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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+ ,0
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+ ,65
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+ ,2
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+ ,32
+ ,117
+ ,66
+ ,100350
+ ,17372
+ ,75882
+ ,80
+ ,72)
+ ,dim=c(15
+ ,164)
+ ,dimnames=list(c('pageviews'
+ ,'timeinrfc'
+ ,'logins'
+ ,'compendiumviewsinfo'
+ ,'compendiumviewspr'
+ ,'sharedcompendiums'
+ ,'bloggedcomputations'
+ ,'compendiumsreviewed'
+ ,'feedbackmessagesp1'
+ ,'feedbackmessagesp120'
+ ,'totsize'
+ ,'totrevisions'
+ ,'totseconds'
+ ,'tothyperlinks'
+ ,'totblogs')
+ ,1:164))
>  y <- array(NA,dim=c(15,164),dimnames=list(c('pageviews','timeinrfc','logins','compendiumviewsinfo','compendiumviewspr','sharedcompendiums','bloggedcomputations','compendiumsreviewed','feedbackmessagesp1','feedbackmessagesp120','totsize','totrevisions','totseconds','tothyperlinks','totblogs'),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 = '3'
> par2 = 'none'
> par1 = '11'
> 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] "totsize"
> x[,par1]
  [1] 140824 110459 105079 112098  43929  76173 187326  22807 144408  66485
 [11]  79089  81625  68788 103297  69446 114948 167949 125081 125818 136588
 [21] 112431 103037  82317 118906  83515 104581 103129  83243  37110 113344
 [31] 139165  86652 112302  69652 119442  69867 101629  70168  31081 103925
 [41]  92622  79011  93487  64520  93473 114360  33032  96125 151911  89256
 [51]  95671   5950 149695  32551  31701 100087 169707 150491 120192  95893
 [61] 151715 176225  59900 104767 114799  72128 143592  89626 131072 126817
 [71]  81351  22618  88977  92059  81897 108146 126372 249771  71154  71571
 [81]  55918 160141  38692 102812  56622  15986 123534 108535  93879 144551
 [91]  56750 127654  65594  59938 146975 143372 168553 183500 165986 184923
[101] 140358 149959  57224  43750  48029 104978 100046 101047 197426 160902
[111] 147172 109432   1168  83248  25162  45724 110529    855 101382  14116
[121]  89506 135356 116066 144244   8773 102153 117440 104128 134238 134047
[131] 279488  79756  66089 102070 146760 154771 165933  64593  92280  67150
[141] 128692 124089 125386  37238 140015 150047 154451 156349      0   6023
[151]      0      0      0      0  84601  68946      0      0   1644   6179
[161]   3926  52789      0 100350
> 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    855   1168   1644   3926   5950   6023   6179   8773  14116  15986 
     8      1      1      1      1      1      1      1      1      1      1 
 22618  22807  25162  31081  31701  32551  33032  37110  37238  38692  43750 
     1      1      1      1      1      1      1      1      1      1      1 
 43929  45724  48029  52789  55918  56622  56750  57224  59900  59938  64520 
     1      1      1      1      1      1      1      1      1      1      1 
 64593  65594  66089  66485  67150  68788  68946  69446  69652  69867  70168 
     1      1      1      1      1      1      1      1      1      1      1 
 71154  71571  72128  76173  79011  79089  79756  81351  81625  81897  82317 
     1      1      1      1      1      1      1      1      1      1      1 
 83243  83248  83515  84601  86652  88977  89256  89506  89626  92059  92280 
     1      1      1      1      1      1      1      1      1      1      1 
 92622  93473  93487  93879  95671  95893  96125 100046 100087 100350 101047 
     1      1      1      1      1      1      1      1      1      1      1 
101382 101629 102070 102153 102812 103037 103129 103297 103925 104128 104581 
     1      1      1      1      1      1      1      1      1      1      1 
104767 104978 105079 108146 108535 109432 110459 110529 112098 112302 112431 
     1      1      1      1      1      1      1      1      1      1      1 
113344 114360 114799 114948 116066 117440 118906 119442 120192 123534 124089 
     1      1      1      1      1      1      1      1      1      1      1 
125081 125386 125818 126372 126817 127654 128692 131072 134047 134238 135356 
     1      1      1      1      1      1      1      1      1      1      1 
136588 139165 140015 140358 140824 143372 143592 144244 144408 144551 146760 
     1      1      1      1      1      1      1      1      1      1      1 
146975 147172 149695 149959 150047 150491 151715 151911 154451 154771 156349 
     1      1      1      1      1      1      1      1      1      1      1 
160141 160902 165933 165986 167949 168553 169707 176225 183500 184923 187326 
     1      1      1      1      1      1      1      1      1      1      1 
197426 249771 279488 
     1      1      1 
> colnames(x)
 [1] "pageviews"            "timeinrfc"            "logins"              
 [4] "compendiumviewsinfo"  "compendiumviewspr"    "sharedcompendiums"   
 [7] "bloggedcomputations"  "compendiumsreviewed"  "feedbackmessagesp1"  
[10] "feedbackmessagesp120" "totsize"              "totrevisions"        
[13] "totseconds"           "tothyperlinks"        "totblogs"            
> colnames(x)[par1]
[1] "totsize"
> x[,par1]
  [1] 140824 110459 105079 112098  43929  76173 187326  22807 144408  66485
 [11]  79089  81625  68788 103297  69446 114948 167949 125081 125818 136588
 [21] 112431 103037  82317 118906  83515 104581 103129  83243  37110 113344
 [31] 139165  86652 112302  69652 119442  69867 101629  70168  31081 103925
 [41]  92622  79011  93487  64520  93473 114360  33032  96125 151911  89256
 [51]  95671   5950 149695  32551  31701 100087 169707 150491 120192  95893
 [61] 151715 176225  59900 104767 114799  72128 143592  89626 131072 126817
 [71]  81351  22618  88977  92059  81897 108146 126372 249771  71154  71571
 [81]  55918 160141  38692 102812  56622  15986 123534 108535  93879 144551
 [91]  56750 127654  65594  59938 146975 143372 168553 183500 165986 184923
[101] 140358 149959  57224  43750  48029 104978 100046 101047 197426 160902
[111] 147172 109432   1168  83248  25162  45724 110529    855 101382  14116
[121]  89506 135356 116066 144244   8773 102153 117440 104128 134238 134047
[131] 279488  79756  66089 102070 146760 154771 165933  64593  92280  67150
[141] 128692 124089 125386  37238 140015 150047 154451 156349      0   6023
[151]      0      0      0      0  84601  68946      0      0   1644   6179
[161]   3926  52789      0 100350
> 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/1uhxm1324146230.tab") 
+ }
+ }
> m

	 Conditional inference tree with 7 terminal nodes

Response:  totsize 
Inputs:  pageviews, timeinrfc, logins, compendiumviewsinfo, compendiumviewspr, sharedcompendiums, bloggedcomputations, compendiumsreviewed, feedbackmessagesp1, feedbackmessagesp120, totrevisions, totseconds, tothyperlinks, totblogs 
Number of observations:  164 

1) totseconds <= 74151; criterion = 1, statistic = 102.666
  2) feedbackmessagesp120 <= 78; criterion = 1, statistic = 29.419
    3) tothyperlinks <= 13; criterion = 1, statistic = 20.262
      4)*  weights = 16 
    3) tothyperlinks > 13
      5)*  weights = 13 
  2) feedbackmessagesp120 > 78
    6)*  weights = 11 
1) totseconds > 74151
  7) totseconds <= 174586; criterion = 1, statistic = 41.371
    8) totrevisions <= 20648; criterion = 1, statistic = 27.52
      9)*  weights = 33 
    8) totrevisions > 20648
      10) tothyperlinks <= 174; criterion = 0.979, statistic = 10.025
        11)*  weights = 53 
      10) tothyperlinks > 174
        12)*  weights = 11 
  7) totseconds > 174586
    13)*  weights = 27 
> postscript(file="/var/wessaorg/rcomp/tmp/2vzoy1324146230.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/3boo71324146230.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    140824 156589.19 -15765.1852
2    110459 108419.55   2039.4528
3    105079  83722.76  21356.2424
4    112098 108419.55   3678.4528
5     43929  83722.76 -39793.7576
6     76173  72925.27   3247.7273
7    187326 156589.19  30736.8148
8     22807   3034.50  19772.5000
9    144408 108419.55  35988.4528
10    66485  83722.76 -17237.7576
11    79089 108419.55 -29330.5472
12    81625  83722.76  -2097.7576
13    68788  72925.27  -4137.2727
14   103297 108419.55  -5122.5472
15    69446  83722.76 -14276.7576
16   114948 108419.55   6528.4528
17   167949 140986.27  26962.7273
18   125081 108419.55  16661.4528
19   125818 108419.55  17398.4528
20   136588 140986.27  -4398.2727
21   112431 108419.55   4011.4528
22   103037  83722.76  19314.2424
23    82317  83722.76  -1405.7576
24   118906 108419.55  10486.4528
25    83515 108419.55 -24904.5472
26   104581 108419.55  -3838.5472
27   103129 108419.55  -5290.5472
28    83243 108419.55 -25176.5472
29    37110  72925.27 -35815.2727
30   113344 156589.19 -43245.1852
31   139165 156589.19 -17424.1852
32    86652  83722.76   2929.2424
33   112302 156589.19 -44287.1852
34    69652  72925.27  -3273.2727
35   119442 156589.19 -37147.1852
36    69867  83722.76 -13855.7576
37   101629 156589.19 -54960.1852
38    70168  83722.76 -13554.7576
39    31081  31186.08   -105.0769
40   103925 108419.55  -4494.5472
41    92622 108419.55 -15797.5472
42    79011  83722.76  -4711.7576
43    93487 108419.55 -14932.5472
44    64520  72925.27  -8405.2727
45    93473  83722.76   9750.2424
46   114360  83722.76  30637.2424
47    33032  31186.08   1845.9231
48    96125  83722.76  12402.2424
49   151911 156589.19  -4678.1852
50    89256 108419.55 -19163.5472
51    95671 108419.55 -12748.5472
52     5950   3034.50   2915.5000
53   149695 156589.19  -6894.1852
54    32551  31186.08   1364.9231
55    31701  31186.08    514.9231
56   100087 108419.55  -8332.5472
57   169707 156589.19  13117.8148
58   150491 156589.19  -6098.1852
59   120192 156589.19 -36397.1852
60    95893 108419.55 -12526.5472
61   151715 156589.19  -4874.1852
62   176225 156589.19  19635.8148
63    59900  83722.76 -23822.7576
64   104767 108419.55  -3652.5472
65   114799 108419.55   6379.4528
66    72128 108419.55 -36291.5472
67   143592 108419.55  35172.4528
68    89626 108419.55 -18793.5472
69   131072 140986.27  -9914.2727
70   126817 108419.55  18397.4528
71    81351  83722.76  -2371.7576
72    22618  31186.08  -8568.0769
73    88977  83722.76   5254.2424
74    92059 108419.55 -16360.5472
75    81897 108419.55 -26522.5472
76   108146 108419.55   -273.5472
77   126372 140986.27 -14614.2727
78   249771 156589.19  93181.8148
79    71154  83722.76 -12568.7576
80    71571  83722.76 -12151.7576
81    55918  83722.76 -27804.7576
82   160141 156589.19   3551.8148
83    38692  72925.27 -34233.2727
84   102812 108419.55  -5607.5472
85    56622  31186.08  25435.9231
86    15986  31186.08 -15200.0769
87   123534 108419.55  15114.4528
88   108535 108419.55    115.4528
89    93879 108419.55 -14540.5472
90   144551 108419.55  36131.4528
91    56750  83722.76 -26972.7576
92   127654 140986.27 -13332.2727
93    65594  83722.76 -18128.7576
94    59938  83722.76 -23784.7576
95   146975 108419.55  38555.4528
96   143372 108419.55  34952.4528
97   168553 140986.27  27566.7273
98   183500 156589.19  26910.8148
99   165986 156589.19   9396.8148
100  184923 156589.19  28333.8148
101  140358 108419.55  31938.4528
102  149959 156589.19  -6630.1852
103   57224  83722.76 -26498.7576
104   43750  31186.08  12563.9231
105   48029  83722.76 -35693.7576
106  104978 140986.27 -36008.2727
107  100046 108419.55  -8373.5472
108  101047 108419.55  -7372.5472
109  197426 156589.19  40836.8148
110  160902  83722.76  77179.2424
111  147172 156589.19  -9417.1852
112  109432 108419.55   1012.4528
113    1168   3034.50  -1866.5000
114   83248 108419.55 -25171.5472
115   25162  31186.08  -6024.0769
116   45724  72925.27 -27201.2727
117  110529 156589.19 -46060.1852
118     855   3034.50  -2179.5000
119  101382  83722.76  17659.2424
120   14116  31186.08 -17070.0769
121   89506 108419.55 -18913.5472
122  135356 108419.55  26936.4528
123  116066  72925.27  43140.7273
124  144244  83722.76  60521.2424
125    8773  31186.08 -22413.0769
126  102153  83722.76  18430.2424
127  117440 108419.55   9020.4528
128  104128 108419.55  -4291.5472
129  134238 156589.19 -22351.1852
130  134047 156589.19 -22542.1852
131  279488 156589.19 122898.8148
132   79756 108419.55 -28663.5472
133   66089  72925.27  -6836.2727
134  102070 108419.55  -6349.5472
135  146760 156589.19  -9829.1852
136  154771  72925.27  81845.7273
137  165933 140986.27  24946.7273
138   64593  72925.27  -8332.2727
139   92280 108419.55 -16139.5472
140   67150  83722.76 -16572.7576
141  128692 108419.55  20272.4528
142  124089  83722.76  40366.2424
143  125386 140986.27 -15600.2727
144   37238  31186.08   6051.9231
145  140015 140986.27   -971.2727
146  150047 108419.55  41627.4528
147  154451 108419.55  46031.4528
148  156349 140986.27  15362.7273
149       0   3034.50  -3034.5000
150    6023   3034.50   2988.5000
151       0   3034.50  -3034.5000
152       0   3034.50  -3034.5000
153       0   3034.50  -3034.5000
154       0   3034.50  -3034.5000
155   84601  83722.76    878.2424
156   68946 108419.55 -39473.5472
157       0   3034.50  -3034.5000
158       0   3034.50  -3034.5000
159    1644   3034.50  -1390.5000
160    6179   3034.50   3144.5000
161    3926   3034.50    891.5000
162   52789  31186.08  21602.9231
163       0   3034.50  -3034.5000
164  100350  83722.76  16627.2424
> 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/4cou51324146230.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/57kmk1324146230.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/6n3x41324146230.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/7ltz71324146230.tab") 
+ }
> 
> try(system("convert tmp/2vzoy1324146230.ps tmp/2vzoy1324146230.png",intern=TRUE))
character(0)
> try(system("convert tmp/3boo71324146230.ps tmp/3boo71324146230.png",intern=TRUE))
character(0)
> try(system("convert tmp/4cou51324146230.ps tmp/4cou51324146230.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  4.914   0.307   5.325