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(97
+ ,197426
+ ,39
+ ,178377
+ ,490
+ ,146
+ ,187326
+ ,173
+ ,250931
+ ,2563
+ ,116
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+ ,898
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+ ,176225
+ ,139
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+ ,1212
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+ ,116
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+ ,738
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+ ,992
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+ ,161
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+ ,1272
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+ ,1293
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+ ,118906
+ ,103
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+ ,636
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+ ,117440
+ ,137
+ ,157384
+ ,1031
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+ ,116066
+ ,55
+ ,24469
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+ ,113344
+ ,155
+ ,230054
+ ,1230
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+ ,112431
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+ ,140303
+ ,847
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+ ,112302
+ ,145
+ ,198299
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+ ,112098
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+ ,106194
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+ ,75
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+ ,510
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+ ,109432
+ ,148
+ ,116938
+ ,698
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+ ,108535
+ ,85
+ ,118845
+ ,1586
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+ ,108146
+ ,140
+ ,100125
+ ,1001
+ ,118
+ ,105079
+ ,70
+ ,99776
+ ,710
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+ ,117
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+ ,906
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+ ,104767
+ ,103
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+ ,1030
+ ,151
+ ,104581
+ ,116
+ ,116136
+ ,1092
+ ,171
+ ,104128
+ ,50
+ ,122975
+ ,511
+ ,156
+ ,103925
+ ,152
+ ,164808
+ ,1319
+ ,171
+ ,103297
+ ,139
+ ,101345
+ ,1186
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+ ,114
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+ ,92622
+ ,126
+ ,134097
+ ,1566
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+ ,92280
+ ,118
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+ ,668
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+ ,101
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+ ,910
+ ,169
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+ ,104864
+ ,1187
+ ,179
+ ,88977
+ ,85
+ ,111563
+ ,784
+ ,149
+ ,86652
+ ,50
+ ,114198
+ ,758
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+ ,816
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+ ,158
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+ ,763
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+ ,132
+ ,94853
+ ,743
+ ,160
+ ,81351
+ ,107
+ ,102204
+ ,900
+ ,152
+ ,79756
+ ,80
+ ,122531
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+ ,147
+ ,79089
+ ,114
+ ,169351
+ ,981
+ ,111
+ ,79011
+ ,97
+ ,80238
+ ,784
+ ,89
+ ,76173
+ ,8
+ ,47552
+ ,179
+ ,87
+ ,72128
+ ,163
+ ,145707
+ ,542
+ ,121
+ ,71571
+ ,102
+ ,75881
+ ,746
+ ,146
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+ ,767
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+ ,174586
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+ ,72558
+ ,790
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+ ,64520
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+ ,86
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+ ,89
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+ ,846
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+ ,49
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+ ,112
+ ,55918
+ ,74
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+ ,795
+ ,89
+ ,52789
+ ,37
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+ ,48029
+ ,120
+ ,88634
+ ,833
+ ,130
+ ,45724
+ ,87
+ ,74151
+ ,641
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+ ,83
+ ,100792
+ ,415
+ ,64
+ ,43750
+ ,13
+ ,19630
+ ,214
+ ,99
+ ,38692
+ ,30
+ ,68580
+ ,657
+ ,78
+ ,37238
+ ,41
+ ,10901
+ ,716
+ ,110
+ ,37110
+ ,67
+ ,64057
+ ,665)
+ ,dim=c(5
+ ,137)
+ ,dimnames=list(c('FbackMess'
+ ,'CompendiumCharacters'
+ ,'BloggedComputations'
+ ,'CompendiumSeconds'
+ ,'CourseViews')
+ ,1:137))
>  y <- array(NA,dim=c(5,137),dimnames=list(c('FbackMess','CompendiumCharacters','BloggedComputations','CompendiumSeconds','CourseViews'),1:137))
>  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 = '3'
> par2 = 'quantiles'
> par1 = '2'
> #'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] "CompendiumCharacters"
> x[,par1]
  [1] 197426 187326 184923 183500 176225 169707 169265 167949 165986 165933
 [11] 165904 160902 160141 156349 154771 154451 151911 151715 150491 150047
 [21] 149959 149695 147172 146975 146760 144551 144408 144244 143592 140824
 [31] 140358 140015 139165 136588 135356 134238 134047 131072 128692 127654
 [41] 126817 126372 125818 125386 125081 124089 123534 120192 119442 118906
 [51] 117440 116066 114948 114799 114360 113344 112431 112302 112098 110529
 [61] 110459 109432 108535 108146 105079 104978 104767 104581 104128 103925
 [71] 103297 103129 103037 102812 102153 102070 101629 101382 101047 100350
 [81] 100087 100046  96125  95893  95676  93879  93487  93473  92622  92280
 [91]  92059  89626  89506  89256  88977  86652  84601  83515  83248  83243
[101]  82317  81897  81625  81351  79756  79089  79011  76173  72128  71571
[111]  71154  70168  69867  69652  69446  68946  68788  67150  66485  66089
[121]  65594  64593  64520  59938  59900  57224  56750  56622  55918  52789
[131]  48029  45724  43929  43750  38692  37238  37110
> 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])

[ 37110, 92059) [ 92059,125081) [125081,197426] 
             46              46              45 
> colnames(x)
[1] "FbackMess"            "CompendiumCharacters" "BloggedComputations" 
[4] "CompendiumSeconds"    "CourseViews"         
> colnames(x)[par1]
[1] "CompendiumCharacters"
> x[,par1]
  [1] [125081,197426] [125081,197426] [125081,197426] [125081,197426]
  [5] [125081,197426] [125081,197426] [125081,197426] [125081,197426]
  [9] [125081,197426] [125081,197426] [125081,197426] [125081,197426]
 [13] [125081,197426] [125081,197426] [125081,197426] [125081,197426]
 [17] [125081,197426] [125081,197426] [125081,197426] [125081,197426]
 [21] [125081,197426] [125081,197426] [125081,197426] [125081,197426]
 [25] [125081,197426] [125081,197426] [125081,197426] [125081,197426]
 [29] [125081,197426] [125081,197426] [125081,197426] [125081,197426]
 [33] [125081,197426] [125081,197426] [125081,197426] [125081,197426]
 [37] [125081,197426] [125081,197426] [125081,197426] [125081,197426]
 [41] [125081,197426] [125081,197426] [125081,197426] [125081,197426]
 [45] [125081,197426] [ 92059,125081) [ 92059,125081) [ 92059,125081)
 [49] [ 92059,125081) [ 92059,125081) [ 92059,125081) [ 92059,125081)
 [53] [ 92059,125081) [ 92059,125081) [ 92059,125081) [ 92059,125081)
 [57] [ 92059,125081) [ 92059,125081) [ 92059,125081) [ 92059,125081)
 [61] [ 92059,125081) [ 92059,125081) [ 92059,125081) [ 92059,125081)
 [65] [ 92059,125081) [ 92059,125081) [ 92059,125081) [ 92059,125081)
 [69] [ 92059,125081) [ 92059,125081) [ 92059,125081) [ 92059,125081)
 [73] [ 92059,125081) [ 92059,125081) [ 92059,125081) [ 92059,125081)
 [77] [ 92059,125081) [ 92059,125081) [ 92059,125081) [ 92059,125081)
 [81] [ 92059,125081) [ 92059,125081) [ 92059,125081) [ 92059,125081)
 [85] [ 92059,125081) [ 92059,125081) [ 92059,125081) [ 92059,125081)
 [89] [ 92059,125081) [ 92059,125081) [ 92059,125081) [ 37110, 92059)
 [93] [ 37110, 92059) [ 37110, 92059) [ 37110, 92059) [ 37110, 92059)
 [97] [ 37110, 92059) [ 37110, 92059) [ 37110, 92059) [ 37110, 92059)
[101] [ 37110, 92059) [ 37110, 92059) [ 37110, 92059) [ 37110, 92059)
[105] [ 37110, 92059) [ 37110, 92059) [ 37110, 92059) [ 37110, 92059)
[109] [ 37110, 92059) [ 37110, 92059) [ 37110, 92059) [ 37110, 92059)
[113] [ 37110, 92059) [ 37110, 92059) [ 37110, 92059) [ 37110, 92059)
[117] [ 37110, 92059) [ 37110, 92059) [ 37110, 92059) [ 37110, 92059)
[121] [ 37110, 92059) [ 37110, 92059) [ 37110, 92059) [ 37110, 92059)
[125] [ 37110, 92059) [ 37110, 92059) [ 37110, 92059) [ 37110, 92059)
[129] [ 37110, 92059) [ 37110, 92059) [ 37110, 92059) [ 37110, 92059)
[133] [ 37110, 92059) [ 37110, 92059) [ 37110, 92059) [ 37110, 92059)
[137] [ 37110, 92059)
Levels: [ 37110, 92059) [ 92059,125081) [125081,197426]
> 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/1al2v1324671746.tab") 
+ }
+ }
> m

	 Conditional inference tree with 3 terminal nodes

Response:  as.factor(CompendiumCharacters) 
Inputs:  FbackMess, BloggedComputations, CompendiumSeconds, CourseViews 
Number of observations:  137 

1) CompendiumSeconds <= 115750; criterion = 1, statistic = 35.178
  2)*  weights = 67 
1) CompendiumSeconds > 115750
  3) CompendiumSeconds <= 174586; criterion = 0.969, statistic = 9.725
    4)*  weights = 45 
  3) CompendiumSeconds > 174586
    5)*  weights = 25 
> postscript(file="/var/wessaorg/rcomp/tmp/2e3p01324671746.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/3xhno1324671746.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,]    3    3
  [2,]    3    3
  [3,]    3    3
  [4,]    3    3
  [5,]    3    3
  [6,]    3    3
  [7,]    3    2
  [8,]    3    2
  [9,]    3    3
 [10,]    3    2
 [11,]    3    2
 [12,]    3    1
 [13,]    3    3
 [14,]    3    2
 [15,]    3    1
 [16,]    3    2
 [17,]    3    3
 [18,]    3    3
 [19,]    3    3
 [20,]    3    1
 [21,]    3    3
 [22,]    3    3
 [23,]    3    3
 [24,]    3    2
 [25,]    3    3
 [26,]    3    1
 [27,]    3    1
 [28,]    3    1
 [29,]    3    1
 [30,]    3    3
 [31,]    3    2
 [32,]    3    2
 [33,]    3    3
 [34,]    3    2
 [35,]    3    1
 [36,]    3    3
 [37,]    3    3
 [38,]    3    1
 [39,]    3    1
 [40,]    3    1
 [41,]    3    1
 [42,]    3    2
 [43,]    3    1
 [44,]    3    2
 [45,]    3    2
 [46,]    2    2
 [47,]    2    1
 [48,]    2    3
 [49,]    2    3
 [50,]    2    1
 [51,]    2    2
 [52,]    2    1
 [53,]    2    2
 [54,]    2    2
 [55,]    2    1
 [56,]    2    3
 [57,]    2    2
 [58,]    2    3
 [59,]    2    1
 [60,]    2    3
 [61,]    2    1
 [62,]    2    2
 [63,]    2    2
 [64,]    2    1
 [65,]    2    1
 [66,]    2    2
 [67,]    2    2
 [68,]    2    2
 [69,]    2    2
 [70,]    2    2
 [71,]    2    1
 [72,]    2    2
 [73,]    2    2
 [74,]    2    2
 [75,]    2    1
 [76,]    2    1
 [77,]    2    3
 [78,]    2    2
 [79,]    2    2
 [80,]    2    1
 [81,]    2    2
 [82,]    2    2
 [83,]    2    1
 [84,]    2    1
 [85,]    2    2
 [86,]    2    2
 [87,]    2    2
 [88,]    2    2
 [89,]    2    2
 [90,]    2    2
 [91,]    2    1
 [92,]    1    2
 [93,]    1    2
 [94,]    1    1
 [95,]    1    1
 [96,]    1    1
 [97,]    1    1
 [98,]    1    1
 [99,]    1    1
[100,]    1    2
[101,]    1    1
[102,]    1    1
[103,]    1    1
[104,]    1    1
[105,]    1    2
[106,]    1    2
[107,]    1    1
[108,]    1    1
[109,]    1    2
[110,]    1    1
[111,]    1    1
[112,]    1    1
[113,]    1    1
[114,]    1    1
[115,]    1    1
[116,]    1    2
[117,]    1    1
[118,]    1    1
[119,]    1    1
[120,]    1    1
[121,]    1    1
[122,]    1    1
[123,]    1    1
[124,]    1    1
[125,]    1    1
[126,]    1    1
[127,]    1    1
[128,]    1    1
[129,]    1    1
[130,]    1    1
[131,]    1    1
[132,]    1    1
[133,]    1    1
[134,]    1    1
[135,]    1    1
[136,]    1    1
[137,]    1    1
                 
                  [ 37110, 92059) [ 92059,125081) [125081,197426]
  [ 37110, 92059)              39               7               0
  [ 92059,125081)              15              25               6
  [125081,197426]              13              13              19
> postscript(file="/var/wessaorg/rcomp/tmp/4s6j91324671746.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/5kwan1324671746.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/6kkei1324671746.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/7d4av1324671746.tab") 
+ }
> 
> try(system("convert tmp/2e3p01324671746.ps tmp/2e3p01324671746.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xhno1324671746.ps tmp/3xhno1324671746.png",intern=TRUE))
character(0)
> try(system("convert tmp/4s6j91324671746.ps tmp/4s6j91324671746.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.553   0.221   2.774