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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+ ,668
+ ,2
+ ,34
+ ,71
+ ,100350
+ ,17372
+ ,80
+ ,72)
+ ,dim=c(9
+ ,164)
+ ,dimnames=list(c('TotalTime'
+ ,'CourseCompendiumViews'
+ ,'SharedbyotherAuthors'
+ ,'ReviewedCompendiums'
+ ,'PeerReviews'
+ ,'CWnumberOfCharacters'
+ ,'CWNumberOfRevisions'
+ ,'CWNumberOfHyperlinks'
+ ,'CWNumberOfBlogs')
+ ,1:164))
>  y <- array(NA,dim=c(9,164),dimnames=list(c('TotalTime','CourseCompendiumViews','SharedbyotherAuthors','ReviewedCompendiums','PeerReviews','CWnumberOfCharacters','CWNumberOfRevisions','CWNumberOfHyperlinks','CWNumberOfBlogs'),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 = '6'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Dr. Ian E. Holliday
> #To cite this work: Ian E. Holliday, 2009, YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: 
> #Technical description: 
> 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] "CWnumberOfCharacters"
> 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]  95676   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 165904 169265 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  95676  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 143592 144244 144408 144551 146760 146975 
     1      1      1      1      1      1      1      1      1      1      1 
147172 149695 149959 150047 150491 151715 151911 154451 154771 156349 160141 
     1      1      1      1      1      1      1      1      1      1      1 
160902 165904 165933 165986 167949 169265 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] "TotalTime"             "CourseCompendiumViews" "SharedbyotherAuthors" 
[4] "ReviewedCompendiums"   "PeerReviews"           "CWnumberOfCharacters" 
[7] "CWNumberOfRevisions"   "CWNumberOfHyperlinks"  "CWNumberOfBlogs"      
> colnames(x)[par1]
[1] "CWnumberOfCharacters"
> 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]  95676   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 165904 169265 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/1iijz1324657778.tab") 
+ }
+ }
> m

	 Conditional inference tree with 5 terminal nodes

Response:  CWnumberOfCharacters 
Inputs:  TotalTime, CourseCompendiumViews, SharedbyotherAuthors, ReviewedCompendiums, PeerReviews, CWNumberOfRevisions, CWNumberOfHyperlinks, CWNumberOfBlogs 
Number of observations:  164 

1) CWNumberOfRevisions <= 18213; criterion = 1, statistic = 97.495
  2) ReviewedCompendiums <= 27; criterion = 1, statistic = 42.414
    3) CWNumberOfHyperlinks <= 13; criterion = 1, statistic = 24.1
      4)*  weights = 16 
    3) CWNumberOfHyperlinks > 13
      5)*  weights = 16 
  2) ReviewedCompendiums > 27
    6)*  weights = 33 
1) CWNumberOfRevisions > 18213
  7) CWNumberOfRevisions <= 41517; criterion = 1, statistic = 21.952
    8)*  weights = 89 
  7) CWNumberOfRevisions > 41517
    9)*  weights = 10 
> postscript(file="/var/wessaorg/rcomp/tmp/28vcr1324657778.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/39plt1324657778.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 118015.71  22808.2921
2    110459 118015.71  -7556.7079
3    105079  82172.12  22906.8788
4    112098 118015.71  -5917.7079
5     43929  35349.81   8579.1875
6     76173  82172.12  -5999.1212
7    187326 173801.50  13524.5000
8     22807   3034.50  19772.5000
9    144408 118015.71  26392.2921
10    66485 118015.71 -51530.7079
11    79089 118015.71 -38926.7079
12    81625 118015.71 -36390.7079
13    68788  82172.12 -13384.1212
14   103297 118015.71 -14718.7079
15    69446  82172.12 -12726.1212
16   114948 118015.71  -3067.7079
17   167949 118015.71  49933.2921
18   125081 118015.71   7065.2921
19   125818 118015.71   7802.2921
20   136588 118015.71  18572.2921
21   112431 118015.71  -5584.7079
22   103037 118015.71 -14978.7079
23    82317  35349.81  46967.1875
24   118906 118015.71    890.2921
25    83515 118015.71 -34500.7079
26   104581 118015.71 -13434.7079
27   103129 118015.71 -14886.7079
28    83243 118015.71 -34772.7079
29    37110  82172.12 -45062.1212
30   113344 118015.71  -4671.7079
31   139165 173801.50 -34636.5000
32    86652  82172.12   4479.8788
33   112302 118015.71  -5713.7079
34    69652  82172.12 -12520.1212
35   119442 118015.71   1426.2921
36    69867  82172.12 -12305.1212
37   101629 118015.71 -16386.7079
38    70168 118015.71 -47847.7079
39    31081  35349.81  -4268.8125
40   103925 118015.71 -14090.7079
41    92622 118015.71 -25393.7079
42    79011  82172.12  -3161.1212
43    93487 118015.71 -24528.7079
44    64520  82172.12 -17652.1212
45    93473  82172.12  11300.8788
46   114360  82172.12  32187.8788
47    33032  35349.81  -2317.8125
48    96125 118015.71 -21890.7079
49   151911 118015.71  33895.2921
50    89256 118015.71 -28759.7079
51    95676 118015.71 -22339.7079
52     5950   3034.50   2915.5000
53   149695 118015.71  31679.2921
54    32551  35349.81  -2798.8125
55    31701  35349.81  -3648.8125
56   100087 118015.71 -17928.7079
57   169707 118015.71  51691.2921
58   150491 173801.50 -23310.5000
59   120192 118015.71   2176.2921
60    95893 118015.71 -22122.7079
61   151715 118015.71  33699.2921
62   176225 173801.50   2423.5000
63    59900  82172.12 -22272.1212
64   104767 118015.71 -13248.7079
65   114799 118015.71  -3216.7079
66    72128 118015.71 -45887.7079
67   143592 118015.71  25576.2921
68    89626 118015.71 -28389.7079
69   131072 118015.71  13056.2921
70   126817 118015.71   8801.2921
71    81351  82172.12   -821.1212
72    22618  35349.81 -12731.8125
73    88977 118015.71 -29038.7079
74    92059 118015.71 -25956.7079
75    81897 118015.71 -36118.7079
76   108146 118015.71  -9869.7079
77   126372 118015.71   8356.2921
78   249771 118015.71 131755.2921
79    71154  82172.12 -11018.1212
80    71571  82172.12 -10601.1212
81    55918  82172.12 -26254.1212
82   160141 118015.71  42125.2921
83    38692  35349.81   3342.1875
84   102812 118015.71 -15203.7079
85    56622  35349.81  21272.1875
86    15986  35349.81 -19363.8125
87   123534 118015.71   5518.2921
88   108535 118015.71  -9480.7079
89    93879 118015.71 -24136.7079
90   144551 118015.71  26535.2921
91    56750  82172.12 -25422.1212
92   127654 118015.71   9638.2921
93    65594  82172.12 -16578.1212
94    59938  82172.12 -22234.1212
95   146975 118015.71  28959.2921
96   165904 118015.71  47888.2921
97   169265 118015.71  51249.2921
98   183500 173801.50   9698.5000
99   165986 173801.50  -7815.5000
100  184923 173801.50  11121.5000
101  140358 118015.71  22342.2921
102  149959 118015.71  31943.2921
103   57224  82172.12 -24948.1212
104   43750  35349.81   8400.1875
105   48029  35349.81  12679.1875
106  104978 173801.50 -68823.5000
107  100046 118015.71 -17969.7079
108  101047 118015.71 -16968.7079
109  197426 118015.71  79410.2921
110  160902  82172.12  78729.8788
111  147172 118015.71  29156.2921
112  109432 118015.71  -8583.7079
113    1168   3034.50  -1866.5000
114   83248 118015.71 -34767.7079
115   25162  35349.81 -10187.8125
116   45724  82172.12 -36448.1212
117  110529 118015.71  -7486.7079
118     855   3034.50  -2179.5000
119  101382  82172.12  19209.8788
120   14116  35349.81 -21233.8125
121   89506 118015.71 -28509.7079
122  135356 118015.71  17340.2921
123  116066  82172.12  33893.8788
124  144244  82172.12  62071.8788
125    8773  35349.81 -26576.8125
126  102153 118015.71 -15862.7079
127  117440 118015.71   -575.7079
128  104128 118015.71 -13887.7079
129  134238 118015.71  16222.2921
130  134047 118015.71  16031.2921
131  279488 173801.50 105686.5000
132   79756 118015.71 -38259.7079
133   66089  82172.12 -16083.1212
134  102070 118015.71 -15945.7079
135  146760 118015.71  28744.2921
136  154771  82172.12  72598.8788
137  165933 173801.50  -7868.5000
138   64593  82172.12 -17579.1212
139   92280 118015.71 -25735.7079
140   67150  82172.12 -15022.1212
141  128692 118015.71  10676.2921
142  124089  82172.12  41916.8788
143  125386 118015.71   7370.2921
144   37238  35349.81   1888.1875
145  140015 118015.71  21999.2921
146  150047 118015.71  32031.2921
147  154451 118015.71  36435.2921
148  156349 118015.71  38333.2921
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 118015.71 -33414.7079
156   68946 118015.71 -49069.7079
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  82172.12 -29383.1212
163       0   3034.50  -3034.5000
164  100350  82172.12  18177.8788
> 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/4th761324657778.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/5fo8i1324657778.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/6vkjb1324657778.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/7irbe1324657778.tab") 
+ }
> 
> try(system("convert tmp/28vcr1324657778.ps tmp/28vcr1324657778.png",intern=TRUE))
character(0)
> try(system("convert tmp/39plt1324657778.ps tmp/39plt1324657778.png",intern=TRUE))
character(0)
> try(system("convert tmp/4th761324657778.ps tmp/4th761324657778.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.796   0.297   4.088