R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(60.9,0,61.1,0,60.2,1,60.1,0,59.7,0,60.5,0,59.5,1,59.5,0,59.7,0,60.4,0,60,1,59,0,59.3,0,59.7,0,60.4,1,59.9,0,60.5,0,60.4,0,60.6,1,60.9,0,61,0,61.2,0,61.2,1,60.3,0,60.4,0,61.2,0,62.1,1,61.7,0,61.6,0,62.1,0,62.7,1,62.6,0,62,0),dim=c(2,33),dimnames=list(c('Werkgelegenheid','dummy'),1:33))
> y <- array(NA,dim=c(2,33),dimnames=list(c('Werkgelegenheid','dummy'),1:33))
> 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
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> 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
Werkgelegenheid dummy
1 60.9 0
2 61.1 0
3 60.2 1
4 60.1 0
5 59.7 0
6 60.5 0
7 59.5 1
8 59.5 0
9 59.7 0
10 60.4 0
11 60.0 1
12 59.0 0
13 59.3 0
14 59.7 0
15 60.4 1
16 59.9 0
17 60.5 0
18 60.4 0
19 60.6 1
20 60.9 0
21 61.0 0
22 61.2 0
23 61.2 1
24 60.3 0
25 60.4 0
26 61.2 0
27 62.1 1
28 61.7 0
29 61.6 0
30 62.1 0
31 62.7 1
32 62.6 0
33 62.0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dummy
60.6280 0.2095
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.628 -0.728 -0.228 0.572 1.972
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 60.6280 0.1931 313.911 <2e-16 ***
dummy 0.2095 0.3923 0.534 0.597
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9657 on 31 degrees of freedom
Multiple R-squared: 0.009117, Adjusted R-squared: -0.02285
F-statistic: 0.2852 on 1 and 31 DF, p-value: 0.5971
> 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,] 0.26034368 0.52068736 0.7396563
[2,] 0.12761730 0.25523459 0.8723827
[3,] 0.09441838 0.18883675 0.9055816
[4,] 0.11915644 0.23831289 0.8808436
[5,] 0.09350696 0.18701392 0.9064930
[6,] 0.05069082 0.10138163 0.9493092
[7,] 0.03195871 0.06391741 0.9680413
[8,] 0.10363897 0.20727793 0.8963610
[9,] 0.15118349 0.30236697 0.8488165
[10,] 0.15204305 0.30408610 0.8479569
[11,] 0.14853660 0.29707319 0.8514634
[12,] 0.15426201 0.30852402 0.8457380
[13,] 0.13942029 0.27884059 0.8605797
[14,] 0.13280119 0.26560239 0.8671988
[15,] 0.18428031 0.36856061 0.8157197
[16,] 0.18758789 0.37517578 0.8124121
[17,] 0.18528218 0.37056435 0.8147178
[18,] 0.18221085 0.36442171 0.8177891
[19,] 0.24967464 0.49934927 0.7503254
[20,] 0.38663122 0.77326243 0.6133688
[21,] 0.74925429 0.50149141 0.2507457
[22,] 0.85039544 0.29920911 0.1496046
[23,] 0.86952073 0.26095854 0.1304793
[24,] 0.82219727 0.35560545 0.1778027
> postscript(file="/var/www/html/rcomp/tmp/1rqrf1227693708.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2npyi1227693708.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/3alzz1227693708.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4he291227693708.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ehvd1227693708.ps",horizontal=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 = 33
Frequency = 1
1 2 3 4 5 6 7 8 9 10
0.2720 0.4720 -0.6375 -0.5280 -0.9280 -0.1280 -1.3375 -1.1280 -0.9280 -0.2280
11 12 13 14 15 16 17 18 19 20
-0.8375 -1.6280 -1.3280 -0.9280 -0.4375 -0.7280 -0.1280 -0.2280 -0.2375 0.2720
21 22 23 24 25 26 27 28 29 30
0.3720 0.5720 0.3625 -0.3280 -0.2280 0.5720 1.2625 1.0720 0.9720 1.4720
31 32 33
1.8625 1.9720 1.3720
> postscript(file="/var/www/html/rcomp/tmp/66swx1227693708.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 33
Frequency = 1
lag(myerror, k = 1) myerror
0 0.2720 NA
1 0.4720 0.2720
2 -0.6375 0.4720
3 -0.5280 -0.6375
4 -0.9280 -0.5280
5 -0.1280 -0.9280
6 -1.3375 -0.1280
7 -1.1280 -1.3375
8 -0.9280 -1.1280
9 -0.2280 -0.9280
10 -0.8375 -0.2280
11 -1.6280 -0.8375
12 -1.3280 -1.6280
13 -0.9280 -1.3280
14 -0.4375 -0.9280
15 -0.7280 -0.4375
16 -0.1280 -0.7280
17 -0.2280 -0.1280
18 -0.2375 -0.2280
19 0.2720 -0.2375
20 0.3720 0.2720
21 0.5720 0.3720
22 0.3625 0.5720
23 -0.3280 0.3625
24 -0.2280 -0.3280
25 0.5720 -0.2280
26 1.2625 0.5720
27 1.0720 1.2625
28 0.9720 1.0720
29 1.4720 0.9720
30 1.8625 1.4720
31 1.9720 1.8625
32 1.3720 1.9720
33 NA 1.3720
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.4720 0.2720
[2,] -0.6375 0.4720
[3,] -0.5280 -0.6375
[4,] -0.9280 -0.5280
[5,] -0.1280 -0.9280
[6,] -1.3375 -0.1280
[7,] -1.1280 -1.3375
[8,] -0.9280 -1.1280
[9,] -0.2280 -0.9280
[10,] -0.8375 -0.2280
[11,] -1.6280 -0.8375
[12,] -1.3280 -1.6280
[13,] -0.9280 -1.3280
[14,] -0.4375 -0.9280
[15,] -0.7280 -0.4375
[16,] -0.1280 -0.7280
[17,] -0.2280 -0.1280
[18,] -0.2375 -0.2280
[19,] 0.2720 -0.2375
[20,] 0.3720 0.2720
[21,] 0.5720 0.3720
[22,] 0.3625 0.5720
[23,] -0.3280 0.3625
[24,] -0.2280 -0.3280
[25,] 0.5720 -0.2280
[26,] 1.2625 0.5720
[27,] 1.0720 1.2625
[28,] 0.9720 1.0720
[29,] 1.4720 0.9720
[30,] 1.8625 1.4720
[31,] 1.9720 1.8625
[32,] 1.3720 1.9720
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.4720 0.2720
2 -0.6375 0.4720
3 -0.5280 -0.6375
4 -0.9280 -0.5280
5 -0.1280 -0.9280
6 -1.3375 -0.1280
7 -1.1280 -1.3375
8 -0.9280 -1.1280
9 -0.2280 -0.9280
10 -0.8375 -0.2280
11 -1.6280 -0.8375
12 -1.3280 -1.6280
13 -0.9280 -1.3280
14 -0.4375 -0.9280
15 -0.7280 -0.4375
16 -0.1280 -0.7280
17 -0.2280 -0.1280
18 -0.2375 -0.2280
19 0.2720 -0.2375
20 0.3720 0.2720
21 0.5720 0.3720
22 0.3625 0.5720
23 -0.3280 0.3625
24 -0.2280 -0.3280
25 0.5720 -0.2280
26 1.2625 0.5720
27 1.0720 1.2625
28 0.9720 1.0720
29 1.4720 0.9720
30 1.8625 1.4720
31 1.9720 1.8625
32 1.3720 1.9720
> 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/html/rcomp/tmp/7v2ax1227693708.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8xpc61227693708.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9gg9o1227693708.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/101s061227693708.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/html/rcomp/tmp/11yngo1227693708.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/html/rcomp/tmp/12ehqs1227693708.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/html/rcomp/tmp/139svr1227693708.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/html/rcomp/tmp/14lcxn1227693708.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/html/rcomp/tmp/157m4x1227693708.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/html/rcomp/tmp/16msgx1227693708.tab")
+ }
>
> system("convert tmp/1rqrf1227693708.ps tmp/1rqrf1227693708.png")
> system("convert tmp/2npyi1227693708.ps tmp/2npyi1227693708.png")
> system("convert tmp/3alzz1227693708.ps tmp/3alzz1227693708.png")
> system("convert tmp/4he291227693708.ps tmp/4he291227693708.png")
> system("convert tmp/5ehvd1227693708.ps tmp/5ehvd1227693708.png")
> system("convert tmp/66swx1227693708.ps tmp/66swx1227693708.png")
> system("convert tmp/7v2ax1227693708.ps tmp/7v2ax1227693708.png")
> system("convert tmp/8xpc61227693708.ps tmp/8xpc61227693708.png")
> system("convert tmp/9gg9o1227693708.ps tmp/9gg9o1227693708.png")
> system("convert tmp/101s061227693708.ps tmp/101s061227693708.png")
>
>
> proc.time()
user system elapsed
2.291 1.578 3.014