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(6.3,0.819543936,3,2.1,3.663040975,4,9.1,2.254064453,4,15.8,-0.522878745,1,5.2,2.227886705,4,10.9,1.408239965,1,8.3,2.643452676,1,11,0.806179974,4,3.2,2.626340367,5,6.3,0.079181246,1,6.6,0.544068044,2,9.5,0.698970004,2,3.3,2.06069784,5,11,0,2,4.7,2.511883361,1,10.4,0.602059991,3,7.4,0.740362689,4,2.1,2.8162413,5,17.9,-0.602059991,1,6.1,3.120573931,1,11.9,-0.397940009,3,13.8,0.799340549,1,14.3,1.033423755,1,15.2,1.190331698,2,10,2.06069784,4,11.9,1.056904851,2,6.5,2.255272505,4,7.5,1.08278537,5,10.6,0.278753601,3,7.4,1.702430536,1,8.4,2.252853031,2,5.7,1.089905111,2,4.9,1.322219295,3,3.2,2.243038049,5,11,0.414973348,2,4.9,1.089905111,3,13.2,0.397940009,2,9.7,1.763427994,4,12.8,0.591064607,1),dim=c(3,39),dimnames=list(c('SWS','Wbr','D'),1:39))
> y <- array(NA,dim=c(3,39),dimnames=list(c('SWS','Wbr','D'),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
SWS Wbr D
1 6.3 0.81954394 3
2 2.1 3.66304097 4
3 9.1 2.25406445 4
4 15.8 -0.52287874 1
5 5.2 2.22788670 4
6 10.9 1.40823996 1
7 8.3 2.64345268 1
8 11.0 0.80617997 4
9 3.2 2.62634037 5
10 6.3 0.07918125 1
11 6.6 0.54406804 2
12 9.5 0.69897000 2
13 3.3 2.06069784 5
14 11.0 0.00000000 2
15 4.7 2.51188336 1
16 10.4 0.60205999 3
17 7.4 0.74036269 4
18 2.1 2.81624130 5
19 17.9 -0.60205999 1
20 6.1 3.12057393 1
21 11.9 -0.39794001 3
22 13.8 0.79934055 1
23 14.3 1.03342376 1
24 15.2 1.19033170 2
25 10.0 2.06069784 4
26 11.9 1.05690485 2
27 6.5 2.25527251 4
28 7.5 1.08278537 5
29 10.6 0.27875360 3
30 7.4 1.70243054 1
31 8.4 2.25285303 2
32 5.7 1.08990511 2
33 4.9 1.32221930 3
34 3.2 2.24303805 5
35 11.0 0.41497335 2
36 4.9 1.08990511 3
37 13.2 0.39794001 2
38 9.7 1.76342799 4
39 12.8 0.59106461 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Wbr D
13.9513 -2.1101 -0.9323
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.552 -1.308 -0.094 1.815 5.625
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.9513 0.9691 14.397 < 2e-16 ***
Wbr -2.1101 0.4569 -4.618 4.8e-05 ***
D -0.9323 0.3347 -2.785 0.00847 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.715 on 36 degrees of freedom
Multiple R-squared: 0.5566, Adjusted R-squared: 0.532
F-statistic: 22.6 on 2 and 36 DF, p-value: 4.383e-07
> 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,] 0.6183693 0.7632613 0.3816307
[2,] 0.4490331 0.8980663 0.5509669
[3,] 0.3830105 0.7660209 0.6169895
[4,] 0.2690118 0.5380236 0.7309882
[5,] 0.7604535 0.4790929 0.2395465
[6,] 0.8172135 0.3655730 0.1827865
[7,] 0.7432670 0.5134661 0.2567330
[8,] 0.6810077 0.6379845 0.3189923
[9,] 0.6020839 0.7958323 0.3979161
[10,] 0.6010437 0.7979127 0.3989563
[11,] 0.5148675 0.9702650 0.4851325
[12,] 0.4317604 0.8635207 0.5682396
[13,] 0.3558847 0.7117695 0.6441153
[14,] 0.4636465 0.9272930 0.5363535
[15,] 0.3825115 0.7650230 0.6174885
[16,] 0.2911928 0.5823855 0.7088072
[17,] 0.2727442 0.5454884 0.7272558
[18,] 0.3065461 0.6130922 0.6934539
[19,] 0.5938266 0.8123468 0.4061734
[20,] 0.6988017 0.6023966 0.3011983
[21,] 0.6735750 0.6528500 0.3264250
[22,] 0.5937745 0.8124511 0.4062255
[23,] 0.4842811 0.9685621 0.5157189
[24,] 0.3690538 0.7381077 0.6309462
[25,] 0.2947439 0.5894878 0.7052561
[26,] 0.2440385 0.4880769 0.7559615
[27,] 0.2616878 0.5233757 0.7383122
[28,] 0.2820022 0.5640044 0.7179978
> postscript(file="/var/www/html/rcomp/tmp/11lyr1292164543.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/21lyr1292164543.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3cvyc1292164543.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4cvyc1292164543.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5cvyc1292164543.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
-3.12493426 -0.39242084 3.63443299 1.67771883 -0.32080588 0.85266125
7 8 9 10 11 12
0.85914041 2.47918535 -0.54769095 -6.55184669 -4.33854814 -1.11168239
13 14 15 16 17 18
-1.64127926 -1.08661115 -3.01849006 0.51614286 -1.25969873 -1.24697225
19 20 21 22 23 24
3.61063484 -0.33406409 -0.09400314 2.46779458 3.46174432 5.62516252
25 26 27 28 29 30
4.12640121 2.04361239 1.03698216 0.49518265 0.03391918 -2.02655370
31 32 33 34 35 36
1.06723766 -4.08675224 -3.46421587 -1.35651480 -0.21095680 -3.95443271
37 38 39
1.95310037 3.19911843 1.02830194
> postscript(file="/var/www/html/rcomp/tmp/64mxe1292164543.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 -3.12493426 NA
1 -0.39242084 -3.12493426
2 3.63443299 -0.39242084
3 1.67771883 3.63443299
4 -0.32080588 1.67771883
5 0.85266125 -0.32080588
6 0.85914041 0.85266125
7 2.47918535 0.85914041
8 -0.54769095 2.47918535
9 -6.55184669 -0.54769095
10 -4.33854814 -6.55184669
11 -1.11168239 -4.33854814
12 -1.64127926 -1.11168239
13 -1.08661115 -1.64127926
14 -3.01849006 -1.08661115
15 0.51614286 -3.01849006
16 -1.25969873 0.51614286
17 -1.24697225 -1.25969873
18 3.61063484 -1.24697225
19 -0.33406409 3.61063484
20 -0.09400314 -0.33406409
21 2.46779458 -0.09400314
22 3.46174432 2.46779458
23 5.62516252 3.46174432
24 4.12640121 5.62516252
25 2.04361239 4.12640121
26 1.03698216 2.04361239
27 0.49518265 1.03698216
28 0.03391918 0.49518265
29 -2.02655370 0.03391918
30 1.06723766 -2.02655370
31 -4.08675224 1.06723766
32 -3.46421587 -4.08675224
33 -1.35651480 -3.46421587
34 -0.21095680 -1.35651480
35 -3.95443271 -0.21095680
36 1.95310037 -3.95443271
37 3.19911843 1.95310037
38 1.02830194 3.19911843
39 NA 1.02830194
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.39242084 -3.12493426
[2,] 3.63443299 -0.39242084
[3,] 1.67771883 3.63443299
[4,] -0.32080588 1.67771883
[5,] 0.85266125 -0.32080588
[6,] 0.85914041 0.85266125
[7,] 2.47918535 0.85914041
[8,] -0.54769095 2.47918535
[9,] -6.55184669 -0.54769095
[10,] -4.33854814 -6.55184669
[11,] -1.11168239 -4.33854814
[12,] -1.64127926 -1.11168239
[13,] -1.08661115 -1.64127926
[14,] -3.01849006 -1.08661115
[15,] 0.51614286 -3.01849006
[16,] -1.25969873 0.51614286
[17,] -1.24697225 -1.25969873
[18,] 3.61063484 -1.24697225
[19,] -0.33406409 3.61063484
[20,] -0.09400314 -0.33406409
[21,] 2.46779458 -0.09400314
[22,] 3.46174432 2.46779458
[23,] 5.62516252 3.46174432
[24,] 4.12640121 5.62516252
[25,] 2.04361239 4.12640121
[26,] 1.03698216 2.04361239
[27,] 0.49518265 1.03698216
[28,] 0.03391918 0.49518265
[29,] -2.02655370 0.03391918
[30,] 1.06723766 -2.02655370
[31,] -4.08675224 1.06723766
[32,] -3.46421587 -4.08675224
[33,] -1.35651480 -3.46421587
[34,] -0.21095680 -1.35651480
[35,] -3.95443271 -0.21095680
[36,] 1.95310037 -3.95443271
[37,] 3.19911843 1.95310037
[38,] 1.02830194 3.19911843
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.39242084 -3.12493426
2 3.63443299 -0.39242084
3 1.67771883 3.63443299
4 -0.32080588 1.67771883
5 0.85266125 -0.32080588
6 0.85914041 0.85266125
7 2.47918535 0.85914041
8 -0.54769095 2.47918535
9 -6.55184669 -0.54769095
10 -4.33854814 -6.55184669
11 -1.11168239 -4.33854814
12 -1.64127926 -1.11168239
13 -1.08661115 -1.64127926
14 -3.01849006 -1.08661115
15 0.51614286 -3.01849006
16 -1.25969873 0.51614286
17 -1.24697225 -1.25969873
18 3.61063484 -1.24697225
19 -0.33406409 3.61063484
20 -0.09400314 -0.33406409
21 2.46779458 -0.09400314
22 3.46174432 2.46779458
23 5.62516252 3.46174432
24 4.12640121 5.62516252
25 2.04361239 4.12640121
26 1.03698216 2.04361239
27 0.49518265 1.03698216
28 0.03391918 0.49518265
29 -2.02655370 0.03391918
30 1.06723766 -2.02655370
31 -4.08675224 1.06723766
32 -3.46421587 -4.08675224
33 -1.35651480 -3.46421587
34 -0.21095680 -1.35651480
35 -3.95443271 -0.21095680
36 1.95310037 -3.95443271
37 3.19911843 1.95310037
38 1.02830194 3.19911843
> 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/74mxe1292164543.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8fvez1292164543.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9fvez1292164543.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1084vk1292164543.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11t5u81292164543.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/12e6te1292164543.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/13bx851292164543.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/14wy7t1292164543.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/15p76e1292164543.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/16lzm41292164543.tab")
+ }
>
> try(system("convert tmp/11lyr1292164543.ps tmp/11lyr1292164543.png",intern=TRUE))
character(0)
> try(system("convert tmp/21lyr1292164543.ps tmp/21lyr1292164543.png",intern=TRUE))
character(0)
> try(system("convert tmp/3cvyc1292164543.ps tmp/3cvyc1292164543.png",intern=TRUE))
character(0)
> try(system("convert tmp/4cvyc1292164543.ps tmp/4cvyc1292164543.png",intern=TRUE))
character(0)
> try(system("convert tmp/5cvyc1292164543.ps tmp/5cvyc1292164543.png",intern=TRUE))
character(0)
> try(system("convert tmp/64mxe1292164543.ps tmp/64mxe1292164543.png",intern=TRUE))
character(0)
> try(system("convert tmp/74mxe1292164543.ps tmp/74mxe1292164543.png",intern=TRUE))
character(0)
> try(system("convert tmp/8fvez1292164543.ps tmp/8fvez1292164543.png",intern=TRUE))
character(0)
> try(system("convert tmp/9fvez1292164543.ps tmp/9fvez1292164543.png",intern=TRUE))
character(0)
> try(system("convert tmp/1084vk1292164543.ps tmp/1084vk1292164543.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.267 1.578 6.057