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 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

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+ ,0
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+ ,894
+ ,72535
+ ,14
+ ,329
+ ,44
+ ,0
+ ,17)
+ ,dim=c(7
+ ,289)
+ ,dimnames=list(c('pageviews'
+ ,'time_in_rfc'
+ ,'logins'
+ ,'compendium_views_info'
+ ,'compendium_views_pr'
+ ,'shared_compendiums'
+ ,'blogged_computations')
+ ,1:289))
>  y <- array(NA,dim=c(7,289),dimnames=list(c('pageviews','time_in_rfc','logins','compendium_views_info','compendium_views_pr','shared_compendiums','blogged_computations'),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 = '2'
> par4 <- 'no'
> par3 <- '3'
> par2 <- 'none'
> par1 <- '2'
> #'GNU S' R Code compiled by R2WASP v. 1.2.291 ()
> #Author: root
> #To cite this work: Wessa P., 2012, Recursive Partitioning (Regression Trees) (v1.0.3) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_regression_trees.wasp/
> #Source of accompanying publication: 
> #
> 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] "pageviews"             "time_in_rfc"           "logins"               
[4] "compendium_views_info" "compendium_views_pr"   "shared_compendiums"   
[7] "blogged_computations" 
> 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/1c9vz1355274422.tab") 
+ }
+ }
> m

	 Conditional inference tree with 11 terminal nodes

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

1) pageviews <= 1613; criterion = 1, statistic = 229.566
  2) pageviews <= 1212; criterion = 1, statistic = 126.863
    3) pageviews <= 800; criterion = 1, statistic = 72.955
      4) pageviews <= 578; criterion = 1, statistic = 31.889
        5) pageviews <= 391; criterion = 0.998, statistic = 13.203
          6)*  weights = 10 
        5) pageviews > 391
          7)*  weights = 15 
      4) pageviews > 578
        8)*  weights = 36 
    3) pageviews > 800
      9) blogged_computations <= 42; criterion = 1, statistic = 21.332
        10)*  weights = 53 
      9) blogged_computations > 42
        11)*  weights = 11 
  2) pageviews > 1212
    12) compendium_views_pr <= 87; criterion = 0.999, statistic = 14.049
      13)*  weights = 57 
    12) compendium_views_pr > 87
      14)*  weights = 13 
1) pageviews > 1613
  15) compendium_views_info <= 967; criterion = 1, statistic = 46.779
    16) blogged_computations <= 55; criterion = 1, statistic = 19.203
      17)*  weights = 15 
    16) blogged_computations > 55
      18) logins <= 71; criterion = 0.954, statistic = 7.089
        19)*  weights = 32 
      18) logins > 71
        20)*  weights = 30 
  15) compendium_views_info > 967
    21)*  weights = 17 
> postscript(file="/var/fisher/rcomp/tmp/241f61355274422.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/3lynl1355274422.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 145472.61  65434.38596
2    120982 125386.18  -4404.18182
3    176508 145472.61  31035.38596
4    179321 236424.60 -57103.60000
5    123185 125386.18  -2201.18182
6     52746  39424.13  13321.86667
7    385534 322592.29  62941.70588
8     33170  19387.40  13782.60000
9    101645  84318.92  17326.07547
10   149061 145472.61   3588.38596
11   165446 145472.61  19973.38596
12   237213 236424.60    788.40000
13   173326 145472.61  27853.38596
14   133131 145472.61 -12341.61404
15   258873 209288.16  49584.84375
16   180083 145472.61  34610.38596
17   324799 322592.29   2206.70588
18   230964 209288.16  21675.84375
19   236785 236424.60    360.40000
20   135473 125386.18  10086.81818
21   202925 209288.16  -6363.15625
22   215147 209288.16   5858.84375
23   344297 236424.60 107872.40000
24   153935 145472.61   8462.38596
25   132943 145472.61 -12529.61404
26   174724 236424.60 -61700.60000
27   174415 236424.60 -62009.60000
28   225548 236424.60 -10876.60000
29   223632 236424.60 -12792.60000
30   124817 145472.61 -20655.61404
31   221698 209288.16  12409.84375
32   210767 209288.16   1478.84375
33   170266 145472.61  24793.38596
34   260561 236424.60  24136.40000
35    84853  84318.92    534.07547
36   294424 322592.29 -28168.29412
37   101011  84318.92  16692.07547
38   215641 209288.16   6352.84375
39   325107 236424.60  88682.40000
40     7176  19387.40 -12211.40000
41   167542 145472.61  22069.38596
42   106408  84318.92  22089.07547
43    96560 144951.27 -48391.26667
44   265769 236424.60  29344.40000
45   269651 322592.29 -52941.29412
46   149112 145472.61   3639.38596
47   175824 236424.60 -60600.60000
48   152871 145472.61   7398.38596
49   111665  84318.92  27346.07547
50   116408 144951.27 -28543.26667
51   362301 322592.29  39708.70588
52    78800  84318.92  -5518.92453
53   183167 209288.16 -26121.15625
54   277965 322592.29 -44627.29412
55   150629 209288.16 -58659.15625
56   168809 145472.61  23336.38596
57    24188  39424.13 -15236.13333
58   329267 236424.60  92842.40000
59    65029  60147.00   4882.00000
60   101097  84318.92  16778.07547
61   218946 209288.16   9657.84375
62   244052 209288.16  34763.84375
63   341570 322592.29  18977.70588
64   103597  84318.92  19278.07547
65   233328 236424.60  -3096.60000
66   256462 236424.60  20037.40000
67   206161 209288.16  -3127.15625
68   311473 322592.29 -11119.29412
69   235800 236424.60   -624.60000
70   177939 144951.27  32987.73333
71   207176 209288.16  -2112.15625
72   196553 145472.61  51080.38596
73   174184 145472.61  28711.38596
74   143246 145472.61  -2226.61404
75   187559 236424.60 -48865.60000
76   187681 209288.16 -21607.15625
77   119016 145472.61 -26456.61404
78   182192 209288.16 -27096.15625
79    73566  84318.92 -10752.92453
80   194979 209288.16 -14309.15625
81   167488 145472.61  22015.38596
82   143756 145472.61  -1716.61404
83   275541 209288.16  66252.84375
84   243199 236424.60   6774.40000
85   182999 145472.61  37526.38596
86   135649 145472.61  -9823.61404
87   152299 145472.61   6826.38596
88   120221 145472.61 -25251.61404
89   346485 322592.29  23892.70588
90   145790 145472.61    317.38596
91   193339 236424.60 -43085.60000
92    80953 125386.18 -44433.18182
93   122774 144951.27 -22177.26667
94   130585 125386.18   5198.81818
95   112611 125386.18 -12775.18182
96   286468 322592.29 -36124.29412
97   241066 236424.60   4641.40000
98   148446 236424.60 -87978.60000
99   204713 209288.16  -4575.15625
100  182079 209288.16 -27209.15625
101  140344 145472.61  -5128.61404
102  220516 209288.16  11227.84375
103  243060 209288.16  33771.84375
104  162765 145472.61  17292.38596
105  182613 145472.61  37140.38596
106  232138 209288.16  22849.84375
107  265318 236424.60  28893.40000
108   85574 145472.61 -59898.61404
109  310839 322592.29 -11753.29412
110  225060 236424.60 -11364.60000
111  232317 209288.16  23028.84375
112  144966 145472.61   -506.61404
113   43287  60147.00 -16860.00000
114  155754 209288.16 -53534.15625
115  164709 145472.61  19236.38596
116  201940 209288.16  -7348.15625
117  235454 236424.60   -970.60000
118  220801 144951.27  75849.73333
119   99466  84318.92  15147.07547
120   92661 105271.69 -12610.69231
121  133328 145472.61 -12144.61404
122   61361  84318.92 -22957.92453
123  125930 144951.27 -19021.26667
124  100750 145472.61 -44722.61404
125  224549 145472.61  79076.38596
126   82316  84318.92  -2002.92453
127  102010  84318.92  17691.07547
128  101523 145472.61 -43949.61404
129  243511 209288.16  34222.84375
130   22938  19387.40   3550.60000
131   41566  60147.00 -18581.00000
132  152474 145472.61   7001.38596
133   61857  39424.13  22432.86667
134   99923 144951.27 -45028.26667
135  132487 105271.69  27215.30769
136  317394 322592.29  -5198.29412
137   21054  19387.40   1666.60000
138  209641 209288.16    352.84375
139   22648  60147.00 -37499.00000
140   31414  39424.13  -8010.13333
141   46698  60147.00 -13449.00000
142  131698 209288.16 -77590.15625
143   91735  84318.92   7416.07547
144  244749 236424.60   8324.40000
145  184510 145472.61  39037.38596
146   79863 105271.69 -25408.69231
147  128423  84318.92  44104.07547
148   97839  84318.92  13520.07547
149   38214  39424.13  -1210.13333
150  151101 105271.69  45829.30769
151  272458 209288.16  63169.84375
152  172494 125386.18  47107.81818
153  108043 145472.61 -37429.61404
154  328107 322592.29   5514.70588
155  250579 236424.60  14154.40000
156  351067 322592.29  28474.70588
157  158015 125386.18  32628.81818
158   98866  84318.92  14547.07547
159   85439  84318.92   1120.07547
160  229242 236424.60  -7182.60000
161  351619 322592.29  29026.70588
162   84207  84318.92   -111.92453
163  120445 145472.61 -25027.61404
164  324598 322592.29   2005.70588
165  131069 145472.61 -14403.61404
166  204271 145472.61  58798.38596
167  165543 209288.16 -43745.15625
168  141722 145472.61  -3750.61404
169  116048 125386.18  -9338.18182
170  250047 144951.27 105095.73333
171  299775 322592.29 -22817.29412
172  195838 209288.16 -13450.15625
173  173260 144951.27  28308.73333
174  254488 236424.60  18063.40000
175  104389 105271.69   -882.69231
176  136084 145472.61  -9388.61404
177  199476 209288.16  -9812.15625
178   92499  84318.92   8180.07547
179  224330 236424.60 -12094.60000
180  135781 105271.69  30509.30769
181   74408 145472.61 -71064.61404
182   81240 105271.69 -24031.69231
183   14688  19387.40  -4699.40000
184  181633 144951.27  36681.73333
185  271856 236424.60  35431.40000
186    7199  19387.40 -12188.40000
187   46660  39424.13   7235.86667
188   17547  19387.40  -1840.40000
189  133368 144951.27 -11583.26667
190   95227  84318.92  10908.07547
191  152601 145472.61   7128.38596
192   98146  84318.92  13827.07547
193   79619 105271.69 -25652.69231
194   59194 105271.69 -46077.69231
195  139942 145472.61  -5530.61404
196  118612 145472.61 -26860.61404
197   72880  84318.92 -11438.92453
198   65475  60147.00   5328.00000
199   99643 145472.61 -45829.61404
200   71965  60147.00  11818.00000
201   77272  60147.00  17125.00000
202   49289  39424.13   9864.86667
203  135131 145472.61 -10341.61404
204  108446 105271.69   3174.30769
205   89746 105271.69 -15525.69231
206   44296  39424.13   4871.86667
207   77648  84318.92  -6670.92453
208  181528 145472.61  36055.38596
209  134019 145472.61 -11453.61404
210  124064 125386.18  -1322.18182
211   92630  84318.92   8311.07547
212  121848 145472.61 -23624.61404
213   52915  60147.00  -7232.00000
214   81872  60147.00  21725.00000
215   58981  84318.92 -25337.92453
216   53515  60147.00  -6632.00000
217   60812  84318.92 -23506.92453
218   56375  60147.00  -3772.00000
219   65490  60147.00   5343.00000
220   80949  60147.00  20802.00000
221   76302  84318.92  -8016.92453
222  104011 145472.61 -41461.61404
223   98104 144951.27 -46847.26667
224   67989  60147.00   7842.00000
225   30989  19387.40  11601.60000
226  135458 144951.27  -9493.26667
227   73504  60147.00  13357.00000
228   63123  84318.92 -21195.92453
229   61254  84318.92 -23064.92453
230   74914 145472.61 -70558.61404
231   31774  60147.00 -28373.00000
232   81437  84318.92  -2881.92453
233   87186 145472.61 -58286.61404
234   50090  60147.00 -10057.00000
235   65745  84318.92 -18573.92453
236   56653  60147.00  -3494.00000
237  158399 145472.61  12926.38596
238   46455  60147.00 -13692.00000
239   73624  84318.92 -10694.92453
240   38395  60147.00 -21752.00000
241   91899  84318.92   7580.07547
242  139526 144951.27  -5425.26667
243   52164  60147.00  -7983.00000
244   51567  60147.00  -8580.00000
245   70551  84318.92 -13767.92453
246   84856  84318.92    537.07547
247  102538 144951.27 -42413.26667
248   86678  60147.00  26531.00000
249   85709  84318.92   1390.07547
250   34662  39424.13  -4762.13333
251  150580 105271.69  45308.30769
252   99611  84318.92  15292.07547
253   19349  19387.40    -38.40000
254   99373  84318.92  15054.07547
255   86230  84318.92   1911.07547
256   30837  39424.13  -8587.13333
257   31706  39424.13  -7718.13333
258   89806  84318.92   5487.07547
259   62088  60147.00   1941.00000
260   40151  39424.13    726.86667
261   27634  39424.13 -11790.13333
262   76990  84318.92  -7328.92453
263   37460  39424.13  -1964.13333
264   54157  60147.00  -5990.00000
265   49862  60147.00 -10285.00000
266   84337  84318.92     18.07547
267   64175  84318.92 -20143.92453
268   59382  60147.00   -765.00000
269  119308  60147.00  59161.00000
270   76702  84318.92  -7616.92453
271  103425 105271.69  -1846.69231
272   70344  60147.00  10197.00000
273   43410  60147.00 -16737.00000
274  104838 125386.18 -20548.18182
275   62215  60147.00   2068.00000
276   69304  84318.92 -15014.92453
277   53117  60147.00  -7030.00000
278   19764  19387.40    376.60000
279   86680  84318.92   2361.07547
280   84105  60147.00  23958.00000
281   77945  84318.92  -6373.92453
282   89113  84318.92   4794.07547
283   91005  84318.92   6686.07547
284   40248  39424.13    823.86667
285   64187  60147.00   4040.00000
286   50857  84318.92 -33461.92453
287   56613  84318.92 -27705.92453
288   62792  60147.00   2645.00000
289   72535  84318.92 -11783.92453
> 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/4gglc1355274422.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/5fzyv1355274422.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/6gqej1355274422.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/7mkxo1355274422.tab") 
+ }
> 
> try(system("convert tmp/241f61355274422.ps tmp/241f61355274422.png",intern=TRUE))
character(0)
> try(system("convert tmp/3lynl1355274422.ps tmp/3lynl1355274422.png",intern=TRUE))
character(0)
> try(system("convert tmp/4gglc1355274422.ps tmp/4gglc1355274422.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  7.580   0.621   8.190