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(98.6,0,98,0,106.8,0,96.7,0,100.2,0,107.7,0,92,0,98.4,0,107.4,0,117.7,0,105.7,0,97.5,0,99.9,0,98.2,0,104.5,0,100.8,0,101.5,0,103.9,0,99.6,0,98.4,0,112.7,0,118.4,0,108.1,0,105.4,0,114.6,0,106.9,0,115.9,0,109.8,0,101.8,0,114.2,0,110.8,0,108.4,0,127.5,1,128.6,1,116.6,1,127.4,1,105,1,108.3,1,125,1,111.6,1,106.5,1,130.3,1,115,1,116.1,1,134,1,126.5,1,125.8,1,136.4,1,114.9,1,110.9,1,125.5,1,116.8,1,116.8,1,125.5,1,104.2,1,115.1,1,132.8,1,123.3,1,124.8,1,122,1,117.4,1,117.9,1,137.4,1,114.6,1,124.7,1,129.6,1,109.4,1,120.9,1,134.9,1,136.3,1,133.2,1,127.2,1),dim=c(2,72),dimnames=list(c('y','x'),1:72))
> y <- array(NA,dim=c(2,72),dimnames=list(c('y','x'),1:72))
> 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
y x
1 98.6 0
2 98.0 0
3 106.8 0
4 96.7 0
5 100.2 0
6 107.7 0
7 92.0 0
8 98.4 0
9 107.4 0
10 117.7 0
11 105.7 0
12 97.5 0
13 99.9 0
14 98.2 0
15 104.5 0
16 100.8 0
17 101.5 0
18 103.9 0
19 99.6 0
20 98.4 0
21 112.7 0
22 118.4 0
23 108.1 0
24 105.4 0
25 114.6 0
26 106.9 0
27 115.9 0
28 109.8 0
29 101.8 0
30 114.2 0
31 110.8 0
32 108.4 0
33 127.5 1
34 128.6 1
35 116.6 1
36 127.4 1
37 105.0 1
38 108.3 1
39 125.0 1
40 111.6 1
41 106.5 1
42 130.3 1
43 115.0 1
44 116.1 1
45 134.0 1
46 126.5 1
47 125.8 1
48 136.4 1
49 114.9 1
50 110.9 1
51 125.5 1
52 116.8 1
53 116.8 1
54 125.5 1
55 104.2 1
56 115.1 1
57 132.8 1
58 123.3 1
59 124.8 1
60 122.0 1
61 117.4 1
62 117.9 1
63 137.4 1
64 114.6 1
65 124.7 1
66 129.6 1
67 109.4 1
68 120.9 1
69 134.9 1
70 136.3 1
71 133.2 1
72 127.2 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x
105.02 16.90
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-17.7175 -6.4656 0.5344 5.5075 15.4825
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 105.016 1.457 72.061 < 2e-16 ***
x 16.902 1.955 8.645 1.19e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.244 on 70 degrees of freedom
Multiple R-squared: 0.5163, Adjusted R-squared: 0.5094
F-statistic: 74.73 on 1 and 70 DF, p-value: 1.188e-12
> postscript(file="/var/www/html/rcomp/tmp/14g9x1227544760.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/2pqqa1227544760.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3d3an1227544760.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4mciw1227544760.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5wwy81227544760.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 72
Frequency = 1
1 2 3 4 5 6 7
-6.415625 -7.015625 1.784375 -8.315625 -4.815625 2.684375 -13.015625
8 9 10 11 12 13 14
-6.615625 2.384375 12.684375 0.684375 -7.515625 -5.115625 -6.815625
15 16 17 18 19 20 21
-0.515625 -4.215625 -3.515625 -1.115625 -5.415625 -6.615625 7.684375
22 23 24 25 26 27 28
13.384375 3.084375 0.384375 9.584375 1.884375 10.884375 4.784375
29 30 31 32 33 34 35
-3.215625 9.184375 5.784375 3.384375 5.582500 6.682500 -5.317500
36 37 38 39 40 41 42
5.482500 -16.917500 -13.617500 3.082500 -10.317500 -15.417500 8.382500
43 44 45 46 47 48 49
-6.917500 -5.817500 12.082500 4.582500 3.882500 14.482500 -7.017500
50 51 52 53 54 55 56
-11.017500 3.582500 -5.117500 -5.117500 3.582500 -17.717500 -6.817500
57 58 59 60 61 62 63
10.882500 1.382500 2.882500 0.082500 -4.517500 -4.017500 15.482500
64 65 66 67 68 69 70
-7.317500 2.782500 7.682500 -12.517500 -1.017500 12.982500 14.382500
71 72
11.282500 5.282500
> postscript(file="/var/www/html/rcomp/tmp/6gcew1227544760.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 = 72
Frequency = 1
lag(myerror, k = 1) myerror
0 -6.415625 NA
1 -7.015625 -6.415625
2 1.784375 -7.015625
3 -8.315625 1.784375
4 -4.815625 -8.315625
5 2.684375 -4.815625
6 -13.015625 2.684375
7 -6.615625 -13.015625
8 2.384375 -6.615625
9 12.684375 2.384375
10 0.684375 12.684375
11 -7.515625 0.684375
12 -5.115625 -7.515625
13 -6.815625 -5.115625
14 -0.515625 -6.815625
15 -4.215625 -0.515625
16 -3.515625 -4.215625
17 -1.115625 -3.515625
18 -5.415625 -1.115625
19 -6.615625 -5.415625
20 7.684375 -6.615625
21 13.384375 7.684375
22 3.084375 13.384375
23 0.384375 3.084375
24 9.584375 0.384375
25 1.884375 9.584375
26 10.884375 1.884375
27 4.784375 10.884375
28 -3.215625 4.784375
29 9.184375 -3.215625
30 5.784375 9.184375
31 3.384375 5.784375
32 5.582500 3.384375
33 6.682500 5.582500
34 -5.317500 6.682500
35 5.482500 -5.317500
36 -16.917500 5.482500
37 -13.617500 -16.917500
38 3.082500 -13.617500
39 -10.317500 3.082500
40 -15.417500 -10.317500
41 8.382500 -15.417500
42 -6.917500 8.382500
43 -5.817500 -6.917500
44 12.082500 -5.817500
45 4.582500 12.082500
46 3.882500 4.582500
47 14.482500 3.882500
48 -7.017500 14.482500
49 -11.017500 -7.017500
50 3.582500 -11.017500
51 -5.117500 3.582500
52 -5.117500 -5.117500
53 3.582500 -5.117500
54 -17.717500 3.582500
55 -6.817500 -17.717500
56 10.882500 -6.817500
57 1.382500 10.882500
58 2.882500 1.382500
59 0.082500 2.882500
60 -4.517500 0.082500
61 -4.017500 -4.517500
62 15.482500 -4.017500
63 -7.317500 15.482500
64 2.782500 -7.317500
65 7.682500 2.782500
66 -12.517500 7.682500
67 -1.017500 -12.517500
68 12.982500 -1.017500
69 14.382500 12.982500
70 11.282500 14.382500
71 5.282500 11.282500
72 NA 5.282500
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -7.015625 -6.415625
[2,] 1.784375 -7.015625
[3,] -8.315625 1.784375
[4,] -4.815625 -8.315625
[5,] 2.684375 -4.815625
[6,] -13.015625 2.684375
[7,] -6.615625 -13.015625
[8,] 2.384375 -6.615625
[9,] 12.684375 2.384375
[10,] 0.684375 12.684375
[11,] -7.515625 0.684375
[12,] -5.115625 -7.515625
[13,] -6.815625 -5.115625
[14,] -0.515625 -6.815625
[15,] -4.215625 -0.515625
[16,] -3.515625 -4.215625
[17,] -1.115625 -3.515625
[18,] -5.415625 -1.115625
[19,] -6.615625 -5.415625
[20,] 7.684375 -6.615625
[21,] 13.384375 7.684375
[22,] 3.084375 13.384375
[23,] 0.384375 3.084375
[24,] 9.584375 0.384375
[25,] 1.884375 9.584375
[26,] 10.884375 1.884375
[27,] 4.784375 10.884375
[28,] -3.215625 4.784375
[29,] 9.184375 -3.215625
[30,] 5.784375 9.184375
[31,] 3.384375 5.784375
[32,] 5.582500 3.384375
[33,] 6.682500 5.582500
[34,] -5.317500 6.682500
[35,] 5.482500 -5.317500
[36,] -16.917500 5.482500
[37,] -13.617500 -16.917500
[38,] 3.082500 -13.617500
[39,] -10.317500 3.082500
[40,] -15.417500 -10.317500
[41,] 8.382500 -15.417500
[42,] -6.917500 8.382500
[43,] -5.817500 -6.917500
[44,] 12.082500 -5.817500
[45,] 4.582500 12.082500
[46,] 3.882500 4.582500
[47,] 14.482500 3.882500
[48,] -7.017500 14.482500
[49,] -11.017500 -7.017500
[50,] 3.582500 -11.017500
[51,] -5.117500 3.582500
[52,] -5.117500 -5.117500
[53,] 3.582500 -5.117500
[54,] -17.717500 3.582500
[55,] -6.817500 -17.717500
[56,] 10.882500 -6.817500
[57,] 1.382500 10.882500
[58,] 2.882500 1.382500
[59,] 0.082500 2.882500
[60,] -4.517500 0.082500
[61,] -4.017500 -4.517500
[62,] 15.482500 -4.017500
[63,] -7.317500 15.482500
[64,] 2.782500 -7.317500
[65,] 7.682500 2.782500
[66,] -12.517500 7.682500
[67,] -1.017500 -12.517500
[68,] 12.982500 -1.017500
[69,] 14.382500 12.982500
[70,] 11.282500 14.382500
[71,] 5.282500 11.282500
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -7.015625 -6.415625
2 1.784375 -7.015625
3 -8.315625 1.784375
4 -4.815625 -8.315625
5 2.684375 -4.815625
6 -13.015625 2.684375
7 -6.615625 -13.015625
8 2.384375 -6.615625
9 12.684375 2.384375
10 0.684375 12.684375
11 -7.515625 0.684375
12 -5.115625 -7.515625
13 -6.815625 -5.115625
14 -0.515625 -6.815625
15 -4.215625 -0.515625
16 -3.515625 -4.215625
17 -1.115625 -3.515625
18 -5.415625 -1.115625
19 -6.615625 -5.415625
20 7.684375 -6.615625
21 13.384375 7.684375
22 3.084375 13.384375
23 0.384375 3.084375
24 9.584375 0.384375
25 1.884375 9.584375
26 10.884375 1.884375
27 4.784375 10.884375
28 -3.215625 4.784375
29 9.184375 -3.215625
30 5.784375 9.184375
31 3.384375 5.784375
32 5.582500 3.384375
33 6.682500 5.582500
34 -5.317500 6.682500
35 5.482500 -5.317500
36 -16.917500 5.482500
37 -13.617500 -16.917500
38 3.082500 -13.617500
39 -10.317500 3.082500
40 -15.417500 -10.317500
41 8.382500 -15.417500
42 -6.917500 8.382500
43 -5.817500 -6.917500
44 12.082500 -5.817500
45 4.582500 12.082500
46 3.882500 4.582500
47 14.482500 3.882500
48 -7.017500 14.482500
49 -11.017500 -7.017500
50 3.582500 -11.017500
51 -5.117500 3.582500
52 -5.117500 -5.117500
53 3.582500 -5.117500
54 -17.717500 3.582500
55 -6.817500 -17.717500
56 10.882500 -6.817500
57 1.382500 10.882500
58 2.882500 1.382500
59 0.082500 2.882500
60 -4.517500 0.082500
61 -4.017500 -4.517500
62 15.482500 -4.017500
63 -7.317500 15.482500
64 2.782500 -7.317500
65 7.682500 2.782500
66 -12.517500 7.682500
67 -1.017500 -12.517500
68 12.982500 -1.017500
69 14.382500 12.982500
70 11.282500 14.382500
71 5.282500 11.282500
> 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/72c8d1227544760.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8qw561227544760.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/950qn1227544760.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10zqjl1227544760.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/112ukc1227544761.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/12mlys1227544761.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/13qi781227544761.tab")
>
> system("convert tmp/14g9x1227544760.ps tmp/14g9x1227544760.png")
> system("convert tmp/2pqqa1227544760.ps tmp/2pqqa1227544760.png")
> system("convert tmp/3d3an1227544760.ps tmp/3d3an1227544760.png")
> system("convert tmp/4mciw1227544760.ps tmp/4mciw1227544760.png")
> system("convert tmp/5wwy81227544760.ps tmp/5wwy81227544760.png")
> system("convert tmp/6gcew1227544760.ps tmp/6gcew1227544760.png")
> system("convert tmp/72c8d1227544760.ps tmp/72c8d1227544760.png")
> system("convert tmp/8qw561227544760.ps tmp/8qw561227544760.png")
> system("convert tmp/950qn1227544760.ps tmp/950qn1227544760.png")
>
>
> proc.time()
user system elapsed
1.943 1.415 2.449