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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+ ,22)
+ ,dim=c(13
+ ,144)
+ ,dimnames=list(c('pageviews'
+ ,'timeRFC'
+ ,'logins'
+ ,'compviews'
+ ,'prviews'
+ ,'bloggedcomp'
+ ,'reviewedcomp'
+ ,'submittedfbpr'
+ ,'characters'
+ ,'revisions'
+ ,'seconds'
+ ,'inclhyperlinks'
+ ,'inclblogs')
+ ,1:144))
>  y <- array(NA,dim=c(13,144),dimnames=list(c('pageviews','timeRFC','logins','compviews','prviews','bloggedcomp','reviewedcomp','submittedfbpr','characters','revisions','seconds','inclhyperlinks','inclblogs'),1:144))
>  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 = '3'
> par2 = 'none'
> par1 = '5'
> 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] "prviews"
> x[,par1]
  [1] 109  68   1 146 124 267  83  48  87 129 146  95  57 240  46  81  85  62
 [19] 126  44  37  94 127 159  41 153  86  55  78  84  71 111  71 243  66   0
 [37]  58 131 258  56  90  57  35  53  46  37  45 111 104 150  37  49  83  67
 [55]  39  69  58  68  30  54  65  81  84  45  52  36  80 144  45  40 126  75
 [73]  54  82  86  62  99  63  76  92  45  57  44 132  44  67  82  71  44  68
 [91]  54  86  59  74  18 156  87  15 104  54  11  37  80  66  27  59 113  24
[109]   0  55  43  45  55  66   5   0  67  67 117  51  63  84  35  57  29  19
[127]  51  63  96  22   7  34   5  43   1  34   0  49  44   0   4  40  52  47
> 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   1   4   5   7  11  15  18  19  22  24  27  29  30  34  35  36  37  39  40 
  5   2   1   2   1   1   1   1   1   1   1   1   1   1   2   2   1   4   1   2 
 41  43  44  45  46  47  48  49  51  52  53  54  55  56  57  58  59  62  63  65 
  1   2   5   5   2   1   1   2   2   2   1   4   3   1   4   2   2   2   3   1 
 66  67  68  69  71  74  75  76  78  80  81  82  83  84  85  86  87  90  92  94 
  3   4   3   1   3   1   1   1   1   2   2   2   2   3   1   3   2   1   1   1 
 95  96  99 104 109 111 113 117 124 126 127 129 131 132 144 146 150 153 156 159 
  1   1   1   2   1   2   1   1   1   2   1   1   1   1   1   2   1   1   1   1 
240 243 258 267 
  1   1   1   1 
> colnames(x)
 [1] "pageviews"      "timeRFC"        "logins"         "compviews"     
 [5] "prviews"        "bloggedcomp"    "reviewedcomp"   "submittedfbpr" 
 [9] "characters"     "revisions"      "seconds"        "inclhyperlinks"
[13] "inclblogs"     
> colnames(x)[par1]
[1] "prviews"
> x[,par1]
  [1] 109  68   1 146 124 267  83  48  87 129 146  95  57 240  46  81  85  62
 [19] 126  44  37  94 127 159  41 153  86  55  78  84  71 111  71 243  66   0
 [37]  58 131 258  56  90  57  35  53  46  37  45 111 104 150  37  49  83  67
 [55]  39  69  58  68  30  54  65  81  84  45  52  36  80 144  45  40 126  75
 [73]  54  82  86  62  99  63  76  92  45  57  44 132  44  67  82  71  44  68
 [91]  54  86  59  74  18 156  87  15 104  54  11  37  80  66  27  59 113  24
[109]   0  55  43  45  55  66   5   0  67  67 117  51  63  84  35  57  29  19
[127]  51  63  96  22   7  34   5  43   1  34   0  49  44   0   4  40  52  47
> 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/1lux61323895300.tab") 
+ }
+ }
> m

	 Conditional inference tree with 5 terminal nodes

Response:  prviews 
Inputs:  pageviews, timeRFC, logins, compviews, bloggedcomp, reviewedcomp, submittedfbpr, characters, revisions, seconds, inclhyperlinks, inclblogs 
Number of observations:  144 

1) pageviews <= 1782; criterion = 1, statistic = 82.844
  2) pageviews <= 708; criterion = 1, statistic = 60.546
    3) submittedfbpr <= 4; criterion = 1, statistic = 18
      4)*  weights = 12 
    3) submittedfbpr > 4
      5)*  weights = 14 
  2) pageviews > 708
    6) pageviews <= 1234; criterion = 0.998, statistic = 14.322
      7)*  weights = 50 
    6) pageviews > 1234
      8)*  weights = 40 
1) pageviews > 1782
  9)*  weights = 28 
> postscript(file="/var/www/rcomp/tmp/2wdiw1323895300.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/3sels1323895300.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)
+ }
    Actuals  Forecasts   Residuals
1       109  77.775000  31.2250000
2        68  57.440000  10.5600000
3         1   3.166667  -2.1666667
4       146 126.892857  19.1071429
5       124 126.892857  -2.8928571
6       267 126.892857 140.1071429
7        83  77.775000   5.2250000
8        48  57.440000  -9.4400000
9        87  77.775000   9.2250000
10      129 126.892857   2.1071429
11      146 126.892857  19.1071429
12       95 126.892857 -31.8928571
13       57  77.775000 -20.7750000
14      240 126.892857 113.1071429
15       46  57.440000 -11.4400000
16       81  77.775000   3.2250000
17       85  77.775000   7.2250000
18       62  57.440000   4.5600000
19      126 126.892857  -0.8928571
20       44  31.857143  12.1428571
21       37  57.440000 -20.4400000
22       94 126.892857 -32.8928571
23      127  77.775000  49.2250000
24      159 126.892857  32.1071429
25       41  77.775000 -36.7750000
26      153  57.440000  95.5600000
27       86  77.775000   8.2250000
28       55  57.440000  -2.4400000
29       78 126.892857 -48.8928571
30       84 126.892857 -42.8928571
31       71  77.775000  -6.7750000
32      111  77.775000  33.2250000
33       71 126.892857 -55.8928571
34      243 126.892857 116.1071429
35       66 126.892857 -60.8928571
36        0   3.166667  -3.1666667
37       58  77.775000 -19.7750000
38      131  77.775000  53.2250000
39      258 126.892857 131.1071429
40       56  77.775000 -21.7750000
41       90  77.775000  12.2250000
42       57  77.775000 -20.7750000
43       35  57.440000 -22.4400000
44       53  77.775000 -24.7750000
45       46  57.440000 -11.4400000
46       37  57.440000 -20.4400000
47       45  31.857143  13.1428571
48      111  77.775000  33.2250000
49      104  77.775000  26.2250000
50      150 126.892857  23.1071429
51       37  31.857143   5.1428571
52       49  57.440000  -8.4400000
53       83 126.892857 -43.8928571
54       67  77.775000 -10.7750000
55       39  77.775000 -38.7750000
56       69  77.775000  -8.7750000
57       58  77.775000 -19.7750000
58       68  57.440000  10.5600000
59       30  57.440000 -27.4400000
60       54 126.892857 -72.8928571
61       65  77.775000 -12.7750000
62       81  77.775000   3.2250000
63       84  57.440000  26.5600000
64       45  57.440000 -12.4400000
65       52  57.440000  -5.4400000
66       36  57.440000 -21.4400000
67       80 126.892857 -46.8928571
68      144 126.892857  17.1071429
69       45  57.440000 -12.4400000
70       40  31.857143   8.1428571
71      126 126.892857  -0.8928571
72       75  57.440000  17.5600000
73       54  57.440000  -3.4400000
74       82  77.775000   4.2250000
75       86 126.892857 -40.8928571
76       62  57.440000   4.5600000
77       99  77.775000  21.2250000
78       63 126.892857 -63.8928571
79       76  57.440000  18.5600000
80       92  57.440000  34.5600000
81       45  57.440000 -12.4400000
82       57  77.775000 -20.7750000
83       44  57.440000 -13.4400000
84      132  77.775000  54.2250000
85       44  57.440000 -13.4400000
86       67  77.775000 -10.7750000
87       82 126.892857 -44.8928571
88       71  57.440000  13.5600000
89       44  57.440000 -13.4400000
90       68  57.440000  10.5600000
91       54  77.775000 -23.7750000
92       86 126.892857 -40.8928571
93       59  57.440000   1.5600000
94       74  57.440000  16.5600000
95       18  31.857143 -13.8571429
96      156 126.892857  29.1071429
97       87  77.775000   9.2250000
98       15   3.166667  11.8333333
99      104  77.775000  26.2250000
100      54  77.775000 -23.7750000
101      11  31.857143 -20.8571429
102      37  31.857143   5.1428571
103      80  57.440000  22.5600000
104      66  57.440000   8.5600000
105      27  31.857143  -4.8571429
106      59  31.857143  27.1428571
107     113  57.440000  55.5600000
108      24  31.857143  -7.8571429
109       0   3.166667  -3.1666667
110      55  57.440000  -2.4400000
111      43  57.440000 -14.4400000
112      45  57.440000 -12.4400000
113      55  57.440000  -2.4400000
114      66  57.440000   8.5600000
115       5   3.166667   1.8333333
116       0   3.166667  -3.1666667
117      67  57.440000   9.5600000
118      67  77.775000 -10.7750000
119     117 126.892857  -9.8928571
120      51  57.440000  -6.4400000
121      63  57.440000   5.5600000
122      84  77.775000   6.2250000
123      35  57.440000 -22.4400000
124      57  77.775000 -20.7750000
125      29  31.857143  -2.8571429
126      19  31.857143 -12.8571429
127      51  77.775000 -26.7750000
128      63  57.440000   5.5600000
129      96  77.775000  18.2250000
130      22  31.857143  -9.8571429
131       7   3.166667   3.8333333
132      34  57.440000 -23.4400000
133       5   3.166667   1.8333333
134      43  77.775000 -34.7750000
135       1   3.166667  -2.1666667
136      34  31.857143   2.1428571
137       0   3.166667  -3.1666667
138      49  57.440000  -8.4400000
139      44  57.440000 -13.4400000
140       0   3.166667  -3.1666667
141       4   3.166667   0.8333333
142      40  57.440000 -17.4400000
143      52  57.440000  -5.4400000
144      47  57.440000 -10.4400000
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
> postscript(file="/var/www/rcomp/tmp/4l8z31323895300.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/545rn1323895300.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/687g81323895300.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/7r25w1323895300.tab") 
+ }
> 
> try(system("convert tmp/2wdiw1323895300.ps tmp/2wdiw1323895300.png",intern=TRUE))
character(0)
> try(system("convert tmp/3sels1323895300.ps tmp/3sels1323895300.png",intern=TRUE))
character(0)
> try(system("convert tmp/4l8z31323895300.ps tmp/4l8z31323895300.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  4.608   0.256   4.890