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(6.3,0,3,2.1,3.40602894496362,4,9.1,1.02325245963371,4,15.8,-1.69897000433602,1,5.2,2.20411998265592,4,10.9,0.51851393987789,1,8.3,1.71733758272386,1,11,-0.36653154442041,4,3.2,2.66745295288995,5,6.3,-1.09691001300806,1,6.6,-0.10237290870956,2,9.5,-0.69897000433602,2,3.3,1.44185217577329,5,11,-0.92081875395238,2,4.7,1.92941892571429,1,10.4,-1,3,7.4,0.01703333929878,4,2.1,2.71683772329952,5,17.9,-2,1,6.1,1.79239168949825,1,11.9,-1.69897000433602,3,13.8,0.23044892137827,1,14.3,0.54406804435028,1,15.2,-0.31875876262441,2,10,1,4,11.9,0.20951501454263,2,6.5,2.28330122870355,4,7.5,0.39794000867204,5,10.6,-0.55284196865778,3,7.4,0.62736585659273,1,8.4,0.83250891270624,2,5.7,-0.1249387366083,2,4.9,0.55630250076729,3,3.2,1.74429298312268,5,11,-0.045757490560675,2,4.9,0.30102999566398,3,13.2,-1,2,9.7,0.6222140229663,4,12.8,0.54406804435028,1),dim=c(3,39),dimnames=list(c('SWS','logWb','D'),1:39))
> y <- array(NA,dim=c(3,39),dimnames=list(c('SWS','logWb','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 logWb D
1 6.3 0.00000000 3
2 2.1 3.40602894 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 11.9 0.20951501 2
27 6.5 2.28330123 4
28 7.5 0.39794001 5
29 10.6 -0.55284197 3
30 7.4 0.62736586 1
31 8.4 0.83250891 2
32 5.7 -0.12493874 2
33 4.9 0.55630250 3
34 3.2 1.74429298 5
35 11.0 -0.04575749 2
36 4.9 0.30103000 3
37 13.2 -1.00000000 2
38 9.7 0.62221402 4
39 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
11.6923 -1.8128 -0.8059
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.5750 -1.6431 0.3231 2.0186 4.5416
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.6923 0.9393 12.448 1.32e-14 ***
logWb -1.8128 0.3706 -4.892 2.09e-05 ***
D -0.8059 0.3361 -2.398 0.0218 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.655 on 36 degrees of freedom
Multiple R-squared: 0.5759, Adjusted R-squared: 0.5524
F-statistic: 24.44 on 2 and 36 DF, p-value: 1.969e-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,] 0.4832784 0.9665568 0.5167216
[2,] 0.3103763 0.6207526 0.6896237
[3,] 0.2098385 0.4196769 0.7901615
[4,] 0.1172387 0.2344773 0.8827613
[5,] 0.6755192 0.6489616 0.3244808
[6,] 0.7046082 0.5907837 0.2953918
[7,] 0.6289855 0.7420290 0.3710145
[8,] 0.5737237 0.8525525 0.4262763
[9,] 0.4808187 0.9616373 0.5191813
[10,] 0.4532449 0.9064898 0.5467551
[11,] 0.3602716 0.7205432 0.6397284
[12,] 0.2800542 0.5601085 0.7199458
[13,] 0.2069738 0.4139477 0.7930262
[14,] 0.2986207 0.5972413 0.7013793
[15,] 0.2558316 0.5116631 0.7441684
[16,] 0.1820564 0.3641127 0.8179436
[17,] 0.2221257 0.4442514 0.7778743
[18,] 0.3347294 0.6694588 0.6652706
[19,] 0.4997517 0.9995035 0.5002483
[20,] 0.5363812 0.9272375 0.4636188
[21,] 0.5103888 0.9792225 0.4896112
[22,] 0.4884303 0.9768606 0.5115697
[23,] 0.3886926 0.7773851 0.6113074
[24,] 0.2870768 0.5741535 0.7129232
[25,] 0.2457997 0.4915995 0.7542003
[26,] 0.1542562 0.3085124 0.8457438
[27,] 0.2924047 0.5848094 0.7075953
[28,] 0.3323445 0.6646889 0.6676555
> postscript(file="/var/www/html/rcomp/tmp/1proc1268939542.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/201if1268939542.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3bzy61268939542.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4nzs21268939542.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5gzg51268939542.ps",horizontal=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
-2.9747062 -0.1942720 2.4861483 1.8336082 0.7268658 0.9535400 0.5268090
8 9 10 11 12 13 14
1.8666997 0.3726789 -6.5749565 -3.6661583 -1.8476902 -1.7491327 -0.7498653
15 16 17 18 19 20 21
-2.6887226 -0.6875408 -1.0379606 -0.6377947 3.3878906 -1.5371303 -0.4546578
22 23 24 25 26 27 28
3.3313257 4.3998653 4.5415700 3.3439954 2.1992430 2.1704084 0.5584272
29 30 31 32 33 34 35
0.3230828 -2.3491295 -0.1713721 -4.6070664 -3.3662217 -1.3008575 0.8364761
36 37 38 39
-3.8289886 1.3065922 2.3591319 2.8998653
> postscript(file="/var/www/html/rcomp/tmp/6880x1268939542.ps",horizontal=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 -2.9747062 NA
1 -0.1942720 -2.9747062
2 2.4861483 -0.1942720
3 1.8336082 2.4861483
4 0.7268658 1.8336082
5 0.9535400 0.7268658
6 0.5268090 0.9535400
7 1.8666997 0.5268090
8 0.3726789 1.8666997
9 -6.5749565 0.3726789
10 -3.6661583 -6.5749565
11 -1.8476902 -3.6661583
12 -1.7491327 -1.8476902
13 -0.7498653 -1.7491327
14 -2.6887226 -0.7498653
15 -0.6875408 -2.6887226
16 -1.0379606 -0.6875408
17 -0.6377947 -1.0379606
18 3.3878906 -0.6377947
19 -1.5371303 3.3878906
20 -0.4546578 -1.5371303
21 3.3313257 -0.4546578
22 4.3998653 3.3313257
23 4.5415700 4.3998653
24 3.3439954 4.5415700
25 2.1992430 3.3439954
26 2.1704084 2.1992430
27 0.5584272 2.1704084
28 0.3230828 0.5584272
29 -2.3491295 0.3230828
30 -0.1713721 -2.3491295
31 -4.6070664 -0.1713721
32 -3.3662217 -4.6070664
33 -1.3008575 -3.3662217
34 0.8364761 -1.3008575
35 -3.8289886 0.8364761
36 1.3065922 -3.8289886
37 2.3591319 1.3065922
38 2.8998653 2.3591319
39 NA 2.8998653
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.1942720 -2.9747062
[2,] 2.4861483 -0.1942720
[3,] 1.8336082 2.4861483
[4,] 0.7268658 1.8336082
[5,] 0.9535400 0.7268658
[6,] 0.5268090 0.9535400
[7,] 1.8666997 0.5268090
[8,] 0.3726789 1.8666997
[9,] -6.5749565 0.3726789
[10,] -3.6661583 -6.5749565
[11,] -1.8476902 -3.6661583
[12,] -1.7491327 -1.8476902
[13,] -0.7498653 -1.7491327
[14,] -2.6887226 -0.7498653
[15,] -0.6875408 -2.6887226
[16,] -1.0379606 -0.6875408
[17,] -0.6377947 -1.0379606
[18,] 3.3878906 -0.6377947
[19,] -1.5371303 3.3878906
[20,] -0.4546578 -1.5371303
[21,] 3.3313257 -0.4546578
[22,] 4.3998653 3.3313257
[23,] 4.5415700 4.3998653
[24,] 3.3439954 4.5415700
[25,] 2.1992430 3.3439954
[26,] 2.1704084 2.1992430
[27,] 0.5584272 2.1704084
[28,] 0.3230828 0.5584272
[29,] -2.3491295 0.3230828
[30,] -0.1713721 -2.3491295
[31,] -4.6070664 -0.1713721
[32,] -3.3662217 -4.6070664
[33,] -1.3008575 -3.3662217
[34,] 0.8364761 -1.3008575
[35,] -3.8289886 0.8364761
[36,] 1.3065922 -3.8289886
[37,] 2.3591319 1.3065922
[38,] 2.8998653 2.3591319
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.1942720 -2.9747062
2 2.4861483 -0.1942720
3 1.8336082 2.4861483
4 0.7268658 1.8336082
5 0.9535400 0.7268658
6 0.5268090 0.9535400
7 1.8666997 0.5268090
8 0.3726789 1.8666997
9 -6.5749565 0.3726789
10 -3.6661583 -6.5749565
11 -1.8476902 -3.6661583
12 -1.7491327 -1.8476902
13 -0.7498653 -1.7491327
14 -2.6887226 -0.7498653
15 -0.6875408 -2.6887226
16 -1.0379606 -0.6875408
17 -0.6377947 -1.0379606
18 3.3878906 -0.6377947
19 -1.5371303 3.3878906
20 -0.4546578 -1.5371303
21 3.3313257 -0.4546578
22 4.3998653 3.3313257
23 4.5415700 4.3998653
24 3.3439954 4.5415700
25 2.1992430 3.3439954
26 2.1704084 2.1992430
27 0.5584272 2.1704084
28 0.3230828 0.5584272
29 -2.3491295 0.3230828
30 -0.1713721 -2.3491295
31 -4.6070664 -0.1713721
32 -3.3662217 -4.6070664
33 -1.3008575 -3.3662217
34 0.8364761 -1.3008575
35 -3.8289886 0.8364761
36 1.3065922 -3.8289886
37 2.3591319 1.3065922
38 2.8998653 2.3591319
> 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/7wrib1268939542.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8s96s1268939542.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9b1sy1268939542.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10o9jo1268939542.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/114wnm1268939542.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/12yeef1268939542.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/13pcoi1268939542.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/146b831268939542.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/15ofsa1268939542.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/16wg9s1268939542.tab")
+ }
>
> try(system("convert tmp/1proc1268939542.ps tmp/1proc1268939542.png",intern=TRUE))
character(0)
> try(system("convert tmp/201if1268939542.ps tmp/201if1268939542.png",intern=TRUE))
character(0)
> try(system("convert tmp/3bzy61268939542.ps tmp/3bzy61268939542.png",intern=TRUE))
character(0)
> try(system("convert tmp/4nzs21268939542.ps tmp/4nzs21268939542.png",intern=TRUE))
character(0)
> try(system("convert tmp/5gzg51268939542.ps tmp/5gzg51268939542.png",intern=TRUE))
character(0)
> try(system("convert tmp/6880x1268939542.ps tmp/6880x1268939542.png",intern=TRUE))
character(0)
> try(system("convert tmp/7wrib1268939542.ps tmp/7wrib1268939542.png",intern=TRUE))
character(0)
> try(system("convert tmp/8s96s1268939542.ps tmp/8s96s1268939542.png",intern=TRUE))
character(0)
> try(system("convert tmp/9b1sy1268939542.ps tmp/9b1sy1268939542.png",intern=TRUE))
character(0)
> try(system("convert tmp/10o9jo1268939542.ps tmp/10o9jo1268939542.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.256 1.538 3.493