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 = 'yes'
> par3 = '2'
> par2 = 'quantiles'
> 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])

[0e+00,100350) [1e+05,279488] 
            82             82 
> colnames(x)
[1] "TotalTime"             "CourseCompendiumViews" "SharedbyotherAuthors" 
[4] "ReviewedCompendiums"   "PeerReviews"           "CWnumberOfCharacters" 
[7] "CWNumberOfRevisions"   "CWNumberOfHyperlinks"  "CWNumberOfBlogs"      
> colnames(x)[par1]
[1] "CWnumberOfCharacters"
> x[,par1]
  [1] [1e+05,279488] [1e+05,279488] [1e+05,279488] [1e+05,279488] [0e+00,100350)
  [6] [0e+00,100350) [1e+05,279488] [0e+00,100350) [1e+05,279488] [0e+00,100350)
 [11] [0e+00,100350) [0e+00,100350) [0e+00,100350) [1e+05,279488] [0e+00,100350)
 [16] [1e+05,279488] [1e+05,279488] [1e+05,279488] [1e+05,279488] [1e+05,279488]
 [21] [1e+05,279488] [1e+05,279488] [0e+00,100350) [1e+05,279488] [0e+00,100350)
 [26] [1e+05,279488] [1e+05,279488] [0e+00,100350) [0e+00,100350) [1e+05,279488]
 [31] [1e+05,279488] [0e+00,100350) [1e+05,279488] [0e+00,100350) [1e+05,279488]
 [36] [0e+00,100350) [1e+05,279488] [0e+00,100350) [0e+00,100350) [1e+05,279488]
 [41] [0e+00,100350) [0e+00,100350) [0e+00,100350) [0e+00,100350) [0e+00,100350)
 [46] [1e+05,279488] [0e+00,100350) [0e+00,100350) [1e+05,279488] [0e+00,100350)
 [51] [0e+00,100350) [0e+00,100350) [1e+05,279488] [0e+00,100350) [0e+00,100350)
 [56] [0e+00,100350) [1e+05,279488] [1e+05,279488] [1e+05,279488] [0e+00,100350)
 [61] [1e+05,279488] [1e+05,279488] [0e+00,100350) [1e+05,279488] [1e+05,279488]
 [66] [0e+00,100350) [1e+05,279488] [0e+00,100350) [1e+05,279488] [1e+05,279488]
 [71] [0e+00,100350) [0e+00,100350) [0e+00,100350) [0e+00,100350) [0e+00,100350)
 [76] [1e+05,279488] [1e+05,279488] [1e+05,279488] [0e+00,100350) [0e+00,100350)
 [81] [0e+00,100350) [1e+05,279488] [0e+00,100350) [1e+05,279488] [0e+00,100350)
 [86] [0e+00,100350) [1e+05,279488] [1e+05,279488] [0e+00,100350) [1e+05,279488]
 [91] [0e+00,100350) [1e+05,279488] [0e+00,100350) [0e+00,100350) [1e+05,279488]
 [96] [1e+05,279488] [1e+05,279488] [1e+05,279488] [1e+05,279488] [1e+05,279488]
[101] [1e+05,279488] [1e+05,279488] [0e+00,100350) [0e+00,100350) [0e+00,100350)
[106] [1e+05,279488] [0e+00,100350) [1e+05,279488] [1e+05,279488] [1e+05,279488]
[111] [1e+05,279488] [1e+05,279488] [0e+00,100350) [0e+00,100350) [0e+00,100350)
[116] [0e+00,100350) [1e+05,279488] [0e+00,100350) [1e+05,279488] [0e+00,100350)
[121] [0e+00,100350) [1e+05,279488] [1e+05,279488] [1e+05,279488] [0e+00,100350)
[126] [1e+05,279488] [1e+05,279488] [1e+05,279488] [1e+05,279488] [1e+05,279488]
[131] [1e+05,279488] [0e+00,100350) [0e+00,100350) [1e+05,279488] [1e+05,279488]
[136] [1e+05,279488] [1e+05,279488] [0e+00,100350) [0e+00,100350) [0e+00,100350)
[141] [1e+05,279488] [1e+05,279488] [1e+05,279488] [0e+00,100350) [1e+05,279488]
[146] [1e+05,279488] [1e+05,279488] [1e+05,279488] [0e+00,100350) [0e+00,100350)
[151] [0e+00,100350) [0e+00,100350) [0e+00,100350) [0e+00,100350) [0e+00,100350)
[156] [0e+00,100350) [0e+00,100350) [0e+00,100350) [0e+00,100350) [0e+00,100350)
[161] [0e+00,100350) [0e+00,100350) [0e+00,100350) [1e+05,279488]
Levels: [0e+00,100350) [1e+05,279488]
> 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/1r4641324658945.tab") 
+ }
+ }
           m.ct.i.pred
m.ct.i.actu   1   2
          1 592 146
          2 137 604
[1] 0.802168
[1] 0.8151147
[1] 0.8086545
           m.ct.x.pred
m.ct.x.actu  1  2
          1 62 20
          2 26 53
[1] 0.7560976
[1] 0.6708861
[1] 0.7142857
> m

	 Conditional inference tree with 5 terminal nodes

Response:  as.factor(CWnumberOfCharacters) 
Inputs:  TotalTime, CourseCompendiumViews, SharedbyotherAuthors, ReviewedCompendiums, PeerReviews, CWNumberOfRevisions, CWNumberOfHyperlinks, CWNumberOfBlogs 
Number of observations:  164 

1) CWNumberOfHyperlinks <= 140; criterion = 1, statistic = 61.934
  2) PeerReviews <= 123; criterion = 1, statistic = 22.217
    3) CWNumberOfRevisions <= 12558; criterion = 0.987, statistic = 9.934
      4)*  weights = 41 
    3) CWNumberOfRevisions > 12558
      5)*  weights = 24 
  2) PeerReviews > 123
    6)*  weights = 44 
1) CWNumberOfHyperlinks > 140
  7) CWNumberOfRevisions <= 24409; criterion = 0.994, statistic = 11.474
    8)*  weights = 15 
  7) CWNumberOfRevisions > 24409
    9)*  weights = 40 
> postscript(file="/var/wessaorg/rcomp/tmp/2qwm71324658945.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/3q9yy1324658945.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)
+ }
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
       [,1] [,2]
  [1,]    2    2
  [2,]    2    2
  [3,]    2    1
  [4,]    2    2
  [5,]    1    1
  [6,]    1    1
  [7,]    2    2
  [8,]    1    1
  [9,]    2    1
 [10,]    1    1
 [11,]    1    2
 [12,]    1    2
 [13,]    1    1
 [14,]    2    2
 [15,]    1    1
 [16,]    2    2
 [17,]    2    2
 [18,]    2    1
 [19,]    2    2
 [20,]    2    2
 [21,]    2    2
 [22,]    2    2
 [23,]    1    1
 [24,]    2    2
 [25,]    1    2
 [26,]    2    2
 [27,]    2    2
 [28,]    1    2
 [29,]    1    1
 [30,]    2    2
 [31,]    2    2
 [32,]    1    2
 [33,]    2    2
 [34,]    1    2
 [35,]    2    2
 [36,]    1    2
 [37,]    2    2
 [38,]    1    1
 [39,]    1    1
 [40,]    2    2
 [41,]    1    2
 [42,]    1    1
 [43,]    1    1
 [44,]    1    2
 [45,]    1    1
 [46,]    2    2
 [47,]    1    1
 [48,]    1    2
 [49,]    2    2
 [50,]    1    2
 [51,]    1    2
 [52,]    1    1
 [53,]    2    2
 [54,]    1    1
 [55,]    1    1
 [56,]    1    1
 [57,]    2    2
 [58,]    2    2
 [59,]    2    2
 [60,]    1    2
 [61,]    2    2
 [62,]    2    2
 [63,]    1    1
 [64,]    2    2
 [65,]    2    2
 [66,]    1    1
 [67,]    2    2
 [68,]    1    2
 [69,]    2    2
 [70,]    2    1
 [71,]    1    2
 [72,]    1    1
 [73,]    1    2
 [74,]    1    1
 [75,]    1    2
 [76,]    2    2
 [77,]    2    2
 [78,]    2    2
 [79,]    1    2
 [80,]    1    1
 [81,]    1    1
 [82,]    2    2
 [83,]    1    1
 [84,]    2    2
 [85,]    1    1
 [86,]    1    1
 [87,]    2    2
 [88,]    2    2
 [89,]    1    1
 [90,]    2    2
 [91,]    1    1
 [92,]    2    2
 [93,]    1    1
 [94,]    1    2
 [95,]    2    2
 [96,]    2    2
 [97,]    2    2
 [98,]    2    2
 [99,]    2    2
[100,]    2    2
[101,]    2    2
[102,]    2    2
[103,]    1    1
[104,]    1    1
[105,]    1    1
[106,]    2    2
[107,]    1    2
[108,]    2    2
[109,]    2    1
[110,]    2    2
[111,]    2    2
[112,]    2    2
[113,]    1    1
[114,]    1    1
[115,]    1    1
[116,]    1    1
[117,]    2    2
[118,]    1    1
[119,]    2    1
[120,]    1    1
[121,]    1    2
[122,]    2    2
[123,]    2    2
[124,]    2    1
[125,]    1    1
[126,]    2    2
[127,]    2    2
[128,]    2    2
[129,]    2    2
[130,]    2    2
[131,]    2    2
[132,]    1    2
[133,]    1    1
[134,]    2    2
[135,]    2    2
[136,]    2    2
[137,]    2    2
[138,]    1    1
[139,]    1    2
[140,]    1    2
[141,]    2    2
[142,]    2    2
[143,]    2    2
[144,]    1    1
[145,]    2    2
[146,]    2    2
[147,]    2    2
[148,]    2    2
[149,]    1    1
[150,]    1    1
[151,]    1    1
[152,]    1    1
[153,]    1    1
[154,]    1    1
[155,]    1    1
[156,]    1    2
[157,]    1    1
[158,]    1    1
[159,]    1    1
[160,]    1    1
[161,]    1    1
[162,]    1    1
[163,]    1    1
[164,]    2    1
                
                 [0e+00,100350) [1e+05,279488]
  [0e+00,100350)             57             25
  [1e+05,279488]              8             74
> postscript(file="/var/wessaorg/rcomp/tmp/4lrpk1324658945.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/5q9ww1324658945.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/6hlia1324658945.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/756j41324658945.tab") 
+ }
> 
> try(system("convert tmp/2qwm71324658945.ps tmp/2qwm71324658945.png",intern=TRUE))
character(0)
> try(system("convert tmp/3q9yy1324658945.ps tmp/3q9yy1324658945.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lrpk1324658945.ps tmp/4lrpk1324658945.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.936   0.283   4.217