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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> x <- array(list(210907
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+ ,29
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+ ,3
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+ ,16
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+ ,1
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+ ,27
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+ ,0
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+ ,21
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+ ,0
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+ ,19
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+ ,1
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+ ,176
+ ,46
+ ,0
+ ,28
+ ,15
+ ,72535
+ ,14
+ ,329
+ ,44
+ ,0
+ ,17
+ ,16)
+ ,dim=c(7
+ ,289)
+ ,dimnames=list(c('time_in_rfc'
+ ,'logins'
+ ,'compendium_views_info'
+ ,'compendium_views_pr'
+ ,'shared_compendiums'
+ ,'blogged_computations'
+ ,'compendiums_reviewed')
+ ,1:289))
>  y <- array(NA,dim=c(7,289),dimnames=list(c('time_in_rfc','logins','compendium_views_info','compendium_views_pr','shared_compendiums','blogged_computations','compendiums_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 = '3'
> par2 = 'none'
> par1 = '1'
> 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] "time_in_rfc"
> x[,par1]
  [1] 210907 120982 176508 179321 123185  52746 385534  33170 101645 149061
 [11] 165446 237213 173326 133131 258873 180083 324799 230964 236785 135473
 [21] 202925 215147 344297 153935 132943 174724 174415 225548 223632 124817
 [31] 221698 210767 170266 260561  84853 294424 101011 215641 325107   7176
 [41] 167542 106408  96560 265769 269651 149112 175824 152871 111665 116408
 [51] 362301  78800 183167 277965 150629 168809  24188 329267  65029 101097
 [61] 218946 244052 341570 103597 233328 256462 206161 311473 235800 177939
 [71] 207176 196553 174184 143246 187559 187681 119016 182192  73566 194979
 [81] 167488 143756 275541 243199 182999 135649 152299 120221 346485 145790
 [91] 193339  80953 122774 130585 112611 286468 241066 148446 204713 182079
[101] 140344 220516 243060 162765 182613 232138 265318  85574 310839 225060
[111] 232317 144966  43287 155754 164709 201940 235454 220801  99466  92661
[121] 133328  61361 125930 100750 224549  82316 102010 101523 243511  22938
[131]  41566 152474  61857  99923 132487 317394  21054 209641  22648  31414
[141]  46698 131698  91735 244749 184510  79863 128423  97839  38214 151101
[151] 272458 172494 108043 328107 250579 351067 158015  98866  85439 229242
[161] 351619  84207 120445 324598 131069 204271 165543 141722 116048 250047
[171] 299775 195838 173260 254488 104389 136084 199476  92499 224330 135781
[181]  74408  81240  14688 181633 271856   7199  46660  17547 133368  95227
[191] 152601  98146  79619  59194 139942 118612  72880  65475  99643  71965
[201]  77272  49289 135131 108446  89746  44296  77648 181528 134019 124064
[211]  92630 121848  52915  81872  58981  53515  60812  56375  65490  80949
[221]  76302 104011  98104  67989  30989 135458  73504  63123  61254  74914
[231]  31774  81437  87186  50090  65745  56653 158399  46455  73624  38395
[241]  91899 139526  52164  51567  70551  84856 102538  86678  85709  34662
[251] 150580  99611  19349  99373  86230  30837  31706  89806  62088  40151
[261]  27634  76990  37460  54157  49862  84337  64175  59382 119308  76702
[271] 103425  70344  43410 104838  62215  69304  53117  19764  86680  84105
[281]  77945  89113  91005  40248  64187  50857  56613  62792  72535
> 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] "time_in_rfc"           "logins"                "compendium_views_info"
[4] "compendium_views_pr"   "shared_compendiums"    "blogged_computations" 
[7] "compendiums_reviewed" 
> colnames(x)[par1]
[1] "time_in_rfc"
> x[,par1]
  [1] 210907 120982 176508 179321 123185  52746 385534  33170 101645 149061
 [11] 165446 237213 173326 133131 258873 180083 324799 230964 236785 135473
 [21] 202925 215147 344297 153935 132943 174724 174415 225548 223632 124817
 [31] 221698 210767 170266 260561  84853 294424 101011 215641 325107   7176
 [41] 167542 106408  96560 265769 269651 149112 175824 152871 111665 116408
 [51] 362301  78800 183167 277965 150629 168809  24188 329267  65029 101097
 [61] 218946 244052 341570 103597 233328 256462 206161 311473 235800 177939
 [71] 207176 196553 174184 143246 187559 187681 119016 182192  73566 194979
 [81] 167488 143756 275541 243199 182999 135649 152299 120221 346485 145790
 [91] 193339  80953 122774 130585 112611 286468 241066 148446 204713 182079
[101] 140344 220516 243060 162765 182613 232138 265318  85574 310839 225060
[111] 232317 144966  43287 155754 164709 201940 235454 220801  99466  92661
[121] 133328  61361 125930 100750 224549  82316 102010 101523 243511  22938
[131]  41566 152474  61857  99923 132487 317394  21054 209641  22648  31414
[141]  46698 131698  91735 244749 184510  79863 128423  97839  38214 151101
[151] 272458 172494 108043 328107 250579 351067 158015  98866  85439 229242
[161] 351619  84207 120445 324598 131069 204271 165543 141722 116048 250047
[171] 299775 195838 173260 254488 104389 136084 199476  92499 224330 135781
[181]  74408  81240  14688 181633 271856   7199  46660  17547 133368  95227
[191] 152601  98146  79619  59194 139942 118612  72880  65475  99643  71965
[201]  77272  49289 135131 108446  89746  44296  77648 181528 134019 124064
[211]  92630 121848  52915  81872  58981  53515  60812  56375  65490  80949
[221]  76302 104011  98104  67989  30989 135458  73504  63123  61254  74914
[231]  31774  81437  87186  50090  65745  56653 158399  46455  73624  38395
[241]  91899 139526  52164  51567  70551  84856 102538  86678  85709  34662
[251] 150580  99611  19349  99373  86230  30837  31706  89806  62088  40151
[261]  27634  76990  37460  54157  49862  84337  64175  59382 119308  76702
[271] 103425  70344  43410 104838  62215  69304  53117  19764  86680  84105
[281]  77945  89113  91005  40248  64187  50857  56613  62792  72535
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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/fisher/rcomp/tmp/1m3121355240903.tab") 
+ }
+ }
> m

	 Conditional inference tree with 13 terminal nodes

Response:  time_in_rfc 
Inputs:  logins, compendium_views_info, compendium_views_pr, shared_compendiums, blogged_computations, compendiums_reviewed 
Number of observations:  289 

1) compendium_views_info <= 555; criterion = 1, statistic = 229.425
  2) compendium_views_info <= 361; criterion = 1, statistic = 117.828
    3) blogged_computations <= 18; criterion = 1, statistic = 61.106
      4) compendium_views_info <= 200; criterion = 1, statistic = 23.096
        5) logins <= 20; criterion = 0.999, statistic = 14.149
          6)*  weights = 17 
        5) logins > 20
          7)*  weights = 7 
      4) compendium_views_info > 200
        8)*  weights = 23 
    3) blogged_computations > 18
      9) blogged_computations <= 42; criterion = 1, statistic = 20.966
        10) compendium_views_info <= 230; criterion = 0.998, statistic = 12.858
          11)*  weights = 16 
        10) compendium_views_info > 230
          12)*  weights = 34 
      9) blogged_computations > 42
        13)*  weights = 7 
  2) compendium_views_info > 361
    14) compendiums_reviewed <= 24; criterion = 1, statistic = 40.126
      15) blogged_computations <= 35; criterion = 0.957, statistic = 7.199
        16)*  weights = 31 
      15) blogged_computations > 35
        17)*  weights = 20 
    14) compendiums_reviewed > 24
      18)*  weights = 38 
1) compendium_views_info > 555
  19) compendium_views_info <= 967; criterion = 1, statistic = 47.205
    20) blogged_computations <= 55; criterion = 1, statistic = 21.03
      21)*  weights = 17 
    20) blogged_computations > 55
      22) compendium_views_info <= 599; criterion = 0.962, statistic = 7.432
        23)*  weights = 10 
      22) compendium_views_info > 599
        24)*  weights = 52 
  19) compendium_views_info > 967
    25)*  weights = 17 
> postscript(file="/var/fisher/rcomp/tmp/2080w1355240903.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/fisher/rcomp/tmp/3d67z1355240903.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    210907 161692.71  49214.28947
2    120982 115681.71   5300.28571
3    176508 183594.90  -7086.90000
4    179321 228546.94 -49225.94231
5    123185 115681.71   7503.28571
6     52746  47861.29   4884.71429
7    385534 322592.29  62941.70588
8     33170  25494.35   7675.64706
9    101645  95634.61   6010.38710
10   149061 149150.29    -89.29412
11   165446 161692.71   3753.28947
12   237213 228546.94   8666.05769
13   173326 161692.71  11633.28947
14   133131 161692.71 -28561.71053
15   258873 228546.94  30326.05769
16   180083 161692.71  18390.28947
17   324799 322592.29   2206.70588
18   230964 228546.94   2417.05769
19   236785 228546.94   8238.05769
20   135473 117064.70  18408.30000
21   202925 183594.90  19330.10000
22   215147 228546.94 -13399.94231
23   344297 228546.94 115750.05769
24   153935 161692.71  -7757.71053
25   132943 161692.71 -28749.71053
26   174724 228546.94 -53822.94231
27   174415 228546.94 -54131.94231
28   225548 228546.94  -2998.94231
29   223632 161692.71  61939.28947
30   124817 161692.71 -36875.71053
31   221698 228546.94  -6848.94231
32   210767 228546.94 -17779.94231
33   170266 161692.71   8573.28947
34   260561 228546.94  32014.05769
35    84853  84594.88    258.11765
36   294424 322592.29 -28168.29412
37   101011  84594.88  16416.11765
38   215641 228546.94 -12905.94231
39   325107 228546.94  96560.05769
40     7176  25494.35 -18318.35294
41   167542 161692.71   5849.28947
42   106408  84594.88  21813.11765
43    96560 117064.70 -20504.70000
44   265769 228546.94  37222.05769
45   269651 322592.29 -52941.29412
46   149112 161692.71 -12580.71053
47   175824 228546.94 -52722.94231
48   152871 161692.71  -8821.71053
49   111665  84594.88  27070.11765
50   116408 149150.29 -32742.29412
51   362301 322592.29  39708.70588
52    78800  84594.88  -5794.88235
53   183167 183594.90   -427.90000
54   277965 322592.29 -44627.29412
55   150629 228546.94 -77917.94231
56   168809 161692.71   7116.28947
57    24188  57076.70 -32888.69565
58   329267 228546.94 100720.05769
59    65029  84594.88 -19565.88235
60   101097  95634.61   5462.38710
61   218946 228546.94  -9600.94231
62   244052 183594.90  60457.10000
63   341570 322592.29  18977.70588
64   103597  95634.61   7962.38710
65   233328 228546.94   4781.05769
66   256462 228546.94  27915.05769
67   206161 228546.94 -22385.94231
68   311473 322592.29 -11119.29412
69   235800 228546.94   7253.05769
70   177939 149150.29  28788.70588
71   207176 228546.94 -21370.94231
72   196553 161692.71  34860.28947
73   174184 161692.71  12491.28947
74   143246 161692.71 -18446.71053
75   187559 228546.94 -40987.94231
76   187681 228546.94 -40865.94231
77   119016 117064.70   1951.30000
78   182192 228546.94 -46354.94231
79    73566  95634.61 -22068.61290
80   194979 183594.90  11384.10000
81   167488 228546.94 -61058.94231
82   143756 161692.71 -17936.71053
83   275541 228546.94  46994.05769
84   243199 228546.94  14652.05769
85   182999 161692.71  21306.28947
86   135649 161692.71 -26043.71053
87   152299 161692.71  -9393.71053
88   120221 117064.70   3156.30000
89   346485 322592.29  23892.70588
90   145790 149150.29  -3360.29412
91   193339 161692.71  31646.28947
92    80953 117064.70 -36111.70000
93   122774 149150.29 -26376.29412
94   130585 115681.71  14903.28571
95   112611 115681.71  -3070.71429
96   286468 322592.29 -36124.29412
97   241066 228546.94  12519.05769
98   148446 228546.94 -80100.94231
99   204713 228546.94 -23833.94231
100  182079 161692.71  20386.28947
101  140344 161692.71 -21348.71053
102  220516 228546.94  -8030.94231
103  243060 228546.94  14513.05769
104  162765 161692.71   1072.28947
105  182613 161692.71  20920.28947
106  232138 228546.94   3591.05769
107  265318 228546.94  36771.05769
108   85574  84594.88    979.11765
109  310839 322592.29 -11753.29412
110  225060 228546.94  -3486.94231
111  232317 228546.94   3770.05769
112  144966 161692.71 -16726.71053
113   43287  57076.70 -13789.69565
114  155754 183594.90 -27840.90000
115  164709 183594.90 -18885.90000
116  201940 228546.94 -26606.94231
117  235454 228546.94   6907.05769
118  220801 149150.29  71650.70588
119   99466  84594.88  14871.11765
120   92661 117064.70 -24403.70000
121  133328 117064.70  16263.30000
122   61361  95634.61 -34273.61290
123  125930 149150.29 -23220.29412
124  100750 161692.71 -60942.71053
125  224549 161692.71  62856.28947
126   82316  84594.88  -2278.88235
127  102010  95634.61   6375.38710
128  101523 115681.71 -14158.71429
129  243511 228546.94  14964.05769
130   22938  25494.35  -2556.35294
131   41566  57076.70 -15510.69565
132  152474 183594.90 -31120.90000
133   61857  59371.75   2485.25000
134   99923 149150.29 -49227.29412
135  132487 161692.71 -29205.71053
136  317394 322592.29  -5198.29412
137   21054  25494.35  -4440.35294
138  209641 228546.94 -18905.94231
139   22648  25494.35  -2846.35294
140   31414  25494.35   5919.64706
141   46698  57076.70 -10378.69565
142  131698 117064.70  14633.30000
143   91735  95634.61  -3899.61290
144  244749 228546.94  16202.05769
145  184510 161692.71  22817.28947
146   79863  95634.61 -15771.61290
147  128423 161692.71 -33269.71053
148   97839  95634.61   2204.38710
149   38214  57076.70 -18862.69565
150  151101 161692.71 -10591.71053
151  272458 228546.94  43911.05769
152  172494 161692.71  10801.28947
153  108043 117064.70  -9021.70000
154  328107 322592.29   5514.70588
155  250579 228546.94  22032.05769
156  351067 322592.29  28474.70588
157  158015 161692.71  -3677.71053
158   98866  95634.61   3231.38710
159   85439  84594.88    844.11765
160  229242 228546.94    695.05769
161  351619 322592.29  29026.70588
162   84207  57076.70  27130.30435
163  120445 117064.70   3380.30000
164  324598 322592.29   2005.70588
165  131069 149150.29 -18081.29412
166  204271 161692.71  42578.28947
167  165543 183594.90 -18051.90000
168  141722 161692.71 -19970.71053
169  116048 115681.71    366.28571
170  250047 149150.29 100896.70588
171  299775 322592.29 -22817.29412
172  195838 183594.90  12243.10000
173  173260 149150.29  24109.70588
174  254488 228546.94  25941.05769
175  104389 161692.71 -57303.71053
176  136084 149150.29 -13066.29412
177  199476 228546.94 -29070.94231
178   92499  84594.88   7904.11765
179  224330 228546.94  -4216.94231
180  135781 117064.70  18716.30000
181   74408  95634.61 -21226.61290
182   81240 117064.70 -35824.70000
183   14688  25494.35 -10806.35294
184  181633 149150.29  32482.70588
185  271856 228546.94  43309.05769
186    7199  25494.35 -18295.35294
187   46660  57076.70 -10416.69565
188   17547  25494.35  -7947.35294
189  133368 117064.70  16303.30000
190   95227  84594.88  10632.11765
191  152601 117064.70  35536.30000
192   98146  95634.61   2511.38710
193   79619 117064.70 -37445.70000
194   59194  57076.70   2117.30435
195  139942 117064.70  22877.30000
196  118612 117064.70   1547.30000
197   72880  95634.61 -22754.61290
198   65475  57076.70   8398.30435
199   99643  95634.61   4008.38710
200   71965  84594.88 -12629.88235
201   77272  59371.75  17900.25000
202   49289  25494.35  23794.64706
203  135131 117064.70  18066.30000
204  108446  95634.61  12811.38710
205   89746 149150.29 -59404.29412
206   44296  47861.29  -3565.28571
207   77648  84594.88  -6946.88235
208  181528 149150.29  32377.70588
209  134019  95634.61  38384.38710
210  124064 117064.70   6999.30000
211   92630  95634.61  -3004.61290
212  121848  84594.88  37253.11765
213   52915  59371.75  -6456.75000
214   81872  84594.88  -2722.88235
215   58981  95634.61 -36653.61290
216   53515  57076.70  -3561.69565
217   60812  95634.61 -34822.61290
218   56375  57076.70   -701.69565
219   65490  59371.75   6118.25000
220   80949  57076.70  23872.30435
221   76302  84594.88  -8292.88235
222  104011  95634.61   8376.38710
223   98104 149150.29 -51046.29412
224   67989  59371.75   8617.25000
225   30989  25494.35   5494.64706
226  135458 149150.29 -13692.29412
227   73504  84594.88 -11090.88235
228   63123  84594.88 -21471.88235
229   61254  84594.88 -23340.88235
230   74914  95634.61 -20720.61290
231   31774  47861.29 -16087.28571
232   81437  84594.88  -3157.88235
233   87186  95634.61  -8448.61290
234   50090  57076.70  -6986.69565
235   65745  84594.88 -18849.88235
236   56653  59371.75  -2718.75000
237  158399  95634.61  62764.38710
238   46455  59371.75 -12916.75000
239   73624  95634.61 -22010.61290
240   38395  57076.70 -18681.69565
241   91899  57076.70  34822.30435
242  139526  95634.61  43891.38710
243   52164  59371.75  -7207.75000
244   51567  59371.75  -7804.75000
245   70551  84594.88 -14043.88235
246   84856  95634.61 -10778.61290
247  102538 117064.70 -14526.70000
248   86678  57076.70  29601.30435
249   85709  84594.88   1114.11765
250   34662  57076.70 -22414.69565
251  150580  95634.61  54945.38710
252   99611  84594.88  15016.11765
253   19349  25494.35  -6145.35294
254   99373  95634.61   3738.38710
255   86230  95634.61  -9404.61290
256   30837  25494.35   5342.64706
257   31706  59371.75 -27665.75000
258   89806  84594.88   5211.11765
259   62088  47861.29  14226.71429
260   40151  47861.29  -7710.28571
261   27634  25494.35   2139.64706
262   76990  59371.75  17618.25000
263   37460  25494.35  11965.64706
264   54157  59371.75  -5214.75000
265   49862  57076.70  -7214.69565
266   84337  84594.88   -257.88235
267   64175  84594.88 -20419.88235
268   59382  59371.75     10.25000
269  119308  84594.88  34713.11765
270   76702  84594.88  -7892.88235
271  103425  95634.61   7790.38710
272   70344  59371.75  10972.25000
273   43410  57076.70 -13666.69565
274  104838 115681.71 -10843.71429
275   62215  59371.75   2843.25000
276   69304  84594.88 -15290.88235
277   53117  47861.29   5255.71429
278   19764  25494.35  -5730.35294
279   86680  84594.88   2085.11765
280   84105  57076.70  27028.30435
281   77945  84594.88  -6649.88235
282   89113  84594.88   4518.11765
283   91005  95634.61  -4629.61290
284   40248  25494.35  14753.64706
285   64187  57076.70   7110.30435
286   50857  47861.29   2995.71429
287   56613  57076.70   -463.69565
288   62792  59371.75   3420.25000
289   72535  57076.70  15458.30435
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
> postscript(file="/var/fisher/rcomp/tmp/4k8vb1355240903.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/fisher/rcomp/tmp/5wr1j1355240903.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/fisher/rcomp/tmp/660a91355240903.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/fisher/rcomp/tmp/73akd1355240903.tab") 
+ }
> 
> try(system("convert tmp/2080w1355240903.ps tmp/2080w1355240903.png",intern=TRUE))
character(0)
> try(system("convert tmp/3d67z1355240903.ps tmp/3d67z1355240903.png",intern=TRUE))
character(0)
> try(system("convert tmp/4k8vb1355240903.ps tmp/4k8vb1355240903.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  8.251   0.634   8.896