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.
Natural language support but running in an English locale
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(6.3,1,3,2.1,2547,4,9.1,10.55,4,15.8,0.023,1,5.2,160,4,10.9,3.3,1,8.3,52.16,1,11,0.425,4,3.2,465,5,6.3,0.075,1,8.6,3,2,6.6,0.785,2,9.5,0.2,2,3.3,27.66,5,11,0.12,2,4.7,85,1,10.4,0.101,3,7.4,1.04,4,2.1,521,5,7.7,0.005,4,17.9,0.01,1,6.1,62,1,11.9,0.023,3,10.8,0.048,3,13.8,1.7,1,14.3,3.5,1,15.2,0.48,2,10,10,4,11.9,1.62,2,6.5,192,4,7.5,2.5,5,10.6,0.28,3,7.4,4.235,1,8.4,6.8,2,5.7,0.75,2,4.9,3.6,3,3.2,55.5,5,11,0.9,2,4.9,2,3,13.2,0.104,2,9.7,4.19,4,12.8,3.5,1),dim=c(3,42),dimnames=list(c('SWS','BW','ODI'),1:42))
> y <- array(NA,dim=c(3,42),dimnames=list(c('SWS','BW','ODI'),1:42))
> 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 BW ODI
1 6.3 1.000 3
2 2.1 2547.000 4
3 9.1 10.550 4
4 15.8 0.023 1
5 5.2 160.000 4
6 10.9 3.300 1
7 8.3 52.160 1
8 11.0 0.425 4
9 3.2 465.000 5
10 6.3 0.075 1
11 8.6 3.000 2
12 6.6 0.785 2
13 9.5 0.200 2
14 3.3 27.660 5
15 11.0 0.120 2
16 4.7 85.000 1
17 10.4 0.101 3
18 7.4 1.040 4
19 2.1 521.000 5
20 7.7 0.005 4
21 17.9 0.010 1
22 6.1 62.000 1
23 11.9 0.023 3
24 10.8 0.048 3
25 13.8 1.700 1
26 14.3 3.500 1
27 15.2 0.480 2
28 10.0 10.000 4
29 11.9 1.620 2
30 6.5 192.000 4
31 7.5 2.500 5
32 10.6 0.280 3
33 7.4 4.235 1
34 8.4 6.800 2
35 5.7 0.750 2
36 4.9 3.600 3
37 3.2 55.500 5
38 11.0 0.900 2
39 4.9 2.000 3
40 13.2 0.104 2
41 9.7 4.190 4
42 12.8 3.500 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) BW ODI
12.455580 -0.002609 -1.282185
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.2516 -2.6507 0.2245 2.1466 6.7266
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.455580 1.081005 11.522 3.99e-14 ***
BW -0.002609 0.001270 -2.054 0.04673 *
ODI -1.282185 0.368019 -3.484 0.00124 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.159 on 39 degrees of freedom
Multiple R-squared: 0.3555, Adjusted R-squared: 0.3225
F-statistic: 10.76 on 2 and 39 DF, p-value: 0.0001903
> 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,] 0.5357164 0.9285672 0.4642836
[2,] 0.5758783 0.8482434 0.4241217
[3,] 0.5941004 0.8117992 0.4058996
[4,] 0.5093701 0.9812598 0.4906299
[5,] 0.6368308 0.7263384 0.3631692
[6,] 0.5300576 0.9398849 0.4699424
[7,] 0.5044596 0.9910807 0.4955404
[8,] 0.3986056 0.7972111 0.6013944
[9,] 0.3707913 0.7415826 0.6292087
[10,] 0.3031954 0.6063908 0.6968046
[11,] 0.5037177 0.9925646 0.4962823
[12,] 0.4515830 0.9031659 0.5484170
[13,] 0.3581376 0.7162751 0.6418624
[14,] 0.3263373 0.6526746 0.6736627
[15,] 0.2491441 0.4982882 0.7508559
[16,] 0.5820599 0.8358803 0.4179401
[17,] 0.6651141 0.6697718 0.3348859
[18,] 0.6602262 0.6795476 0.3397738
[19,] 0.6041967 0.7916066 0.3958033
[20,] 0.5616745 0.8766510 0.4383255
[21,] 0.5464652 0.9070695 0.4535348
[22,] 0.7079923 0.5840155 0.2920077
[23,] 0.6775648 0.6448703 0.3224352
[24,] 0.6337825 0.7324350 0.3662175
[25,] 0.6106071 0.7787859 0.3893929
[26,] 0.5019067 0.9961866 0.4980933
[27,] 0.4364017 0.8728034 0.5635983
[28,] 0.4289153 0.8578305 0.5710847
[29,] 0.3156016 0.6312031 0.6843984
[30,] 0.3974709 0.7949417 0.6025291
[31,] 0.4454954 0.8909909 0.5545046
> postscript(file="/var/www/html/freestat/rcomp/tmp/183p61292263588.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/283p61292263588.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/383p61292263588.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/41c6q1292263588.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/51c6q1292263588.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 = 42
Frequency = 1
1 2 3 4 5 6
-2.30641660 1.41857985 1.80068519 4.62666487 -1.70938229 -0.26478505
7 8 9 10 11 12
-2.73730360 3.67426788 -1.63141691 -4.87319946 -1.28338308 -3.28916227
13 14 15 16 17 18
-0.39068860 -2.67248811 1.10910267 -6.25162020 1.79123781 0.07587248
19 20 21 22 23 24
-2.58530636 0.37317205 6.72663095 -4.91162989 3.29103430 2.19109952
25 26 27 28 29 30
2.63104036 3.13573677 5.31004195 2.69925017 2.01301634 -0.32589055
31 32 33 34 35 36
1.46186651 1.99170484 -3.76234553 -1.47346843 -4.18925359 -3.69963289
37 38 39 40 41 42
-2.69985029 1.11113778 -3.70380748 3.30906092 2.38409120 1.63573677
> postscript(file="/var/www/html/freestat/rcomp/tmp/61c6q1292263588.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 = 42
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.30641660 NA
1 1.41857985 -2.30641660
2 1.80068519 1.41857985
3 4.62666487 1.80068519
4 -1.70938229 4.62666487
5 -0.26478505 -1.70938229
6 -2.73730360 -0.26478505
7 3.67426788 -2.73730360
8 -1.63141691 3.67426788
9 -4.87319946 -1.63141691
10 -1.28338308 -4.87319946
11 -3.28916227 -1.28338308
12 -0.39068860 -3.28916227
13 -2.67248811 -0.39068860
14 1.10910267 -2.67248811
15 -6.25162020 1.10910267
16 1.79123781 -6.25162020
17 0.07587248 1.79123781
18 -2.58530636 0.07587248
19 0.37317205 -2.58530636
20 6.72663095 0.37317205
21 -4.91162989 6.72663095
22 3.29103430 -4.91162989
23 2.19109952 3.29103430
24 2.63104036 2.19109952
25 3.13573677 2.63104036
26 5.31004195 3.13573677
27 2.69925017 5.31004195
28 2.01301634 2.69925017
29 -0.32589055 2.01301634
30 1.46186651 -0.32589055
31 1.99170484 1.46186651
32 -3.76234553 1.99170484
33 -1.47346843 -3.76234553
34 -4.18925359 -1.47346843
35 -3.69963289 -4.18925359
36 -2.69985029 -3.69963289
37 1.11113778 -2.69985029
38 -3.70380748 1.11113778
39 3.30906092 -3.70380748
40 2.38409120 3.30906092
41 1.63573677 2.38409120
42 NA 1.63573677
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.41857985 -2.30641660
[2,] 1.80068519 1.41857985
[3,] 4.62666487 1.80068519
[4,] -1.70938229 4.62666487
[5,] -0.26478505 -1.70938229
[6,] -2.73730360 -0.26478505
[7,] 3.67426788 -2.73730360
[8,] -1.63141691 3.67426788
[9,] -4.87319946 -1.63141691
[10,] -1.28338308 -4.87319946
[11,] -3.28916227 -1.28338308
[12,] -0.39068860 -3.28916227
[13,] -2.67248811 -0.39068860
[14,] 1.10910267 -2.67248811
[15,] -6.25162020 1.10910267
[16,] 1.79123781 -6.25162020
[17,] 0.07587248 1.79123781
[18,] -2.58530636 0.07587248
[19,] 0.37317205 -2.58530636
[20,] 6.72663095 0.37317205
[21,] -4.91162989 6.72663095
[22,] 3.29103430 -4.91162989
[23,] 2.19109952 3.29103430
[24,] 2.63104036 2.19109952
[25,] 3.13573677 2.63104036
[26,] 5.31004195 3.13573677
[27,] 2.69925017 5.31004195
[28,] 2.01301634 2.69925017
[29,] -0.32589055 2.01301634
[30,] 1.46186651 -0.32589055
[31,] 1.99170484 1.46186651
[32,] -3.76234553 1.99170484
[33,] -1.47346843 -3.76234553
[34,] -4.18925359 -1.47346843
[35,] -3.69963289 -4.18925359
[36,] -2.69985029 -3.69963289
[37,] 1.11113778 -2.69985029
[38,] -3.70380748 1.11113778
[39,] 3.30906092 -3.70380748
[40,] 2.38409120 3.30906092
[41,] 1.63573677 2.38409120
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.41857985 -2.30641660
2 1.80068519 1.41857985
3 4.62666487 1.80068519
4 -1.70938229 4.62666487
5 -0.26478505 -1.70938229
6 -2.73730360 -0.26478505
7 3.67426788 -2.73730360
8 -1.63141691 3.67426788
9 -4.87319946 -1.63141691
10 -1.28338308 -4.87319946
11 -3.28916227 -1.28338308
12 -0.39068860 -3.28916227
13 -2.67248811 -0.39068860
14 1.10910267 -2.67248811
15 -6.25162020 1.10910267
16 1.79123781 -6.25162020
17 0.07587248 1.79123781
18 -2.58530636 0.07587248
19 0.37317205 -2.58530636
20 6.72663095 0.37317205
21 -4.91162989 6.72663095
22 3.29103430 -4.91162989
23 2.19109952 3.29103430
24 2.63104036 2.19109952
25 3.13573677 2.63104036
26 5.31004195 3.13573677
27 2.69925017 5.31004195
28 2.01301634 2.69925017
29 -0.32589055 2.01301634
30 1.46186651 -0.32589055
31 1.99170484 1.46186651
32 -3.76234553 1.99170484
33 -1.47346843 -3.76234553
34 -4.18925359 -1.47346843
35 -3.69963289 -4.18925359
36 -2.69985029 -3.69963289
37 1.11113778 -2.69985029
38 -3.70380748 1.11113778
39 3.30906092 -3.70380748
40 2.38409120 3.30906092
41 1.63573677 2.38409120
> 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/freestat/rcomp/tmp/7u4ob1292263588.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/84vne1292263588.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/94vne1292263588.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/104vne1292263588.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11jnl51292263588.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/freestat/rcomp/tmp/12bwkq1292263588.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/freestat/rcomp/tmp/13ixhk1292263588.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/freestat/rcomp/tmp/14b6gn1292263588.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/freestat/rcomp/tmp/15eofs1292263588.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/freestat/rcomp/tmp/16agcj1292263588.tab")
+ }
>
> try(system("convert tmp/183p61292263588.ps tmp/183p61292263588.png",intern=TRUE))
character(0)
> try(system("convert tmp/283p61292263588.ps tmp/283p61292263588.png",intern=TRUE))
character(0)
> try(system("convert tmp/383p61292263588.ps tmp/383p61292263588.png",intern=TRUE))
character(0)
> try(system("convert tmp/41c6q1292263588.ps tmp/41c6q1292263588.png",intern=TRUE))
character(0)
> try(system("convert tmp/51c6q1292263588.ps tmp/51c6q1292263588.png",intern=TRUE))
character(0)
> try(system("convert tmp/61c6q1292263588.ps tmp/61c6q1292263588.png",intern=TRUE))
character(0)
> try(system("convert tmp/7u4ob1292263588.ps tmp/7u4ob1292263588.png",intern=TRUE))
character(0)
> try(system("convert tmp/84vne1292263588.ps tmp/84vne1292263588.png",intern=TRUE))
character(0)
> try(system("convert tmp/94vne1292263588.ps tmp/94vne1292263588.png",intern=TRUE))
character(0)
> try(system("convert tmp/104vne1292263588.ps tmp/104vne1292263588.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.679 2.508 4.161