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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> x <- array(list(87.4
+ ,0
+ ,104.5
+ ,98.1
+ ,102.7
+ ,105.4
+ ,97
+ ,97.4
+ ,89.9
+ ,0
+ ,87.4
+ ,104.5
+ ,98.1
+ ,102.7
+ ,105.4
+ ,97
+ ,109.8
+ ,0
+ ,89.9
+ ,87.4
+ ,104.5
+ ,98.1
+ ,102.7
+ ,105.4
+ ,111.7
+ ,0
+ ,109.8
+ ,89.9
+ ,87.4
+ ,104.5
+ ,98.1
+ ,102.7
+ ,98.6
+ ,0
+ ,111.7
+ ,109.8
+ ,89.9
+ ,87.4
+ ,104.5
+ ,98.1
+ ,96.9
+ ,0
+ ,98.6
+ ,111.7
+ ,109.8
+ ,89.9
+ ,87.4
+ ,104.5
+ ,95.1
+ ,0
+ ,96.9
+ ,98.6
+ ,111.7
+ ,109.8
+ ,89.9
+ ,87.4
+ ,97
+ ,0
+ ,95.1
+ ,96.9
+ ,98.6
+ ,111.7
+ ,109.8
+ ,89.9
+ ,112.7
+ ,0
+ ,97
+ ,95.1
+ ,96.9
+ ,98.6
+ ,111.7
+ ,109.8
+ ,102.9
+ ,0
+ ,112.7
+ ,97
+ ,95.1
+ ,96.9
+ ,98.6
+ ,111.7
+ ,97.4
+ ,0
+ ,102.9
+ ,112.7
+ ,97
+ ,95.1
+ ,96.9
+ ,98.6
+ ,111.4
+ ,0
+ ,97.4
+ ,102.9
+ ,112.7
+ ,97
+ ,95.1
+ ,96.9
+ ,87.4
+ ,0
+ ,111.4
+ ,97.4
+ ,102.9
+ ,112.7
+ ,97
+ ,95.1
+ ,96.8
+ ,0
+ ,87.4
+ ,111.4
+ ,97.4
+ ,102.9
+ ,112.7
+ ,97
+ ,114.1
+ ,0
+ ,96.8
+ ,87.4
+ ,111.4
+ ,97.4
+ ,102.9
+ ,112.7
+ ,110.3
+ ,0
+ ,114.1
+ ,96.8
+ ,87.4
+ ,111.4
+ ,97.4
+ ,102.9
+ ,103.9
+ ,0
+ ,110.3
+ ,114.1
+ ,96.8
+ ,87.4
+ ,111.4
+ ,97.4
+ ,101.6
+ ,0
+ ,103.9
+ ,110.3
+ ,114.1
+ ,96.8
+ ,87.4
+ ,111.4
+ ,94.6
+ ,0
+ ,101.6
+ ,103.9
+ ,110.3
+ ,114.1
+ ,96.8
+ ,87.4
+ ,95.9
+ ,0
+ ,94.6
+ ,101.6
+ ,103.9
+ ,110.3
+ ,114.1
+ ,96.8
+ ,104.7
+ ,0
+ ,95.9
+ ,94.6
+ ,101.6
+ ,103.9
+ ,110.3
+ ,114.1
+ ,102.8
+ ,0
+ ,104.7
+ ,95.9
+ ,94.6
+ ,101.6
+ ,103.9
+ ,110.3
+ ,98.1
+ ,0
+ ,102.8
+ ,104.7
+ ,95.9
+ ,94.6
+ ,101.6
+ ,103.9
+ ,113.9
+ ,0
+ ,98.1
+ ,102.8
+ ,104.7
+ ,95.9
+ ,94.6
+ ,101.6
+ ,80.9
+ ,0
+ ,113.9
+ ,98.1
+ ,102.8
+ ,104.7
+ ,95.9
+ ,94.6
+ ,95.7
+ ,0
+ ,80.9
+ ,113.9
+ ,98.1
+ ,102.8
+ ,104.7
+ ,95.9
+ ,113.2
+ ,0
+ ,95.7
+ ,80.9
+ ,113.9
+ ,98.1
+ ,102.8
+ ,104.7
+ ,105.9
+ ,0
+ ,113.2
+ ,95.7
+ ,80.9
+ ,113.9
+ ,98.1
+ ,102.8
+ ,108.8
+ ,0
+ ,105.9
+ ,113.2
+ ,95.7
+ ,80.9
+ ,113.9
+ ,98.1
+ ,102.3
+ ,0
+ ,108.8
+ ,105.9
+ ,113.2
+ ,95.7
+ ,80.9
+ ,113.9
+ ,99
+ ,0
+ ,102.3
+ ,108.8
+ ,105.9
+ ,113.2
+ ,95.7
+ ,80.9
+ ,100.7
+ ,0
+ ,99
+ ,102.3
+ ,108.8
+ ,105.9
+ ,113.2
+ ,95.7
+ ,115.5
+ ,0
+ ,100.7
+ ,99
+ ,102.3
+ ,108.8
+ ,105.9
+ ,113.2
+ ,100.7
+ ,0
+ ,115.5
+ ,100.7
+ ,99
+ ,102.3
+ ,108.8
+ ,105.9
+ ,109.9
+ ,0
+ ,100.7
+ ,115.5
+ ,100.7
+ ,99
+ ,102.3
+ ,108.8
+ ,114.6
+ ,0
+ ,109.9
+ ,100.7
+ ,115.5
+ ,100.7
+ ,99
+ ,102.3
+ ,85.4
+ ,0
+ ,114.6
+ ,109.9
+ ,100.7
+ ,115.5
+ ,100.7
+ ,99
+ ,100.5
+ ,0
+ ,85.4
+ ,114.6
+ ,109.9
+ ,100.7
+ ,115.5
+ ,100.7
+ ,114.8
+ ,0
+ ,100.5
+ ,85.4
+ ,114.6
+ ,109.9
+ ,100.7
+ ,115.5
+ ,116.5
+ ,0
+ ,114.8
+ ,100.5
+ ,85.4
+ ,114.6
+ ,109.9
+ ,100.7
+ ,112.9
+ ,0
+ ,116.5
+ ,114.8
+ ,100.5
+ ,85.4
+ ,114.6
+ ,109.9
+ ,102
+ ,0
+ ,112.9
+ ,116.5
+ ,114.8
+ ,100.5
+ ,85.4
+ ,114.6
+ ,106
+ ,0
+ ,102
+ ,112.9
+ ,116.5
+ ,114.8
+ ,100.5
+ ,85.4
+ ,105.3
+ ,0
+ ,106
+ ,102
+ ,112.9
+ ,116.5
+ ,114.8
+ ,100.5
+ ,118.8
+ ,0
+ ,105.3
+ ,106
+ ,102
+ ,112.9
+ ,116.5
+ ,114.8
+ ,106.1
+ ,0
+ ,118.8
+ ,105.3
+ ,106
+ ,102
+ ,112.9
+ ,116.5
+ ,109.3
+ ,0
+ ,106.1
+ ,118.8
+ ,105.3
+ ,106
+ ,102
+ ,112.9
+ ,117.2
+ ,0
+ ,109.3
+ ,106.1
+ ,118.8
+ ,105.3
+ ,106
+ ,102
+ ,92.5
+ ,0
+ ,117.2
+ ,109.3
+ ,106.1
+ ,118.8
+ ,105.3
+ ,106
+ ,104.2
+ ,0
+ ,92.5
+ ,117.2
+ ,109.3
+ ,106.1
+ ,118.8
+ ,105.3
+ ,112.5
+ ,0
+ ,104.2
+ ,92.5
+ ,117.2
+ ,109.3
+ ,106.1
+ ,118.8
+ ,122.4
+ ,0
+ ,112.5
+ ,104.2
+ ,92.5
+ ,117.2
+ ,109.3
+ ,106.1
+ ,113.3
+ ,0
+ ,122.4
+ ,112.5
+ ,104.2
+ ,92.5
+ ,117.2
+ ,109.3
+ ,100
+ ,0
+ ,113.3
+ ,122.4
+ ,112.5
+ ,104.2
+ ,92.5
+ ,117.2
+ ,110.7
+ ,0
+ ,100
+ ,113.3
+ ,122.4
+ ,112.5
+ ,104.2
+ ,92.5
+ ,112.8
+ ,0
+ ,110.7
+ ,100
+ ,113.3
+ ,122.4
+ ,112.5
+ ,104.2
+ ,109.8
+ ,0
+ ,112.8
+ ,110.7
+ ,100
+ ,113.3
+ ,122.4
+ ,112.5
+ ,117.3
+ ,0
+ ,109.8
+ ,112.8
+ ,110.7
+ ,100
+ ,113.3
+ ,122.4
+ ,109.1
+ ,0
+ ,117.3
+ ,109.8
+ ,112.8
+ ,110.7
+ ,100
+ ,113.3
+ ,115.9
+ ,0
+ ,109.1
+ ,117.3
+ ,109.8
+ ,112.8
+ ,110.7
+ ,100
+ ,96
+ ,0
+ ,115.9
+ ,109.1
+ ,117.3
+ ,109.8
+ ,112.8
+ ,110.7
+ ,99.8
+ ,0
+ ,96
+ ,115.9
+ ,109.1
+ ,117.3
+ ,109.8
+ ,112.8
+ ,116.8
+ ,0
+ ,99.8
+ ,96
+ ,115.9
+ ,109.1
+ ,117.3
+ ,109.8
+ ,115.7
+ ,1
+ ,116.8
+ ,99.8
+ ,96
+ ,115.9
+ ,109.1
+ ,117.3
+ ,99.4
+ ,1
+ ,115.7
+ ,116.8
+ ,99.8
+ ,96
+ ,115.9
+ ,109.1
+ ,94.3
+ ,1
+ ,99.4
+ ,115.7
+ ,116.8
+ ,99.8
+ ,96
+ ,115.9
+ ,91
+ ,1
+ ,94.3
+ ,99.4
+ ,115.7
+ ,116.8
+ ,99.8
+ ,96
+ ,93.2
+ ,1
+ ,91
+ ,94.3
+ ,99.4
+ ,115.7
+ ,116.8
+ ,99.8
+ ,103.1
+ ,1
+ ,93.2
+ ,91
+ ,94.3
+ ,99.4
+ ,115.7
+ ,116.8
+ ,94.1
+ ,1
+ ,103.1
+ ,93.2
+ ,91
+ ,94.3
+ ,99.4
+ ,115.7
+ ,91.8
+ ,1
+ ,94.1
+ ,103.1
+ ,93.2
+ ,91
+ ,94.3
+ ,99.4
+ ,102.7
+ ,1
+ ,91.8
+ ,94.1
+ ,103.1
+ ,93.2
+ ,91
+ ,94.3
+ ,82.6
+ ,1
+ ,102.7
+ ,91.8
+ ,94.1
+ ,103.1
+ ,93.2
+ ,91
+ ,89.1
+ ,1
+ ,82.6
+ ,102.7
+ ,91.8
+ ,94.1
+ ,103.1
+ ,93.2
+ ,104.5
+ ,1
+ ,89.1
+ ,82.6
+ ,102.7
+ ,91.8
+ ,94.1
+ ,103.1)
+ ,dim=c(8
+ ,75)
+ ,dimnames=list(c('Productie'
+ ,'Dummy'
+ ,'Yt-1'
+ ,'Yt-2'
+ ,'Yt-3'
+ ,'Yt-4'
+ ,'Yt-5'
+ ,'Yt-6')
+ ,1:75))
> y <- array(NA,dim=c(8,75),dimnames=list(c('Productie','Dummy','Yt-1','Yt-2','Yt-3','Yt-4','Yt-5','Yt-6'),1:75))
> 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
Productie Dummy Yt-1 Yt-2 Yt-3 Yt-4 Yt-5 Yt-6 M1 M2 M3 M4 M5 M6 M7 M8
1 87.4 0 104.5 98.1 102.7 105.4 97.0 97.4 1 0 0 0 0 0 0 0
2 89.9 0 87.4 104.5 98.1 102.7 105.4 97.0 0 1 0 0 0 0 0 0
3 109.8 0 89.9 87.4 104.5 98.1 102.7 105.4 0 0 1 0 0 0 0 0
4 111.7 0 109.8 89.9 87.4 104.5 98.1 102.7 0 0 0 1 0 0 0 0
5 98.6 0 111.7 109.8 89.9 87.4 104.5 98.1 0 0 0 0 1 0 0 0
6 96.9 0 98.6 111.7 109.8 89.9 87.4 104.5 0 0 0 0 0 1 0 0
7 95.1 0 96.9 98.6 111.7 109.8 89.9 87.4 0 0 0 0 0 0 1 0
8 97.0 0 95.1 96.9 98.6 111.7 109.8 89.9 0 0 0 0 0 0 0 1
9 112.7 0 97.0 95.1 96.9 98.6 111.7 109.8 0 0 0 0 0 0 0 0
10 102.9 0 112.7 97.0 95.1 96.9 98.6 111.7 0 0 0 0 0 0 0 0
11 97.4 0 102.9 112.7 97.0 95.1 96.9 98.6 0 0 0 0 0 0 0 0
12 111.4 0 97.4 102.9 112.7 97.0 95.1 96.9 0 0 0 0 0 0 0 0
13 87.4 0 111.4 97.4 102.9 112.7 97.0 95.1 1 0 0 0 0 0 0 0
14 96.8 0 87.4 111.4 97.4 102.9 112.7 97.0 0 1 0 0 0 0 0 0
15 114.1 0 96.8 87.4 111.4 97.4 102.9 112.7 0 0 1 0 0 0 0 0
16 110.3 0 114.1 96.8 87.4 111.4 97.4 102.9 0 0 0 1 0 0 0 0
17 103.9 0 110.3 114.1 96.8 87.4 111.4 97.4 0 0 0 0 1 0 0 0
18 101.6 0 103.9 110.3 114.1 96.8 87.4 111.4 0 0 0 0 0 1 0 0
19 94.6 0 101.6 103.9 110.3 114.1 96.8 87.4 0 0 0 0 0 0 1 0
20 95.9 0 94.6 101.6 103.9 110.3 114.1 96.8 0 0 0 0 0 0 0 1
21 104.7 0 95.9 94.6 101.6 103.9 110.3 114.1 0 0 0 0 0 0 0 0
22 102.8 0 104.7 95.9 94.6 101.6 103.9 110.3 0 0 0 0 0 0 0 0
23 98.1 0 102.8 104.7 95.9 94.6 101.6 103.9 0 0 0 0 0 0 0 0
24 113.9 0 98.1 102.8 104.7 95.9 94.6 101.6 0 0 0 0 0 0 0 0
25 80.9 0 113.9 98.1 102.8 104.7 95.9 94.6 1 0 0 0 0 0 0 0
26 95.7 0 80.9 113.9 98.1 102.8 104.7 95.9 0 1 0 0 0 0 0 0
27 113.2 0 95.7 80.9 113.9 98.1 102.8 104.7 0 0 1 0 0 0 0 0
28 105.9 0 113.2 95.7 80.9 113.9 98.1 102.8 0 0 0 1 0 0 0 0
29 108.8 0 105.9 113.2 95.7 80.9 113.9 98.1 0 0 0 0 1 0 0 0
30 102.3 0 108.8 105.9 113.2 95.7 80.9 113.9 0 0 0 0 0 1 0 0
31 99.0 0 102.3 108.8 105.9 113.2 95.7 80.9 0 0 0 0 0 0 1 0
32 100.7 0 99.0 102.3 108.8 105.9 113.2 95.7 0 0 0 0 0 0 0 1
33 115.5 0 100.7 99.0 102.3 108.8 105.9 113.2 0 0 0 0 0 0 0 0
34 100.7 0 115.5 100.7 99.0 102.3 108.8 105.9 0 0 0 0 0 0 0 0
35 109.9 0 100.7 115.5 100.7 99.0 102.3 108.8 0 0 0 0 0 0 0 0
36 114.6 0 109.9 100.7 115.5 100.7 99.0 102.3 0 0 0 0 0 0 0 0
37 85.4 0 114.6 109.9 100.7 115.5 100.7 99.0 1 0 0 0 0 0 0 0
38 100.5 0 85.4 114.6 109.9 100.7 115.5 100.7 0 1 0 0 0 0 0 0
39 114.8 0 100.5 85.4 114.6 109.9 100.7 115.5 0 0 1 0 0 0 0 0
40 116.5 0 114.8 100.5 85.4 114.6 109.9 100.7 0 0 0 1 0 0 0 0
41 112.9 0 116.5 114.8 100.5 85.4 114.6 109.9 0 0 0 0 1 0 0 0
42 102.0 0 112.9 116.5 114.8 100.5 85.4 114.6 0 0 0 0 0 1 0 0
43 106.0 0 102.0 112.9 116.5 114.8 100.5 85.4 0 0 0 0 0 0 1 0
44 105.3 0 106.0 102.0 112.9 116.5 114.8 100.5 0 0 0 0 0 0 0 1
45 118.8 0 105.3 106.0 102.0 112.9 116.5 114.8 0 0 0 0 0 0 0 0
46 106.1 0 118.8 105.3 106.0 102.0 112.9 116.5 0 0 0 0 0 0 0 0
47 109.3 0 106.1 118.8 105.3 106.0 102.0 112.9 0 0 0 0 0 0 0 0
48 117.2 0 109.3 106.1 118.8 105.3 106.0 102.0 0 0 0 0 0 0 0 0
49 92.5 0 117.2 109.3 106.1 118.8 105.3 106.0 1 0 0 0 0 0 0 0
50 104.2 0 92.5 117.2 109.3 106.1 118.8 105.3 0 1 0 0 0 0 0 0
51 112.5 0 104.2 92.5 117.2 109.3 106.1 118.8 0 0 1 0 0 0 0 0
52 122.4 0 112.5 104.2 92.5 117.2 109.3 106.1 0 0 0 1 0 0 0 0
53 113.3 0 122.4 112.5 104.2 92.5 117.2 109.3 0 0 0 0 1 0 0 0
54 100.0 0 113.3 122.4 112.5 104.2 92.5 117.2 0 0 0 0 0 1 0 0
55 110.7 0 100.0 113.3 122.4 112.5 104.2 92.5 0 0 0 0 0 0 1 0
56 112.8 0 110.7 100.0 113.3 122.4 112.5 104.2 0 0 0 0 0 0 0 1
57 109.8 0 112.8 110.7 100.0 113.3 122.4 112.5 0 0 0 0 0 0 0 0
58 117.3 0 109.8 112.8 110.7 100.0 113.3 122.4 0 0 0 0 0 0 0 0
59 109.1 0 117.3 109.8 112.8 110.7 100.0 113.3 0 0 0 0 0 0 0 0
60 115.9 0 109.1 117.3 109.8 112.8 110.7 100.0 0 0 0 0 0 0 0 0
61 96.0 0 115.9 109.1 117.3 109.8 112.8 110.7 1 0 0 0 0 0 0 0
62 99.8 0 96.0 115.9 109.1 117.3 109.8 112.8 0 1 0 0 0 0 0 0
63 116.8 0 99.8 96.0 115.9 109.1 117.3 109.8 0 0 1 0 0 0 0 0
64 115.7 1 116.8 99.8 96.0 115.9 109.1 117.3 0 0 0 1 0 0 0 0
65 99.4 1 115.7 116.8 99.8 96.0 115.9 109.1 0 0 0 0 1 0 0 0
66 94.3 1 99.4 115.7 116.8 99.8 96.0 115.9 0 0 0 0 0 1 0 0
67 91.0 1 94.3 99.4 115.7 116.8 99.8 96.0 0 0 0 0 0 0 1 0
68 93.2 1 91.0 94.3 99.4 115.7 116.8 99.8 0 0 0 0 0 0 0 1
69 103.1 1 93.2 91.0 94.3 99.4 115.7 116.8 0 0 0 0 0 0 0 0
70 94.1 1 103.1 93.2 91.0 94.3 99.4 115.7 0 0 0 0 0 0 0 0
71 91.8 1 94.1 103.1 93.2 91.0 94.3 99.4 0 0 0 0 0 0 0 0
72 102.7 1 91.8 94.1 103.1 93.2 91.0 94.3 0 0 0 0 0 0 0 0
73 82.6 1 102.7 91.8 94.1 103.1 93.2 91.0 1 0 0 0 0 0 0 0
74 89.1 1 82.6 102.7 91.8 94.1 103.1 93.2 0 1 0 0 0 0 0 0
75 104.5 1 89.1 82.6 102.7 91.8 94.1 103.1 0 0 1 0 0 0 0 0
M9 M10 M11 t
1 0 0 0 1
2 0 0 0 2
3 0 0 0 3
4 0 0 0 4
5 0 0 0 5
6 0 0 0 6
7 0 0 0 7
8 0 0 0 8
9 1 0 0 9
10 0 1 0 10
11 0 0 1 11
12 0 0 0 12
13 0 0 0 13
14 0 0 0 14
15 0 0 0 15
16 0 0 0 16
17 0 0 0 17
18 0 0 0 18
19 0 0 0 19
20 0 0 0 20
21 1 0 0 21
22 0 1 0 22
23 0 0 1 23
24 0 0 0 24
25 0 0 0 25
26 0 0 0 26
27 0 0 0 27
28 0 0 0 28
29 0 0 0 29
30 0 0 0 30
31 0 0 0 31
32 0 0 0 32
33 1 0 0 33
34 0 1 0 34
35 0 0 1 35
36 0 0 0 36
37 0 0 0 37
38 0 0 0 38
39 0 0 0 39
40 0 0 0 40
41 0 0 0 41
42 0 0 0 42
43 0 0 0 43
44 0 0 0 44
45 1 0 0 45
46 0 1 0 46
47 0 0 1 47
48 0 0 0 48
49 0 0 0 49
50 0 0 0 50
51 0 0 0 51
52 0 0 0 52
53 0 0 0 53
54 0 0 0 54
55 0 0 0 55
56 0 0 0 56
57 1 0 0 57
58 0 1 0 58
59 0 0 1 59
60 0 0 0 60
61 0 0 0 61
62 0 0 0 62
63 0 0 0 63
64 0 0 0 64
65 0 0 0 65
66 0 0 0 66
67 0 0 0 67
68 0 0 0 68
69 1 0 0 69
70 0 1 0 70
71 0 0 1 71
72 0 0 0 72
73 0 0 0 73
74 0 0 0 74
75 0 0 0 75
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dummy `Yt-1` `Yt-2` `Yt-3` `Yt-4`
70.8432 -12.1409 -0.1705 0.1273 0.4067 -0.1159
`Yt-5` `Yt-6` M1 M2 M3 M4
-0.0322 0.1257 -19.0625 -15.1805 -0.5265 14.7572
M5 M6 M7 M8 M9 M10
-1.1565 -16.4847 -11.7529 -7.2183 2.7078 -3.6809
M11 t
-7.4643 0.1282
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.29938 -1.95590 0.09114 2.18801 6.77637
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 70.84317 15.45691 4.583 2.68e-05 ***
Dummy -12.14094 2.59814 -4.673 1.96e-05 ***
`Yt-1` -0.17054 0.12766 -1.336 0.187110
`Yt-2` 0.12734 0.12784 0.996 0.323573
`Yt-3` 0.40667 0.13151 3.092 0.003117 **
`Yt-4` -0.11593 0.12141 -0.955 0.343833
`Yt-5` -0.03220 0.12028 -0.268 0.789909
`Yt-6` 0.12570 0.12817 0.981 0.330995
M1 -19.06247 2.38246 -8.001 8.68e-11 ***
M2 -15.18054 3.05158 -4.975 6.78e-06 ***
M3 -0.52645 3.47989 -0.151 0.880305
M4 14.75716 4.45542 3.312 0.001640 **
M5 -1.15645 4.75196 -0.243 0.808629
M6 -16.48471 3.51219 -4.694 1.82e-05 ***
M7 -11.75294 3.37145 -3.486 0.000971 ***
M8 -7.21826 3.44345 -2.096 0.040676 *
M9 2.70776 4.28173 0.632 0.529747
M10 -3.68094 4.09629 -0.899 0.372782
M11 -7.46426 2.88189 -2.590 0.012259 *
t 0.12825 0.03862 3.321 0.001599 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.249 on 55 degrees of freedom
Multiple R-squared: 0.914, Adjusted R-squared: 0.8843
F-statistic: 30.77 on 19 and 55 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,] 0.7589498 0.4821004 0.2410502
[2,] 0.6948078 0.6103844 0.3051922
[3,] 0.7159134 0.5681732 0.2840866
[4,] 0.7961561 0.4076879 0.2038439
[5,] 0.7584852 0.4830297 0.2415148
[6,] 0.8332946 0.3334109 0.1667054
[7,] 0.7948731 0.4102538 0.2051269
[8,] 0.8047284 0.3905432 0.1952716
[9,] 0.7835918 0.4328163 0.2164082
[10,] 0.8118522 0.3762956 0.1881478
[11,] 0.8406491 0.3187017 0.1593509
[12,] 0.8099503 0.3800995 0.1900497
[13,] 0.8719528 0.2560943 0.1280472
[14,] 0.8154527 0.3690946 0.1845473
[15,] 0.8143689 0.3712621 0.1856311
[16,] 0.7877505 0.4244990 0.2122495
[17,] 0.7221804 0.5556393 0.2778196
[18,] 0.6924187 0.6151626 0.3075813
[19,] 0.6185667 0.7628666 0.3814333
[20,] 0.5248263 0.9503474 0.4751737
[21,] 0.4581741 0.9163482 0.5418259
[22,] 0.5578657 0.8842686 0.4421343
[23,] 0.5601575 0.8796849 0.4398425
[24,] 0.5780810 0.8438379 0.4219190
[25,] 0.5774707 0.8450587 0.4225293
[26,] 0.4938023 0.9876047 0.5061977
[27,] 0.4368831 0.8737663 0.5631169
[28,] 0.3271960 0.6543920 0.6728040
[29,] 0.2603821 0.5207642 0.7396179
[30,] 0.2224405 0.4448810 0.7775595
> postscript(file="/var/www/html/rcomp/tmp/19cvg1261341644.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ywbb1261341644.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33htw1261341644.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/41ps21261341644.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ngmb1261341644.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 = 75
Frequency = 1
1 2 3 4 5 6
2.15418755 -1.20867705 2.23402075 -0.31522786 -2.05471112 -0.18875524
7 8 9 10 11 12
-1.70607989 1.31473819 4.24600078 3.01614100 -1.88865473 -1.17988420
13 14 15 16 17 18
2.93517243 3.81666419 2.37339834 -2.64727172 -1.57580043 2.23831035
19 20 21 22 23 24
-2.32836444 -5.05451947 -7.29938272 1.24786579 -1.85142812 2.43220645
25 26 27 28 29 30
-5.62593008 -0.66472638 0.64147686 -5.63132199 0.83579968 2.51019230
31 32 33 34 35 36
2.49476121 -3.52573557 2.47478457 -2.15782858 4.64089427 0.09114156
37 38 39 40 41 42
-2.34057414 -2.02314875 0.60616743 1.98646775 2.10970013 -0.01664722
43 44 45 46 47 48
2.84625224 -0.22304733 4.86646710 -2.40167652 1.41841310 -0.18335562
49 50 51 52 53 54
1.19504252 1.41569212 -4.87374432 2.20007052 1.74739380 -2.97258774
55 56 57 58 59 60
2.17595443 6.77636983 -3.65312846 1.89751055 0.11571113 0.44965845
61 62 63 64 65 66
-2.98751013 -3.61366762 -1.31123991 4.40728329 -1.06238206 -1.57051245
67 68 69 70 71 72
-3.48252355 0.71219436 -0.63474127 -1.60201223 -2.43493566 -1.60976663
73 74 75
4.66961185 2.27786349 0.32992086
> postscript(file="/var/www/html/rcomp/tmp/6dyko1261341644.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 = 75
Frequency = 1
lag(myerror, k = 1) myerror
0 2.15418755 NA
1 -1.20867705 2.15418755
2 2.23402075 -1.20867705
3 -0.31522786 2.23402075
4 -2.05471112 -0.31522786
5 -0.18875524 -2.05471112
6 -1.70607989 -0.18875524
7 1.31473819 -1.70607989
8 4.24600078 1.31473819
9 3.01614100 4.24600078
10 -1.88865473 3.01614100
11 -1.17988420 -1.88865473
12 2.93517243 -1.17988420
13 3.81666419 2.93517243
14 2.37339834 3.81666419
15 -2.64727172 2.37339834
16 -1.57580043 -2.64727172
17 2.23831035 -1.57580043
18 -2.32836444 2.23831035
19 -5.05451947 -2.32836444
20 -7.29938272 -5.05451947
21 1.24786579 -7.29938272
22 -1.85142812 1.24786579
23 2.43220645 -1.85142812
24 -5.62593008 2.43220645
25 -0.66472638 -5.62593008
26 0.64147686 -0.66472638
27 -5.63132199 0.64147686
28 0.83579968 -5.63132199
29 2.51019230 0.83579968
30 2.49476121 2.51019230
31 -3.52573557 2.49476121
32 2.47478457 -3.52573557
33 -2.15782858 2.47478457
34 4.64089427 -2.15782858
35 0.09114156 4.64089427
36 -2.34057414 0.09114156
37 -2.02314875 -2.34057414
38 0.60616743 -2.02314875
39 1.98646775 0.60616743
40 2.10970013 1.98646775
41 -0.01664722 2.10970013
42 2.84625224 -0.01664722
43 -0.22304733 2.84625224
44 4.86646710 -0.22304733
45 -2.40167652 4.86646710
46 1.41841310 -2.40167652
47 -0.18335562 1.41841310
48 1.19504252 -0.18335562
49 1.41569212 1.19504252
50 -4.87374432 1.41569212
51 2.20007052 -4.87374432
52 1.74739380 2.20007052
53 -2.97258774 1.74739380
54 2.17595443 -2.97258774
55 6.77636983 2.17595443
56 -3.65312846 6.77636983
57 1.89751055 -3.65312846
58 0.11571113 1.89751055
59 0.44965845 0.11571113
60 -2.98751013 0.44965845
61 -3.61366762 -2.98751013
62 -1.31123991 -3.61366762
63 4.40728329 -1.31123991
64 -1.06238206 4.40728329
65 -1.57051245 -1.06238206
66 -3.48252355 -1.57051245
67 0.71219436 -3.48252355
68 -0.63474127 0.71219436
69 -1.60201223 -0.63474127
70 -2.43493566 -1.60201223
71 -1.60976663 -2.43493566
72 4.66961185 -1.60976663
73 2.27786349 4.66961185
74 0.32992086 2.27786349
75 NA 0.32992086
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.20867705 2.15418755
[2,] 2.23402075 -1.20867705
[3,] -0.31522786 2.23402075
[4,] -2.05471112 -0.31522786
[5,] -0.18875524 -2.05471112
[6,] -1.70607989 -0.18875524
[7,] 1.31473819 -1.70607989
[8,] 4.24600078 1.31473819
[9,] 3.01614100 4.24600078
[10,] -1.88865473 3.01614100
[11,] -1.17988420 -1.88865473
[12,] 2.93517243 -1.17988420
[13,] 3.81666419 2.93517243
[14,] 2.37339834 3.81666419
[15,] -2.64727172 2.37339834
[16,] -1.57580043 -2.64727172
[17,] 2.23831035 -1.57580043
[18,] -2.32836444 2.23831035
[19,] -5.05451947 -2.32836444
[20,] -7.29938272 -5.05451947
[21,] 1.24786579 -7.29938272
[22,] -1.85142812 1.24786579
[23,] 2.43220645 -1.85142812
[24,] -5.62593008 2.43220645
[25,] -0.66472638 -5.62593008
[26,] 0.64147686 -0.66472638
[27,] -5.63132199 0.64147686
[28,] 0.83579968 -5.63132199
[29,] 2.51019230 0.83579968
[30,] 2.49476121 2.51019230
[31,] -3.52573557 2.49476121
[32,] 2.47478457 -3.52573557
[33,] -2.15782858 2.47478457
[34,] 4.64089427 -2.15782858
[35,] 0.09114156 4.64089427
[36,] -2.34057414 0.09114156
[37,] -2.02314875 -2.34057414
[38,] 0.60616743 -2.02314875
[39,] 1.98646775 0.60616743
[40,] 2.10970013 1.98646775
[41,] -0.01664722 2.10970013
[42,] 2.84625224 -0.01664722
[43,] -0.22304733 2.84625224
[44,] 4.86646710 -0.22304733
[45,] -2.40167652 4.86646710
[46,] 1.41841310 -2.40167652
[47,] -0.18335562 1.41841310
[48,] 1.19504252 -0.18335562
[49,] 1.41569212 1.19504252
[50,] -4.87374432 1.41569212
[51,] 2.20007052 -4.87374432
[52,] 1.74739380 2.20007052
[53,] -2.97258774 1.74739380
[54,] 2.17595443 -2.97258774
[55,] 6.77636983 2.17595443
[56,] -3.65312846 6.77636983
[57,] 1.89751055 -3.65312846
[58,] 0.11571113 1.89751055
[59,] 0.44965845 0.11571113
[60,] -2.98751013 0.44965845
[61,] -3.61366762 -2.98751013
[62,] -1.31123991 -3.61366762
[63,] 4.40728329 -1.31123991
[64,] -1.06238206 4.40728329
[65,] -1.57051245 -1.06238206
[66,] -3.48252355 -1.57051245
[67,] 0.71219436 -3.48252355
[68,] -0.63474127 0.71219436
[69,] -1.60201223 -0.63474127
[70,] -2.43493566 -1.60201223
[71,] -1.60976663 -2.43493566
[72,] 4.66961185 -1.60976663
[73,] 2.27786349 4.66961185
[74,] 0.32992086 2.27786349
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.20867705 2.15418755
2 2.23402075 -1.20867705
3 -0.31522786 2.23402075
4 -2.05471112 -0.31522786
5 -0.18875524 -2.05471112
6 -1.70607989 -0.18875524
7 1.31473819 -1.70607989
8 4.24600078 1.31473819
9 3.01614100 4.24600078
10 -1.88865473 3.01614100
11 -1.17988420 -1.88865473
12 2.93517243 -1.17988420
13 3.81666419 2.93517243
14 2.37339834 3.81666419
15 -2.64727172 2.37339834
16 -1.57580043 -2.64727172
17 2.23831035 -1.57580043
18 -2.32836444 2.23831035
19 -5.05451947 -2.32836444
20 -7.29938272 -5.05451947
21 1.24786579 -7.29938272
22 -1.85142812 1.24786579
23 2.43220645 -1.85142812
24 -5.62593008 2.43220645
25 -0.66472638 -5.62593008
26 0.64147686 -0.66472638
27 -5.63132199 0.64147686
28 0.83579968 -5.63132199
29 2.51019230 0.83579968
30 2.49476121 2.51019230
31 -3.52573557 2.49476121
32 2.47478457 -3.52573557
33 -2.15782858 2.47478457
34 4.64089427 -2.15782858
35 0.09114156 4.64089427
36 -2.34057414 0.09114156
37 -2.02314875 -2.34057414
38 0.60616743 -2.02314875
39 1.98646775 0.60616743
40 2.10970013 1.98646775
41 -0.01664722 2.10970013
42 2.84625224 -0.01664722
43 -0.22304733 2.84625224
44 4.86646710 -0.22304733
45 -2.40167652 4.86646710
46 1.41841310 -2.40167652
47 -0.18335562 1.41841310
48 1.19504252 -0.18335562
49 1.41569212 1.19504252
50 -4.87374432 1.41569212
51 2.20007052 -4.87374432
52 1.74739380 2.20007052
53 -2.97258774 1.74739380
54 2.17595443 -2.97258774
55 6.77636983 2.17595443
56 -3.65312846 6.77636983
57 1.89751055 -3.65312846
58 0.11571113 1.89751055
59 0.44965845 0.11571113
60 -2.98751013 0.44965845
61 -3.61366762 -2.98751013
62 -1.31123991 -3.61366762
63 4.40728329 -1.31123991
64 -1.06238206 4.40728329
65 -1.57051245 -1.06238206
66 -3.48252355 -1.57051245
67 0.71219436 -3.48252355
68 -0.63474127 0.71219436
69 -1.60201223 -0.63474127
70 -2.43493566 -1.60201223
71 -1.60976663 -2.43493566
72 4.66961185 -1.60976663
73 2.27786349 4.66961185
74 0.32992086 2.27786349
> 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/7lnrc1261341644.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/85ys41261341644.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9q3481261341644.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10pwta1261341644.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11qp6w1261341644.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/123lzs1261341644.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/131kvz1261341645.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/140h7y1261341645.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/15g7xq1261341645.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/160gvz1261341645.tab")
+ }
>
> try(system("convert tmp/19cvg1261341644.ps tmp/19cvg1261341644.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ywbb1261341644.ps tmp/2ywbb1261341644.png",intern=TRUE))
character(0)
> try(system("convert tmp/33htw1261341644.ps tmp/33htw1261341644.png",intern=TRUE))
character(0)
> try(system("convert tmp/41ps21261341644.ps tmp/41ps21261341644.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ngmb1261341644.ps tmp/5ngmb1261341644.png",intern=TRUE))
character(0)
> try(system("convert tmp/6dyko1261341644.ps tmp/6dyko1261341644.png",intern=TRUE))
character(0)
> try(system("convert tmp/7lnrc1261341644.ps tmp/7lnrc1261341644.png",intern=TRUE))
character(0)
> try(system("convert tmp/85ys41261341644.ps tmp/85ys41261341644.png",intern=TRUE))
character(0)
> try(system("convert tmp/9q3481261341644.ps tmp/9q3481261341644.png",intern=TRUE))
character(0)
> try(system("convert tmp/10pwta1261341644.ps tmp/10pwta1261341644.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.637 1.610 4.024