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(6.3,0,3,2.1,3.406028945,4,9.1,1.02325246,4,15.8,-1.638272164,1,5.2,2.204119983,4,10.9,0.51851394,1,8.3,1.717337583,1,11,-0.37161107,4,3.2,2.667452953,5,6.3,-1.124938737,1,6.6,-0.105130343,2,9.5,-0.698970004,2,3.3,1.441852176,5,11,-0.920818754,2,4.7,1.929418926,1,10.4,-0.995678626,3,7.4,0.017033339,4,2.1,2.716837723,5,7.7,-2.301029996,4,17.9,-2,1,6.1,1.792391689,1,11.9,-1.638272164,3,10.8,-1.318758763,3,13.8,0.230448921,1,14.3,0.544068044,1,15.2,-0.318758763,2,10,1,4,11.9,0.209515015,2,6.5,2.283301229,4,7.5,0.397940009,5,10.6,-0.552841969,3,7.4,0.626853415,1,8.4,0.832508913,2,5.7,-0.124938737,2,4.9,0.556302501,3,3.2,1.744292983,5,11,-0.045757491,2,4.9,0.301029996,3,13.2,-0.982966661,2,9.7,0.622214023,4,12.8,0.544068044,1),dim=c(3,41),dimnames=list(c('SWS','logbody','D
'),1:41))
> y <- array(NA,dim=c(3,41),dimnames=list(c('SWS','logbody','D
'),1:41))
> 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 logbody D\r\r
1 6.3 0.00000000 3
2 2.1 3.40602895 4
3 9.1 1.02325246 4
4 15.8 -1.63827216 1
5 5.2 2.20411998 4
6 10.9 0.51851394 1
7 8.3 1.71733758 1
8 11.0 -0.37161107 4
9 3.2 2.66745295 5
10 6.3 -1.12493874 1
11 6.6 -0.10513034 2
12 9.5 -0.69897000 2
13 3.3 1.44185218 5
14 11.0 -0.92081875 2
15 4.7 1.92941893 1
16 10.4 -0.99567863 3
17 7.4 0.01703334 4
18 2.1 2.71683772 5
19 7.7 -2.30103000 4
20 17.9 -2.00000000 1
21 6.1 1.79239169 1
22 11.9 -1.63827216 3
23 10.8 -1.31875876 3
24 13.8 0.23044892 1
25 14.3 0.54406804 1
26 15.2 -0.31875876 2
27 10.0 1.00000000 4
28 11.9 0.20951501 2
29 6.5 2.28330123 4
30 7.5 0.39794001 5
31 10.6 -0.55284197 3
32 7.4 0.62685342 1
33 8.4 0.83250891 2
34 5.7 -0.12493874 2
35 4.9 0.55630250 3
36 3.2 1.74429298 5
37 11.0 -0.04575749 2
38 4.9 0.30103000 3
39 13.2 -0.98296666 2
40 9.7 0.62221402 4
41 12.8 0.54406804 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) logbody `D\r\r`
11.9287 -1.5545 -0.9768
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.40058 -1.56166 0.01763 2.25054 4.72936
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.9287 0.9401 12.689 3.09e-15 ***
logbody -1.5545 0.3350 -4.640 4.06e-05 ***
`D\r\r` -0.9768 0.3227 -3.026 0.00443 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.687 on 38 degrees of freedom
Multiple R-squared: 0.5456, Adjusted R-squared: 0.5216
F-statistic: 22.81 on 2 and 38 DF, p-value: 3.105e-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,] 0.4829009 0.9658017 0.5170991
[2,] 0.3098668 0.6197335 0.6901332
[3,] 0.2116684 0.4233369 0.7883316
[4,] 0.1186287 0.2372575 0.8813713
[5,] 0.6687665 0.6624670 0.3312335
[6,] 0.6884950 0.6230099 0.3115050
[7,] 0.6016500 0.7966999 0.3983500
[8,] 0.5422979 0.9154042 0.4577021
[9,] 0.4425866 0.8851732 0.5574134
[10,] 0.4413953 0.8827907 0.5586047
[11,] 0.3433809 0.6867618 0.6566191
[12,] 0.2599357 0.5198714 0.7400643
[13,] 0.1921651 0.3843302 0.8078349
[14,] 0.2751019 0.5502038 0.7248981
[15,] 0.4106452 0.8212903 0.5893548
[16,] 0.3774610 0.7549220 0.6225390
[17,] 0.2930439 0.5860878 0.7069561
[18,] 0.2169541 0.4339082 0.7830459
[19,] 0.2436912 0.4873825 0.7563088
[20,] 0.3408781 0.6817562 0.6591219
[21,] 0.5113921 0.9772157 0.4886079
[22,] 0.5547365 0.8905269 0.4452635
[23,] 0.5301293 0.9397415 0.4698707
[24,] 0.5071680 0.9856640 0.4928320
[25,] 0.4106345 0.8212690 0.5893655
[26,] 0.3082058 0.6164116 0.6917942
[27,] 0.2675114 0.5350228 0.7324886
[28,] 0.1711247 0.3422494 0.8288753
[29,] 0.3062277 0.6124555 0.6937723
[30,] 0.3419697 0.6839393 0.6580303
> postscript(file="/var/www/html/rcomp/tmp/1ecsk1292892382.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2p3rn1292892382.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3p3rn1292892382.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4p3rn1292892382.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/50c871292892382.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 = 41
Frequency = 1
1 2 3 4 5 6
-2.69838011 -0.62708359 2.66898676 2.30146747 0.60459755 0.75410423
7 8 9 10 11 12
0.01762708 2.40072782 0.30159222 -6.40057648 -3.53856328 -1.56166301
13 14 15 16 17 18
-1.50355461 -0.40651792 -3.25270105 -0.14612225 -0.59514016 -0.72164115
19 20 21 22 23 24
-3.89847585 3.83917619 -2.06570434 0.35499209 -0.24833726 3.20631881
25 26 27 28 29 30
4.19382705 4.72935999 3.53284175 2.25054020 2.02768160 1.07372615
31 32 33 34 35 36
0.74224942 -2.57748644 -0.28104098 -4.46935462 -3.23363037 -1.13342261
37 38 39 40 41
0.95372942 -3.63044115 1.69687568 2.64558873 2.69382705
> postscript(file="/var/www/html/rcomp/tmp/60c871292892382.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 = 41
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.69838011 NA
1 -0.62708359 -2.69838011
2 2.66898676 -0.62708359
3 2.30146747 2.66898676
4 0.60459755 2.30146747
5 0.75410423 0.60459755
6 0.01762708 0.75410423
7 2.40072782 0.01762708
8 0.30159222 2.40072782
9 -6.40057648 0.30159222
10 -3.53856328 -6.40057648
11 -1.56166301 -3.53856328
12 -1.50355461 -1.56166301
13 -0.40651792 -1.50355461
14 -3.25270105 -0.40651792
15 -0.14612225 -3.25270105
16 -0.59514016 -0.14612225
17 -0.72164115 -0.59514016
18 -3.89847585 -0.72164115
19 3.83917619 -3.89847585
20 -2.06570434 3.83917619
21 0.35499209 -2.06570434
22 -0.24833726 0.35499209
23 3.20631881 -0.24833726
24 4.19382705 3.20631881
25 4.72935999 4.19382705
26 3.53284175 4.72935999
27 2.25054020 3.53284175
28 2.02768160 2.25054020
29 1.07372615 2.02768160
30 0.74224942 1.07372615
31 -2.57748644 0.74224942
32 -0.28104098 -2.57748644
33 -4.46935462 -0.28104098
34 -3.23363037 -4.46935462
35 -1.13342261 -3.23363037
36 0.95372942 -1.13342261
37 -3.63044115 0.95372942
38 1.69687568 -3.63044115
39 2.64558873 1.69687568
40 2.69382705 2.64558873
41 NA 2.69382705
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.62708359 -2.69838011
[2,] 2.66898676 -0.62708359
[3,] 2.30146747 2.66898676
[4,] 0.60459755 2.30146747
[5,] 0.75410423 0.60459755
[6,] 0.01762708 0.75410423
[7,] 2.40072782 0.01762708
[8,] 0.30159222 2.40072782
[9,] -6.40057648 0.30159222
[10,] -3.53856328 -6.40057648
[11,] -1.56166301 -3.53856328
[12,] -1.50355461 -1.56166301
[13,] -0.40651792 -1.50355461
[14,] -3.25270105 -0.40651792
[15,] -0.14612225 -3.25270105
[16,] -0.59514016 -0.14612225
[17,] -0.72164115 -0.59514016
[18,] -3.89847585 -0.72164115
[19,] 3.83917619 -3.89847585
[20,] -2.06570434 3.83917619
[21,] 0.35499209 -2.06570434
[22,] -0.24833726 0.35499209
[23,] 3.20631881 -0.24833726
[24,] 4.19382705 3.20631881
[25,] 4.72935999 4.19382705
[26,] 3.53284175 4.72935999
[27,] 2.25054020 3.53284175
[28,] 2.02768160 2.25054020
[29,] 1.07372615 2.02768160
[30,] 0.74224942 1.07372615
[31,] -2.57748644 0.74224942
[32,] -0.28104098 -2.57748644
[33,] -4.46935462 -0.28104098
[34,] -3.23363037 -4.46935462
[35,] -1.13342261 -3.23363037
[36,] 0.95372942 -1.13342261
[37,] -3.63044115 0.95372942
[38,] 1.69687568 -3.63044115
[39,] 2.64558873 1.69687568
[40,] 2.69382705 2.64558873
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.62708359 -2.69838011
2 2.66898676 -0.62708359
3 2.30146747 2.66898676
4 0.60459755 2.30146747
5 0.75410423 0.60459755
6 0.01762708 0.75410423
7 2.40072782 0.01762708
8 0.30159222 2.40072782
9 -6.40057648 0.30159222
10 -3.53856328 -6.40057648
11 -1.56166301 -3.53856328
12 -1.50355461 -1.56166301
13 -0.40651792 -1.50355461
14 -3.25270105 -0.40651792
15 -0.14612225 -3.25270105
16 -0.59514016 -0.14612225
17 -0.72164115 -0.59514016
18 -3.89847585 -0.72164115
19 3.83917619 -3.89847585
20 -2.06570434 3.83917619
21 0.35499209 -2.06570434
22 -0.24833726 0.35499209
23 3.20631881 -0.24833726
24 4.19382705 3.20631881
25 4.72935999 4.19382705
26 3.53284175 4.72935999
27 2.25054020 3.53284175
28 2.02768160 2.25054020
29 1.07372615 2.02768160
30 0.74224942 1.07372615
31 -2.57748644 0.74224942
32 -0.28104098 -2.57748644
33 -4.46935462 -0.28104098
34 -3.23363037 -4.46935462
35 -1.13342261 -3.23363037
36 0.95372942 -1.13342261
37 -3.63044115 0.95372942
38 1.69687568 -3.63044115
39 2.64558873 1.69687568
40 2.69382705 2.64558873
> 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/7al7a1292892382.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8al7a1292892382.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/93d7d1292892382.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/103d7d1292892382.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/116v511292892382.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/12ad371292892382.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/13o6jg1292892382.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/14ro041292892382.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/15uogr1292892382.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/16y7xf1292892382.tab")
+ }
>
> try(system("convert tmp/1ecsk1292892382.ps tmp/1ecsk1292892382.png",intern=TRUE))
character(0)
> try(system("convert tmp/2p3rn1292892382.ps tmp/2p3rn1292892382.png",intern=TRUE))
character(0)
> try(system("convert tmp/3p3rn1292892382.ps tmp/3p3rn1292892382.png",intern=TRUE))
character(0)
> try(system("convert tmp/4p3rn1292892382.ps tmp/4p3rn1292892382.png",intern=TRUE))
character(0)
> try(system("convert tmp/50c871292892382.ps tmp/50c871292892382.png",intern=TRUE))
character(0)
> try(system("convert tmp/60c871292892382.ps tmp/60c871292892382.png",intern=TRUE))
character(0)
> try(system("convert tmp/7al7a1292892382.ps tmp/7al7a1292892382.png",intern=TRUE))
character(0)
> try(system("convert tmp/8al7a1292892382.ps tmp/8al7a1292892382.png",intern=TRUE))
character(0)
> try(system("convert tmp/93d7d1292892382.ps tmp/93d7d1292892382.png",intern=TRUE))
character(0)
> try(system("convert tmp/103d7d1292892382.ps tmp/103d7d1292892382.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.328 1.659 6.062