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 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
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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(463
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+ ,0
+ ,17
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+ ,39
+ ,19540
+ ,6095
+ ,35873
+ ,27
+ ,21)
+ ,dim=c(15
+ ,284)
+ ,dimnames=list(c('pageviews'
+ ,'time_in_rfc'
+ ,'logins'
+ ,'compendium_views_info'
+ ,'compendium_views_pr'
+ ,'shared_compendiums'
+ ,'blogged_computations'
+ ,'compendiums_reviewed'
+ ,'feedback_messages_p1'
+ ,'feedback_messages_p120'
+ ,'totsize'
+ ,'totrevisions'
+ ,'totseconds'
+ ,'tothyperlinks'
+ ,'totblogs')
+ ,1:284))
>  y <- array(NA,dim=c(15,284),dimnames=list(c('pageviews','time_in_rfc','logins','compendium_views_info','compendium_views_pr','shared_compendiums','blogged_computations','compendiums_reviewed','feedback_messages_p1','feedback_messages_p120','totsize','totrevisions','totseconds','tothyperlinks','totblogs'),1:284))
>  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 = '13'
> 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] "totseconds"
> x[,par1]
  [1]  42551 195663   6853  21529  95757  85584 143983  75851  59238  93163
 [11]  96037 151511 136368 112642  94728 105499 121527 127766  98958  77900
 [21]  85646  98579 130767 131741  53907 178812 146761  82036 163253  27032
 [31] 171975  65990  86572 159676   1929  85371  58391  31580 136815 120642
 [41]  69107  50495 108016  46341  78348  79336  56968  93176 161632  87850
 [51] 127969  15049 155135  25109  45824 102996 160604 158051  44547 162647
 [61] 174141  60622 179566 184301  75661  96144 129847 117286  71180 109377
 [71]  85298  73631  86767  23824  93487  82981  73815  94552 132190 128754
 [81]  66363  67808  61724 131722  68580 106175  55792  25157  76669  57283
 [91] 105805 129484  72413  87831  96971  71299  77494 120336  93913 136048
[101] 181248 146123  32036 186646 102255 168237  64219  19630  76825 115338
[111] 109427 118168  84845 153197  29877  63506  22445  47695  68370 146304
[121]  38233  42071  50517 103950   5841   2341  84396  24610  35753  55515
[131] 209056   6622 115814  11609  13155  18274  72875  10112 142775  68847
[141]  17659  20112  61023  13983  65176 132432 112494  45109 170875 180759
[151] 214921 100226  32043  54454  78876 170745   6940  49025 122037  53782
[161] 127748  86839  44830  77395  89324 103300 112283  10901 120691  58106
[171]  57140 122422  25899 139296  52678  23853  17306   7953  89455 147866
[181]   4245  21509   7670  66675  14336  53608  30059  29668  22097  96841
[191]  41907  27080  35885  41247  28313  36845  16548  36134  55764  28910
[201]  13339  25319  66956  47487  52785  44683  35619  21920  45608   7721
[211]  20634  29788  31931  37754  32505  40557  94238  44197  43228   4103
[221]  44144  32868  27640  14063  28990   4694  42648  64329  21928  25836
[231]  22779  40820  27530  32378  10824  39613  60865  19787  20107  36605
[241]  40961  48231  39725  21455  23430  62991  49363   9604  24552  31493
[251]   3439  19555  21228  23177  22094   2342  38798   3255  24261  18511
[261]  40798  28893  21425  50276  37643  30377  27126     13  42097  24451
[271]  14335   5084   9927  43527  27184  21610  20484  20156   6012  18475
[281]  12645  11017  37623  35873
> 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])

    13   1929   2341   2342   3255   3439   4103   4245   4694   5084   5841 
     1      1      1      1      1      1      1      1      1      1      1 
  6012   6622   6853   6940   7670   7721   7953   9604   9927  10112  10824 
     1      1      1      1      1      1      1      1      1      1      1 
 10901  11017  11609  12645  13155  13339  13983  14063  14335  14336  15049 
     1      1      1      1      1      1      1      1      1      1      1 
 16548  17306  17659  18274  18475  18511  19555  19630  19787  20107  20112 
     1      1      1      1      1      1      1      1      1      1      1 
 20156  20484  20634  21228  21425  21455  21509  21529  21610  21920  21928 
     1      1      1      1      1      1      1      1      1      1      1 
 22094  22097  22445  22779  23177  23430  23824  23853  24261  24451  24552 
     1      1      1      1      1      1      1      1      1      1      1 
 24610  25109  25157  25319  25836  25899  27032  27080  27126  27184  27530 
     1      1      1      1      1      1      1      1      1      1      1 
 27640  28313  28893  28910  28990  29668  29788  29877  30059  30377  31493 
     1      1      1      1      1      1      1      1      1      1      1 
 31580  31931  32036  32043  32378  32505  32868  35619  35753  35873  35885 
     1      1      1      1      1      1      1      1      1      1      1 
 36134  36605  36845  37623  37643  37754  38233  38798  39613  39725  40557 
     1      1      1      1      1      1      1      1      1      1      1 
 40798  40820  40961  41247  41907  42071  42097  42551  42648  43228  43527 
     1      1      1      1      1      1      1      1      1      1      1 
 44144  44197  44547  44683  44830  45109  45608  45824  46341  47487  47695 
     1      1      1      1      1      1      1      1      1      1      1 
 48231  49025  49363  50276  50495  50517  52678  52785  53608  53782  53907 
     1      1      1      1      1      1      1      1      1      1      1 
 54454  55515  55764  55792  56968  57140  57283  58106  58391  59238  60622 
     1      1      1      1      1      1      1      1      1      1      1 
 60865  61023  61724  62991  63506  64219  64329  65176  65990  66363  66675 
     1      1      1      1      1      1      1      1      1      1      1 
 66956  67808  68370  68580  68847  69107  71180  71299  72413  72875  73631 
     1      1      1      1      1      1      1      1      1      1      1 
 73815  75661  75851  76669  76825  77395  77494  77900  78348  78876  79336 
     1      1      1      1      1      1      1      1      1      1      1 
 82036  82981  84396  84845  85298  85371  85584  85646  86572  86767  86839 
     1      1      1      1      1      1      1      1      1      1      1 
 87831  87850  89324  89455  93163  93176  93487  93913  94238  94552  94728 
     1      1      1      1      1      1      1      1      1      1      1 
 95757  96037  96144  96841  96971  98579  98958 100226 102255 102996 103300 
     1      1      1      1      1      1      1      1      1      1      1 
103950 105499 105805 106175 108016 109377 109427 112283 112494 112642 115338 
     1      1      1      1      1      1      1      1      1      1      1 
115814 117286 118168 120336 120642 120691 121527 122037 122422 127748 127766 
     1      1      1      1      1      1      1      1      1      1      1 
127969 128754 129484 129847 130767 131722 131741 132190 132432 136048 136368 
     1      1      1      1      1      1      1      1      1      1      1 
136815 139296 142775 143983 146123 146304 146761 147866 151511 153197 155135 
     1      1      1      1      1      1      1      1      1      1      1 
158051 159676 160604 161632 162647 163253 168237 170745 170875 171975 174141 
     1      1      1      1      1      1      1      1      1      1      1 
178812 179566 180759 181248 184301 186646 195663 209056 214921 
     1      1      1      1      1      1      1      1      1 
> colnames(x)
 [1] "pageviews"              "time_in_rfc"            "logins"                
 [4] "compendium_views_info"  "compendium_views_pr"    "shared_compendiums"    
 [7] "blogged_computations"   "compendiums_reviewed"   "feedback_messages_p1"  
[10] "feedback_messages_p120" "totsize"                "totrevisions"          
[13] "totseconds"             "tothyperlinks"          "totblogs"              
> colnames(x)[par1]
[1] "totseconds"
> x[,par1]
  [1]  42551 195663   6853  21529  95757  85584 143983  75851  59238  93163
 [11]  96037 151511 136368 112642  94728 105499 121527 127766  98958  77900
 [21]  85646  98579 130767 131741  53907 178812 146761  82036 163253  27032
 [31] 171975  65990  86572 159676   1929  85371  58391  31580 136815 120642
 [41]  69107  50495 108016  46341  78348  79336  56968  93176 161632  87850
 [51] 127969  15049 155135  25109  45824 102996 160604 158051  44547 162647
 [61] 174141  60622 179566 184301  75661  96144 129847 117286  71180 109377
 [71]  85298  73631  86767  23824  93487  82981  73815  94552 132190 128754
 [81]  66363  67808  61724 131722  68580 106175  55792  25157  76669  57283
 [91] 105805 129484  72413  87831  96971  71299  77494 120336  93913 136048
[101] 181248 146123  32036 186646 102255 168237  64219  19630  76825 115338
[111] 109427 118168  84845 153197  29877  63506  22445  47695  68370 146304
[121]  38233  42071  50517 103950   5841   2341  84396  24610  35753  55515
[131] 209056   6622 115814  11609  13155  18274  72875  10112 142775  68847
[141]  17659  20112  61023  13983  65176 132432 112494  45109 170875 180759
[151] 214921 100226  32043  54454  78876 170745   6940  49025 122037  53782
[161] 127748  86839  44830  77395  89324 103300 112283  10901 120691  58106
[171]  57140 122422  25899 139296  52678  23853  17306   7953  89455 147866
[181]   4245  21509   7670  66675  14336  53608  30059  29668  22097  96841
[191]  41907  27080  35885  41247  28313  36845  16548  36134  55764  28910
[201]  13339  25319  66956  47487  52785  44683  35619  21920  45608   7721
[211]  20634  29788  31931  37754  32505  40557  94238  44197  43228   4103
[221]  44144  32868  27640  14063  28990   4694  42648  64329  21928  25836
[231]  22779  40820  27530  32378  10824  39613  60865  19787  20107  36605
[241]  40961  48231  39725  21455  23430  62991  49363   9604  24552  31493
[251]   3439  19555  21228  23177  22094   2342  38798   3255  24261  18511
[261]  40798  28893  21425  50276  37643  30377  27126     13  42097  24451
[271]  14335   5084   9927  43527  27184  21610  20484  20156   6012  18475
[281]  12645  11017  37623  35873
> 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/1moon1354800605.tab") 
+ }
+ }
> m

	 Conditional inference tree with 15 terminal nodes

Response:  totseconds 
Inputs:  pageviews, time_in_rfc, logins, compendium_views_info, compendium_views_pr, shared_compendiums, blogged_computations, compendiums_reviewed, feedback_messages_p1, feedback_messages_p120, totsize, totrevisions, tothyperlinks, totblogs 
Number of observations:  284 

1) time_in_rfc <= 152601; criterion = 1, statistic = 227.444
  2) totrevisions <= 6943; criterion = 1, statistic = 124.149
    3) totrevisions <= 3997; criterion = 1, statistic = 56.211
      4) totrevisions <= 1421; criterion = 1, statistic = 25.884
        5)*  weights = 18 
      4) totrevisions > 1421
        6)*  weights = 32 
    3) totrevisions > 3997
      7) blogged_computations <= 32; criterion = 1, statistic = 19.658
        8)*  weights = 36 
      7) blogged_computations > 32
        9)*  weights = 11 
  2) totrevisions > 6943
    10) totsize <= 51513; criterion = 1, statistic = 42.196
      11) time_in_rfc <= 99923; criterion = 1, statistic = 33.111
        12) time_in_rfc <= 65745; criterion = 0.973, statistic = 9.581
          13)*  weights = 8 
        12) time_in_rfc > 65745
          14)*  weights = 19 
      11) time_in_rfc > 99923
        15)*  weights = 27 
    10) totsize > 51513
      16) totsize <= 95364; criterion = 0.987, statistic = 10.963
        17) pageviews <= 1318; criterion = 0.984, statistic = 10.599
          18)*  weights = 10 
        17) pageviews > 1318
          19)*  weights = 14 
      16) totsize > 95364
        20)*  weights = 7 
1) time_in_rfc > 152601
  21) tothyperlinks <= 131; criterion = 1, statistic = 53.54
    22) tothyperlinks <= 84; criterion = 1, statistic = 22.669
      23)*  weights = 24 
    22) tothyperlinks > 84
      24)*  weights = 43 
  21) tothyperlinks > 131
    25) time_in_rfc <= 230964; criterion = 0.999, statistic = 15.744
      26)*  weights = 10 
    25) time_in_rfc > 230964
      27) tothyperlinks <= 140; criterion = 0.969, statistic = 9.345
        28)*  weights = 8 
      27) tothyperlinks > 140
        29)*  weights = 17 
> postscript(file="/var/wessaorg/rcomp/tmp/2r98a1354800605.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/3sr0m1354800605.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     42551  54896.000 -12345.00000
2    195663 177377.235  18285.76471
3      6853   5873.056    979.94444
4     21529  28790.917  -7261.91667
5     95757  89040.143   6716.85714
6     85584  82775.333   2808.66667
7    143983 115016.326  28966.67442
8     75851  82775.333  -6924.33333
9     59238  75120.214 -15882.21429
10    93163 115016.326 -21853.32558
11    96037  82775.333  13261.66667
12   151511 140454.875  11056.12500
13   136368 114577.000  21791.00000
14   112642 115016.326  -2374.32558
15    94728  89040.143   5687.85714
16   105499 114577.000  -9078.00000
17   121527 115016.326   6510.67442
18   127766 115016.326  12749.67442
19    98958  82775.333  16182.66667
20    77900  89040.143 -11140.14286
21    85646 115016.326 -29370.32558
22    98579 115016.326 -16437.32558
23   130767 115016.326  15750.67442
24   131741 115016.326  16724.67442
25    53907  55586.407  -1679.40741
26   178812 115016.326  63795.67442
27   146761 115016.326  31744.67442
28    82036 115016.326 -32980.32558
29   163253 115016.326  48236.67442
30    27032  17430.812   9601.18750
31   171975 177377.235  -5402.23529
32    65990  55586.407  10403.59259
33    86572  82775.333   3796.66667
34   159676 115016.326  44659.67442
35     1929   5873.056  -3944.05556
36    85371  82775.333   2595.66667
37    58391  55586.407   2804.59259
38    31580  38321.842  -6741.84211
39   136815 115016.326  21798.67442
40   120642 115016.326   5625.67442
41    69107  75120.214  -6013.21429
42    50495  82775.333 -32280.33333
43   108016  82775.333  25240.66667
44    46341  55586.407  -9245.40741
45    78348  75120.214   3227.78571
46    79336  82775.333  -3439.33333
47    56968  38321.842  18646.15789
48    93176 115016.326 -21840.32558
49   161632 177377.235 -15745.23529
50    87850  75120.214  12729.78571
51   127969 115016.326  12952.67442
52    15049  17430.812  -2381.81250
53   155135 177377.235 -22242.23529
54    25109  17430.812   7678.18750
55    45824  28790.917  17033.08333
56   102996 115016.326 -12020.32558
57   160604 140454.875  20149.12500
58   158051 140454.875  17596.12500
59    44547  55586.407 -11039.40741
60   162647 177377.235 -14730.23529
61   174141 177377.235  -3236.23529
62    60622  82775.333 -22153.33333
63   179566 177377.235   2188.76471
64   184301 177377.235   6923.76471
65    75661  82775.333  -7114.33333
66    96144 115016.326 -18872.32558
67   129847 115016.326  14830.67442
68   117286 115016.326   2269.67442
69    71180  89040.143 -17860.14286
70   109377 115016.326  -5639.32558
71    85298 114577.000 -29279.00000
72    73631  54896.000  18735.00000
73    86767 115016.326 -28249.32558
74    23824  17430.812   6393.18750
75    93487  82775.333  10711.66667
76    82981 115016.326 -32035.32558
77    73815  75120.214  -1305.21429
78    94552 115016.326 -20464.32558
79   132190 140454.875  -8264.87500
80   128754  82775.333  45978.66667
81    66363  75120.214  -8757.21429
82    67808  75120.214  -7312.21429
83    61724  55586.407   6137.59259
84   131722 115016.326  16705.67442
85    68580  55586.407  12993.59259
86   106175 114577.000  -8402.00000
87    55792  54896.000    896.00000
88    25157  28790.917  -3633.91667
89    76669  54896.000  21773.00000
90    57283  55586.407   1696.59259
91   105805  82775.333  23029.66667
92   129484  82775.333  46708.66667
93    72413  89040.143 -16627.14286
94    87831  82775.333   5055.66667
95    96971 114577.000 -17606.00000
96    71299  75120.214  -3821.21429
97    77494 115016.326 -37522.32558
98   120336 115016.326   5319.67442
99    93913 115016.326 -21103.32558
100  136048 114577.000  21471.00000
101  181248 177377.235   3870.76471
102  146123 140454.875   5668.12500
103   32036  39743.636  -7707.63636
104  186646 177377.235   9268.76471
105  102255 115016.326 -12761.32558
106  168237 177377.235  -9140.23529
107   64219  55586.407   8632.59259
108   19630  25163.250  -5533.25000
109   76825  82775.333  -5950.33333
110  115338 114577.000    761.00000
111  109427 115016.326  -5589.32558
112  118168 115016.326   3151.67442
113   84845  82775.333   2069.66667
114  153197  89040.143  64156.85714
115   29877  39743.636  -9866.63636
116   63506  55586.407   7919.59259
117   22445  28790.917  -6345.91667
118   47695  55586.407  -7891.40741
119   68370  39743.636  28626.36364
120  146304 115016.326  31287.67442
121   38233  28790.917   9442.08333
122   42071  28790.917  13280.08333
123   50517  54896.000  -4379.00000
124  103950 115016.326 -11066.32558
125    5841   5873.056    -32.05556
126    2341   5873.056  -3532.05556
127   84396  75120.214   9275.78571
128   24610  17430.812   7179.18750
129   35753  38321.842  -2568.84211
130   55515  55586.407    -71.40741
131  209056 177377.235  31678.76471
132    6622   5873.056    748.94444
133  115814 115016.326    797.67442
134   11609  17430.812  -5821.81250
135   13155  17430.812  -4275.81250
136   18274  17430.812    843.18750
137   72875  75120.214  -2245.21429
138   10112   5873.056   4238.94444
139  142775 115016.326  27758.67442
140   68847  82775.333 -13928.33333
141   17659  17430.812    228.18750
142   20112  17430.812   2681.18750
143   61023  54896.000   6127.00000
144   13983  17430.812  -3447.81250
145   65176  75120.214  -9944.21429
146  132432 140454.875  -8022.87500
147  112494 114577.000  -2083.00000
148   45109  39743.636   5365.36364
149  170875 177377.235  -6502.23529
150  180759 177377.235   3381.76471
151  214921 177377.235  37543.76471
152  100226  82775.333  17450.66667
153   32043  28790.917   3252.08333
154   54454  54896.000   -442.00000
155   78876 115016.326 -36140.32558
156  170745 177377.235  -6632.23529
157    6940   5873.056   1066.94444
158   49025  55586.407  -6561.40741
159  122037 140454.875 -18417.87500
160   53782  55586.407  -1804.40741
161  127748 115016.326  12731.67442
162   86839 115016.326 -28177.32558
163   44830  54896.000 -10066.00000
164   77395  55586.407  21808.59259
165   89324  82775.333   6548.66667
166  103300 115016.326 -11716.32558
167  112283 114577.000  -2294.00000
168   10901  82775.333 -71874.33333
169  120691 140454.875 -19763.87500
170   58106  89040.143 -30934.14286
171   57140  55586.407   1553.59259
172  122422 115016.326   7405.67442
173   25899  28790.917  -2891.91667
174  139296 114577.000  24719.00000
175   52678  55586.407  -2908.40741
176   23853  28790.917  -4937.91667
177   17306  17430.812   -124.81250
178    7953  17430.812  -9477.81250
179   89455 115016.326 -25561.32558
180  147866 177377.235 -29511.23529
181    4245   5873.056  -1628.05556
182   21509  17430.812   4078.18750
183    7670  17430.812  -9760.81250
184   66675  55586.407  11088.59259
185   14336  17430.812  -3094.81250
186   53608  54896.000  -1288.00000
187   30059  38321.842  -8262.84211
188   29668  38321.842  -8653.84211
189   22097  28790.917  -6693.91667
190   96841  75120.214  21720.78571
191   41907  55586.407 -13679.40741
192   27080  38321.842 -11241.84211
193   35885  54896.000 -19011.00000
194   41247  39743.636   1503.36364
195   28313  38321.842 -10008.84211
196   36845  38321.842  -1476.84211
197   16548  17430.812   -882.81250
198   36134  55586.407 -19452.40741
199   55764  55586.407    177.59259
200   28910  28790.917    119.08333
201   13339  17430.812  -4091.81250
202   25319  38321.842 -13002.84211
203   66956  82775.333 -15819.33333
204   47487  55586.407  -8099.40741
205   52785  55586.407  -2801.40741
206   44683  28790.917  15892.08333
207   35619  39743.636  -4124.63636
208   21920  28790.917  -6870.91667
209   45608  38321.842   7286.15789
210    7721  17430.812  -9709.81250
211   20634  28790.917  -8156.91667
212   29788  28790.917    997.08333
213   31931  28790.917   3140.08333
214   37754  28790.917   8963.08333
215   32505  28790.917   3714.08333
216   40557  38321.842   2235.15789
217   94238  75120.214  19117.78571
218   44197  38321.842   5875.15789
219   43228  38321.842   4906.15789
220    4103   5873.056  -1770.05556
221   44144  39743.636   4400.36364
222   32868  17430.812  15437.18750
223   27640  25163.250   2476.75000
224   14063  17430.812  -3367.81250
225   28990  39743.636 -10753.63636
226    4694  17430.812 -12736.81250
227   42648  38321.842   4326.15789
228   64329  75120.214 -10791.21429
229   21928  28790.917  -6862.91667
230   25836  25163.250    672.75000
231   22779  25163.250  -2384.25000
232   40820  82775.333 -41955.33333
233   27530  17430.812  10099.18750
234   32378  28790.917   3587.08333
235   10824  17430.812  -6606.81250
236   39613  28790.917  10822.08333
237   60865  55586.407   5278.59259
238   19787  28790.917  -9003.91667
239   20107  25163.250  -5056.25000
240   36605  28790.917   7814.08333
241   40961  38321.842   2639.15789
242   48231  55586.407  -7355.40741
243   39725  28790.917  10934.08333
244   21455  17430.812   4024.18750
245   23430  25163.250  -1733.25000
246   62991  55586.407   7404.59259
247   49363  38321.842  11041.15789
248    9604   5873.056   3730.94444
249   24552  28790.917  -4238.91667
250   31493  28790.917   2702.08333
251    3439   5873.056  -2434.05556
252   19555  28790.917  -9235.91667
253   21228  28790.917  -7562.91667
254   23177  28790.917  -5613.91667
255   22094  17430.812   4663.18750
256    2342   5873.056  -3531.05556
257   38798  38321.842    476.15789
258    3255   5873.056  -2618.05556
259   24261  25163.250   -902.25000
260   18511  28790.917 -10279.91667
261   40798  39743.636   1054.36364
262   28893  39743.636 -10850.63636
263   21425  17430.812   3994.18750
264   50276  55586.407  -5310.40741
265   37643  38321.842   -678.84211
266   30377  28790.917   1586.08333
267   27126  28790.917  -1664.91667
268      13   5873.056  -5860.05556
269   42097  39743.636   2353.36364
270   24451  17430.812   7020.18750
271   14335   5873.056   8461.94444
272    5084   5873.056   -789.05556
273    9927  17430.812  -7503.81250
274   43527  38321.842   5205.15789
275   27184  28790.917  -1606.91667
276   21610  28790.917  -7180.91667
277   20484  17430.812   3053.18750
278   20156  17430.812   2725.18750
279    6012   5873.056    138.94444
280   18475  28790.917 -10315.91667
281   12645   5873.056   6771.94444
282   11017  17430.812  -6413.81250
283   37623  25163.250  12459.75000
284   35873  28790.917   7082.08333
> 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/4glad1354800606.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/5btd51354800606.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/6rnv41354800606.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/7f28v1354800606.tab") 
+ }
> 
> try(system("convert tmp/2r98a1354800605.ps tmp/2r98a1354800605.png",intern=TRUE))
character(0)
> try(system("convert tmp/3sr0m1354800605.ps tmp/3sr0m1354800605.png",intern=TRUE))
character(0)
> try(system("convert tmp/4glad1354800606.ps tmp/4glad1354800606.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
 15.819   0.794  16.568