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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+ ,37
+ ,12
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+ ,16
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+ ,176
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+ ,72
+ ,19540
+ ,329
+ ,44
+ ,16
+ ,39
+ ,72535
+ ,6095
+ ,27
+ ,21)
+ ,dim=c(9
+ ,289)
+ ,dimnames=list(c('totsize'
+ ,'compviewsinfo'
+ ,'compviewspr'
+ ,'reviews'
+ ,'feedbackm'
+ ,'time'
+ ,'totrevisions'
+ ,'tothyperlinks'
+ ,'totblogs')
+ ,1:289))
>  y <- array(NA,dim=c(9,289),dimnames=list(c('totsize','compviewsinfo','compviewspr','reviews','feedbackm','time','totrevisions','tothyperlinks','totblogs'),1:289))
>  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] "totsize"
> x[,par1]
  [1] 112285  84786  83123 101193  38361  68504 119182  22807  17140 116174
 [11]  57635  66198  71701  57793  80444  53855  97668 133824 101481  99645
 [21] 114789  99052  67654  65553  97500  69112  82753  85323  72654  30727
 [31]  77873 117478  74007  90183  61542 101494  27570  55813  79215   1423
 [41]  55461  31081  22996  83122  70106  60578  39992  79892  49810  71570
 [51] 100708  33032  82875 139077  71595  72260   5950 115762  32551  31701
 [61]  80670 143558 117105  23789 120733 105195  73107 132068 149193  46821
 [71]  87011  95260  55183 106671  73511  92945  78664  70054  22618  74011
 [81]  83737  69094  93133  95536 225920  62133  61370  43836 106117  38692
 [91]  84651  56622  15986  95364  26706  89691  67267 126846  41140 102860
[101]  51715  55801 111813 120293 138599 161647 115929  24266 162901 109825
[111] 129838  37510  43750  40652  87771  85872  89275  44418 192565  35232
[121]  40909  13294  32387 140867 120662  21233  44332  61056 101338   1168
[131]  13497  65567  25162  32334  40735  91413    855  97068  44339  14116
[141]  10288  65622  16563  76643 110681  29011  92696  94785   8773  83209
[151]  93815  86687  34553 105547 103487 213688  71220  23517  56926  91721
[161] 115168 111194  51009 135777  51513  74163  51633  75345  33416  83305
[171]  98952 102372  37238 103772 123969  27142 135400  21399 130115  24874
[181]  34988  45549   6023  64466  54990   1644   6179   3926  32755  34777
[191]  73224  27114  20760  37636  65461  30080  24094  69008  54968  46090
[201]  27507  10672  34029  46300  24760  18779  21280  40662  28987  22827
[211]  18513  30594  24006  27913  42744  12934  22574  41385  18653  18472
[221]  30976  63339  25568  33747   4154  19474  35130  39067  13310  65892
[231]   4143  28579  51776  21152  38084  27717  32928  11342  19499  16380
[241]  36874  48259  16734  28207  30143  41369  45833  29156  35944  36278
[251]  45588  45097   3895  28394  18632   2325  25139  27975  14483  13127
[261]   5839  24069   3738  18625  36341  24548  21792  26263  23686  49303
[271]  25659  28904   2781  29236  19546  22818  32689   5752  22197  20055
[281]  25272  82206  32073   5444  20154  36944   8019  30884  19540
> 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])

   855   1168   1423   1644   2325   2781   3738   3895   3926   4143   4154 
     1      1      1      1      1      1      1      1      1      1      1 
  5444   5752   5839   5950   6023   6179   8019   8773  10288  10672  11342 
     1      1      1      1      1      1      1      1      1      1      1 
 12934  13127  13294  13310  13497  14116  14483  15986  16380  16563  16734 
     1      1      1      1      1      1      1      1      1      1      1 
 17140  18472  18513  18625  18632  18653  18779  19474  19499  19540  19546 
     1      1      1      1      1      1      1      1      1      1      1 
 20055  20154  20760  21152  21233  21280  21399  21792  22197  22574  22618 
     1      1      1      1      1      1      1      1      1      1      1 
 22807  22818  22827  22996  23517  23686  23789  24006  24069  24094  24266 
     1      1      1      1      1      1      1      1      1      1      1 
 24548  24760  24874  25139  25162  25272  25568  25659  26263  26706  27114 
     1      1      1      1      1      1      1      1      1      1      1 
 27142  27507  27570  27717  27913  27975  28207  28394  28579  28904  28987 
     1      1      1      1      1      1      1      1      1      1      1 
 29011  29156  29236  30080  30143  30594  30727  30884  30976  31081  31701 
     1      1      1      1      1      1      1      1      1      1      1 
 32073  32334  32387  32551  32689  32755  32928  33032  33416  33747  34029 
     1      1      1      1      1      1      1      1      1      1      1 
 34553  34777  34988  35130  35232  35944  36278  36341  36874  36944  37238 
     1      1      1      1      1      1      1      1      1      1      1 
 37510  37636  38084  38361  38692  39067  39992  40652  40662  40735  40909 
     1      1      1      1      1      1      1      1      1      1      1 
 41140  41369  41385  42744  43750  43836  44332  44339  44418  45097  45549 
     1      1      1      1      1      1      1      1      1      1      1 
 45588  45833  46090  46300  46821  48259  49303  49810  51009  51513  51633 
     1      1      1      1      1      1      1      1      1      1      1 
 51715  51776  53855  54968  54990  55183  55461  55801  55813  56622  56926 
     1      1      1      1      1      1      1      1      1      1      1 
 57635  57793  60578  61056  61370  61542  62133  63339  64466  65461  65553 
     1      1      1      1      1      1      1      1      1      1      1 
 65567  65622  65892  66198  67267  67654  68504  69008  69094  69112  70054 
     1      1      1      1      1      1      1      1      1      1      1 
 70106  71220  71570  71595  71701  72260  72654  73107  73224  73511  74007 
     1      1      1      1      1      1      1      1      1      1      1 
 74011  74163  75345  76643  77873  78664  79215  79892  80444  80670  82206 
     1      1      1      1      1      1      1      1      1      1      1 
 82753  82875  83122  83123  83209  83305  83737  84651  84786  85323  85872 
     1      1      1      1      1      1      1      1      1      1      1 
 86687  87011  87771  89275  89691  90183  91413  91721  92696  92945  93133 
     1      1      1      1      1      1      1      1      1      1      1 
 93815  94785  95260  95364  95536  97068  97500  97668  98952  99052  99645 
     1      1      1      1      1      1      1      1      1      1      1 
100708 101193 101338 101481 101494 102372 102860 103487 103772 105195 105547 
     1      1      1      1      1      1      1      1      1      1      1 
106117 106671 109825 110681 111194 111813 112285 114789 115168 115762 115929 
     1      1      1      1      1      1      1      1      1      1      1 
116174 117105 117478 119182 120293 120662 120733 123969 126846 129838 130115 
     1      1      1      1      1      1      1      1      1      1      1 
132068 133824 135400 135777 138599 139077 140867 143558 149193 161647 162901 
     1      1      1      1      1      1      1      1      1      1      1 
192565 213688 225920 
     1      1      1 
> colnames(x)
[1] "totsize"       "compviewsinfo" "compviewspr"   "reviews"      
[5] "feedbackm"     "time"          "totrevisions"  "tothyperlinks"
[9] "totblogs"     
> colnames(x)[par1]
[1] "totsize"
> x[,par1]
  [1] 112285  84786  83123 101193  38361  68504 119182  22807  17140 116174
 [11]  57635  66198  71701  57793  80444  53855  97668 133824 101481  99645
 [21] 114789  99052  67654  65553  97500  69112  82753  85323  72654  30727
 [31]  77873 117478  74007  90183  61542 101494  27570  55813  79215   1423
 [41]  55461  31081  22996  83122  70106  60578  39992  79892  49810  71570
 [51] 100708  33032  82875 139077  71595  72260   5950 115762  32551  31701
 [61]  80670 143558 117105  23789 120733 105195  73107 132068 149193  46821
 [71]  87011  95260  55183 106671  73511  92945  78664  70054  22618  74011
 [81]  83737  69094  93133  95536 225920  62133  61370  43836 106117  38692
 [91]  84651  56622  15986  95364  26706  89691  67267 126846  41140 102860
[101]  51715  55801 111813 120293 138599 161647 115929  24266 162901 109825
[111] 129838  37510  43750  40652  87771  85872  89275  44418 192565  35232
[121]  40909  13294  32387 140867 120662  21233  44332  61056 101338   1168
[131]  13497  65567  25162  32334  40735  91413    855  97068  44339  14116
[141]  10288  65622  16563  76643 110681  29011  92696  94785   8773  83209
[151]  93815  86687  34553 105547 103487 213688  71220  23517  56926  91721
[161] 115168 111194  51009 135777  51513  74163  51633  75345  33416  83305
[171]  98952 102372  37238 103772 123969  27142 135400  21399 130115  24874
[181]  34988  45549   6023  64466  54990   1644   6179   3926  32755  34777
[191]  73224  27114  20760  37636  65461  30080  24094  69008  54968  46090
[201]  27507  10672  34029  46300  24760  18779  21280  40662  28987  22827
[211]  18513  30594  24006  27913  42744  12934  22574  41385  18653  18472
[221]  30976  63339  25568  33747   4154  19474  35130  39067  13310  65892
[231]   4143  28579  51776  21152  38084  27717  32928  11342  19499  16380
[241]  36874  48259  16734  28207  30143  41369  45833  29156  35944  36278
[251]  45588  45097   3895  28394  18632   2325  25139  27975  14483  13127
[261]   5839  24069   3738  18625  36341  24548  21792  26263  23686  49303
[271]  25659  28904   2781  29236  19546  22818  32689   5752  22197  20055
[281]  25272  82206  32073   5444  20154  36944   8019  30884  19540
> 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/13fxg1324653738.tab") 
+ }
+ }
> m

	 Conditional inference tree with 12 terminal nodes

Response:  totsize 
Inputs:  compviewsinfo, compviewspr, reviews, feedbackm, time, totrevisions, tothyperlinks, totblogs 
Number of observations:  289 

1) totrevisions <= 12770; criterion = 1, statistic = 187.948
  2) reviews <= 22; criterion = 1, statistic = 67.591
    3) tothyperlinks <= 16; criterion = 1, statistic = 44.779
      4) time <= 46698; criterion = 1, statistic = 16.78
        5) reviews <= 7; criterion = 0.985, statistic = 9.625
          6)*  weights = 14 
        5) reviews > 7
          7)*  weights = 9 
      4) time > 46698
        8)*  weights = 9 
    3) tothyperlinks > 16
      9) time <= 135458; criterion = 0.994, statistic = 11.284
        10)*  weights = 98 
      9) time > 135458
        11)*  weights = 7 
  2) reviews > 22
    12)*  weights = 34 
1) totrevisions > 12770
  13) totrevisions <= 22583; criterion = 1, statistic = 33.402
    14) reviews <= 30; criterion = 0.991, statistic = 10.691
      15) tothyperlinks <= 102; criterion = 0.997, statistic = 12.664
        16) reviews <= 21; criterion = 0.961, statistic = 7.891
          17)*  weights = 7 
        16) reviews > 21
          18)*  weights = 15 
      15) tothyperlinks > 102
        19)*  weights = 8 
    14) reviews > 30
      20)*  weights = 38 
  13) totrevisions > 22583
    21) tothyperlinks <= 176; criterion = 0.984, statistic = 9.51
      22)*  weights = 42 
    21) tothyperlinks > 176
      23)*  weights = 8 
> postscript(file="/var/wessaorg/rcomp/tmp/2wtc71324653738.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/3zhhj1324653738.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    112285 106116.833  6.168167e+03
2     84786  90331.125 -5.545125e+03
3     83123  90345.816 -7.222816e+03
4    101193 106116.833 -4.923833e+03
5     38361  30130.429  8.230571e+03
6     68504  57313.529  1.119047e+04
7    119182 106116.833  1.306517e+04
8     22807  11267.444  1.153956e+04
9     17140  30130.429 -1.299043e+04
10   116174 106116.833  1.005717e+04
11    57635  67808.333 -1.017333e+04
12    66198  90345.816 -2.414782e+04
13    71701  90345.816 -1.864482e+04
14    57793  57313.529  4.794706e+02
15    80444  90345.816 -9.901816e+03
16    53855  57313.529 -3.458529e+03
17    97668 106116.833 -8.448833e+03
18   133824 106116.833  2.770717e+04
19   101481  90345.816  1.113518e+04
20    99645  90331.125  9.313875e+03
21   114789  90345.816  2.444318e+04
22    99052 106116.833 -7.064833e+03
23    67654  90331.125 -2.267712e+04
24    65553  67808.333 -2.255333e+03
25    97500  90345.816  7.154184e+03
26    69112 106116.833 -3.700483e+04
27    82753 106116.833 -2.336383e+04
28    85323  90345.816 -5.022816e+03
29    72654 106116.833 -3.346283e+04
30    30727  57313.529 -2.658653e+04
31    77873  90345.816 -1.247282e+04
32   117478 106116.833  1.136117e+04
33    74007  57313.529  1.669347e+04
34    90183 106116.833 -1.593383e+04
35    61542  57313.529  4.228471e+03
36   101494 106116.833 -4.622833e+03
37    27570  30130.429 -2.560429e+03
38    55813  57313.529 -1.500529e+03
39    79215 106116.833 -2.690183e+04
40     1423   3650.214 -2.227214e+03
41    55461  67808.333 -1.234733e+04
42    31081  30130.429  9.505714e+02
43    22996  30130.429 -7.134429e+03
44    83122 106116.833 -2.299483e+04
45    70106  90331.125 -2.022512e+04
46    60578  90345.816 -2.976782e+04
47    39992  47534.714 -7.542714e+03
48    79892  67808.333  1.208367e+04
49    49810  57313.529 -7.503529e+03
50    71570  57313.529  1.425647e+04
51   100708  57313.529  4.339447e+04
52    33032  57313.529 -2.428153e+04
53    82875  90345.816 -7.470816e+03
54   139077  90345.816  4.873118e+04
55    71595  90345.816 -1.875082e+04
56    72260  67808.333  4.451667e+03
57     5950   3650.214  2.299786e+03
58   115762  90345.816  2.541618e+04
59    32551  30130.429  2.420571e+03
60    31701  30130.429  1.570571e+03
61    80670 106116.833 -2.544683e+04
62   143558 106116.833  3.744117e+04
63   117105  90331.125  2.677388e+04
64    23789  30130.429 -6.341429e+03
65   120733 143195.875 -2.246288e+04
66   105195 143195.875 -3.800088e+04
67    73107  67808.333  5.298667e+03
68   132068 106116.833  2.595117e+04
69   149193 143195.875  5.997125e+03
70    46821  57313.529 -1.049253e+04
71    87011  90345.816 -3.334816e+03
72    95260 106116.833 -1.085683e+04
73    55183  67808.333 -1.262533e+04
74   106671  90331.125  1.633988e+04
75    73511  90345.816 -1.683482e+04
76    92945  90331.125  2.613875e+03
77    78664  67808.333  1.085567e+04
78    70054  90345.816 -2.029182e+04
79    22618  57313.529 -3.469553e+04
80    74011  90345.816 -1.633482e+04
81    83737  90331.125 -6.594125e+03
82    69094  90345.816 -2.125182e+04
83    93133 106116.833 -1.298383e+04
84    95536 106116.833 -1.058083e+04
85   225920  90345.816  1.355742e+05
86    62133  67808.333 -5.675333e+03
87    61370  90345.816 -2.897582e+04
88    43836  30130.429  1.370557e+04
89   106117 106116.833  1.666667e-01
90    38692  57313.529 -1.862153e+04
91    84651  90345.816 -5.694816e+03
92    56622  30130.429  2.649157e+04
93    15986  57313.529 -4.132753e+04
94    95364 106116.833 -1.075283e+04
95    26706  30130.429 -3.424429e+03
96    89691  67808.333  2.188267e+04
97    67267 106116.833 -3.884983e+04
98   126846 106116.833  2.072917e+04
99    41140  57313.529 -1.617353e+04
100  102860  90345.816  1.251418e+04
101   51715  57313.529 -5.598529e+03
102   55801  90345.816 -3.454482e+04
103  111813 106116.833  5.696167e+03
104  120293 106116.833  1.417617e+04
105  138599 106116.833  3.248217e+04
106  161647 143195.875  1.845112e+04
107  115929 106116.833  9.812167e+03
108   24266  30130.429 -5.864429e+03
109  162901 143195.875  1.970512e+04
110  109825 106116.833  3.708167e+03
111  129838 143195.875 -1.335788e+04
112   37510  57313.529 -1.980353e+04
113   43750  30130.429  1.361957e+04
114   40652  47534.714 -6.882714e+03
115   87771 106116.833 -1.834583e+04
116   85872 106116.833 -2.024483e+04
117   89275 106116.833 -1.684183e+04
118   44418  42702.429  1.715571e+03
119  192565 106116.833  8.644817e+04
120   35232  30130.429  5.101571e+03
121   40909  47534.714 -6.625714e+03
122   13294  30130.429 -1.683643e+04
123   32387  30130.429  2.256571e+03
124  140867  57313.529  8.355347e+04
125  120662  90345.816  3.031618e+04
126   21233  30130.429 -8.897429e+03
127   44332  30130.429  1.420157e+04
128   61056  67808.333 -6.752333e+03
129  101338  90345.816  1.099218e+04
130    1168   3650.214 -2.482214e+03
131   13497  11267.444  2.229556e+03
132   65567  90345.816 -2.477882e+04
133   25162  30130.429 -4.968429e+03
134   32334  57313.529 -2.497953e+04
135   40735  57313.529 -1.657853e+04
136   91413  90345.816  1.067184e+03
137     855   3650.214 -2.795214e+03
138   97068  57313.529  3.975447e+04
139   44339  30130.429  1.420857e+04
140   14116  30130.429 -1.601443e+04
141   10288  11267.444 -9.794444e+02
142   65622  47534.714  1.808729e+04
143   16563  22900.111 -6.337111e+03
144   76643  90345.816 -1.370282e+04
145  110681  90345.816  2.033518e+04
146   29011  30130.429 -1.119429e+03
147   92696  57313.529  3.538247e+04
148   94785  57313.529  3.747147e+04
149    8773  11267.444 -2.494444e+03
150   83209  90345.816 -7.136816e+03
151   93815 106116.833 -1.230183e+04
152   86687  90345.816 -3.658816e+03
153   34553  30130.429  4.422571e+03
154  105547 106116.833 -5.698333e+02
155  103487  90345.816  1.314118e+04
156  213688 143195.875  7.049212e+04
157   71220  90345.816 -1.912582e+04
158   23517  30130.429 -6.613429e+03
159   56926  57313.529 -3.875294e+02
160   91721  90345.816  1.375184e+03
161  115168 106116.833  9.051167e+03
162  111194  57313.529  5.388047e+04
163   51009  47534.714  3.474286e+03
164  135777 106116.833  2.966017e+04
165   51513  57313.529 -5.800529e+03
166   74163 106116.833 -3.195383e+04
167   51633  57313.529 -5.680529e+03
168   75345  57313.529  1.803147e+04
169   33416  30130.429  3.285571e+03
170   83305  42702.429  4.060257e+04
171   98952  90345.816  8.606184e+03
172  102372 143195.875 -4.082388e+04
173   37238  22900.111  1.433789e+04
174  103772 106116.833 -2.344833e+03
175  123969  90345.816  3.362318e+04
176   27142  42702.429 -1.556043e+04
177  135400 106116.833  2.928317e+04
178   21399  30130.429 -8.731429e+03
179  130115 106116.833  2.399817e+04
180   24874  42702.429 -1.782843e+04
181   34988  30130.429  4.857571e+03
182   45549  22900.111  2.264889e+04
183    6023   3650.214  2.372786e+03
184   64466  67808.333 -3.342333e+03
185   54990  90345.816 -3.535582e+04
186    1644   3650.214 -2.006214e+03
187    6179   3650.214  2.528786e+03
188    3926   3650.214  2.757857e+02
189   32755  30130.429  2.624571e+03
190   34777  57313.529 -2.253653e+04
191   73224  67808.333  5.415667e+03
192   27114  30130.429 -3.016429e+03
193   20760  30130.429 -9.370429e+03
194   37636  57313.529 -1.967753e+04
195   65461  67808.333 -2.347333e+03
196   30080  30130.429 -5.042857e+01
197   24094  30130.429 -6.036429e+03
198   69008  30130.429  3.887757e+04
199   54968  30130.429  2.483757e+04
200   46090  30130.429  1.595957e+04
201   27507  30130.429 -2.623429e+03
202   10672  57313.529 -4.664153e+04
203   34029  30130.429  3.898571e+03
204   46300  47534.714 -1.234714e+03
205   24760  30130.429 -5.370429e+03
206   18779  11267.444  7.511556e+03
207   21280  30130.429 -8.850429e+03
208   40662  42702.429 -2.040429e+03
209   28987  30130.429 -1.143429e+03
210   22827  30130.429 -7.303429e+03
211   18513  30130.429 -1.161743e+04
212   30594  30130.429  4.635714e+02
213   24006  30130.429 -6.124429e+03
214   27913  30130.429 -2.217429e+03
215   42744  57313.529 -1.456953e+04
216   12934  22900.111 -9.966111e+03
217   22574  30130.429 -7.556429e+03
218   41385  30130.429  1.125457e+04
219   18653  30130.429 -1.147743e+04
220   18472  22900.111 -4.428111e+03
221   30976  30130.429  8.455714e+02
222   63339  67808.333 -4.469333e+03
223   25568  30130.429 -4.562429e+03
224   33747  30130.429  3.616571e+03
225    4154  11267.444 -7.113444e+03
226   19474  30130.429 -1.065643e+04
227   35130  30130.429  4.999571e+03
228   39067  30130.429  8.936571e+03
229   13310  30130.429 -1.682043e+04
230   65892  57313.529  8.578471e+03
231    4143  11267.444 -7.124444e+03
232   28579  30130.429 -1.551429e+03
233   51776  30130.429  2.164557e+04
234   21152  30130.429 -8.978429e+03
235   38084  30130.429  7.953571e+03
236   27717  30130.429 -2.413429e+03
237   32928  42702.429 -9.774429e+03
238   11342  30130.429 -1.878843e+04
239   19499  30130.429 -1.063143e+04
240   16380  30130.429 -1.375043e+04
241   36874  30130.429  6.743571e+03
242   48259  47534.714  7.242857e+02
243   16734  30130.429 -1.339643e+04
244   28207  30130.429 -1.923429e+03
245   30143  30130.429  1.257143e+01
246   41369  30130.429  1.123857e+04
247   45833  30130.429  1.570257e+04
248   29156  30130.429 -9.744286e+02
249   35944  30130.429  5.813571e+03
250   36278  30130.429  6.147571e+03
251   45588  42702.429  2.885571e+03
252   45097  30130.429  1.496657e+04
253    3895   3650.214  2.447857e+02
254   28394  30130.429 -1.736429e+03
255   18632  30130.429 -1.149843e+04
256    2325   3650.214 -1.325214e+03
257   25139  30130.429 -4.991429e+03
258   27975  30130.429 -2.155429e+03
259   14483  22900.111 -8.417111e+03
260   13127  11267.444  1.859556e+03
261    5839  11267.444 -5.428444e+03
262   24069  30130.429 -6.061429e+03
263    3738   3650.214  8.778571e+01
264   18625  30130.429 -1.150543e+04
265   36341  30130.429  6.210571e+03
266   24548  30130.429 -5.582429e+03
267   21792  30130.429 -8.338429e+03
268   26263  30130.429 -3.867429e+03
269   23686  30130.429 -6.444429e+03
270   49303  30130.429  1.917257e+04
271   25659  30130.429 -4.471429e+03
272   28904  30130.429 -1.226429e+03
273    2781   3650.214 -8.692143e+02
274   29236  30130.429 -8.944286e+02
275   19546  30130.429 -1.058443e+04
276   22818  30130.429 -7.312429e+03
277   32689  22900.111  9.788889e+03
278    5752   3650.214  2.101786e+03
279   22197  30130.429 -7.933429e+03
280   20055  30130.429 -1.007543e+04
281   25272  30130.429 -4.858429e+03
282   82206  30130.429  5.207557e+04
283   32073  30130.429  1.942571e+03
284    5444   3650.214  1.793786e+03
285   20154  22900.111 -2.746111e+03
286   36944  30130.429  6.813571e+03
287    8019  22900.111 -1.488111e+04
288   30884  30130.429  7.535714e+02
289   19540  30130.429 -1.059043e+04
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
> postscript(file="/var/wessaorg/rcomp/tmp/4vh271324653738.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/5klyk1324653738.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/6kst61324653738.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/7f6p41324653738.tab") 
+ }
> 
> try(system("convert tmp/2wtc71324653738.ps tmp/2wtc71324653738.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zhhj1324653738.ps tmp/3zhhj1324653738.png",intern=TRUE))
character(0)
> try(system("convert tmp/4vh271324653738.ps tmp/4vh271324653738.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  6.407   0.338   6.749