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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> x <- array(list(144244
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+ ,0
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+ ,151
+ ,88
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+ ,139292
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+ ,24188
+ ,24
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+ ,0
+ ,8
+ ,4
+ ,12
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+ ,15049
+ ,7
+ ,7
+ ,3926
+ ,141
+ ,17547
+ ,5
+ ,69
+ ,3
+ ,0
+ ,0
+ ,1
+ ,4
+ ,4
+ ,2658
+ ,7670
+ ,3
+ ,3)
+ ,dim=c(15
+ ,144)
+ ,dimnames=list(c('#karakters'
+ ,'#PageViews'
+ ,'#SecRFC'
+ ,'#LogIns'
+ ,'#CourseCompViews'
+ ,'#CompViewsPR'
+ ,'#shared'
+ ,'#Blogs'
+ ,'#Reviews'
+ ,'#FBMinPR'
+ ,'#FBMinPR+120'
+ ,'#revisions'
+ ,'#seconden'
+ ,'#hyperlinks'
+ ,'#blogs')
+ ,1:144))
>  y <- array(NA,dim=c(15,144),dimnames=list(c('#karakters','#PageViews','#SecRFC','#LogIns','#CourseCompViews','#CompViewsPR','#shared','#Blogs','#Reviews','#FBMinPR','#FBMinPR+120','#revisions','#seconden','#hyperlinks','#blogs'),1:144))
>  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 = '15'
> 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] "X.karakters"
> x[,par1]
  [1] 144244 197426  86652  65594 101382  76173 124089  66089  22618 149695
 [11]  56622 150047 151911  25162 105079  69446 136588 128692 134047  31701
 [21]  43750 143592 100350 151715 113344 279488 125081  68788 103037 102153
 [31] 147172 146760 127654 110459 131072 126817 108535  82317  57224 135356
 [41]  96125   1168 102070 118906  59900  79011 103297 143372 109432 167949
 [51]   8773  45724 149959  81351 103129 154451  88977 140824  84601 169707
 [61] 187326 156349 108146 168553 144408 183500 104128  33032  43929  56750
 [71] 126372 160141  71571 125818  38692  95893  67150 110529  59938  81625
 [81]  71154 104767 125386 165933  64520 165986 102812  81897  37110 146975
 [91]  92059 144551 184923  79756 140015  89506  64593  70168 134238 101047
[101]  92622  14116  15986  89256 150491 140358 114948  95671 176225  93487
[111]  89626  66485  79089  55918 112302 104581 117440 101629 112098  68946
[121] 114799 119442 100087 139165  83243 123534   6179   1644   6023 120192
[131]  83248 103925  72128 112431  92280  83515  48029  93879    855 100046
[141]  31081 104978   5950   3926
> 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])

   855   1168   1644   3926   5950   6023   6179   8773  14116  15986  22618 
     1      1      1      1      1      1      1      1      1      1      1 
 25162  31081  31701  33032  37110  38692  43750  43929  45724  48029  55918 
     1      1      1      1      1      1      1      1      1      1      1 
 56622  56750  57224  59900  59938  64520  64593  65594  66089  66485  67150 
     1      1      1      1      1      1      1      1      1      1      1 
 68788  68946  69446  70168  71154  71571  72128  76173  79011  79089  79756 
     1      1      1      1      1      1      1      1      1      1      1 
 81351  81625  81897  82317  83243  83248  83515  84601  86652  88977  89256 
     1      1      1      1      1      1      1      1      1      1      1 
 89506  89626  92059  92280  92622  93487  93879  95671  95893  96125 100046 
     1      1      1      1      1      1      1      1      1      1      1 
100087 100350 101047 101382 101629 102070 102153 102812 103037 103129 103297 
     1      1      1      1      1      1      1      1      1      1      1 
103925 104128 104581 104767 104978 105079 108146 108535 109432 110459 110529 
     1      1      1      1      1      1      1      1      1      1      1 
112098 112302 112431 113344 114799 114948 117440 118906 119442 120192 123534 
     1      1      1      1      1      1      1      1      1      1      1 
124089 125081 125386 125818 126372 126817 127654 128692 131072 134047 134238 
     1      1      1      1      1      1      1      1      1      1      1 
135356 136588 139165 140015 140358 140824 143372 143592 144244 144408 144551 
     1      1      1      1      1      1      1      1      1      1      1 
146760 146975 147172 149695 149959 150047 150491 151715 151911 154451 156349 
     1      1      1      1      1      1      1      1      1      1      1 
160141 165933 165986 167949 168553 169707 176225 183500 184923 187326 197426 
     1      1      1      1      1      1      1      1      1      1      1 
279488 
     1 
> colnames(x)
 [1] "X.karakters"       "X.PageViews"       "X.SecRFC"         
 [4] "X.LogIns"          "X.CourseCompViews" "X.CompViewsPR"    
 [7] "X.shared"          "X.Blogs"           "X.Reviews"        
[10] "X.FBMinPR"         "X.FBMinPR.120"     "X.revisions"      
[13] "X.seconden"        "X.hyperlinks"      "X.blogs"          
> colnames(x)[par1]
[1] "X.karakters"
> x[,par1]
  [1] 144244 197426  86652  65594 101382  76173 124089  66089  22618 149695
 [11]  56622 150047 151911  25162 105079  69446 136588 128692 134047  31701
 [21]  43750 143592 100350 151715 113344 279488 125081  68788 103037 102153
 [31] 147172 146760 127654 110459 131072 126817 108535  82317  57224 135356
 [41]  96125   1168 102070 118906  59900  79011 103297 143372 109432 167949
 [51]   8773  45724 149959  81351 103129 154451  88977 140824  84601 169707
 [61] 187326 156349 108146 168553 144408 183500 104128  33032  43929  56750
 [71] 126372 160141  71571 125818  38692  95893  67150 110529  59938  81625
 [81]  71154 104767 125386 165933  64520 165986 102812  81897  37110 146975
 [91]  92059 144551 184923  79756 140015  89506  64593  70168 134238 101047
[101]  92622  14116  15986  89256 150491 140358 114948  95671 176225  93487
[111]  89626  66485  79089  55918 112302 104581 117440 101629 112098  68946
[121] 114799 119442 100087 139165  83243 123534   6179   1644   6023 120192
[131]  83248 103925  72128 112431  92280  83515  48029  93879    855 100046
[141]  31081 104978   5950   3926
> 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/1sgko1324115718.tab") 
+ }
+ }
> m

	 Conditional inference tree with 6 terminal nodes

Response:  X.karakters 
Inputs:  X.PageViews, X.SecRFC, X.LogIns, X.CourseCompViews, X.CompViewsPR, X.shared, X.Blogs, X.Reviews, X.FBMinPR, X.FBMinPR.120, X.revisions, X.seconden, X.hyperlinks, X.blogs 
Number of observations:  144 

1) X.revisions <= 18213; criterion = 1, statistic = 95.75
  2) X.revisions <= 5296; criterion = 1, statistic = 30.34
    3)*  weights = 13 
  2) X.revisions > 5296
    4) X.blogs <= 101; criterion = 0.978, statistic = 10.006
      5)*  weights = 24 
    4) X.blogs > 101
      6)*  weights = 9 
1) X.revisions > 18213
  7) X.revisions <= 41517; criterion = 1, statistic = 30.39
    8) X.revisions <= 34245; criterion = 0.993, statistic = 12.026
      9)*  weights = 65 
    8) X.revisions > 34245
      10)*  weights = 23 
  7) X.revisions > 41517
    11)*  weights = 10 
> postscript(file="/var/wessaorg/rcomp/tmp/2asio1324115718.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/30j2c1324115718.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    144244  90963.11  53280.8889
2    197426 110347.62  87078.3846
3     86652  90963.11  -4311.1111
4     65594  59941.00   5653.0000
5    101382  59941.00  41441.0000
6     76173  59941.00  16232.0000
7    124089  90963.11  33125.8889
8     66089  59941.00   6148.0000
9     22618  11084.69  11533.3077
10   149695 110347.62  39347.3846
11    56622  59941.00  -3319.0000
12   150047 110347.62  39699.3846
13   151911 110347.62  41563.3846
14    25162  11084.69  14077.3077
15   105079  90963.11  14115.8889
16    69446  59941.00   9505.0000
17   136588 110347.62  26240.3846
18   128692 110347.62  18344.3846
19   134047 110347.62  23699.3846
20    31701  11084.69  20616.3077
21    43750  59941.00 -16191.0000
22   143592 110347.62  33244.3846
23   100350  59941.00  40409.0000
24   151715 110347.62  41367.3846
25   113344 110347.62   2996.3846
26   279488 173801.50 105686.5000
27   125081 110347.62  14733.3846
28    68788  59941.00   8847.0000
29   103037 110347.62  -7310.6154
30   102153 110347.62  -8194.6154
31   147172 110347.62  36824.3846
32   146760 110347.62  36412.3846
33   127654 110347.62  17306.3846
34   110459 110347.62    111.3846
35   131072 110347.62  20724.3846
36   126817 110347.62  16469.3846
37   108535 110347.62  -1812.6154
38    82317  59941.00  22376.0000
39    57224  59941.00  -2717.0000
40   135356 110347.62  25008.3846
41    96125 110347.62 -14222.6154
42     1168  11084.69  -9916.6923
43   102070 110347.62  -8277.6154
44   118906 110347.62   8558.3846
45    59900  59941.00    -41.0000
46    79011  90963.11 -11952.1111
47   103297 110347.62  -7050.6154
48   143372 110347.62  33024.3846
49   109432 110347.62   -915.6154
50   167949 132947.09  35001.9130
51     8773  11084.69  -2311.6923
52    45724  59941.00 -14217.0000
53   149959 110347.62  39611.3846
54    81351  90963.11  -9612.1111
55   103129 110347.62  -7218.6154
56   154451 132947.09  21503.9130
57    88977 110347.62 -21370.6154
58   140824 110347.62  30476.3846
59    84601 110347.62 -25746.6154
60   169707 132947.09  36759.9130
61   187326 173801.50  13524.5000
62   156349 132947.09  23401.9130
63   108146 110347.62  -2201.6154
64   168553 132947.09  35605.9130
65   144408 132947.09  11460.9130
66   183500 173801.50   9698.5000
67   104128 110347.62  -6219.6154
68    33032  59941.00 -26909.0000
69    43929  59941.00 -16012.0000
70    56750  59941.00  -3191.0000
71   126372 110347.62  16024.3846
72   160141 132947.09  27193.9130
73    71571  59941.00  11630.0000
74   125818 110347.62  15470.3846
75    38692  59941.00 -21249.0000
76    95893 110347.62 -14454.6154
77    67150  90963.11 -23813.1111
78   110529 110347.62    181.3846
79    59938  90963.11 -31025.1111
80    81625 110347.62 -28722.6154
81    71154  90963.11 -19809.1111
82   104767 110347.62  -5580.6154
83   125386 110347.62  15038.3846
84   165933 173801.50  -7868.5000
85    64520  59941.00   4579.0000
86   165986 173801.50  -7815.5000
87   102812 110347.62  -7535.6154
88    81897 110347.62 -28450.6154
89    37110  59941.00 -22831.0000
90   146975 132947.09  14027.9130
91    92059 110347.62 -18288.6154
92   144551 132947.09  11603.9130
93   184923 173801.50  11121.5000
94    79756 110347.62 -30591.6154
95   140015 132947.09   7067.9130
96    89506 110347.62 -20841.6154
97    64593  59941.00   4652.0000
98    70168 110347.62 -40179.6154
99   134238 132947.09   1290.9130
100  101047 110347.62  -9300.6154
101   92622 110347.62 -17725.6154
102   14116  11084.69   3031.3077
103   15986  11084.69   4901.3077
104   89256 110347.62 -21091.6154
105  150491 173801.50 -23310.5000
106  140358 132947.09   7410.9130
107  114948 110347.62   4600.3846
108   95671 110347.62 -14676.6154
109  176225 173801.50   2423.5000
110   93487 110347.62 -16860.6154
111   89626 110347.62 -20721.6154
112   66485 110347.62 -43862.6154
113   79089 110347.62 -31258.6154
114   55918  59941.00  -4023.0000
115  112302 132947.09 -20645.0870
116  104581 110347.62  -5766.6154
117  117440 132947.09 -15507.0870
118  101629 110347.62  -8718.6154
119  112098 132947.09 -20849.0870
120   68946 110347.62 -41401.6154
121  114799 132947.09 -18148.0870
122  119442 132947.09 -13505.0870
123  100087 110347.62 -10260.6154
124  139165 173801.50 -34636.5000
125   83243 110347.62 -27104.6154
126  123534 132947.09  -9413.0870
127    6179  11084.69  -4905.6923
128    1644  11084.69  -9440.6923
129    6023  11084.69  -5061.6923
130  120192 132947.09 -12755.0870
131   83248 110347.62 -27099.6154
132  103925 132947.09 -29022.0870
133   72128 110347.62 -38219.6154
134  112431 132947.09 -20516.0870
135   92280 110347.62 -18067.6154
136   83515 110347.62 -26832.6154
137   48029  59941.00 -11912.0000
138   93879 132947.09 -39068.0870
139     855  11084.69 -10229.6923
140  100046 132947.09 -32901.0870
141   31081  59941.00 -28860.0000
142  104978 173801.50 -68823.5000
143    5950  11084.69  -5134.6923
144    3926  11084.69  -7158.6923
> 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/4aml51324115718.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/5z6sz1324115718.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/6vx7d1324115718.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/76ff11324115718.tab") 
+ }
> 
> try(system("convert tmp/2asio1324115718.ps tmp/2asio1324115718.png",intern=TRUE))
character(0)
> try(system("convert tmp/30j2c1324115718.ps tmp/30j2c1324115718.png",intern=TRUE))
character(0)
> try(system("convert tmp/4aml51324115718.ps tmp/4aml51324115718.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  4.112   0.280   4.621