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.
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(124.1,0,124.4,0,115.7,1,108.3,1,102.3,0,104.6,0,104,1,103.5,1,96,1,96.6,1,95.4,1,92.1,1,93,0,90.4,1,93.3,0,97.1,0,111,0,114.1,0,113.3,1,111,1,107.2,1,118.3,1,134.1,0,139,0,116.7,0,112.5,0,122.8,0,130,0,125.6,0,123.8,0,135.8,0,136.4,0,135.3,0,149.5,0,159.6,0,161.4,0,175.2,0,199.5,0,245,0,257.8,0),dim=c(2,40),dimnames=list(c('Prijsindexcijfer','dummy'),1:40))
> y <- array(NA,dim=c(2,40),dimnames=list(c('Prijsindexcijfer','dummy'),1:40))
> 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 = 'Include Quarterly 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
Prijsindexcijfer dummy Q1 Q2 Q3
1 124.1 0 1 0 0
2 124.4 0 0 1 0
3 115.7 1 0 0 1
4 108.3 1 0 0 0
5 102.3 0 1 0 0
6 104.6 0 0 1 0
7 104.0 1 0 0 1
8 103.5 1 0 0 0
9 96.0 1 1 0 0
10 96.6 1 0 1 0
11 95.4 1 0 0 1
12 92.1 1 0 0 0
13 93.0 0 1 0 0
14 90.4 1 0 1 0
15 93.3 0 0 0 1
16 97.1 0 0 0 0
17 111.0 0 1 0 0
18 114.1 0 0 1 0
19 113.3 1 0 0 1
20 111.0 1 0 0 0
21 107.2 1 1 0 0
22 118.3 1 0 1 0
23 134.1 0 0 0 1
24 139.0 0 0 0 0
25 116.7 0 1 0 0
26 112.5 0 0 1 0
27 122.8 0 0 0 1
28 130.0 0 0 0 0
29 125.6 0 1 0 0
30 123.8 0 0 1 0
31 135.8 0 0 0 1
32 136.4 0 0 0 0
33 135.3 0 1 0 0
34 149.5 0 0 1 0
35 159.6 0 0 0 1
36 161.4 0 0 0 0
37 175.2 0 1 0 0
38 199.5 0 0 1 0
39 245.0 0 0 0 1
40 257.8 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dummy Q1 Q2 Q3
148.62 -37.39 -22.50 -14.03 -1.76
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-53.56 -15.99 -7.26 7.75 109.18
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 148.62 11.86 12.536 1.68e-14 ***
dummy -37.39 11.80 -3.170 0.00316 **
Q1 -22.50 15.56 -1.446 0.15710
Q2 -14.03 15.43 -0.909 0.36932
Q3 -1.76 15.38 -0.114 0.90955
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 34.39 on 35 degrees of freedom
Multiple R-squared: 0.2445, Adjusted R-squared: 0.1582
F-statistic: 2.832 on 4 and 35 DF, p-value: 0.03904
> 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,] 5.091902e-02 1.018380e-01 0.9490810
[2,] 1.365275e-02 2.730549e-02 0.9863473
[3,] 3.262085e-03 6.524171e-03 0.9967379
[4,] 1.407564e-03 2.815127e-03 0.9985924
[5,] 5.870787e-04 1.174157e-03 0.9994129
[6,] 4.947877e-04 9.895754e-04 0.9995052
[7,] 1.512180e-04 3.024361e-04 0.9998488
[8,] 2.381875e-04 4.763750e-04 0.9997618
[9,] 1.467690e-04 2.935380e-04 0.9998532
[10,] 4.960180e-05 9.920360e-05 0.9999504
[11,] 1.821100e-05 3.642201e-05 0.9999818
[12,] 7.324106e-06 1.464821e-05 0.9999927
[13,] 2.933704e-06 5.867409e-06 0.9999971
[14,] 7.652614e-07 1.530523e-06 0.9999992
[15,] 3.189345e-07 6.378690e-07 0.9999997
[16,] 5.832839e-07 1.166568e-06 0.9999994
[17,] 1.281838e-06 2.563676e-06 0.9999987
[18,] 4.358351e-07 8.716702e-07 0.9999996
[19,] 1.743743e-07 3.487485e-07 0.9999998
[20,] 1.136676e-07 2.273352e-07 0.9999999
[21,] 1.064443e-07 2.128886e-07 0.9999999
[22,] 4.865863e-08 9.731727e-08 1.0000000
[23,] 2.990742e-08 5.981484e-08 1.0000000
[24,] 6.585308e-08 1.317062e-07 0.9999999
[25,] 3.352830e-07 6.705660e-07 0.9999997
> postscript(file="/var/www/html/rcomp/tmp/1mzrn1229525382.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2reae1229525382.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3tvol1229525382.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/446ht1229525382.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5qrzc1229525382.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 = 40
Frequency = 1
1 2 3 4 5 6
-2.0183529 -10.1875294 6.2350588 -2.9249412 -23.8183529 -29.9875294
7 8 9 10 11 12
-5.4649412 -7.7249412 7.2734118 -0.5957647 -14.0649412 -19.1249412
13 14 15 16 17 18
-33.1183529 -6.7957647 -53.5567059 -51.5167059 -15.1183529 -20.4875294
19 20 21 22 23 24
3.8350588 -0.2249412 18.4734118 21.1042353 -12.7567059 -9.6167059
25 26 27 28 29 30
-9.4183529 -22.0875294 -24.0567059 -18.6167059 -0.5183529 -10.7875294
31 32 33 34 35 36
-11.0567059 -12.2167059 9.1816471 14.9124706 12.7432941 12.7832941
37 38 39 40
49.0816471 64.9124706 98.1432941 109.1832941
> postscript(file="/var/www/html/rcomp/tmp/60tfk1229525382.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 = 40
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.0183529 NA
1 -10.1875294 -2.0183529
2 6.2350588 -10.1875294
3 -2.9249412 6.2350588
4 -23.8183529 -2.9249412
5 -29.9875294 -23.8183529
6 -5.4649412 -29.9875294
7 -7.7249412 -5.4649412
8 7.2734118 -7.7249412
9 -0.5957647 7.2734118
10 -14.0649412 -0.5957647
11 -19.1249412 -14.0649412
12 -33.1183529 -19.1249412
13 -6.7957647 -33.1183529
14 -53.5567059 -6.7957647
15 -51.5167059 -53.5567059
16 -15.1183529 -51.5167059
17 -20.4875294 -15.1183529
18 3.8350588 -20.4875294
19 -0.2249412 3.8350588
20 18.4734118 -0.2249412
21 21.1042353 18.4734118
22 -12.7567059 21.1042353
23 -9.6167059 -12.7567059
24 -9.4183529 -9.6167059
25 -22.0875294 -9.4183529
26 -24.0567059 -22.0875294
27 -18.6167059 -24.0567059
28 -0.5183529 -18.6167059
29 -10.7875294 -0.5183529
30 -11.0567059 -10.7875294
31 -12.2167059 -11.0567059
32 9.1816471 -12.2167059
33 14.9124706 9.1816471
34 12.7432941 14.9124706
35 12.7832941 12.7432941
36 49.0816471 12.7832941
37 64.9124706 49.0816471
38 98.1432941 64.9124706
39 109.1832941 98.1432941
40 NA 109.1832941
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -10.1875294 -2.0183529
[2,] 6.2350588 -10.1875294
[3,] -2.9249412 6.2350588
[4,] -23.8183529 -2.9249412
[5,] -29.9875294 -23.8183529
[6,] -5.4649412 -29.9875294
[7,] -7.7249412 -5.4649412
[8,] 7.2734118 -7.7249412
[9,] -0.5957647 7.2734118
[10,] -14.0649412 -0.5957647
[11,] -19.1249412 -14.0649412
[12,] -33.1183529 -19.1249412
[13,] -6.7957647 -33.1183529
[14,] -53.5567059 -6.7957647
[15,] -51.5167059 -53.5567059
[16,] -15.1183529 -51.5167059
[17,] -20.4875294 -15.1183529
[18,] 3.8350588 -20.4875294
[19,] -0.2249412 3.8350588
[20,] 18.4734118 -0.2249412
[21,] 21.1042353 18.4734118
[22,] -12.7567059 21.1042353
[23,] -9.6167059 -12.7567059
[24,] -9.4183529 -9.6167059
[25,] -22.0875294 -9.4183529
[26,] -24.0567059 -22.0875294
[27,] -18.6167059 -24.0567059
[28,] -0.5183529 -18.6167059
[29,] -10.7875294 -0.5183529
[30,] -11.0567059 -10.7875294
[31,] -12.2167059 -11.0567059
[32,] 9.1816471 -12.2167059
[33,] 14.9124706 9.1816471
[34,] 12.7432941 14.9124706
[35,] 12.7832941 12.7432941
[36,] 49.0816471 12.7832941
[37,] 64.9124706 49.0816471
[38,] 98.1432941 64.9124706
[39,] 109.1832941 98.1432941
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -10.1875294 -2.0183529
2 6.2350588 -10.1875294
3 -2.9249412 6.2350588
4 -23.8183529 -2.9249412
5 -29.9875294 -23.8183529
6 -5.4649412 -29.9875294
7 -7.7249412 -5.4649412
8 7.2734118 -7.7249412
9 -0.5957647 7.2734118
10 -14.0649412 -0.5957647
11 -19.1249412 -14.0649412
12 -33.1183529 -19.1249412
13 -6.7957647 -33.1183529
14 -53.5567059 -6.7957647
15 -51.5167059 -53.5567059
16 -15.1183529 -51.5167059
17 -20.4875294 -15.1183529
18 3.8350588 -20.4875294
19 -0.2249412 3.8350588
20 18.4734118 -0.2249412
21 21.1042353 18.4734118
22 -12.7567059 21.1042353
23 -9.6167059 -12.7567059
24 -9.4183529 -9.6167059
25 -22.0875294 -9.4183529
26 -24.0567059 -22.0875294
27 -18.6167059 -24.0567059
28 -0.5183529 -18.6167059
29 -10.7875294 -0.5183529
30 -11.0567059 -10.7875294
31 -12.2167059 -11.0567059
32 9.1816471 -12.2167059
33 14.9124706 9.1816471
34 12.7432941 14.9124706
35 12.7832941 12.7432941
36 49.0816471 12.7832941
37 64.9124706 49.0816471
38 98.1432941 64.9124706
39 109.1832941 98.1432941
> 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/7knc01229525382.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ahq11229525382.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ayve1229525382.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10fhum1229525382.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11g8o91229525382.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/12nkpf1229525382.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/13c3oi1229525382.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/1496uh1229525382.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/15tol21229525382.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/16wt3j1229525382.tab")
+ }
>
> system("convert tmp/1mzrn1229525382.ps tmp/1mzrn1229525382.png")
> system("convert tmp/2reae1229525382.ps tmp/2reae1229525382.png")
> system("convert tmp/3tvol1229525382.ps tmp/3tvol1229525382.png")
> system("convert tmp/446ht1229525382.ps tmp/446ht1229525382.png")
> system("convert tmp/5qrzc1229525382.ps tmp/5qrzc1229525382.png")
> system("convert tmp/60tfk1229525382.ps tmp/60tfk1229525382.png")
> system("convert tmp/7knc01229525382.ps tmp/7knc01229525382.png")
> system("convert tmp/8ahq11229525382.ps tmp/8ahq11229525382.png")
> system("convert tmp/9ayve1229525382.ps tmp/9ayve1229525382.png")
> system("convert tmp/10fhum1229525382.ps tmp/10fhum1229525382.png")
>
>
> proc.time()
user system elapsed
2.499 1.695 3.425