R version 2.12.0 (2010-10-15)
Copyright (C) 2010 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(1330
+ ,35
+ ,27
+ ,37.13
+ ,1030
+ ,50
+ ,28
+ ,36.21
+ ,895
+ ,40
+ ,28
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+ ,43
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+ ,1321
+ ,46
+ ,26
+ ,31.91
+ ,901
+ ,30
+ ,23
+ ,30.69
+ ,1125
+ ,57
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+ ,50
+ ,29
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+ ,32
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+ ,23
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+ ,13
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+ ,637
+ ,20
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+ ,822
+ ,10
+ ,34
+ ,16.2
+ ,405
+ ,42
+ ,23
+ ,16.02
+ ,744
+ ,25
+ ,18
+ ,15.84
+ ,873
+ ,20
+ ,35
+ ,15.71
+ ,592
+ ,26
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+ ,37
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+ ,725
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+ ,19
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+ ,644
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+ ,22
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+ ,19
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+ ,577
+ ,18
+ ,33
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+ ,676
+ ,15
+ ,13
+ ,13.6
+ ,1058
+ ,18
+ ,2
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+ ,462
+ ,42
+ ,11
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+ ,10
+ ,25
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+ ,16
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+ ,18
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+ ,12
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+ ,17
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+ ,563
+ ,7
+ ,21
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+ ,7
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+ ,14
+ ,25
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+ ,55
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+ ,28
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+ ,8
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+ ,13
+ ,34
+ ,7.31
+ ,343
+ ,11
+ ,24
+ ,7.24
+ ,287
+ ,10
+ ,27
+ ,7.11
+ ,281
+ ,13
+ ,17
+ ,6.9
+ ,376
+ ,8
+ ,7
+ ,6.48
+ ,227
+ ,2
+ ,23
+ ,6.34
+ ,209
+ ,14
+ ,3
+ ,6.2
+ ,413
+ ,1
+ ,13
+ ,5.94
+ ,336
+ ,12
+ ,0
+ ,5.46
+ ,224
+ ,0
+ ,21
+ ,5.44
+ ,171
+ ,1
+ ,11
+ ,5.09
+ ,417
+ ,11
+ ,12
+ ,4.99
+ ,307
+ ,4
+ ,0
+ ,4.72
+ ,147
+ ,0
+ ,0
+ ,4.71
+ ,206
+ ,4
+ ,0
+ ,4.08
+ ,214
+ ,2
+ ,2
+ ,3.93
+ ,166
+ ,0
+ ,7
+ ,2.97
+ ,76
+ ,0
+ ,8
+ ,2.4
+ ,151
+ ,7
+ ,0
+ ,2
+ ,29
+ ,0
+ ,0
+ ,0.27
+ ,8
+ ,0
+ ,0
+ ,0.13
+ ,4
+ ,0
+ ,0
+ ,0.06
+ ,5
+ ,0
+ ,0
+ ,0.03
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0)
+ ,dim=c(4
+ ,164)
+ ,dimnames=list(c('PAGES'
+ ,'BLOGS'
+ ,'LPRM'
+ ,'HRS')
+ ,1:164))
>  y <- array(NA,dim=c(4,164),dimnames=list(c('PAGES','BLOGS','LPRM','HRS'),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 = '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] 27 28 28 27 26 23 33 30 23 33 46 28 25 31 22 32 38 26 24 31 44 34 29 26 28
 [26] 35 51 20 13 29 35 40 28 38 25 22 28 26 32 31 35 30 49 28 20 28 33 25 28 30
 [51] 27 26 27 14 27 29 28 27 28 23 20 28 28 24 29  9 23 16 39 19 16 34 23 18 35
 [76] 40 17 28 19 22 27 32 29 18 31 24 27 33 13  2 11 53 24  5  9 23 34 13 29 30
[101] 24 28 22  8 35 25 35 33 32 28 26 25 22 35 19 29 19 21 35 26 25 30 31 32 10
[126] 12  9 32 17 21 17 25 16 24 15  8 34 24 27 17  7 23  3 13  0 21 11 12  0  0
[151]  0  2  7  8  0  0  0  0  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  2  3  5  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 
14  2  1  1  2  3  3  1  2  2  4  1  1  3  4  2  4  3  3  5  7  7  7  7  9 16 
29 30 31 32 33 34 35 38 39 40 44 46 49 51 53 
 7  5  5  6  5  4  8  2  1  2  1  1  1  1  1 
> colnames(x)
[1] "PAGES" "BLOGS" "LPRM"  "HRS"  
> colnames(x)[par1]
[1] "LPRM"
> x[,par1]
  [1] 27 28 28 27 26 23 33 30 23 33 46 28 25 31 22 32 38 26 24 31 44 34 29 26 28
 [26] 35 51 20 13 29 35 40 28 38 25 22 28 26 32 31 35 30 49 28 20 28 33 25 28 30
 [51] 27 26 27 14 27 29 28 27 28 23 20 28 28 24 29  9 23 16 39 19 16 34 23 18 35
 [76] 40 17 28 19 22 27 32 29 18 31 24 27 33 13  2 11 53 24  5  9 23 34 13 29 30
[101] 24 28 22  8 35 25 35 33 32 28 26 25 22 35 19 29 19 21 35 26 25 30 31 32 10
[126] 12  9 32 17 21 17 25 16 24 15  8 34 24 27 17  7 23  3 13  0 21 11 12  0  0
[151]  0  2  7  8  0  0  0  0  0  0  0  0  0  0
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/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/www/rcomp/tmp/137jk1319053935.tab") 
+ }
+ }
> m

	 Conditional inference tree with 4 terminal nodes

Response:  LPRM 
Inputs:  PAGES, BLOGS, HRS 
Number of observations:  164 

1) HRS <= 6.48; criterion = 1, statistic = 57.37
  2) HRS <= 4.72; criterion = 0.989, statistic = 8.519
    3)*  weights = 16 
  2) HRS > 4.72
    4)*  weights = 8 
1) HRS > 6.48
  5) HRS <= 17.9; criterion = 0.998, statistic = 11.244
    6)*  weights = 90 
  5) HRS > 17.9
    7)*  weights = 50 
> postscript(file="/var/www/rcomp/tmp/2yy821319053935.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/www/rcomp/tmp/3meyo1319053935.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        27  30.02000  -3.02000000
2        28  30.02000  -2.02000000
3        28  30.02000  -2.02000000
4        27  30.02000  -3.02000000
5        26  30.02000  -4.02000000
6        23  30.02000  -7.02000000
7        33  30.02000   2.98000000
8        30  30.02000  -0.02000000
9        23  30.02000  -7.02000000
10       33  30.02000   2.98000000
11       46  30.02000  15.98000000
12       28  30.02000  -2.02000000
13       25  30.02000  -5.02000000
14       31  30.02000   0.98000000
15       22  30.02000  -8.02000000
16       32  30.02000   1.98000000
17       38  30.02000   7.98000000
18       26  30.02000  -4.02000000
19       24  30.02000  -6.02000000
20       31  30.02000   0.98000000
21       44  30.02000  13.98000000
22       34  30.02000   3.98000000
23       29  30.02000  -1.02000000
24       26  30.02000  -4.02000000
25       28  30.02000  -2.02000000
26       35  30.02000   4.98000000
27       51  30.02000  20.98000000
28       20  30.02000 -10.02000000
29       13  30.02000 -17.02000000
30       29  30.02000  -1.02000000
31       35  30.02000   4.98000000
32       40  30.02000   9.98000000
33       28  30.02000  -2.02000000
34       38  30.02000   7.98000000
35       25  30.02000  -5.02000000
36       22  30.02000  -8.02000000
37       28  30.02000  -2.02000000
38       26  30.02000  -4.02000000
39       32  30.02000   1.98000000
40       31  30.02000   0.98000000
41       35  30.02000   4.98000000
42       30  30.02000  -0.02000000
43       49  30.02000  18.98000000
44       28  30.02000  -2.02000000
45       20  30.02000 -10.02000000
46       28  30.02000  -2.02000000
47       33  30.02000   2.98000000
48       25  30.02000  -5.02000000
49       28  30.02000  -2.02000000
50       30  30.02000  -0.02000000
51       27  24.03333   2.96666667
52       26  24.03333   1.96666667
53       27  24.03333   2.96666667
54       14  24.03333 -10.03333333
55       27  24.03333   2.96666667
56       29  24.03333   4.96666667
57       28  24.03333   3.96666667
58       27  24.03333   2.96666667
59       28  24.03333   3.96666667
60       23  24.03333  -1.03333333
61       20  24.03333  -4.03333333
62       28  24.03333   3.96666667
63       28  24.03333   3.96666667
64       24  24.03333  -0.03333333
65       29  24.03333   4.96666667
66        9  24.03333 -15.03333333
67       23  24.03333  -1.03333333
68       16  24.03333  -8.03333333
69       39  24.03333  14.96666667
70       19  24.03333  -5.03333333
71       16  24.03333  -8.03333333
72       34  24.03333   9.96666667
73       23  24.03333  -1.03333333
74       18  24.03333  -6.03333333
75       35  24.03333  10.96666667
76       40  24.03333  15.96666667
77       17  24.03333  -7.03333333
78       28  24.03333   3.96666667
79       19  24.03333  -5.03333333
80       22  24.03333  -2.03333333
81       27  24.03333   2.96666667
82       32  24.03333   7.96666667
83       29  24.03333   4.96666667
84       18  24.03333  -6.03333333
85       31  24.03333   6.96666667
86       24  24.03333  -0.03333333
87       27  24.03333   2.96666667
88       33  24.03333   8.96666667
89       13  24.03333 -11.03333333
90        2  24.03333 -22.03333333
91       11  24.03333 -13.03333333
92       53  24.03333  28.96666667
93       24  24.03333  -0.03333333
94        5  24.03333 -19.03333333
95        9  24.03333 -15.03333333
96       23  24.03333  -1.03333333
97       34  24.03333   9.96666667
98       13  24.03333 -11.03333333
99       29  24.03333   4.96666667
100      30  24.03333   5.96666667
101      24  24.03333  -0.03333333
102      28  24.03333   3.96666667
103      22  24.03333  -2.03333333
104       8  24.03333 -16.03333333
105      35  24.03333  10.96666667
106      25  24.03333   0.96666667
107      35  24.03333  10.96666667
108      33  24.03333   8.96666667
109      32  24.03333   7.96666667
110      28  24.03333   3.96666667
111      26  24.03333   1.96666667
112      25  24.03333   0.96666667
113      22  24.03333  -2.03333333
114      35  24.03333  10.96666667
115      19  24.03333  -5.03333333
116      29  24.03333   4.96666667
117      19  24.03333  -5.03333333
118      21  24.03333  -3.03333333
119      35  24.03333  10.96666667
120      26  24.03333   1.96666667
121      25  24.03333   0.96666667
122      30  24.03333   5.96666667
123      31  24.03333   6.96666667
124      32  24.03333   7.96666667
125      10  24.03333 -14.03333333
126      12  24.03333 -12.03333333
127       9  24.03333 -15.03333333
128      32  24.03333   7.96666667
129      17  24.03333  -7.03333333
130      21  24.03333  -3.03333333
131      17  24.03333  -7.03333333
132      25  24.03333   0.96666667
133      16  24.03333  -8.03333333
134      24  24.03333  -0.03333333
135      15  24.03333  -9.03333333
136       8  24.03333 -16.03333333
137      34  24.03333   9.96666667
138      24  24.03333  -0.03333333
139      27  24.03333   2.96666667
140      17  24.03333  -7.03333333
141       7  11.25000  -4.25000000
142      23  11.25000  11.75000000
143       3  11.25000  -8.25000000
144      13  11.25000   1.75000000
145       0  11.25000 -11.25000000
146      21  11.25000   9.75000000
147      11  11.25000  -0.25000000
148      12  11.25000   0.75000000
149       0   1.06250  -1.06250000
150       0   1.06250  -1.06250000
151       0   1.06250  -1.06250000
152       2   1.06250   0.93750000
153       7   1.06250   5.93750000
154       8   1.06250   6.93750000
155       0   1.06250  -1.06250000
156       0   1.06250  -1.06250000
157       0   1.06250  -1.06250000
158       0   1.06250  -1.06250000
159       0   1.06250  -1.06250000
160       0   1.06250  -1.06250000
161       0   1.06250  -1.06250000
162       0   1.06250  -1.06250000
163       0   1.06250  -1.06250000
164       0   1.06250  -1.06250000
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
> postscript(file="/var/www/rcomp/tmp/49zgf1319053935.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/www/rcomp/tmp/5wrgd1319053935.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/www/rcomp/tmp/62myo1319053935.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/www/rcomp/tmp/7z1t81319053935.tab") 
+ }
> 
> try(system("convert tmp/2yy821319053935.ps tmp/2yy821319053935.png",intern=TRUE))
character(0)
> try(system("convert tmp/3meyo1319053935.ps tmp/3meyo1319053935.png",intern=TRUE))
character(0)
> try(system("convert tmp/49zgf1319053935.ps tmp/49zgf1319053935.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
   2.58    0.09    2.65