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)
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
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(127,13,1235,115,12,1080,127,7,845,150,9,1522,156,6,1047,182,11,1979,156,12,1822,132,10,1253,137,9,1297,113,9,946,137,15,1713,117,11,1024,137,8,1147,153,6,1092,117,13,1152,126,10,1336,170,14,1131,182,8,1550,162,11,1884,184,10,2041,143,6,845,159,9,1483,108,14,1055,175,8,1545,108,6,729,179,9,1792,111,15,1175,187,8,1593,111,7,785,115,7,744,194,5,1356,168,7,1262),dim=c(3,32),dimnames=list(c('ouderdom','aanbieders','veilingprijs
'),1:32))
> y <- array(NA,dim=c(3,32),dimnames=list(c('ouderdom','aanbieders','veilingprijs
'),1:32))
> 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'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '3'
> #'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, 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
veilingprijs\r ouderdom aanbieders
1 1235 127 13
2 1080 115 12
3 845 127 7
4 1522 150 9
5 1047 156 6
6 1979 182 11
7 1822 156 12
8 1253 132 10
9 1297 137 9
10 946 113 9
11 1713 137 15
12 1024 117 11
13 1147 137 8
14 1092 153 6
15 1152 117 13
16 1336 126 10
17 1131 170 14
18 1550 182 8
19 1884 162 11
20 2041 184 10
21 845 143 6
22 1483 159 9
23 1055 108 14
24 1545 175 8
25 729 108 6
26 1792 179 9
27 1175 111 15
28 1593 187 8
29 785 111 7
30 744 115 7
31 1356 194 5
32 1262 168 7
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) ouderdom aanbieders
-921.50 11.09 64.03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-728.60 -85.31 -13.83 130.73 305.17
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -921.503 258.686 -3.562 0.00129 **
ouderdom 11.087 1.347 8.233 4.46e-09 ***
aanbieders 64.027 12.991 4.929 3.09e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 198.7 on 29 degrees of freedom
Multiple R-squared: 0.7249, Adjusted R-squared: 0.7059
F-statistic: 38.2 on 2 and 29 DF, p-value: 7.469e-09
> 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,] 0.279423333 0.558846666 0.72057667
[2,] 0.215157141 0.430314281 0.78484286
[3,] 0.131107349 0.262214699 0.86889265
[4,] 0.097315365 0.194630731 0.90268463
[5,] 0.063019347 0.126038693 0.93698065
[6,] 0.035781703 0.071563406 0.96421830
[7,] 0.017684647 0.035369294 0.98231535
[8,] 0.007874030 0.015748060 0.99212597
[9,] 0.003928661 0.007857321 0.99607134
[10,] 0.002020492 0.004040985 0.99797951
[11,] 0.004975121 0.009950241 0.99502488
[12,] 0.910185754 0.179628491 0.08981425
[13,] 0.861875163 0.276249674 0.13812484
[14,] 0.928196562 0.143606875 0.07180344
[15,] 0.976004725 0.047990550 0.02399527
[16,] 0.979473835 0.041052330 0.02052617
[17,] 0.966439421 0.067121158 0.03356058
[18,] 0.939680934 0.120638133 0.06031907
[19,] 0.886600059 0.226799882 0.11339994
[20,] 0.812320164 0.375359672 0.18767984
[21,] 0.962743691 0.074512619 0.03725631
> postscript(file="/var/wessaorg/rcomp/tmp/1rg2m1356088460.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2eolz1356088460.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3tuxd1356088460.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4r5xj1356088460.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ixwz1356088460.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 = 32
Frequency = 1
1 2 3 4 5 6 7
-83.85133 -41.78463 -89.69017 204.26309 -145.17626 178.43647 245.66259
8 9 10 11 12 13 14
70.79598 123.38958 38.46925 155.22843 -55.93108 37.41644 -66.91630
15 16 17 18 19 20 21
-55.98479 220.31590 -728.60427 -58.48295 305.16953 282.29002 -203.04977
22 23 24 25 26 27 28
65.48321 -117.23177 14.12362 68.98309 152.75015 -94.51859 -70.91622
29 30 31 32
27.69628 -57.65034 -193.44222 -127.24295
> postscript(file="/var/wessaorg/rcomp/tmp/6bi7z1356088460.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 = 32
Frequency = 1
lag(myerror, k = 1) myerror
0 -83.85133 NA
1 -41.78463 -83.85133
2 -89.69017 -41.78463
3 204.26309 -89.69017
4 -145.17626 204.26309
5 178.43647 -145.17626
6 245.66259 178.43647
7 70.79598 245.66259
8 123.38958 70.79598
9 38.46925 123.38958
10 155.22843 38.46925
11 -55.93108 155.22843
12 37.41644 -55.93108
13 -66.91630 37.41644
14 -55.98479 -66.91630
15 220.31590 -55.98479
16 -728.60427 220.31590
17 -58.48295 -728.60427
18 305.16953 -58.48295
19 282.29002 305.16953
20 -203.04977 282.29002
21 65.48321 -203.04977
22 -117.23177 65.48321
23 14.12362 -117.23177
24 68.98309 14.12362
25 152.75015 68.98309
26 -94.51859 152.75015
27 -70.91622 -94.51859
28 27.69628 -70.91622
29 -57.65034 27.69628
30 -193.44222 -57.65034
31 -127.24295 -193.44222
32 NA -127.24295
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -41.78463 -83.85133
[2,] -89.69017 -41.78463
[3,] 204.26309 -89.69017
[4,] -145.17626 204.26309
[5,] 178.43647 -145.17626
[6,] 245.66259 178.43647
[7,] 70.79598 245.66259
[8,] 123.38958 70.79598
[9,] 38.46925 123.38958
[10,] 155.22843 38.46925
[11,] -55.93108 155.22843
[12,] 37.41644 -55.93108
[13,] -66.91630 37.41644
[14,] -55.98479 -66.91630
[15,] 220.31590 -55.98479
[16,] -728.60427 220.31590
[17,] -58.48295 -728.60427
[18,] 305.16953 -58.48295
[19,] 282.29002 305.16953
[20,] -203.04977 282.29002
[21,] 65.48321 -203.04977
[22,] -117.23177 65.48321
[23,] 14.12362 -117.23177
[24,] 68.98309 14.12362
[25,] 152.75015 68.98309
[26,] -94.51859 152.75015
[27,] -70.91622 -94.51859
[28,] 27.69628 -70.91622
[29,] -57.65034 27.69628
[30,] -193.44222 -57.65034
[31,] -127.24295 -193.44222
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -41.78463 -83.85133
2 -89.69017 -41.78463
3 204.26309 -89.69017
4 -145.17626 204.26309
5 178.43647 -145.17626
6 245.66259 178.43647
7 70.79598 245.66259
8 123.38958 70.79598
9 38.46925 123.38958
10 155.22843 38.46925
11 -55.93108 155.22843
12 37.41644 -55.93108
13 -66.91630 37.41644
14 -55.98479 -66.91630
15 220.31590 -55.98479
16 -728.60427 220.31590
17 -58.48295 -728.60427
18 305.16953 -58.48295
19 282.29002 305.16953
20 -203.04977 282.29002
21 65.48321 -203.04977
22 -117.23177 65.48321
23 14.12362 -117.23177
24 68.98309 14.12362
25 152.75015 68.98309
26 -94.51859 152.75015
27 -70.91622 -94.51859
28 27.69628 -70.91622
29 -57.65034 27.69628
30 -193.44222 -57.65034
31 -127.24295 -193.44222
> 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/wessaorg/rcomp/tmp/7sfhi1356088460.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8ya4v1356088460.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/99e2t1356088460.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/1030ax1356088460.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11qh351356088460.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/wessaorg/rcomp/tmp/12k2cu1356088460.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/wessaorg/rcomp/tmp/13cktu1356088460.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/wessaorg/rcomp/tmp/14dy0c1356088460.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/wessaorg/rcomp/tmp/15anz11356088460.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/wessaorg/rcomp/tmp/16h8ye1356088460.tab")
+ }
>
> try(system("convert tmp/1rg2m1356088460.ps tmp/1rg2m1356088460.png",intern=TRUE))
character(0)
> try(system("convert tmp/2eolz1356088460.ps tmp/2eolz1356088460.png",intern=TRUE))
character(0)
> try(system("convert tmp/3tuxd1356088460.ps tmp/3tuxd1356088460.png",intern=TRUE))
character(0)
> try(system("convert tmp/4r5xj1356088460.ps tmp/4r5xj1356088460.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ixwz1356088460.ps tmp/5ixwz1356088460.png",intern=TRUE))
character(0)
> try(system("convert tmp/6bi7z1356088460.ps tmp/6bi7z1356088460.png",intern=TRUE))
character(0)
> try(system("convert tmp/7sfhi1356088460.ps tmp/7sfhi1356088460.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ya4v1356088460.ps tmp/8ya4v1356088460.png",intern=TRUE))
character(0)
> try(system("convert tmp/99e2t1356088460.ps tmp/99e2t1356088460.png",intern=TRUE))
character(0)
> try(system("convert tmp/1030ax1356088460.ps tmp/1030ax1356088460.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.682 1.024 6.712