R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(35.323,186.577,186.59,35.478,244.642,244.665,4.39,248.18,248.18,41.667,253.568,253.568,22.173,171.239,171.242,28.021,413.945,413.971,18.109,216.89,216.89,13.962,227.901,227.901,40.174,259.813,259.823,16.065,148.438,148.438,18.145,240.984,241.013,18.439,206.248,206.248,10.603,108.873,108.908,34.811,267.945,267.952,69.064,314.171,314.219,51.202,235.115,235.115,14.786,203.023,203.027,33.01,365.415,365.415,81.101,350.881,350.933,89.232,263.287,263.304,21.223,738.743,738.751,15.173,959.072,959.073,241.66,483.618,483.828,26.848,212.996,213.016,8.752,177.326,177.341,60.535,352.594,352.622,60.535,352.594,352.622,26.052,217.305,217.307),dim=c(3,28),dimnames=list(c('TFC','TLC','TP'),1:28))
> y <- array(NA,dim=c(3,28),dimnames=list(c('TFC','TLC','TP'),1:28))
> 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 = '3'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
TP TFC TLC
1 186.590 35.323 186.577
2 244.665 35.478 244.642
3 248.180 4.390 248.180
4 253.568 41.667 253.568
5 171.242 22.173 171.239
6 413.971 28.021 413.945
7 216.890 18.109 216.890
8 227.901 13.962 227.901
9 259.823 40.174 259.813
10 148.438 16.065 148.438
11 241.013 18.145 240.984
12 206.248 18.439 206.248
13 108.908 10.603 108.873
14 267.952 34.811 267.945
15 314.219 69.064 314.171
16 235.115 51.202 235.115
17 203.027 14.786 203.023
18 365.415 33.010 365.415
19 350.933 81.101 350.881
20 263.304 89.232 263.287
21 738.751 21.223 738.743
22 959.073 15.173 959.072
23 483.828 241.660 483.618
24 213.016 26.848 212.996
25 177.341 8.752 177.326
26 352.622 60.535 352.594
27 352.622 60.535 352.594
28 217.307 26.052 217.305
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) TFC TLC
-0.0121356 0.0008057 1.0000004
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.042854 -0.008767 -0.001099 0.006973 0.038553
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.214e-02 6.854e-03 -1.771 0.0888 .
TFC 8.057e-04 7.600e-05 10.601 9.75e-11 ***
TLC 1.000e+00 1.927e-05 51897.382 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.01746 on 25 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 1.401e+09 on 2 and 25 DF, p-value: < 2.2e-16
> 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)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.214676802 0.42935360 0.7853232
[2,] 0.104758703 0.20951741 0.8952413
[3,] 0.044840140 0.08968028 0.9551599
[4,] 0.019367406 0.03873481 0.9806326
[5,] 0.007017156 0.01403431 0.9929828
[6,] 0.034511387 0.06902277 0.9654886
[7,] 0.016681643 0.03336329 0.9833184
[8,] 0.217588013 0.43517603 0.7824120
[9,] 0.140750892 0.28150178 0.8592491
[10,] 0.164630016 0.32926003 0.8353700
[11,] 0.219337340 0.43867468 0.7806627
[12,] 0.150290375 0.30058075 0.8497096
[13,] 0.118427347 0.23685469 0.8815727
[14,] 0.087082852 0.17416570 0.9129171
[15,] 0.559857769 0.88028446 0.4401422
[16,] 0.411703571 0.82340714 0.5882964
[17,] 0.693388879 0.61322224 0.3066111
> postscript(file="/var/fisher/rcomp/tmp/19jdp1353323150.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2i6jq1353323150.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3u09b1353323150.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4diz51353323150.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5a4ef1353323150.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 28
Frequency = 1
1 2 3 4 5
-0.0033924689 0.0064611953 0.0085069877 -0.0215284802 -0.0027920588
6 7 8 9 10
0.0154066229 -0.0025346276 0.0008024749 -0.0103279017 -0.0008625170
11 12 13 14 15
0.0264274652 -0.0027965710 0.0385527474 -0.0090100240 0.0043758066
16 17 18 19 20
-0.0292038575 0.0041477837 -0.0145950022 -0.0013357742 -0.0428544239
21 22 23 24 25
0.0027636499 0.0005566264 0.0272553858 0.0104259401 0.0200187750
26 27 28
-0.0087667130 -0.0087667130 -0.0069343278
> postscript(file="/var/fisher/rcomp/tmp/6r4ui1353323150.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 = 28
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.0033924689 NA
1 0.0064611953 -0.0033924689
2 0.0085069877 0.0064611953
3 -0.0215284802 0.0085069877
4 -0.0027920588 -0.0215284802
5 0.0154066229 -0.0027920588
6 -0.0025346276 0.0154066229
7 0.0008024749 -0.0025346276
8 -0.0103279017 0.0008024749
9 -0.0008625170 -0.0103279017
10 0.0264274652 -0.0008625170
11 -0.0027965710 0.0264274652
12 0.0385527474 -0.0027965710
13 -0.0090100240 0.0385527474
14 0.0043758066 -0.0090100240
15 -0.0292038575 0.0043758066
16 0.0041477837 -0.0292038575
17 -0.0145950022 0.0041477837
18 -0.0013357742 -0.0145950022
19 -0.0428544239 -0.0013357742
20 0.0027636499 -0.0428544239
21 0.0005566264 0.0027636499
22 0.0272553858 0.0005566264
23 0.0104259401 0.0272553858
24 0.0200187750 0.0104259401
25 -0.0087667130 0.0200187750
26 -0.0087667130 -0.0087667130
27 -0.0069343278 -0.0087667130
28 NA -0.0069343278
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.0064611953 -0.0033924689
[2,] 0.0085069877 0.0064611953
[3,] -0.0215284802 0.0085069877
[4,] -0.0027920588 -0.0215284802
[5,] 0.0154066229 -0.0027920588
[6,] -0.0025346276 0.0154066229
[7,] 0.0008024749 -0.0025346276
[8,] -0.0103279017 0.0008024749
[9,] -0.0008625170 -0.0103279017
[10,] 0.0264274652 -0.0008625170
[11,] -0.0027965710 0.0264274652
[12,] 0.0385527474 -0.0027965710
[13,] -0.0090100240 0.0385527474
[14,] 0.0043758066 -0.0090100240
[15,] -0.0292038575 0.0043758066
[16,] 0.0041477837 -0.0292038575
[17,] -0.0145950022 0.0041477837
[18,] -0.0013357742 -0.0145950022
[19,] -0.0428544239 -0.0013357742
[20,] 0.0027636499 -0.0428544239
[21,] 0.0005566264 0.0027636499
[22,] 0.0272553858 0.0005566264
[23,] 0.0104259401 0.0272553858
[24,] 0.0200187750 0.0104259401
[25,] -0.0087667130 0.0200187750
[26,] -0.0087667130 -0.0087667130
[27,] -0.0069343278 -0.0087667130
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.0064611953 -0.0033924689
2 0.0085069877 0.0064611953
3 -0.0215284802 0.0085069877
4 -0.0027920588 -0.0215284802
5 0.0154066229 -0.0027920588
6 -0.0025346276 0.0154066229
7 0.0008024749 -0.0025346276
8 -0.0103279017 0.0008024749
9 -0.0008625170 -0.0103279017
10 0.0264274652 -0.0008625170
11 -0.0027965710 0.0264274652
12 0.0385527474 -0.0027965710
13 -0.0090100240 0.0385527474
14 0.0043758066 -0.0090100240
15 -0.0292038575 0.0043758066
16 0.0041477837 -0.0292038575
17 -0.0145950022 0.0041477837
18 -0.0013357742 -0.0145950022
19 -0.0428544239 -0.0013357742
20 0.0027636499 -0.0428544239
21 0.0005566264 0.0027636499
22 0.0272553858 0.0005566264
23 0.0104259401 0.0272553858
24 0.0200187750 0.0104259401
25 -0.0087667130 0.0200187750
26 -0.0087667130 -0.0087667130
27 -0.0069343278 -0.0087667130
> 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/fisher/rcomp/tmp/7p6ii1353323150.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/fisher/rcomp/tmp/86rxg1353323150.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/fisher/rcomp/tmp/93wh91353323150.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/fisher/rcomp/tmp/10cw051353323150.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11g20r1353323150.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/129lqr1353323150.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13aszx1353323150.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14lsxe1353323150.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/fisher/rcomp/tmp/15hugf1353323151.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/fisher/rcomp/tmp/16j8vj1353323151.tab")
+ }
>
> try(system("convert tmp/19jdp1353323150.ps tmp/19jdp1353323150.png",intern=TRUE))
character(0)
> try(system("convert tmp/2i6jq1353323150.ps tmp/2i6jq1353323150.png",intern=TRUE))
character(0)
> try(system("convert tmp/3u09b1353323150.ps tmp/3u09b1353323150.png",intern=TRUE))
character(0)
> try(system("convert tmp/4diz51353323150.ps tmp/4diz51353323150.png",intern=TRUE))
character(0)
> try(system("convert tmp/5a4ef1353323150.ps tmp/5a4ef1353323150.png",intern=TRUE))
character(0)
> try(system("convert tmp/6r4ui1353323150.ps tmp/6r4ui1353323150.png",intern=TRUE))
character(0)
> try(system("convert tmp/7p6ii1353323150.ps tmp/7p6ii1353323150.png",intern=TRUE))
character(0)
> try(system("convert tmp/86rxg1353323150.ps tmp/86rxg1353323150.png",intern=TRUE))
character(0)
> try(system("convert tmp/93wh91353323150.ps tmp/93wh91353323150.png",intern=TRUE))
character(0)
> try(system("convert tmp/10cw051353323150.ps tmp/10cw051353323150.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.033 1.340 7.412