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.
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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(124.1,0,124.4,0,115.7,0,108.3,0,102.3,0,104.6,0,104,0,103.5,0,96,0,96.6,0,95.4,0,92.1,0,93,0,90.4,0,93.3,0,97.1,0,111,1,114.1,1,113.3,1,111,1,107.2,1,118.3,1,134.1,1,139,1,116.7,1,112.5,1,122.8,1,130,1,125.6,1,123.8,1,135.8,1,136.4,1,135.3,1,149.5,1,159.6,1,161.4,1,175.2,1,199.5,1,245,1,257.8,1),dim=c(2,40),dimnames=list(c('Prijsindex','x'),1:40))
> y <- array(NA,dim=c(2,40),dimnames=list(c('Prijsindex','x'),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 = '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
Prijsindex x Q1 Q2 Q3 t
1 124.1 0 1 0 0 1
2 124.4 0 0 1 0 2
3 115.7 0 0 0 1 3
4 108.3 0 0 0 0 4
5 102.3 0 1 0 0 5
6 104.6 0 0 1 0 6
7 104.0 0 0 0 1 7
8 103.5 0 0 0 0 8
9 96.0 0 1 0 0 9
10 96.6 0 0 1 0 10
11 95.4 0 0 0 1 11
12 92.1 0 0 0 0 12
13 93.0 0 1 0 0 13
14 90.4 0 0 1 0 14
15 93.3 0 0 0 1 15
16 97.1 0 0 0 0 16
17 111.0 1 1 0 0 17
18 114.1 1 0 1 0 18
19 113.3 1 0 0 1 19
20 111.0 1 0 0 0 20
21 107.2 1 1 0 0 21
22 118.3 1 0 1 0 22
23 134.1 1 0 0 1 23
24 139.0 1 0 0 0 24
25 116.7 1 1 0 0 25
26 112.5 1 0 1 0 26
27 122.8 1 0 0 1 27
28 130.0 1 0 0 0 28
29 125.6 1 1 0 0 29
30 123.8 1 0 1 0 30
31 135.8 1 0 0 1 31
32 136.4 1 0 0 0 32
33 135.3 1 1 0 0 33
34 149.5 1 0 1 0 34
35 159.6 1 0 0 1 35
36 161.4 1 0 0 0 36
37 175.2 1 1 0 0 37
38 199.5 1 0 1 0 38
39 245.0 1 0 0 1 39
40 257.8 1 0 0 0 40
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x Q1 Q2 Q3 t
79.161 -19.743 -5.973 -4.259 1.256 3.016
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-32.352 -18.232 -6.602 7.948 77.755
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 79.1606 12.1825 6.498 1.96e-07 ***
x -19.7431 16.5504 -1.193 0.241165
Q1 -5.9729 12.1620 -0.491 0.626502
Q2 -4.2586 12.0592 -0.353 0.726162
Q3 1.2557 11.9971 0.105 0.917255
t 3.0157 0.7057 4.273 0.000147 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 26.78 on 34 degrees of freedom
Multiple R-squared: 0.555, Adjusted R-squared: 0.4896
F-statistic: 8.481 on 5 and 34 DF, p-value: 2.725e-05
> 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,] 2.201833e-02 4.403667e-02 0.9779817
[2,] 4.881071e-03 9.762141e-03 0.9951189
[3,] 1.181687e-03 2.363374e-03 0.9988183
[4,] 2.649815e-04 5.299630e-04 0.9997350
[5,] 1.220644e-04 2.441287e-04 0.9998779
[6,] 2.433708e-05 4.867416e-05 0.9999757
[7,] 1.100950e-05 2.201900e-05 0.9999890
[8,] 1.921059e-05 3.842117e-05 0.9999808
[9,] 6.113619e-06 1.222724e-05 0.9999939
[10,] 2.075223e-06 4.150446e-06 0.9999979
[11,] 4.914230e-07 9.828461e-07 0.9999995
[12,] 9.809539e-08 1.961908e-07 0.9999999
[13,] 2.697238e-08 5.394476e-08 1.0000000
[14,] 6.599271e-08 1.319854e-07 0.9999999
[15,] 1.179114e-05 2.358229e-05 0.9999882
[16,] 5.129841e-04 1.025968e-03 0.9994870
[17,] 8.486112e-04 1.697222e-03 0.9991514
[18,] 6.797647e-04 1.359529e-03 0.9993202
[19,] 5.248323e-04 1.049665e-03 0.9994752
[20,] 1.133683e-03 2.267366e-03 0.9988663
[21,] 7.521261e-03 1.504252e-02 0.9924787
[22,] 1.315550e-02 2.631100e-02 0.9868445
[23,] 1.613064e-02 3.226129e-02 0.9838694
> postscript(file="/var/www/html/rcomp/tmp/17puv1264273729.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/28owc1264273729.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3beh91264273729.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4kurg1264273729.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/50k0h1264273729.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
47.8966667 43.4666667 26.2366667 17.0766667 14.0338889 11.6038889
7 8 9 10 11 12
2.4738889 0.2138889 -4.3288889 -8.4588889 -18.1888889 -23.2488889
13 14 15 16 17 18
-19.3916667 -26.7216667 -32.3516667 -30.3116667 6.2886111 4.6586111
19 20 21 22 23 24
-4.6713889 -8.7313889 -9.5741667 -3.2041667 4.0658333 7.2058333
25 26 27 28 29 30
-12.1369444 -21.0669444 -19.2969444 -13.8569444 -15.2997222 -21.8297222
31 32 33 34 35 36
-18.3597222 -19.5197222 -17.6625000 -8.1925000 -6.6225000 -6.5825000
37 38 39 40
10.1747222 29.7447222 66.7147222 77.7547222
> postscript(file="/var/www/html/rcomp/tmp/6i5ja1264273729.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 47.8966667 NA
1 43.4666667 47.8966667
2 26.2366667 43.4666667
3 17.0766667 26.2366667
4 14.0338889 17.0766667
5 11.6038889 14.0338889
6 2.4738889 11.6038889
7 0.2138889 2.4738889
8 -4.3288889 0.2138889
9 -8.4588889 -4.3288889
10 -18.1888889 -8.4588889
11 -23.2488889 -18.1888889
12 -19.3916667 -23.2488889
13 -26.7216667 -19.3916667
14 -32.3516667 -26.7216667
15 -30.3116667 -32.3516667
16 6.2886111 -30.3116667
17 4.6586111 6.2886111
18 -4.6713889 4.6586111
19 -8.7313889 -4.6713889
20 -9.5741667 -8.7313889
21 -3.2041667 -9.5741667
22 4.0658333 -3.2041667
23 7.2058333 4.0658333
24 -12.1369444 7.2058333
25 -21.0669444 -12.1369444
26 -19.2969444 -21.0669444
27 -13.8569444 -19.2969444
28 -15.2997222 -13.8569444
29 -21.8297222 -15.2997222
30 -18.3597222 -21.8297222
31 -19.5197222 -18.3597222
32 -17.6625000 -19.5197222
33 -8.1925000 -17.6625000
34 -6.6225000 -8.1925000
35 -6.5825000 -6.6225000
36 10.1747222 -6.5825000
37 29.7447222 10.1747222
38 66.7147222 29.7447222
39 77.7547222 66.7147222
40 NA 77.7547222
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 43.4666667 47.8966667
[2,] 26.2366667 43.4666667
[3,] 17.0766667 26.2366667
[4,] 14.0338889 17.0766667
[5,] 11.6038889 14.0338889
[6,] 2.4738889 11.6038889
[7,] 0.2138889 2.4738889
[8,] -4.3288889 0.2138889
[9,] -8.4588889 -4.3288889
[10,] -18.1888889 -8.4588889
[11,] -23.2488889 -18.1888889
[12,] -19.3916667 -23.2488889
[13,] -26.7216667 -19.3916667
[14,] -32.3516667 -26.7216667
[15,] -30.3116667 -32.3516667
[16,] 6.2886111 -30.3116667
[17,] 4.6586111 6.2886111
[18,] -4.6713889 4.6586111
[19,] -8.7313889 -4.6713889
[20,] -9.5741667 -8.7313889
[21,] -3.2041667 -9.5741667
[22,] 4.0658333 -3.2041667
[23,] 7.2058333 4.0658333
[24,] -12.1369444 7.2058333
[25,] -21.0669444 -12.1369444
[26,] -19.2969444 -21.0669444
[27,] -13.8569444 -19.2969444
[28,] -15.2997222 -13.8569444
[29,] -21.8297222 -15.2997222
[30,] -18.3597222 -21.8297222
[31,] -19.5197222 -18.3597222
[32,] -17.6625000 -19.5197222
[33,] -8.1925000 -17.6625000
[34,] -6.6225000 -8.1925000
[35,] -6.5825000 -6.6225000
[36,] 10.1747222 -6.5825000
[37,] 29.7447222 10.1747222
[38,] 66.7147222 29.7447222
[39,] 77.7547222 66.7147222
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 43.4666667 47.8966667
2 26.2366667 43.4666667
3 17.0766667 26.2366667
4 14.0338889 17.0766667
5 11.6038889 14.0338889
6 2.4738889 11.6038889
7 0.2138889 2.4738889
8 -4.3288889 0.2138889
9 -8.4588889 -4.3288889
10 -18.1888889 -8.4588889
11 -23.2488889 -18.1888889
12 -19.3916667 -23.2488889
13 -26.7216667 -19.3916667
14 -32.3516667 -26.7216667
15 -30.3116667 -32.3516667
16 6.2886111 -30.3116667
17 4.6586111 6.2886111
18 -4.6713889 4.6586111
19 -8.7313889 -4.6713889
20 -9.5741667 -8.7313889
21 -3.2041667 -9.5741667
22 4.0658333 -3.2041667
23 7.2058333 4.0658333
24 -12.1369444 7.2058333
25 -21.0669444 -12.1369444
26 -19.2969444 -21.0669444
27 -13.8569444 -19.2969444
28 -15.2997222 -13.8569444
29 -21.8297222 -15.2997222
30 -18.3597222 -21.8297222
31 -19.5197222 -18.3597222
32 -17.6625000 -19.5197222
33 -8.1925000 -17.6625000
34 -6.6225000 -8.1925000
35 -6.5825000 -6.6225000
36 10.1747222 -6.5825000
37 29.7447222 10.1747222
38 66.7147222 29.7447222
39 77.7547222 66.7147222
> 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/7rlwl1264273729.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8jo1j1264273729.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/94n0b1264273729.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/100jtl1264273729.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/118teb1264273729.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/12aq2x1264273729.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/13i8hr1264273729.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/14buk21264273729.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/15sf2i1264273729.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/166hku1264273729.tab")
+ }
> try(system("convert tmp/17puv1264273729.ps tmp/17puv1264273729.png",intern=TRUE))
character(0)
> try(system("convert tmp/28owc1264273729.ps tmp/28owc1264273729.png",intern=TRUE))
character(0)
> try(system("convert tmp/3beh91264273729.ps tmp/3beh91264273729.png",intern=TRUE))
character(0)
> try(system("convert tmp/4kurg1264273729.ps tmp/4kurg1264273729.png",intern=TRUE))
character(0)
> try(system("convert tmp/50k0h1264273729.ps tmp/50k0h1264273729.png",intern=TRUE))
character(0)
> try(system("convert tmp/6i5ja1264273729.ps tmp/6i5ja1264273729.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rlwl1264273729.ps tmp/7rlwl1264273729.png",intern=TRUE))
character(0)
> try(system("convert tmp/8jo1j1264273729.ps tmp/8jo1j1264273729.png",intern=TRUE))
character(0)
> try(system("convert tmp/94n0b1264273729.ps tmp/94n0b1264273729.png",intern=TRUE))
character(0)
> try(system("convert tmp/100jtl1264273729.ps tmp/100jtl1264273729.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.205 1.523 2.773