905 223701  80953 130805 135082 300170 271806 150949
 [91] 225805 197389 156583 222599 261601 178489 200657 259084 313075 346933
[101] 246440 252444 159965  43287 172239 181897 227681 260464 106288 109632
[111] 268905 266805  23623 152474  61857 144889 346600  21054 224051  31414
[121] 261043 197819 154984 112933  38214 158671 302148 177918 350552 275578
[131] 366217 172464  94381 243875 382487 114525 335681 147989 216638 192862
[141] 184818 336707 215836 173260 271773 130908 204009 245514      1  14688
[151]     98    455      0      0 195765 326038      0    203   7199  46660
[161]  17547 107465    969 173102
> 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])

[     0,192648) [192648,439387] 
             82              82 
> colnames(x)
[1] "Time"            "Logins"          "Views"           "Blogs"          
[5] "Reviews"         "LFM"             "Compendia_time."
> colnames(x)[par1]
[1] "Time"
> x[,par1]
  [1] [192648,439387] [     0,192648) [192648,439387] [     0,192648)
  [5] [     0,192648) [     0,192648) [192648,439387] [     0,192648)
  [9] [     0,192648) [     0,192648) [192648,439387] [     0,192648)
 [13] [     0,192648) [192648,439387] [192648,439387] [192648,439387]
 [17] [192648,439387] [192648,439387] [     0,192648) [192648,439387]
 [21] [192648,439387] [192648,439387] [     0,192648) [     0,192648)
 [25] [     0,192648) [     0,192648) [192648,439387] [192648,439387]
 [29] [     0,192648) [192648,439387] [192648,439387] [     0,192648)
 [33] [192648,439387] [     0,192648) [192648,439387] [192648,439387]
 [37] [192648,439387] [     0,192648) [     0,192648) [192648,439387]
 [41] [192648,439387] [     0,192648) [     0,192648) [     0,192648)
 [45] [     0,192648) [192648,439387] [     0,192648) [192648,439387]
 [49] [192648,439387] [     0,192648) [192648,439387] [     0,192648)
 [53] [192648,439387] [     0,192648) [     0,192648) [192648,439387]
 [57] [192648,439387] [192648,439387] [192648,439387] [192648,439387]
 [61] [192648,439387] [192648,439387] [     0,192648) [192648,439387]
 [65] [192648,439387] [192648,439387] [     0,192648) [192648,439387]
 [69] [192648,439387] [     0,192648) [192648,439387] [     0,192648)
 [73] [192648,439387] [     0,192648) [     0,192648) [192648,439387]
 [77] [192648,439387] [192648,439387] [     0,192648) [     0,192648)
 [81] [     0,192648) [192648,439387] [     0,192648) [192648,439387]
 [85] [     0,192648) [     0,192648) [     0,192648) [192648,439387]
 [89] [192648,439387] [     0,192648) [192648,439387] [192648,439387]
 [93] [     0,192648) [192648,439387] [192648,439387] [     0,192648)
 [97] [192648,439387] [192648,439387] [192648,439387] [192648,439387]
[101] [192648,439387] [192648,439387] [     0,192648) [     0,192648)
[105] [     0,192648) [     0,192648) [192648,439387] [192648,439387]
[109] [     0,192648) [     0,192648) [192648,439387] [192648,439387]
[113] [     0,192648) [     0,192648) [     0,192648) [     0,192648)
[117] [192648,439387] [     0,192648) [192648,439387] [     0,192648)
[121] [192648,439387] [192648,439387] [     0,192648) [     0,192648)
[125] [     0,192648) [     0,192648) [192648,439387] [     0,192648)
[129] [192648,439387] [192648,439387] [192648,439387] [     0,192648)
[133] [     0,192648) [192648,439387] [192648,439387] [     0,192648)
[137] [192648,439387] [     0,192648) [192648,439387] [192648,439387]
[141] [     0,192648) [192648,439387] [192648,439387] [     0,192648)
[145] [192648,439387] [     0,192648) [192648,439387] [192648,439387]
[149] [     0,192648) [     0,192648) [     0,192648) [     0,192648)
[153] [     0,192648) [     0,192648) [192648,439387] [192648,439387]
[157] [     0,192648) [     0,192648) [     0,192648) [     0,192648)
[161] [     0,192648) [     0,192648) [     0,192648) [     0,192648)
Levels: [     0,192648) [192648,439387]
> 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/1vksb1324647978.tab") 
+ }
+ }
> m

	 Conditional inference tree with 4 terminal nodes

Response:  as.factor(Time) 
Inputs:  Logins, Views, Blogs, Reviews, LFM, Compendia_time. 
Number of observations:  164 

1) Compendia_time. <= 93116; criterion = 1, statistic = 85.119
  2) Views <= 664; criterion = 1, statistic = 17.124
    3)*  weights = 61 
  2) Views > 664
    4)*  weights = 9 
1) Compendia_time. > 93116
  5) Blogs <= 83; criterion = 1, statistic = 21.701
    6)*  weights = 30 
  5) Blogs > 83
    7)*  weights = 64 
> postscript(file="/var/www/rcomp/tmp/2rax11324647978.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/3yo381324647978.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,]    1    2
  [3,]    2    2
  [4,]    1    2
  [5,]    1    1
  [6,]    1    1
  [7,]    2    2
  [8,]    1    1
  [9,]    1    2
 [10,]    1    2
 [11,]    2    2
 [12,]    1    1
 [13,]    1    1
 [14,]    2    2
 [15,]    2    2
 [16,]    2    2
 [17,]    2    2
 [18,]    2    2
 [19,]    1    2
 [20,]    2    2
 [21,]    2    2
 [22,]    2    2
 [23,]    1    2
 [24,]    1    1
 [25,]    1    1
 [26,]    1    2
 [27,]    2    2
 [28,]    2    2
 [29,]    1    1
 [30,]    2    2
 [31,]    2    2
 [32,]    1    2
 [33,]    2    2
 [34,]    1    1
 [35,]    2    2
 [36,]    2    2
 [37,]    2    2
 [38,]    1    2
 [39,]    1    1
 [40,]    2    2
 [41,]    2    2
 [42,]    1    1
 [43,]    1    2
 [44,]    1    1
 [45,]    1    2
 [46,]    2    1
 [47,]    1    1
 [48,]    2    2
 [49,]    2    2
 [50,]    1    2
 [51,]    2    2
 [52,]    1    1
 [53,]    2    2
 [54,]    1    1
 [55,]    1    1
 [56,]    2    2
 [57,]    2    2
 [58,]    2    2
 [59,]    2    2
 [60,]    2    1
 [61,]    2    2
 [62,]    2    2
 [63,]    1    1
 [64,]    2    2
 [65,]    2    2
 [66,]    2    2
 [67,]    1    1
 [68,]    2    2
 [69,]    2    2
 [70,]    1    1
 [71,]    2    2
 [72,]    1    1
 [73,]    2    2
 [74,]    1    1
 [75,]    1    1
 [76,]    2    2
 [77,]    2    2
 [78,]    2    2
 [79,]    1    1
 [80,]    1    1
 [81,]    1    1
 [82,]    2    2
 [83,]    1    1
 [84,]    2    2
 [85,]    1    1
 [86,]    1    1
 [87,]    1    1
 [88,]    2    2
 [89,]    2    2
 [90,]    1    1
 [91,]    2    2
 [92,]    2    2
 [93,]    1    1
 [94,]    2    1
 [95,]    2    2
 [96,]    1    2
 [97,]    2    2
 [98,]    2    2
 [99,]    2    2
[100,]    2    2
[101,]    2    2
[102,]    2    2
[103,]    1    1
[104,]    1    1
[105,]    1    1
[106,]    1    2
[107,]    2    2
[108,]    2    2
[109,]    1    2
[110,]    1    1
[111,]    2    2
[112,]    2    2
[113,]    1    1
[114,]    1    1
[115,]    1    1
[116,]    1    1
[117,]    2    2
[118,]    1    1
[119,]    2    2
[120,]    1    1
[121,]    2    2
[122,]    2    1
[123,]    1    1
[124,]    1    1
[125,]    1    1
[126,]    1    1
[127,]    2    2
[128,]    1    2
[129,]    2    2
[130,]    2    2
[131,]    2    2
[132,]    1    2
[133,]    1    1
[134,]    2    1
[135,]    2    2
[136,]    1    1
[137,]    2    2
[138,]    1    1
[139,]    2    2
[140,]    2    2
[141,]    1    1
[142,]    2    2
[143,]    2    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
                 
                  [     0,192648) [192648,439387]
  [     0,192648)              65              17
  [192648,439387]               5              77
> postscript(file="/var/www/rcomp/tmp/4b8uq1324647978.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/59g8g1324647978.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/6a8y31324647978.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/7hbsv1324647978.tab") 
+ }
> 
> try(system("convert tmp/2rax11324647978.ps tmp/2rax11324647978.png",intern=TRUE))
character(0)
> try(system("convert tmp/3yo381324647978.ps tmp/3yo381324647978.png",intern=TRUE))
character(0)
> try(system("convert tmp/4b8uq1324647978.ps tmp/4b8uq1324647978.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.420   0.120   2.534