R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(0.301029996,162.325,3,0.491361694,207.918,1,-0.15490196,225.527,4,0.591064607,154.407,1,0.556302501,179.934,1,0.146128036,236.173,1,0.176091259,204.922,4,-0.15490196,244.871,5,0.255272505,279.518,4,0.380211242,171.600,1,0.079181246,207.918,2,-0.301029996,217.026,5,-0.045757491,235.218,2,-0.096910013,183.251,4,0.531478917,120.412,2,0.612783857,162.325,2,-0.096910013,252.634,5,0.301029996,169.897,1,0.819543936,114.613,1,0.278753601,242.651,1,0.322219295,162.325,1,0.113943352,127.875,3,0.748188027,107.918,1,0.255272505,214.613,2,-0.045757491,223.045,4,0.255272505,123.045,2,0.278753601,206.070,4,-0.045757491,149.136,5,0.414973348,132.222,3,0.079181246,221.484,2,-0.301029996,235.218,3,0.176091259,249.136,1,-0.22184875,217.898,5,0.531478917,144.716,3,0,259.329,4,0.361727836,177.815,2,-0.301029996,230.103,3,0.414973348,166.276,2,-0.22184875,232.222,4),dim=c(3,39),dimnames=list(c('PS','log(tg)','D'),1:39))
> y <- array(NA,dim=c(3,39),dimnames=list(c('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
> 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
PS log(tg) D
1 0.30103000 162.325 3
2 0.49136169 207.918 1
3 -0.15490196 225.527 4
4 0.59106461 154.407 1
5 0.55630250 179.934 1
6 0.14612804 236.173 1
7 0.17609126 204.922 4
8 -0.15490196 244.871 5
9 0.25527250 279.518 4
10 0.38021124 171.600 1
11 0.07918125 207.918 2
12 -0.30103000 217.026 5
13 -0.04575749 235.218 2
14 -0.09691001 183.251 4
15 0.53147892 120.412 2
16 0.61278386 162.325 2
17 -0.09691001 252.634 5
18 0.30103000 169.897 1
19 0.81954394 114.613 1
20 0.27875360 242.651 1
21 0.32221930 162.325 1
22 0.11394335 127.875 3
23 0.74818803 107.918 1
24 0.25527250 214.613 2
25 -0.04575749 223.045 4
26 0.25527250 123.045 2
27 0.27875360 206.070 4
28 -0.04575749 149.136 5
29 0.41497335 132.222 3
30 0.07918125 221.484 2
31 -0.30103000 235.218 3
32 0.17609126 249.136 1
33 -0.22184875 217.898 5
34 0.53147892 144.716 3
35 0.00000000 259.329 4
36 0.36172784 177.815 2
37 -0.30103000 230.103 3
38 0.41497335 166.276 2
39 -0.22184875 232.222 4
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `log(tg)` D
1.074507 -0.003035 -0.110510
> (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) 1.074507 0.128751 8.346 6.16e-10 ***
`log(tg)` -0.003035 0.000689 -4.405 9.09e-05 ***
D -0.110510 0.022191 -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,] 0.1260931 0.2521862 0.8739069
[2,] 0.1827706 0.3655413 0.8172294
[3,] 0.1134901 0.2269803 0.8865099
[4,] 0.6488441 0.7023117 0.3511559
[5,] 0.5568681 0.8862637 0.4431319
[6,] 0.5744900 0.8510201 0.4255100
[7,] 0.6035164 0.7929672 0.3964836
[8,] 0.6517341 0.6965319 0.3482659
[9,] 0.6201956 0.7596087 0.3798044
[10,] 0.5412167 0.9175667 0.4587833
[11,] 0.6168750 0.7662500 0.3831250
[12,] 0.5780698 0.8438604 0.4219302
[13,] 0.5421829 0.9156341 0.4578171
[14,] 0.5556019 0.8887962 0.4443981
[15,] 0.4719031 0.9438062 0.5280969
[16,] 0.4314463 0.8628927 0.5685537
[17,] 0.4977259 0.9954518 0.5022741
[18,] 0.4296111 0.8592222 0.5703889
[19,] 0.3502339 0.7004678 0.6497661
[20,] 0.2622748 0.5245497 0.7377252
[21,] 0.3299366 0.6598732 0.6700634
[22,] 0.5156798 0.9686404 0.4843202
[23,] 0.4675166 0.9350332 0.5324834
[24,] 0.3671692 0.7343383 0.6328308
[25,] 0.2717493 0.5434985 0.7282507
[26,] 0.3902865 0.7805729 0.6097135
[27,] 0.2821165 0.5642330 0.7178835
[28,] 0.2109618 0.4219236 0.7890382
> postscript(file="/var/www/rcomp/tmp/1psif1293013331.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2psif1293013331.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/302ii1293013331.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/402ii1293013331.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/502ii1293013331.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
0.050773690 0.158476846 -0.102805343 0.095753213 0.138475408 -0.100991978
7 8 9 10 11 12
0.165643746 0.066421617 0.471252653 -0.062912759 -0.143193152 -0.164226745
13 14 15 16 17 18
-0.185265848 -0.173137378 0.043490023 0.252017101 0.147977266 -0.147263267
19 20 21 22 23 24
0.203442387 0.051296818 -0.149057912 -0.240882005 0.111764568 0.053220017
25 26 27 28 29 30
-0.001194702 -0.224724217 0.271790711 -0.115026602 0.073342815 -0.102015104
31 32 33 34 35 36
-0.330027903 -0.031681045 -0.082398642 0.227772498 0.154698738 0.047979210
37 38 39
-0.345553902 0.066199402 -0.149430223
> postscript(file="/var/www/rcomp/tmp/6bbh31293013331.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 0.050773690 NA
1 0.158476846 0.050773690
2 -0.102805343 0.158476846
3 0.095753213 -0.102805343
4 0.138475408 0.095753213
5 -0.100991978 0.138475408
6 0.165643746 -0.100991978
7 0.066421617 0.165643746
8 0.471252653 0.066421617
9 -0.062912759 0.471252653
10 -0.143193152 -0.062912759
11 -0.164226745 -0.143193152
12 -0.185265848 -0.164226745
13 -0.173137378 -0.185265848
14 0.043490023 -0.173137378
15 0.252017101 0.043490023
16 0.147977266 0.252017101
17 -0.147263267 0.147977266
18 0.203442387 -0.147263267
19 0.051296818 0.203442387
20 -0.149057912 0.051296818
21 -0.240882005 -0.149057912
22 0.111764568 -0.240882005
23 0.053220017 0.111764568
24 -0.001194702 0.053220017
25 -0.224724217 -0.001194702
26 0.271790711 -0.224724217
27 -0.115026602 0.271790711
28 0.073342815 -0.115026602
29 -0.102015104 0.073342815
30 -0.330027903 -0.102015104
31 -0.031681045 -0.330027903
32 -0.082398642 -0.031681045
33 0.227772498 -0.082398642
34 0.154698738 0.227772498
35 0.047979210 0.154698738
36 -0.345553902 0.047979210
37 0.066199402 -0.345553902
38 -0.149430223 0.066199402
39 NA -0.149430223
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.158476846 0.050773690
[2,] -0.102805343 0.158476846
[3,] 0.095753213 -0.102805343
[4,] 0.138475408 0.095753213
[5,] -0.100991978 0.138475408
[6,] 0.165643746 -0.100991978
[7,] 0.066421617 0.165643746
[8,] 0.471252653 0.066421617
[9,] -0.062912759 0.471252653
[10,] -0.143193152 -0.062912759
[11,] -0.164226745 -0.143193152
[12,] -0.185265848 -0.164226745
[13,] -0.173137378 -0.185265848
[14,] 0.043490023 -0.173137378
[15,] 0.252017101 0.043490023
[16,] 0.147977266 0.252017101
[17,] -0.147263267 0.147977266
[18,] 0.203442387 -0.147263267
[19,] 0.051296818 0.203442387
[20,] -0.149057912 0.051296818
[21,] -0.240882005 -0.149057912
[22,] 0.111764568 -0.240882005
[23,] 0.053220017 0.111764568
[24,] -0.001194702 0.053220017
[25,] -0.224724217 -0.001194702
[26,] 0.271790711 -0.224724217
[27,] -0.115026602 0.271790711
[28,] 0.073342815 -0.115026602
[29,] -0.102015104 0.073342815
[30,] -0.330027903 -0.102015104
[31,] -0.031681045 -0.330027903
[32,] -0.082398642 -0.031681045
[33,] 0.227772498 -0.082398642
[34,] 0.154698738 0.227772498
[35,] 0.047979210 0.154698738
[36,] -0.345553902 0.047979210
[37,] 0.066199402 -0.345553902
[38,] -0.149430223 0.066199402
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.158476846 0.050773690
2 -0.102805343 0.158476846
3 0.095753213 -0.102805343
4 0.138475408 0.095753213
5 -0.100991978 0.138475408
6 0.165643746 -0.100991978
7 0.066421617 0.165643746
8 0.471252653 0.066421617
9 -0.062912759 0.471252653
10 -0.143193152 -0.062912759
11 -0.164226745 -0.143193152
12 -0.185265848 -0.164226745
13 -0.173137378 -0.185265848
14 0.043490023 -0.173137378
15 0.252017101 0.043490023
16 0.147977266 0.252017101
17 -0.147263267 0.147977266
18 0.203442387 -0.147263267
19 0.051296818 0.203442387
20 -0.149057912 0.051296818
21 -0.240882005 -0.149057912
22 0.111764568 -0.240882005
23 0.053220017 0.111764568
24 -0.001194702 0.053220017
25 -0.224724217 -0.001194702
26 0.271790711 -0.224724217
27 -0.115026602 0.271790711
28 0.073342815 -0.115026602
29 -0.102015104 0.073342815
30 -0.330027903 -0.102015104
31 -0.031681045 -0.330027903
32 -0.082398642 -0.031681045
33 0.227772498 -0.082398642
34 0.154698738 0.227772498
35 0.047979210 0.154698738
36 -0.345553902 0.047979210
37 0.066199402 -0.345553902
38 -0.149430223 0.066199402
> 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/7lkg61293013331.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8lkg61293013331.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9lkg61293013331.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10eugr1293013331.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11zuex1293013331.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/123dul1293013331.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/13z4at1293013331.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/14sesf1293013331.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/15vwqk1293013331.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/16966b1293013331.tab")
+ }
>
> try(system("convert tmp/1psif1293013331.ps tmp/1psif1293013331.png",intern=TRUE))
character(0)
> try(system("convert tmp/2psif1293013331.ps tmp/2psif1293013331.png",intern=TRUE))
character(0)
> try(system("convert tmp/302ii1293013331.ps tmp/302ii1293013331.png",intern=TRUE))
character(0)
> try(system("convert tmp/402ii1293013331.ps tmp/402ii1293013331.png",intern=TRUE))
character(0)
> try(system("convert tmp/502ii1293013331.ps tmp/502ii1293013331.png",intern=TRUE))
character(0)
> try(system("convert tmp/6bbh31293013331.ps tmp/6bbh31293013331.png",intern=TRUE))
character(0)
> try(system("convert tmp/7lkg61293013331.ps tmp/7lkg61293013331.png",intern=TRUE))
character(0)
> try(system("convert tmp/8lkg61293013331.ps tmp/8lkg61293013331.png",intern=TRUE))
character(0)
> try(system("convert tmp/9lkg61293013331.ps tmp/9lkg61293013331.png",intern=TRUE))
character(0)
> try(system("convert tmp/10eugr1293013331.ps tmp/10eugr1293013331.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.92 1.67 4.64