R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'license()' or 'licence()' for distribution details.
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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(0.30103,1.62325,3.00,0.491362,2.07918,1.00,-0.1549,2.25527,4.00,0.591065,1.54407,1.00,0.556303,1.79934,1.00,0.146128,2.36173,1.00,0.176091,2.04922,4.00,-0.1549,2.44871,5.00,0.255273,2.79518,4.00,0.380211,1.71600,1.00,0.079181,2.07918,2.00,-0.30103,2.17026,5.00,-0.04576,2.35218,2.00,-0.09691,1.83251,4.00,0.531479,1.20412,2.00,0.612784,1.62325,2.00,-0.09691,2.52634,5.00,0.30103,1.69897,1.00,0.819544,1.14613,1.00,0.278754,2.42651,1.00,0.322219,1.62325,1.00,0.113943,1.27875,3.00,0.748188,1.07918,1.00,0.255273,2.14613,2.00,-0.04576,2.23045,4.00,0.255273,1.23045,2.00,0.278754,2.06070,4.00,-0.04576,1.49136,5.00,0.414973,1.32222,3.00,0.079181,2.21484,2.00,-0.30103,2.35218,3.00,0.176091,2.49136,1.00,-0.22185,2.17898,5.00,0.531479,1.44716,3.00,0,2.59329,4.00,0.361728,1.77815,2.00,-0.30103,2.30103,3.00,0.414973,1.66276,2.00,-0.22185,2.32222,4.00),dim=c(3,39),dimnames=list(c('log(ps)','log(tg)','D'),1:39))
> y <- array(NA,dim=c(3,39),dimnames=list(c('log(ps)','log(tg)','D'),1:39))
> 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
log(ps) log(tg) D
1 0.301030 1.62325 3
2 0.491362 2.07918 1
3 -0.154900 2.25527 4
4 0.591065 1.54407 1
5 0.556303 1.79934 1
6 0.146128 2.36173 1
7 0.176091 2.04922 4
8 -0.154900 2.44871 5
9 0.255273 2.79518 4
10 0.380211 1.71600 1
11 0.079181 2.07918 2
12 -0.301030 2.17026 5
13 -0.045760 2.35218 2
14 -0.096910 1.83251 4
15 0.531479 1.20412 2
16 0.612784 1.62325 2
17 -0.096910 2.52634 5
18 0.301030 1.69897 1
19 0.819544 1.14613 1
20 0.278754 2.42651 1
21 0.322219 1.62325 1
22 0.113943 1.27875 3
23 0.748188 1.07918 1
24 0.255273 2.14613 2
25 -0.045760 2.23045 4
26 0.255273 1.23045 2
27 0.278754 2.06070 4
28 -0.045760 1.49136 5
29 0.414973 1.32222 3
30 0.079181 2.21484 2
31 -0.301030 2.35218 3
32 0.176091 2.49136 1
33 -0.221850 2.17898 5
34 0.531479 1.44716 3
35 0.000000 2.59329 4
36 0.361728 1.77815 2
37 -0.301030 2.30103 3
38 0.414973 1.66276 2
39 -0.221850 2.32222 4
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `log(tg)` D
1.0745 -0.3035 -0.1105
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.34555 -0.14523 0.04349 0.12512 0.47125
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.07451 0.12875 8.346 6.16e-10 ***
`log(tg)` -0.30354 0.06890 -4.405 9.10e-05 ***
D -0.11051 0.02219 -4.980 1.60e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1818 on 36 degrees of freedom
Multiple R-squared: 0.6546, Adjusted R-squared: 0.6354
F-statistic: 34.12 on 2 and 36 DF, p-value: 4.888e-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,] 0.1260932 0.2521863 0.8739068
[2,] 0.1827682 0.3655363 0.8172318
[3,] 0.1134888 0.2269776 0.8865112
[4,] 0.6488405 0.7023189 0.3511595
[5,] 0.5568645 0.8862710 0.4431355
[6,] 0.5744872 0.8510256 0.4255128
[7,] 0.6035145 0.7929710 0.3964855
[8,] 0.6517353 0.6965295 0.3482647
[9,] 0.6201968 0.7596064 0.3798032
[10,] 0.5412178 0.9175644 0.4587822
[11,] 0.6168758 0.7662484 0.3831242
[12,] 0.5780709 0.8438583 0.4219291
[13,] 0.5421839 0.9156322 0.4578161
[14,] 0.5556026 0.8887949 0.4443974
[15,] 0.4719037 0.9438075 0.5280963
[16,] 0.4314472 0.8628944 0.5685528
[17,] 0.4977266 0.9954532 0.5022734
[18,] 0.4296116 0.8592233 0.5703884
[19,] 0.3502345 0.7004691 0.6497655
[20,] 0.2622753 0.5245507 0.7377247
[21,] 0.3299359 0.6598717 0.6700641
[22,] 0.5156801 0.9686399 0.4843199
[23,] 0.4675182 0.9350364 0.5324818
[24,] 0.3671706 0.7343413 0.6328294
[25,] 0.2717506 0.5435011 0.7282494
[26,] 0.3902869 0.7805739 0.6097131
[27,] 0.2821166 0.5642332 0.7178834
[28,] 0.2109620 0.4219239 0.7890380
> postscript(file="/var/www/html/rcomp/tmp/193vv1293049140.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/293vv1293049140.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/393vv1293049140.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4kcuf1293049140.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5kcuf1293049140.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 = 39
Frequency = 1
1 2 3 4 5 6
0.050773907 0.158477081 -0.102803189 0.095753636 0.138475889 -0.100992137
7 8 9 10 11 12
0.165643719 0.066423833 0.471253241 -0.062913003 -0.143193369 -0.164226441
13 14 15 16 17 18
-0.185268380 -0.173137093 0.043490298 0.252017358 0.147977521 -0.147263263
19 20 21 22 23 24
0.203442555 0.051297082 -0.149058192 -0.240882080 0.111764658 0.053220528
25 26 27 28 29 30
-0.001197013 -0.224723535 0.271791340 -0.115028677 0.073342735 -0.102015347
31 32 33 34 35 36
-0.330027830 -0.031681451 -0.082399586 0.227772826 0.154698869 0.047979459
37 38 39
-0.345553820 0.066199161 -0.149431292
> postscript(file="/var/www/html/rcomp/tmp/6kcuf1293049140.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 = 39
Frequency = 1
lag(myerror, k = 1) myerror
0 0.050773907 NA
1 0.158477081 0.050773907
2 -0.102803189 0.158477081
3 0.095753636 -0.102803189
4 0.138475889 0.095753636
5 -0.100992137 0.138475889
6 0.165643719 -0.100992137
7 0.066423833 0.165643719
8 0.471253241 0.066423833
9 -0.062913003 0.471253241
10 -0.143193369 -0.062913003
11 -0.164226441 -0.143193369
12 -0.185268380 -0.164226441
13 -0.173137093 -0.185268380
14 0.043490298 -0.173137093
15 0.252017358 0.043490298
16 0.147977521 0.252017358
17 -0.147263263 0.147977521
18 0.203442555 -0.147263263
19 0.051297082 0.203442555
20 -0.149058192 0.051297082
21 -0.240882080 -0.149058192
22 0.111764658 -0.240882080
23 0.053220528 0.111764658
24 -0.001197013 0.053220528
25 -0.224723535 -0.001197013
26 0.271791340 -0.224723535
27 -0.115028677 0.271791340
28 0.073342735 -0.115028677
29 -0.102015347 0.073342735
30 -0.330027830 -0.102015347
31 -0.031681451 -0.330027830
32 -0.082399586 -0.031681451
33 0.227772826 -0.082399586
34 0.154698869 0.227772826
35 0.047979459 0.154698869
36 -0.345553820 0.047979459
37 0.066199161 -0.345553820
38 -0.149431292 0.066199161
39 NA -0.149431292
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.158477081 0.050773907
[2,] -0.102803189 0.158477081
[3,] 0.095753636 -0.102803189
[4,] 0.138475889 0.095753636
[5,] -0.100992137 0.138475889
[6,] 0.165643719 -0.100992137
[7,] 0.066423833 0.165643719
[8,] 0.471253241 0.066423833
[9,] -0.062913003 0.471253241
[10,] -0.143193369 -0.062913003
[11,] -0.164226441 -0.143193369
[12,] -0.185268380 -0.164226441
[13,] -0.173137093 -0.185268380
[14,] 0.043490298 -0.173137093
[15,] 0.252017358 0.043490298
[16,] 0.147977521 0.252017358
[17,] -0.147263263 0.147977521
[18,] 0.203442555 -0.147263263
[19,] 0.051297082 0.203442555
[20,] -0.149058192 0.051297082
[21,] -0.240882080 -0.149058192
[22,] 0.111764658 -0.240882080
[23,] 0.053220528 0.111764658
[24,] -0.001197013 0.053220528
[25,] -0.224723535 -0.001197013
[26,] 0.271791340 -0.224723535
[27,] -0.115028677 0.271791340
[28,] 0.073342735 -0.115028677
[29,] -0.102015347 0.073342735
[30,] -0.330027830 -0.102015347
[31,] -0.031681451 -0.330027830
[32,] -0.082399586 -0.031681451
[33,] 0.227772826 -0.082399586
[34,] 0.154698869 0.227772826
[35,] 0.047979459 0.154698869
[36,] -0.345553820 0.047979459
[37,] 0.066199161 -0.345553820
[38,] -0.149431292 0.066199161
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.158477081 0.050773907
2 -0.102803189 0.158477081
3 0.095753636 -0.102803189
4 0.138475889 0.095753636
5 -0.100992137 0.138475889
6 0.165643719 -0.100992137
7 0.066423833 0.165643719
8 0.471253241 0.066423833
9 -0.062913003 0.471253241
10 -0.143193369 -0.062913003
11 -0.164226441 -0.143193369
12 -0.185268380 -0.164226441
13 -0.173137093 -0.185268380
14 0.043490298 -0.173137093
15 0.252017358 0.043490298
16 0.147977521 0.252017358
17 -0.147263263 0.147977521
18 0.203442555 -0.147263263
19 0.051297082 0.203442555
20 -0.149058192 0.051297082
21 -0.240882080 -0.149058192
22 0.111764658 -0.240882080
23 0.053220528 0.111764658
24 -0.001197013 0.053220528
25 -0.224723535 -0.001197013
26 0.271791340 -0.224723535
27 -0.115028677 0.271791340
28 0.073342735 -0.115028677
29 -0.102015347 0.073342735
30 -0.330027830 -0.102015347
31 -0.031681451 -0.330027830
32 -0.082399586 -0.031681451
33 0.227772826 -0.082399586
34 0.154698869 0.227772826
35 0.047979459 0.154698869
36 -0.345553820 0.047979459
37 0.066199161 -0.345553820
38 -0.149431292 0.066199161
> 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/7cmt11293049140.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ndtl1293049140.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ndtl1293049140.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ndtl1293049140.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111nqc1293049140.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/12nn7i1293049140.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/13t77m1293049141.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/14wq6s1293049141.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/150qmf1293049141.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/16l9l31293049141.tab")
+ }
>
> try(system("convert tmp/193vv1293049140.ps tmp/193vv1293049140.png",intern=TRUE))
character(0)
> try(system("convert tmp/293vv1293049140.ps tmp/293vv1293049140.png",intern=TRUE))
character(0)
> try(system("convert tmp/393vv1293049140.ps tmp/393vv1293049140.png",intern=TRUE))
character(0)
> try(system("convert tmp/4kcuf1293049140.ps tmp/4kcuf1293049140.png",intern=TRUE))
character(0)
> try(system("convert tmp/5kcuf1293049140.ps tmp/5kcuf1293049140.png",intern=TRUE))
character(0)
> try(system("convert tmp/6kcuf1293049140.ps tmp/6kcuf1293049140.png",intern=TRUE))
character(0)
> try(system("convert tmp/7cmt11293049140.ps tmp/7cmt11293049140.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ndtl1293049140.ps tmp/8ndtl1293049140.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ndtl1293049140.ps tmp/9ndtl1293049140.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ndtl1293049140.ps tmp/10ndtl1293049140.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.311 1.591 5.776