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(2.0,3,42.0,1.8,4,624.0,0.7,4,180.0,3.9,1,35.0,1.0,4,392.0,3.6,1,63.0,1.4,1,230.0,1.5,4,112.0,0.7,5,281.0,2.1,1,42.0,4.1,2,42.0,1.2,2,120.0,0.5,5,148.0,3.4,2,16.0,1.5,1,310.0,3.4,3,28.0,0.8,4,68.0,0.8,5,336.0,2.0,1,50.0,1.9,1,267.0,1.3,3,19.0,5.6,1,12.0,3.1,1,120.0,1.8,2,140.0,0.9,4,170.0,1.8,2,17.0,1.9,4,115.0,0.9,5,31.0,2.6,3,21.0,2.4,1,52.0,1.2,2,164.0,0.9,2,225.0,0.5,3,225.0,0.6,5,151.0,2.3,2,60.0,0.5,3,200.0,2.6,2,46.0,0.6,4,210.0,6.6,1,14.0),dim=c(3,39),dimnames=list(c('PS','D','Tg'),1:39))
> y <- array(NA,dim=c(3,39),dimnames=list(c('PS','D','Tg'),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
PS D Tg
1 2.0 3 42
2 1.8 4 624
3 0.7 4 180
4 3.9 1 35
5 1.0 4 392
6 3.6 1 63
7 1.4 1 230
8 1.5 4 112
9 0.7 5 281
10 2.1 1 42
11 4.1 2 42
12 1.2 2 120
13 0.5 5 148
14 3.4 2 16
15 1.5 1 310
16 3.4 3 28
17 0.8 4 68
18 0.8 5 336
19 2.0 1 50
20 1.9 1 267
21 1.3 3 19
22 5.6 1 12
23 3.1 1 120
24 1.8 2 140
25 0.9 4 170
26 1.8 2 17
27 1.9 4 115
28 0.9 5 31
29 2.6 3 21
30 2.4 1 52
31 1.2 2 164
32 0.9 2 225
33 0.5 3 225
34 0.6 5 151
35 2.3 2 60
36 0.5 3 200
37 2.6 2 46
38 0.6 4 210
39 6.6 1 14
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) D Tg
3.737274 -0.498628 -0.003253
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.1496 -0.8048 -0.2448 0.5320 3.4069
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.737274 0.379110 9.858 9.09e-12 ***
D -0.498628 0.130456 -3.822 0.000505 ***
Tg -0.003253 0.001433 -2.270 0.029267 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.076 on 36 degrees of freedom
Multiple R-squared: 0.4445, Adjusted R-squared: 0.4137
F-statistic: 14.4 on 2 and 36 DF, p-value: 2.536e-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,] 0.03361496 0.06722992 0.9663850
[2,] 0.58262920 0.83474159 0.4173708
[3,] 0.43102812 0.86205625 0.5689719
[4,] 0.31335163 0.62670325 0.6866484
[5,] 0.29123598 0.58247196 0.7087640
[6,] 0.46247497 0.92494994 0.5375250
[7,] 0.49797672 0.99595343 0.5020233
[8,] 0.39866963 0.79733925 0.6013304
[9,] 0.35403972 0.70807944 0.6459603
[10,] 0.32276352 0.64552703 0.6772365
[11,] 0.34151000 0.68302000 0.6584900
[12,] 0.30774279 0.61548557 0.6922572
[13,] 0.33628162 0.67256324 0.6637184
[14,] 0.37664930 0.75329860 0.6233507
[15,] 0.29942306 0.59884612 0.7005769
[16,] 0.32574587 0.65149174 0.6742541
[17,] 0.65313649 0.69372702 0.3468635
[18,] 0.56572190 0.86855619 0.4342781
[19,] 0.47410401 0.94820803 0.5258960
[20,] 0.38457922 0.76915845 0.6154208
[21,] 0.44675893 0.89351785 0.5532411
[22,] 0.38608408 0.77216817 0.6139159
[23,] 0.32072561 0.64145122 0.6792744
[24,] 0.24644644 0.49289288 0.7535536
[25,] 0.29998082 0.59996163 0.7000192
[26,] 0.24514631 0.49029261 0.7548537
[27,] 0.16588222 0.33176444 0.8341178
[28,] 0.09580114 0.19160229 0.9041989
> postscript(file="/var/www/html/rcomp/tmp/1gesd1292081460.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2gesd1292081460.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/39n9g1292081460.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/49n9g1292081460.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/59n9g1292081460.ps",horizontal=F,onefile=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
-0.10474954 2.08730827 -0.45716376 0.77522073 0.53253910 0.56631356
7 8 9 10 11 12
-1.09038277 0.12161079 0.37004935 -1.00200606 1.49662220 -1.14961920
13 14 15 16 17 18
-0.26264160 0.71203600 -0.73011754 1.24970404 -0.72153509 0.64898169
19 20 21 22 23 24
-1.07597954 -0.47001010 -0.87957580 2.40039448 0.25175254 -0.48455289
25 26 27 28 29 30
-0.28969692 -0.88471069 0.53137074 -0.24327950 0.42693083 -0.66947290
31 32 33 34 35 36
-1.00647332 -1.10802109 -1.00939283 -0.15288165 -0.24481812 -1.09072571
37 38 39
0.00963546 -0.45956430 3.40690111
> postscript(file="/var/www/html/rcomp/tmp/6jeqj1292081460.ps",horizontal=F,onefile=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 -0.10474954 NA
1 2.08730827 -0.10474954
2 -0.45716376 2.08730827
3 0.77522073 -0.45716376
4 0.53253910 0.77522073
5 0.56631356 0.53253910
6 -1.09038277 0.56631356
7 0.12161079 -1.09038277
8 0.37004935 0.12161079
9 -1.00200606 0.37004935
10 1.49662220 -1.00200606
11 -1.14961920 1.49662220
12 -0.26264160 -1.14961920
13 0.71203600 -0.26264160
14 -0.73011754 0.71203600
15 1.24970404 -0.73011754
16 -0.72153509 1.24970404
17 0.64898169 -0.72153509
18 -1.07597954 0.64898169
19 -0.47001010 -1.07597954
20 -0.87957580 -0.47001010
21 2.40039448 -0.87957580
22 0.25175254 2.40039448
23 -0.48455289 0.25175254
24 -0.28969692 -0.48455289
25 -0.88471069 -0.28969692
26 0.53137074 -0.88471069
27 -0.24327950 0.53137074
28 0.42693083 -0.24327950
29 -0.66947290 0.42693083
30 -1.00647332 -0.66947290
31 -1.10802109 -1.00647332
32 -1.00939283 -1.10802109
33 -0.15288165 -1.00939283
34 -0.24481812 -0.15288165
35 -1.09072571 -0.24481812
36 0.00963546 -1.09072571
37 -0.45956430 0.00963546
38 3.40690111 -0.45956430
39 NA 3.40690111
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.08730827 -0.10474954
[2,] -0.45716376 2.08730827
[3,] 0.77522073 -0.45716376
[4,] 0.53253910 0.77522073
[5,] 0.56631356 0.53253910
[6,] -1.09038277 0.56631356
[7,] 0.12161079 -1.09038277
[8,] 0.37004935 0.12161079
[9,] -1.00200606 0.37004935
[10,] 1.49662220 -1.00200606
[11,] -1.14961920 1.49662220
[12,] -0.26264160 -1.14961920
[13,] 0.71203600 -0.26264160
[14,] -0.73011754 0.71203600
[15,] 1.24970404 -0.73011754
[16,] -0.72153509 1.24970404
[17,] 0.64898169 -0.72153509
[18,] -1.07597954 0.64898169
[19,] -0.47001010 -1.07597954
[20,] -0.87957580 -0.47001010
[21,] 2.40039448 -0.87957580
[22,] 0.25175254 2.40039448
[23,] -0.48455289 0.25175254
[24,] -0.28969692 -0.48455289
[25,] -0.88471069 -0.28969692
[26,] 0.53137074 -0.88471069
[27,] -0.24327950 0.53137074
[28,] 0.42693083 -0.24327950
[29,] -0.66947290 0.42693083
[30,] -1.00647332 -0.66947290
[31,] -1.10802109 -1.00647332
[32,] -1.00939283 -1.10802109
[33,] -0.15288165 -1.00939283
[34,] -0.24481812 -0.15288165
[35,] -1.09072571 -0.24481812
[36,] 0.00963546 -1.09072571
[37,] -0.45956430 0.00963546
[38,] 3.40690111 -0.45956430
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.08730827 -0.10474954
2 -0.45716376 2.08730827
3 0.77522073 -0.45716376
4 0.53253910 0.77522073
5 0.56631356 0.53253910
6 -1.09038277 0.56631356
7 0.12161079 -1.09038277
8 0.37004935 0.12161079
9 -1.00200606 0.37004935
10 1.49662220 -1.00200606
11 -1.14961920 1.49662220
12 -0.26264160 -1.14961920
13 0.71203600 -0.26264160
14 -0.73011754 0.71203600
15 1.24970404 -0.73011754
16 -0.72153509 1.24970404
17 0.64898169 -0.72153509
18 -1.07597954 0.64898169
19 -0.47001010 -1.07597954
20 -0.87957580 -0.47001010
21 2.40039448 -0.87957580
22 0.25175254 2.40039448
23 -0.48455289 0.25175254
24 -0.28969692 -0.48455289
25 -0.88471069 -0.28969692
26 0.53137074 -0.88471069
27 -0.24327950 0.53137074
28 0.42693083 -0.24327950
29 -0.66947290 0.42693083
30 -1.00647332 -0.66947290
31 -1.10802109 -1.00647332
32 -1.00939283 -1.10802109
33 -0.15288165 -1.00939283
34 -0.24481812 -0.15288165
35 -1.09072571 -0.24481812
36 0.00963546 -1.09072571
37 -0.45956430 0.00963546
38 3.40690111 -0.45956430
> 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/7u5741292081460.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8u5741292081460.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9u5741292081460.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/105x671292081460.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11qf5v1292081460.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/12tg3j1292081460.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/13ihid1292081460.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/14mzh01292081460.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/1570yo1292081460.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/16ladf1292081460.tab")
+ }
>
> try(system("convert tmp/1gesd1292081460.ps tmp/1gesd1292081460.png",intern=TRUE))
character(0)
> try(system("convert tmp/2gesd1292081460.ps tmp/2gesd1292081460.png",intern=TRUE))
character(0)
> try(system("convert tmp/39n9g1292081460.ps tmp/39n9g1292081460.png",intern=TRUE))
character(0)
> try(system("convert tmp/49n9g1292081460.ps tmp/49n9g1292081460.png",intern=TRUE))
character(0)
> try(system("convert tmp/59n9g1292081460.ps tmp/59n9g1292081460.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jeqj1292081460.ps tmp/6jeqj1292081460.png",intern=TRUE))
character(0)
> try(system("convert tmp/7u5741292081460.ps tmp/7u5741292081460.png",intern=TRUE))
character(0)
> try(system("convert tmp/8u5741292081460.ps tmp/8u5741292081460.png",intern=TRUE))
character(0)
> try(system("convert tmp/9u5741292081460.ps tmp/9u5741292081460.png",intern=TRUE))
character(0)
> try(system("convert tmp/105x671292081460.ps tmp/105x671292081460.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.321 1.646 8.176