R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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.
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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
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> x <- array(list(1.0137,0,0.9834,0,0.9643,0,0.947,0,0.906,0,0.9492,0,0.9397,0,0.9041,0,0.8721,0,0.8552,0,0.8564,0,0.8973,0,0.9383,0,0.9217,0,0.9095,0,0.892,0,0.8742,0,0.8532,0,0.8607,0,0.9005,0,0.9111,1,0.9059,1,0.8883,1,0.8924,1,0.8833,1,0.87,1,0.8758,1,0.8858,1,0.917,1,0.9554,1,0.9922,1,0.9778,1,0.9808,1,0.9811,1,1.0014,1,1.0183,1),dim=c(2,36),dimnames=list(c('Koers','Dummy'),1:36))
> y <- array(NA,dim=c(2,36),dimnames=list(c('Koers','Dummy'),1:36))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly 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
Koers Dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 1.0137 0 1 0 0 0 0 0 0 0 0 0 0 1
2 0.9834 0 0 1 0 0 0 0 0 0 0 0 0 2
3 0.9643 0 0 0 1 0 0 0 0 0 0 0 0 3
4 0.9470 0 0 0 0 1 0 0 0 0 0 0 0 4
5 0.9060 0 0 0 0 0 1 0 0 0 0 0 0 5
6 0.9492 0 0 0 0 0 0 1 0 0 0 0 0 6
7 0.9397 0 0 0 0 0 0 0 1 0 0 0 0 7
8 0.9041 0 0 0 0 0 0 0 0 1 0 0 0 8
9 0.8721 0 0 0 0 0 0 0 0 0 1 0 0 9
10 0.8552 0 0 0 0 0 0 0 0 0 0 1 0 10
11 0.8564 0 0 0 0 0 0 0 0 0 0 0 1 11
12 0.8973 0 0 0 0 0 0 0 0 0 0 0 0 12
13 0.9383 0 1 0 0 0 0 0 0 0 0 0 0 13
14 0.9217 0 0 1 0 0 0 0 0 0 0 0 0 14
15 0.9095 0 0 0 1 0 0 0 0 0 0 0 0 15
16 0.8920 0 0 0 0 1 0 0 0 0 0 0 0 16
17 0.8742 0 0 0 0 0 1 0 0 0 0 0 0 17
18 0.8532 0 0 0 0 0 0 1 0 0 0 0 0 18
19 0.8607 0 0 0 0 0 0 0 1 0 0 0 0 19
20 0.9005 0 0 0 0 0 0 0 0 1 0 0 0 20
21 0.9111 1 0 0 0 0 0 0 0 0 1 0 0 21
22 0.9059 1 0 0 0 0 0 0 0 0 0 1 0 22
23 0.8883 1 0 0 0 0 0 0 0 0 0 0 1 23
24 0.8924 1 0 0 0 0 0 0 0 0 0 0 0 24
25 0.8833 1 1 0 0 0 0 0 0 0 0 0 0 25
26 0.8700 1 0 1 0 0 0 0 0 0 0 0 0 26
27 0.8758 1 0 0 1 0 0 0 0 0 0 0 0 27
28 0.8858 1 0 0 0 1 0 0 0 0 0 0 0 28
29 0.9170 1 0 0 0 0 1 0 0 0 0 0 0 29
30 0.9554 1 0 0 0 0 0 1 0 0 0 0 0 30
31 0.9922 1 0 0 0 0 0 0 1 0 0 0 0 31
32 0.9778 1 0 0 0 0 0 0 0 1 0 0 0 32
33 0.9808 1 0 0 0 0 0 0 0 0 1 0 0 33
34 0.9811 1 0 0 0 0 0 0 0 0 0 1 0 34
35 1.0014 1 0 0 0 0 0 0 0 0 0 0 1 35
36 1.0183 1 0 0 0 0 0 0 0 0 0 0 0 36
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dummy M1 M2 M3 M4
0.9261611 0.0331083 0.0145292 -0.0050278 -0.0130181 -0.0207750
M5 M6 M7 M8 M9 M10
-0.0294653 -0.0087556 0.0033542 0.0004639 -0.0161958 -0.0229528
M11 t
-0.0211431 -0.0005097
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.077756 -0.038183 -0.001626 0.037053 0.081114
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.9261611 0.0427788 21.650 2.53e-16 ***
Dummy 0.0331083 0.0405835 0.816 0.423
M1 0.0145292 0.0483435 0.301 0.767
M2 -0.0050278 0.0479873 -0.105 0.918
M3 -0.0130181 0.0477084 -0.273 0.788
M4 -0.0207750 0.0475082 -0.437 0.666
M5 -0.0294653 0.0473877 -0.622 0.540
M6 -0.0087556 0.0473474 -0.185 0.855
M7 0.0033542 0.0473877 0.071 0.944
M8 0.0004639 0.0475082 0.010 0.992
M9 -0.0161958 0.0472265 -0.343 0.735
M10 -0.0229528 0.0470242 -0.488 0.630
M11 -0.0211431 0.0469025 -0.451 0.657
t -0.0005097 0.0019526 -0.261 0.796
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.05739 on 22 degrees of freedom
Multiple R-squared: 0.1204, Adjusted R-squared: -0.3994
F-statistic: 0.2316 on 13 and 22 DF, p-value: 0.9955
> 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,] 0.2649464 0.5298927 0.7350536
[2,] 0.3278696 0.6557393 0.6721304
[3,] 0.3240584 0.6481168 0.6759416
> postscript(file="/var/www/html/rcomp/tmp/1k4li1227839048.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/256o61227839048.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3910i1227839048.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4qve51227839048.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5kd0w1227839048.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 = 36
Frequency = 1
1 2 3 4 5 6
0.073519444 0.063286111 0.052686111 0.043652778 0.011852778 0.034852778
7 8 9 10 11 12
0.013752778 -0.018447222 -0.033277778 -0.042911111 -0.043011111 -0.022744444
13 14 15 16 17 18
0.004236111 0.007702778 0.004002778 -0.005230556 -0.013830556 -0.055030556
19 20 21 22 23 24
-0.059130556 -0.015930556 -0.021269444 -0.019202778 -0.038102778 -0.054636111
25 26 27 28 29 30
-0.077755556 -0.070988889 -0.056688889 -0.038422222 0.001977778 0.020177778
31 32 33 34 35 36
0.045377778 0.034377778 0.054547222 0.062113889 0.081113889 0.077380556
> postscript(file="/var/www/html/rcomp/tmp/6iyzh1227839048.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 = 36
Frequency = 1
lag(myerror, k = 1) myerror
0 0.073519444 NA
1 0.063286111 0.073519444
2 0.052686111 0.063286111
3 0.043652778 0.052686111
4 0.011852778 0.043652778
5 0.034852778 0.011852778
6 0.013752778 0.034852778
7 -0.018447222 0.013752778
8 -0.033277778 -0.018447222
9 -0.042911111 -0.033277778
10 -0.043011111 -0.042911111
11 -0.022744444 -0.043011111
12 0.004236111 -0.022744444
13 0.007702778 0.004236111
14 0.004002778 0.007702778
15 -0.005230556 0.004002778
16 -0.013830556 -0.005230556
17 -0.055030556 -0.013830556
18 -0.059130556 -0.055030556
19 -0.015930556 -0.059130556
20 -0.021269444 -0.015930556
21 -0.019202778 -0.021269444
22 -0.038102778 -0.019202778
23 -0.054636111 -0.038102778
24 -0.077755556 -0.054636111
25 -0.070988889 -0.077755556
26 -0.056688889 -0.070988889
27 -0.038422222 -0.056688889
28 0.001977778 -0.038422222
29 0.020177778 0.001977778
30 0.045377778 0.020177778
31 0.034377778 0.045377778
32 0.054547222 0.034377778
33 0.062113889 0.054547222
34 0.081113889 0.062113889
35 0.077380556 0.081113889
36 NA 0.077380556
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.063286111 0.073519444
[2,] 0.052686111 0.063286111
[3,] 0.043652778 0.052686111
[4,] 0.011852778 0.043652778
[5,] 0.034852778 0.011852778
[6,] 0.013752778 0.034852778
[7,] -0.018447222 0.013752778
[8,] -0.033277778 -0.018447222
[9,] -0.042911111 -0.033277778
[10,] -0.043011111 -0.042911111
[11,] -0.022744444 -0.043011111
[12,] 0.004236111 -0.022744444
[13,] 0.007702778 0.004236111
[14,] 0.004002778 0.007702778
[15,] -0.005230556 0.004002778
[16,] -0.013830556 -0.005230556
[17,] -0.055030556 -0.013830556
[18,] -0.059130556 -0.055030556
[19,] -0.015930556 -0.059130556
[20,] -0.021269444 -0.015930556
[21,] -0.019202778 -0.021269444
[22,] -0.038102778 -0.019202778
[23,] -0.054636111 -0.038102778
[24,] -0.077755556 -0.054636111
[25,] -0.070988889 -0.077755556
[26,] -0.056688889 -0.070988889
[27,] -0.038422222 -0.056688889
[28,] 0.001977778 -0.038422222
[29,] 0.020177778 0.001977778
[30,] 0.045377778 0.020177778
[31,] 0.034377778 0.045377778
[32,] 0.054547222 0.034377778
[33,] 0.062113889 0.054547222
[34,] 0.081113889 0.062113889
[35,] 0.077380556 0.081113889
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.063286111 0.073519444
2 0.052686111 0.063286111
3 0.043652778 0.052686111
4 0.011852778 0.043652778
5 0.034852778 0.011852778
6 0.013752778 0.034852778
7 -0.018447222 0.013752778
8 -0.033277778 -0.018447222
9 -0.042911111 -0.033277778
10 -0.043011111 -0.042911111
11 -0.022744444 -0.043011111
12 0.004236111 -0.022744444
13 0.007702778 0.004236111
14 0.004002778 0.007702778
15 -0.005230556 0.004002778
16 -0.013830556 -0.005230556
17 -0.055030556 -0.013830556
18 -0.059130556 -0.055030556
19 -0.015930556 -0.059130556
20 -0.021269444 -0.015930556
21 -0.019202778 -0.021269444
22 -0.038102778 -0.019202778
23 -0.054636111 -0.038102778
24 -0.077755556 -0.054636111
25 -0.070988889 -0.077755556
26 -0.056688889 -0.070988889
27 -0.038422222 -0.056688889
28 0.001977778 -0.038422222
29 0.020177778 0.001977778
30 0.045377778 0.020177778
31 0.034377778 0.045377778
32 0.054547222 0.034377778
33 0.062113889 0.054547222
34 0.081113889 0.062113889
35 0.077380556 0.081113889
> 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/73dr51227839048.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8eboj1227839048.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9tq631227839048.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
hat values (leverages) are all = 0.3888889
and there are no factor predictors; no plot no. 5
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10708p1227839048.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11db5y1227839048.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/1203ww1227839049.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/133wwa1227839049.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/14djsl1227839049.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/15brpo1227839049.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/16cofb1227839049.tab")
+ }
>
> system("convert tmp/1k4li1227839048.ps tmp/1k4li1227839048.png")
> system("convert tmp/256o61227839048.ps tmp/256o61227839048.png")
> system("convert tmp/3910i1227839048.ps tmp/3910i1227839048.png")
> system("convert tmp/4qve51227839048.ps tmp/4qve51227839048.png")
> system("convert tmp/5kd0w1227839048.ps tmp/5kd0w1227839048.png")
> system("convert tmp/6iyzh1227839048.ps tmp/6iyzh1227839048.png")
> system("convert tmp/73dr51227839048.ps tmp/73dr51227839048.png")
> system("convert tmp/8eboj1227839048.ps tmp/8eboj1227839048.png")
> system("convert tmp/9tq631227839048.ps tmp/9tq631227839048.png")
> system("convert tmp/10708p1227839048.ps tmp/10708p1227839048.png")
>
>
> proc.time()
user system elapsed
2.102 1.484 2.483