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.
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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.
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> x <- array(list(1000,6.3,3,2547000,2.1,4,10550,9.1,4,0.023,15.8,1,160000,5.2,4,3300,10.9,1,52160,8.3,1,0.425,11.0,4,465000,3.2,5,0.075,6.3,1,3000,8.6,2,0.785,6.6,2,0.2,9.5,2,27660,3.3,5,0.12,11.0,2,85000,4.7,1,0.101,10.4,3,1040,7.4,4,521000,2.1,5,0.005,7.7,4,0.01,17.9,1,62000,6.1,1,0.023,11.9,3,0.048,10.8,3,1700,13.8,1,3500,14.3,1,0.48,15.2,2,10000,10.0,4,1620,11.9,2,192000,6.5,4,2500,7.5,5,0.28,10.6,3,4235,7.4,1,6800,8.4,2,0.75,5.7,2,3600,4.9,3,55500,3.2,5,0.9,11.0,2,2000,4.9,3,0.104,13.2,2,4190,9.7,4,3500,12.8,1),dim=c(3,42),dimnames=list(c('Wb','SWS','D'),1:42))
> y <- array(NA,dim=c(3,42),dimnames=list(c('Wb','SWS','D'),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 = '2'
> #'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 Wb D
1 6.3 1.000e+03 3
2 2.1 2.547e+06 4
3 9.1 1.055e+04 4
4 15.8 2.300e-02 1
5 5.2 1.600e+05 4
6 10.9 3.300e+03 1
7 8.3 5.216e+04 1
8 11.0 4.250e-01 4
9 3.2 4.650e+05 5
10 6.3 7.500e-02 1
11 8.6 3.000e+03 2
12 6.6 7.850e-01 2
13 9.5 2.000e-01 2
14 3.3 2.766e+04 5
15 11.0 1.200e-01 2
16 4.7 8.500e+04 1
17 10.4 1.010e-01 3
18 7.4 1.040e+03 4
19 2.1 5.210e+05 5
20 7.7 5.000e-03 4
21 17.9 1.000e-02 1
22 6.1 6.200e+04 1
23 11.9 2.300e-02 3
24 10.8 4.800e-02 3
25 13.8 1.700e+03 1
26 14.3 3.500e+03 1
27 15.2 4.800e-01 2
28 10.0 1.000e+04 4
29 11.9 1.620e+03 2
30 6.5 1.920e+05 4
31 7.5 2.500e+03 5
32 10.6 2.800e-01 3
33 7.4 4.235e+03 1
34 8.4 6.800e+03 2
35 5.7 7.500e-01 2
36 4.9 3.600e+03 3
37 3.2 5.550e+04 5
38 11.0 9.000e-01 2
39 4.9 2.000e+03 3
40 13.2 1.040e-01 2
41 9.7 4.190e+03 4
42 12.8 3.500e+03 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Wb D
1.246e+01 -2.609e-06 -1.282e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.2513 -2.6506 0.2247 2.1468 6.7270
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.246e+01 1.081e+00 11.522 4e-14 ***
Wb -2.609e-06 1.270e-06 -2.054 0.04672 *
D -1.282e+00 3.680e-01 -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,] 0.5357102 0.9285795 0.4642898
[2,] 0.5758611 0.8482779 0.4241389
[3,] 0.5939797 0.8120405 0.4060203
[4,] 0.5092250 0.9815501 0.4907750
[5,] 0.6366818 0.7266364 0.3633182
[6,] 0.5298811 0.9402378 0.4701189
[7,] 0.5044899 0.9910201 0.4955101
[8,] 0.3986385 0.7972771 0.6013615
[9,] 0.3708051 0.7416102 0.6291949
[10,] 0.3032087 0.6064173 0.6967913
[11,] 0.5036544 0.9926911 0.4963456
[12,] 0.4515210 0.9030420 0.5484790
[13,] 0.3580755 0.7161510 0.6419245
[14,] 0.3261756 0.6523513 0.6738244
[15,] 0.2489927 0.4979855 0.7510073
[16,] 0.5820490 0.8359020 0.4179510
[17,] 0.6650234 0.6699532 0.3349766
[18,] 0.6601785 0.6796430 0.3398215
[19,] 0.6041682 0.7916637 0.3958318
[20,] 0.5616968 0.8766064 0.4383032
[21,] 0.5465927 0.9068146 0.4534073
[22,] 0.7078992 0.5842015 0.2921008
[23,] 0.6775106 0.6449787 0.3224894
[24,] 0.6337871 0.7324258 0.3662129
[25,] 0.6103385 0.7793231 0.3896615
[26,] 0.5017381 0.9965238 0.4982619
[27,] 0.4361100 0.8722199 0.5638900
[28,] 0.4283849 0.8567697 0.5716151
[29,] 0.3151363 0.6302726 0.6848637
[30,] 0.3977948 0.7955895 0.6022052
[31,] 0.4457010 0.8914021 0.5542990
> postscript(file="/var/www/html/rcomp/tmp/1r6mv1292318366.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2r6mv1292318366.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32x3y1292318366.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/42x3y1292318366.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/52x3y1292318366.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
-2.30616076 1.41866803 1.80088499 4.62697201 -1.70918906 -0.26441811
7 8 9 10 11 12
-2.73693881 3.67336038 -1.63129271 -4.87302785 -1.28307172 -3.29089689
13 14 15 16 17 18
-0.39089842 -2.67234466 1.10910137 -6.25125685 1.79123043 0.07607271
19 20 21 22 23 24
-2.58518462 0.37335929 6.72697198 -4.91126553 3.29123022 2.19123029
25 26 27 28 29 30
2.63140737 3.13610371 5.30910231 2.69945000 2.01332776 -0.32569873
31 32 33 34 35 36
1.46201106 1.99123090 -3.76197863 -1.47315725 -4.19089699 -3.69937717
37 38 39 40 41 42
-2.69970807 1.10910341 -3.70355169 3.30910133 2.38429129 1.63610371
> postscript(file="/var/www/html/rcomp/tmp/6c7kj1292318366.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 -2.30616076 NA
1 1.41866803 -2.30616076
2 1.80088499 1.41866803
3 4.62697201 1.80088499
4 -1.70918906 4.62697201
5 -0.26441811 -1.70918906
6 -2.73693881 -0.26441811
7 3.67336038 -2.73693881
8 -1.63129271 3.67336038
9 -4.87302785 -1.63129271
10 -1.28307172 -4.87302785
11 -3.29089689 -1.28307172
12 -0.39089842 -3.29089689
13 -2.67234466 -0.39089842
14 1.10910137 -2.67234466
15 -6.25125685 1.10910137
16 1.79123043 -6.25125685
17 0.07607271 1.79123043
18 -2.58518462 0.07607271
19 0.37335929 -2.58518462
20 6.72697198 0.37335929
21 -4.91126553 6.72697198
22 3.29123022 -4.91126553
23 2.19123029 3.29123022
24 2.63140737 2.19123029
25 3.13610371 2.63140737
26 5.30910231 3.13610371
27 2.69945000 5.30910231
28 2.01332776 2.69945000
29 -0.32569873 2.01332776
30 1.46201106 -0.32569873
31 1.99123090 1.46201106
32 -3.76197863 1.99123090
33 -1.47315725 -3.76197863
34 -4.19089699 -1.47315725
35 -3.69937717 -4.19089699
36 -2.69970807 -3.69937717
37 1.10910341 -2.69970807
38 -3.70355169 1.10910341
39 3.30910133 -3.70355169
40 2.38429129 3.30910133
41 1.63610371 2.38429129
42 NA 1.63610371
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.41866803 -2.30616076
[2,] 1.80088499 1.41866803
[3,] 4.62697201 1.80088499
[4,] -1.70918906 4.62697201
[5,] -0.26441811 -1.70918906
[6,] -2.73693881 -0.26441811
[7,] 3.67336038 -2.73693881
[8,] -1.63129271 3.67336038
[9,] -4.87302785 -1.63129271
[10,] -1.28307172 -4.87302785
[11,] -3.29089689 -1.28307172
[12,] -0.39089842 -3.29089689
[13,] -2.67234466 -0.39089842
[14,] 1.10910137 -2.67234466
[15,] -6.25125685 1.10910137
[16,] 1.79123043 -6.25125685
[17,] 0.07607271 1.79123043
[18,] -2.58518462 0.07607271
[19,] 0.37335929 -2.58518462
[20,] 6.72697198 0.37335929
[21,] -4.91126553 6.72697198
[22,] 3.29123022 -4.91126553
[23,] 2.19123029 3.29123022
[24,] 2.63140737 2.19123029
[25,] 3.13610371 2.63140737
[26,] 5.30910231 3.13610371
[27,] 2.69945000 5.30910231
[28,] 2.01332776 2.69945000
[29,] -0.32569873 2.01332776
[30,] 1.46201106 -0.32569873
[31,] 1.99123090 1.46201106
[32,] -3.76197863 1.99123090
[33,] -1.47315725 -3.76197863
[34,] -4.19089699 -1.47315725
[35,] -3.69937717 -4.19089699
[36,] -2.69970807 -3.69937717
[37,] 1.10910341 -2.69970807
[38,] -3.70355169 1.10910341
[39,] 3.30910133 -3.70355169
[40,] 2.38429129 3.30910133
[41,] 1.63610371 2.38429129
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.41866803 -2.30616076
2 1.80088499 1.41866803
3 4.62697201 1.80088499
4 -1.70918906 4.62697201
5 -0.26441811 -1.70918906
6 -2.73693881 -0.26441811
7 3.67336038 -2.73693881
8 -1.63129271 3.67336038
9 -4.87302785 -1.63129271
10 -1.28307172 -4.87302785
11 -3.29089689 -1.28307172
12 -0.39089842 -3.29089689
13 -2.67234466 -0.39089842
14 1.10910137 -2.67234466
15 -6.25125685 1.10910137
16 1.79123043 -6.25125685
17 0.07607271 1.79123043
18 -2.58518462 0.07607271
19 0.37335929 -2.58518462
20 6.72697198 0.37335929
21 -4.91126553 6.72697198
22 3.29123022 -4.91126553
23 2.19123029 3.29123022
24 2.63140737 2.19123029
25 3.13610371 2.63140737
26 5.30910231 3.13610371
27 2.69945000 5.30910231
28 2.01332776 2.69945000
29 -0.32569873 2.01332776
30 1.46201106 -0.32569873
31 1.99123090 1.46201106
32 -3.76197863 1.99123090
33 -1.47315725 -3.76197863
34 -4.19089699 -1.47315725
35 -3.69937717 -4.19089699
36 -2.69970807 -3.69937717
37 1.10910341 -2.69970807
38 -3.70355169 1.10910341
39 3.30910133 -3.70355169
40 2.38429129 3.30910133
41 1.63610371 2.38429129
> 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/7ng2m1292318366.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ng2m1292318366.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ng2m1292318366.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10y71o1292318366.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1118hc1292318366.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/1248g01292318366.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/1310e91292318366.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/14micf1292318366.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/15pjs31292318366.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/16t2rr1292318366.tab")
+ }
>
> try(system("convert tmp/1r6mv1292318366.ps tmp/1r6mv1292318366.png",intern=TRUE))
character(0)
> try(system("convert tmp/2r6mv1292318366.ps tmp/2r6mv1292318366.png",intern=TRUE))
character(0)
> try(system("convert tmp/32x3y1292318366.ps tmp/32x3y1292318366.png",intern=TRUE))
character(0)
> try(system("convert tmp/42x3y1292318366.ps tmp/42x3y1292318366.png",intern=TRUE))
character(0)
> try(system("convert tmp/52x3y1292318366.ps tmp/52x3y1292318366.png",intern=TRUE))
character(0)
> try(system("convert tmp/6c7kj1292318366.ps tmp/6c7kj1292318366.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ng2m1292318366.ps tmp/7ng2m1292318366.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ng2m1292318366.ps tmp/8ng2m1292318366.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ng2m1292318366.ps tmp/9ng2m1292318366.png",intern=TRUE))
character(0)
> try(system("convert tmp/10y71o1292318366.ps tmp/10y71o1292318366.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.308 1.609 5.706