R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
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.

> x <- array(list(210907
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+ ,6
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+ ,35
+ ,28
+ ,15
+ ,30884
+ ,72
+ ,72535
+ ,894
+ ,14
+ ,17
+ ,16
+ ,19540
+ ,27)
+ ,dim=c(7
+ ,289)
+ ,dimnames=list(c('time_in_rfc'
+ ,'pageviews'
+ ,'logins'
+ ,'blogged_computations'
+ ,'compendiums_reviewed'
+ ,'totsize'
+ ,'tothyperlinks
')
+ ,1:289))
>  y <- array(NA,dim=c(7,289),dimnames=list(c('time_in_rfc','pageviews','logins','blogged_computations','compendiums_reviewed','totsize','tothyperlinks
'),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 objects 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 is masked from 'package:survival':

    untangle.specials

The following objects 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"          "pageviews"            "logins"              
[4] "blogged_computations" "compendiums_reviewed" "totsize"             
[7] "tothyperlinks."      
> 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/1xpkw1386535310.tab") 
+ }
+ }
> m

	 Conditional inference tree with 13 terminal nodes

Response:  time_in_rfc 
Inputs:  pageviews, logins, blogged_computations, compendiums_reviewed, totsize, tothyperlinks. 
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) tothyperlinks. <= 59; criterion = 1, statistic = 24.861
        10)*  weights = 55 
      9) tothyperlinks. > 59
        11)*  weights = 9 
  2) pageviews > 1212
    12) tothyperlinks. <= 47; criterion = 1, statistic = 27.915
      13)*  weights = 21 
    12) tothyperlinks. > 47
      14) tothyperlinks. <= 96; criterion = 0.986, statistic = 9.225
        15)*  weights = 33 
      14) tothyperlinks. > 96
        16)*  weights = 16 
1) pageviews > 1613
  17) pageviews <= 2702; criterion = 1, statistic = 42.52
    18) tothyperlinks. <= 83; criterion = 1, statistic = 21.471
      19) totsize <= 32755; criterion = 0.951, statistic = 6.944
        20)*  weights = 7 
      19) totsize > 32755
        21)*  weights = 14 
    18) tothyperlinks. > 83
      22) pageviews <= 2201; criterion = 0.964, statistic = 7.537
        23)*  weights = 40 
      22) pageviews > 2201
        24)*  weights = 17 
  17) pageviews > 2702
    25)*  weights = 16 
> postscript(file="/var/fisher/rcomp/tmp/2o5cl1386535310.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/3c1f21386535310.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 170896.50   40010.5000
2    120982 124193.78   -3211.7778
3    176508 170896.50    5611.5000
4    179321 217375.42  -38054.4250
5    123185 124193.78   -1008.7778
6     52746  39424.13   13321.8667
7    385534 321098.06   64435.9375
8     33170  19387.40   13782.6000
9    101645  86007.40   15637.6000
10   149061 141926.97    7134.0303
11   165446 141926.97   23519.0303
12   237213 217375.42   19837.5750
13   173326 141926.97   31399.0303
14   133131 141926.97   -8795.9697
15   258873 217375.42   41497.5750
16   180083 141926.97   38156.0303
17   324799 321098.06    3700.9375
18   230964 217375.42   13588.5750
19   236785 217375.42   19409.5750
20   135473 124193.78   11279.2222
21   202925 217375.42  -14450.4250
22   215147 217375.42   -2228.4250
23   344297 252483.41   91813.5882
24   153935 141926.97   12008.0303
25   132943 170896.50  -37953.5000
26   174724 252483.41  -77759.4118
27   174415 217375.42  -42960.4250
28   225548 252483.41  -26935.4118
29   223632 217375.42    6256.5750
30   124817 141926.97  -17109.9697
31   221698 217375.42    4322.5750
32   210767 217375.42   -6608.4250
33   170266 170896.50    -630.5000
34   260561 217375.42   43185.5750
35    84853  86007.40   -1154.4000
36   294424 252483.41   41940.5882
37   101011  86007.40   15003.6000
38   215641 179384.00   36257.0000
39   325107 252483.41   72623.5882
40     7176  19387.40  -12211.4000
41   167542 141926.97   25615.0303
42   106408  86007.40   20400.6000
43    96560 116016.71  -19456.7143
44   265769 252483.41   13285.5882
45   269651 321098.06  -51447.0625
46   149112 141926.97    7185.0303
47   175824 179384.00   -3560.0000
48   152871 141926.97   10944.0303
49   111665  86007.40   25657.6000
50   116408 179384.00  -62976.0000
51   362301 321098.06   41202.9375
52    78800  86007.40   -7207.4000
53   183167 217375.42  -34208.4250
54   277965 321098.06  -43133.0625
55   150629 217375.42  -66746.4250
56   168809 170896.50   -2087.5000
57    24188  39424.13  -15236.1333
58   329267 252483.41   76783.5882
59    65029  60147.00    4882.0000
60   101097  86007.40   15089.6000
61   218946 252483.41  -33537.4118
62   244052 217375.42   26676.5750
63   341570 321098.06   20471.9375
64   103597  86007.40   17589.6000
65   233328 252483.41  -19155.4118
66   256462 217375.42   39086.5750
67   206161 179384.00   26777.0000
68   311473 321098.06   -9625.0625
69   235800 252483.41  -16683.4118
70   177939 179384.00   -1445.0000
71   207176 217375.42  -10199.4250
72   196553 170896.50   25656.5000
73   174184 141926.97   32257.0303
74   143246 170896.50  -27650.5000
75   187559 252483.41  -64924.4118
76   187681 217375.42  -29694.4250
77   119016 141926.97  -22910.9697
78   182192 217375.42  -35183.4250
79    73566  86007.40  -12441.4000
80   194979 217375.42  -22396.4250
81   167488 170896.50   -3408.5000
82   143756 170896.50  -27140.5000
83   275541 217375.42   58165.5750
84   243199 217375.42   25823.5750
85   182999 141926.97   41072.0303
86   135649 141926.97   -6277.9697
87   152299 141926.97   10372.0303
88   120221 141926.97  -21705.9697
89   346485 321098.06   25386.9375
90   145790 106787.48   39002.5238
91   193339 217375.42  -24036.4250
92    80953  86007.40   -5054.4000
93   122774 116016.71    6757.2857
94   130585 124193.78    6391.2222
95   112611  86007.40   26603.6000
96   286468 321098.06  -34630.0625
97   241066 179384.00   61682.0000
98   148446 252483.41 -104037.4118
99   204713 179384.00   25329.0000
100  182079 217375.42  -35296.4250
101  140344 141926.97   -1582.9697
102  220516 252483.41  -31967.4118
103  243060 217375.42   25684.5750
104  162765 170896.50   -8131.5000
105  182613 170896.50   11716.5000
106  232138 217375.42   14762.5750
107  265318 321098.06  -55780.0625
108   85574 106787.48  -21213.4762
109  310839 321098.06  -10259.0625
110  225060 217375.42    7684.5750
111  232317 217375.42   14941.5750
112  144966 141926.97    3039.0303
113   43287  60147.00  -16860.0000
114  155754 179384.00  -23630.0000
115  164709 170896.50   -6187.5000
116  201940 217375.42  -15435.4250
117  235454 217375.42   18078.5750
118  220801 179384.00   41417.0000
119   99466  86007.40   13458.6000
120   92661 106787.48  -14126.4762
121  133328 141926.97   -8598.9697
122   61361  86007.40  -24646.4000
123  125930 116016.71    9913.2857
124  100750 141926.97  -41176.9697
125  224549 170896.50   53652.5000
126   82316  86007.40   -3691.4000
127  102010  86007.40   16002.6000
128  101523 141926.97  -40403.9697
129  243511 217375.42   26135.5750
130   22938  19387.40    3550.6000
131   41566  60147.00  -18581.0000
132  152474 170896.50  -18422.5000
133   61857  39424.13   22432.8667
134   99923 116016.71  -16093.7143
135  132487 170896.50  -38409.5000
136  317394 252483.41   64910.5882
137   21054  19387.40    1666.6000
138  209641 217375.42   -7734.4250
139   22648  60147.00  -37499.0000
140   31414  39424.13   -8010.1333
141   46698  60147.00  -13449.0000
142  131698 179384.00  -47686.0000
143   91735  86007.40    5727.6000
144  244749 252483.41   -7734.4118
145  184510 141926.97   42583.0303
146   79863 106787.48  -26924.4762
147  128423  86007.40   42415.6000
148   97839 124193.78  -26354.7778
149   38214  39424.13   -1210.1333
150  151101 141926.97    9174.0303
151  272458 217375.42   55082.5750
152  172494 124193.78   48300.2222
153  108043 106787.48    1255.5238
154  328107 321098.06    7008.9375
155  250579 217375.42   33203.5750
156  351067 321098.06   29968.9375
157  158015 124193.78   33821.2222
158   98866  86007.40   12858.6000
159   85439  86007.40    -568.4000
160  229242 217375.42   11866.5750
161  351619 321098.06   30520.9375
162   84207  86007.40   -1800.4000
163  120445 141926.97  -21481.9697
164  324598 321098.06    3499.9375
165  131069 141926.97  -10857.9697
166  204271 170896.50   33374.5000
167  165543 217375.42  -51832.4250
168  141722 141926.97    -204.9697
169  116048 124193.78   -8145.7778
170  250047 179384.00   70663.0000
171  299775 321098.06  -21323.0625
172  195838 217375.42  -21537.4250
173  173260 179384.00   -6124.0000
174  254488 252483.41    2004.5882
175  104389 141926.97  -37537.9697
176  136084 106787.48   29296.5238
177  199476 217375.42  -17899.4250
178   92499  86007.40    6491.6000
179  224330 217375.42    6954.5750
180  135781 106787.48   28993.5238
181   74408 106787.48  -32379.4762
182   81240 106787.48  -25547.4762
183   14688  19387.40   -4699.4000
184  181633 217375.42  -35742.4250
185  271856 252483.41   19372.5882
186    7199  19387.40  -12188.4000
187   46660  39424.13    7235.8667
188   17547  19387.40   -1840.4000
189  133368 116016.71   17351.2857
190   95227  86007.40    9219.6000
191  152601 141926.97   10674.0303
192   98146  86007.40   12138.6000
193   79619 106787.48  -27168.4762
194   59194 106787.48  -47593.4762
195  139942 141926.97   -1984.9697
196  118612 141926.97  -23314.9697
197   72880  86007.40  -13127.4000
198   65475  60147.00    5328.0000
199   99643 141926.97  -42283.9697
200   71965  60147.00   11818.0000
201   77272  60147.00   17125.0000
202   49289  39424.13    9864.8667
203  135131 106787.48   28343.5238
204  108446 106787.48    1658.5238
205   89746 106787.48  -17041.4762
206   44296  39424.13    4871.8667
207   77648  86007.40   -8359.4000
208  181528 141926.97   39601.0303
209  134019 106787.48   27231.5238
210  124064  86007.40   38056.6000
211   92630  86007.40    6622.6000
212  121848 106787.48   15060.5238
213   52915  60147.00   -7232.0000
214   81872  60147.00   21725.0000
215   58981  86007.40  -27026.4000
216   53515  60147.00   -6632.0000
217   60812  86007.40  -25195.4000
218   56375  60147.00   -3772.0000
219   65490  60147.00    5343.0000
220   80949  60147.00   20802.0000
221   76302  86007.40   -9705.4000
222  104011 106787.48   -2776.4762
223   98104 116016.71  -17912.7143
224   67989  60147.00    7842.0000
225   30989  19387.40   11601.6000
226  135458 116016.71   19441.2857
227   73504  60147.00   13357.0000
228   63123 124193.78  -61070.7778
229   61254  86007.40  -24753.4000
230   74914 106787.48  -31873.4762
231   31774  60147.00  -28373.0000
232   81437  86007.40   -4570.4000
233   87186 106787.48  -19601.4762
234   50090  60147.00  -10057.0000
235   65745  86007.40  -20262.4000
236   56653  60147.00   -3494.0000
237  158399 106787.48   51611.5238
238   46455  60147.00  -13692.0000
239   73624  86007.40  -12383.4000
240   38395  60147.00  -21752.0000
241   91899  86007.40    5891.6000
242  139526 179384.00  -39858.0000
243   52164  60147.00   -7983.0000
244   51567  60147.00   -8580.0000
245   70551  86007.40  -15456.4000
246   84856  86007.40   -1151.4000
247  102538 179384.00  -76846.0000
248   86678  60147.00   26531.0000
249   85709  86007.40    -298.4000
250   34662  39424.13   -4762.1333
251  150580 106787.48   43792.5238
252   99611  86007.40   13603.6000
253   19349  19387.40     -38.4000
254   99373  86007.40   13365.6000
255   86230  86007.40     222.6000
256   30837  39424.13   -8587.1333
257   31706  39424.13   -7718.1333
258   89806  86007.40    3798.6000
259   62088  60147.00    1941.0000
260   40151  39424.13     726.8667
261   27634  39424.13  -11790.1333
262   76990  86007.40   -9017.4000
263   37460  39424.13   -1964.1333
264   54157  60147.00   -5990.0000
265   49862  60147.00  -10285.0000
266   84337  86007.40   -1670.4000
267   64175  86007.40  -21832.4000
268   59382  60147.00    -765.0000
269  119308  60147.00   59161.0000
270   76702  86007.40   -9305.4000
271  103425 141926.97  -38501.9697
272   70344  60147.00   10197.0000
273   43410  60147.00  -16737.0000
274  104838  86007.40   18830.6000
275   62215  60147.00    2068.0000
276   69304  86007.40  -16703.4000
277   53117  60147.00   -7030.0000
278   19764  19387.40     376.6000
279   86680  86007.40     672.6000
280   84105  60147.00   23958.0000
281   77945  86007.40   -8062.4000
282   89113  86007.40    3105.6000
283   91005  86007.40    4997.6000
284   40248  39424.13     823.8667
285   64187  60147.00    4040.0000
286   50857  86007.40  -35150.4000
287   56613  86007.40  -29394.4000
288   62792  60147.00    2645.0000
289   72535  86007.40  -13472.4000
> 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/4c0ke1386535310.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/5m4w41386535310.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/6v9q51386535310.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/7ho9v1386535310.tab") 
+ }
> 
> try(system("convert tmp/2o5cl1386535310.ps tmp/2o5cl1386535310.png",intern=TRUE))
character(0)
> try(system("convert tmp/3c1f21386535310.ps tmp/3c1f21386535310.png",intern=TRUE))
character(0)
> try(system("convert tmp/4c0ke1386535310.ps tmp/4c0ke1386535310.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
 16.151   1.903  18.015