R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(20.7246301,0,21.44580352,0,22.09413114,0,21.53321848,0,23.3470789,0,23.5656163,0,26.42117166,0,25.21193138,0,26.43574082,0,29.33500366,0,29.40056488,0,33.05013946,0,28.38072368,0,26.0059506,0,29.31314992,0,30.36212944,0,35.74543406,0,36.15337054,0,34.20838768,0,37.90895432,0,38.70297354,0,42.11944156,0,42.16314904,0,39.79566054,0,37.36261082,0,38.3533137,0,42.60022384,0,41.24529196,0,42.15586446,0,46.94183352,0,47.42990038,0,47.0583868,0,50.18347162,0,50.12519498,0,43.22669772,0,40.04333626,0,40.37114236,0,42.2141411,0,36.99838182,0,39.74466848,0,42.68035422,0,46.2935059,0,46.97097184,0,48.72655562,0,52.36884562,0,50.05234918,0,54.03701444,0,57.78128856,0,64.71620872,0,63.4122689,0,64.3592643,0,66.02743312,0,72.13919574,0,76.60464328,0,86.97060062,0,93.48301514,0,95.58825876,0,81.88596378,1,70.5511573,1,50.38015528,1,36.24807008,1),dim=c(2,61),dimnames=list(c('Olie','Dumivariabele'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Olie','Dumivariabele'),1:61))
> 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)
> 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
Olie Dumivariabele
1 20.72463 0
2 21.44580 0
3 22.09413 0
4 21.53322 0
5 23.34708 0
6 23.56562 0
7 26.42117 0
8 25.21193 0
9 26.43574 0
10 29.33500 0
11 29.40056 0
12 33.05014 0
13 28.38072 0
14 26.00595 0
15 29.31315 0
16 30.36213 0
17 35.74543 0
18 36.15337 0
19 34.20839 0
20 37.90895 0
21 38.70297 0
22 42.11944 0
23 42.16315 0
24 39.79566 0
25 37.36261 0
26 38.35331 0
27 42.60022 0
28 41.24529 0
29 42.15586 0
30 46.94183 0
31 47.42990 0
32 47.05839 0
33 50.18347 0
34 50.12519 0
35 43.22670 0
36 40.04334 0
37 40.37114 0
38 42.21414 0
39 36.99838 0
40 39.74467 0
41 42.68035 0
42 46.29351 0
43 46.97097 0
44 48.72656 0
45 52.36885 0
46 50.05235 0
47 54.03701 0
48 57.78129 0
49 64.71621 0
50 63.41227 0
51 64.35926 0
52 66.02743 0
53 72.13920 0
54 76.60464 0
55 86.97060 0
56 93.48302 0
57 95.58826 0
58 81.88596 1
59 70.55116 1
60 50.38016 1
61 36.24807 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dumivariabele
43.71 16.05
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-23.518 -13.352 -2.469 6.411 51.874
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 43.714 2.325 18.806 <2e-16 ***
Dumivariabele 16.052 9.077 1.768 0.0822 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 17.55 on 59 degrees of freedom
Multiple R-squared: 0.05034, Adjusted R-squared: 0.03424
F-statistic: 3.127 on 1 and 59 DF, p-value: 0.08217
> postscript(file="/var/www/html/rcomp/tmp/12cz91229870727.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/2syxp1229870727.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3v2j21229870727.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4yg0l1229870727.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/56woy1229870727.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 61
Frequency = 1
1 2 3 4 5 6
-22.9892399 -22.2680665 -21.6197388 -22.1806515 -20.3667911 -20.1482537
7 8 9 10 11 12
-17.2926983 -18.5019386 -17.2781292 -14.3788663 -14.3133051 -10.6637305
13 14 15 16 17 18
-15.3331463 -17.7079194 -14.4007201 -13.3517405 -7.9684359 -7.5604994
19 20 21 22 23 24
-9.5054823 -5.8049157 -5.0108964 -1.5944284 -1.5507209 -3.9182094
25 26 27 28 29 30
-6.3512592 -5.3605563 -1.1136461 -2.4685780 -1.5580055 3.2279635
31 32 33 34 35 36
3.7160304 3.3445168 6.4696016 6.4113250 -0.4871723 -3.6705337
37 38 39 40 41 42
-3.3427276 -1.4997289 -6.7154882 -3.9692015 -1.0335158 2.5796359
43 44 45 46 47 48
3.2571019 5.0126856 8.6549756 6.3384792 10.3231445 14.0674186
49 50 51 52 53 54
21.0023387 19.6983989 20.6453943 22.3135631 28.4253258 32.8907733
55 56 57 58 59 60
43.2567306 49.7691452 51.8743888 22.1196272 10.7848207 -9.3861813
61
-23.5182665
> postscript(file="/var/www/html/rcomp/tmp/6od1h1229870727.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -22.9892399 NA
1 -22.2680665 -22.9892399
2 -21.6197388 -22.2680665
3 -22.1806515 -21.6197388
4 -20.3667911 -22.1806515
5 -20.1482537 -20.3667911
6 -17.2926983 -20.1482537
7 -18.5019386 -17.2926983
8 -17.2781292 -18.5019386
9 -14.3788663 -17.2781292
10 -14.3133051 -14.3788663
11 -10.6637305 -14.3133051
12 -15.3331463 -10.6637305
13 -17.7079194 -15.3331463
14 -14.4007201 -17.7079194
15 -13.3517405 -14.4007201
16 -7.9684359 -13.3517405
17 -7.5604994 -7.9684359
18 -9.5054823 -7.5604994
19 -5.8049157 -9.5054823
20 -5.0108964 -5.8049157
21 -1.5944284 -5.0108964
22 -1.5507209 -1.5944284
23 -3.9182094 -1.5507209
24 -6.3512592 -3.9182094
25 -5.3605563 -6.3512592
26 -1.1136461 -5.3605563
27 -2.4685780 -1.1136461
28 -1.5580055 -2.4685780
29 3.2279635 -1.5580055
30 3.7160304 3.2279635
31 3.3445168 3.7160304
32 6.4696016 3.3445168
33 6.4113250 6.4696016
34 -0.4871723 6.4113250
35 -3.6705337 -0.4871723
36 -3.3427276 -3.6705337
37 -1.4997289 -3.3427276
38 -6.7154882 -1.4997289
39 -3.9692015 -6.7154882
40 -1.0335158 -3.9692015
41 2.5796359 -1.0335158
42 3.2571019 2.5796359
43 5.0126856 3.2571019
44 8.6549756 5.0126856
45 6.3384792 8.6549756
46 10.3231445 6.3384792
47 14.0674186 10.3231445
48 21.0023387 14.0674186
49 19.6983989 21.0023387
50 20.6453943 19.6983989
51 22.3135631 20.6453943
52 28.4253258 22.3135631
53 32.8907733 28.4253258
54 43.2567306 32.8907733
55 49.7691452 43.2567306
56 51.8743888 49.7691452
57 22.1196272 51.8743888
58 10.7848207 22.1196272
59 -9.3861813 10.7848207
60 -23.5182665 -9.3861813
61 NA -23.5182665
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -22.2680665 -22.9892399
[2,] -21.6197388 -22.2680665
[3,] -22.1806515 -21.6197388
[4,] -20.3667911 -22.1806515
[5,] -20.1482537 -20.3667911
[6,] -17.2926983 -20.1482537
[7,] -18.5019386 -17.2926983
[8,] -17.2781292 -18.5019386
[9,] -14.3788663 -17.2781292
[10,] -14.3133051 -14.3788663
[11,] -10.6637305 -14.3133051
[12,] -15.3331463 -10.6637305
[13,] -17.7079194 -15.3331463
[14,] -14.4007201 -17.7079194
[15,] -13.3517405 -14.4007201
[16,] -7.9684359 -13.3517405
[17,] -7.5604994 -7.9684359
[18,] -9.5054823 -7.5604994
[19,] -5.8049157 -9.5054823
[20,] -5.0108964 -5.8049157
[21,] -1.5944284 -5.0108964
[22,] -1.5507209 -1.5944284
[23,] -3.9182094 -1.5507209
[24,] -6.3512592 -3.9182094
[25,] -5.3605563 -6.3512592
[26,] -1.1136461 -5.3605563
[27,] -2.4685780 -1.1136461
[28,] -1.5580055 -2.4685780
[29,] 3.2279635 -1.5580055
[30,] 3.7160304 3.2279635
[31,] 3.3445168 3.7160304
[32,] 6.4696016 3.3445168
[33,] 6.4113250 6.4696016
[34,] -0.4871723 6.4113250
[35,] -3.6705337 -0.4871723
[36,] -3.3427276 -3.6705337
[37,] -1.4997289 -3.3427276
[38,] -6.7154882 -1.4997289
[39,] -3.9692015 -6.7154882
[40,] -1.0335158 -3.9692015
[41,] 2.5796359 -1.0335158
[42,] 3.2571019 2.5796359
[43,] 5.0126856 3.2571019
[44,] 8.6549756 5.0126856
[45,] 6.3384792 8.6549756
[46,] 10.3231445 6.3384792
[47,] 14.0674186 10.3231445
[48,] 21.0023387 14.0674186
[49,] 19.6983989 21.0023387
[50,] 20.6453943 19.6983989
[51,] 22.3135631 20.6453943
[52,] 28.4253258 22.3135631
[53,] 32.8907733 28.4253258
[54,] 43.2567306 32.8907733
[55,] 49.7691452 43.2567306
[56,] 51.8743888 49.7691452
[57,] 22.1196272 51.8743888
[58,] 10.7848207 22.1196272
[59,] -9.3861813 10.7848207
[60,] -23.5182665 -9.3861813
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -22.2680665 -22.9892399
2 -21.6197388 -22.2680665
3 -22.1806515 -21.6197388
4 -20.3667911 -22.1806515
5 -20.1482537 -20.3667911
6 -17.2926983 -20.1482537
7 -18.5019386 -17.2926983
8 -17.2781292 -18.5019386
9 -14.3788663 -17.2781292
10 -14.3133051 -14.3788663
11 -10.6637305 -14.3133051
12 -15.3331463 -10.6637305
13 -17.7079194 -15.3331463
14 -14.4007201 -17.7079194
15 -13.3517405 -14.4007201
16 -7.9684359 -13.3517405
17 -7.5604994 -7.9684359
18 -9.5054823 -7.5604994
19 -5.8049157 -9.5054823
20 -5.0108964 -5.8049157
21 -1.5944284 -5.0108964
22 -1.5507209 -1.5944284
23 -3.9182094 -1.5507209
24 -6.3512592 -3.9182094
25 -5.3605563 -6.3512592
26 -1.1136461 -5.3605563
27 -2.4685780 -1.1136461
28 -1.5580055 -2.4685780
29 3.2279635 -1.5580055
30 3.7160304 3.2279635
31 3.3445168 3.7160304
32 6.4696016 3.3445168
33 6.4113250 6.4696016
34 -0.4871723 6.4113250
35 -3.6705337 -0.4871723
36 -3.3427276 -3.6705337
37 -1.4997289 -3.3427276
38 -6.7154882 -1.4997289
39 -3.9692015 -6.7154882
40 -1.0335158 -3.9692015
41 2.5796359 -1.0335158
42 3.2571019 2.5796359
43 5.0126856 3.2571019
44 8.6549756 5.0126856
45 6.3384792 8.6549756
46 10.3231445 6.3384792
47 14.0674186 10.3231445
48 21.0023387 14.0674186
49 19.6983989 21.0023387
50 20.6453943 19.6983989
51 22.3135631 20.6453943
52 28.4253258 22.3135631
53 32.8907733 28.4253258
54 43.2567306 32.8907733
55 49.7691452 43.2567306
56 51.8743888 49.7691452
57 22.1196272 51.8743888
58 10.7848207 22.1196272
59 -9.3861813 10.7848207
60 -23.5182665 -9.3861813
> 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/7lksf1229870727.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8tlie1229870727.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9uucg1229870727.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/109jw91229870727.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/11v47w1229870727.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/12emij1229870727.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/13m6cs1229870727.tab")
>
> system("convert tmp/12cz91229870727.ps tmp/12cz91229870727.png")
> system("convert tmp/2syxp1229870727.ps tmp/2syxp1229870727.png")
> system("convert tmp/3v2j21229870727.ps tmp/3v2j21229870727.png")
> system("convert tmp/4yg0l1229870727.ps tmp/4yg0l1229870727.png")
> system("convert tmp/56woy1229870727.ps tmp/56woy1229870727.png")
> system("convert tmp/6od1h1229870727.ps tmp/6od1h1229870727.png")
> system("convert tmp/7lksf1229870727.ps tmp/7lksf1229870727.png")
> system("convert tmp/8tlie1229870727.ps tmp/8tlie1229870727.png")
> system("convert tmp/9uucg1229870727.ps tmp/9uucg1229870727.png")
>
>
> proc.time()
user system elapsed
1.788 1.325 2.350