R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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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(101.6
+ ,79.8
+ ,103.9
+ ,110.3
+ ,114.1
+ ,96.8
+ ,94.6
+ ,71.9
+ ,101.6
+ ,103.9
+ ,110.3
+ ,114.1
+ ,95.9
+ ,82.9
+ ,94.6
+ ,101.6
+ ,103.9
+ ,110.3
+ ,104.7
+ ,90.1
+ ,95.9
+ ,94.6
+ ,101.6
+ ,103.9
+ ,102.8
+ ,100.7
+ ,104.7
+ ,95.9
+ ,94.6
+ ,101.6
+ ,98.1
+ ,90.7
+ ,102.8
+ ,104.7
+ ,95.9
+ ,94.6
+ ,113.9
+ ,108.8
+ ,98.1
+ ,102.8
+ ,104.7
+ ,95.9
+ ,80.9
+ ,44.1
+ ,113.9
+ ,98.1
+ ,102.8
+ ,104.7
+ ,95.7
+ ,93.6
+ ,80.9
+ ,113.9
+ ,98.1
+ ,102.8
+ ,113.2
+ ,107.4
+ ,95.7
+ ,80.9
+ ,113.9
+ ,98.1
+ ,105.9
+ ,96.5
+ ,113.2
+ ,95.7
+ ,80.9
+ ,113.9
+ ,108.8
+ ,93.6
+ ,105.9
+ ,113.2
+ ,95.7
+ ,80.9
+ ,102.3
+ ,76.5
+ ,108.8
+ ,105.9
+ ,113.2
+ ,95.7
+ ,99
+ ,76.7
+ ,102.3
+ ,108.8
+ ,105.9
+ ,113.2
+ ,100.7
+ ,84
+ ,99
+ ,102.3
+ ,108.8
+ ,105.9
+ ,115.5
+ ,103.3
+ ,100.7
+ ,99
+ ,102.3
+ ,108.8
+ ,100.7
+ ,88.5
+ ,115.5
+ ,100.7
+ ,99
+ ,102.3
+ ,109.9
+ ,99
+ ,100.7
+ ,115.5
+ ,100.7
+ ,99
+ ,114.6
+ ,105.9
+ ,109.9
+ ,100.7
+ ,115.5
+ ,100.7
+ ,85.4
+ ,44.7
+ ,114.6
+ ,109.9
+ ,100.7
+ ,115.5
+ ,100.5
+ ,94
+ ,85.4
+ ,114.6
+ ,109.9
+ ,100.7
+ ,114.8
+ ,107.1
+ ,100.5
+ ,85.4
+ ,114.6
+ ,109.9
+ ,116.5
+ ,104.8
+ ,114.8
+ ,100.5
+ ,85.4
+ ,114.6
+ ,112.9
+ ,102.5
+ ,116.5
+ ,114.8
+ ,100.5
+ ,85.4
+ ,102
+ ,77.7
+ ,112.9
+ ,116.5
+ ,114.8
+ ,100.5
+ ,106
+ ,85.2
+ ,102
+ ,112.9
+ ,116.5
+ ,114.8
+ ,105.3
+ ,91.3
+ ,106
+ ,102
+ ,112.9
+ ,116.5
+ ,118.8
+ ,106.5
+ ,105.3
+ ,106
+ ,102
+ ,112.9
+ ,106.1
+ ,92.4
+ ,118.8
+ ,105.3
+ ,106
+ ,102
+ ,109.3
+ ,97.5
+ ,106.1
+ ,118.8
+ ,105.3
+ ,106
+ ,117.2
+ ,107
+ ,109.3
+ ,106.1
+ ,118.8
+ ,105.3
+ ,92.5
+ ,51.1
+ ,117.2
+ ,109.3
+ ,106.1
+ ,118.8
+ ,104.2
+ ,98.6
+ ,92.5
+ ,117.2
+ ,109.3
+ ,106.1
+ ,112.5
+ ,102.2
+ ,104.2
+ ,92.5
+ ,117.2
+ ,109.3
+ ,122.4
+ ,114.3
+ ,112.5
+ ,104.2
+ ,92.5
+ ,117.2
+ ,113.3
+ ,99.4
+ ,122.4
+ ,112.5
+ ,104.2
+ ,92.5
+ ,100
+ ,72.5
+ ,113.3
+ ,122.4
+ ,112.5
+ ,104.2
+ ,110.7
+ ,92.3
+ ,100
+ ,113.3
+ ,122.4
+ ,112.5
+ ,112.8
+ ,99.4
+ ,110.7
+ ,100
+ ,113.3
+ ,122.4
+ ,109.8
+ ,85.9
+ ,112.8
+ ,110.7
+ ,100
+ ,113.3
+ ,117.3
+ ,109.4
+ ,109.8
+ ,112.8
+ ,110.7
+ ,100
+ ,109.1
+ ,97.6
+ ,117.3
+ ,109.8
+ ,112.8
+ ,110.7
+ ,115.9
+ ,104.7
+ ,109.1
+ ,117.3
+ ,109.8
+ ,112.8
+ ,96
+ ,56.9
+ ,115.9
+ ,109.1
+ ,117.3
+ ,109.8
+ ,99.8
+ ,86.7
+ ,96
+ ,115.9
+ ,109.1
+ ,117.3
+ ,116.8
+ ,108.5
+ ,99.8
+ ,96
+ ,115.9
+ ,109.1
+ ,115.7
+ ,103.4
+ ,116.8
+ ,99.8
+ ,96
+ ,115.9
+ ,99.4
+ ,86.2
+ ,115.7
+ ,116.8
+ ,99.8
+ ,96
+ ,94.3
+ ,71
+ ,99.4
+ ,115.7
+ ,116.8
+ ,99.8
+ ,91
+ ,75.9
+ ,94.3
+ ,99.4
+ ,115.7
+ ,116.8
+ ,93.2
+ ,87.1
+ ,91
+ ,94.3
+ ,99.4
+ ,115.7
+ ,103.1
+ ,102
+ ,93.2
+ ,91
+ ,94.3
+ ,99.4
+ ,94.1
+ ,88.5
+ ,103.1
+ ,93.2
+ ,91
+ ,94.3
+ ,91.8
+ ,87.8
+ ,94.1
+ ,103.1
+ ,93.2
+ ,91
+ ,102.7
+ ,100.8
+ ,91.8
+ ,94.1
+ ,103.1
+ ,93.2
+ ,82.6
+ ,50.6
+ ,102.7
+ ,91.8
+ ,94.1
+ ,103.1)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('Totind'
+ ,'Bouw'
+ ,'Yt-1'
+ ,'Yt-2'
+ ,'Yt-3'
+ ,'Yt-4')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('Totind','Bouw','Yt-1','Yt-2','Yt-3','Yt-4'),1:56))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly 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
Totind Bouw Yt-1 Yt-2 Yt-3 Yt-4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 101.6 79.8 103.9 110.3 114.1 96.8 1 0 0 0 0 0 0 0 0 0 0 1
2 94.6 71.9 101.6 103.9 110.3 114.1 0 1 0 0 0 0 0 0 0 0 0 2
3 95.9 82.9 94.6 101.6 103.9 110.3 0 0 1 0 0 0 0 0 0 0 0 3
4 104.7 90.1 95.9 94.6 101.6 103.9 0 0 0 1 0 0 0 0 0 0 0 4
5 102.8 100.7 104.7 95.9 94.6 101.6 0 0 0 0 1 0 0 0 0 0 0 5
6 98.1 90.7 102.8 104.7 95.9 94.6 0 0 0 0 0 1 0 0 0 0 0 6
7 113.9 108.8 98.1 102.8 104.7 95.9 0 0 0 0 0 0 1 0 0 0 0 7
8 80.9 44.1 113.9 98.1 102.8 104.7 0 0 0 0 0 0 0 1 0 0 0 8
9 95.7 93.6 80.9 113.9 98.1 102.8 0 0 0 0 0 0 0 0 1 0 0 9
10 113.2 107.4 95.7 80.9 113.9 98.1 0 0 0 0 0 0 0 0 0 1 0 10
11 105.9 96.5 113.2 95.7 80.9 113.9 0 0 0 0 0 0 0 0 0 0 1 11
12 108.8 93.6 105.9 113.2 95.7 80.9 0 0 0 0 0 0 0 0 0 0 0 12
13 102.3 76.5 108.8 105.9 113.2 95.7 1 0 0 0 0 0 0 0 0 0 0 13
14 99.0 76.7 102.3 108.8 105.9 113.2 0 1 0 0 0 0 0 0 0 0 0 14
15 100.7 84.0 99.0 102.3 108.8 105.9 0 0 1 0 0 0 0 0 0 0 0 15
16 115.5 103.3 100.7 99.0 102.3 108.8 0 0 0 1 0 0 0 0 0 0 0 16
17 100.7 88.5 115.5 100.7 99.0 102.3 0 0 0 0 1 0 0 0 0 0 0 17
18 109.9 99.0 100.7 115.5 100.7 99.0 0 0 0 0 0 1 0 0 0 0 0 18
19 114.6 105.9 109.9 100.7 115.5 100.7 0 0 0 0 0 0 1 0 0 0 0 19
20 85.4 44.7 114.6 109.9 100.7 115.5 0 0 0 0 0 0 0 1 0 0 0 20
21 100.5 94.0 85.4 114.6 109.9 100.7 0 0 0 0 0 0 0 0 1 0 0 21
22 114.8 107.1 100.5 85.4 114.6 109.9 0 0 0 0 0 0 0 0 0 1 0 22
23 116.5 104.8 114.8 100.5 85.4 114.6 0 0 0 0 0 0 0 0 0 0 1 23
24 112.9 102.5 116.5 114.8 100.5 85.4 0 0 0 0 0 0 0 0 0 0 0 24
25 102.0 77.7 112.9 116.5 114.8 100.5 1 0 0 0 0 0 0 0 0 0 0 25
26 106.0 85.2 102.0 112.9 116.5 114.8 0 1 0 0 0 0 0 0 0 0 0 26
27 105.3 91.3 106.0 102.0 112.9 116.5 0 0 1 0 0 0 0 0 0 0 0 27
28 118.8 106.5 105.3 106.0 102.0 112.9 0 0 0 1 0 0 0 0 0 0 0 28
29 106.1 92.4 118.8 105.3 106.0 102.0 0 0 0 0 1 0 0 0 0 0 0 29
30 109.3 97.5 106.1 118.8 105.3 106.0 0 0 0 0 0 1 0 0 0 0 0 30
31 117.2 107.0 109.3 106.1 118.8 105.3 0 0 0 0 0 0 1 0 0 0 0 31
32 92.5 51.1 117.2 109.3 106.1 118.8 0 0 0 0 0 0 0 1 0 0 0 32
33 104.2 98.6 92.5 117.2 109.3 106.1 0 0 0 0 0 0 0 0 1 0 0 33
34 112.5 102.2 104.2 92.5 117.2 109.3 0 0 0 0 0 0 0 0 0 1 0 34
35 122.4 114.3 112.5 104.2 92.5 117.2 0 0 0 0 0 0 0 0 0 0 1 35
36 113.3 99.4 122.4 112.5 104.2 92.5 0 0 0 0 0 0 0 0 0 0 0 36
37 100.0 72.5 113.3 122.4 112.5 104.2 1 0 0 0 0 0 0 0 0 0 0 37
38 110.7 92.3 100.0 113.3 122.4 112.5 0 1 0 0 0 0 0 0 0 0 0 38
39 112.8 99.4 110.7 100.0 113.3 122.4 0 0 1 0 0 0 0 0 0 0 0 39
40 109.8 85.9 112.8 110.7 100.0 113.3 0 0 0 1 0 0 0 0 0 0 0 40
41 117.3 109.4 109.8 112.8 110.7 100.0 0 0 0 0 1 0 0 0 0 0 0 41
42 109.1 97.6 117.3 109.8 112.8 110.7 0 0 0 0 0 1 0 0 0 0 0 42
43 115.9 104.7 109.1 117.3 109.8 112.8 0 0 0 0 0 0 1 0 0 0 0 43
44 96.0 56.9 115.9 109.1 117.3 109.8 0 0 0 0 0 0 0 1 0 0 0 44
45 99.8 86.7 96.0 115.9 109.1 117.3 0 0 0 0 0 0 0 0 1 0 0 45
46 116.8 108.5 99.8 96.0 115.9 109.1 0 0 0 0 0 0 0 0 0 1 0 46
47 115.7 103.4 116.8 99.8 96.0 115.9 0 0 0 0 0 0 0 0 0 0 1 47
48 99.4 86.2 115.7 116.8 99.8 96.0 0 0 0 0 0 0 0 0 0 0 0 48
49 94.3 71.0 99.4 115.7 116.8 99.8 1 0 0 0 0 0 0 0 0 0 0 49
50 91.0 75.9 94.3 99.4 115.7 116.8 0 1 0 0 0 0 0 0 0 0 0 50
51 93.2 87.1 91.0 94.3 99.4 115.7 0 0 1 0 0 0 0 0 0 0 0 51
52 103.1 102.0 93.2 91.0 94.3 99.4 0 0 0 1 0 0 0 0 0 0 0 52
53 94.1 88.5 103.1 93.2 91.0 94.3 0 0 0 0 1 0 0 0 0 0 0 53
54 91.8 87.8 94.1 103.1 93.2 91.0 0 0 0 0 0 1 0 0 0 0 0 54
55 102.7 100.8 91.8 94.1 103.1 93.2 0 0 0 0 0 0 1 0 0 0 0 55
56 82.6 50.6 102.7 91.8 94.1 103.1 0 0 0 0 0 0 0 1 0 0 0 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Bouw `Yt-1` `Yt-2` `Yt-3` `Yt-4`
-29.51396 0.66562 0.28437 0.28934 0.15726 -0.06413
M1 M2 M3 M4 M5 M6
5.11065 7.13200 5.96647 9.41343 0.97286 0.39130
M7 M8 M9 M10 M11 t
3.03306 14.01947 0.11724 8.83733 8.13094 -0.04215
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.78856 -0.85799 -0.03611 0.86488 3.78876
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -29.51396 6.19505 -4.764 2.77e-05 ***
Bouw 0.66562 0.04901 13.583 3.67e-16 ***
`Yt-1` 0.28437 0.07164 3.969 0.000309 ***
`Yt-2` 0.28934 0.05543 5.219 6.68e-06 ***
`Yt-3` 0.15726 0.06775 2.321 0.025746 *
`Yt-4` -0.06413 0.07538 -0.851 0.400233
M1 5.11065 2.57610 1.984 0.054529 .
M2 7.13200 3.66090 1.948 0.058810 .
M3 5.96647 3.43576 1.737 0.090564 .
M4 9.41343 2.70924 3.475 0.001295 **
M5 0.97286 1.87042 0.520 0.605992
M6 0.39130 1.96699 0.199 0.843375
M7 3.03306 2.39700 1.265 0.213448
M8 14.01947 3.31330 4.231 0.000141 ***
M9 0.11724 3.37804 0.035 0.972496
M10 8.83733 3.49659 2.527 0.015771 *
M11 8.13094 2.85192 2.851 0.007006 **
t -0.04215 0.01623 -2.597 0.013292 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.764 on 38 degrees of freedom
Multiple R-squared: 0.9768, Adjusted R-squared: 0.9665
F-statistic: 94.25 on 17 and 38 DF, p-value: < 2.2e-16
> 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,] 0.2676884 0.5353767 0.7323116
[2,] 0.1619965 0.3239931 0.8380035
[3,] 0.1084263 0.2168526 0.8915737
[4,] 0.1517944 0.3035887 0.8482056
[5,] 0.5273558 0.9452883 0.4726442
[6,] 0.6087191 0.7825619 0.3912809
[7,] 0.4848866 0.9697731 0.5151134
[8,] 0.3849730 0.7699460 0.6150270
[9,] 0.2959097 0.5918193 0.7040903
[10,] 0.2610851 0.5221702 0.7389149
[11,] 0.1794410 0.3588820 0.8205590
[12,] 0.1324213 0.2648426 0.8675787
[13,] 0.1447301 0.2894602 0.8552699
[14,] 0.1756549 0.3513098 0.8243451
[15,] 0.1035931 0.2071862 0.8964069
> postscript(file="/var/www/html/rcomp/tmp/1oe741258731166.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2st7v1258731166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3mxu81258731166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4qy1e1258731166.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5cq1d1258731166.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 = 56
Frequency = 1
1 2 3 4 5 6
-0.266051517 0.225969439 -1.169313028 1.040379901 -1.357719649 -1.437079822
7 8 9 10 11 12
0.301426291 -2.847430932 -1.621198763 0.568791197 -1.783365404 3.788756737
13 14 15 16 17 18
3.086926887 0.954177496 0.897782437 1.126107035 0.061520988 2.343723924
19 20 21 22 23 24
-0.701012417 -0.831437698 -0.054186250 0.853910339 1.291201579 -1.472963798
25 26 27 28 29 30
-1.682733910 0.136905195 -0.724202618 -0.221453071 -0.018032624 0.483037305
31 32 33 34 35 36
0.056821312 1.311069987 -1.240893692 -1.232616756 0.007318433 0.357395138
37 38 39 40 41 42
-0.937627931 -0.005520053 1.447683971 1.843529336 -0.105838934 -0.736653842
43 44 45 46 47 48
-0.493914259 -0.454648297 2.916278704 -0.190084780 0.484845393 -2.673188077
49 50 51 52 53 54
-0.200513529 -1.311532077 -0.451950762 -3.788563202 1.420070219 -0.653027565
55 56
0.836679072 2.822446941
> postscript(file="/var/www/html/rcomp/tmp/66phb1258731166.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 = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.266051517 NA
1 0.225969439 -0.266051517
2 -1.169313028 0.225969439
3 1.040379901 -1.169313028
4 -1.357719649 1.040379901
5 -1.437079822 -1.357719649
6 0.301426291 -1.437079822
7 -2.847430932 0.301426291
8 -1.621198763 -2.847430932
9 0.568791197 -1.621198763
10 -1.783365404 0.568791197
11 3.788756737 -1.783365404
12 3.086926887 3.788756737
13 0.954177496 3.086926887
14 0.897782437 0.954177496
15 1.126107035 0.897782437
16 0.061520988 1.126107035
17 2.343723924 0.061520988
18 -0.701012417 2.343723924
19 -0.831437698 -0.701012417
20 -0.054186250 -0.831437698
21 0.853910339 -0.054186250
22 1.291201579 0.853910339
23 -1.472963798 1.291201579
24 -1.682733910 -1.472963798
25 0.136905195 -1.682733910
26 -0.724202618 0.136905195
27 -0.221453071 -0.724202618
28 -0.018032624 -0.221453071
29 0.483037305 -0.018032624
30 0.056821312 0.483037305
31 1.311069987 0.056821312
32 -1.240893692 1.311069987
33 -1.232616756 -1.240893692
34 0.007318433 -1.232616756
35 0.357395138 0.007318433
36 -0.937627931 0.357395138
37 -0.005520053 -0.937627931
38 1.447683971 -0.005520053
39 1.843529336 1.447683971
40 -0.105838934 1.843529336
41 -0.736653842 -0.105838934
42 -0.493914259 -0.736653842
43 -0.454648297 -0.493914259
44 2.916278704 -0.454648297
45 -0.190084780 2.916278704
46 0.484845393 -0.190084780
47 -2.673188077 0.484845393
48 -0.200513529 -2.673188077
49 -1.311532077 -0.200513529
50 -0.451950762 -1.311532077
51 -3.788563202 -0.451950762
52 1.420070219 -3.788563202
53 -0.653027565 1.420070219
54 0.836679072 -0.653027565
55 2.822446941 0.836679072
56 NA 2.822446941
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.225969439 -0.266051517
[2,] -1.169313028 0.225969439
[3,] 1.040379901 -1.169313028
[4,] -1.357719649 1.040379901
[5,] -1.437079822 -1.357719649
[6,] 0.301426291 -1.437079822
[7,] -2.847430932 0.301426291
[8,] -1.621198763 -2.847430932
[9,] 0.568791197 -1.621198763
[10,] -1.783365404 0.568791197
[11,] 3.788756737 -1.783365404
[12,] 3.086926887 3.788756737
[13,] 0.954177496 3.086926887
[14,] 0.897782437 0.954177496
[15,] 1.126107035 0.897782437
[16,] 0.061520988 1.126107035
[17,] 2.343723924 0.061520988
[18,] -0.701012417 2.343723924
[19,] -0.831437698 -0.701012417
[20,] -0.054186250 -0.831437698
[21,] 0.853910339 -0.054186250
[22,] 1.291201579 0.853910339
[23,] -1.472963798 1.291201579
[24,] -1.682733910 -1.472963798
[25,] 0.136905195 -1.682733910
[26,] -0.724202618 0.136905195
[27,] -0.221453071 -0.724202618
[28,] -0.018032624 -0.221453071
[29,] 0.483037305 -0.018032624
[30,] 0.056821312 0.483037305
[31,] 1.311069987 0.056821312
[32,] -1.240893692 1.311069987
[33,] -1.232616756 -1.240893692
[34,] 0.007318433 -1.232616756
[35,] 0.357395138 0.007318433
[36,] -0.937627931 0.357395138
[37,] -0.005520053 -0.937627931
[38,] 1.447683971 -0.005520053
[39,] 1.843529336 1.447683971
[40,] -0.105838934 1.843529336
[41,] -0.736653842 -0.105838934
[42,] -0.493914259 -0.736653842
[43,] -0.454648297 -0.493914259
[44,] 2.916278704 -0.454648297
[45,] -0.190084780 2.916278704
[46,] 0.484845393 -0.190084780
[47,] -2.673188077 0.484845393
[48,] -0.200513529 -2.673188077
[49,] -1.311532077 -0.200513529
[50,] -0.451950762 -1.311532077
[51,] -3.788563202 -0.451950762
[52,] 1.420070219 -3.788563202
[53,] -0.653027565 1.420070219
[54,] 0.836679072 -0.653027565
[55,] 2.822446941 0.836679072
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.225969439 -0.266051517
2 -1.169313028 0.225969439
3 1.040379901 -1.169313028
4 -1.357719649 1.040379901
5 -1.437079822 -1.357719649
6 0.301426291 -1.437079822
7 -2.847430932 0.301426291
8 -1.621198763 -2.847430932
9 0.568791197 -1.621198763
10 -1.783365404 0.568791197
11 3.788756737 -1.783365404
12 3.086926887 3.788756737
13 0.954177496 3.086926887
14 0.897782437 0.954177496
15 1.126107035 0.897782437
16 0.061520988 1.126107035
17 2.343723924 0.061520988
18 -0.701012417 2.343723924
19 -0.831437698 -0.701012417
20 -0.054186250 -0.831437698
21 0.853910339 -0.054186250
22 1.291201579 0.853910339
23 -1.472963798 1.291201579
24 -1.682733910 -1.472963798
25 0.136905195 -1.682733910
26 -0.724202618 0.136905195
27 -0.221453071 -0.724202618
28 -0.018032624 -0.221453071
29 0.483037305 -0.018032624
30 0.056821312 0.483037305
31 1.311069987 0.056821312
32 -1.240893692 1.311069987
33 -1.232616756 -1.240893692
34 0.007318433 -1.232616756
35 0.357395138 0.007318433
36 -0.937627931 0.357395138
37 -0.005520053 -0.937627931
38 1.447683971 -0.005520053
39 1.843529336 1.447683971
40 -0.105838934 1.843529336
41 -0.736653842 -0.105838934
42 -0.493914259 -0.736653842
43 -0.454648297 -0.493914259
44 2.916278704 -0.454648297
45 -0.190084780 2.916278704
46 0.484845393 -0.190084780
47 -2.673188077 0.484845393
48 -0.200513529 -2.673188077
49 -1.311532077 -0.200513529
50 -0.451950762 -1.311532077
51 -3.788563202 -0.451950762
52 1.420070219 -3.788563202
53 -0.653027565 1.420070219
54 0.836679072 -0.653027565
55 2.822446941 0.836679072
> 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/7re201258731167.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8tnu51258731167.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/91kpr1258731167.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10h59o1258731167.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ce0k1258731167.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/12fd0r1258731167.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/134ra31258731167.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/14vnq61258731167.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/15umdk1258731167.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/16y4g81258731167.tab")
+ }
>
> system("convert tmp/1oe741258731166.ps tmp/1oe741258731166.png")
> system("convert tmp/2st7v1258731166.ps tmp/2st7v1258731166.png")
> system("convert tmp/3mxu81258731166.ps tmp/3mxu81258731166.png")
> system("convert tmp/4qy1e1258731166.ps tmp/4qy1e1258731166.png")
> system("convert tmp/5cq1d1258731166.ps tmp/5cq1d1258731166.png")
> system("convert tmp/66phb1258731166.ps tmp/66phb1258731166.png")
> system("convert tmp/7re201258731167.ps tmp/7re201258731167.png")
> system("convert tmp/8tnu51258731167.ps tmp/8tnu51258731167.png")
> system("convert tmp/91kpr1258731167.ps tmp/91kpr1258731167.png")
> system("convert tmp/10h59o1258731167.ps tmp/10h59o1258731167.png")
>
>
> proc.time()
user system elapsed
2.348 1.558 2.935