R version 2.12.1 (2010-12-16)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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.
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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
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Type 'q()' to quit R.
> x <- array(list(34,71,152,74,99,765,36,54,99,79,128,1,37,71,92,80,57,2,17,75,138,37,95,232,25,61,106,55,205,230,21,686,95,46,51,828,21,88,145,46,59,2,29,7,181,63,194,906,36,90,190,78,27,2,24,40,150,53,9,1,22,50,186,48,24,1,21,14,174,45,189,1,30,63,151,66,37,820,22,91,112,48,81,107,37,89,143,81,72,1,31,83,120,68,81,870,19,22,169,42,90,1,31,24,135,69,216,731,18,74,161,40,216,2,30,24,98,66,13,521,21,12,142,46,153,2,17,23,190,36,185,2,38,49,169,84,131,100,30,68,130,65,136,34,35,87,160,77,182,325,26,69,176,57,139,2,29,16,111,64,42,2,23,78,165,50,213,477,32,89,117,69,184,1,34,35,122,75,44,2),dim=c(6,30),dimnames=list(c('Y_t','X_1t','X_2t','X_3t','X_4t','X_5t'),1:30))
> y <- array(NA,dim=c(6,30),dimnames=list(c('Y_t','X_1t','X_2t','X_3t','X_4t','X_5t'),1:30))
> 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'
> 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
Y_t X_1t X_2t X_3t X_4t X_5t
1 34 71 152 74 99 765
2 36 54 99 79 128 1
3 37 71 92 80 57 2
4 17 75 138 37 95 232
5 25 61 106 55 205 230
6 21 686 95 46 51 828
7 21 88 145 46 59 2
8 29 7 181 63 194 906
9 36 90 190 78 27 2
10 24 40 150 53 9 1
11 22 50 186 48 24 1
12 21 14 174 45 189 1
13 30 63 151 66 37 820
14 22 91 112 48 81 107
15 37 89 143 81 72 1
16 31 83 120 68 81 870
17 19 22 169 42 90 1
18 31 24 135 69 216 731
19 18 74 161 40 216 2
20 30 24 98 66 13 521
21 21 12 142 46 153 2
22 17 23 190 36 185 2
23 38 49 169 84 131 100
24 30 68 130 65 136 34
25 35 87 160 77 182 325
26 26 69 176 57 139 2
27 29 16 111 64 42 2
28 23 78 165 50 213 477
29 32 89 117 69 184 1
30 34 35 122 75 44 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X_1t X_2t X_3t X_4t X_5t
-0.1873581 0.0003319 0.0014476 0.4562736 0.0003834 -0.0001591
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.46540 -0.18466 -0.04234 0.21147 0.50720
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1873581 0.4333189 -0.432 0.669
X_1t 0.0003319 0.0005189 0.640 0.529
X_2t 0.0014476 0.0019371 0.747 0.462
X_3t 0.4562736 0.0038957 117.123 <2e-16 ***
X_4t 0.0003834 0.0008135 0.471 0.642
X_5t -0.0001591 0.0001729 -0.920 0.367
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2833 on 24 degrees of freedom
Multiple R-squared: 0.9985, Adjusted R-squared: 0.9982
F-statistic: 3229 on 5 and 24 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)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.31846479 0.6369296 0.6815352
[2,] 0.46315166 0.9263033 0.5368483
[3,] 0.30302749 0.6060550 0.6969725
[4,] 0.42172739 0.8434548 0.5782726
[5,] 0.30824902 0.6164980 0.6917510
[6,] 0.23496094 0.4699219 0.7650391
[7,] 0.18880109 0.3776022 0.8111989
[8,] 0.11153116 0.2230623 0.8884688
[9,] 0.09612356 0.1922471 0.9038764
[10,] 0.20313086 0.4062617 0.7968691
[11,] 0.63983968 0.7203206 0.3601603
[12,] 0.64789687 0.7042063 0.3521031
[13,] 0.89035018 0.2192996 0.1096498
> postscript(file="/var/www/rcomp/tmp/1ck9m1321626013.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2lhel1321626013.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3g4b11321626013.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4gj1p1321626013.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5x85r1321626013.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 30
Frequency = 1
1 2 3 4 5 6
0.263288216 -0.068399254 0.507197963 0.081072744 -0.123373163 -0.054217117
7 8 9 10 11 12
-0.062631672 0.247571241 0.283077933 -0.228844173 -0.008658937 0.326221420
13 14 15 16 17 18
-0.049899317 0.079868564 -0.034786008 0.066877913 -0.262419694 -0.465403153
19 20 21 22 23 24
-0.403697147 0.001391109 -0.069104688 0.408227883 -0.434836036 0.272092962
25 26 27 28 29 30
-0.224255660 -0.150881163 -0.195925189 0.103180099 0.435194472 -0.237930148
> postscript(file="/var/www/rcomp/tmp/64jnh1321626013.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 = 30
Frequency = 1
lag(myerror, k = 1) myerror
0 0.263288216 NA
1 -0.068399254 0.263288216
2 0.507197963 -0.068399254
3 0.081072744 0.507197963
4 -0.123373163 0.081072744
5 -0.054217117 -0.123373163
6 -0.062631672 -0.054217117
7 0.247571241 -0.062631672
8 0.283077933 0.247571241
9 -0.228844173 0.283077933
10 -0.008658937 -0.228844173
11 0.326221420 -0.008658937
12 -0.049899317 0.326221420
13 0.079868564 -0.049899317
14 -0.034786008 0.079868564
15 0.066877913 -0.034786008
16 -0.262419694 0.066877913
17 -0.465403153 -0.262419694
18 -0.403697147 -0.465403153
19 0.001391109 -0.403697147
20 -0.069104688 0.001391109
21 0.408227883 -0.069104688
22 -0.434836036 0.408227883
23 0.272092962 -0.434836036
24 -0.224255660 0.272092962
25 -0.150881163 -0.224255660
26 -0.195925189 -0.150881163
27 0.103180099 -0.195925189
28 0.435194472 0.103180099
29 -0.237930148 0.435194472
30 NA -0.237930148
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.068399254 0.263288216
[2,] 0.507197963 -0.068399254
[3,] 0.081072744 0.507197963
[4,] -0.123373163 0.081072744
[5,] -0.054217117 -0.123373163
[6,] -0.062631672 -0.054217117
[7,] 0.247571241 -0.062631672
[8,] 0.283077933 0.247571241
[9,] -0.228844173 0.283077933
[10,] -0.008658937 -0.228844173
[11,] 0.326221420 -0.008658937
[12,] -0.049899317 0.326221420
[13,] 0.079868564 -0.049899317
[14,] -0.034786008 0.079868564
[15,] 0.066877913 -0.034786008
[16,] -0.262419694 0.066877913
[17,] -0.465403153 -0.262419694
[18,] -0.403697147 -0.465403153
[19,] 0.001391109 -0.403697147
[20,] -0.069104688 0.001391109
[21,] 0.408227883 -0.069104688
[22,] -0.434836036 0.408227883
[23,] 0.272092962 -0.434836036
[24,] -0.224255660 0.272092962
[25,] -0.150881163 -0.224255660
[26,] -0.195925189 -0.150881163
[27,] 0.103180099 -0.195925189
[28,] 0.435194472 0.103180099
[29,] -0.237930148 0.435194472
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.068399254 0.263288216
2 0.507197963 -0.068399254
3 0.081072744 0.507197963
4 -0.123373163 0.081072744
5 -0.054217117 -0.123373163
6 -0.062631672 -0.054217117
7 0.247571241 -0.062631672
8 0.283077933 0.247571241
9 -0.228844173 0.283077933
10 -0.008658937 -0.228844173
11 0.326221420 -0.008658937
12 -0.049899317 0.326221420
13 0.079868564 -0.049899317
14 -0.034786008 0.079868564
15 0.066877913 -0.034786008
16 -0.262419694 0.066877913
17 -0.465403153 -0.262419694
18 -0.403697147 -0.465403153
19 0.001391109 -0.403697147
20 -0.069104688 0.001391109
21 0.408227883 -0.069104688
22 -0.434836036 0.408227883
23 0.272092962 -0.434836036
24 -0.224255660 0.272092962
25 -0.150881163 -0.224255660
26 -0.195925189 -0.150881163
27 0.103180099 -0.195925189
28 0.435194472 0.103180099
29 -0.237930148 0.435194472
> 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/rcomp/tmp/7kpxl1321626013.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/rcomp/tmp/8ulh71321626013.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/rcomp/tmp/96qtr1321626013.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/rcomp/tmp/1088do1321626013.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/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11doqu1321626013.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/127rj51321626013.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13zow41321626013.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/148kls1321626013.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/rcomp/tmp/159jd31321626013.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/rcomp/tmp/165vbb1321626013.tab")
+ }
>
> try(system("convert tmp/1ck9m1321626013.ps tmp/1ck9m1321626013.png",intern=TRUE))
character(0)
> try(system("convert tmp/2lhel1321626013.ps tmp/2lhel1321626013.png",intern=TRUE))
character(0)
> try(system("convert tmp/3g4b11321626013.ps tmp/3g4b11321626013.png",intern=TRUE))
character(0)
> try(system("convert tmp/4gj1p1321626013.ps tmp/4gj1p1321626013.png",intern=TRUE))
character(0)
> try(system("convert tmp/5x85r1321626013.ps tmp/5x85r1321626013.png",intern=TRUE))
character(0)
> try(system("convert tmp/64jnh1321626013.ps tmp/64jnh1321626013.png",intern=TRUE))
character(0)
> try(system("convert tmp/7kpxl1321626013.ps tmp/7kpxl1321626013.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ulh71321626013.ps tmp/8ulh71321626013.png",intern=TRUE))
character(0)
> try(system("convert tmp/96qtr1321626013.ps tmp/96qtr1321626013.png",intern=TRUE))
character(0)
> try(system("convert tmp/1088do1321626013.ps tmp/1088do1321626013.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.248 0.532 3.812