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)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
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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(112285
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+ ,24)
+ ,dim=c(5
+ ,156)
+ ,dimnames=list(c('Total_size'
+ ,'Time_RFC'
+ ,'PR_views'
+ ,'Blogged'
+ ,'Reviewed')
+ ,1:156))
>  y <- array(NA,dim=c(5,156),dimnames=list(c('Total_size','Time_RFC','PR_views','Blogged','Reviewed'),1:156))
>  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 = ''
> par2 = 'none'
> par1 = '2'
> 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] "Time_RFC"
> x[,par1]
  [1] 210907 120982 176508 179321 123185  52746 385534  33170 149061 165446
 [11] 237213 173326 133131 258873 180083 324799 230964 236785 135473 202925
 [21] 215147 344297 153935 132943 174724 174415 225548 223632 124817 221698
 [31] 210767 170266 260561  84853 294424 215641 325107 167542 106408 265769
 [41] 269651 149112 152871 111665 116408 362301  78800 183167 277965 150629
 [51] 168809  24188 329267  65029 101097 218946 244052 233328 256462 206161
 [61] 311473 235800 177939 207176 196553 174184 143246 187559 187681 119016
 [71] 182192  73566 194979 167488 143756 275541 243199 182999 135649 152299
 [81] 120221 346485 145790 193339  80953 122774 130585 286468 241066 148446
 [91] 204713 182079 140344 220516 243060 162765 182613 232138 265318 310839
[101] 225060 232317 144966  43287 155754 164709 201940 235454  99466 100750
[111] 224549 243511  22938 152474  61857 132487 317394  21054 209641  31414
[121] 244749 184510 128423  97839  38214 151101 272458 172494 328107 250579
[131] 351067 158015  85439 229242 351619  84207 324598 131069 204271 165543
[141] 141722 299775 195838 173260 254488 104389 199476 224330  14688 181633
[151] 271856   7199  46660  17547  95227 152601
> 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])

  7199  14688  17547  21054  22938  24188  31414  33170  38214  43287  46660 
     1      1      1      1      1      1      1      1      1      1      1 
 52746  61857  65029  73566  78800  80953  84207  84853  85439  95227  97839 
     1      1      1      1      1      1      1      1      1      1      1 
 99466 100750 101097 104389 106408 111665 116408 119016 120221 120982 122774 
     1      1      1      1      1      1      1      1      1      1      1 
123185 124817 128423 130585 131069 132487 132943 133131 135473 135649 140344 
     1      1      1      1      1      1      1      1      1      1      1 
141722 143246 143756 144966 145790 148446 149061 149112 150629 151101 152299 
     1      1      1      1      1      1      1      1      1      1      1 
152474 152601 152871 153935 155754 158015 162765 164709 165446 165543 167488 
     1      1      1      1      1      1      1      1      1      1      1 
167542 168809 170266 172494 173260 173326 174184 174415 174724 176508 177939 
     1      1      1      1      1      1      1      1      1      1      1 
179321 180083 181633 182079 182192 182613 182999 183167 184510 187559 187681 
     1      1      1      1      1      1      1      1      1      1      1 
193339 194979 195838 196553 199476 201940 202925 204271 204713 206161 207176 
     1      1      1      1      1      1      1      1      1      1      1 
209641 210767 210907 215147 215641 218946 220516 221698 223632 224330 224549 
     1      1      1      1      1      1      1      1      1      1      1 
225060 225548 229242 230964 232138 232317 233328 235454 235800 236785 237213 
     1      1      1      1      1      1      1      1      1      1      1 
241066 243060 243199 243511 244052 244749 250579 254488 256462 258873 260561 
     1      1      1      1      1      1      1      1      1      1      1 
265318 265769 269651 271856 272458 275541 277965 286468 294424 299775 310839 
     1      1      1      1      1      1      1      1      1      1      1 
311473 317394 324598 324799 325107 328107 329267 344297 346485 351067 351619 
     1      1      1      1      1      1      1      1      1      1      1 
362301 385534 
     1      1 
> colnames(x)
[1] "Total_size" "Time_RFC"   "PR_views"   "Blogged"    "Reviewed"  
> colnames(x)[par1]
[1] "Time_RFC"
> x[,par1]
  [1] 210907 120982 176508 179321 123185  52746 385534  33170 149061 165446
 [11] 237213 173326 133131 258873 180083 324799 230964 236785 135473 202925
 [21] 215147 344297 153935 132943 174724 174415 225548 223632 124817 221698
 [31] 210767 170266 260561  84853 294424 215641 325107 167542 106408 265769
 [41] 269651 149112 152871 111665 116408 362301  78800 183167 277965 150629
 [51] 168809  24188 329267  65029 101097 218946 244052 233328 256462 206161
 [61] 311473 235800 177939 207176 196553 174184 143246 187559 187681 119016
 [71] 182192  73566 194979 167488 143756 275541 243199 182999 135649 152299
 [81] 120221 346485 145790 193339  80953 122774 130585 286468 241066 148446
 [91] 204713 182079 140344 220516 243060 162765 182613 232138 265318 310839
[101] 225060 232317 144966  43287 155754 164709 201940 235454  99466 100750
[111] 224549 243511  22938 152474  61857 132487 317394  21054 209641  31414
[121] 244749 184510 128423  97839  38214 151101 272458 172494 328107 250579
[131] 351067 158015  85439 229242 351619  84207 324598 131069 204271 165543
[141] 141722 299775 195838 173260 254488 104389 199476 224330  14688 181633
[151] 271856   7199  46660  17547  95227 152601
> 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/1bmp41324132823.tab") 
+ }
+ }
> m

	 Conditional inference tree with 6 terminal nodes

Response:  Time_RFC 
Inputs:  Total_size, PR_views, Blogged, Reviewed 
Number of observations:  156 

1) Blogged <= 53; criterion = 1, statistic = 86.781
  2) Blogged <= 23; criterion = 1, statistic = 31.592
    3)*  weights = 18 
  2) Blogged > 23
    4)*  weights = 29 
1) Blogged > 53
  5) Blogged <= 74; criterion = 1, statistic = 16.234
    6) PR_views <= 68; criterion = 0.982, statistic = 8.035
      7)*  weights = 22 
    6) PR_views > 68
      8)*  weights = 8 
  5) Blogged > 74
    9) PR_views <= 85; criterion = 0.983, statistic = 8.11
      10)*  weights = 55 
    9) PR_views > 85
      11)*  weights = 24 
> postscript(file="/var/wessaorg/rcomp/tmp/2kmd11324132823.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/3il861324132823.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    210907 218589.04   -7682.0364
2    120982 172611.00  -51629.0000
3    176508 172611.00    3897.0000
4    179321 268541.33  -89220.3333
5    123185 130861.34   -7676.3448
6     52746  47682.56    5063.4444
7    385534 218589.04  166944.9636
8     33170  47682.56  -14512.5556
9    149061 130861.34   18199.6552
10   165446 172611.00   -7165.0000
11   237213 218589.04   18623.9636
12   173326 218589.04  -45263.0364
13   133131 130861.34    2269.6552
14   258873 268541.33   -9668.3333
15   180083 172611.00    7472.0000
16   324799 268541.33   56257.6667
17   230964 218589.04   12374.9636
18   236785 218589.04   18195.9636
19   135473 218589.04  -83116.0364
20   202925 218589.04  -15664.0364
21   215147 218589.04   -3442.0364
22   344297 268541.33   75755.6667
23   153935 130861.34   23073.6552
24   132943 218589.04  -85646.0364
25   174724 268541.33  -93817.3333
26   174415 172611.00    1804.0000
27   225548 218589.04    6958.9636
28   223632 218589.04    5042.9636
29   124817 130861.34   -6044.3448
30   221698 268541.33  -46843.3333
31   210767 218589.04   -7822.0364
32   170266 130861.34   39404.6552
33   260561 218589.04   41971.9636
34    84853 130861.34  -46008.3448
35   294424 218589.04   75834.9636
36   215641 199100.00   16541.0000
37   325107 218589.04  106517.9636
38   167542 172611.00   -5069.0000
39   106408 130861.34  -24453.3448
40   265769 218589.04   47179.9636
41   269651 218589.04   51061.9636
42   149112 172611.00  -23499.0000
43   152871 199100.00  -46229.0000
44   111665 130861.34  -19196.3448
45   116408 130861.34  -14453.3448
46   362301 268541.33   93759.6667
47    78800  47682.56   31117.4444
48   183167 218589.04  -35422.0364
49   277965 268541.33    9423.6667
50   150629 218589.04  -67960.0364
51   168809 218589.04  -49780.0364
52    24188  47682.56  -23494.5556
53   329267 218589.04  110677.9636
54    65029  47682.56   17346.4444
55   101097 130861.34  -29764.3448
56   218946 268541.33  -49595.3333
57   244052 218589.04   25462.9636
58   233328 268541.33  -35213.3333
59   256462 218589.04   37872.9636
60   206161 218589.04  -12428.0364
61   311473 218589.04   92883.9636
62   235800 268541.33  -32741.3333
63   177939 172611.00    5328.0000
64   207176 172611.00   34565.0000
65   196553 130861.34   65691.6552
66   174184 172611.00    1573.0000
67   143246 172611.00  -29365.0000
68   187559 218589.04  -31030.0364
69   187681 218589.04  -30908.0364
70   119016 218589.04  -99573.0364
71   182192 218589.04  -36397.0364
72    73566  47682.56   25883.4444
73   194979 172611.00   22368.0000
74   167488 199100.00  -31612.0000
75   143756 218589.04  -74833.0364
76   275541 218589.04   56951.9636
77   243199 218589.04   24609.9636
78   182999 172611.00   10388.0000
79   135649 218589.04  -82940.0364
80   152299 172611.00  -20312.0000
81   120221 130861.34  -10640.3448
82   346485 268541.33   77943.6667
83   145790 130861.34   14928.6552
84   193339 218589.04  -25250.0364
85    80953 130861.34  -49908.3448
86   122774 130861.34   -8087.3448
87   130585 172611.00  -42026.0000
88   286468 199100.00   87368.0000
89   241066 218589.04   22476.9636
90   148446 218589.04  -70143.0364
91   204713 172611.00   32102.0000
92   182079 218589.04  -36510.0364
93   140344 130861.34    9482.6552
94   220516 218589.04    1926.9636
95   243060 199100.00   43960.0000
96   162765 172611.00   -9846.0000
97   182613 218589.04  -35976.0364
98   232138 218589.04   13548.9636
99   265318 268541.33   -3223.3333
100  310839 218589.04   92249.9636
101  225060 218589.04    6470.9636
102  232317 218589.04   13727.9636
103  144966 130861.34   14104.6552
104   43287  47682.56   -4395.5556
105  155754 172611.00  -16857.0000
106  164709 218589.04  -53880.0364
107  201940 268541.33  -66601.3333
108  235454 218589.04   16864.9636
109   99466 130861.34  -31395.3448
110  100750 218589.04 -117839.0364
111  224549 172611.00   51938.0000
112  243511 218589.04   24921.9636
113   22938  47682.56  -24744.5556
114  152474 218589.04  -66115.0364
115   61857  47682.56   14174.4444
116  132487 199100.00  -66613.0000
117  317394 218589.04   98804.9636
118   21054  47682.56  -26628.5556
119  209641 172611.00   37030.0000
120   31414  47682.56  -16268.5556
121  244749 268541.33  -23792.3333
122  184510 172611.00   11899.0000
123  128423 130861.34   -2438.3448
124   97839 130861.34  -33022.3448
125   38214  47682.56   -9468.5556
126  151101 130861.34   20239.6552
127  272458 268541.33    3916.6667
128  172494 130861.34   41632.6552
129  328107 268541.33   59565.6667
130  250579 268541.33  -17962.3333
131  351067 268541.33   82525.6667
132  158015 172611.00  -14596.0000
133   85439 130861.34  -45422.3448
134  229242 199100.00   30142.0000
135  351619 268541.33   83077.6667
136   84207  47682.56   36524.4444
137  324598 268541.33   56056.6667
138  131069 130861.34     207.6552
139  204271 218589.04  -14318.0364
140  165543 199100.00  -33557.0000
141  141722  47682.56   94039.4444
142  299775 268541.33   31233.6667
143  195838 218589.04  -22751.0364
144  173260 130861.34   42398.6552
145  254488 218589.04   35898.9636
146  104389 268541.33 -164152.3333
147  199476 218589.04  -19113.0364
148  224330 218589.04    5740.9636
149   14688  47682.56  -32994.5556
150  181633 130861.34   50771.6552
151  271856 268541.33    3314.6667
152    7199  47682.56  -40483.5556
153   46660  47682.56   -1022.5556
154   17547  47682.56  -30135.5556
155   95227 130861.34  -35634.3448
156  152601 130861.34   21739.6552
> 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/4n2ze1324132823.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/5khjb1324132823.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/6ibnp1324132823.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/7dru61324132823.tab") 
+ }
> 
> try(system("convert tmp/2kmd11324132823.ps tmp/2kmd11324132823.png",intern=TRUE))
character(0)
> try(system("convert tmp/3il861324132823.ps tmp/3il861324132823.png",intern=TRUE))
character(0)
> try(system("convert tmp/4n2ze1324132823.ps tmp/4n2ze1324132823.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.193   0.273   3.463