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)
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
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(0.30103,3,1.62325,0.25527,4,2.79518,-0.15490,4,2.25527,0.59106,1,1.54407,0.00000,4,2.59329,0.55630,1,1.79934,0.14613,1,2.36173,0.17609,4,2.04922,-0.15490,5,2.44871,0.32222,1,1.62325,0.61278,2,1.62325,0.07918,2,2.07918,-0.30103,5,2.17026,0.53148,2,1.20412,0.17609,1,2.49136,0.53148,3,1.44716,-0.09691,4,1.83251,-0.09691,5,2.52634,0.30103,1,1.69897,0.27875,1,2.42651,0.11394,3,1.27875,0.74819,1,1.07918,0.49136,1,2.07918,0.25527,2,2.14613,-0.04576,4,2.23045,0.25527,2,1.23045,0.27875,4,2.06070,-0.04576,5,1.49136,0.41497,3,1.32222,0.38021,1,1.71600,0.07918,2,2.21484,-0.04576,2,2.35218,-0.30103,3,2.35218,-0.22185,5,2.17898,0.36173,2,1.77815,-0.30103,3,2.30103,0.41497,2,1.66276,-0.22185,4,2.32222,0.81954,1,1.14613),dim=c(3,39),dimnames=list(c('Paradoxicalsleep','Overalldangerindex','Gestationtime'),1:39))
> y <- array(NA,dim=c(3,39),dimnames=list(c('Paradoxicalsleep','Overalldangerindex','Gestationtime'),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
Paradoxicalsleep Overalldangerindex Gestationtime
1 0.30103 3 1.62325
2 0.25527 4 2.79518
3 -0.15490 4 2.25527
4 0.59106 1 1.54407
5 0.00000 4 2.59329
6 0.55630 1 1.79934
7 0.14613 1 2.36173
8 0.17609 4 2.04922
9 -0.15490 5 2.44871
10 0.32222 1 1.62325
11 0.61278 2 1.62325
12 0.07918 2 2.07918
13 -0.30103 5 2.17026
14 0.53148 2 1.20412
15 0.17609 1 2.49136
16 0.53148 3 1.44716
17 -0.09691 4 1.83251
18 -0.09691 5 2.52634
19 0.30103 1 1.69897
20 0.27875 1 2.42651
21 0.11394 3 1.27875
22 0.74819 1 1.07918
23 0.49136 1 2.07918
24 0.25527 2 2.14613
25 -0.04576 4 2.23045
26 0.25527 2 1.23045
27 0.27875 4 2.06070
28 -0.04576 5 1.49136
29 0.41497 3 1.32222
30 0.38021 1 1.71600
31 0.07918 2 2.21484
32 -0.04576 2 2.35218
33 -0.30103 3 2.35218
34 -0.22185 5 2.17898
35 0.36173 2 1.77815
36 -0.30103 3 2.30103
37 0.41497 2 1.66276
38 -0.22185 4 2.32222
39 0.81954 1 1.14613
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Overalldangerindex Gestationtime
1.0745 -0.1105 -0.3035
> (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) 1.07450 0.12875 8.346 6.16e-10 ***
Overalldangerindex -0.11051 0.02219 -4.980 1.60e-05 ***
Gestationtime -0.30354 0.06890 -4.405 9.09e-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,] 0.5979222 0.80415557 0.40207779
[2,] 0.8058044 0.38839117 0.19419558
[3,] 0.7209684 0.55806319 0.27903160
[4,] 0.6497497 0.70050057 0.35025029
[5,] 0.6129877 0.77402469 0.38701234
[6,] 0.6900895 0.61982106 0.30991053
[7,] 0.6911842 0.61763159 0.30881579
[8,] 0.7378867 0.52422663 0.26211331
[9,] 0.6517605 0.69647901 0.34823950
[10,] 0.5666298 0.86674034 0.43337017
[11,] 0.5946800 0.81063999 0.40531999
[12,] 0.6108716 0.77825673 0.38912836
[13,] 0.6134338 0.77313244 0.38656622
[14,] 0.5891970 0.82160608 0.41080304
[15,] 0.5034181 0.99316372 0.49658186
[16,] 0.5913960 0.81720804 0.40860402
[17,] 0.5262785 0.94744302 0.47372151
[18,] 0.5343489 0.93130229 0.46565114
[19,] 0.4829110 0.96582210 0.51708895
[20,] 0.4142985 0.82859701 0.58570150
[21,] 0.6028516 0.79429689 0.39714845
[22,] 0.9605562 0.07888753 0.03944376
[23,] 0.9705521 0.05889572 0.02944786
[24,] 0.9617221 0.07655577 0.03827789
[25,] 0.9327463 0.13450749 0.06725374
[26,] 0.9136056 0.17278888 0.08639444
[27,] 0.9363519 0.12729621 0.06364811
[28,] 0.8803553 0.23928940 0.11964470
> postscript(file="/var/www/rcomp/tmp/1wdev1292426965.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2wdev1292426965.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/37mwg1292426965.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/47mwg1292426965.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/57mwg1292426965.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
0.050774927 0.471250737 -0.102802563 0.095750159 0.154699414 0.138474351
7 8 9 10 11 12
-0.100988810 0.165643394 0.066424171 -0.149055687 0.252014620 -0.143193216
13 14 15 16 17 18
-0.164226037 0.043492661 -0.031681155 0.227774889 -0.173136365 0.147977840
19 20 21 22 23 24
-0.147261776 0.051294394 -0.240883977 0.111768293 0.158476476 0.053218665
25 26 27 28 29 30
-0.001196381 -0.224725178 0.271788013 -0.115028109 0.073340828 -0.062912521
31 32 33 34 35 36
-0.102015226 -0.185267292 -0.330026985 -0.082399184 0.047982685 -0.345552963
37 38 39
0.066197414 -0.149430682 0.203440175
> postscript(file="/var/www/rcomp/tmp/6zdvj1292426965.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 0.050774927 NA
1 0.471250737 0.050774927
2 -0.102802563 0.471250737
3 0.095750159 -0.102802563
4 0.154699414 0.095750159
5 0.138474351 0.154699414
6 -0.100988810 0.138474351
7 0.165643394 -0.100988810
8 0.066424171 0.165643394
9 -0.149055687 0.066424171
10 0.252014620 -0.149055687
11 -0.143193216 0.252014620
12 -0.164226037 -0.143193216
13 0.043492661 -0.164226037
14 -0.031681155 0.043492661
15 0.227774889 -0.031681155
16 -0.173136365 0.227774889
17 0.147977840 -0.173136365
18 -0.147261776 0.147977840
19 0.051294394 -0.147261776
20 -0.240883977 0.051294394
21 0.111768293 -0.240883977
22 0.158476476 0.111768293
23 0.053218665 0.158476476
24 -0.001196381 0.053218665
25 -0.224725178 -0.001196381
26 0.271788013 -0.224725178
27 -0.115028109 0.271788013
28 0.073340828 -0.115028109
29 -0.062912521 0.073340828
30 -0.102015226 -0.062912521
31 -0.185267292 -0.102015226
32 -0.330026985 -0.185267292
33 -0.082399184 -0.330026985
34 0.047982685 -0.082399184
35 -0.345552963 0.047982685
36 0.066197414 -0.345552963
37 -0.149430682 0.066197414
38 0.203440175 -0.149430682
39 NA 0.203440175
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.471250737 0.050774927
[2,] -0.102802563 0.471250737
[3,] 0.095750159 -0.102802563
[4,] 0.154699414 0.095750159
[5,] 0.138474351 0.154699414
[6,] -0.100988810 0.138474351
[7,] 0.165643394 -0.100988810
[8,] 0.066424171 0.165643394
[9,] -0.149055687 0.066424171
[10,] 0.252014620 -0.149055687
[11,] -0.143193216 0.252014620
[12,] -0.164226037 -0.143193216
[13,] 0.043492661 -0.164226037
[14,] -0.031681155 0.043492661
[15,] 0.227774889 -0.031681155
[16,] -0.173136365 0.227774889
[17,] 0.147977840 -0.173136365
[18,] -0.147261776 0.147977840
[19,] 0.051294394 -0.147261776
[20,] -0.240883977 0.051294394
[21,] 0.111768293 -0.240883977
[22,] 0.158476476 0.111768293
[23,] 0.053218665 0.158476476
[24,] -0.001196381 0.053218665
[25,] -0.224725178 -0.001196381
[26,] 0.271788013 -0.224725178
[27,] -0.115028109 0.271788013
[28,] 0.073340828 -0.115028109
[29,] -0.062912521 0.073340828
[30,] -0.102015226 -0.062912521
[31,] -0.185267292 -0.102015226
[32,] -0.330026985 -0.185267292
[33,] -0.082399184 -0.330026985
[34,] 0.047982685 -0.082399184
[35,] -0.345552963 0.047982685
[36,] 0.066197414 -0.345552963
[37,] -0.149430682 0.066197414
[38,] 0.203440175 -0.149430682
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.471250737 0.050774927
2 -0.102802563 0.471250737
3 0.095750159 -0.102802563
4 0.154699414 0.095750159
5 0.138474351 0.154699414
6 -0.100988810 0.138474351
7 0.165643394 -0.100988810
8 0.066424171 0.165643394
9 -0.149055687 0.066424171
10 0.252014620 -0.149055687
11 -0.143193216 0.252014620
12 -0.164226037 -0.143193216
13 0.043492661 -0.164226037
14 -0.031681155 0.043492661
15 0.227774889 -0.031681155
16 -0.173136365 0.227774889
17 0.147977840 -0.173136365
18 -0.147261776 0.147977840
19 0.051294394 -0.147261776
20 -0.240883977 0.051294394
21 0.111768293 -0.240883977
22 0.158476476 0.111768293
23 0.053218665 0.158476476
24 -0.001196381 0.053218665
25 -0.224725178 -0.001196381
26 0.271788013 -0.224725178
27 -0.115028109 0.271788013
28 0.073340828 -0.115028109
29 -0.062912521 0.073340828
30 -0.102015226 -0.062912521
31 -0.185267292 -0.102015226
32 -0.330026985 -0.185267292
33 -0.082399184 -0.330026985
34 0.047982685 -0.082399184
35 -0.345552963 0.047982685
36 0.066197414 -0.345552963
37 -0.149430682 0.066197414
38 0.203440175 -0.149430682
> 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/7a5cm1292426965.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8a5cm1292426965.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9a5cm1292426965.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10lwu71292426965.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/116fsd1292426965.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/12sx9j1292426965.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/13o7os1292426965.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/142zqa1292426966.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/15ni6g1292426966.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/1680m41292426966.tab")
+ }
>
> try(system("convert tmp/1wdev1292426965.ps tmp/1wdev1292426965.png",intern=TRUE))
character(0)
> try(system("convert tmp/2wdev1292426965.ps tmp/2wdev1292426965.png",intern=TRUE))
character(0)
> try(system("convert tmp/37mwg1292426965.ps tmp/37mwg1292426965.png",intern=TRUE))
character(0)
> try(system("convert tmp/47mwg1292426965.ps tmp/47mwg1292426965.png",intern=TRUE))
character(0)
> try(system("convert tmp/57mwg1292426965.ps tmp/57mwg1292426965.png",intern=TRUE))
character(0)
> try(system("convert tmp/6zdvj1292426965.ps tmp/6zdvj1292426965.png",intern=TRUE))
character(0)
> try(system("convert tmp/7a5cm1292426965.ps tmp/7a5cm1292426965.png",intern=TRUE))
character(0)
> try(system("convert tmp/8a5cm1292426965.ps tmp/8a5cm1292426965.png",intern=TRUE))
character(0)
> try(system("convert tmp/9a5cm1292426965.ps tmp/9a5cm1292426965.png",intern=TRUE))
character(0)
> try(system("convert tmp/10lwu71292426965.ps tmp/10lwu71292426965.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.990 1.620 4.558