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(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,-1.124938737,1,6.6,-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(3,40),dimnames=list(c('SWS_(non_dreaming)','logWb','D_(overall_danger)'),1:40))
> y <- array(NA,dim=c(3,40),dimnames=list(c('SWS_(non_dreaming)','logWb','D_(overall_danger)'),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
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 6.6 -0.10513034 2
12 9.5 -0.69897000 2
13 3.3 1.44185218 5
14 11.0 -0.92081875 2
15 4.7 1.92941893 1
16 10.4 -0.99567863 3
17 7.4 0.01703334 4
18 2.1 2.71683772 5
19 7.7 -2.30103000 4
20 17.9 -2.00000000 1
21 6.1 1.79239169 1
22 11.9 -1.63827216 3
23 10.8 -1.31875876 3
24 13.8 0.23044892 1
25 14.3 0.54406804 1
26 10.0 1.00000000 4
27 11.9 0.20951501 2
28 6.5 2.28330123 4
29 7.5 0.39794001 5
30 10.6 -0.55284197 3
31 7.4 3.62685341 1
32 8.4 0.83250891 2
33 5.7 -0.12493874 2
34 4.9 0.55630250 3
35 3.2 1.74429298 5
36 11.0 -0.04575749 2
37 4.9 0.30103000 3
38 13.2 -0.98296666 2
39 9.7 0.62221402 4
40 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)`
12.129 -1.401 -1.076
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.3295 -1.4375 0.1444 1.9578 4.0447
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.1295 0.9004 13.471 7.70e-16 ***
logWb -1.4008 0.2938 -4.767 2.89e-05 ***
`D_(overall_danger)` -1.0757 0.3027 -3.554 0.00106 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.587 on 37 degrees of freedom
Multiple R-squared: 0.5587, Adjusted R-squared: 0.5348
F-statistic: 23.42 on 2 and 37 DF, p-value: 2.679e-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,] 0.5182315 0.9635370 0.4817685
[2,] 0.3435154 0.6870307 0.6564846
[3,] 0.2452528 0.4905056 0.7547472
[4,] 0.1427536 0.2855071 0.8572464
[5,] 0.7326463 0.5347074 0.2673537
[6,] 0.7597357 0.4805287 0.2402643
[7,] 0.6831130 0.6337740 0.3168870
[8,] 0.6284988 0.7430024 0.3715012
[9,] 0.5305454 0.9389092 0.4694546
[10,] 0.5678211 0.8643579 0.4321789
[11,] 0.4656684 0.9313368 0.5343316
[12,] 0.3713106 0.7426212 0.6286894
[13,] 0.2898226 0.5796452 0.7101774
[14,] 0.3721221 0.7442442 0.6278779
[15,] 0.5386504 0.9226993 0.4613496
[16,] 0.5483866 0.9032268 0.4516134
[17,] 0.4563694 0.9127388 0.5436306
[18,] 0.3596141 0.7192281 0.6403859
[19,] 0.3898574 0.7797148 0.6101426
[20,] 0.4942396 0.9884791 0.5057604
[21,] 0.5549027 0.8901946 0.4450973
[22,] 0.5172052 0.9655896 0.4827948
[23,] 0.4488219 0.8976437 0.5511781
[24,] 0.3884076 0.7768151 0.6115924
[25,] 0.3089074 0.6178148 0.6910926
[26,] 0.2178271 0.4356543 0.7821729
[27,] 0.1345141 0.2690282 0.8654859
[28,] 0.2651504 0.5303009 0.7348496
[29,] 0.3060762 0.6121523 0.6939238
> postscript(file="/var/www/html/rcomp/tmp/1rwh21292355952.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2rwh21292355952.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/325yn1292355952.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/425yn1292355952.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/525yn1292355952.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 = 40
Frequency = 1
1 2 3 4 5 6
-2.60224934 -0.95539573 2.70685333 2.45139051 0.46099157 0.57257814
7 8 9 10 11 12
-0.34813101 2.65295330 0.18576897 -6.32954115 -3.52526287 -1.45710291
13 14 15 16 17 18
-1.43103081 -0.26786469 -3.65105124 0.10302174 -0.40264052 -0.84505372
19 20 21 22 23 24
-3.34974243 4.04468858 -2.44299656 0.70288814 0.05045683 3.06906178
25 26 27 28 29 30
4.00837387 3.57428170 2.21548675 1.87190725 1.30667558 0.92333946
31 32 33 34 35 36
1.42668482 -0.41183447 -4.45301012 -3.22299068 -1.10737710 0.95790557
37 38 39 40
-3.58057187 1.84507962 2.74508582 2.50837387
> postscript(file="/var/www/html/rcomp/tmp/6dwg81292355952.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 = 40
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.60224934 NA
1 -0.95539573 -2.60224934
2 2.70685333 -0.95539573
3 2.45139051 2.70685333
4 0.46099157 2.45139051
5 0.57257814 0.46099157
6 -0.34813101 0.57257814
7 2.65295330 -0.34813101
8 0.18576897 2.65295330
9 -6.32954115 0.18576897
10 -3.52526287 -6.32954115
11 -1.45710291 -3.52526287
12 -1.43103081 -1.45710291
13 -0.26786469 -1.43103081
14 -3.65105124 -0.26786469
15 0.10302174 -3.65105124
16 -0.40264052 0.10302174
17 -0.84505372 -0.40264052
18 -3.34974243 -0.84505372
19 4.04468858 -3.34974243
20 -2.44299656 4.04468858
21 0.70288814 -2.44299656
22 0.05045683 0.70288814
23 3.06906178 0.05045683
24 4.00837387 3.06906178
25 3.57428170 4.00837387
26 2.21548675 3.57428170
27 1.87190725 2.21548675
28 1.30667558 1.87190725
29 0.92333946 1.30667558
30 1.42668482 0.92333946
31 -0.41183447 1.42668482
32 -4.45301012 -0.41183447
33 -3.22299068 -4.45301012
34 -1.10737710 -3.22299068
35 0.95790557 -1.10737710
36 -3.58057187 0.95790557
37 1.84507962 -3.58057187
38 2.74508582 1.84507962
39 2.50837387 2.74508582
40 NA 2.50837387
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.95539573 -2.60224934
[2,] 2.70685333 -0.95539573
[3,] 2.45139051 2.70685333
[4,] 0.46099157 2.45139051
[5,] 0.57257814 0.46099157
[6,] -0.34813101 0.57257814
[7,] 2.65295330 -0.34813101
[8,] 0.18576897 2.65295330
[9,] -6.32954115 0.18576897
[10,] -3.52526287 -6.32954115
[11,] -1.45710291 -3.52526287
[12,] -1.43103081 -1.45710291
[13,] -0.26786469 -1.43103081
[14,] -3.65105124 -0.26786469
[15,] 0.10302174 -3.65105124
[16,] -0.40264052 0.10302174
[17,] -0.84505372 -0.40264052
[18,] -3.34974243 -0.84505372
[19,] 4.04468858 -3.34974243
[20,] -2.44299656 4.04468858
[21,] 0.70288814 -2.44299656
[22,] 0.05045683 0.70288814
[23,] 3.06906178 0.05045683
[24,] 4.00837387 3.06906178
[25,] 3.57428170 4.00837387
[26,] 2.21548675 3.57428170
[27,] 1.87190725 2.21548675
[28,] 1.30667558 1.87190725
[29,] 0.92333946 1.30667558
[30,] 1.42668482 0.92333946
[31,] -0.41183447 1.42668482
[32,] -4.45301012 -0.41183447
[33,] -3.22299068 -4.45301012
[34,] -1.10737710 -3.22299068
[35,] 0.95790557 -1.10737710
[36,] -3.58057187 0.95790557
[37,] 1.84507962 -3.58057187
[38,] 2.74508582 1.84507962
[39,] 2.50837387 2.74508582
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.95539573 -2.60224934
2 2.70685333 -0.95539573
3 2.45139051 2.70685333
4 0.46099157 2.45139051
5 0.57257814 0.46099157
6 -0.34813101 0.57257814
7 2.65295330 -0.34813101
8 0.18576897 2.65295330
9 -6.32954115 0.18576897
10 -3.52526287 -6.32954115
11 -1.45710291 -3.52526287
12 -1.43103081 -1.45710291
13 -0.26786469 -1.43103081
14 -3.65105124 -0.26786469
15 0.10302174 -3.65105124
16 -0.40264052 0.10302174
17 -0.84505372 -0.40264052
18 -3.34974243 -0.84505372
19 4.04468858 -3.34974243
20 -2.44299656 4.04468858
21 0.70288814 -2.44299656
22 0.05045683 0.70288814
23 3.06906178 0.05045683
24 4.00837387 3.06906178
25 3.57428170 4.00837387
26 2.21548675 3.57428170
27 1.87190725 2.21548675
28 1.30667558 1.87190725
29 0.92333946 1.30667558
30 1.42668482 0.92333946
31 -0.41183447 1.42668482
32 -4.45301012 -0.41183447
33 -3.22299068 -4.45301012
34 -1.10737710 -3.22299068
35 0.95790557 -1.10737710
36 -3.58057187 0.95790557
37 1.84507962 -3.58057187
38 2.74508582 1.84507962
39 2.50837387 2.74508582
> 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/755ft1292355952.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/855ft1292355952.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/955ft1292355952.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10gxww1292355952.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111fvk1292355952.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/125yb81292355952.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/13czq21292355952.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/1448741292355952.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/15886a1292355952.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/16m0mj1292355952.tab")
+ }
>
> try(system("convert tmp/1rwh21292355952.ps tmp/1rwh21292355952.png",intern=TRUE))
character(0)
> try(system("convert tmp/2rwh21292355952.ps tmp/2rwh21292355952.png",intern=TRUE))
character(0)
> try(system("convert tmp/325yn1292355952.ps tmp/325yn1292355952.png",intern=TRUE))
character(0)
> try(system("convert tmp/425yn1292355952.ps tmp/425yn1292355952.png",intern=TRUE))
character(0)
> try(system("convert tmp/525yn1292355952.ps tmp/525yn1292355952.png",intern=TRUE))
character(0)
> try(system("convert tmp/6dwg81292355952.ps tmp/6dwg81292355952.png",intern=TRUE))
character(0)
> try(system("convert tmp/755ft1292355952.ps tmp/755ft1292355952.png",intern=TRUE))
character(0)
> try(system("convert tmp/855ft1292355952.ps tmp/855ft1292355952.png",intern=TRUE))
character(0)
> try(system("convert tmp/955ft1292355952.ps tmp/955ft1292355952.png",intern=TRUE))
character(0)
> try(system("convert tmp/10gxww1292355952.ps tmp/10gxww1292355952.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.359 1.639 5.731