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(6.3,0,3,14.3,0.544068044,1,9.1,1.02325246,4,15.8,-1.638272164,1,10.9,0.51851394,1,8.3,1.717337583,1,11,-0.37161107,4,3.2,2.667452953,5,2.1,3.406028945,4,7.4,0.626853415,1,9.5,-0.698970004,2,3.3,1.441852176,5,5.7,-0.124938737,2,7.4,0.017033339,4,11,-0.920818754,2,6.6,-0.105130343,2,2.1,2.716837723,5,17.9,-2,1,12.8,0.544068044,1,6.1,1.792391689,1,6.3,-1.124938737,1,11.9,-1.638272164,3,13.8,0.230448921,1,15.2,-0.318758763,2,10,1,4,11.9,0.209515015,2,6.5,2.283301229,4,7.5,0.397940009,5,10.6,-0.552841969,3,8.4,0.832508913,2,4.9,0.556302501,3,4.7,1.929418926,1,3.2,1.744292983,5,10.4,-0.995678626,3,5.2,2.204119983,4,11,-0.045757491,2,4.9,0.301029996,3,13.2,-0.982966661,2,9.7,0.622214023,4),dim=c(3,39),dimnames=list(c('SWS','log(wb)','D'),1:39))
> y <- array(NA,dim=c(3,39),dimnames=list(c('SWS','log(wb)','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
SWS log(wb) D
1 6.3 0.00000000 3
2 14.3 0.54406804 1
3 9.1 1.02325246 4
4 15.8 -1.63827216 1
5 10.9 0.51851394 1
6 8.3 1.71733758 1
7 11.0 -0.37161107 4
8 3.2 2.66745295 5
9 2.1 3.40602895 4
10 7.4 0.62685342 1
11 9.5 -0.69897000 2
12 3.3 1.44185218 5
13 5.7 -0.12493874 2
14 7.4 0.01703334 4
15 11.0 -0.92081875 2
16 6.6 -0.10513034 2
17 2.1 2.71683772 5
18 17.9 -2.00000000 1
19 12.8 0.54406804 1
20 6.1 1.79239169 1
21 6.3 -1.12493874 1
22 11.9 -1.63827216 3
23 13.8 0.23044892 1
24 15.2 -0.31875876 2
25 10.0 1.00000000 4
26 11.9 0.20951501 2
27 6.5 2.28330123 4
28 7.5 0.39794001 5
29 10.6 -0.55284197 3
30 8.4 0.83250891 2
31 4.9 0.55630250 3
32 4.7 1.92941893 1
33 3.2 1.74429298 5
34 10.4 -0.99567863 3
35 5.2 2.20411998 4
36 11.0 -0.04575749 2
37 4.9 0.30103000 3
38 13.2 -0.98296666 2
39 9.7 0.62221402 4
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `log(wb)` D
11.6991 -1.8149 -0.8062
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.6345 -1.6456 0.3162 2.0518 4.5348
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.6991 0.9411 12.431 1.37e-14 ***
`log(wb)` -1.8149 0.3729 -4.866 2.26e-05 ***
D -0.8062 0.3370 -2.393 0.0221 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.661 on 36 degrees of freedom
Multiple R-squared: 0.5741, Adjusted R-squared: 0.5505
F-statistic: 24.27 on 2 and 36 DF, p-value: 2.124e-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,] 0.7121873 0.5756254 0.2878127
[2,] 0.5960312 0.8079377 0.4039688
[3,] 0.4434691 0.8869382 0.5565309
[4,] 0.3149135 0.6298270 0.6850865
[5,] 0.3836928 0.7673856 0.6163072
[6,] 0.3639318 0.7278637 0.6360682
[7,] 0.3027256 0.6054511 0.6972744
[8,] 0.5064160 0.9871679 0.4935840
[9,] 0.4075957 0.8151913 0.5924043
[10,] 0.3120331 0.6240661 0.6879669
[11,] 0.3728204 0.7456407 0.6271796
[12,] 0.2839981 0.5679961 0.7160019
[13,] 0.3466173 0.6932346 0.6533827
[14,] 0.3554755 0.7109510 0.6445245
[15,] 0.2956958 0.5913916 0.7043042
[16,] 0.7392816 0.5214368 0.2607184
[17,] 0.6659483 0.6681034 0.3340517
[18,] 0.6974278 0.6051444 0.3025722
[19,] 0.8412804 0.3174393 0.1587196
[20,] 0.8711348 0.2577303 0.1288652
[21,] 0.8745770 0.2508459 0.1254230
[22,] 0.8811257 0.2377487 0.1188743
[23,] 0.8094343 0.3811314 0.1905657
[24,] 0.7125472 0.5749055 0.2874528
[25,] 0.6065690 0.7868619 0.3934310
[26,] 0.6324572 0.7350857 0.3675428
[27,] 0.5420139 0.9159723 0.4579861
[28,] 0.4010893 0.8021785 0.5989107
> postscript(file="/var/www/html/rcomp/tmp/1yz181292936077.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2yz181292936077.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/38r0t1292936077.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/48r0t1292936077.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/58r0t1292936077.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 7
-2.9804580 4.3945145 2.4828170 1.9338766 0.9481374 0.5238323 1.8513376
8 9 10 11 12 13 14
0.3730246 -0.1927817 -2.3552418 -1.8552063 -1.7512670 -4.6134210 -1.0433279
15 16 17 18 19 20 21
-0.7578303 -3.6774715 -0.6373490 3.3773919 2.8945145 -1.5399551 -6.6344960
22 23 24 25 26 27 28
-0.3536895 3.3253403 4.5348232 3.3406171 2.1935651 2.1696268 0.5541805
29 30 31 32 33 34 35
0.3162123 -0.1757893 -3.3708478 -2.6912701 -1.3023798 -0.6874734 0.7259241
36 37 38 39
0.8302818 -3.8341312 1.3293801 2.3549891
> postscript(file="/var/www/html/rcomp/tmp/6jihe1292936077.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 -2.9804580 NA
1 4.3945145 -2.9804580
2 2.4828170 4.3945145
3 1.9338766 2.4828170
4 0.9481374 1.9338766
5 0.5238323 0.9481374
6 1.8513376 0.5238323
7 0.3730246 1.8513376
8 -0.1927817 0.3730246
9 -2.3552418 -0.1927817
10 -1.8552063 -2.3552418
11 -1.7512670 -1.8552063
12 -4.6134210 -1.7512670
13 -1.0433279 -4.6134210
14 -0.7578303 -1.0433279
15 -3.6774715 -0.7578303
16 -0.6373490 -3.6774715
17 3.3773919 -0.6373490
18 2.8945145 3.3773919
19 -1.5399551 2.8945145
20 -6.6344960 -1.5399551
21 -0.3536895 -6.6344960
22 3.3253403 -0.3536895
23 4.5348232 3.3253403
24 3.3406171 4.5348232
25 2.1935651 3.3406171
26 2.1696268 2.1935651
27 0.5541805 2.1696268
28 0.3162123 0.5541805
29 -0.1757893 0.3162123
30 -3.3708478 -0.1757893
31 -2.6912701 -3.3708478
32 -1.3023798 -2.6912701
33 -0.6874734 -1.3023798
34 0.7259241 -0.6874734
35 0.8302818 0.7259241
36 -3.8341312 0.8302818
37 1.3293801 -3.8341312
38 2.3549891 1.3293801
39 NA 2.3549891
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.3945145 -2.9804580
[2,] 2.4828170 4.3945145
[3,] 1.9338766 2.4828170
[4,] 0.9481374 1.9338766
[5,] 0.5238323 0.9481374
[6,] 1.8513376 0.5238323
[7,] 0.3730246 1.8513376
[8,] -0.1927817 0.3730246
[9,] -2.3552418 -0.1927817
[10,] -1.8552063 -2.3552418
[11,] -1.7512670 -1.8552063
[12,] -4.6134210 -1.7512670
[13,] -1.0433279 -4.6134210
[14,] -0.7578303 -1.0433279
[15,] -3.6774715 -0.7578303
[16,] -0.6373490 -3.6774715
[17,] 3.3773919 -0.6373490
[18,] 2.8945145 3.3773919
[19,] -1.5399551 2.8945145
[20,] -6.6344960 -1.5399551
[21,] -0.3536895 -6.6344960
[22,] 3.3253403 -0.3536895
[23,] 4.5348232 3.3253403
[24,] 3.3406171 4.5348232
[25,] 2.1935651 3.3406171
[26,] 2.1696268 2.1935651
[27,] 0.5541805 2.1696268
[28,] 0.3162123 0.5541805
[29,] -0.1757893 0.3162123
[30,] -3.3708478 -0.1757893
[31,] -2.6912701 -3.3708478
[32,] -1.3023798 -2.6912701
[33,] -0.6874734 -1.3023798
[34,] 0.7259241 -0.6874734
[35,] 0.8302818 0.7259241
[36,] -3.8341312 0.8302818
[37,] 1.3293801 -3.8341312
[38,] 2.3549891 1.3293801
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.3945145 -2.9804580
2 2.4828170 4.3945145
3 1.9338766 2.4828170
4 0.9481374 1.9338766
5 0.5238323 0.9481374
6 1.8513376 0.5238323
7 0.3730246 1.8513376
8 -0.1927817 0.3730246
9 -2.3552418 -0.1927817
10 -1.8552063 -2.3552418
11 -1.7512670 -1.8552063
12 -4.6134210 -1.7512670
13 -1.0433279 -4.6134210
14 -0.7578303 -1.0433279
15 -3.6774715 -0.7578303
16 -0.6373490 -3.6774715
17 3.3773919 -0.6373490
18 2.8945145 3.3773919
19 -1.5399551 2.8945145
20 -6.6344960 -1.5399551
21 -0.3536895 -6.6344960
22 3.3253403 -0.3536895
23 4.5348232 3.3253403
24 3.3406171 4.5348232
25 2.1935651 3.3406171
26 2.1696268 2.1935651
27 0.5541805 2.1696268
28 0.3162123 0.5541805
29 -0.1757893 0.3162123
30 -3.3708478 -0.1757893
31 -2.6912701 -3.3708478
32 -1.3023798 -2.6912701
33 -0.6874734 -1.3023798
34 0.7259241 -0.6874734
35 0.8302818 0.7259241
36 -3.8341312 0.8302818
37 1.3293801 -3.8341312
38 2.3549891 1.3293801
> 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/7u9zh1292936077.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8u9zh1292936077.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9u9zh1292936077.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10miy11292936077.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1181ep1292936077.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/12tkdv1292936077.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/13ptt41292936077.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/14bcrs1292936077.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/15l39v1292936077.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/16hd6m1292936077.tab")
+ }
>
> try(system("convert tmp/1yz181292936077.ps tmp/1yz181292936077.png",intern=TRUE))
character(0)
> try(system("convert tmp/2yz181292936077.ps tmp/2yz181292936077.png",intern=TRUE))
character(0)
> try(system("convert tmp/38r0t1292936077.ps tmp/38r0t1292936077.png",intern=TRUE))
character(0)
> try(system("convert tmp/48r0t1292936077.ps tmp/48r0t1292936077.png",intern=TRUE))
character(0)
> try(system("convert tmp/58r0t1292936077.ps tmp/58r0t1292936077.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jihe1292936077.ps tmp/6jihe1292936077.png",intern=TRUE))
character(0)
> try(system("convert tmp/7u9zh1292936077.ps tmp/7u9zh1292936077.png",intern=TRUE))
character(0)
> try(system("convert tmp/8u9zh1292936077.ps tmp/8u9zh1292936077.png",intern=TRUE))
character(0)
> try(system("convert tmp/9u9zh1292936077.ps tmp/9u9zh1292936077.png",intern=TRUE))
character(0)
> try(system("convert tmp/10miy11292936077.ps tmp/10miy11292936077.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.296 1.595 5.374