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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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> x <- array(list(848
+ ,33
+ ,25
+ ,31.72
+ ,742
+ ,56
+ ,25
+ ,22.33
+ ,714
+ ,28
+ ,21
+ ,16.5
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+ ,16
+ ,22
+ ,14.74
+ ,384
+ ,20
+ ,35
+ ,14.28
+ ,461
+ ,12
+ ,21
+ ,13.94
+ ,507
+ ,18
+ ,25
+ ,13.78
+ ,546
+ ,22
+ ,25
+ ,12.48
+ ,999
+ ,4
+ ,0
+ ,11.72
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+ ,11.1
+ ,378
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+ ,10.98
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+ ,380
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+ ,10.14
+ ,374
+ ,14
+ ,14
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+ ,316
+ ,18
+ ,24
+ ,10.01
+ ,389
+ ,19
+ ,25
+ ,8.97
+ ,341
+ ,15
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+ ,11
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+ ,15
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+ ,8.66
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+ ,14
+ ,16
+ ,8.61
+ ,333
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+ ,8.57
+ ,254
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+ ,14
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+ ,11
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+ ,7.92
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+ ,16
+ ,25
+ ,7.91
+ ,196
+ ,20
+ ,25
+ ,7.87
+ ,303
+ ,7
+ ,25
+ ,7.85
+ ,311
+ ,13
+ ,15
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+ ,12
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+ ,24
+ ,7.47
+ ,503
+ ,20
+ ,10
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+ ,327
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+ ,13
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+ ,7
+ ,445
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+ ,9
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+ ,6.73
+ ,264
+ ,19
+ ,13
+ ,6.71
+ ,261
+ ,19
+ ,21
+ ,6.67
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+ ,7
+ ,17
+ ,6.66
+ ,237
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+ ,6.48
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+ ,19
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+ ,6.47
+ ,185
+ ,11
+ ,25
+ ,6.37
+ ,240
+ ,18
+ ,7
+ ,6.34
+ ,275
+ ,13
+ ,19
+ ,6.28
+ ,302
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+ ,15
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+ ,323
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+ ,240
+ ,11
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+ ,6.03
+ ,219
+ ,7
+ ,24
+ ,6.02
+ ,378
+ ,22
+ ,26
+ ,5.99
+ ,320
+ ,20
+ ,20
+ ,5.94
+ ,337
+ ,11
+ ,5
+ ,5.89
+ ,218
+ ,4
+ ,17
+ ,5.75
+ ,194
+ ,8
+ ,24
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+ ,18
+ ,16
+ ,5.67
+ ,332
+ ,9
+ ,25
+ ,5.45
+ ,243
+ ,11
+ ,25
+ ,5.36
+ ,256
+ ,11
+ ,23
+ ,5.32
+ ,271
+ ,18
+ ,23
+ ,5.2
+ ,201
+ ,0
+ ,25
+ ,5.16
+ ,319
+ ,17
+ ,19
+ ,5.01
+ ,160
+ ,5
+ ,10
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+ ,12
+ ,23
+ ,4.9
+ ,297
+ ,15
+ ,12
+ ,4.89
+ ,191
+ ,12
+ ,3
+ ,4.87
+ ,212
+ ,12
+ ,25
+ ,4.84
+ ,254
+ ,10
+ ,10
+ ,4.79
+ ,314
+ ,14
+ ,7
+ ,4.72
+ ,230
+ ,18
+ ,10
+ ,4.7
+ ,173
+ ,14
+ ,11
+ ,4.66
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+ ,12
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+ ,7
+ ,10
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+ ,12
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+ ,237
+ ,11
+ ,0
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+ ,185
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+ ,9
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+ ,14
+ ,24
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+ ,15
+ ,1
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+ ,13
+ ,14
+ ,3.88
+ ,218
+ ,18
+ ,10
+ ,3.86
+ ,294
+ ,16
+ ,11
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+ ,3.66
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+ ,9
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+ ,11
+ ,10
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+ ,87
+ ,8
+ ,20
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+ ,1
+ ,4
+ ,3.22
+ ,228
+ ,14
+ ,3
+ ,3.2
+ ,140
+ ,10
+ ,25
+ ,3.19
+ ,110
+ ,9
+ ,4
+ ,3.18
+ ,278
+ ,15
+ ,19
+ ,2.84
+ ,102
+ ,3
+ ,0
+ ,2.78
+ ,172
+ ,11
+ ,0
+ ,2.49
+ ,128
+ ,8
+ ,3
+ ,2.37
+ ,186
+ ,5
+ ,4
+ ,2.31
+ ,84
+ ,5
+ ,0
+ ,2.13
+ ,246
+ ,0
+ ,5
+ ,1.86
+ ,78
+ ,6
+ ,0
+ ,1.78
+ ,40
+ ,0
+ ,0
+ ,0.59
+ ,7
+ ,0
+ ,0
+ ,0.01
+ ,0
+ ,3
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0)
+ ,dim=c(4
+ ,131)
+ ,dimnames=list(c('PAGES'
+ ,'BLOGS'
+ ,'LPRM'
+ ,'HRS')
+ ,1:131))
>  y <- array(NA,dim=c(4,131),dimnames=list(c('PAGES','BLOGS','LPRM','HRS'),1:131))
>  for (i in 1:dim(x)[1])
+  {
+  	for (j in 1:dim(x)[2])
+  	{
+  		y[i,j] <- as.numeric(x[i,j])
+  	}
+  }
> par4 = 'yes'
> par3 = '2'
> par2 = 'none'
> par1 = '3'
> #'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] "LPRM"
> x[,par1]
  [1] 25 25 21 22 35 21 25 25  0 23 29 22 24 19 14 24 25 21 14 24 16 17 24 14 24
 [26] 20 20 25 25 25 25 25 15  9 29 23 25 24 23 24 10 13 25 20 23 16  0 13 23 24
 [51] 24 25 17  9 25 13 21 17 10 11 25 25 22 17 25  7 19 15 25 11 19 24 26 20  5
 [76] 17 24 16 25 25 23 23 25 19 10 23 12  3 25 10  7 10 11 28 19 20 19 10  0  0
[101] 14 20 24  1  4 14 10 11 25  0 10 20  4  3 25  4 19  0  0  3  4  0  5  0  0
[126]  0  0  0  0  0  0
> 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  3  4  5  7  9 10 11 12 13 14 15 16 17 19 20 21 22 23 24 25 26 28 29 35 
16  1  3  4  2  2  2  8  4  1  3  5  2  3  5  7  7  4  3  8 12 24  1  1  2  1 
> colnames(x)
[1] "PAGES" "BLOGS" "LPRM"  "HRS"  
> colnames(x)[par1]
[1] "LPRM"
> x[,par1]
  [1] 25 25 21 22 35 21 25 25  0 23 29 22 24 19 14 24 25 21 14 24 16 17 24 14 24
 [26] 20 20 25 25 25 25 25 15  9 29 23 25 24 23 24 10 13 25 20 23 16  0 13 23 24
 [51] 24 25 17  9 25 13 21 17 10 11 25 25 22 17 25  7 19 15 25 11 19 24 26 20  5
 [76] 17 24 16 25 25 23 23 25 19 10 23 12  3 25 10  7 10 11 28 19 20 19 10  0  0
[101] 14 20 24  1  4 14 10 11 25  0 10 20  4  3 25  4 19  0  0  3  4  0  5  0  0
[126]  0  0  0  0  0  0
> 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/1u5211319055679.tab") 
+ }
+ }
> m

	 Conditional inference tree with 3 terminal nodes

Response:  LPRM 
Inputs:  PAGES, BLOGS, HRS 
Number of observations:  131 

1) HRS <= 4.36; criterion = 1, statistic = 33.859
  2) HRS <= 2.78; criterion = 0.986, statistic = 8.028
    3)*  weights = 14 
  2) HRS > 2.78
    4)*  weights = 20 
1) HRS > 4.36
  5)*  weights = 97 
> postscript(file="/var/wessaorg/rcomp/tmp/2btuh1319055679.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/3kcyv1319055679.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        25 19.4639175   5.5360825
2        25 19.4639175   5.5360825
3        21 19.4639175   1.5360825
4        22 19.4639175   2.5360825
5        35 19.4639175  15.5360825
6        21 19.4639175   1.5360825
7        25 19.4639175   5.5360825
8        25 19.4639175   5.5360825
9         0 19.4639175 -19.4639175
10       23 19.4639175   3.5360825
11       29 19.4639175   9.5360825
12       22 19.4639175   2.5360825
13       24 19.4639175   4.5360825
14       19 19.4639175  -0.4639175
15       14 19.4639175  -5.4639175
16       24 19.4639175   4.5360825
17       25 19.4639175   5.5360825
18       21 19.4639175   1.5360825
19       14 19.4639175  -5.4639175
20       24 19.4639175   4.5360825
21       16 19.4639175  -3.4639175
22       17 19.4639175  -2.4639175
23       24 19.4639175   4.5360825
24       14 19.4639175  -5.4639175
25       24 19.4639175   4.5360825
26       20 19.4639175   0.5360825
27       20 19.4639175   0.5360825
28       25 19.4639175   5.5360825
29       25 19.4639175   5.5360825
30       25 19.4639175   5.5360825
31       25 19.4639175   5.5360825
32       25 19.4639175   5.5360825
33       15 19.4639175  -4.4639175
34        9 19.4639175 -10.4639175
35       29 19.4639175   9.5360825
36       23 19.4639175   3.5360825
37       25 19.4639175   5.5360825
38       24 19.4639175   4.5360825
39       23 19.4639175   3.5360825
40       24 19.4639175   4.5360825
41       10 19.4639175  -9.4639175
42       13 19.4639175  -6.4639175
43       25 19.4639175   5.5360825
44       20 19.4639175   0.5360825
45       23 19.4639175   3.5360825
46       16 19.4639175  -3.4639175
47        0 19.4639175 -19.4639175
48       13 19.4639175  -6.4639175
49       23 19.4639175   3.5360825
50       24 19.4639175   4.5360825
51       24 19.4639175   4.5360825
52       25 19.4639175   5.5360825
53       17 19.4639175  -2.4639175
54        9 19.4639175 -10.4639175
55       25 19.4639175   5.5360825
56       13 19.4639175  -6.4639175
57       21 19.4639175   1.5360825
58       17 19.4639175  -2.4639175
59       10 19.4639175  -9.4639175
60       11 19.4639175  -8.4639175
61       25 19.4639175   5.5360825
62       25 19.4639175   5.5360825
63       22 19.4639175   2.5360825
64       17 19.4639175  -2.4639175
65       25 19.4639175   5.5360825
66        7 19.4639175 -12.4639175
67       19 19.4639175  -0.4639175
68       15 19.4639175  -4.4639175
69       25 19.4639175   5.5360825
70       11 19.4639175  -8.4639175
71       19 19.4639175  -0.4639175
72       24 19.4639175   4.5360825
73       26 19.4639175   6.5360825
74       20 19.4639175   0.5360825
75        5 19.4639175 -14.4639175
76       17 19.4639175  -2.4639175
77       24 19.4639175   4.5360825
78       16 19.4639175  -3.4639175
79       25 19.4639175   5.5360825
80       25 19.4639175   5.5360825
81       23 19.4639175   3.5360825
82       23 19.4639175   3.5360825
83       25 19.4639175   5.5360825
84       19 19.4639175  -0.4639175
85       10 19.4639175  -9.4639175
86       23 19.4639175   3.5360825
87       12 19.4639175  -7.4639175
88        3 19.4639175 -16.4639175
89       25 19.4639175   5.5360825
90       10 19.4639175  -9.4639175
91        7 19.4639175 -12.4639175
92       10 19.4639175  -9.4639175
93       11 19.4639175  -8.4639175
94       28 19.4639175   8.5360825
95       19 19.4639175  -0.4639175
96       20 19.4639175   0.5360825
97       19 19.4639175  -0.4639175
98       10 10.9000000  -0.9000000
99        0 10.9000000 -10.9000000
100       0 10.9000000 -10.9000000
101      14 10.9000000   3.1000000
102      20 10.9000000   9.1000000
103      24 10.9000000  13.1000000
104       1 10.9000000  -9.9000000
105       4 10.9000000  -6.9000000
106      14 10.9000000   3.1000000
107      10 10.9000000  -0.9000000
108      11 10.9000000   0.1000000
109      25 10.9000000  14.1000000
110       0 10.9000000 -10.9000000
111      10 10.9000000  -0.9000000
112      20 10.9000000   9.1000000
113       4 10.9000000  -6.9000000
114       3 10.9000000  -7.9000000
115      25 10.9000000  14.1000000
116       4 10.9000000  -6.9000000
117      19 10.9000000   8.1000000
118       0  0.8571429  -0.8571429
119       0  0.8571429  -0.8571429
120       3  0.8571429   2.1428571
121       4  0.8571429   3.1428571
122       0  0.8571429  -0.8571429
123       5  0.8571429   4.1428571
124       0  0.8571429  -0.8571429
125       0  0.8571429  -0.8571429
126       0  0.8571429  -0.8571429
127       0  0.8571429  -0.8571429
128       0  0.8571429  -0.8571429
129       0  0.8571429  -0.8571429
130       0  0.8571429  -0.8571429
131       0  0.8571429  -0.8571429
> 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/4t1351319055679.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/5l5ms1319055679.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/674r81319055679.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/726r11319055679.tab") 
+ }
> 
> try(system("convert tmp/2btuh1319055679.ps tmp/2btuh1319055679.png",intern=TRUE))
character(0)
> try(system("convert tmp/3kcyv1319055679.ps tmp/3kcyv1319055679.png",intern=TRUE))
character(0)
> try(system("convert tmp/4t1351319055679.ps tmp/4t1351319055679.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.003   0.200   3.204