R version 2.9.0 (2009-04-17)
Copyright (C) 2009 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(70.5,4,370,67,53.5,315,6166,54,65,4,684,62,76.5,17,449,73,70,8,643,68,71,56,1551,68,60.5,15,616,60,51.5,503,36660,50,78,26,403,74,76,26,346,73,57.5,44,2471,57,61,24,7427,59,64.5,23,2992,64,78.5,38,233,75,79,18,609,76,61,96,7615,59,70,90,370,67,70,49,1066,67,72,66,600,68,64.5,21,4873,63,54.5,592,3485,53,56.5,73,2364,56,64.5,14,1016,62,64.5,88,1062,62,73,39,480,69,72,6,559,69,69,32,259,64,64,11,1340,61,78.5,26,275,75,53,23,12550,52,75,32,965,72,52.5,NA,25229,50,68.5,11,4883,66,70,5,1189,68,70.5,3,226,66,76,3,611,73,75.5,13,404,72,74.5,56,576,71,65,29,3096,63,54,NA,23193,52),dim=c(4,40),dimnames=list(c('Yt','X1t','X2t','X4t'),1:40))
> y <- array(NA,dim=c(4,40),dimnames=list(c('Yt','X1t','X2t','X4t'),1:40))
> 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)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
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
Yt X1t X2t X4t
1 70.5 4 370 67
2 53.5 315 6166 54
3 65.0 4 684 62
4 76.5 17 449 73
5 70.0 8 643 68
6 71.0 56 1551 68
7 60.5 15 616 60
8 51.5 503 36660 50
9 78.0 26 403 74
10 76.0 26 346 73
11 57.5 44 2471 57
12 61.0 24 7427 59
13 64.5 23 2992 64
14 78.5 38 233 75
15 79.0 18 609 76
16 61.0 96 7615 59
17 70.0 90 370 67
18 70.0 49 1066 67
19 72.0 66 600 68
20 64.5 21 4873 63
21 54.5 592 3485 53
22 56.5 73 2364 56
23 64.5 14 1016 62
24 64.5 88 1062 62
25 73.0 39 480 69
26 72.0 6 559 69
27 69.0 32 259 64
28 64.0 11 1340 61
29 78.5 26 275 75
30 53.0 23 12550 52
31 75.0 32 965 72
32 52.5 NA 25229 50
33 68.5 11 4883 66
34 70.0 5 1189 68
35 70.5 3 226 66
36 76.0 3 611 73
37 75.5 13 404 72
38 74.5 56 576 71
39 65.0 29 3096 63
40 54.0 NA 23193 52
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X1t X2t X4t
-6.076e+00 3.513e-04 9.304e-06 1.132e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.8789 -0.5379 0.0965 0.3754 2.6434
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.076e+00 2.007e+00 -3.028 0.00468 **
X1t 3.513e-04 1.606e-03 0.219 0.82818
X2t 9.304e-06 3.375e-05 0.276 0.78445
X4t 1.132e+00 2.954e-02 38.308 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9409 on 34 degrees of freedom
(2 observations deleted due to missingness)
Multiple R-squared: 0.9866, Adjusted R-squared: 0.9855
F-statistic: 836.8 on 3 and 34 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,] 0.7239237 0.5521527 0.27607634
[2,] 0.6442824 0.7114353 0.35571764
[3,] 0.5039388 0.9921223 0.49606117
[4,] 0.4178831 0.8357662 0.58211692
[5,] 0.3961691 0.7923381 0.60383094
[6,] 0.2863592 0.5727184 0.71364078
[7,] 0.6780099 0.6439802 0.32199010
[8,] 0.5763665 0.8472670 0.42363352
[9,] 0.5622397 0.8755205 0.43776026
[10,] 0.4890384 0.9780769 0.51096156
[11,] 0.4551547 0.9103093 0.54484534
[12,] 0.3816289 0.7632578 0.61837110
[13,] 0.4708489 0.9416978 0.52915111
[14,] 0.4276764 0.8553528 0.57232358
[15,] 0.4101792 0.8203583 0.58982084
[16,] 0.5882972 0.8234056 0.41170279
[17,] 0.5889159 0.8221681 0.41108406
[18,] 0.6688559 0.6622882 0.33114412
[19,] 0.6234417 0.7531166 0.37655831
[20,] 0.5153385 0.9693230 0.48466152
[21,] 0.8349015 0.3301971 0.16509853
[22,] 0.7600177 0.4799645 0.23998227
[23,] 0.6480717 0.7038566 0.35192830
[24,] 0.5329500 0.9341000 0.46704999
[25,] 0.3653343 0.7306686 0.63466570
[26,] 0.5642397 0.8715206 0.43576028
[27,] 0.9353285 0.1293431 0.06467154
> postscript(file="/var/www/html/rcomp/tmp/1oej81290525016.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$resid)
Warning message:
In x[, 1] - mysum$resid :
longer object length is not a multiple of shorter object length
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2oej81290525016.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3hn0b1290525016.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4hn0b1290525016.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5hn0b1290525016.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 38
Frequency = 1
1 2 3 4 5 6
0.757516860 -1.695533062 0.912339359 -0.037077944 -0.877977149 0.096711827
7 8 9 10 11 12
-1.327794859 0.480901666 0.328639424 -0.539281468 -0.960595361 0.237223183
13 14 15 16 17 18
-1.878906598 -0.305543466 -0.933564239 0.210179434 0.227303849 0.235232206
19 20 21 22 23 24
1.102046703 -0.764155835 0.363645499 -0.838239166 0.405737321 0.379312099
25 26 27 28 29 30
0.981099866 -0.008041777 2.643359155 1.035325582 -0.301718461 0.110752074
31 33 34 35 36 37
-0.415599686 -0.155382090 -0.882003140 1.890756722 -0.533666780 0.096294774
38 39
0.211136781 -0.250433304
> postscript(file="/var/www/html/rcomp/tmp/6sxzw1290525016.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 = 38
Frequency = 1
lag(myerror, k = 1) myerror
0 0.757516860 NA
1 -1.695533062 0.757516860
2 0.912339359 -1.695533062
3 -0.037077944 0.912339359
4 -0.877977149 -0.037077944
5 0.096711827 -0.877977149
6 -1.327794859 0.096711827
7 0.480901666 -1.327794859
8 0.328639424 0.480901666
9 -0.539281468 0.328639424
10 -0.960595361 -0.539281468
11 0.237223183 -0.960595361
12 -1.878906598 0.237223183
13 -0.305543466 -1.878906598
14 -0.933564239 -0.305543466
15 0.210179434 -0.933564239
16 0.227303849 0.210179434
17 0.235232206 0.227303849
18 1.102046703 0.235232206
19 -0.764155835 1.102046703
20 0.363645499 -0.764155835
21 -0.838239166 0.363645499
22 0.405737321 -0.838239166
23 0.379312099 0.405737321
24 0.981099866 0.379312099
25 -0.008041777 0.981099866
26 2.643359155 -0.008041777
27 1.035325582 2.643359155
28 -0.301718461 1.035325582
29 0.110752074 -0.301718461
30 -0.415599686 0.110752074
31 -0.155382090 -0.415599686
32 -0.882003140 -0.155382090
33 1.890756722 -0.882003140
34 -0.533666780 1.890756722
35 0.096294774 -0.533666780
36 0.211136781 0.096294774
37 -0.250433304 0.211136781
38 NA -0.250433304
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.695533062 0.757516860
[2,] 0.912339359 -1.695533062
[3,] -0.037077944 0.912339359
[4,] -0.877977149 -0.037077944
[5,] 0.096711827 -0.877977149
[6,] -1.327794859 0.096711827
[7,] 0.480901666 -1.327794859
[8,] 0.328639424 0.480901666
[9,] -0.539281468 0.328639424
[10,] -0.960595361 -0.539281468
[11,] 0.237223183 -0.960595361
[12,] -1.878906598 0.237223183
[13,] -0.305543466 -1.878906598
[14,] -0.933564239 -0.305543466
[15,] 0.210179434 -0.933564239
[16,] 0.227303849 0.210179434
[17,] 0.235232206 0.227303849
[18,] 1.102046703 0.235232206
[19,] -0.764155835 1.102046703
[20,] 0.363645499 -0.764155835
[21,] -0.838239166 0.363645499
[22,] 0.405737321 -0.838239166
[23,] 0.379312099 0.405737321
[24,] 0.981099866 0.379312099
[25,] -0.008041777 0.981099866
[26,] 2.643359155 -0.008041777
[27,] 1.035325582 2.643359155
[28,] -0.301718461 1.035325582
[29,] 0.110752074 -0.301718461
[30,] -0.415599686 0.110752074
[31,] -0.155382090 -0.415599686
[32,] -0.882003140 -0.155382090
[33,] 1.890756722 -0.882003140
[34,] -0.533666780 1.890756722
[35,] 0.096294774 -0.533666780
[36,] 0.211136781 0.096294774
[37,] -0.250433304 0.211136781
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.695533062 0.757516860
2 0.912339359 -1.695533062
3 -0.037077944 0.912339359
4 -0.877977149 -0.037077944
5 0.096711827 -0.877977149
6 -1.327794859 0.096711827
7 0.480901666 -1.327794859
8 0.328639424 0.480901666
9 -0.539281468 0.328639424
10 -0.960595361 -0.539281468
11 0.237223183 -0.960595361
12 -1.878906598 0.237223183
13 -0.305543466 -1.878906598
14 -0.933564239 -0.305543466
15 0.210179434 -0.933564239
16 0.227303849 0.210179434
17 0.235232206 0.227303849
18 1.102046703 0.235232206
19 -0.764155835 1.102046703
20 0.363645499 -0.764155835
21 -0.838239166 0.363645499
22 0.405737321 -0.838239166
23 0.379312099 0.405737321
24 0.981099866 0.379312099
25 -0.008041777 0.981099866
26 2.643359155 -0.008041777
27 1.035325582 2.643359155
28 -0.301718461 1.035325582
29 0.110752074 -0.301718461
30 -0.415599686 0.110752074
31 -0.155382090 -0.415599686
32 -0.882003140 -0.155382090
33 1.890756722 -0.882003140
34 -0.533666780 1.890756722
35 0.096294774 -0.533666780
36 0.211136781 0.096294774
37 -0.250433304 0.211136781
> 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/7k6hh1290525016.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/8k6hh1290525016.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/9k6hh1290525016.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
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10dxg21290525016.ps",horizontal=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/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11ygeq1290525016.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/122gve1290525016.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13qiv01290525017.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14u0c51290525017.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/html/rcomp/tmp/15x1st1290525017.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/html/rcomp/tmp/161j9h1290525017.tab")
+ }
>
> try(system("convert tmp/1oej81290525016.ps tmp/1oej81290525016.png",intern=TRUE))
character(0)
> try(system("convert tmp/2oej81290525016.ps tmp/2oej81290525016.png",intern=TRUE))
character(0)
> try(system("convert tmp/3hn0b1290525016.ps tmp/3hn0b1290525016.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hn0b1290525016.ps tmp/4hn0b1290525016.png",intern=TRUE))
character(0)
> try(system("convert tmp/5hn0b1290525016.ps tmp/5hn0b1290525016.png",intern=TRUE))
character(0)
> try(system("convert tmp/6sxzw1290525016.ps tmp/6sxzw1290525016.png",intern=TRUE))
character(0)
> try(system("convert tmp/7k6hh1290525016.ps tmp/7k6hh1290525016.png",intern=TRUE))
character(0)
> try(system("convert tmp/8k6hh1290525016.ps tmp/8k6hh1290525016.png",intern=TRUE))
character(0)
> try(system("convert tmp/9k6hh1290525016.ps tmp/9k6hh1290525016.png",intern=TRUE))
character(0)
> try(system("convert tmp/10dxg21290525016.ps tmp/10dxg21290525016.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.261 1.561 5.129