R version 2.6.1 (2007-11-26)
Copyright (C) 2007 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(5.38,5.70,5.32,5.70,5.26,5.20,5.20,5.50,5.47,6.10,5.17,5.10,5.43,5.50,5.57,5.80,4.99,5.30,4.93,5.10,4.54,5.10,4.67,5.00,5.53,6.00,4.84,5.00,4.83,4.90,4.71,5.00,5.06,5.30,4.93,5.20,5.49,5.70,4.95,5.10,4.96,5.10,5.04,5.30,5.52,5.90,5.44,5.50,5.44,5.70,5.14,5.30,4.81,5.30,5.35,5.50,4.50,4.80,5.18,5.30,4.79,5.10,5.31,5.30,5.13,5.20,5.45,6.00,4.60,5.50,4.51,4.90,4.72,4.80,4.57,4.90,4.59,5.00,4.55,4.90,5.18,5.40,4.79,5.10),dim=c(2,42),dimnames=list(c('P','U'),1:42))
> y <- array(NA,dim=c(2,42),dimnames=list(c('P','U'),1:42))
> 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 = '2'
> #'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
U P
1 5.7 5.38
2 5.7 5.32
3 5.2 5.26
4 5.5 5.20
5 6.1 5.47
6 5.1 5.17
7 5.5 5.43
8 5.8 5.57
9 5.3 4.99
10 5.1 4.93
11 5.1 4.54
12 5.0 4.67
13 6.0 5.53
14 5.0 4.84
15 4.9 4.83
16 5.0 4.71
17 5.3 5.06
18 5.2 4.93
19 5.7 5.49
20 5.1 4.95
21 5.1 4.96
22 5.3 5.04
23 5.9 5.52
24 5.5 5.44
25 5.7 5.44
26 5.3 5.14
27 5.3 4.81
28 5.5 5.35
29 4.8 4.50
30 5.3 5.18
31 5.1 4.79
32 5.3 5.31
33 5.2 5.13
34 6.0 5.45
35 5.5 4.60
36 4.9 4.51
37 4.8 4.72
38 4.9 4.57
39 5.0 4.59
40 4.9 4.55
41 5.4 5.18
42 5.1 4.79
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) P
0.9283 0.8691
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.321577 -0.130348 -0.004356 0.052270 0.573809
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.92834 0.43891 2.115 0.0407 *
P 0.86910 0.08683 10.009 1.88e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1867 on 40 degrees of freedom
Multiple R-Squared: 0.7146, Adjusted R-squared: 0.7075
F-statistic: 100.2 on 1 and 40 DF, p-value: 1.882e-12
> 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,] 0.73152729 0.53694542 0.268472712
[2,] 0.66030581 0.67938837 0.339694186
[3,] 0.85908256 0.28183488 0.140917441
[4,] 0.85722123 0.28555753 0.142778766
[5,] 0.86331946 0.27336109 0.136680543
[6,] 0.80033077 0.39933847 0.199669235
[7,] 0.87526869 0.24946262 0.124731311
[8,] 0.81293983 0.37412034 0.187060171
[9,] 0.85286362 0.29427275 0.147136375
[10,] 0.81848186 0.36303627 0.181518136
[11,] 0.82930560 0.34138880 0.170694401
[12,] 0.76373099 0.47253802 0.236269008
[13,] 0.68485940 0.63028120 0.315140602
[14,] 0.59674683 0.80650634 0.403253172
[15,] 0.50819445 0.98361110 0.491805550
[16,] 0.45157857 0.90315714 0.548421432
[17,] 0.40233977 0.80467953 0.597660234
[18,] 0.31552246 0.63104491 0.684477544
[19,] 0.30739213 0.61478426 0.692607870
[20,] 0.28069228 0.56138457 0.719307717
[21,] 0.21601936 0.43203871 0.783980644
[22,] 0.16305432 0.32610865 0.836945677
[23,] 0.16966717 0.33933434 0.830332828
[24,] 0.12054438 0.24108876 0.879455619
[25,] 0.08355909 0.16711817 0.916440913
[26,] 0.06136877 0.12273755 0.938631225
[27,] 0.03659010 0.07318020 0.963409899
[28,] 0.04983216 0.09966432 0.950167841
[29,] 0.06368057 0.12736114 0.936319430
[30,] 0.11489516 0.22979032 0.885104841
[31,] 0.91728658 0.16542684 0.082713422
[32,] 0.84073801 0.31852399 0.159261993
[33,] 0.99132798 0.01734405 0.008672023
> postscript(file="/var/www/html/rcomp/tmp/1w6x81206529324.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2b8bu1206529324.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jvc21206529324.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4kosy1206529324.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5hm6v1206529324.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 = 42
Frequency = 1
1 2 3 4 5
0.0959126471 0.1480585486 -0.2997955499 0.0523503516 0.4176937948
6 7 8 9 10
-0.3215766976 -0.1475422709 0.0307839589 0.0348610069 -0.1129930916
11 12 13 14 15
0.2259552683 0.0129724817 0.2655478933 -0.1347742393 -0.2260832557
16 17 18 19 20
-0.0217914527 -0.0259758782 -0.0129930916 0.0003118276 -0.1303750587
21 22 23 24 25
-0.1390660423 -0.0085939110 0.1742388768 -0.1562332545 0.0437667455
26 27 28 29 30
-0.0955037469 0.1912987115 -0.0780144022 -0.0392807974 -0.1302676812
31 32 33 34 35
0.0086806786 -0.2432504678 -0.1868127633 0.3350757619 0.5738093668
36 37 38 39 40
0.0520282191 -0.2304824363 -0.0001176825 0.0825003504 0.0172642847
41 42
-0.0302676812 0.0086806786
> postscript(file="/var/www/html/rcomp/tmp/6tnu71206529324.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 = 42
Frequency = 1
lag(myerror, k = 1) myerror
0 0.0959126471 NA
1 0.1480585486 0.0959126471
2 -0.2997955499 0.1480585486
3 0.0523503516 -0.2997955499
4 0.4176937948 0.0523503516
5 -0.3215766976 0.4176937948
6 -0.1475422709 -0.3215766976
7 0.0307839589 -0.1475422709
8 0.0348610069 0.0307839589
9 -0.1129930916 0.0348610069
10 0.2259552683 -0.1129930916
11 0.0129724817 0.2259552683
12 0.2655478933 0.0129724817
13 -0.1347742393 0.2655478933
14 -0.2260832557 -0.1347742393
15 -0.0217914527 -0.2260832557
16 -0.0259758782 -0.0217914527
17 -0.0129930916 -0.0259758782
18 0.0003118276 -0.0129930916
19 -0.1303750587 0.0003118276
20 -0.1390660423 -0.1303750587
21 -0.0085939110 -0.1390660423
22 0.1742388768 -0.0085939110
23 -0.1562332545 0.1742388768
24 0.0437667455 -0.1562332545
25 -0.0955037469 0.0437667455
26 0.1912987115 -0.0955037469
27 -0.0780144022 0.1912987115
28 -0.0392807974 -0.0780144022
29 -0.1302676812 -0.0392807974
30 0.0086806786 -0.1302676812
31 -0.2432504678 0.0086806786
32 -0.1868127633 -0.2432504678
33 0.3350757619 -0.1868127633
34 0.5738093668 0.3350757619
35 0.0520282191 0.5738093668
36 -0.2304824363 0.0520282191
37 -0.0001176825 -0.2304824363
38 0.0825003504 -0.0001176825
39 0.0172642847 0.0825003504
40 -0.0302676812 0.0172642847
41 0.0086806786 -0.0302676812
42 NA 0.0086806786
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.1480585486 0.0959126471
[2,] -0.2997955499 0.1480585486
[3,] 0.0523503516 -0.2997955499
[4,] 0.4176937948 0.0523503516
[5,] -0.3215766976 0.4176937948
[6,] -0.1475422709 -0.3215766976
[7,] 0.0307839589 -0.1475422709
[8,] 0.0348610069 0.0307839589
[9,] -0.1129930916 0.0348610069
[10,] 0.2259552683 -0.1129930916
[11,] 0.0129724817 0.2259552683
[12,] 0.2655478933 0.0129724817
[13,] -0.1347742393 0.2655478933
[14,] -0.2260832557 -0.1347742393
[15,] -0.0217914527 -0.2260832557
[16,] -0.0259758782 -0.0217914527
[17,] -0.0129930916 -0.0259758782
[18,] 0.0003118276 -0.0129930916
[19,] -0.1303750587 0.0003118276
[20,] -0.1390660423 -0.1303750587
[21,] -0.0085939110 -0.1390660423
[22,] 0.1742388768 -0.0085939110
[23,] -0.1562332545 0.1742388768
[24,] 0.0437667455 -0.1562332545
[25,] -0.0955037469 0.0437667455
[26,] 0.1912987115 -0.0955037469
[27,] -0.0780144022 0.1912987115
[28,] -0.0392807974 -0.0780144022
[29,] -0.1302676812 -0.0392807974
[30,] 0.0086806786 -0.1302676812
[31,] -0.2432504678 0.0086806786
[32,] -0.1868127633 -0.2432504678
[33,] 0.3350757619 -0.1868127633
[34,] 0.5738093668 0.3350757619
[35,] 0.0520282191 0.5738093668
[36,] -0.2304824363 0.0520282191
[37,] -0.0001176825 -0.2304824363
[38,] 0.0825003504 -0.0001176825
[39,] 0.0172642847 0.0825003504
[40,] -0.0302676812 0.0172642847
[41,] 0.0086806786 -0.0302676812
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.1480585486 0.0959126471
2 -0.2997955499 0.1480585486
3 0.0523503516 -0.2997955499
4 0.4176937948 0.0523503516
5 -0.3215766976 0.4176937948
6 -0.1475422709 -0.3215766976
7 0.0307839589 -0.1475422709
8 0.0348610069 0.0307839589
9 -0.1129930916 0.0348610069
10 0.2259552683 -0.1129930916
11 0.0129724817 0.2259552683
12 0.2655478933 0.0129724817
13 -0.1347742393 0.2655478933
14 -0.2260832557 -0.1347742393
15 -0.0217914527 -0.2260832557
16 -0.0259758782 -0.0217914527
17 -0.0129930916 -0.0259758782
18 0.0003118276 -0.0129930916
19 -0.1303750587 0.0003118276
20 -0.1390660423 -0.1303750587
21 -0.0085939110 -0.1390660423
22 0.1742388768 -0.0085939110
23 -0.1562332545 0.1742388768
24 0.0437667455 -0.1562332545
25 -0.0955037469 0.0437667455
26 0.1912987115 -0.0955037469
27 -0.0780144022 0.1912987115
28 -0.0392807974 -0.0780144022
29 -0.1302676812 -0.0392807974
30 0.0086806786 -0.1302676812
31 -0.2432504678 0.0086806786
32 -0.1868127633 -0.2432504678
33 0.3350757619 -0.1868127633
34 0.5738093668 0.3350757619
35 0.0520282191 0.5738093668
36 -0.2304824363 0.0520282191
37 -0.0001176825 -0.2304824363
38 0.0825003504 -0.0001176825
39 0.0172642847 0.0825003504
40 -0.0302676812 0.0172642847
41 0.0086806786 -0.0302676812
> 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/7lbmk1206529324.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8hp2t1206529324.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9tr5w1206529324.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10khxr1206529324.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11wkrc1206529324.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/12q9yw1206529324.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/133dnt1206529324.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/145dsu1206529325.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/15m9gm1206529325.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/16361l1206529325.tab")
+ }
>
> system("convert tmp/1w6x81206529324.ps tmp/1w6x81206529324.png")
> system("convert tmp/2b8bu1206529324.ps tmp/2b8bu1206529324.png")
> system("convert tmp/3jvc21206529324.ps tmp/3jvc21206529324.png")
> system("convert tmp/4kosy1206529324.ps tmp/4kosy1206529324.png")
> system("convert tmp/5hm6v1206529324.ps tmp/5hm6v1206529324.png")
> system("convert tmp/6tnu71206529324.ps tmp/6tnu71206529324.png")
> system("convert tmp/7lbmk1206529324.ps tmp/7lbmk1206529324.png")
> system("convert tmp/8hp2t1206529324.ps tmp/8hp2t1206529324.png")
> system("convert tmp/9tr5w1206529324.ps tmp/9tr5w1206529324.png")
> system("convert tmp/10khxr1206529324.ps tmp/10khxr1206529324.png")
>
>
> proc.time()
user system elapsed
4.747 2.733 5.102