R version 2.12.1 (2010-12-16)
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(210907
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+ ,dim=c(5
+ ,197)
+ ,dimnames=list(c('time_in_rfc'
+ ,'logins'
+ ,'feedback_messages_p1'
+ ,'totsize'
+ ,'totblogs')
+ ,1:197))
>  y <- array(NA,dim=c(5,197),dimnames=list(c('time_in_rfc','logins','feedback_messages_p1','totsize','totblogs'),1:197))
>  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] "time_in_rfc"
> x[,par1]
  [1] 210907 120982 176508 179321 123185  52746 385534  33170 101645 149061
 [11] 165446 237213 173326 133131 258873 180083 324799 230964 236785 135473
 [21] 202925 215147 344297 153935 132943 174724 174415 225548 223632 124817
 [31] 221698 210767 170266 260561  84853 294424 101011 215641 325107   7176
 [41] 167542 106408  96560 265769 269651 149112 175824 152871 111665 116408
 [51] 362301  78800 183167 277965 150629 168809  24188 329267  65029 101097
 [61] 218946 244052 341570 103597 233328 256462 206161 311473 235800 177939
 [71] 207176 196553 174184 143246 187559 187681 119016 182192  73566 194979
 [81] 167488 143756 275541 243199 182999 135649 152299 120221 346485 145790
 [91] 193339  80953 122774 130585 112611 286468 241066 148446 204713 182079
[101] 140344 220516 243060 162765 182613 232138 265318  85574 310839 225060
[111] 232317 144966  43287 155754 164709 201940 235454 220801  99466  92661
[121] 133328  61361 125930 100750 224549  82316 102010 101523 243511  22938
[131]  41566 152474  61857  99923 132487 317394  21054 209641  22648  31414
[141]  46698 131698  91735 244749 184510  79863 128423  97839  38214 151101
[151] 272458 172494 108043 328107 250579 351067 158015  98866  85439 229242
[161] 351619  84207 120445 324598 131069 204271 165543 141722 116048 250047
[171] 299775 195838 173260 254488 104389 136084 199476  92499 224330 135781
[181]  74408  81240  14688 181633 271856   7199  46660  17547 133368  95227
[191] 152601  98146  79619  59194 139942 118612  72880
> 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])

[  7176,164709) [164709,385534] 
             99              98 
> colnames(x)
[1] "time_in_rfc"          "logins"               "feedback_messages_p1"
[4] "totsize"              "totblogs"            
> colnames(x)[par1]
[1] "time_in_rfc"
> x[,par1]
  [1] [164709,385534] [  7176,164709) [164709,385534] [164709,385534]
  [5] [  7176,164709) [  7176,164709) [164709,385534] [  7176,164709)
  [9] [  7176,164709) [  7176,164709) [164709,385534] [164709,385534]
 [13] [164709,385534] [  7176,164709) [164709,385534] [164709,385534]
 [17] [164709,385534] [164709,385534] [164709,385534] [  7176,164709)
 [21] [164709,385534] [164709,385534] [164709,385534] [  7176,164709)
 [25] [  7176,164709) [164709,385534] [164709,385534] [164709,385534]
 [29] [164709,385534] [  7176,164709) [164709,385534] [164709,385534]
 [33] [164709,385534] [164709,385534] [  7176,164709) [164709,385534]
 [37] [  7176,164709) [164709,385534] [164709,385534] [  7176,164709)
 [41] [164709,385534] [  7176,164709) [  7176,164709) [164709,385534]
 [45] [164709,385534] [  7176,164709) [164709,385534] [  7176,164709)
 [49] [  7176,164709) [  7176,164709) [164709,385534] [  7176,164709)
 [53] [164709,385534] [164709,385534] [  7176,164709) [164709,385534]
 [57] [  7176,164709) [164709,385534] [  7176,164709) [  7176,164709)
 [61] [164709,385534] [164709,385534] [164709,385534] [  7176,164709)
 [65] [164709,385534] [164709,385534] [164709,385534] [164709,385534]
 [69] [164709,385534] [164709,385534] [164709,385534] [164709,385534]
 [73] [164709,385534] [  7176,164709) [164709,385534] [164709,385534]
 [77] [  7176,164709) [164709,385534] [  7176,164709) [164709,385534]
 [81] [164709,385534] [  7176,164709) [164709,385534] [164709,385534]
 [85] [164709,385534] [  7176,164709) [  7176,164709) [  7176,164709)
 [89] [164709,385534] [  7176,164709) [164709,385534] [  7176,164709)
 [93] [  7176,164709) [  7176,164709) [  7176,164709) [164709,385534]
 [97] [164709,385534] [  7176,164709) [164709,385534] [164709,385534]
[101] [  7176,164709) [164709,385534] [164709,385534] [  7176,164709)
[105] [164709,385534] [164709,385534] [164709,385534] [  7176,164709)
[109] [164709,385534] [164709,385534] [164709,385534] [  7176,164709)
[113] [  7176,164709) [  7176,164709) [164709,385534] [164709,385534]
[117] [164709,385534] [164709,385534] [  7176,164709) [  7176,164709)
[121] [  7176,164709) [  7176,164709) [  7176,164709) [  7176,164709)
[125] [164709,385534] [  7176,164709) [  7176,164709) [  7176,164709)
[129] [164709,385534] [  7176,164709) [  7176,164709) [  7176,164709)
[133] [  7176,164709) [  7176,164709) [  7176,164709) [164709,385534]
[137] [  7176,164709) [164709,385534] [  7176,164709) [  7176,164709)
[141] [  7176,164709) [  7176,164709) [  7176,164709) [164709,385534]
[145] [164709,385534] [  7176,164709) [  7176,164709) [  7176,164709)
[149] [  7176,164709) [  7176,164709) [164709,385534] [164709,385534]
[153] [  7176,164709) [164709,385534] [164709,385534] [164709,385534]
[157] [  7176,164709) [  7176,164709) [  7176,164709) [164709,385534]
[161] [164709,385534] [  7176,164709) [  7176,164709) [164709,385534]
[165] [  7176,164709) [164709,385534] [164709,385534] [  7176,164709)
[169] [  7176,164709) [164709,385534] [164709,385534] [164709,385534]
[173] [164709,385534] [164709,385534] [  7176,164709) [  7176,164709)
[177] [164709,385534] [  7176,164709) [164709,385534] [  7176,164709)
[181] [  7176,164709) [  7176,164709) [  7176,164709) [164709,385534]
[185] [164709,385534] [  7176,164709) [  7176,164709) [  7176,164709)
[189] [  7176,164709) [  7176,164709) [  7176,164709) [  7176,164709)
[193] [  7176,164709) [  7176,164709) [  7176,164709) [  7176,164709)
[197] [  7176,164709)
Levels: [  7176,164709) [164709,385534]
> 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/1yfrp1323963151.tab") 
+ }
+ }
> m

	 Conditional inference tree with 4 terminal nodes

Response:  as.factor(time_in_rfc) 
Inputs:  logins, feedback_messages_p1, totsize, totblogs 
Number of observations:  197 

1) totblogs <= 79; criterion = 1, statistic = 95.105
  2) logins <= 67; criterion = 1, statistic = 23.789
    3)*  weights = 80 
  2) logins > 67
    4)*  weights = 18 
1) totblogs > 79
  5) logins <= 46; criterion = 0.983, statistic = 8.186
    6)*  weights = 18 
  5) logins > 46
    7)*  weights = 81 
> postscript(file="/var/www/rcomp/tmp/2r59u1323963151.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/3o34o1323963151.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,]    2    2
  [5,]    1    1
  [6,]    1    1
  [7,]    2    2
  [8,]    1    1
  [9,]    1    1
 [10,]    1    1
 [11,]    2    1
 [12,]    2    2
 [13,]    2    2
 [14,]    1    1
 [15,]    2    2
 [16,]    2    2
 [17,]    2    2
 [18,]    2    2
 [19,]    2    2
 [20,]    1    1
 [21,]    2    2
 [22,]    2    2
 [23,]    2    2
 [24,]    1    1
 [25,]    1    1
 [26,]    2    2
 [27,]    2    2
 [28,]    2    2
 [29,]    2    2
 [30,]    1    1
 [31,]    2    1
 [32,]    2    2
 [33,]    2    2
 [34,]    2    2
 [35,]    1    1
 [36,]    2    2
 [37,]    1    1
 [38,]    2    1
 [39,]    2    2
 [40,]    1    1
 [41,]    2    2
 [42,]    1    1
 [43,]    1    2
 [44,]    2    2
 [45,]    2    2
 [46,]    1    1
 [47,]    2    2
 [48,]    1    1
 [49,]    1    1
 [50,]    1    1
 [51,]    2    2
 [52,]    1    1
 [53,]    2    2
 [54,]    2    2
 [55,]    1    1
 [56,]    2    2
 [57,]    1    1
 [58,]    2    2
 [59,]    1    1
 [60,]    1    1
 [61,]    2    1
 [62,]    2    2
 [63,]    2    2
 [64,]    1    1
 [65,]    2    2
 [66,]    2    2
 [67,]    2    2
 [68,]    2    2
 [69,]    2    2
 [70,]    2    2
 [71,]    2    2
 [72,]    2    2
 [73,]    2    2
 [74,]    1    2
 [75,]    2    2
 [76,]    2    2
 [77,]    1    1
 [78,]    2    2
 [79,]    1    1
 [80,]    2    2
 [81,]    2    1
 [82,]    1    1
 [83,]    2    2
 [84,]    2    2
 [85,]    2    2
 [86,]    1    1
 [87,]    1    1
 [88,]    1    1
 [89,]    2    2
 [90,]    1    1
 [91,]    2    2
 [92,]    1    1
 [93,]    1    1
 [94,]    1    1
 [95,]    1    1
 [96,]    2    2
 [97,]    2    2
 [98,]    1    2
 [99,]    2    2
[100,]    2    2
[101,]    1    1
[102,]    2    2
[103,]    2    2
[104,]    1    1
[105,]    2    1
[106,]    2    2
[107,]    2    2
[108,]    1    1
[109,]    2    2
[110,]    2    2
[111,]    2    2
[112,]    1    2
[113,]    1    1
[114,]    1    1
[115,]    2    2
[116,]    2    1
[117,]    2    2
[118,]    2    2
[119,]    1    1
[120,]    1    1
[121,]    1    1
[122,]    1    2
[123,]    1    2
[124,]    1    2
[125,]    2    2
[126,]    1    1
[127,]    1    1
[128,]    1    1
[129,]    2    2
[130,]    1    1
[131,]    1    1
[132,]    1    2
[133,]    1    1
[134,]    1    1
[135,]    1    1
[136,]    2    2
[137,]    1    1
[138,]    2    1
[139,]    1    1
[140,]    1    1
[141,]    1    1
[142,]    1    1
[143,]    1    1
[144,]    2    2
[145,]    2    2
[146,]    1    1
[147,]    1    1
[148,]    1    1
[149,]    1    1
[150,]    1    1
[151,]    2    2
[152,]    2    2
[153,]    1    1
[154,]    2    2
[155,]    2    2
[156,]    2    2
[157,]    1    1
[158,]    1    1
[159,]    1    1
[160,]    2    2
[161,]    2    2
[162,]    1    1
[163,]    1    2
[164,]    2    2
[165,]    1    1
[166,]    2    1
[167,]    2    2
[168,]    1    2
[169,]    1    1
[170,]    2    2
[171,]    2    2
[172,]    2    2
[173,]    2    1
[174,]    2    2
[175,]    1    1
[176,]    1    1
[177,]    2    2
[178,]    1    1
[179,]    2    2
[180,]    1    1
[181,]    1    1
[182,]    1    1
[183,]    1    1
[184,]    2    2
[185,]    2    2
[186,]    1    1
[187,]    1    1
[188,]    1    1
[189,]    1    1
[190,]    1    1
[191,]    1    1
[192,]    1    1
[193,]    1    1
[194,]    1    1
[195,]    1    1
[196,]    1    1
[197,]    1    1
                 
                  [  7176,164709) [164709,385534]
  [  7176,164709)              88              11
  [164709,385534]              10              88
> postscript(file="/var/www/rcomp/tmp/45jrm1323963151.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/5iiee1323963151.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/6w9gd1323963151.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/7o80r1323963151.tab") 
+ }
> 
> try(system("convert tmp/2r59u1323963151.ps tmp/2r59u1323963151.png",intern=TRUE))
character(0)
> try(system("convert tmp/3o34o1323963151.ps tmp/3o34o1323963151.png",intern=TRUE))
character(0)
> try(system("convert tmp/45jrm1323963151.ps tmp/45jrm1323963151.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.036   0.248   3.313