R version 2.12.1 (2010-12-16)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: x86_64-redhat-linux-gnu (64-bit)
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> x <- array(list(1235,127,13,1080,115,12,845,127,7,1522,150,9,1047,156,6,1979,182,11,1822,156,12,1253,132,10,1297,137,9,946,113,9,1713,137,15,1024,117,11,1147,137,8,1092,153,6,1152,117,13,1336,126,10,2131,170,14,1550,182,8,1884,162,11,2041,184,10,845,143,6,1483,159,9,1055,108,14,1545,175,8,729,108,6,1792,179,9,1175,111,15,1593,187,8,785,111,7,744,115,7,1356,194,5,1262,168,7),dim=c(3,32),dimnames=list(c('Veilingprijs','Ouderdom','Aanbieders'),1:32))
> y <- array(NA,dim=c(3,32),dimnames=list(c('Veilingprijs','Ouderdom','Aanbieders'),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 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'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 Aanbieders
1 1235 127 13
2 1080 115 12
3 845 127 7
4 1522 150 9
5 1047 156 6
6 1979 182 11
7 1822 156 12
8 1253 132 10
9 1297 137 9
10 946 113 9
11 1713 137 15
12 1024 117 11
13 1147 137 8
14 1092 153 6
15 1152 117 13
16 1336 126 10
17 2131 170 14
18 1550 182 8
19 1884 162 11
20 2041 184 10
21 845 143 6
22 1483 159 9
23 1055 108 14
24 1545 175 8
25 729 108 6
26 1792 179 9
27 1175 111 15
28 1593 187 8
29 785 111 7
30 744 115 7
31 1356 194 5
32 1262 168 7
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Ouderdom Aanbieders
-1338.95 12.74 85.95
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-206.49 -117.34 16.66 102.55 213.50
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1338.9513 173.8095 -7.704 1.71e-08 ***
Ouderdom 12.7406 0.9047 14.082 1.69e-14 ***
Aanbieders 85.9530 8.7285 9.847 9.34e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 133.5 on 29 degrees of freedom
Multiple R-squared: 0.8923, Adjusted R-squared: 0.8849
F-statistic: 120.2 on 2 and 29 DF, p-value: 9.216e-15
> 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,] 0.61676019 0.7664796 0.3832398
[2,] 0.56916757 0.8616649 0.4308324
[3,] 0.46032136 0.9206427 0.5396786
[4,] 0.42973037 0.8594607 0.5702696
[5,] 0.36696167 0.7339233 0.6330383
[6,] 0.25326816 0.5065363 0.7467318
[7,] 0.17865755 0.3573151 0.8213424
[8,] 0.11513872 0.2302774 0.8848613
[9,] 0.07716865 0.1543373 0.9228313
[10,] 0.06104873 0.1220975 0.9389513
[11,] 0.13918100 0.2783620 0.8608190
[12,] 0.10579204 0.2115841 0.8942080
[13,] 0.11643994 0.2328799 0.8835601
[14,] 0.22983646 0.4596729 0.7701635
[15,] 0.45103833 0.9020767 0.5489617
[16,] 0.55056403 0.8988719 0.4494360
[17,] 0.49717677 0.9943535 0.5028232
[18,] 0.46895183 0.9379037 0.5310482
[19,] 0.37690181 0.7538036 0.6230982
[20,] 0.36302758 0.7260552 0.6369724
[21,] 0.84191899 0.3161620 0.1580810
> postscript(file="/var/www/wessaorg/rcomp/tmp/1566j1298402059.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/wessaorg/rcomp/tmp/2vfsj1298402059.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/wessaorg/rcomp/tmp/30fwk1298402059.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/wessaorg/rcomp/tmp/48ow21298402059.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/wessaorg/rcomp/tmp/5pchz1298402059.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
-161.49037 -77.65049 -35.77246 176.28837 -117.29612 53.68403 141.98597
8 9 10 11 12 13 14
50.66572 116.91583 71.68961 17.19792 -73.17866 52.86881 -34.07440
15 16 17 18 19 20 21
-117.08463 210.10916 100.71196 -117.45702 213.49551 176.15586 -153.66866
22 23 24 25 26 27 28
22.62320 -185.37244 -33.27300 176.25143 76.81172 -189.54715 -138.15989
29 30 31 32
108.07673 16.11443 -206.48496 -141.13600
> postscript(file="/var/www/wessaorg/rcomp/tmp/6rt8k1298402059.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 -161.49037 NA
1 -77.65049 -161.49037
2 -35.77246 -77.65049
3 176.28837 -35.77246
4 -117.29612 176.28837
5 53.68403 -117.29612
6 141.98597 53.68403
7 50.66572 141.98597
8 116.91583 50.66572
9 71.68961 116.91583
10 17.19792 71.68961
11 -73.17866 17.19792
12 52.86881 -73.17866
13 -34.07440 52.86881
14 -117.08463 -34.07440
15 210.10916 -117.08463
16 100.71196 210.10916
17 -117.45702 100.71196
18 213.49551 -117.45702
19 176.15586 213.49551
20 -153.66866 176.15586
21 22.62320 -153.66866
22 -185.37244 22.62320
23 -33.27300 -185.37244
24 176.25143 -33.27300
25 76.81172 176.25143
26 -189.54715 76.81172
27 -138.15989 -189.54715
28 108.07673 -138.15989
29 16.11443 108.07673
30 -206.48496 16.11443
31 -141.13600 -206.48496
32 NA -141.13600
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -77.65049 -161.49037
[2,] -35.77246 -77.65049
[3,] 176.28837 -35.77246
[4,] -117.29612 176.28837
[5,] 53.68403 -117.29612
[6,] 141.98597 53.68403
[7,] 50.66572 141.98597
[8,] 116.91583 50.66572
[9,] 71.68961 116.91583
[10,] 17.19792 71.68961
[11,] -73.17866 17.19792
[12,] 52.86881 -73.17866
[13,] -34.07440 52.86881
[14,] -117.08463 -34.07440
[15,] 210.10916 -117.08463
[16,] 100.71196 210.10916
[17,] -117.45702 100.71196
[18,] 213.49551 -117.45702
[19,] 176.15586 213.49551
[20,] -153.66866 176.15586
[21,] 22.62320 -153.66866
[22,] -185.37244 22.62320
[23,] -33.27300 -185.37244
[24,] 176.25143 -33.27300
[25,] 76.81172 176.25143
[26,] -189.54715 76.81172
[27,] -138.15989 -189.54715
[28,] 108.07673 -138.15989
[29,] 16.11443 108.07673
[30,] -206.48496 16.11443
[31,] -141.13600 -206.48496
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -77.65049 -161.49037
2 -35.77246 -77.65049
3 176.28837 -35.77246
4 -117.29612 176.28837
5 53.68403 -117.29612
6 141.98597 53.68403
7 50.66572 141.98597
8 116.91583 50.66572
9 71.68961 116.91583
10 17.19792 71.68961
11 -73.17866 17.19792
12 52.86881 -73.17866
13 -34.07440 52.86881
14 -117.08463 -34.07440
15 210.10916 -117.08463
16 100.71196 210.10916
17 -117.45702 100.71196
18 213.49551 -117.45702
19 176.15586 213.49551
20 -153.66866 176.15586
21 22.62320 -153.66866
22 -185.37244 22.62320
23 -33.27300 -185.37244
24 176.25143 -33.27300
25 76.81172 176.25143
26 -189.54715 76.81172
27 -138.15989 -189.54715
28 108.07673 -138.15989
29 16.11443 108.07673
30 -206.48496 16.11443
31 -141.13600 -206.48496
> 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/wessaorg/rcomp/tmp/79cc81298402059.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/wessaorg/rcomp/tmp/8daht1298402059.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/wessaorg/rcomp/tmp/9oahj1298402059.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/wessaorg/rcomp/tmp/105tyq1298402059.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11cgll1298402059.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/wessaorg/rcomp/tmp/1271h01298402059.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/wessaorg/rcomp/tmp/13380r1298402059.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/wessaorg/rcomp/tmp/14m0g51298402059.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/wessaorg/rcomp/tmp/15nld11298402059.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/wessaorg/rcomp/tmp/16ne941298402059.tab")
+ }
>
> try(system("convert tmp/1566j1298402059.ps tmp/1566j1298402059.png",intern=TRUE))
character(0)
> try(system("convert tmp/2vfsj1298402059.ps tmp/2vfsj1298402059.png",intern=TRUE))
character(0)
> try(system("convert tmp/30fwk1298402059.ps tmp/30fwk1298402059.png",intern=TRUE))
character(0)
> try(system("convert tmp/48ow21298402059.ps tmp/48ow21298402059.png",intern=TRUE))
character(0)
> try(system("convert tmp/5pchz1298402059.ps tmp/5pchz1298402059.png",intern=TRUE))
character(0)
> try(system("convert tmp/6rt8k1298402059.ps tmp/6rt8k1298402059.png",intern=TRUE))
character(0)
> try(system("convert tmp/79cc81298402059.ps tmp/79cc81298402059.png",intern=TRUE))
character(0)
> try(system("convert tmp/8daht1298402059.ps tmp/8daht1298402059.png",intern=TRUE))
character(0)
> try(system("convert tmp/9oahj1298402059.ps tmp/9oahj1298402059.png",intern=TRUE))
character(0)
> try(system("convert tmp/105tyq1298402059.ps tmp/105tyq1298402059.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.000 0.390 3.548