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,-1.698970004,1,5.2,2.204119983,4,10.9,0.51851394,1,8.3,1.717337583,1,11,-0.366531544,4,3.2,2.667452953,5,6.3,-1.096910013,1,6.6,-0.102372909,2,9.5,-0.698970004,2,3.3,1.441852176,5,11,-0.920818754,2,4.7,1.929418926,1,10.4,-1,3,7.4,0.017033339,4,2.1,2.716837723,5,17.9,-2,1,6.1,1.792391689,1,11.9,-1.698970004,3,13.8,0.230448921,1,14.3,0.544068044,1,15.2,-0.318758763,2,10,1,4,6.5,0.209515015,4,7.5,2.283301229,5,10.6,0.397940009,3,7.4,-0.552841969,1,8.4,0.627365857,2,5.7,0.832508913,2,4.9,-0.124938737,3,3.2,0.556302501,5,11,1.744292983,2,4.9,-0.045757491,3,13.2,0.301029996,2,9.7,-1,4,12.8,0.622214023,1,11.9,0.544068044,2),dim=c(3,39),dimnames=list(c('SWS','logWb','ODI
'),1:39))
> y <- array(NA,dim=c(3,39),dimnames=list(c('SWS','logWb','ODI
'),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 logWb ODI\r
1 6.3 0.00000000 3
2 2.1 3.40602895 4
3 9.1 1.02325246 4
4 15.8 -1.69897000 1
5 5.2 2.20411998 4
6 10.9 0.51851394 1
7 8.3 1.71733758 1
8 11.0 -0.36653154 4
9 3.2 2.66745295 5
10 6.3 -1.09691001 1
11 6.6 -0.10237291 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 -1.00000000 3
17 7.4 0.01703334 4
18 2.1 2.71683772 5
19 17.9 -2.00000000 1
20 6.1 1.79239169 1
21 11.9 -1.69897000 3
22 13.8 0.23044892 1
23 14.3 0.54406804 1
24 15.2 -0.31875876 2
25 10.0 1.00000000 4
26 6.5 0.20951501 4
27 7.5 2.28330123 5
28 10.6 0.39794001 3
29 7.4 -0.55284197 1
30 8.4 0.62736586 2
31 5.7 0.83250891 2
32 4.9 -0.12493874 3
33 3.2 0.55630250 5
34 11.0 1.74429298 2
35 4.9 -0.04575749 3
36 13.2 0.30103000 2
37 9.7 -1.00000000 4
38 12.8 0.62221402 1
39 11.9 0.54406804 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) logWb `ODI\r`
12.141 -1.406 -1.043
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.33959 -2.03751 -0.01644 2.57408 4.69773
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.1411 1.0329 11.755 6.98e-14 ***
logWb -1.4057 0.4008 -3.507 0.00123 **
`ODI\r` -1.0435 0.3635 -2.870 0.00683 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.958 on 36 degrees of freedom
Multiple R-squared: 0.4738, Adjusted R-squared: 0.4445
F-statistic: 16.2 on 2 and 36 DF, p-value: 9.58e-06
> 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,] 0.39107167 0.7821433 0.6089283
[2,] 0.22818081 0.4563616 0.7718192
[3,] 0.14383169 0.2876634 0.8561683
[4,] 0.07263451 0.1452690 0.9273655
[5,] 0.48992056 0.9798411 0.5100794
[6,] 0.49423421 0.9884684 0.5057658
[7,] 0.39447368 0.7889474 0.6055263
[8,] 0.33369120 0.6673824 0.6663088
[9,] 0.24420148 0.4884030 0.7557985
[10,] 0.24142018 0.4828404 0.7585798
[11,] 0.16662090 0.3332418 0.8333791
[12,] 0.11175792 0.2235158 0.8882421
[13,] 0.07470691 0.1494138 0.9252931
[14,] 0.14436880 0.2887376 0.8556312
[15,] 0.15618179 0.3123636 0.8438182
[16,] 0.13071414 0.2614283 0.8692859
[17,] 0.15661908 0.3132382 0.8433809
[18,] 0.20850928 0.4170186 0.7914907
[19,] 0.41830853 0.8366171 0.5816915
[20,] 0.41876261 0.8375252 0.5812374
[21,] 0.32786796 0.6557359 0.6721320
[22,] 0.29036227 0.5807245 0.7096377
[23,] 0.25977076 0.5195415 0.7402292
[24,] 0.35473290 0.7094658 0.6452671
[25,] 0.26315406 0.5263081 0.7368459
[26,] 0.36569160 0.7313832 0.6343084
[27,] 0.50040239 0.9991952 0.4995976
[28,] 0.35850056 0.7170011 0.6414994
> postscript(file="/var/www/html/rcomp/tmp/17t2a1292846450.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/27t2a1292846450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3i21d1292846450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4i21d1292846450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5i21d1292846450.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
-2.71072592 -1.07935352 2.57113516 2.31408793 0.33110166 0.53124459
7 8 9 10 11 12
-0.38354767 2.51749104 0.02587394 -6.33958562 -3.59809023 -1.53673738
13 14 15 16 17 18
-1.59697489 -0.34859412 -3.68542115 -0.01644373 -0.54332497 -1.00470501
19 20 21 22 23 24
3.99092471 -2.47804278 0.50100169 3.02630646 3.96716645 4.69773233
25 26 27 28 29 30
3.43844876 -1.17275006 3.78586503 2.14866543 -4.47477949 -0.77228345
31 32 33 34 35 36
-3.18391021 -4.28635453 -2.94180783 3.39780089 -4.17504804 3.56898042
37 38 39
0.32701315 2.57701764 2.61062333
> postscript(file="/var/www/html/rcomp/tmp/6tujg1292846450.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 -2.71072592 NA
1 -1.07935352 -2.71072592
2 2.57113516 -1.07935352
3 2.31408793 2.57113516
4 0.33110166 2.31408793
5 0.53124459 0.33110166
6 -0.38354767 0.53124459
7 2.51749104 -0.38354767
8 0.02587394 2.51749104
9 -6.33958562 0.02587394
10 -3.59809023 -6.33958562
11 -1.53673738 -3.59809023
12 -1.59697489 -1.53673738
13 -0.34859412 -1.59697489
14 -3.68542115 -0.34859412
15 -0.01644373 -3.68542115
16 -0.54332497 -0.01644373
17 -1.00470501 -0.54332497
18 3.99092471 -1.00470501
19 -2.47804278 3.99092471
20 0.50100169 -2.47804278
21 3.02630646 0.50100169
22 3.96716645 3.02630646
23 4.69773233 3.96716645
24 3.43844876 4.69773233
25 -1.17275006 3.43844876
26 3.78586503 -1.17275006
27 2.14866543 3.78586503
28 -4.47477949 2.14866543
29 -0.77228345 -4.47477949
30 -3.18391021 -0.77228345
31 -4.28635453 -3.18391021
32 -2.94180783 -4.28635453
33 3.39780089 -2.94180783
34 -4.17504804 3.39780089
35 3.56898042 -4.17504804
36 0.32701315 3.56898042
37 2.57701764 0.32701315
38 2.61062333 2.57701764
39 NA 2.61062333
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.07935352 -2.71072592
[2,] 2.57113516 -1.07935352
[3,] 2.31408793 2.57113516
[4,] 0.33110166 2.31408793
[5,] 0.53124459 0.33110166
[6,] -0.38354767 0.53124459
[7,] 2.51749104 -0.38354767
[8,] 0.02587394 2.51749104
[9,] -6.33958562 0.02587394
[10,] -3.59809023 -6.33958562
[11,] -1.53673738 -3.59809023
[12,] -1.59697489 -1.53673738
[13,] -0.34859412 -1.59697489
[14,] -3.68542115 -0.34859412
[15,] -0.01644373 -3.68542115
[16,] -0.54332497 -0.01644373
[17,] -1.00470501 -0.54332497
[18,] 3.99092471 -1.00470501
[19,] -2.47804278 3.99092471
[20,] 0.50100169 -2.47804278
[21,] 3.02630646 0.50100169
[22,] 3.96716645 3.02630646
[23,] 4.69773233 3.96716645
[24,] 3.43844876 4.69773233
[25,] -1.17275006 3.43844876
[26,] 3.78586503 -1.17275006
[27,] 2.14866543 3.78586503
[28,] -4.47477949 2.14866543
[29,] -0.77228345 -4.47477949
[30,] -3.18391021 -0.77228345
[31,] -4.28635453 -3.18391021
[32,] -2.94180783 -4.28635453
[33,] 3.39780089 -2.94180783
[34,] -4.17504804 3.39780089
[35,] 3.56898042 -4.17504804
[36,] 0.32701315 3.56898042
[37,] 2.57701764 0.32701315
[38,] 2.61062333 2.57701764
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.07935352 -2.71072592
2 2.57113516 -1.07935352
3 2.31408793 2.57113516
4 0.33110166 2.31408793
5 0.53124459 0.33110166
6 -0.38354767 0.53124459
7 2.51749104 -0.38354767
8 0.02587394 2.51749104
9 -6.33958562 0.02587394
10 -3.59809023 -6.33958562
11 -1.53673738 -3.59809023
12 -1.59697489 -1.53673738
13 -0.34859412 -1.59697489
14 -3.68542115 -0.34859412
15 -0.01644373 -3.68542115
16 -0.54332497 -0.01644373
17 -1.00470501 -0.54332497
18 3.99092471 -1.00470501
19 -2.47804278 3.99092471
20 0.50100169 -2.47804278
21 3.02630646 0.50100169
22 3.96716645 3.02630646
23 4.69773233 3.96716645
24 3.43844876 4.69773233
25 -1.17275006 3.43844876
26 3.78586503 -1.17275006
27 2.14866543 3.78586503
28 -4.47477949 2.14866543
29 -0.77228345 -4.47477949
30 -3.18391021 -0.77228345
31 -4.28635453 -3.18391021
32 -2.94180783 -4.28635453
33 3.39780089 -2.94180783
34 -4.17504804 3.39780089
35 3.56898042 -4.17504804
36 0.32701315 3.56898042
37 2.57701764 0.32701315
38 2.61062333 2.57701764
> 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/732z01292846450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/832z01292846450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/932z01292846450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10wuz41292846450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11huxr1292846450.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/12ldef1292846450.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/13z5co1292846450.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/14knau1292846450.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/15no9i1292846450.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/169opo1292846450.tab")
+ }
>
> try(system("convert tmp/17t2a1292846450.ps tmp/17t2a1292846450.png",intern=TRUE))
character(0)
> try(system("convert tmp/27t2a1292846450.ps tmp/27t2a1292846450.png",intern=TRUE))
character(0)
> try(system("convert tmp/3i21d1292846450.ps tmp/3i21d1292846450.png",intern=TRUE))
character(0)
> try(system("convert tmp/4i21d1292846450.ps tmp/4i21d1292846450.png",intern=TRUE))
character(0)
> try(system("convert tmp/5i21d1292846450.ps tmp/5i21d1292846450.png",intern=TRUE))
character(0)
> try(system("convert tmp/6tujg1292846450.ps tmp/6tujg1292846450.png",intern=TRUE))
character(0)
> try(system("convert tmp/732z01292846450.ps tmp/732z01292846450.png",intern=TRUE))
character(0)
> try(system("convert tmp/832z01292846450.ps tmp/832z01292846450.png",intern=TRUE))
character(0)
> try(system("convert tmp/932z01292846450.ps tmp/932z01292846450.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wuz41292846450.ps tmp/10wuz41292846450.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.318 1.606 6.482