R version 2.12.1 (2010-12-16)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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> x <- array(list(210907
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+ ,21
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+ ,1383
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+ ,42
+ ,11
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+ ,43
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+ ,32
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+ ,1314
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+ ,7
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+ ,20
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+ ,1403
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+ ,41907
+ ,43
+ ,72880
+ ,910
+ ,376
+ ,14
+ ,19
+ ,24094
+ ,25
+ ,27080
+ ,67)
+ ,dim=c(9
+ ,197)
+ ,dimnames=list(c('time_in_rfc'
+ ,'pageviews'
+ ,'compendium_views_info'
+ ,'blogged_computations'
+ ,'compendiums_reviewed'
+ ,'totale_size'
+ ,'totale_hyperlinks'
+ ,'totale_seconds'
+ ,'feedback_messages_p120')
+ ,1:197))
>  y <- array(NA,dim=c(9,197),dimnames=list(c('time_in_rfc','pageviews','compendium_views_info','blogged_computations','compendiums_reviewed','totale_size','totale_hyperlinks','totale_seconds','feedback_messages_p120'),1:197))
>  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
Loading required package: sandwich
Loading required package: strucchange
Loading required package: vcd
Loading required package: MASS
Loading required package: colorspace
> library(Hmisc)

Attaching package: 'Hmisc'

The following object(s) are masked from 'package:survival':

    untangle.specials

The following object(s) are masked from 'package:base':

    format.pval, round.POSIXt, trunc.POSIXt, units

> par1 <- as.numeric(par1)
> par3 <- as.numeric(par3)
> x <- data.frame(t(y))
> is.data.frame(x)
[1] TRUE
> x <- x[!is.na(x[,par1]),]
> k <- length(x[1,])
> n <- length(x[,1])
> colnames(x)[par1]
[1] "time_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
> 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  21054  22648  22938  24188  31414  33170  38214 
     1      1      1      1      1      1      1      1      1      1      1 
 41566  43287  46660  46698  52746  59194  61361  61857  65029  72880  73566 
     1      1      1      1      1      1      1      1      1      1      1 
 74408  78800  79619  79863  80953  81240  82316  84207  84853  85439  85574 
     1      1      1      1      1      1      1      1      1      1      1 
 91735  92499  92661  95227  96560  97839  98146  98866  99466  99923 100750 
     1      1      1      1      1      1      1      1      1      1      1 
101011 101097 101523 101645 102010 103597 104389 106408 108043 111665 112611 
     1      1      1      1      1      1      1      1      1      1      1 
116048 116408 118612 119016 120221 120445 120982 122774 123185 124817 125930 
     1      1      1      1      1      1      1      1      1      1      1 
128423 130585 131069 131698 132487 132943 133131 133328 133368 135473 135649 
     1      1      1      1      1      1      1      1      1      1      1 
135781 136084 139942 140344 141722 143246 143756 144966 145790 148446 149061 
     1      1      1      1      1      1      1      1      1      1      1 
149112 150629 151101 152299 152474 152601 152871 153935 155754 158015 162765 
     1      1      1      1      1      1      1      1      1      1      1 
164709 165446 165543 167488 167542 168809 170266 172494 173260 173326 174184 
     1      1      1      1      1      1      1      1      1      1      1 
174415 174724 175824 176508 177939 179321 180083 181633 182079 182192 182613 
     1      1      1      1      1      1      1      1      1      1      1 
182999 183167 184510 187559 187681 193339 194979 195838 196553 199476 201940 
     1      1      1      1      1      1      1      1      1      1      1 
202925 204271 204713 206161 207176 209641 210767 210907 215147 215641 218946 
     1      1      1      1      1      1      1      1      1      1      1 
220516 220801 221698 223632 224330 224549 225060 225548 229242 230964 232138 
     1      1      1      1      1      1      1      1      1      1      1 
232317 233328 235454 235800 236785 237213 241066 243060 243199 243511 244052 
     1      1      1      1      1      1      1      1      1      1      1 
244749 250047 250579 254488 256462 258873 260561 265318 265769 269651 271856 
     1      1      1      1      1      1      1      1      1      1      1 
272458 275541 277965 286468 294424 299775 310839 311473 317394 324598 324799 
     1      1      1      1      1      1      1      1      1      1      1 
325107 328107 329267 341570 344297 346485 351067 351619 362301 385534 
     1      1      1      1      1      1      1      1      1      1 
> colnames(x)
[1] "time_in_rfc"            "pageviews"              "compendium_views_info" 
[4] "blogged_computations"   "compendiums_reviewed"   "totale_size"           
[7] "totale_hyperlinks"      "totale_seconds"         "feedback_messages_p120"
> 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
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/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/www/rcomp/tmp/1sz871324117947.tab") 
+ }
+ }
> m

	 Conditional inference tree with 10 terminal nodes

Response:  time_in_rfc 
Inputs:  pageviews, compendium_views_info, blogged_computations, compendiums_reviewed, totale_size, totale_hyperlinks, totale_seconds, feedback_messages_p120 
Number of observations:  197 

1) pageviews <= 1605; criterion = 1, statistic = 148.734
  2) totale_seconds <= 42551; criterion = 1, statistic = 76.652
    3) pageviews <= 800; criterion = 1, statistic = 25.537
      4)*  weights = 18 
    3) pageviews > 800
      5)*  weights = 22 
  2) totale_seconds > 42551
    6) totale_seconds <= 79234; criterion = 1, statistic = 31.043
      7) pageviews <= 1281; criterion = 1, statistic = 16.845
        8)*  weights = 19 
      7) pageviews > 1281
        9)*  weights = 21 
    6) totale_seconds > 79234
      10)*  weights = 26 
1) pageviews > 1605
  11) totale_seconds <= 115814; criterion = 1, statistic = 45.76
    12) compendium_views_info <= 801; criterion = 1, statistic = 16.731
      13) totale_size <= 40652; criterion = 0.999, statistic = 14.155
        14)*  weights = 7 
      13) totale_size > 40652
        15)*  weights = 24 
    12) compendium_views_info > 801
      16)*  weights = 16 
  11) totale_seconds > 115814
    17) compendium_views_info <= 927; criterion = 1, statistic = 24.784
      18)*  weights = 27 
    17) compendium_views_info > 927
      19)*  weights = 17 
> postscript(file="/var/www/rcomp/tmp/2ibkx1324117947.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/www/rcomp/tmp/3de641324117947.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 166707.88   44199.11538
2    120982 166707.88  -45725.88462
3    176508 166707.88    9800.11538
4    179321 221840.50  -42519.50000
5    123185 109097.21   14087.78947
6     52746  33226.61   19519.38889
7    385534 320569.29   64964.70588
8     33170  33226.61     -56.61111
9    101645  88132.05   13512.95455
10   149061 166707.88  -17646.88462
11   165446 166707.88   -1261.88462
12   237213 242812.85   -5599.85185
13   173326 137349.05   35976.95238
14   133131 137349.05   -4218.04762
15   258873 221840.50   37032.50000
16   180083 166707.88   13375.11538
17   324799 320569.29    4229.70588
18   230964 242812.85  -11848.85185
19   236785 221840.50   14944.50000
20   135473 166707.88  -31234.88462
21   202925 195605.79    7319.20833
22   215147 242812.85  -27665.85185
23   344297 320569.29   23727.70588
24   153935 166707.88  -12772.88462
25   132943 137349.05   -4406.04762
26   174724 221840.50  -47116.50000
27   174415 195605.79  -21190.79167
28   225548 242812.85  -17264.85185
29   223632 242812.85  -19180.85185
30   124817 109097.21   15719.78947
31   221698 242812.85  -21114.85185
32   210767 242812.85  -32045.85185
33   170266 166707.88    3558.11538
34   260561 242812.85   17748.14815
35    84853  88132.05   -3279.04545
36   294424 320569.29  -26145.29412
37   101011 109097.21   -8086.21053
38   215641 221840.50   -6199.50000
39   325107 320569.29    4537.70588
40     7176  33226.61  -26050.61111
41   167542 166707.88     834.11538
42   106408 109097.21   -2689.21053
43    96560 129652.71  -33092.71429
44   265769 242812.85   22956.14815
45   269651 320569.29  -50918.29412
46   149112 137349.05   11762.95238
47   175824 221840.50  -46016.50000
48   152871 166707.88  -13836.88462
49   111665 109097.21    2567.78947
50   116408 221840.50 -105432.50000
51   362301 221840.50  140460.50000
52    78800 109097.21  -30297.21053
53   183167 195605.79  -12438.79167
54   277965 320569.29  -42604.29412
55   150629 195605.79  -44976.79167
56   168809 166707.88    2101.11538
57    24188  33226.61   -9038.61111
58   329267 242812.85   86454.14815
59    65029  33226.61   31802.38889
60   101097 109097.21   -8000.21053
61   218946 221840.50   -2894.50000
62   244052 242812.85    1239.14815
63   341570 320569.29   21000.70588
64   103597 109097.21   -5500.21053
65   233328 242812.85   -9484.85185
66   256462 242812.85   13649.14815
67   206161 195605.79   10555.20833
68   311473 320569.29   -9096.29412
69   235800 242812.85   -7012.85185
70   177939 221840.50  -43901.50000
71   207176 195605.79   11570.20833
72   196553 166707.88   29845.11538
73   174184 166707.88    7476.11538
74   143246 137349.05    5896.95238
75   187559 195605.79   -8046.79167
76   187681 195605.79   -7924.79167
77   119016 109097.21    9918.78947
78   182192 195605.79  -13413.79167
79    73566  88132.05  -14566.04545
80   194979 195605.79    -626.79167
81   167488 166707.88     780.11538
82   143756 137349.05    6406.95238
83   275541 221840.50   53700.50000
84   243199 242812.85     386.14815
85   182999 166707.88   16291.11538
86   135649 137349.05   -1700.04762
87   152299 137349.05   14949.95238
88   120221 137349.05  -17128.04762
89   346485 320569.29   25915.70588
90   145790 137349.05    8440.95238
91   193339 195605.79   -2266.79167
92    80953 109097.21  -28144.21053
93   122774 129652.71   -6878.71429
94   130585 109097.21   21487.78947
95   112611 109097.21    3513.78947
96   286468 221840.50   64627.50000
97   241066 242812.85   -1746.85185
98   148446 221840.50  -73394.50000
99   204713 195605.79    9107.20833
100  182079 195605.79  -13526.79167
101  140344 137349.05    2994.95238
102  220516 221840.50   -1324.50000
103  243060 242812.85     247.14815
104  162765 166707.88   -3942.88462
105  182613 166707.88   15905.11538
106  232138 242812.85  -10674.85185
107  265318 242812.85   22505.14815
108   85574  88132.05   -2558.04545
109  310839 320569.29   -9730.29412
110  225060 195605.79   29454.20833
111  232317 242812.85  -10495.85185
112  144966 137349.05    7616.95238
113   43287  33226.61   10060.38889
114  155754 129652.71   26101.28571
115  164709 166707.88   -1998.88462
116  201940 221840.50  -19900.50000
117  235454 242812.85   -7358.85185
118  220801 195605.79   25195.20833
119   99466 166707.88  -67241.88462
120   92661  88132.05    4528.95455
121  133328 137349.05   -4021.04762
122   61361  88132.05  -26771.04545
123  125930 129652.71   -3722.71429
124  100750 109097.21   -8347.21053
125  224549 166707.88   57841.11538
126   82316  88132.05   -5816.04545
127  102010  88132.05   13877.95455
128  101523 109097.21   -7574.21053
129  243511 195605.79   47905.20833
130   22938  33226.61  -10288.61111
131   41566  33226.61    8339.38889
132  152474 166707.88  -14233.88462
133   61857  33226.61   28630.38889
134   99923 129652.71  -29729.71429
135  132487 137349.05   -4862.04762
136  317394 320569.29   -3175.29412
137   21054  33226.61  -12172.61111
138  209641 195605.79   14035.20833
139   22648  33226.61  -10578.61111
140   31414  33226.61   -1812.61111
141   46698  33226.61   13471.38889
142  131698 195605.79  -63907.79167
143   91735  88132.05    3602.95455
144  244749 320569.29  -75820.29412
145  184510 195605.79  -11095.79167
146   79863  88132.05   -8269.04545
147  128423  88132.05   40290.95455
148   97839 109097.21  -11258.21053
149   38214  33226.61    4987.38889
150  151101 137349.05   13751.95238
151  272458 242812.85   29645.14815
152  172494 166707.88    5786.11538
153  108043 137349.05  -29306.04762
154  328107 320569.29    7537.70588
155  250579 242812.85    7766.14815
156  351067 320569.29   30497.70588
157  158015 166707.88   -8692.88462
158   98866  88132.05   10733.95455
159   85439 109097.21  -23658.21053
160  229242 195605.79   33636.20833
161  351619 320569.29   31049.70588
162   84207  88132.05   -3925.04545
163  120445 137349.05  -16904.04762
164  324598 320569.29    4028.70588
165  131069 137349.05   -6280.04762
166  204271 166707.88   37563.11538
167  165543 195605.79  -30062.79167
168  141722 109097.21   32624.78947
169  116048 109097.21    6950.78947
170  250047 195605.79   54441.20833
171  299775 221840.50   77934.50000
172  195838 195605.79     232.20833
173  173260 129652.71   43607.28571
174  254488 242812.85   11675.14815
175  104389 137349.05  -32960.04762
176  136084 137349.05   -1265.04762
177  199476 242812.85  -43336.85185
178   92499  88132.05    4366.95455
179  224330 242812.85  -18482.85185
180  135781 109097.21   26683.78947
181   74408  88132.05  -13724.04545
182   81240  88132.05   -6892.04545
183   14688  33226.61  -18538.61111
184  181633 195605.79  -13972.79167
185  271856 242812.85   29043.14815
186    7199  33226.61  -26027.61111
187   46660  33226.61   13433.38889
188   17547  33226.61  -15679.61111
189  133368 129652.71    3715.28571
190   95227  88132.05    7094.95455
191  152601 137349.05   15251.95238
192   98146  88132.05   10013.95455
193   79619  88132.05   -8513.04545
194   59194  88132.05  -28938.04545
195  139942 166707.88  -26765.88462
196  118612  88132.05   30479.95455
197   72880  88132.05  -15252.04545
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
> postscript(file="/var/www/rcomp/tmp/4fj7m1324117947.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/www/rcomp/tmp/59fbi1324117948.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/www/rcomp/tmp/6enif1324117948.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/www/rcomp/tmp/75xj51324117948.tab") 
+ }
> 
> try(system("convert tmp/2ibkx1324117947.ps tmp/2ibkx1324117947.png",intern=TRUE))
character(0)
> try(system("convert tmp/3de641324117947.ps tmp/3de641324117947.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fj7m1324117947.ps tmp/4fj7m1324117947.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  5.400   0.260   5.716