R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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+ ,16541
+ ,63891
+ ,67
+ ,63)
+ ,dim=c(15
+ ,164)
+ ,dimnames=list(c('#Pageviews'
+ ,'TimeRFC'
+ ,'#Logins'
+ ,'#CourseCompendiumViews'
+ ,'#CourseCompendiumViews(onlyPR)'
+ ,'#SharedCompendiums'
+ ,'#BloggedComputations'
+ ,'#ReviewedCompendiums'
+ ,'#FeedbackMessagesInPR'
+ ,'#FBMIPR+120'
+ ,'#CompendiumCharacters'
+ ,'#Revisions'
+ ,'CompendiumWrintingTime'
+ ,'#IncludedHyperlinks'
+ ,'#IncludedBlogs')
+ ,1:164))
>  y <- array(NA,dim=c(15,164),dimnames=list(c('#Pageviews','TimeRFC','#Logins','#CourseCompendiumViews','#CourseCompendiumViews(onlyPR)','#SharedCompendiums','#BloggedComputations','#ReviewedCompendiums','#FeedbackMessagesInPR','#FBMIPR+120','#CompendiumCharacters','#Revisions','CompendiumWrintingTime','#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 = ''
> 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] 252101 134577 198520 189326 137449  65295 439387  33186 178368 186657
 [11] 261949 191051 138866 296878 192648 333462 243571 263451 155679 227053
 [21] 240028 388549 156540 148421 177732 191441 249893 236812 142329 259667
 [31] 231625 176062 286683  87485 322865 247082 346011 191653 114673 284224
 [41] 284195 155363 177306 144571 140319 405267  78800 201970 302674 164733
 [51] 194221  24188 342263  65029 101097 246088 273108 282220 273495 214872
 [61] 335121 267171 187938 229512 209798 201345 163833 204250 197813 132955
 [71] 216092  73566 213198 181713 148698 300103 251437 197295 158163 155529
 [81] 132672 377205 145905 223701  80953 130805 135082 300805 271806 150949
 [91] 225805 197389 156583 222599 261601 178489 200657 259084 313075 346933
[101] 246440 252444 159965  43287 172239 183738 227681 260464 106288 109632
[111] 268905 266805  23623 152474  61857 144889 346600  21054 224051  31414
[121] 261043 197819 154984 112933  38214 158671 302148 177918 350552 275578
[131] 368746 172464  94381 243875 382487 114525 335681 147989 216638 192862
[141] 184818 336707 215836 173260 271773 130908 204009 245514      1  14688
[151]     98    455      0      0 195765 326038      0    203   7199  46660
[161]  17547 107465    969 173102
> 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  23623 
     3      1      1      1      1      1      1      1      1      1      1 
 24188  31414  33186  38214  43287  46660  61857  65029  65295  73566  78800 
     1      1      1      1      1      1      1      1      1      1      1 
 80953  87485  94381 101097 106288 107465 109632 112933 114525 114673 130805 
     1      1      1      1      1      1      1      1      1      1      1 
130908 132672 132955 134577 135082 137449 138866 140319 142329 144571 144889 
     1      1      1      1      1      1      1      1      1      1      1 
145905 147989 148421 148698 150949 152474 154984 155363 155529 155679 156540 
     1      1      1      1      1      1      1      1      1      1      1 
156583 158163 158671 159965 163833 164733 172239 172464 173102 173260 176062 
     1      1      1      1      1      1      1      1      1      1      1 
177306 177732 177918 178368 178489 181713 183738 184818 186657 187938 189326 
     1      1      1      1      1      1      1      1      1      1      1 
191051 191441 191653 192648 192862 194221 195765 197295 197389 197813 197819 
     1      1      1      1      1      1      1      1      1      1      1 
198520 200657 201345 201970 204009 204250 209798 213198 214872 215836 216092 
     1      1      1      1      1      1      1      1      1      1      1 
216638 222599 223701 224051 225805 227053 227681 229512 231625 236812 240028 
     1      1      1      1      1      1      1      1      1      1      1 
243571 243875 245514 246088 246440 247082 249893 251437 252101 252444 259084 
     1      1      1      1      1      1      1      1      1      1      1 
259667 260464 261043 261601 261949 263451 266805 267171 268905 271773 271806 
     1      1      1      1      1      1      1      1      1      1      1 
273108 273495 275578 282220 284195 284224 286683 296878 300103 300805 302148 
     1      1      1      1      1      1      1      1      1      1      1 
302674 313075 322865 326038 333462 335121 335681 336707 342263 346011 346600 
     1      1      1      1      1      1      1      1      1      1      1 
346933 350552 368746 377205 382487 388549 405267 439387 
     1      1      1      1      1      1      1      1 
> colnames(x)
 [1] "X.Pageviews"                     "TimeRFC"                        
 [3] "X.Logins"                        "X.CourseCompendiumViews"        
 [5] "X.CourseCompendiumViews.onlyPR." "X.SharedCompendiums"            
 [7] "X.BloggedComputations"           "X.ReviewedCompendiums"          
 [9] "X.FeedbackMessagesInPR"          "X.FBMIPR.120"                   
[11] "X.CompendiumCharacters"          "X.Revisions"                    
[13] "CompendiumWrintingTime"          "X.IncludedHyperlinks"           
[15] "X.IncludedBlogs"                
> colnames(x)[par1]
[1] "TimeRFC"
> x[,par1]
  [1] 252101 134577 198520 189326 137449  65295 439387  33186 178368 186657
 [11] 261949 191051 138866 296878 192648 333462 243571 263451 155679 227053
 [21] 240028 388549 156540 148421 177732 191441 249893 236812 142329 259667
 [31] 231625 176062 286683  87485 322865 247082 346011 191653 114673 284224
 [41] 284195 155363 177306 144571 140319 405267  78800 201970 302674 164733
 [51] 194221  24188 342263  65029 101097 246088 273108 282220 273495 214872
 [61] 335121 267171 187938 229512 209798 201345 163833 204250 197813 132955
 [71] 216092  73566 213198 181713 148698 300103 251437 197295 158163 155529
 [81] 132672 377205 145905 223701  80953 130805 135082 300805 271806 150949
 [91] 225805 197389 156583 222599 261601 178489 200657 259084 313075 346933
[101] 246440 252444 159965  43287 172239 183738 227681 260464 106288 109632
[111] 268905 266805  23623 152474  61857 144889 346600  21054 224051  31414
[121] 261043 197819 154984 112933  38214 158671 302148 177918 350552 275578
[131] 368746 172464  94381 243875 382487 114525 335681 147989 216638 192862
[141] 184818 336707 215836 173260 271773 130908 204009 245514      1  14688
[151]     98    455      0      0 195765 326038      0    203   7199  46660
[161]  17547 107465    969 173102
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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/wessaorg/rcomp/tmp/1dvjk1323612724.tab") 
+ }
+ }
> m

	 Conditional inference tree with 10 terminal nodes

Response:  TimeRFC 
Inputs:  X.Pageviews, X.Logins, X.CourseCompendiumViews, X.CourseCompendiumViews.onlyPR., X.SharedCompendiums, X.BloggedComputations, X.ReviewedCompendiums, X.FeedbackMessagesInPR, X.FBMIPR.120, X.CompendiumCharacters, X.Revisions, CompendiumWrintingTime, X.IncludedHyperlinks, X.IncludedBlogs 
Number of observations:  164 

1) X.Pageviews <= 1503; criterion = 1, statistic = 136.613
  2) X.Pageviews <= 893; criterion = 1, statistic = 46.529
    3) X.Pageviews <= 496; criterion = 1, statistic = 21.988
      4)*  weights = 17 
    3) X.Pageviews > 496
      5)*  weights = 8 
  2) X.Pageviews > 893
    6) X.IncludedBlogs <= 61; criterion = 0.996, statistic = 13.25
      7)*  weights = 11 
    6) X.IncludedBlogs > 61
      8)*  weights = 18 
1) X.Pageviews > 1503
  9) X.Pageviews <= 2760; criterion = 1, statistic = 65.282
    10) CompendiumWrintingTime <= 134041; criterion = 1, statistic = 45.438
      11) CompendiumWrintingTime <= 93116; criterion = 0.999, statistic = 16.79
        12)*  weights = 23 
      11) CompendiumWrintingTime > 93116
        13) X.BloggedComputations <= 101; criterion = 0.959, statistic = 8.812
          14)*  weights = 28 
        13) X.BloggedComputations > 101
          15)*  weights = 12 
    10) CompendiumWrintingTime > 134041
      16) X.CourseCompendiumViews <= 783; criterion = 0.996, statistic = 13.203
        17)*  weights = 18 
      16) X.CourseCompendiumViews > 783
        18)*  weights = 10 
  9) X.Pageviews > 2760
    19)*  weights = 19 
> postscript(file="/var/wessaorg/rcomp/tmp/2i63z1323612724.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/wessaorg/rcomp/tmp/3oyr81323612724.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    252101 248802.11   3298.88889
2    134577 153658.44 -19081.44444
3    198520 203954.14  -5434.14286
4    189326 238818.00 -49492.00000
5    137449 153658.44 -16209.44444
6     65295  64460.75    834.25000
7    439387 338295.21 101091.78947
8     33186  13016.76  20169.23529
9    178368 203954.14 -25586.14286
10   186657 203954.14 -17297.14286
11   261949 248802.11  13146.88889
12   191051 172349.13  18701.86957
13   138866 115146.91  23719.09091
14   296878 238818.00  58060.00000
15   192648 203954.14 -11306.14286
16   333462 338295.21  -4833.21053
17   243571 248802.11  -5231.11111
18   263451 203954.14  59496.85714
19   155679 153658.44   2020.55556
20   227053 238818.00 -11765.00000
21   240028 238818.00   1210.00000
22   388549 304898.10  83650.90000
23   156540 153658.44   2881.55556
24   148421 153658.44  -5237.44444
25   177732 172349.13   5382.86957
26   191441 203954.14 -12513.14286
27   249893 304898.10 -55005.10000
28   236812 248802.11 -11990.11111
29   142329 153658.44 -11329.44444
30   259667 248802.11  10864.88889
31   231625 248802.11 -17177.11111
32   176062 203954.14 -27892.14286
33   286683 248802.11  37880.88889
34    87485  64460.75  23024.25000
35   322865 304898.10  17966.90000
36   247082 203954.14  43127.85714
37   346011 338295.21   7715.78947
38   191653 203954.14 -12301.14286
39   114673 115146.91   -473.90909
40   284224 304898.10 -20674.10000
41   284195 338295.21 -54100.21053
42   155363 153658.44   1704.55556
43   177306 203954.14 -26648.14286
44   144571 153658.44  -9087.44444
45   140319 203954.14 -63635.14286
46   405267 338295.21  66971.78947
47    78800 115146.91 -36346.90909
48   201970 203954.14  -1984.14286
49   302674 338295.21 -35621.21053
50   164733 203954.14 -39221.14286
51   194221 248802.11 -54581.11111
52    24188  13016.76  11171.23529
53   342263 304898.10  37364.90000
54    65029  64460.75    568.25000
55   101097 115146.91 -14049.90909
56   246088 203954.14  42133.85714
57   273108 248802.11  24305.88889
58   282220 338295.21 -56075.21053
59   273495 304898.10 -31403.10000
60   214872 172349.13  42522.86957
61   335121 338295.21  -3174.21053
62   267171 304898.10 -37727.10000
63   187938 172349.13  15588.86957
64   229512 203954.14  25557.85714
65   209798 153658.44  56139.55556
66   201345 203954.14  -2609.14286
67   163833 172349.13  -8516.13043
68   204250 203954.14    295.85714
69   197813 238818.00 -41005.00000
70   132955 153658.44 -20703.44444
71   216092 203954.14  12137.85714
72    73566  64460.75   9105.25000
73   213198 203954.14   9243.85714
74   181713 172349.13   9363.86957
75   148698 153658.44  -4960.44444
76   300103 238818.00  61285.00000
77   251437 248802.11   2634.88889
78   197295 248802.11 -51507.11111
79   158163 172349.13 -14186.13043
80   155529 153658.44   1870.55556
81   132672 172349.13 -39677.13043
82   377205 338295.21  38909.78947
83   145905 115146.91  30758.09091
84   223701 203954.14  19746.85714
85    80953  64460.75  16492.25000
86   130805 172349.13 -41544.13043
87   135082 153658.44 -18576.44444
88   300805 338295.21 -37490.21053
89   271806 248802.11  23003.88889
90   150949 172349.13 -21400.13043
91   225805 203954.14  21850.85714
92   197389 238818.00 -41429.00000
93   156583 172349.13 -15766.13043
94   222599 172349.13  50249.86957
95   261601 203954.14  57646.85714
96   178489 203954.14 -25465.14286
97   200657 153658.44  46998.55556
98   259084 248802.11  10281.88889
99   313075 338295.21 -25220.21053
100  346933 338295.21   8637.78947
101  246440 238818.00   7622.00000
102  252444 248802.11   3641.88889
103  159965 172349.13 -12384.13043
104   43287  64460.75 -21173.75000
105  172239 172349.13   -110.13043
106  183738 203954.14 -20216.14286
107  227681 238818.00 -11137.00000
108  260464 238818.00  21646.00000
109  106288 115146.91  -8858.90909
110  109632 115146.91  -5514.90909
111  268905 248802.11  20102.88889
112  266805 238818.00  27987.00000
113   23623  13016.76  10606.23529
114  152474 172349.13 -19875.13043
115   61857  64460.75  -2603.75000
116  144889 153658.44  -8769.44444
117  346600 304898.10  41701.90000
118   21054  13016.76   8037.23529
119  224051 203954.14  20096.85714
120   31414  13016.76  18397.23529
121  261043 338295.21 -77252.21053
122  197819 172349.13  25469.86957
123  154984 115146.91  39837.09091
124  112933 153658.44 -40725.44444
125   38214  64460.75 -26246.75000
126  158671 172349.13 -13678.13043
127  302148 304898.10  -2750.10000
128  177918 153658.44  24259.55556
129  350552 338295.21  12256.78947
130  275578 248802.11  26775.88889
131  368746 338295.21  30450.78947
132  172464 153658.44  18805.55556
133   94381 115146.91 -20765.90909
134  243875 172349.13  71525.86957
135  382487 338295.21  44191.78947
136  114525 115146.91   -621.90909
137  335681 338295.21  -2614.21053
138  147989 172349.13 -24360.13043
139  216638 248802.11 -32164.11111
140  192862 203954.14 -11092.14286
141  184818 172349.13  12468.86957
142  336707 338295.21  -1588.21053
143  215836 238818.00 -22982.00000
144  173260 172349.13    910.86957
145  271773 304898.10 -33125.10000
146  130908 172349.13 -41441.13043
147  204009 203954.14     54.85714
148  245514 248802.11  -3288.11111
149       1  13016.76 -13015.76471
150   14688  13016.76   1671.23529
151      98  13016.76 -12918.76471
152     455  13016.76 -12561.76471
153       0  13016.76 -13016.76471
154       0  13016.76 -13016.76471
155  195765 203954.14  -8189.14286
156  326038 338295.21 -12257.21053
157       0  13016.76 -13016.76471
158     203  13016.76 -12813.76471
159    7199  13016.76  -5817.76471
160   46660  13016.76  33643.23529
161   17547  13016.76   4530.23529
162  107465 115146.91  -7681.90909
163     969  13016.76 -12047.76471
164  173102 172349.13    752.86957
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
> postscript(file="/var/wessaorg/rcomp/tmp/4d6071323612724.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/wessaorg/rcomp/tmp/5g0491323612724.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/wessaorg/rcomp/tmp/63w451323612724.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/wessaorg/rcomp/tmp/7a2jj1323612724.tab") 
+ }
> 
> try(system("convert tmp/2i63z1323612724.ps tmp/2i63z1323612724.png",intern=TRUE))
character(0)
> try(system("convert tmp/3oyr81323612724.ps tmp/3oyr81323612724.png",intern=TRUE))
character(0)
> try(system("convert tmp/4d6071323612724.ps tmp/4d6071323612724.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  5.227   0.246   5.504