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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You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(127,13,1235,115,12,1080,127,7,845,150,9,1522,156,6,1047,182,11,1979,156,12,1822,132,10,1253,137,9,1297,113,9,946,137,15,1713,117,11,1024,137,8,1147,153,6,1092,117,13,1152,126,10,1336,170,14,2131,182,8,1550,162,11,1884,184,10,2041,143,6,845,159,9,1483,108,14,1055,175,8,1545,108,6,729,179,9,1792,111,15,1175,187,8,1593,111,7,785,115,7,744,194,5,1356,168,7,1262),dim=c(3,32),dimnames=list(c('Ouderdom','Aantal_bieders','veilingprijs'),1:32))
> y <- array(NA,dim=c(3,32),dimnames=list(c('Ouderdom','Aantal_bieders','veilingprijs'),1:32))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '3'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), 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: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
veilingprijs Ouderdom Aantal_bieders t
1 1235 127 13 1
2 1080 115 12 2
3 845 127 7 3
4 1522 150 9 4
5 1047 156 6 5
6 1979 182 11 6
7 1822 156 12 7
8 1253 132 10 8
9 1297 137 9 9
10 946 113 9 10
11 1713 137 15 11
12 1024 117 11 12
13 1147 137 8 13
14 1092 153 6 14
15 1152 117 13 15
16 1336 126 10 16
17 2131 170 14 17
18 1550 182 8 18
19 1884 162 11 19
20 2041 184 10 20
21 845 143 6 21
22 1483 159 9 22
23 1055 108 14 23
24 1545 175 8 24
25 729 108 6 25
26 1792 179 9 26
27 1175 111 15 27
28 1593 187 8 28
29 785 111 7 29
30 744 115 7 30
31 1356 194 5 31
32 1262 168 7 32
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Ouderdom Aantal_bieders t
-1294.277 12.854 83.887 -2.509
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-191.18 -112.20 20.89 100.52 220.87
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1294.2768 180.6985 -7.163 8.54e-08 ***
Ouderdom 12.8538 0.9149 14.049 3.32e-14 ***
Aantal_bieders 83.8869 9.0257 9.294 4.72e-10 ***
t -2.5085 2.6945 -0.931 0.36
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 133.8 on 28 degrees of freedom
Multiple R-squared: 0.8956, Adjusted R-squared: 0.8844
F-statistic: 80.05 on 3 and 28 DF, p-value: 7.547e-14
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.6163630 0.7672741 0.3836370
[2,] 0.4821490 0.9642979 0.5178510
[3,] 0.3271133 0.6542267 0.6728867
[4,] 0.2087150 0.4174301 0.7912850
[5,] 0.1786345 0.3572690 0.8213655
[6,] 0.1908680 0.3817359 0.8091320
[7,] 0.1194348 0.2388695 0.8805652
[8,] 0.1257798 0.2515596 0.8742202
[9,] 0.1611733 0.3223467 0.8388267
[10,] 0.1919166 0.3838332 0.8080834
[11,] 0.1325697 0.2651394 0.8674303
[12,] 0.2461003 0.4922005 0.7538997
[13,] 0.2858000 0.5716001 0.7142000
[14,] 0.3901496 0.7802992 0.6098504
[15,] 0.7776902 0.4446196 0.2223098
[16,] 0.6618023 0.6763953 0.3381977
[17,] 0.7672903 0.4654194 0.2327097
[18,] 0.7054980 0.5890040 0.2945020
[19,] 0.7509745 0.4980510 0.2490255
> postscript(file="/var/www/rcomp/tmp/1ssht1322137445.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2g0qk1322137445.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3lo461322137445.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4v62s1322137445.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5rrjj1322137445.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 32
Frequency = 1
1 2 3 4 5 6 7
-191.17469 -105.53391 -72.83633 143.26139 -154.69213 26.18356 122.00351
8 9 10 11 12 13 14
31.77658 97.90306 57.90234 15.59870 -78.26958 41.82395 -48.55428
15 16 17 18 19 20 21
-110.51786 211.96727 108.36178 -121.05376 220.86971 181.48189 -149.45696
22 23 24 25 26 27 28
33.73035 -155.65271 -21.02628 194.45943 95.68870 -148.06695 -117.23767
29 30 31 32
138.04519 48.13856 -185.02796 -110.09491
> postscript(file="/var/www/rcomp/tmp/6y5fk1322137445.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 32
Frequency = 1
lag(myerror, k = 1) myerror
0 -191.17469 NA
1 -105.53391 -191.17469
2 -72.83633 -105.53391
3 143.26139 -72.83633
4 -154.69213 143.26139
5 26.18356 -154.69213
6 122.00351 26.18356
7 31.77658 122.00351
8 97.90306 31.77658
9 57.90234 97.90306
10 15.59870 57.90234
11 -78.26958 15.59870
12 41.82395 -78.26958
13 -48.55428 41.82395
14 -110.51786 -48.55428
15 211.96727 -110.51786
16 108.36178 211.96727
17 -121.05376 108.36178
18 220.86971 -121.05376
19 181.48189 220.86971
20 -149.45696 181.48189
21 33.73035 -149.45696
22 -155.65271 33.73035
23 -21.02628 -155.65271
24 194.45943 -21.02628
25 95.68870 194.45943
26 -148.06695 95.68870
27 -117.23767 -148.06695
28 138.04519 -117.23767
29 48.13856 138.04519
30 -185.02796 48.13856
31 -110.09491 -185.02796
32 NA -110.09491
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -105.53391 -191.17469
[2,] -72.83633 -105.53391
[3,] 143.26139 -72.83633
[4,] -154.69213 143.26139
[5,] 26.18356 -154.69213
[6,] 122.00351 26.18356
[7,] 31.77658 122.00351
[8,] 97.90306 31.77658
[9,] 57.90234 97.90306
[10,] 15.59870 57.90234
[11,] -78.26958 15.59870
[12,] 41.82395 -78.26958
[13,] -48.55428 41.82395
[14,] -110.51786 -48.55428
[15,] 211.96727 -110.51786
[16,] 108.36178 211.96727
[17,] -121.05376 108.36178
[18,] 220.86971 -121.05376
[19,] 181.48189 220.86971
[20,] -149.45696 181.48189
[21,] 33.73035 -149.45696
[22,] -155.65271 33.73035
[23,] -21.02628 -155.65271
[24,] 194.45943 -21.02628
[25,] 95.68870 194.45943
[26,] -148.06695 95.68870
[27,] -117.23767 -148.06695
[28,] 138.04519 -117.23767
[29,] 48.13856 138.04519
[30,] -185.02796 48.13856
[31,] -110.09491 -185.02796
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -105.53391 -191.17469
2 -72.83633 -105.53391
3 143.26139 -72.83633
4 -154.69213 143.26139
5 26.18356 -154.69213
6 122.00351 26.18356
7 31.77658 122.00351
8 97.90306 31.77658
9 57.90234 97.90306
10 15.59870 57.90234
11 -78.26958 15.59870
12 41.82395 -78.26958
13 -48.55428 41.82395
14 -110.51786 -48.55428
15 211.96727 -110.51786
16 108.36178 211.96727
17 -121.05376 108.36178
18 220.86971 -121.05376
19 181.48189 220.86971
20 -149.45696 181.48189
21 33.73035 -149.45696
22 -155.65271 33.73035
23 -21.02628 -155.65271
24 194.45943 -21.02628
25 95.68870 194.45943
26 -148.06695 95.68870
27 -117.23767 -148.06695
28 138.04519 -117.23767
29 48.13856 138.04519
30 -185.02796 48.13856
31 -110.09491 -185.02796
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/7p5as1322137445.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8l65e1322137445.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9ym7f1322137445.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10myz71322137445.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11m3ei1322137445.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12aga11322137445.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/137ul51322137445.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/1400qp1322137445.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/1550ir1322137445.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/166n551322137445.tab")
+ }
>
> try(system("convert tmp/1ssht1322137445.ps tmp/1ssht1322137445.png",intern=TRUE))
character(0)
> try(system("convert tmp/2g0qk1322137445.ps tmp/2g0qk1322137445.png",intern=TRUE))
character(0)
> try(system("convert tmp/3lo461322137445.ps tmp/3lo461322137445.png",intern=TRUE))
character(0)
> try(system("convert tmp/4v62s1322137445.ps tmp/4v62s1322137445.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rrjj1322137445.ps tmp/5rrjj1322137445.png",intern=TRUE))
character(0)
> try(system("convert tmp/6y5fk1322137445.ps tmp/6y5fk1322137445.png",intern=TRUE))
character(0)
> try(system("convert tmp/7p5as1322137445.ps tmp/7p5as1322137445.png",intern=TRUE))
character(0)
> try(system("convert tmp/8l65e1322137445.ps tmp/8l65e1322137445.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ym7f1322137445.ps tmp/9ym7f1322137445.png",intern=TRUE))
character(0)
> try(system("convert tmp/10myz71322137445.ps tmp/10myz71322137445.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.800 0.230 4.036