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

> x <- array(list(79
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+ ,dim=c(5
+ ,156)
+ ,dimnames=list(c('blogged_computations'
+ ,'compendiums_reviewed'
+ ,'feedback_messages_p120'
+ ,'totsize'
+ ,'totseconds')
+ ,1:156))
>  y <- array(NA,dim=c(5,156),dimnames=list(c('blogged_computations','compendiums_reviewed','feedback_messages_p120','totsize','totseconds'),1:156))
>  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 = '3'
> 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] "feedback_messages_p120"
> x[,par1]
  [1]  94 103  93 103  51  70  91  22  93  60 123 148  90 124  70 168 115  71
 [19]  66 134 117 108  84 156 120 114  94 120  81 110 133 122 158 109 124  92
 [37] 126  70  37 120  93  95  90  80  31 110  66 138 133 113 100   7 140  61
 [55]  41  96 164 102 124  99 129  62  73 114  99  70 104 116  91  74 138  67
 [73] 151  72 120 115 105 104 108  98  69 111  99  71  27  69 107 107  93 129
 [91]  69 118  73 119 104 107  99  90 197  85 139 106  50  64  31  63  92 106
[109]  69  93 114 110   0  83  30  98  82   0  60   9 115 140 120  66  21 124
[127] 152 139 144 120 160 114  78 119 141 101 133  83 116  90  36  97  98  78
[145] 117 148 105 132   0  73  86   0  13   4  48  46
> 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])

[  0,100) [100,197] 
       81        75 
> colnames(x)
[1] "blogged_computations"   "compendiums_reviewed"   "feedback_messages_p120"
[4] "totsize"                "totseconds"            
> colnames(x)[par1]
[1] "feedback_messages_p120"
> x[,par1]
  [1] [  0,100) [100,197] [  0,100) [100,197] [  0,100) [  0,100) [  0,100)
  [8] [  0,100) [  0,100) [  0,100) [100,197] [100,197] [  0,100) [100,197]
 [15] [  0,100) [100,197] [100,197] [  0,100) [  0,100) [100,197] [100,197]
 [22] [100,197] [  0,100) [100,197] [100,197] [100,197] [  0,100) [100,197]
 [29] [  0,100) [100,197] [100,197] [100,197] [100,197] [100,197] [100,197]
 [36] [  0,100) [100,197] [  0,100) [  0,100) [100,197] [  0,100) [  0,100)
 [43] [  0,100) [  0,100) [  0,100) [100,197] [  0,100) [100,197] [100,197]
 [50] [100,197] [100,197] [  0,100) [100,197] [  0,100) [  0,100) [  0,100)
 [57] [100,197] [100,197] [100,197] [  0,100) [100,197] [  0,100) [  0,100)
 [64] [100,197] [  0,100) [  0,100) [100,197] [100,197] [  0,100) [  0,100)
 [71] [100,197] [  0,100) [100,197] [  0,100) [100,197] [100,197] [100,197]
 [78] [100,197] [100,197] [  0,100) [  0,100) [100,197] [  0,100) [  0,100)
 [85] [  0,100) [  0,100) [100,197] [100,197] [  0,100) [100,197] [  0,100)
 [92] [100,197] [  0,100) [100,197] [100,197] [100,197] [  0,100) [  0,100)
 [99] [100,197] [  0,100) [100,197] [100,197] [  0,100) [  0,100) [  0,100)
[106] [  0,100) [  0,100) [100,197] [  0,100) [  0,100) [100,197] [100,197]
[113] [  0,100) [  0,100) [  0,100) [  0,100) [  0,100) [  0,100) [  0,100)
[120] [  0,100) [100,197] [100,197] [100,197] [  0,100) [  0,100) [100,197]
[127] [100,197] [100,197] [100,197] [100,197] [100,197] [100,197] [  0,100)
[134] [100,197] [100,197] [100,197] [100,197] [  0,100) [100,197] [  0,100)
[141] [  0,100) [  0,100) [  0,100) [  0,100) [100,197] [100,197] [100,197]
[148] [100,197] [  0,100) [  0,100) [  0,100) [  0,100) [  0,100) [  0,100)
[155] [  0,100) [  0,100)
Levels: [  0,100) [100,197]
> 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/1ly0v1324175246.tab") 
+ }
+ }
> m

	 Conditional inference tree with 4 terminal nodes

Response:  as.factor(feedback_messages_p120) 
Inputs:  blogged_computations, compendiums_reviewed, totsize, totseconds 
Number of observations:  156 

1) compendiums_reviewed <= 26; criterion = 1, statistic = 49.531
  2)*  weights = 40 
1) compendiums_reviewed > 26
  3) totsize <= 61370; criterion = 1, statistic = 16.135
    4)*  weights = 17 
  3) totsize > 61370
    5) compendiums_reviewed <= 31; criterion = 0.998, statistic = 12.352
      6)*  weights = 39 
    5) compendiums_reviewed > 31
      7)*  weights = 60 
> postscript(file="/var/www/rcomp/tmp/286ww1324175246.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/3p8io1324175246.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    1
  [2,]    2    1
  [3,]    1    2
  [4,]    2    1
  [5,]    1    1
  [6,]    1    1
  [7,]    1    1
  [8,]    1    1
  [9,]    1    1
 [10,]    1    1
 [11,]    2    2
 [12,]    2    2
 [13,]    1    1
 [14,]    2    2
 [15,]    1    1
 [16,]    2    2
 [17,]    2    1
 [18,]    1    1
 [19,]    1    1
 [20,]    2    2
 [21,]    2    2
 [22,]    2    1
 [23,]    1    1
 [24,]    2    2
 [25,]    2    2
 [26,]    2    1
 [27,]    1    1
 [28,]    2    2
 [29,]    1    1
 [30,]    2    2
 [31,]    2    2
 [32,]    2    2
 [33,]    2    2
 [34,]    2    1
 [35,]    2    2
 [36,]    1    1
 [37,]    2    2
 [38,]    1    1
 [39,]    1    1
 [40,]    2    2
 [41,]    1    1
 [42,]    1    1
 [43,]    1    1
 [44,]    1    1
 [45,]    1    2
 [46,]    2    2
 [47,]    1    1
 [48,]    2    2
 [49,]    2    2
 [50,]    2    2
 [51,]    2    1
 [52,]    1    1
 [53,]    2    2
 [54,]    1    1
 [55,]    1    1
 [56,]    1    1
 [57,]    2    2
 [58,]    2    1
 [59,]    2    2
 [60,]    1    1
 [61,]    2    2
 [62,]    1    1
 [63,]    1    1
 [64,]    2    2
 [65,]    1    1
 [66,]    1    1
 [67,]    2    1
 [68,]    2    2
 [69,]    1    1
 [70,]    1    1
 [71,]    2    2
 [72,]    1    1
 [73,]    2    2
 [74,]    1    1
 [75,]    2    2
 [76,]    2    2
 [77,]    2    1
 [78,]    2    2
 [79,]    2    1
 [80,]    1    1
 [81,]    1    1
 [82,]    2    2
 [83,]    1    1
 [84,]    1    2
 [85,]    1    1
 [86,]    1    1
 [87,]    2    1
 [88,]    2    1
 [89,]    1    2
 [90,]    2    2
 [91,]    1    1
 [92,]    2    2
 [93,]    1    1
 [94,]    2    1
 [95,]    2    1
 [96,]    2    1
 [97,]    1    1
 [98,]    1    1
 [99,]    2    2
[100,]    1    1
[101,]    2    2
[102,]    2    2
[103,]    1    1
[104,]    1    1
[105,]    1    1
[106,]    1    1
[107,]    1    1
[108,]    2    2
[109,]    1    1
[110,]    1    1
[111,]    2    1
[112,]    2    2
[113,]    1    1
[114,]    1    2
[115,]    1    1
[116,]    1    1
[117,]    1    1
[118,]    1    1
[119,]    1    1
[120,]    1    1
[121,]    2    2
[122,]    2    2
[123,]    2    2
[124,]    1    1
[125,]    1    1
[126,]    2    2
[127,]    2    2
[128,]    2    2
[129,]    2    2
[130,]    2    2
[131,]    2    2
[132,]    2    1
[133,]    1    1
[134,]    2    1
[135,]    2    2
[136,]    2    1
[137,]    2    2
[138,]    1    1
[139,]    2    2
[140,]    1    1
[141,]    1    1
[142,]    1    1
[143,]    1    1
[144,]    1    1
[145,]    2    2
[146,]    2    2
[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,]    1    1
[156,]    1    1
           
            [  0,100) [100,197]
  [  0,100)        76         5
  [100,197]        20        55
> postscript(file="/var/www/rcomp/tmp/49vs71324175246.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/5o1jl1324175246.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/6y8h91324175246.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/7wr8e1324175246.tab") 
+ }
> 
> try(system("convert tmp/286ww1324175246.ps tmp/286ww1324175246.png",intern=TRUE))
character(0)
> try(system("convert tmp/3p8io1324175246.ps tmp/3p8io1324175246.png",intern=TRUE))
character(0)
> try(system("convert tmp/49vs71324175246.ps tmp/49vs71324175246.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.120   0.120   2.223