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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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

> x <- array(list(127476
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+ ,dim=c(5
+ ,144)
+ ,dimnames=list(c('TimeRFC'
+ ,'Blogs'
+ ,'ReviewedComp'
+ ,'Longfeedback'
+ ,'Comptime')
+ ,1:144))
>  y <- array(NA,dim=c(5,144),dimnames=list(c('TimeRFC','Blogs','ReviewedComp','Longfeedback','Comptime'),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 = 'yes'
> 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] "TimeRFC"
> x[,par1]
  [1] 127476 130358   7215 112861 210171 393802 117604 126029  99729 256310
 [11] 113066 156212  69952 152673 125841 125769 123467  56232 108244  22762
 [21]  48554 178697 139115  93773 133398 113933 144781 140711 283337 158146
 [31] 123344 157640  91279 189374 167915      0 175403  92342 100023 178277
 [41] 145062 110980  86039 119514  95535 109894  61554 156520 159121 129362
 [51]  48188  91198 229864 180317 150640 104416 159645  63205 100056 137214
 [61]  99630  84557  91199  83419 101723  94982 129700 110708  81518  31970
 [71] 192268  87611  77890  83261 116290  55254 116173 111488  60138  73422
 [81]  67751 213351  51185  97181  42311 115801 183637  68161  76441 103613
 [91]  98707 126527 136781 105863  38775 179984 164808  19349 143902 108660
[101]  43803  47062 110845  92517  58660  27676  98550  43284      0  66016
[111]  57359  96933  70369  65494   3616      0 143931 109894 122973  84336
[121]  43410 136250  79015  92937  57586  19764 105757  96410 113402  11796
[131]   7627 121085   6836 139563   5118  40248      0  95079  80750   7131
[141]   4194  60378  96971  83484
> 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, 99729) [99729,393802] 
            72             72 
> colnames(x)
[1] "TimeRFC"      "Blogs"        "ReviewedComp" "Longfeedback" "Comptime"    
> colnames(x)[par1]
[1] "TimeRFC"
> x[,par1]
  [1] [99729,393802] [99729,393802] [    0, 99729) [99729,393802] [99729,393802]
  [6] [99729,393802] [99729,393802] [99729,393802] [99729,393802] [99729,393802]
 [11] [99729,393802] [99729,393802] [    0, 99729) [99729,393802] [99729,393802]
 [16] [99729,393802] [99729,393802] [    0, 99729) [99729,393802] [    0, 99729)
 [21] [    0, 99729) [99729,393802] [99729,393802] [    0, 99729) [99729,393802]
 [26] [99729,393802] [99729,393802] [99729,393802] [99729,393802] [99729,393802]
 [31] [99729,393802] [99729,393802] [    0, 99729) [99729,393802] [99729,393802]
 [36] [    0, 99729) [99729,393802] [    0, 99729) [99729,393802] [99729,393802]
 [41] [99729,393802] [99729,393802] [    0, 99729) [99729,393802] [    0, 99729)
 [46] [99729,393802] [    0, 99729) [99729,393802] [99729,393802] [99729,393802]
 [51] [    0, 99729) [    0, 99729) [99729,393802] [99729,393802] [99729,393802]
 [56] [99729,393802] [99729,393802] [    0, 99729) [99729,393802] [99729,393802]
 [61] [    0, 99729) [    0, 99729) [    0, 99729) [    0, 99729) [99729,393802]
 [66] [    0, 99729) [99729,393802] [99729,393802] [    0, 99729) [    0, 99729)
 [71] [99729,393802] [    0, 99729) [    0, 99729) [    0, 99729) [99729,393802]
 [76] [    0, 99729) [99729,393802] [99729,393802] [    0, 99729) [    0, 99729)
 [81] [    0, 99729) [99729,393802] [    0, 99729) [    0, 99729) [    0, 99729)
 [86] [99729,393802] [99729,393802] [    0, 99729) [    0, 99729) [99729,393802]
 [91] [    0, 99729) [99729,393802] [99729,393802] [99729,393802] [    0, 99729)
 [96] [99729,393802] [99729,393802] [    0, 99729) [99729,393802] [99729,393802]
[101] [    0, 99729) [    0, 99729) [99729,393802] [    0, 99729) [    0, 99729)
[106] [    0, 99729) [    0, 99729) [    0, 99729) [    0, 99729) [    0, 99729)
[111] [    0, 99729) [    0, 99729) [    0, 99729) [    0, 99729) [    0, 99729)
[116] [    0, 99729) [99729,393802] [99729,393802] [99729,393802] [    0, 99729)
[121] [    0, 99729) [99729,393802] [    0, 99729) [    0, 99729) [    0, 99729)
[126] [    0, 99729) [99729,393802] [    0, 99729) [99729,393802] [    0, 99729)
[131] [    0, 99729) [99729,393802] [    0, 99729) [99729,393802] [    0, 99729)
[136] [    0, 99729) [    0, 99729) [    0, 99729) [    0, 99729) [    0, 99729)
[141] [    0, 99729) [    0, 99729) [    0, 99729) [    0, 99729)
Levels: [    0, 99729) [99729,393802]
> 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/1gw901324655135.tab") 
+ }
+ }
           m.ct.i.pred
m.ct.i.actu   1   2
          1 472 170
          2  57 592
[1] 0.7352025
[1] 0.9121726
[1] 0.8241673
           m.ct.x.pred
m.ct.x.actu  1  2
          1 48 30
          2  6 65
[1] 0.6153846
[1] 0.915493
[1] 0.7583893
> m

	 Conditional inference tree with 4 terminal nodes

Response:  as.factor(TimeRFC) 
Inputs:  Blogs, ReviewedComp, Longfeedback, Comptime 
Number of observations:  144 

1) Comptime <= 32615; criterion = 1, statistic = 56.787
  2) Longfeedback <= 54; criterion = 0.992, statistic = 9.565
    3)*  weights = 43 
  2) Longfeedback > 54
    4)*  weights = 18 
1) Comptime > 32615
  5) Blogs <= 41; criterion = 0.983, statistic = 8.194
    6)*  weights = 36 
  5) Blogs > 41
    7)*  weights = 47 
> postscript(file="/var/wessaorg/rcomp/tmp/2ek9g1324655135.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/3ylu61324655135.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,]    2    2
  [3,]    1    1
  [4,]    2    2
  [5,]    2    2
  [6,]    2    2
  [7,]    2    2
  [8,]    2    2
  [9,]    2    2
 [10,]    2    2
 [11,]    2    2
 [12,]    2    2
 [13,]    1    1
 [14,]    2    2
 [15,]    2    2
 [16,]    2    2
 [17,]    2    2
 [18,]    1    1
 [19,]    2    2
 [20,]    1    1
 [21,]    1    1
 [22,]    2    2
 [23,]    2    1
 [24,]    1    1
 [25,]    2    2
 [26,]    2    2
 [27,]    2    2
 [28,]    2    2
 [29,]    2    2
 [30,]    2    2
 [31,]    2    2
 [32,]    2    2
 [33,]    1    1
 [34,]    2    1
 [35,]    2    2
 [36,]    1    1
 [37,]    2    2
 [38,]    1    1
 [39,]    2    2
 [40,]    2    2
 [41,]    2    2
 [42,]    2    2
 [43,]    1    2
 [44,]    2    2
 [45,]    1    2
 [46,]    2    2
 [47,]    1    1
 [48,]    2    2
 [49,]    2    2
 [50,]    2    2
 [51,]    1    1
 [52,]    1    1
 [53,]    2    2
 [54,]    2    2
 [55,]    2    2
 [56,]    2    2
 [57,]    2    2
 [58,]    1    1
 [59,]    2    2
 [60,]    2    1
 [61,]    1    2
 [62,]    1    2
 [63,]    1    2
 [64,]    1    2
 [65,]    2    2
 [66,]    1    2
 [67,]    2    2
 [68,]    2    2
 [69,]    1    2
 [70,]    1    1
 [71,]    2    2
 [72,]    1    2
 [73,]    1    2
 [74,]    1    1
 [75,]    2    2
 [76,]    1    1
 [77,]    2    2
 [78,]    2    2
 [79,]    1    1
 [80,]    1    1
 [81,]    1    1
 [82,]    2    2
 [83,]    1    1
 [84,]    1    2
 [85,]    1    1
 [86,]    2    2
 [87,]    2    2
 [88,]    1    1
 [89,]    1    1
 [90,]    2    2
 [91,]    1    2
 [92,]    2    2
 [93,]    2    2
 [94,]    2    1
 [95,]    1    1
 [96,]    2    2
 [97,]    2    2
 [98,]    1    1
 [99,]    2    2
[100,]    2    2
[101,]    1    1
[102,]    1    1
[103,]    2    1
[104,]    1    2
[105,]    1    1
[106,]    1    1
[107,]    1    2
[108,]    1    1
[109,]    1    1
[110,]    1    1
[111,]    1    1
[112,]    1    2
[113,]    1    2
[114,]    1    1
[115,]    1    1
[116,]    1    1
[117,]    2    2
[118,]    2    2
[119,]    2    2
[120,]    1    1
[121,]    1    1
[122,]    2    2
[123,]    1    1
[124,]    1    1
[125,]    1    1
[126,]    1    1
[127,]    2    2
[128,]    1    1
[129,]    2    1
[130,]    1    1
[131,]    1    1
[132,]    2    2
[133,]    1    1
[134,]    2    2
[135,]    1    1
[136,]    1    1
[137,]    1    1
[138,]    1    1
[139,]    1    1
[140,]    1    1
[141,]    1    1
[142,]    1    1
[143,]    1    2
[144,]    1    2
                
                 [    0, 99729) [99729,393802]
  [    0, 99729)             54             18
  [99729,393802]              7             65
> postscript(file="/var/wessaorg/rcomp/tmp/4xl8x1324655135.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/5d22e1324655136.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/6w0yc1324655136.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/7b8701324655136.tab") 
+ }
> 
> try(system("convert tmp/2ek9g1324655135.ps tmp/2ek9g1324655135.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ylu61324655135.ps tmp/3ylu61324655135.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xl8x1324655135.ps tmp/4xl8x1324655135.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.086   0.232   3.318