R version 2.12.0 (2010-10-15)
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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+ ,53
+ ,2
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+ ,125
+ ,71
+ ,100350
+ ,17372
+ ,75882
+ ,80
+ ,72)
+ ,dim=c(15
+ ,164)
+ ,dimnames=list(c('#Pageviews'
+ ,'TimeRFC'
+ ,'#Logins'
+ ,'#CCviews'
+ ,'#Creviews'
+ ,'#Shared'
+ ,'#BloggedComputations'
+ ,'#ReviewedCompendiums'
+ ,'#submittedFBmessages'
+ ,'#Peerreviews+120'
+ ,'#Compendiumcharacters'
+ ,'#Compendiumrevisions'
+ ,'#secondscompendium'
+ ,'#includedhyperlinks'
+ ,'#includedblogs')
+ ,1:164))
>  y <- array(NA,dim=c(15,164),dimnames=list(c('#Pageviews','TimeRFC','#Logins','#CCviews','#Creviews','#Shared','#BloggedComputations','#ReviewedCompendiums','#submittedFBmessages','#Peerreviews+120','#Compendiumcharacters','#Compendiumrevisions','#secondscompendium','#includedhyperlinks','#includedblogs'),1:164))
>  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 = '0'
> par2 = 'none'
> par1 = '2'
> library(party)
Loading required package: survival
Loading required package: splines
Loading required package: grid
Loading required package: modeltools
Loading required package: stats4
Loading required package: coin
Loading required package: mvtnorm
Loading required package: zoo
Loading required package: sandwich
Loading required package: strucchange
Loading required package: vcd
Loading required package: MASS
Loading required package: colorspace
> library(Hmisc)

Attaching package: 'Hmisc'

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

    untangle.specials

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

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

> par1 <- as.numeric(par1)
> par3 <- as.numeric(par3)
> x <- data.frame(t(y))
> is.data.frame(x)
[1] TRUE
> x <- x[!is.na(x[,par1]),]
> k <- length(x[1,])
> n <- length(x[,1])
> colnames(x)[par1]
[1] "TimeRFC"
> x[,par1]
  [1] 279055 212450 233939 222117 189911  70849 605767  33186 227332 267925
 [11] 372083 279589 212638 368577 269455 402600 335735 432711 185822 267365
 [21] 279452 527853 227252 200004 257239 271341 324969 349753 200723 399591
 [31] 327660 269239 397689 130446 430118 273950 429837 254312 120351 395658
 [41] 349119 220517 234780 191784 186112 459665  78800 259887 368086 230299
 [51] 256033  24188 400109  65029 101097 319020 375638 375011 387748 280106
 [61] 400971 322780 291391 295075 280018 267432 217181 258166 277891 192894
 [71] 271853  73566 276269 242619 230030 371391 398698 243355 233519 219936
 [81] 206169 483429 146100 295224  80953 217384 179344 416097 395041 180679
 [91] 311447 292260 199481 282361 329281 234577 310685 352078 416463 429565
[101] 297080 331792 242507  43287 238089 285479 310383 321797 193926 175737
[111] 354041 303566  23668 196743  61857 217543 440711  21054 252805  31961
[121] 360436 251948 187320 180842  38214 289296 358276 211775 447335 348017
[131] 441946 215177 140328 318092 466139 162406 417354 178322 292443 283913
[141] 253950 389698 246963 173260 346748 188437 279125 314070      1  14688
[151]     98    455      0      0 291847 415839      0    203   7199  46660
[161]  17547 121550    969 242774
> 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])

     0      1     98    203    455    969   7199  14688  17547  21054  23668 
     3      1      1      1      1      1      1      1      1      1      1 
 24188  31961  33186  38214  43287  46660  61857  65029  70849  73566  78800 
     1      1      1      1      1      1      1      1      1      1      1 
 80953 101097 120351 121550 130446 140328 146100 162406 173260 175737 178322 
     1      1      1      1      1      1      1      1      1      1      1 
179344 180679 180842 185822 186112 187320 188437 189911 191784 192894 193926 
     1      1      1      1      1      1      1      1      1      1      1 
196743 199481 200004 200723 206169 211775 212450 212638 215177 217181 217384 
     1      1      1      1      1      1      1      1      1      1      1 
217543 219936 220517 222117 227252 227332 230030 230299 233519 233939 234577 
     1      1      1      1      1      1      1      1      1      1      1 
234780 238089 242507 242619 242774 243355 246963 251948 252805 253950 254312 
     1      1      1      1      1      1      1      1      1      1      1 
256033 257239 258166 259887 267365 267432 267925 269239 269455 271341 271853 
     1      1      1      1      1      1      1      1      1      1      1 
273950 276269 277891 279055 279125 279452 279589 280018 280106 282361 283913 
     1      1      1      1      1      1      1      1      1      1      1 
285479 289296 291391 291847 292260 292443 295075 295224 297080 303566 310383 
     1      1      1      1      1      1      1      1      1      1      1 
310685 311447 314070 318092 319020 321797 322780 324969 327660 329281 331792 
     1      1      1      1      1      1      1      1      1      1      1 
335735 346748 348017 349119 349753 352078 354041 358276 360436 368086 368577 
     1      1      1      1      1      1      1      1      1      1      1 
371391 372083 375011 375638 387748 389698 395041 395658 397689 398698 399591 
     1      1      1      1      1      1      1      1      1      1      1 
400109 400971 402600 415839 416097 416463 417354 429565 429837 430118 432711 
     1      1      1      1      1      1      1      1      1      1      1 
440711 441946 447335 459665 466139 483429 527853 605767 
     1      1      1      1      1      1      1      1 
> colnames(x)
 [1] "X.Pageviews"            "TimeRFC"                "X.Logins"              
 [4] "X.CCviews"              "X.Creviews"             "X.Shared"              
 [7] "X.BloggedComputations"  "X.ReviewedCompendiums"  "X.submittedFBmessages" 
[10] "X.Peerreviews.120"      "X.Compendiumcharacters" "X.Compendiumrevisions" 
[13] "X.secondscompendium"    "X.includedhyperlinks"   "X.includedblogs"       
> colnames(x)[par1]
[1] "TimeRFC"
> x[,par1]
  [1] 279055 212450 233939 222117 189911  70849 605767  33186 227332 267925
 [11] 372083 279589 212638 368577 269455 402600 335735 432711 185822 267365
 [21] 279452 527853 227252 200004 257239 271341 324969 349753 200723 399591
 [31] 327660 269239 397689 130446 430118 273950 429837 254312 120351 395658
 [41] 349119 220517 234780 191784 186112 459665  78800 259887 368086 230299
 [51] 256033  24188 400109  65029 101097 319020 375638 375011 387748 280106
 [61] 400971 322780 291391 295075 280018 267432 217181 258166 277891 192894
 [71] 271853  73566 276269 242619 230030 371391 398698 243355 233519 219936
 [81] 206169 483429 146100 295224  80953 217384 179344 416097 395041 180679
 [91] 311447 292260 199481 282361 329281 234577 310685 352078 416463 429565
[101] 297080 331792 242507  43287 238089 285479 310383 321797 193926 175737
[111] 354041 303566  23668 196743  61857 217543 440711  21054 252805  31961
[121] 360436 251948 187320 180842  38214 289296 358276 211775 447335 348017
[131] 441946 215177 140328 318092 466139 162406 417354 178322 292443 283913
[141] 253950 389698 246963 173260 346748 188437 279125 314070      1  14688
[151]     98    455      0      0 291847 415839      0    203   7199  46660
[161]  17547 121550    969 242774
> 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/1ga4y1324679700.tab") 
+ }
+ }
> m

	 Conditional inference tree with 10 terminal nodes

Response:  TimeRFC 
Inputs:  X.Pageviews, X.Logins, X.CCviews, X.Creviews, X.Shared, X.BloggedComputations, X.ReviewedCompendiums, X.submittedFBmessages, X.Peerreviews.120, X.Compendiumcharacters, X.Compendiumrevisions, X.secondscompendium, X.includedhyperlinks, X.includedblogs 
Number of observations:  164 

1) X.Pageviews <= 1380; criterion = 1, statistic = 135.728
  2) X.Pageviews <= 602; criterion = 1, statistic = 27.434
    3) X.secondscompendium <= 4245; criterion = 1, statistic = 17.326
      4)*  weights = 9 
    3) X.secondscompendium > 4245
      5)*  weights = 11 
  2) X.Pageviews > 602
    6)*  weights = 10 
1) X.Pageviews > 1380
  7) X.Pageviews <= 3230; criterion = 1, statistic = 82.032
    8) X.secondscompendium <= 134969; criterion = 1, statistic = 47.273
      9) X.Pageviews <= 2150; criterion = 1, statistic = 28.27
        10) X.secondscompendium <= 72558; criterion = 0.988, statistic = 11.11
          11)*  weights = 7 
        10) X.secondscompendium > 72558
          12)*  weights = 26 
      9) X.Pageviews > 2150
        13)*  weights = 41 
    8) X.secondscompendium > 134969
      14) X.Pageviews <= 2222; criterion = 0.994, statistic = 12.261
        15)*  weights = 8 
      14) X.Pageviews > 2222
        16)*  weights = 20 
  7) X.Pageviews > 3230
    17) X.CCviews <= 1410; criterion = 0.983, statistic = 10.47
      18)*  weights = 21 
    17) X.CCviews > 1410
      19)*  weights = 11 
> postscript(file="/var/www/rcomp/tmp/2wuz91324679700.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/3g3ko1324679700.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    279055 270787.8750    8267.12500
2    212450 216377.5769   -3927.57692
3    233939 216377.5769   17561.42308
4    222117 268631.0732  -46514.07317
5    189911 216377.5769  -26466.57692
6     70849  98874.1000  -28025.10000
7    605767 446472.0000  159295.00000
8     33186  32391.8182     794.18182
9    227332 216377.5769   10954.42308
10   267925 216377.5769   51547.42308
11   372083 339876.7500   32206.25000
12   279589 268631.0732   10957.92683
13   212638 216377.5769   -3739.57692
14   368577 268631.0732   99945.92683
15   269455 268631.0732     823.92683
16   402600 446472.0000  -43872.00000
17   335735 339876.7500   -4141.75000
18   432711 381241.4286   51469.57143
19   185822 216377.5769  -30555.57692
20   267365 268631.0732   -1266.07317
21   279452 339876.7500  -60424.75000
22   527853 446472.0000   81381.00000
23   227252 216377.5769   10874.42308
24   200004 216377.5769  -16373.57692
25   257239 381241.4286 -124002.42857
26   271341 268631.0732    2709.92683
27   324969 339876.7500  -14907.75000
28   349753 339876.7500    9876.25000
29   200723 176306.1429   24416.85714
30   399591 381241.4286   18349.57143
31   327660 339876.7500  -12216.75000
32   269239 268631.0732     607.92683
33   397689 339876.7500   57812.25000
34   130446  98874.1000   31571.90000
35   430118 381241.4286   48876.57143
36   273950 268631.0732    5318.92683
37   429837 381241.4286   48595.57143
38   254312 216377.5769   37934.42308
39   120351  98874.1000   21476.90000
40   395658 381241.4286   14416.57143
41   349119 446472.0000  -97353.00000
42   220517 216377.5769    4139.42308
43   234780 216377.5769   18402.42308
44   191784 176306.1429   15477.85714
45   186112 268631.0732  -82519.07317
46   459665 446472.0000   13193.00000
47    78800  98874.1000  -20074.10000
48   259887 268631.0732   -8744.07317
49   368086 381241.4286  -13155.42857
50   230299 268631.0732  -38332.07317
51   256033 270787.8750  -14754.87500
52    24188  32391.8182   -8203.81818
53   400109 339876.7500   60232.25000
54    65029  98874.1000  -33845.10000
55   101097  98874.1000    2222.90000
56   319020 381241.4286  -62221.42857
57   375638 339876.7500   35761.25000
58   375011 381241.4286   -6230.42857
59   387748 339876.7500   47871.25000
60   280106 268631.0732   11474.92683
61   400971 381241.4286   19729.57143
62   322780 381241.4286  -58461.42857
63   291391 268631.0732   22759.92683
64   295075 268631.0732   26443.92683
65   280018 270787.8750    9230.12500
66   267432 270787.8750   -3355.87500
67   217181 216377.5769     803.42308
68   258166 268631.0732  -10465.07317
69   277891 268631.0732    9259.92683
70   192894 216377.5769  -23483.57692
71   271853 268631.0732    3221.92683
72    73566  98874.1000  -25308.10000
73   276269 268631.0732    7637.92683
74   242619 268631.0732  -26012.07317
75   230030 268631.0732  -38601.07317
76   371391 268631.0732  102759.92683
77   398698 381241.4286   17456.57143
78   243355 270787.8750  -27432.87500
79   233519 268631.0732  -35112.07317
80   219936 216377.5769    3558.42308
81   206169 216377.5769  -10208.57692
82   483429 381241.4286  102187.57143
83   146100  98874.1000   47225.90000
84   295224 339876.7500  -44652.75000
85    80953  98874.1000  -17921.10000
86   217384 268631.0732  -51247.07317
87   179344 216377.5769  -37033.57692
88   416097 446472.0000  -30375.00000
89   395041 381241.4286   13799.57143
90   180679 268631.0732  -87952.07317
91   311447 268631.0732   42815.92683
92   292260 268631.0732   23628.92683
93   199481 216377.5769  -16896.57692
94   282361 268631.0732   13729.92683
95   329281 339876.7500  -10595.75000
96   234577 216377.5769   18199.42308
97   310685 339876.7500  -29191.75000
98   352078 339876.7500   12201.25000
99   416463 381241.4286   35221.57143
100  429565 446472.0000  -16907.00000
101  297080 268631.0732   28448.92683
102  331792 339876.7500   -8084.75000
103  242507 268631.0732  -26124.07317
104   43287  32391.8182   10895.18182
105  238089 268631.0732  -30542.07317
106  285479 339876.7500  -54397.75000
107  310383 268631.0732   41751.92683
108  321797 339876.7500  -18079.75000
109  193926 270787.8750  -76861.87500
110  175737 381241.4286 -205504.42857
111  354041 270787.8750   83253.12500
112  303566 268631.0732   34934.92683
113   23668  32391.8182   -8723.81818
114  196743 268631.0732  -71888.07317
115   61857  32391.8182   29465.18182
116  217543 216377.5769    1165.42308
117  440711 381241.4286   59469.57143
118   21054  32391.8182  -11337.81818
119  252805 216377.5769   36427.42308
120   31961  32391.8182    -430.81818
121  360436 381241.4286  -20805.42857
122  251948 268631.0732  -16683.07317
123  187320 176306.1429   11013.85714
124  180842 216377.5769  -35535.57692
125   38214  32391.8182    5822.18182
126  289296 268631.0732   20664.92683
127  358276 339876.7500   18399.25000
128  211775 216377.5769   -4602.57692
129  447335 446472.0000     863.00000
130  348017 339876.7500    8140.25000
131  441946 381241.4286   60704.57143
132  215177 216377.5769   -1200.57692
133  140328 176306.1429  -35978.14286
134  318092 268631.0732   49460.92683
135  466139 446472.0000   19667.00000
136  162406 176306.1429  -13900.14286
137  417354 446472.0000  -29118.00000
138  178322 176306.1429    2015.85714
139  292443 270787.8750   21655.12500
140  283913 268631.0732   15281.92683
141  253950 268631.0732  -14681.07317
142  389698 446472.0000  -56774.00000
143  246963 268631.0732  -21668.07317
144  173260 176306.1429   -3046.14286
145  346748 381241.4286  -34493.42857
146  188437 216377.5769  -27940.57692
147  279125 268631.0732   10493.92683
148  314070 339876.7500  -25806.75000
149       1    991.6667    -990.66667
150   14688  32391.8182  -17703.81818
151      98    991.6667    -893.66667
152     455    991.6667    -536.66667
153       0    991.6667    -991.66667
154       0    991.6667    -991.66667
155  291847 268631.0732   23215.92683
156  415839 381241.4286   34597.57143
157       0    991.6667    -991.66667
158     203    991.6667    -788.66667
159    7199    991.6667    6207.33333
160   46660  32391.8182   14268.18182
161   17547  32391.8182  -14844.81818
162  121550  98874.1000   22675.90000
163     969    991.6667     -22.66667
164  242774 216377.5769   26396.42308
> 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/4heed1324679700.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/56id61324679700.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/685fh1324679700.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/7cud01324679700.tab") 
+ }
> 
> try(system("convert tmp/2wuz91324679700.ps tmp/2wuz91324679700.png",intern=TRUE))
character(0)
> try(system("convert tmp/3g3ko1324679700.ps tmp/3g3ko1324679700.png",intern=TRUE))
character(0)
> try(system("convert tmp/4heed1324679700.ps tmp/4heed1324679700.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  4.370   0.140   4.534