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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+ ,dimnames=list(c('Total_size'
+ ,'Time_RFC'
+ ,'PR_views'
+ ,'Blogged'
+ ,'Reviewed')
+ ,1:289))
>  y <- array(NA,dim=c(5,289),dimnames=list(c('Total_size','Time_RFC','PR_views','Blogged','Reviewed'),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 = ''
> par2 = 'none'
> par1 = '2'
> 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] "Time_RFC"
> x[,par1]
  [1] 101645 101011   7176  96560 175824 341570 103597 112611  85574 220801
 [11]  92661 133328  61361 125930  82316 102010 101523  41566  99923  22648
 [21]  46698 131698  91735  79863 108043  98866 120445 116048 250047 136084
 [31]  92499 135781  74408  81240 133368  79619  59194 139942 118612  72880
 [41]  65475  99643  71965  77272  49289 135131 108446  89746  44296  77648
 [51] 181528 134019 124064  92630 121848  52915  81872  58981  53515  60812
 [61]  56375  65490  80949  76302 104011  98104  67989  30989 135458  73504
 [71]  63123  61254  74914  31774  81437  87186  50090  65745  56653 158399
 [81]  46455  73624  38395  91899 139526  52164  51567  70551  84856 102538
 [91]  86678  85709  34662 150580  99611  19349  99373  86230  30837  31706
[101]  89806  62088  40151  27634  76990  37460  54157  49862  84337  64175
[111]  59382 119308  76702 103425  70344  43410 104838  62215  69304  53117
[121]  19764  86680  84105  77945  89113  91005  40248  64187  50857  56613
[131]  62792  72535  98146 210907 120982 176508 179321 123185  52746 385534
[141]  33170 149061 165446 237213 173326 133131 258873 180083 324799 230964
[151] 236785 135473 202925 215147 344297 153935 132943 174724 174415 225548
[161] 223632 124817 221698 210767 170266 260561  84853 294424 215641 325107
[171] 167542 106408 265769 269651 149112 152871 111665 116408 362301  78800
[181] 183167 277965 150629 168809  24188 329267  65029 101097 218946 244052
[191] 233328 256462 206161 311473 235800 177939 207176 196553 174184 143246
[201] 187559 187681 119016 182192  73566 194979 167488 143756 275541 243199
[211] 182999 135649 152299 120221 346485 145790 193339  80953 122774 130585
[221] 286468 241066 148446 204713 182079 140344 220516 243060 162765 182613
[231] 232138 265318 310839 225060 232317 144966  43287 155754 164709 201940
[241] 235454  99466 100750 224549 243511  22938 152474  61857 132487 317394
[251]  21054 209641  31414 244749 184510 128423  97839  38214 151101 272458
[261] 172494 328107 250579 351067 158015  85439 229242 351619  84207 324598
[271] 131069 204271 165543 141722 299775 195838 173260 254488 104389 199476
[281] 224330  14688 181633 271856   7199  46660  17547  95227 152601
> 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])

  7176   7199  14688  17547  19349  19764  21054  22648  22938  24188  27634 
     1      1      1      1      1      1      1      1      1      1      1 
 30837  30989  31414  31706  31774  33170  34662  37460  38214  38395  40151 
     1      1      1      1      1      1      1      1      1      1      1 
 40248  41566  43287  43410  44296  46455  46660  46698  49289  49862  50090 
     1      1      1      1      1      1      1      1      1      1      1 
 50857  51567  52164  52746  52915  53117  53515  54157  56375  56613  56653 
     1      1      1      1      1      1      1      1      1      1      1 
 58981  59194  59382  60812  61254  61361  61857  62088  62215  62792  63123 
     1      1      1      1      1      1      1      1      1      1      1 
 64175  64187  65029  65475  65490  65745  67989  69304  70344  70551  71965 
     1      1      1      1      1      1      1      1      1      1      1 
 72535  72880  73504  73566  73624  74408  74914  76302  76702  76990  77272 
     1      1      1      1      1      1      1      1      1      1      1 
 77648  77945  78800  79619  79863  80949  80953  81240  81437  81872  82316 
     1      1      1      1      1      1      1      1      1      1      1 
 84105  84207  84337  84853  84856  85439  85574  85709  86230  86678  86680 
     1      1      1      1      1      1      1      1      1      1      1 
 87186  89113  89746  89806  91005  91735  91899  92499  92630  92661  95227 
     1      1      1      1      1      1      1      1      1      1      1 
 96560  97839  98104  98146  98866  99373  99466  99611  99643  99923 100750 
     1      1      1      1      1      1      1      1      1      1      1 
101011 101097 101523 101645 102010 102538 103425 103597 104011 104389 104838 
     1      1      1      1      1      1      1      1      1      1      1 
106408 108043 108446 111665 112611 116048 116408 118612 119016 119308 120221 
     1      1      1      1      1      1      1      1      1      1      1 
120445 120982 121848 122774 123185 124064 124817 125930 128423 130585 131069 
     1      1      1      1      1      1      1      1      1      1      1 
131698 132487 132943 133131 133328 133368 134019 135131 135458 135473 135649 
     1      1      1      1      1      1      1      1      1      1      1 
135781 136084 139526 139942 140344 141722 143246 143756 144966 145790 148446 
     1      1      1      1      1      1      1      1      1      1      1 
149061 149112 150580 150629 151101 152299 152474 152601 152871 153935 155754 
     1      1      1      1      1      1      1      1      1      1      1 
158015 158399 162765 164709 165446 165543 167488 167542 168809 170266 172494 
     1      1      1      1      1      1      1      1      1      1      1 
173260 173326 174184 174415 174724 175824 176508 177939 179321 180083 181528 
     1      1      1      1      1      1      1      1      1      1      1 
181633 182079 182192 182613 182999 183167 184510 187559 187681 193339 194979 
     1      1      1      1      1      1      1      1      1      1      1 
195838 196553 199476 201940 202925 204271 204713 206161 207176 209641 210767 
     1      1      1      1      1      1      1      1      1      1      1 
210907 215147 215641 218946 220516 220801 221698 223632 224330 224549 225060 
     1      1      1      1      1      1      1      1      1      1      1 
225548 229242 230964 232138 232317 233328 235454 235800 236785 237213 241066 
     1      1      1      1      1      1      1      1      1      1      1 
243060 243199 243511 244052 244749 250047 250579 254488 256462 258873 260561 
     1      1      1      1      1      1      1      1      1      1      1 
265318 265769 269651 271856 272458 275541 277965 286468 294424 299775 310839 
     1      1      1      1      1      1      1      1      1      1      1 
311473 317394 324598 324799 325107 328107 329267 341570 344297 346485 351067 
     1      1      1      1      1      1      1      1      1      1      1 
351619 362301 385534 
     1      1      1 
> colnames(x)
[1] "Total_size" "Time_RFC"   "PR_views"   "Blogged"    "Reviewed"  
> colnames(x)[par1]
[1] "Time_RFC"
> x[,par1]
  [1] 101645 101011   7176  96560 175824 341570 103597 112611  85574 220801
 [11]  92661 133328  61361 125930  82316 102010 101523  41566  99923  22648
 [21]  46698 131698  91735  79863 108043  98866 120445 116048 250047 136084
 [31]  92499 135781  74408  81240 133368  79619  59194 139942 118612  72880
 [41]  65475  99643  71965  77272  49289 135131 108446  89746  44296  77648
 [51] 181528 134019 124064  92630 121848  52915  81872  58981  53515  60812
 [61]  56375  65490  80949  76302 104011  98104  67989  30989 135458  73504
 [71]  63123  61254  74914  31774  81437  87186  50090  65745  56653 158399
 [81]  46455  73624  38395  91899 139526  52164  51567  70551  84856 102538
 [91]  86678  85709  34662 150580  99611  19349  99373  86230  30837  31706
[101]  89806  62088  40151  27634  76990  37460  54157  49862  84337  64175
[111]  59382 119308  76702 103425  70344  43410 104838  62215  69304  53117
[121]  19764  86680  84105  77945  89113  91005  40248  64187  50857  56613
[131]  62792  72535  98146 210907 120982 176508 179321 123185  52746 385534
[141]  33170 149061 165446 237213 173326 133131 258873 180083 324799 230964
[151] 236785 135473 202925 215147 344297 153935 132943 174724 174415 225548
[161] 223632 124817 221698 210767 170266 260561  84853 294424 215641 325107
[171] 167542 106408 265769 269651 149112 152871 111665 116408 362301  78800
[181] 183167 277965 150629 168809  24188 329267  65029 101097 218946 244052
[191] 233328 256462 206161 311473 235800 177939 207176 196553 174184 143246
[201] 187559 187681 119016 182192  73566 194979 167488 143756 275541 243199
[211] 182999 135649 152299 120221 346485 145790 193339  80953 122774 130585
[221] 286468 241066 148446 204713 182079 140344 220516 243060 162765 182613
[231] 232138 265318 310839 225060 232317 144966  43287 155754 164709 201940
[241] 235454  99466 100750 224549 243511  22938 152474  61857 132487 317394
[251]  21054 209641  31414 244749 184510 128423  97839  38214 151101 272458
[261] 172494 328107 250579 351067 158015  85439 229242 351619  84207 324598
[271] 131069 204271 165543 141722 299775 195838 173260 254488 104389 199476
[281] 224330  14688 181633 271856   7199  46660  17547  95227 152601
> 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/1rcwb1324132594.tab") 
+ }
+ }
> m

	 Conditional inference tree with 10 terminal nodes

Response:  Time_RFC 
Inputs:  Total_size, PR_views, Blogged, Reviewed 
Number of observations:  289 

1) Blogged <= 50; criterion = 1, statistic = 198.82
  2) Blogged <= 18; criterion = 1, statistic = 79.237
    3) Reviewed <= 9; criterion = 1, statistic = 21.404
      4)*  weights = 19 
    3) Reviewed > 9
      5)*  weights = 34 
  2) Blogged > 18
    6) Reviewed <= 23; criterion = 1, statistic = 26.563
      7) Blogged <= 40; criterion = 0.994, statistic = 10.059
        8) PR_views <= 68; criterion = 0.992, statistic = 9.475
          9)*  weights = 57 
        8) PR_views > 68
          10)*  weights = 18 
      7) Blogged > 40
        11)*  weights = 16 
    6) Reviewed > 23
      12)*  weights = 25 
1) Blogged > 50
  13) Blogged <= 74; criterion = 1, statistic = 25.667
    14) Reviewed <= 28; criterion = 0.967, statistic = 6.938
      15)*  weights = 20 
    14) Reviewed > 28
      16)*  weights = 20 
  13) Blogged > 74
    17) PR_views <= 85; criterion = 0.99, statistic = 9.216
      18)*  weights = 55 
    17) PR_views > 85
      19)*  weights = 25 
> postscript(file="/var/wessaorg/rcomp/tmp/2ctwd1324132594.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/3ejr81324132594.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    101645 108208.06   -6563.0556
2    101011  80754.93   20256.0702
3      7176  28343.42  -21167.4211
4     96560 119975.38  -23415.3750
5    175824 147060.60   28763.4000
6    341570 271462.48   70107.5200
7    103597  80754.93   22842.0702
8    112611 119975.38   -7364.3750
9     85574 108208.06  -22634.0556
10   220801 147060.60   73740.4000
11    92661 108208.06  -15547.0556
12   133328 147060.60  -13732.6000
13    61361  80754.93  -19393.9298
14   125930 108208.06   17721.9444
15    82316  80754.93    1561.0702
16   102010  80754.93   21255.0702
17   101523 147060.60  -45537.6000
18    41566  28343.42   13222.5789
19    99923 132433.36  -32510.3600
20    22648  60800.35  -38152.3529
21    46698  60800.35  -14102.3529
22   131698 147060.60  -15362.6000
23    91735  60800.35   30934.6471
24    79863 108208.06  -28345.0556
25   108043 119975.38  -11932.3750
26    98866 108208.06   -9342.0556
27   120445 108208.06   12236.9444
28   116048 119975.38   -3927.3750
29   250047 119975.38  130071.6250
30   136084 108208.06   27875.9444
31    92499  80754.93   11744.0702
32   135781 119975.38   15805.6250
33    74408  80754.93   -6346.9298
34    81240 147060.60  -65820.6000
35   133368  80754.93   52613.0702
36    79619 119975.38  -40356.3750
37    59194  60800.35   -1606.3529
38   139942 147060.60   -7118.6000
39   118612 147060.60  -28448.6000
40    72880  60800.35   12079.6471
41    65475  60800.35    4674.6471
42    99643  80754.93   18888.0702
43    71965  80754.93   -8789.9298
44    77272  80754.93   -3482.9298
45    49289  60800.35  -11511.3529
46   135131 108208.06   26922.9444
47   108446 108208.06     237.9444
48    89746 108208.06  -18462.0556
49    44296  60800.35  -16504.3529
50    77648  80754.93   -3106.9298
51   181528 108208.06   73319.9444
52   134019  80754.93   53264.0702
53   124064 119975.38    4088.6250
54    92630  80754.93   11875.0702
55   121848  80754.93   41093.0702
56    52915  80754.93  -27839.9298
57    81872  80754.93    1117.0702
58    58981  60800.35   -1819.3529
59    53515  60800.35   -7285.3529
60    60812  80754.93  -19942.9298
61    56375  60800.35   -4425.3529
62    65490  80754.93  -15264.9298
63    80949  60800.35   20148.6471
64    76302  80754.93   -4452.9298
65   104011  80754.93   23256.0702
66    98104 147060.60  -48956.6000
67    67989  80754.93  -12765.9298
68    30989  60800.35  -29811.3529
69   135458 119975.38   15482.6250
70    73504  80754.93   -7250.9298
71    63123  80754.93  -17631.9298
72    61254 108208.06  -46954.0556
73    74914  80754.93   -5840.9298
74    31774  60800.35  -29026.3529
75    81437  80754.93     682.0702
76    87186  80754.93    6431.0702
77    50090  60800.35  -10710.3529
78    65745 108208.06  -42463.0556
79    56653  80754.93  -24101.9298
80   158399  80754.93   77644.0702
81    46455  80754.93  -34299.9298
82    73624 108208.06  -34584.0556
83    38395  60800.35  -22405.3529
84    91899  60800.35   31098.6471
85   139526 108208.06   31317.9444
86    52164  80754.93  -28590.9298
87    51567  80754.93  -29187.9298
88    70551  80754.93  -10203.9298
89    84856  80754.93    4101.0702
90   102538 119975.38  -17437.3750
91    86678  60800.35   25877.6471
92    85709  80754.93    4954.0702
93    34662  28343.42    6318.5789
94   150580 108208.06   42371.9444
95    99611 119975.38  -20364.3750
96    19349  28343.42   -8994.4211
97    99373  60800.35   38572.6471
98    86230  80754.93    5475.0702
99    30837  28343.42    2493.5789
100   31706  80754.93  -49048.9298
101   89806  80754.93    9051.0702
102   62088  60800.35    1287.6471
103   40151  28343.42   11807.5789
104   27634  60800.35  -33166.3529
105   76990 119975.38  -42985.3750
106   37460  28343.42    9116.5789
107   54157  80754.93  -26597.9298
108   49862  60800.35  -10938.3529
109   84337  80754.93    3582.0702
110   64175  80754.93  -16579.9298
111   59382  80754.93  -21372.9298
112  119308  80754.93   38553.0702
113   76702  80754.93   -4052.9298
114  103425  60800.35   42624.6471
115   70344  80754.93  -10410.9298
116   43410  28343.42   15066.5789
117  104838 119975.38  -15137.3750
118   62215  80754.93  -18539.9298
119   69304  80754.93  -11450.9298
120   53117  60800.35   -7683.3529
121   19764  28343.42   -8579.4211
122   86680  80754.93    5925.0702
123   84105  60800.35   23304.6471
124   77945  80754.93   -2809.9298
125   89113  80754.93    8358.0702
126   91005  80754.93   10250.0702
127   40248  28343.42   11904.5789
128   64187  60800.35    3386.6471
129   50857  60800.35   -9943.3529
130   56613  60800.35   -4187.3529
131   62792  80754.93  -17962.9298
132   72535  60800.35   11734.6471
133   98146  60800.35   37345.6471
134  210907 218589.04   -7682.0364
135  120982 147060.60  -26078.6000
136  176508 188516.15  -12008.1500
137  179321 271462.48  -92141.4800
138  123185 119975.38    3209.6250
139   52746  60800.35   -8054.3529
140  385534 218589.04  166944.9636
141   33170  60800.35  -27630.3529
142  149061 132433.36   16627.6400
143  165446 147060.60   18385.4000
144  237213 218589.04   18623.9636
145  173326 218589.04  -45263.0364
146  133131 132433.36     697.6400
147  258873 271462.48  -12589.4800
148  180083 188516.15   -8433.1500
149  324799 271462.48   53336.5200
150  230964 218589.04   12374.9636
151  236785 218589.04   18195.9636
152  135473 218589.04  -83116.0364
153  202925 218589.04  -15664.0364
154  215147 218589.04   -3442.0364
155  344297 271462.48   72834.5200
156  153935 132433.36   21501.6400
157  132943 218589.04  -85646.0364
158  174724 271462.48  -96738.4800
159  174415 188516.15  -14101.1500
160  225548 218589.04    6958.9636
161  223632 218589.04    5042.9636
162  124817 132433.36   -7616.3600
163  221698 271462.48  -49764.4800
164  210767 218589.04   -7822.0364
165  170266 132433.36   37832.6400
166  260561 218589.04   41971.9636
167   84853 132433.36  -47580.3600
168  294424 218589.04   75834.9636
169  215641 188516.15   27124.8500
170  325107 218589.04  106517.9636
171  167542 147060.60   20481.4000
172  106408  80754.93   25653.0702
173  265769 218589.04   47179.9636
174  269651 218589.04   51061.9636
175  149112 188516.15  -39404.1500
176  152871 147060.60    5810.4000
177  111665 132433.36  -20768.3600
178  116408 132433.36  -16025.3600
179  362301 271462.48   90838.5200
180   78800 132433.36  -53633.3600
181  183167 218589.04  -35422.0364
182  277965 271462.48    6502.5200
183  150629 218589.04  -67960.0364
184  168809 218589.04  -49780.0364
185   24188  28343.42   -4155.4211
186  329267 218589.04  110677.9636
187   65029  80754.93  -15725.9298
188  101097 108208.06   -7111.0556
189  218946 271462.48  -52516.4800
190  244052 218589.04   25462.9636
191  233328 271462.48  -38134.4800
192  256462 218589.04   37872.9636
193  206161 218589.04  -12428.0364
194  311473 218589.04   92883.9636
195  235800 271462.48  -35662.4800
196  177939 188516.15  -10577.1500
197  207176 188516.15   18659.8500
198  196553 132433.36   64119.6400
199  174184 147060.60   27123.4000
200  143246 147060.60   -3814.6000
201  187559 218589.04  -31030.0364
202  187681 218589.04  -30908.0364
203  119016 218589.04  -99573.0364
204  182192 218589.04  -36397.0364
205   73566  80754.93   -7188.9298
206  194979 188516.15    6462.8500
207  167488 147060.60   20427.4000
208  143756 218589.04  -74833.0364
209  275541 218589.04   56951.9636
210  243199 218589.04   24609.9636
211  182999 188516.15   -5517.1500
212  135649 218589.04  -82940.0364
213  152299 188516.15  -36217.1500
214  120221 147060.60  -26839.6000
215  346485 271462.48   75022.5200
216  145790 132433.36   13356.6400
217  193339 218589.04  -25250.0364
218   80953 119975.38  -39022.3750
219  122774 132433.36   -9659.3600
220  130585 188516.15  -57931.1500
221  286468 188516.15   97951.8500
222  241066 218589.04   22476.9636
223  148446 218589.04  -70143.0364
224  204713 188516.15   16196.8500
225  182079 218589.04  -36510.0364
226  140344 132433.36    7910.6400
227  220516 218589.04    1926.9636
228  243060 188516.15   54543.8500
229  162765 147060.60   15704.4000
230  182613 218589.04  -35976.0364
231  232138 218589.04   13548.9636
232  265318 271462.48   -6144.4800
233  310839 218589.04   92249.9636
234  225060 218589.04    6470.9636
235  232317 218589.04   13727.9636
236  144966 132433.36   12532.6400
237   43287  60800.35  -17513.3529
238  155754 147060.60    8693.4000
239  164709 218589.04  -53880.0364
240  201940 271462.48  -69522.4800
241  235454 218589.04   16864.9636
242   99466  80754.93   18711.0702
243  100750 218589.04 -117839.0364
244  224549 188516.15   36032.8500
245  243511 218589.04   24921.9636
246   22938  28343.42   -5405.4211
247  152474 218589.04  -66115.0364
248   61857  80754.93  -18897.9298
249  132487 188516.15  -56029.1500
250  317394 218589.04   98804.9636
251   21054  28343.42   -7289.4211
252  209641 147060.60   62580.4000
253   31414  28343.42    3070.5789
254  244749 271462.48  -26713.4800
255  184510 188516.15   -4006.1500
256  128423 132433.36   -4010.3600
257   97839 132433.36  -34594.3600
258   38214  28343.42    9870.5789
259  151101 132433.36   18667.6400
260  272458 271462.48     995.5200
261  172494 132433.36   40060.6400
262  328107 271462.48   56644.5200
263  250579 271462.48  -20883.4800
264  351067 271462.48   79604.5200
265  158015 188516.15  -30501.1500
266   85439 132433.36  -46994.3600
267  229242 188516.15   40725.8500
268  351619 271462.48   80156.5200
269   84207  60800.35   23406.6471
270  324598 271462.48   53135.5200
271  131069 132433.36   -1364.3600
272  204271 218589.04  -14318.0364
273  165543 188516.15  -22973.1500
274  141722 132433.36    9288.6400
275  299775 271462.48   28312.5200
276  195838 218589.04  -22751.0364
277  173260 119975.38   53284.6250
278  254488 218589.04   35898.9636
279  104389 271462.48 -167073.4800
280  199476 218589.04  -19113.0364
281  224330 218589.04    5740.9636
282   14688  28343.42  -13655.4211
283  181633 132433.36   49199.6400
284  271856 271462.48     393.5200
285    7199  28343.42  -21144.4211
286   46660  28343.42   18316.5789
287   17547  28343.42  -10796.4211
288   95227 132433.36  -37206.3600
289  152601 132433.36   20167.6400
> 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/44cw11324132594.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/58ga51324132594.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/6zf1m1324132594.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/754x01324132594.tab") 
+ }
> 
> try(system("convert tmp/2ctwd1324132594.ps tmp/2ctwd1324132594.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ejr81324132594.ps tmp/3ejr81324132594.png",intern=TRUE))
character(0)
> try(system("convert tmp/44cw11324132594.ps tmp/44cw11324132594.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  5.377   0.306   5.935