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.
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(6.3,0,3,2.1,3.406028945,4,9.1,1.02325246,4,15.8,-1.638272164,1,5.2,2.204119983,4,10.9,0.51851394,1,8.3,1.717337583,1,11,-0.37161107,4,3.2,2.667452953,5,6.3,-1.124938737,1,8.6,0.477121255,2,6.6,-0.105130343,2,9.5,-0.698970004,2,3.3,1.441852176,5,11,-0.920818754,2,4.7,1.929418926,1,10.4,-0.995678626,3,7.4,0.017033339,4,2.1,2.716837723,5,7.7,-2.301029996,4,17.9,-2,1,6.1,1.792391689,1,11.9,-1.638272164,3,10.8,-1.318758763,3,13.8,0.230448921,1,14.3,0.544068044,1,10,1,4,11.9,0.209515015,2,6.5,2.283301229,4,7.5,0.397940009,5,10.6,-0.552841969,3,7.4,3.626853415,1,8.4,0.832508913,2,5.7,-0.124938737,2,4.9,0.556302501,3,3.2,1.744292983,5,11,-0.045757491,2,4.9,0.301029996,3,13.2,-0.982966661,2,9.7,0.622214023,4,12.8,0.544068044,1),dim=c(3,41),dimnames=list(c('SWS_(non_dreaming)','logWb','D_(overall_danger)'),1:41))
> y <- array(NA,dim=c(3,41),dimnames=list(c('SWS_(non_dreaming)','logWb','D_(overall_danger)'),1:41))
> 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
SWS_(non_dreaming) logWb D_(overall_danger)
1 6.3 0.00000000 3
2 2.1 3.40602895 4
3 9.1 1.02325246 4
4 15.8 -1.63827216 1
5 5.2 2.20411998 4
6 10.9 0.51851394 1
7 8.3 1.71733758 1
8 11.0 -0.37161107 4
9 3.2 2.66745295 5
10 6.3 -1.12493874 1
11 8.6 0.47712126 2
12 6.6 -0.10513034 2
13 9.5 -0.69897000 2
14 3.3 1.44185218 5
15 11.0 -0.92081875 2
16 4.7 1.92941893 1
17 10.4 -0.99567863 3
18 7.4 0.01703334 4
19 2.1 2.71683772 5
20 7.7 -2.30103000 4
21 17.9 -2.00000000 1
22 6.1 1.79239169 1
23 11.9 -1.63827216 3
24 10.8 -1.31875876 3
25 13.8 0.23044892 1
26 14.3 0.54406804 1
27 10.0 1.00000000 4
28 11.9 0.20951501 2
29 6.5 2.28330123 4
30 7.5 0.39794001 5
31 10.6 -0.55284197 3
32 7.4 3.62685341 1
33 8.4 0.83250891 2
34 5.7 -0.12493874 2
35 4.9 0.55630250 3
36 3.2 1.74429298 5
37 11.0 -0.04575749 2
38 4.9 0.30103000 3
39 13.2 -0.98296666 2
40 9.7 0.62221402 4
41 12.8 0.54406804 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) logWb `D_(overall_danger)`
12.094 -1.403 -1.069
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.3036 -1.4275 0.1154 1.8840 4.0689
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.0944 0.8800 13.743 2.53e-16 ***
logWb -1.4028 0.2901 -4.835 2.22e-05 ***
`D_(overall_danger)` -1.0688 0.2979 -3.588 0.000938 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.555 on 38 degrees of freedom
Multiple R-squared: 0.5578, Adjusted R-squared: 0.5345
F-statistic: 23.97 on 2 and 38 DF, p-value: 1.847e-07
> 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,] 0.5316624 0.9366752 0.4683376
[2,] 0.3568984 0.7137968 0.6431016
[3,] 0.2578952 0.5157903 0.7421048
[4,] 0.1524007 0.3048013 0.8475993
[5,] 0.7520211 0.4959579 0.2479789
[6,] 0.6528033 0.6943934 0.3471967
[7,] 0.6911771 0.6176459 0.3088229
[8,] 0.6109372 0.7781257 0.3890628
[9,] 0.5574341 0.8851318 0.4425659
[10,] 0.4603199 0.9206399 0.5396801
[11,] 0.5047391 0.9905219 0.4952609
[12,] 0.4053482 0.8106964 0.5946518
[13,] 0.3170578 0.6341156 0.6829422
[14,] 0.2432831 0.4865661 0.7567169
[15,] 0.3239901 0.6479802 0.6760099
[16,] 0.4905071 0.9810143 0.5094929
[17,] 0.5035072 0.9929856 0.4964928
[18,] 0.4133389 0.8266778 0.5866611
[19,] 0.3206982 0.6413964 0.6793018
[20,] 0.3524245 0.7048490 0.6475755
[21,] 0.4593897 0.9187794 0.5406103
[22,] 0.5235247 0.9529506 0.4764753
[23,] 0.4879649 0.9759299 0.5120351
[24,] 0.4216595 0.8433189 0.5783405
[25,] 0.3636830 0.7273659 0.6363170
[26,] 0.2875241 0.5750481 0.7124759
[27,] 0.2012663 0.4025326 0.7987337
[28,] 0.1228620 0.2457239 0.8771380
[29,] 0.2502480 0.5004959 0.7497520
[30,] 0.2926506 0.5853012 0.7073494
> postscript(file="/var/www/rcomp/tmp/1k7ke1292426522.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2uz1z1292426522.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3uz1z1292426522.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4n80k1292426522.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5n80k1292426522.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 = 41
Frequency = 1
1 2 3 4 5 6
-2.58784760 -0.94104508 2.71640368 2.47631745 0.47292087 0.60185010
7 8 9 10 11 12
-0.31644392 2.65969358 0.19171888 -6.30358006 -0.68737943 -3.50416011
13 14 15 16 17 18
-1.43719649 -1.42754998 -0.24840520 -3.61893689 0.11541760 -0.39511729
19 20 21 22 23 24
-0.83900432 -3.34688913 4.06888679 -2.41115826 0.71398944 0.06220182
25 26 27 28 29 30
3.09775341 4.03769732 3.58378521 2.23722340 1.88399607 1.30805337
31 32 33 34 35 36
0.93662745 1.46221879 -0.38884275 -4.43194726 -3.20746823 -1.10328698
37 38 39 40 41
0.97912794 -3.56556368 1.86441391 2.75382824 2.53769732
> postscript(file="/var/www/rcomp/tmp/6n80k1292426522.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 = 41
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.58784760 NA
1 -0.94104508 -2.58784760
2 2.71640368 -0.94104508
3 2.47631745 2.71640368
4 0.47292087 2.47631745
5 0.60185010 0.47292087
6 -0.31644392 0.60185010
7 2.65969358 -0.31644392
8 0.19171888 2.65969358
9 -6.30358006 0.19171888
10 -0.68737943 -6.30358006
11 -3.50416011 -0.68737943
12 -1.43719649 -3.50416011
13 -1.42754998 -1.43719649
14 -0.24840520 -1.42754998
15 -3.61893689 -0.24840520
16 0.11541760 -3.61893689
17 -0.39511729 0.11541760
18 -0.83900432 -0.39511729
19 -3.34688913 -0.83900432
20 4.06888679 -3.34688913
21 -2.41115826 4.06888679
22 0.71398944 -2.41115826
23 0.06220182 0.71398944
24 3.09775341 0.06220182
25 4.03769732 3.09775341
26 3.58378521 4.03769732
27 2.23722340 3.58378521
28 1.88399607 2.23722340
29 1.30805337 1.88399607
30 0.93662745 1.30805337
31 1.46221879 0.93662745
32 -0.38884275 1.46221879
33 -4.43194726 -0.38884275
34 -3.20746823 -4.43194726
35 -1.10328698 -3.20746823
36 0.97912794 -1.10328698
37 -3.56556368 0.97912794
38 1.86441391 -3.56556368
39 2.75382824 1.86441391
40 2.53769732 2.75382824
41 NA 2.53769732
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.94104508 -2.58784760
[2,] 2.71640368 -0.94104508
[3,] 2.47631745 2.71640368
[4,] 0.47292087 2.47631745
[5,] 0.60185010 0.47292087
[6,] -0.31644392 0.60185010
[7,] 2.65969358 -0.31644392
[8,] 0.19171888 2.65969358
[9,] -6.30358006 0.19171888
[10,] -0.68737943 -6.30358006
[11,] -3.50416011 -0.68737943
[12,] -1.43719649 -3.50416011
[13,] -1.42754998 -1.43719649
[14,] -0.24840520 -1.42754998
[15,] -3.61893689 -0.24840520
[16,] 0.11541760 -3.61893689
[17,] -0.39511729 0.11541760
[18,] -0.83900432 -0.39511729
[19,] -3.34688913 -0.83900432
[20,] 4.06888679 -3.34688913
[21,] -2.41115826 4.06888679
[22,] 0.71398944 -2.41115826
[23,] 0.06220182 0.71398944
[24,] 3.09775341 0.06220182
[25,] 4.03769732 3.09775341
[26,] 3.58378521 4.03769732
[27,] 2.23722340 3.58378521
[28,] 1.88399607 2.23722340
[29,] 1.30805337 1.88399607
[30,] 0.93662745 1.30805337
[31,] 1.46221879 0.93662745
[32,] -0.38884275 1.46221879
[33,] -4.43194726 -0.38884275
[34,] -3.20746823 -4.43194726
[35,] -1.10328698 -3.20746823
[36,] 0.97912794 -1.10328698
[37,] -3.56556368 0.97912794
[38,] 1.86441391 -3.56556368
[39,] 2.75382824 1.86441391
[40,] 2.53769732 2.75382824
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.94104508 -2.58784760
2 2.71640368 -0.94104508
3 2.47631745 2.71640368
4 0.47292087 2.47631745
5 0.60185010 0.47292087
6 -0.31644392 0.60185010
7 2.65969358 -0.31644392
8 0.19171888 2.65969358
9 -6.30358006 0.19171888
10 -0.68737943 -6.30358006
11 -3.50416011 -0.68737943
12 -1.43719649 -3.50416011
13 -1.42754998 -1.43719649
14 -0.24840520 -1.42754998
15 -3.61893689 -0.24840520
16 0.11541760 -3.61893689
17 -0.39511729 0.11541760
18 -0.83900432 -0.39511729
19 -3.34688913 -0.83900432
20 4.06888679 -3.34688913
21 -2.41115826 4.06888679
22 0.71398944 -2.41115826
23 0.06220182 0.71398944
24 3.09775341 0.06220182
25 4.03769732 3.09775341
26 3.58378521 4.03769732
27 2.23722340 3.58378521
28 1.88399607 2.23722340
29 1.30805337 1.88399607
30 0.93662745 1.30805337
31 1.46221879 0.93662745
32 -0.38884275 1.46221879
33 -4.43194726 -0.38884275
34 -3.20746823 -4.43194726
35 -1.10328698 -3.20746823
36 0.97912794 -1.10328698
37 -3.56556368 0.97912794
38 1.86441391 -3.56556368
39 2.75382824 1.86441391
40 2.53769732 2.75382824
> 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/7yzin1292426522.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/888hq1292426522.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/988hq1292426522.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/1088hq1292426522.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11n0xh1292426522.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/12xsek1292426522.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/134abd1292426522.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/14f2sy1292426522.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/15i28m1292426522.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/164l7s1292426522.tab")
+ }
>
> try(system("convert tmp/1k7ke1292426522.ps tmp/1k7ke1292426522.png",intern=TRUE))
character(0)
> try(system("convert tmp/2uz1z1292426522.ps tmp/2uz1z1292426522.png",intern=TRUE))
character(0)
> try(system("convert tmp/3uz1z1292426522.ps tmp/3uz1z1292426522.png",intern=TRUE))
character(0)
> try(system("convert tmp/4n80k1292426522.ps tmp/4n80k1292426522.png",intern=TRUE))
character(0)
> try(system("convert tmp/5n80k1292426522.ps tmp/5n80k1292426522.png",intern=TRUE))
character(0)
> try(system("convert tmp/6n80k1292426522.ps tmp/6n80k1292426522.png",intern=TRUE))
character(0)
> try(system("convert tmp/7yzin1292426522.ps tmp/7yzin1292426522.png",intern=TRUE))
character(0)
> try(system("convert tmp/888hq1292426522.ps tmp/888hq1292426522.png",intern=TRUE))
character(0)
> try(system("convert tmp/988hq1292426522.ps tmp/988hq1292426522.png",intern=TRUE))
character(0)
> try(system("convert tmp/1088hq1292426522.ps tmp/1088hq1292426522.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.98 1.69 4.67