R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-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(41
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+ ,dim=c(6
+ ,162)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'age'
+ ,'beloning'
+ ,'totblogs'
+ ,'Login')
+ ,1:162))
>  y <- array(NA,dim=c(6,162),dimnames=list(c('Connected','Separate','age','beloning','totblogs','Login'),1:162))
>  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 = '6'
> 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

Attaching package: 'zoo'

The following object(s) are masked from 'package:base':

    as.Date, as.Date.numeric

Loading required package: sandwich
Loading required package: strucchange
Loading required package: vcd
Loading required package: MASS
Loading required package: colorspace
> library(Hmisc)
Hmisc library by Frank E Harrell Jr

Type library(help='Hmisc'), ?Overview, or ?Hmisc.Overview')
to see overall documentation.

NOTE:Hmisc no longer redefines [.factor to drop unused levels when
subsetting.  To get the old behavior of Hmisc type dropUnusedLevels().


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] "Login"
> x[,par1]
  [1]  56  56  54  89  40  25  92  18  63  44  33  84  88  55  60  66 154  53
 [19] 119  41  61  58  75  33  40  92 100 112  73  40  45  60  62  75  31  77
 [37]  34  46  99  17  66  30  76 146  67  56 107  58  34  61 119  42  66  89
 [55]  44  66  24 259  17  64  41  68 168  43 132 105  71 112  94  82  70  57
 [73]  53 103 121  62  52  52  32  62  45  46  63  75  88  46  53  37  90  63
 [91]  78  25  45  46  41 144  82  91  71  63  53  62  63  32  39  62 117  34
[109]  92  93  54 144  14  61 109  38  73  75  50  61  55  77  75  72  50  32
[127]  53  42  71  10  35  65  25  66  41  86  16  42  19  19  45  65  35  95
[145]  49  37  64  38  34  32  65  52  62  65  83  95  29  18  33 247 139  29
> 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])

[10, 62) [62,259] 
      83       79 
> colnames(x)
[1] "Connected" "Separate"  "age"       "beloning"  "totblogs"  "Login"    
> colnames(x)[par1]
[1] "Login"
> x[,par1]
  [1] [10, 62) [10, 62) [10, 62) [62,259] [10, 62) [10, 62) [62,259] [10, 62)
  [9] [62,259] [10, 62) [10, 62) [62,259] [62,259] [10, 62) [10, 62) [62,259]
 [17] [62,259] [10, 62) [62,259] [10, 62) [10, 62) [10, 62) [62,259] [10, 62)
 [25] [10, 62) [62,259] [62,259] [62,259] [62,259] [10, 62) [10, 62) [10, 62)
 [33] [62,259] [62,259] [10, 62) [62,259] [10, 62) [10, 62) [62,259] [10, 62)
 [41] [62,259] [10, 62) [62,259] [62,259] [62,259] [10, 62) [62,259] [10, 62)
 [49] [10, 62) [10, 62) [62,259] [10, 62) [62,259] [62,259] [10, 62) [62,259]
 [57] [10, 62) [62,259] [10, 62) [62,259] [10, 62) [62,259] [62,259] [10, 62)
 [65] [62,259] [62,259] [62,259] [62,259] [62,259] [62,259] [62,259] [10, 62)
 [73] [10, 62) [62,259] [62,259] [62,259] [10, 62) [10, 62) [10, 62) [62,259]
 [81] [10, 62) [10, 62) [62,259] [62,259] [62,259] [10, 62) [10, 62) [10, 62)
 [89] [62,259] [62,259] [62,259] [10, 62) [10, 62) [10, 62) [10, 62) [62,259]
 [97] [62,259] [62,259] [62,259] [62,259] [10, 62) [62,259] [62,259] [10, 62)
[105] [10, 62) [62,259] [62,259] [10, 62) [62,259] [62,259] [10, 62) [62,259]
[113] [10, 62) [10, 62) [62,259] [10, 62) [62,259] [62,259] [10, 62) [10, 62)
[121] [10, 62) [62,259] [62,259] [62,259] [10, 62) [10, 62) [10, 62) [10, 62)
[129] [62,259] [10, 62) [10, 62) [62,259] [10, 62) [62,259] [10, 62) [62,259]
[137] [10, 62) [10, 62) [10, 62) [10, 62) [10, 62) [62,259] [10, 62) [62,259]
[145] [10, 62) [10, 62) [62,259] [10, 62) [10, 62) [10, 62) [62,259] [10, 62)
[153] [62,259] [62,259] [62,259] [62,259] [10, 62) [10, 62) [10, 62) [62,259]
[161] [62,259] [10, 62)
Levels: [10, 62) [62,259]
> 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/1i2la1354798989.tab") 
+ }
+ }
> m

	 Conditional inference tree with 4 terminal nodes

Response:  as.factor(Login) 
Inputs:  Connected, Separate, age, beloning, totblogs 
Number of observations:  162 

1) totblogs <= 116; criterion = 1, statistic = 30.499
  2) totblogs <= 27; criterion = 0.967, statistic = 7.358
    3) age <= 5; criterion = 0.989, statistic = 9.335
      4)*  weights = 12 
    3) age > 5
      5)*  weights = 9 
  2) totblogs > 27
    6)*  weights = 93 
1) totblogs > 116
  7)*  weights = 48 
> postscript(file="/var/wessaorg/rcomp/tmp/2k8p81354798989.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/3ujeu1354798989.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,]    1    2
  [2,]    1    1
  [3,]    1    1
  [4,]    2    2
  [5,]    1    1
  [6,]    1    1
  [7,]    2    2
  [8,]    1    1
  [9,]    2    1
 [10,]    1    1
 [11,]    1    1
 [12,]    2    2
 [13,]    2    1
 [14,]    1    1
 [15,]    1    2
 [16,]    2    1
 [17,]    2    2
 [18,]    1    2
 [19,]    2    1
 [20,]    1    2
 [21,]    1    2
 [22,]    1    2
 [23,]    2    2
 [24,]    1    1
 [25,]    1    1
 [26,]    2    2
 [27,]    2    1
 [28,]    2    1
 [29,]    2    1
 [30,]    1    1
 [31,]    1    1
 [32,]    1    1
 [33,]    2    2
 [34,]    2    2
 [35,]    1    1
 [36,]    2    2
 [37,]    1    1
 [38,]    1    1
 [39,]    2    1
 [40,]    1    1
 [41,]    2    1
 [42,]    1    1
 [43,]    2    1
 [44,]    2    2
 [45,]    2    1
 [46,]    1    1
 [47,]    2    1
 [48,]    1    1
 [49,]    1    1
 [50,]    1    1
 [51,]    2    1
 [52,]    1    1
 [53,]    2    2
 [54,]    2    2
 [55,]    1    1
 [56,]    2    1
 [57,]    1    1
 [58,]    2    2
 [59,]    1    1
 [60,]    2    1
 [61,]    1    1
 [62,]    2    2
 [63,]    2    2
 [64,]    1    1
 [65,]    2    2
 [66,]    2    2
 [67,]    2    1
 [68,]    2    2
 [69,]    2    2
 [70,]    2    1
 [71,]    2    1
 [72,]    1    1
 [73,]    1    1
 [74,]    2    1
 [75,]    2    1
 [76,]    2    2
 [77,]    1    1
 [78,]    1    1
 [79,]    1    1
 [80,]    2    1
 [81,]    1    1
 [82,]    1    2
 [83,]    2    1
 [84,]    2    2
 [85,]    2    1
 [86,]    1    1
 [87,]    1    1
 [88,]    1    1
 [89,]    2    2
 [90,]    2    1
 [91,]    2    2
 [92,]    1    1
 [93,]    1    1
 [94,]    1    1
 [95,]    1    1
 [96,]    2    1
 [97,]    2    1
 [98,]    2    2
 [99,]    2    1
[100,]    2    2
[101,]    1    1
[102,]    2    1
[103,]    2    2
[104,]    1    1
[105,]    1    2
[106,]    2    2
[107,]    2    2
[108,]    1    1
[109,]    2    2
[110,]    2    1
[111,]    1    2
[112,]    2    1
[113,]    1    1
[114,]    1    1
[115,]    2    2
[116,]    1    1
[117,]    2    2
[118,]    2    1
[119,]    1    1
[120,]    1    1
[121,]    1    1
[122,]    2    1
[123,]    2    1
[124,]    2    1
[125,]    1    1
[126,]    1    1
[127,]    1    1
[128,]    1    1
[129,]    2    2
[130,]    1    1
[131,]    1    1
[132,]    2    1
[133,]    1    1
[134,]    2    1
[135,]    1    1
[136,]    2    2
[137,]    1    1
[138,]    1    1
[139,]    1    1
[140,]    1    1
[141,]    1    1
[142,]    2    1
[143,]    1    1
[144,]    2    1
[145,]    1    1
[146,]    1    1
[147,]    2    1
[148,]    1    1
[149,]    1    1
[150,]    1    1
[151,]    2    2
[152,]    1    2
[153,]    2    1
[154,]    2    2
[155,]    2    2
[156,]    2    2
[157,]    1    1
[158,]    1    1
[159,]    1    1
[160,]    2    1
[161,]    2    2
[162,]    1    1
          
           [10, 62) [62,259]
  [10, 62)       73       10
  [62,259]       41       38
> postscript(file="/var/wessaorg/rcomp/tmp/4l24y1354798989.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/5b8s01354798989.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/6ij2h1354798989.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/71ylg1354798989.tab") 
+ }
> 
> try(system("convert tmp/2k8p81354798989.ps tmp/2k8p81354798989.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ujeu1354798989.ps tmp/3ujeu1354798989.png",intern=TRUE))
character(0)
> try(system("convert tmp/4l24y1354798989.ps tmp/4l24y1354798989.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  4.492   0.511   4.985