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.
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(1.0622,1,1.0183,1,1.0014,1,0.9811,1,0.9808,1,0.9778,1,0.9922,1,0.9554,1,0.917,1,0.8858,1,0.8758,1,0.87,1,0.8833,1,0.8924,1,0.8883,1,0.9059,1,0.9111,1,0.9005,0,0.8607,0,0.8532,0,0.8742,0,0.892,0,0.9095,0,0.9217,0,0.9383,0,0.8973,0,0.8564,0,0.8552,0,0.8721,0,0.9041,0,0.9397,0,0.9492,0,0.906,0,0.947,0,0.9643,0,0.9834,0,1.0137,0,1.011,0,1.0338,0,1.0706,0,1.0501,0,1.0604,0,1.0353,0,1.0378,0,1.0628,0,1.0704,0,1.0883,0,1.1208,0,1.1608,0),dim=c(2,49),dimnames=list(c('wisselkoers','dummy'),1:49))
> y <- array(NA,dim=c(2,49),dimnames=list(c('wisselkoers','dummy'),1:49))
> 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
wisselkoers dummy
1 1.0622 1
2 1.0183 1
3 1.0014 1
4 0.9811 1
5 0.9808 1
6 0.9778 1
7 0.9922 1
8 0.9554 1
9 0.9170 1
10 0.8858 1
11 0.8758 1
12 0.8700 1
13 0.8833 1
14 0.8924 1
15 0.8883 1
16 0.9059 1
17 0.9111 1
18 0.9005 0
19 0.8607 0
20 0.8532 0
21 0.8742 0
22 0.8920 0
23 0.9095 0
24 0.9217 0
25 0.9383 0
26 0.8973 0
27 0.8564 0
28 0.8552 0
29 0.8721 0
30 0.9041 0
31 0.9397 0
32 0.9492 0
33 0.9060 0
34 0.9470 0
35 0.9643 0
36 0.9834 0
37 1.0137 0
38 1.0110 0
39 1.0338 0
40 1.0706 0
41 1.0501 0
42 1.0604 0
43 1.0353 0
44 1.0378 0
45 1.0628 0
46 1.0704 0
47 1.0883 0
48 1.1208 0
49 1.1608 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dummy
0.97002 -0.02891
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.11682 -0.06402 -0.02302 0.06378 0.19078
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.97002 0.01394 69.566 <2e-16 ***
dummy -0.02891 0.02367 -1.221 0.228
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07888 on 47 degrees of freedom
Multiple R-squared: 0.03076, Adjusted R-squared: 0.01014
F-statistic: 1.492 on 1 and 47 DF, p-value: 0.2280
> 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,] 0.138584453 0.27716891 0.8614155
[2,] 0.073361487 0.14672297 0.9266385
[3,] 0.031371807 0.06274361 0.9686282
[4,] 0.025150492 0.05030098 0.9748495
[5,] 0.047917259 0.09583452 0.9520827
[6,] 0.104914792 0.20982958 0.8950852
[7,] 0.156729844 0.31345969 0.8432702
[8,] 0.192279581 0.38455916 0.8077204
[9,] 0.180843798 0.36168760 0.8191562
[10,] 0.150407796 0.30081559 0.8495922
[11,] 0.124050763 0.24810153 0.8759492
[12,] 0.088696377 0.17739275 0.9113036
[13,] 0.059736884 0.11947377 0.9402631
[14,] 0.039781341 0.07956268 0.9602187
[15,] 0.032611393 0.06522279 0.9673886
[16,] 0.028461439 0.05692288 0.9715386
[17,] 0.022340055 0.04468011 0.9776599
[18,] 0.016956675 0.03391335 0.9830433
[19,] 0.012760190 0.02552038 0.9872398
[20,] 0.009595489 0.01919098 0.9904045
[21,] 0.007388064 0.01477613 0.9926119
[22,] 0.005880229 0.01176046 0.9941198
[23,] 0.009079278 0.01815856 0.9909207
[24,] 0.017871928 0.03574386 0.9821281
[25,] 0.033296102 0.06659220 0.9667039
[26,] 0.049473229 0.09894646 0.9505268
[27,] 0.063315965 0.12663193 0.9366840
[28,] 0.081905222 0.16381044 0.9180948
[29,] 0.198638445 0.39727689 0.8013616
[30,] 0.330066746 0.66013349 0.6699333
[31,] 0.494715707 0.98943141 0.5052843
[32,] 0.647265590 0.70546882 0.3527344
[33,] 0.722697950 0.55460410 0.2773021
[34,] 0.796623616 0.40675277 0.2033764
[35,] 0.817482243 0.36503551 0.1825178
[36,] 0.801146871 0.39770626 0.1988531
[37,] 0.767365065 0.46526987 0.2326349
[38,] 0.704986148 0.59002770 0.2950139
[39,] 0.684891398 0.63021720 0.3151086
[40,] 0.688246240 0.62350752 0.3117538
> postscript(file="/var/www/html/rcomp/tmp/1z9jw1227467419.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/22rwz1227467419.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/308wl1227467419.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/405um1227467419.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ikyv1227467419.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 = 49
Frequency = 1
1 2 3 4 5 6
0.12109412 0.07719412 0.06029412 0.03999412 0.03969412 0.03669412
7 8 9 10 11 12
0.05109412 0.01429412 -0.02410588 -0.05530588 -0.06530588 -0.07110588
13 14 15 16 17 18
-0.05780588 -0.04870588 -0.05280588 -0.03520588 -0.03000588 -0.06951875
19 20 21 22 23 24
-0.10931875 -0.11681875 -0.09581875 -0.07801875 -0.06051875 -0.04831875
25 26 27 28 29 30
-0.03171875 -0.07271875 -0.11361875 -0.11481875 -0.09791875 -0.06591875
31 32 33 34 35 36
-0.03031875 -0.02081875 -0.06401875 -0.02301875 -0.00571875 0.01338125
37 38 39 40 41 42
0.04368125 0.04098125 0.06378125 0.10058125 0.08008125 0.09038125
43 44 45 46 47 48
0.06528125 0.06778125 0.09278125 0.10038125 0.11828125 0.15078125
49
0.19078125
> postscript(file="/var/www/html/rcomp/tmp/6zhhs1227467419.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 = 49
Frequency = 1
lag(myerror, k = 1) myerror
0 0.12109412 NA
1 0.07719412 0.12109412
2 0.06029412 0.07719412
3 0.03999412 0.06029412
4 0.03969412 0.03999412
5 0.03669412 0.03969412
6 0.05109412 0.03669412
7 0.01429412 0.05109412
8 -0.02410588 0.01429412
9 -0.05530588 -0.02410588
10 -0.06530588 -0.05530588
11 -0.07110588 -0.06530588
12 -0.05780588 -0.07110588
13 -0.04870588 -0.05780588
14 -0.05280588 -0.04870588
15 -0.03520588 -0.05280588
16 -0.03000588 -0.03520588
17 -0.06951875 -0.03000588
18 -0.10931875 -0.06951875
19 -0.11681875 -0.10931875
20 -0.09581875 -0.11681875
21 -0.07801875 -0.09581875
22 -0.06051875 -0.07801875
23 -0.04831875 -0.06051875
24 -0.03171875 -0.04831875
25 -0.07271875 -0.03171875
26 -0.11361875 -0.07271875
27 -0.11481875 -0.11361875
28 -0.09791875 -0.11481875
29 -0.06591875 -0.09791875
30 -0.03031875 -0.06591875
31 -0.02081875 -0.03031875
32 -0.06401875 -0.02081875
33 -0.02301875 -0.06401875
34 -0.00571875 -0.02301875
35 0.01338125 -0.00571875
36 0.04368125 0.01338125
37 0.04098125 0.04368125
38 0.06378125 0.04098125
39 0.10058125 0.06378125
40 0.08008125 0.10058125
41 0.09038125 0.08008125
42 0.06528125 0.09038125
43 0.06778125 0.06528125
44 0.09278125 0.06778125
45 0.10038125 0.09278125
46 0.11828125 0.10038125
47 0.15078125 0.11828125
48 0.19078125 0.15078125
49 NA 0.19078125
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.07719412 0.12109412
[2,] 0.06029412 0.07719412
[3,] 0.03999412 0.06029412
[4,] 0.03969412 0.03999412
[5,] 0.03669412 0.03969412
[6,] 0.05109412 0.03669412
[7,] 0.01429412 0.05109412
[8,] -0.02410588 0.01429412
[9,] -0.05530588 -0.02410588
[10,] -0.06530588 -0.05530588
[11,] -0.07110588 -0.06530588
[12,] -0.05780588 -0.07110588
[13,] -0.04870588 -0.05780588
[14,] -0.05280588 -0.04870588
[15,] -0.03520588 -0.05280588
[16,] -0.03000588 -0.03520588
[17,] -0.06951875 -0.03000588
[18,] -0.10931875 -0.06951875
[19,] -0.11681875 -0.10931875
[20,] -0.09581875 -0.11681875
[21,] -0.07801875 -0.09581875
[22,] -0.06051875 -0.07801875
[23,] -0.04831875 -0.06051875
[24,] -0.03171875 -0.04831875
[25,] -0.07271875 -0.03171875
[26,] -0.11361875 -0.07271875
[27,] -0.11481875 -0.11361875
[28,] -0.09791875 -0.11481875
[29,] -0.06591875 -0.09791875
[30,] -0.03031875 -0.06591875
[31,] -0.02081875 -0.03031875
[32,] -0.06401875 -0.02081875
[33,] -0.02301875 -0.06401875
[34,] -0.00571875 -0.02301875
[35,] 0.01338125 -0.00571875
[36,] 0.04368125 0.01338125
[37,] 0.04098125 0.04368125
[38,] 0.06378125 0.04098125
[39,] 0.10058125 0.06378125
[40,] 0.08008125 0.10058125
[41,] 0.09038125 0.08008125
[42,] 0.06528125 0.09038125
[43,] 0.06778125 0.06528125
[44,] 0.09278125 0.06778125
[45,] 0.10038125 0.09278125
[46,] 0.11828125 0.10038125
[47,] 0.15078125 0.11828125
[48,] 0.19078125 0.15078125
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.07719412 0.12109412
2 0.06029412 0.07719412
3 0.03999412 0.06029412
4 0.03969412 0.03999412
5 0.03669412 0.03969412
6 0.05109412 0.03669412
7 0.01429412 0.05109412
8 -0.02410588 0.01429412
9 -0.05530588 -0.02410588
10 -0.06530588 -0.05530588
11 -0.07110588 -0.06530588
12 -0.05780588 -0.07110588
13 -0.04870588 -0.05780588
14 -0.05280588 -0.04870588
15 -0.03520588 -0.05280588
16 -0.03000588 -0.03520588
17 -0.06951875 -0.03000588
18 -0.10931875 -0.06951875
19 -0.11681875 -0.10931875
20 -0.09581875 -0.11681875
21 -0.07801875 -0.09581875
22 -0.06051875 -0.07801875
23 -0.04831875 -0.06051875
24 -0.03171875 -0.04831875
25 -0.07271875 -0.03171875
26 -0.11361875 -0.07271875
27 -0.11481875 -0.11361875
28 -0.09791875 -0.11481875
29 -0.06591875 -0.09791875
30 -0.03031875 -0.06591875
31 -0.02081875 -0.03031875
32 -0.06401875 -0.02081875
33 -0.02301875 -0.06401875
34 -0.00571875 -0.02301875
35 0.01338125 -0.00571875
36 0.04368125 0.01338125
37 0.04098125 0.04368125
38 0.06378125 0.04098125
39 0.10058125 0.06378125
40 0.08008125 0.10058125
41 0.09038125 0.08008125
42 0.06528125 0.09038125
43 0.06778125 0.06528125
44 0.09278125 0.06778125
45 0.10038125 0.09278125
46 0.11828125 0.10038125
47 0.15078125 0.11828125
48 0.19078125 0.15078125
> 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/7ggfi1227467419.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ossl1227467419.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9l7231227467419.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10vest1227467419.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111a281227467419.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/12v66c1227467419.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/137cl21227467420.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/142tn31227467420.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/159tm71227467420.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/16r71o1227467420.tab")
+ }
>
> system("convert tmp/1z9jw1227467419.ps tmp/1z9jw1227467419.png")
> system("convert tmp/22rwz1227467419.ps tmp/22rwz1227467419.png")
> system("convert tmp/308wl1227467419.ps tmp/308wl1227467419.png")
> system("convert tmp/405um1227467419.ps tmp/405um1227467419.png")
> system("convert tmp/5ikyv1227467419.ps tmp/5ikyv1227467419.png")
> system("convert tmp/6zhhs1227467419.ps tmp/6zhhs1227467419.png")
> system("convert tmp/7ggfi1227467419.ps tmp/7ggfi1227467419.png")
> system("convert tmp/8ossl1227467419.ps tmp/8ossl1227467419.png")
> system("convert tmp/9l7231227467419.ps tmp/9l7231227467419.png")
> system("convert tmp/10vest1227467419.ps tmp/10vest1227467419.png")
>
>
> proc.time()
user system elapsed
2.366 1.566 2.972