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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> x <- array(list(1536
+ ,78
+ ,20
+ ,17
+ ,66
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+ ,1134
+ ,46
+ ,38
+ ,17
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+ ,42
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+ ,22
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+ ,54
+ ,3230
+ ,124
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+ ,30
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+ ,104
+ ,29
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+ ,157
+ ,1321
+ ,49
+ ,37
+ ,19
+ ,72
+ ,36
+ ,1077
+ ,46
+ ,49
+ ,25
+ ,96
+ ,48
+ ,1462
+ ,37
+ ,42
+ ,30
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+ ,45
+ ,2568
+ ,86
+ ,62
+ ,26
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+ ,34
+ ,1526
+ ,54
+ ,44
+ ,26
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+ ,47
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+ ,16
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+ ,21
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+ ,859
+ ,55
+ ,42
+ ,21
+ ,83
+ ,31
+ ,2315
+ ,71
+ ,28
+ ,34
+ ,124
+ ,40
+ ,1326
+ ,47
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+ ,29
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+ ,1567
+ ,62
+ ,32
+ ,18
+ ,72
+ ,34
+ ,1080
+ ,44
+ ,28
+ ,16
+ ,55
+ ,40
+ ,896
+ ,28
+ ,31
+ ,23
+ ,86
+ ,35
+ ,855
+ ,25
+ ,13
+ ,22
+ ,85
+ ,11
+ ,1229
+ ,37
+ ,38
+ ,29
+ ,107
+ ,43
+ ,1939
+ ,60
+ ,39
+ ,31
+ ,124
+ ,53
+ ,2293
+ ,57
+ ,68
+ ,21
+ ,78
+ ,82
+ ,818
+ ,30
+ ,32
+ ,21
+ ,83
+ ,41)
+ ,dim=c(6
+ ,69)
+ ,dimnames=list(c('Pageviews'
+ ,'#Logins'
+ ,'Blogged_Computations'
+ ,'Reviewed_Compendiums'
+ ,'Feedback_in_PR'
+ ,'Included_Hyperlinks')
+ ,1:69))
>  y <- array(NA,dim=c(6,69),dimnames=list(c('Pageviews','#Logins','Blogged_Computations','Reviewed_Compendiums','Feedback_in_PR','Included_Hyperlinks'),1:69))
>  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 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Dr. Ian E. Holliday
> #To cite this work: Ian E. Holliday, 2009, YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: 
> #Technical description: 
> 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] "Pageviews"
> x[,par1]
 [1] 1536 1134  192 2032 3230 5723 1321 1077 1462 2568 1810 1788 1334 2415 1155
[16] 1374 1503  999 2189  633  837 2167 1451 1790 1645 1179 1688 1100 2258 1767
[31] 1300 1432 1780 2475 1930    1 1782 1505 1820 1648 1668 1366  864 1602 1023
[46]  962  629 1568 1715 2093  658 1198 2059 1574 1447 1342 1526  669  859 2315
[61] 1326 1567 1080  896  855 1229 1939 2293  818
> 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])

   1  192  629  633  658  669  818  837  855  859  864  896  962  999 1023 1077 
   1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1 
1080 1100 1134 1155 1179 1198 1229 1300 1321 1326 1334 1342 1366 1374 1432 1447 
   1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1 
1451 1462 1503 1505 1526 1536 1567 1568 1574 1602 1645 1648 1668 1688 1715 1767 
   1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1 
1780 1782 1788 1790 1810 1820 1930 1939 2032 2059 2093 2167 2189 2258 2293 2315 
   1    1    1    1    1    1    1    1    1    1    1    1    1    1    1    1 
2415 2475 2568 3230 5723 
   1    1    1    1    1 
> colnames(x)
[1] "Pageviews"            "X.Logins"             "Blogged_Computations"
[4] "Reviewed_Compendiums" "Feedback_in_PR"       "Included_Hyperlinks" 
> colnames(x)[par1]
[1] "Pageviews"
> x[,par1]
 [1] 1536 1134  192 2032 3230 5723 1321 1077 1462 2568 1810 1788 1334 2415 1155
[16] 1374 1503  999 2189  633  837 2167 1451 1790 1645 1179 1688 1100 2258 1767
[31] 1300 1432 1780 2475 1930    1 1782 1505 1820 1648 1668 1366  864 1602 1023
[46]  962  629 1568 1715 2093  658 1198 2059 1574 1447 1342 1526  669  859 2315
[61] 1326 1567 1080  896  855 1229 1939 2293  818
> 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/1cw5o1323868151.tab") 
+ }
+ }
> m

	 Conditional inference tree with 5 terminal nodes

Response:  Pageviews 
Inputs:  X.Logins, Blogged_Computations, Reviewed_Compendiums, Feedback_in_PR, Included_Hyperlinks 
Number of observations:  69 

1) X.Logins <= 85; criterion = 1, statistic = 47.741
  2) X.Logins <= 30; criterion = 1, statistic = 29.391
    3)*  weights = 11 
  2) X.Logins > 30
    4) Blogged_Computations <= 44; criterion = 0.985, statistic = 8.823
      5) Reviewed_Compendiums <= 25; criterion = 0.98, statistic = 8.308
        6)*  weights = 20 
      5) Reviewed_Compendiums > 25
        7)*  weights = 11 
    4) Blogged_Computations > 44
      8)*  weights = 20 
1) X.Logins > 85
  9)*  weights = 7 
> postscript(file="/var/www/rcomp/tmp/26dyv1323868151.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/3qx2t1323868151.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     1536 1295.1000   240.900000
2     1134 1295.1000  -161.100000
3      192  672.1818  -480.181818
4     2032 1754.5000   277.500000
5     3230 2817.4286   412.571429
6     5723 2817.4286  2905.571429
7     1321 1295.1000    25.900000
8     1077 1754.5000  -677.500000
9     1462 1642.0000  -180.000000
10    2568 2817.4286  -249.428571
11    1810 1754.5000    55.500000
12    1788 1754.5000    33.500000
13    1334 1295.1000    38.900000
14    2415 1754.5000   660.500000
15    1155 1295.1000  -140.100000
16    1374 1754.5000  -380.500000
17    1503 1754.5000  -251.500000
18     999 1295.1000  -296.100000
19    2189 1754.5000   434.500000
20     633  672.1818   -39.181818
21     837 1295.1000  -458.100000
22    2167 1754.5000   412.500000
23    1451 1295.1000   155.900000
24    1790 1642.0000   148.000000
25    1645 1754.5000  -109.500000
26    1179  672.1818   506.818182
27    1688 2817.4286 -1129.428571
28    1100 1754.5000  -654.500000
29    2258 2817.4286  -559.428571
30    1767 1295.1000   471.900000
31    1300 1295.1000     4.900000
32    1432 1754.5000  -322.500000
33    1780 2817.4286 -1037.428571
34    2475 2817.4286  -342.428571
35    1930 1295.1000   634.900000
36       1  672.1818  -671.181818
37    1782 1754.5000    27.500000
38    1505 1295.1000   209.900000
39    1820 1642.0000   178.000000
40    1648 1754.5000  -106.500000
41    1668 1754.5000   -86.500000
42    1366 1642.0000  -276.000000
43     864  672.1818   191.818182
44    1602 1295.1000   306.900000
45    1023 1295.1000  -272.100000
46     962 1295.1000  -333.100000
47     629  672.1818   -43.181818
48    1568 1754.5000  -186.500000
49    1715 1642.0000    73.000000
50    2093 1754.5000   338.500000
51     658  672.1818   -14.181818
52    1198 1295.1000   -97.100000
53    2059 1754.5000   304.500000
54    1574 1642.0000   -68.000000
55    1447 1754.5000  -307.500000
56    1342 1295.1000    46.900000
57    1526 1642.0000  -116.000000
58     669  672.1818    -3.181818
59     859 1295.1000  -436.100000
60    2315 1642.0000   673.000000
61    1326 1642.0000  -316.000000
62    1567 1295.1000   271.900000
63    1080 1295.1000  -215.100000
64     896  672.1818   223.818182
65     855  672.1818   182.818182
66    1229 1642.0000  -413.000000
67    1939 1642.0000   297.000000
68    2293 1754.5000   538.500000
69     818  672.1818   145.818182
> 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/41uf41323868151.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/5ww1x1323868151.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/6vest1323868151.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/7zln81323868151.tab") 
+ }
> 
> try(system("convert tmp/26dyv1323868151.ps tmp/26dyv1323868151.png",intern=TRUE))
character(0)
> try(system("convert tmp/3qx2t1323868151.ps tmp/3qx2t1323868151.png",intern=TRUE))
character(0)
> try(system("convert tmp/41uf41323868151.ps tmp/41uf41323868151.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.790   0.170   2.963