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(6.3,1,3,2.1,2547,4,9.1,10.55,4,15.8,0.023,1,5.2,160,4,10.9,3.3,1,8.3,52.16,1,11,0.425,4,3.2,465,5,6.3,0.075,1,8.6,3,2,6.6,0.785,2,9.5,0.2,2,3.3,27.66,5,11,0.12,2,4.7,85,1,10.4,0.101,3,7.4,1.04,4,2.1,521,5,7.7,0.005,4,17.9,0.01,1,6.1,62,1,11.9,0.023,3,10.8,0.048,3,13.8,1.7,1,14.3,3.5,1,15.2,0.48,2,10,10,4,11.9,1.62,2,6.5,192,4,7.5,2.5,5,10.6,0.28,3,7.4,4.235,1,8.4,6.8,2,5.7,0.75,2,4.9,3.6,3,3.2,55.5,5,11,0.9,2,4.9,2,3,13.2,0.104,2,9.7,4.19,4,12.8,3.5,1),dim=c(3,42),dimnames=list(c('SWS','BodyW','ODI'),1:42))
> y <- array(NA,dim=c(3,42),dimnames=list(c('SWS','BodyW','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])
+ }
+ }
> par6 = '0'
> par5 = '0'
> par4 = '0'
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par6 <- '0'
> par5 <- '0'
> par4 <- '0'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Dr. Ian E. Holliday
> #To cite this work: Ian E. Holliday, 2009, 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:
> #Technical description:
> 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 BodyW 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) BodyW 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 ***
BodyW -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/rcomp/tmp/1w3yx1292319528.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2w3yx1292319528.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/37cxi1292319528.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/47cxi1292319528.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/57cxi1292319528.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 = 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/rcomp/tmp/6z3w31292319528.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 = 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/rcomp/tmp/7sceo1292319528.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8sceo1292319528.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9sceo1292319528.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10k4vq1292319528.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/116mte1292319528.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/12rna21292319528.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/135xqt1292319528.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/14rf6h1292319528.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/15cyn51292319528.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/16yg3t1292319528.tab")
+ }
>
> try(system("convert tmp/1w3yx1292319528.ps tmp/1w3yx1292319528.png",intern=TRUE))
character(0)
> try(system("convert tmp/2w3yx1292319528.ps tmp/2w3yx1292319528.png",intern=TRUE))
character(0)
> try(system("convert tmp/37cxi1292319528.ps tmp/37cxi1292319528.png",intern=TRUE))
character(0)
> try(system("convert tmp/47cxi1292319528.ps tmp/47cxi1292319528.png",intern=TRUE))
character(0)
> try(system("convert tmp/57cxi1292319528.ps tmp/57cxi1292319528.png",intern=TRUE))
character(0)
> try(system("convert tmp/6z3w31292319528.ps tmp/6z3w31292319528.png",intern=TRUE))
character(0)
> try(system("convert tmp/7sceo1292319528.ps tmp/7sceo1292319528.png",intern=TRUE))
character(0)
> try(system("convert tmp/8sceo1292319528.ps tmp/8sceo1292319528.png",intern=TRUE))
character(0)
> try(system("convert tmp/9sceo1292319528.ps tmp/9sceo1292319528.png",intern=TRUE))
character(0)
> try(system("convert tmp/10k4vq1292319528.ps tmp/10k4vq1292319528.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.345 1.620 7.788