R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(6.3
+ ,101.9
+ ,1.7
+ ,1
+ ,1.2
+ ,1.4
+ ,6
+ ,106.2
+ ,6.3
+ ,1.7
+ ,1
+ ,1.2
+ ,6.2
+ ,81
+ ,6
+ ,6.3
+ ,1.7
+ ,1
+ ,6.4
+ ,94.7
+ ,6.2
+ ,6
+ ,6.3
+ ,1.7
+ ,6.8
+ ,101
+ ,6.4
+ ,6.2
+ ,6
+ ,6.3
+ ,7.5
+ ,109.4
+ ,6.8
+ ,6.4
+ ,6.2
+ ,6
+ ,7.5
+ ,102.3
+ ,7.5
+ ,6.8
+ ,6.4
+ ,6.2
+ ,7.6
+ ,90.7
+ ,7.5
+ ,7.5
+ ,6.8
+ ,6.4
+ ,7.6
+ ,96.2
+ ,7.6
+ ,7.5
+ ,7.5
+ ,6.8
+ ,7.4
+ ,96.1
+ ,7.6
+ ,7.6
+ ,7.5
+ ,7.5
+ ,7.3
+ ,106
+ ,7.4
+ ,7.6
+ ,7.6
+ ,7.5
+ ,7.1
+ ,103.1
+ ,7.3
+ ,7.4
+ ,7.6
+ ,7.6
+ ,6.9
+ ,102
+ ,7.1
+ ,7.3
+ ,7.4
+ ,7.6
+ ,6.8
+ ,104.7
+ ,6.9
+ ,7.1
+ ,7.3
+ ,7.4
+ ,7.5
+ ,86
+ ,6.8
+ ,6.9
+ ,7.1
+ ,7.3
+ ,7.6
+ ,92.1
+ ,7.5
+ ,6.8
+ ,6.9
+ ,7.1
+ ,7.8
+ ,106.9
+ ,7.6
+ ,7.5
+ ,6.8
+ ,6.9
+ ,8
+ ,112.6
+ ,7.8
+ ,7.6
+ ,7.5
+ ,6.8
+ ,8.1
+ ,101.7
+ ,8
+ ,7.8
+ ,7.6
+ ,7.5
+ ,8.2
+ ,92
+ ,8.1
+ ,8
+ ,7.8
+ ,7.6
+ ,8.3
+ ,97.4
+ ,8.2
+ ,8.1
+ ,8
+ ,7.8
+ ,8.2
+ ,97
+ ,8.3
+ ,8.2
+ ,8.1
+ ,8
+ ,8
+ ,105.4
+ ,8.2
+ ,8.3
+ ,8.2
+ ,8.1
+ ,7.9
+ ,102.7
+ ,8
+ ,8.2
+ ,8.3
+ ,8.2
+ ,7.6
+ ,98.1
+ ,7.9
+ ,8
+ ,8.2
+ ,8.3
+ ,7.6
+ ,104.5
+ ,7.6
+ ,7.9
+ ,8
+ ,8.2
+ ,8.3
+ ,87.4
+ ,7.6
+ ,7.6
+ ,7.9
+ ,8
+ ,8.4
+ ,89.9
+ ,8.3
+ ,7.6
+ ,7.6
+ ,7.9
+ ,8.4
+ ,109.8
+ ,8.4
+ ,8.3
+ ,7.6
+ ,7.6
+ ,8.4
+ ,111.7
+ ,8.4
+ ,8.4
+ ,8.3
+ ,7.6
+ ,8.4
+ ,98.6
+ ,8.4
+ ,8.4
+ ,8.4
+ ,8.3
+ ,8.6
+ ,96.9
+ ,8.4
+ ,8.4
+ ,8.4
+ ,8.4
+ ,8.9
+ ,95.1
+ ,8.6
+ ,8.4
+ ,8.4
+ ,8.4
+ ,8.8
+ ,97
+ ,8.9
+ ,8.6
+ ,8.4
+ ,8.4
+ ,8.3
+ ,112.7
+ ,8.8
+ ,8.9
+ ,8.6
+ ,8.4
+ ,7.5
+ ,102.9
+ ,8.3
+ ,8.8
+ ,8.9
+ ,8.6
+ ,7.2
+ ,97.4
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.9
+ ,7.4
+ ,111.4
+ ,7.2
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.8
+ ,87.4
+ ,7.4
+ ,7.2
+ ,7.5
+ ,8.3
+ ,9.3
+ ,96.8
+ ,8.8
+ ,7.4
+ ,7.2
+ ,7.5
+ ,9.3
+ ,114.1
+ ,9.3
+ ,8.8
+ ,7.4
+ ,7.2
+ ,8.7
+ ,110.3
+ ,9.3
+ ,9.3
+ ,8.8
+ ,7.4
+ ,8.2
+ ,103.9
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.8
+ ,8.3
+ ,101.6
+ ,8.2
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.5
+ ,94.6
+ ,8.3
+ ,8.2
+ ,8.7
+ ,9.3
+ ,8.6
+ ,95.9
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.7
+ ,8.5
+ ,104.7
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.2
+ ,102.8
+ ,8.5
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.1
+ ,98.1
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.5
+ ,7.9
+ ,113.9
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.6
+ ,80.9
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.7
+ ,95.7
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.7
+ ,113.2
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.5
+ ,105.9
+ ,8.7
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.4
+ ,108.8
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.6
+ ,8.5
+ ,102.3
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.7
+ ,99
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,100.7
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.6
+ ,115.5
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,100.7
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.3
+ ,109.9
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8
+ ,114.6
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.2
+ ,85.4
+ ,8
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.1
+ ,100.5
+ ,8.2
+ ,8
+ ,8.3
+ ,8.5
+ ,8.1
+ ,114.8
+ ,8.1
+ ,8.2
+ ,8
+ ,8.3
+ ,8
+ ,116.5
+ ,8.1
+ ,8.1
+ ,8.2
+ ,8
+ ,7.9
+ ,112.9
+ ,8
+ ,8.1
+ ,8.1
+ ,8.2
+ ,7.9
+ ,102
+ ,7.9
+ ,8
+ ,8.1
+ ,8.1
+ ,8
+ ,106
+ ,7.9
+ ,7.9
+ ,8
+ ,8.1
+ ,8
+ ,105.3
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,7.9
+ ,118.8
+ ,8
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,106.1
+ ,7.9
+ ,8
+ ,8
+ ,7.9
+ ,7.7
+ ,109.3
+ ,8
+ ,7.9
+ ,8
+ ,8
+ ,7.2
+ ,117.2
+ ,7.7
+ ,8
+ ,7.9
+ ,8
+ ,7.5
+ ,92.5
+ ,7.2
+ ,7.7
+ ,8
+ ,7.9
+ ,7.3
+ ,104.2
+ ,7.5
+ ,7.2
+ ,7.7
+ ,8
+ ,7
+ ,112.5
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7.7
+ ,7
+ ,122.4
+ ,7
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7
+ ,113.3
+ ,7
+ ,7
+ ,7.3
+ ,7.5
+ ,7.2
+ ,100
+ ,7
+ ,7
+ ,7
+ ,7.3
+ ,7.3
+ ,110.7
+ ,7.2
+ ,7
+ ,7
+ ,7
+ ,7.1
+ ,112.8
+ ,7.3
+ ,7.2
+ ,7
+ ,7
+ ,6.8
+ ,109.8
+ ,7.1
+ ,7.3
+ ,7.2
+ ,7
+ ,6.4
+ ,117.3
+ ,6.8
+ ,7.1
+ ,7.3
+ ,7.2
+ ,6.1
+ ,109.1
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.3
+ ,6.5
+ ,115.9
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.7
+ ,96
+ ,6.5
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.9
+ ,99.8
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.4
+ ,7.5
+ ,116.8
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.9
+ ,115.7
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.6
+ ,99.4
+ ,6.9
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.9
+ ,94.3
+ ,6.6
+ ,6.9
+ ,7.5
+ ,7.9
+ ,7.7
+ ,91
+ ,6.9
+ ,6.6
+ ,6.9
+ ,7.5)
+ ,dim=c(6
+ ,93)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:93))
> y <- array(NA,dim=c(6,93),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:93))
> 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
Y X Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 6.3 101.9 1.7 1.0 1.2 1.4 1 0 0 0 0 0 0 0 0 0 0 1
2 6.0 106.2 6.3 1.7 1.0 1.2 0 1 0 0 0 0 0 0 0 0 0 2
3 6.2 81.0 6.0 6.3 1.7 1.0 0 0 1 0 0 0 0 0 0 0 0 3
4 6.4 94.7 6.2 6.0 6.3 1.7 0 0 0 1 0 0 0 0 0 0 0 4
5 6.8 101.0 6.4 6.2 6.0 6.3 0 0 0 0 1 0 0 0 0 0 0 5
6 7.5 109.4 6.8 6.4 6.2 6.0 0 0 0 0 0 1 0 0 0 0 0 6
7 7.5 102.3 7.5 6.8 6.4 6.2 0 0 0 0 0 0 1 0 0 0 0 7
8 7.6 90.7 7.5 7.5 6.8 6.4 0 0 0 0 0 0 0 1 0 0 0 8
9 7.6 96.2 7.6 7.5 7.5 6.8 0 0 0 0 0 0 0 0 1 0 0 9
10 7.4 96.1 7.6 7.6 7.5 7.5 0 0 0 0 0 0 0 0 0 1 0 10
11 7.3 106.0 7.4 7.6 7.6 7.5 0 0 0 0 0 0 0 0 0 0 1 11
12 7.1 103.1 7.3 7.4 7.6 7.6 0 0 0 0 0 0 0 0 0 0 0 12
13 6.9 102.0 7.1 7.3 7.4 7.6 1 0 0 0 0 0 0 0 0 0 0 13
14 6.8 104.7 6.9 7.1 7.3 7.4 0 1 0 0 0 0 0 0 0 0 0 14
15 7.5 86.0 6.8 6.9 7.1 7.3 0 0 1 0 0 0 0 0 0 0 0 15
16 7.6 92.1 7.5 6.8 6.9 7.1 0 0 0 1 0 0 0 0 0 0 0 16
17 7.8 106.9 7.6 7.5 6.8 6.9 0 0 0 0 1 0 0 0 0 0 0 17
18 8.0 112.6 7.8 7.6 7.5 6.8 0 0 0 0 0 1 0 0 0 0 0 18
19 8.1 101.7 8.0 7.8 7.6 7.5 0 0 0 0 0 0 1 0 0 0 0 19
20 8.2 92.0 8.1 8.0 7.8 7.6 0 0 0 0 0 0 0 1 0 0 0 20
21 8.3 97.4 8.2 8.1 8.0 7.8 0 0 0 0 0 0 0 0 1 0 0 21
22 8.2 97.0 8.3 8.2 8.1 8.0 0 0 0 0 0 0 0 0 0 1 0 22
23 8.0 105.4 8.2 8.3 8.2 8.1 0 0 0 0 0 0 0 0 0 0 1 23
24 7.9 102.7 8.0 8.2 8.3 8.2 0 0 0 0 0 0 0 0 0 0 0 24
25 7.6 98.1 7.9 8.0 8.2 8.3 1 0 0 0 0 0 0 0 0 0 0 25
26 7.6 104.5 7.6 7.9 8.0 8.2 0 1 0 0 0 0 0 0 0 0 0 26
27 8.3 87.4 7.6 7.6 7.9 8.0 0 0 1 0 0 0 0 0 0 0 0 27
28 8.4 89.9 8.3 7.6 7.6 7.9 0 0 0 1 0 0 0 0 0 0 0 28
29 8.4 109.8 8.4 8.3 7.6 7.6 0 0 0 0 1 0 0 0 0 0 0 29
30 8.4 111.7 8.4 8.4 8.3 7.6 0 0 0 0 0 1 0 0 0 0 0 30
31 8.4 98.6 8.4 8.4 8.4 8.3 0 0 0 0 0 0 1 0 0 0 0 31
32 8.6 96.9 8.4 8.4 8.4 8.4 0 0 0 0 0 0 0 1 0 0 0 32
33 8.9 95.1 8.6 8.4 8.4 8.4 0 0 0 0 0 0 0 0 1 0 0 33
34 8.8 97.0 8.9 8.6 8.4 8.4 0 0 0 0 0 0 0 0 0 1 0 34
35 8.3 112.7 8.8 8.9 8.6 8.4 0 0 0 0 0 0 0 0 0 0 1 35
36 7.5 102.9 8.3 8.8 8.9 8.6 0 0 0 0 0 0 0 0 0 0 0 36
37 7.2 97.4 7.5 8.3 8.8 8.9 1 0 0 0 0 0 0 0 0 0 0 37
38 7.4 111.4 7.2 7.5 8.3 8.8 0 1 0 0 0 0 0 0 0 0 0 38
39 8.8 87.4 7.4 7.2 7.5 8.3 0 0 1 0 0 0 0 0 0 0 0 39
40 9.3 96.8 8.8 7.4 7.2 7.5 0 0 0 1 0 0 0 0 0 0 0 40
41 9.3 114.1 9.3 8.8 7.4 7.2 0 0 0 0 1 0 0 0 0 0 0 41
42 8.7 110.3 9.3 9.3 8.8 7.4 0 0 0 0 0 1 0 0 0 0 0 42
43 8.2 103.9 8.7 9.3 9.3 8.8 0 0 0 0 0 0 1 0 0 0 0 43
44 8.3 101.6 8.2 8.7 9.3 9.3 0 0 0 0 0 0 0 1 0 0 0 44
45 8.5 94.6 8.3 8.2 8.7 9.3 0 0 0 0 0 0 0 0 1 0 0 45
46 8.6 95.9 8.5 8.3 8.2 8.7 0 0 0 0 0 0 0 0 0 1 0 46
47 8.5 104.7 8.6 8.5 8.3 8.2 0 0 0 0 0 0 0 0 0 0 1 47
48 8.2 102.8 8.5 8.6 8.5 8.3 0 0 0 0 0 0 0 0 0 0 0 48
49 8.1 98.1 8.2 8.5 8.6 8.5 1 0 0 0 0 0 0 0 0 0 0 49
50 7.9 113.9 8.1 8.2 8.5 8.6 0 1 0 0 0 0 0 0 0 0 0 50
51 8.6 80.9 7.9 8.1 8.2 8.5 0 0 1 0 0 0 0 0 0 0 0 51
52 8.7 95.7 8.6 7.9 8.1 8.2 0 0 0 1 0 0 0 0 0 0 0 52
53 8.7 113.2 8.7 8.6 7.9 8.1 0 0 0 0 1 0 0 0 0 0 0 53
54 8.5 105.9 8.7 8.7 8.6 7.9 0 0 0 0 0 1 0 0 0 0 0 54
55 8.4 108.8 8.5 8.7 8.7 8.6 0 0 0 0 0 0 1 0 0 0 0 55
56 8.5 102.3 8.4 8.5 8.7 8.7 0 0 0 0 0 0 0 1 0 0 0 56
57 8.7 99.0 8.5 8.4 8.5 8.7 0 0 0 0 0 0 0 0 1 0 0 57
58 8.7 100.7 8.7 8.5 8.4 8.5 0 0 0 0 0 0 0 0 0 1 0 58
59 8.6 115.5 8.7 8.7 8.5 8.4 0 0 0 0 0 0 0 0 0 0 1 59
60 8.5 100.7 8.6 8.7 8.7 8.5 0 0 0 0 0 0 0 0 0 0 0 60
61 8.3 109.9 8.5 8.6 8.7 8.7 1 0 0 0 0 0 0 0 0 0 0 61
62 8.0 114.6 8.3 8.5 8.6 8.7 0 1 0 0 0 0 0 0 0 0 0 62
63 8.2 85.4 8.0 8.3 8.5 8.6 0 0 1 0 0 0 0 0 0 0 0 63
64 8.1 100.5 8.2 8.0 8.3 8.5 0 0 0 1 0 0 0 0 0 0 0 64
65 8.1 114.8 8.1 8.2 8.0 8.3 0 0 0 0 1 0 0 0 0 0 0 65
66 8.0 116.5 8.1 8.1 8.2 8.0 0 0 0 0 0 1 0 0 0 0 0 66
67 7.9 112.9 8.0 8.1 8.1 8.2 0 0 0 0 0 0 1 0 0 0 0 67
68 7.9 102.0 7.9 8.0 8.1 8.1 0 0 0 0 0 0 0 1 0 0 0 68
69 8.0 106.0 7.9 7.9 8.0 8.1 0 0 0 0 0 0 0 0 1 0 0 69
70 8.0 105.3 8.0 7.9 7.9 8.0 0 0 0 0 0 0 0 0 0 1 0 70
71 7.9 118.8 8.0 8.0 7.9 7.9 0 0 0 0 0 0 0 0 0 0 1 71
72 8.0 106.1 7.9 8.0 8.0 7.9 0 0 0 0 0 0 0 0 0 0 0 72
73 7.7 109.3 8.0 7.9 8.0 8.0 1 0 0 0 0 0 0 0 0 0 0 73
74 7.2 117.2 7.7 8.0 7.9 8.0 0 1 0 0 0 0 0 0 0 0 0 74
75 7.5 92.5 7.2 7.7 8.0 7.9 0 0 1 0 0 0 0 0 0 0 0 75
76 7.3 104.2 7.5 7.2 7.7 8.0 0 0 0 1 0 0 0 0 0 0 0 76
77 7.0 112.5 7.3 7.5 7.2 7.7 0 0 0 0 1 0 0 0 0 0 0 77
78 7.0 122.4 7.0 7.3 7.5 7.2 0 0 0 0 0 1 0 0 0 0 0 78
79 7.0 113.3 7.0 7.0 7.3 7.5 0 0 0 0 0 0 1 0 0 0 0 79
80 7.2 100.0 7.0 7.0 7.0 7.3 0 0 0 0 0 0 0 1 0 0 0 80
81 7.3 110.7 7.2 7.0 7.0 7.0 0 0 0 0 0 0 0 0 1 0 0 81
82 7.1 112.8 7.3 7.2 7.0 7.0 0 0 0 0 0 0 0 0 0 1 0 82
83 6.8 109.8 7.1 7.3 7.2 7.0 0 0 0 0 0 0 0 0 0 0 1 83
84 6.4 117.3 6.8 7.1 7.3 7.2 0 0 0 0 0 0 0 0 0 0 0 84
85 6.1 109.1 6.4 6.8 7.1 7.3 1 0 0 0 0 0 0 0 0 0 0 85
86 6.5 115.9 6.1 6.4 6.8 7.1 0 1 0 0 0 0 0 0 0 0 0 86
87 7.7 96.0 6.5 6.1 6.4 6.8 0 0 1 0 0 0 0 0 0 0 0 87
88 7.9 99.8 7.7 6.5 6.1 6.4 0 0 0 1 0 0 0 0 0 0 0 88
89 7.5 116.8 7.9 7.7 6.5 6.1 0 0 0 0 1 0 0 0 0 0 0 89
90 6.9 115.7 7.5 7.9 7.7 6.5 0 0 0 0 0 1 0 0 0 0 0 90
91 6.6 99.4 6.9 7.5 7.9 7.7 0 0 0 0 0 0 1 0 0 0 0 91
92 6.9 94.3 6.6 6.9 7.5 7.9 0 0 0 0 0 0 0 1 0 0 0 92
93 7.7 91.0 6.9 6.6 6.9 7.5 0 0 0 0 0 0 0 0 1 0 0 93
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
2.808717 0.002191 0.687299 -0.239085 -0.040973 0.210960
M1 M2 M3 M4 M5 M6
0.141197 -0.242649 0.681888 0.334352 0.298794 0.308872
M7 M8 M9 M10 M11 t
0.129615 0.324046 0.429470 0.248536 0.138288 -0.005054
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.92435 -0.23155 0.01784 0.20965 1.95639
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.808717 1.173255 2.394 0.0192 *
X 0.002191 0.010836 0.202 0.8403
Y1 0.687299 0.104841 6.556 6.24e-09 ***
Y2 -0.239085 0.131833 -1.814 0.0737 .
Y3 -0.040973 0.131618 -0.311 0.7564
Y4 0.210960 0.085342 2.472 0.0157 *
M1 0.141197 0.215567 0.655 0.5145
M2 -0.242649 0.225890 -1.074 0.2862
M3 0.681888 0.288252 2.366 0.0206 *
M4 0.334352 0.243105 1.375 0.1731
M5 0.298794 0.229515 1.302 0.1970
M6 0.308872 0.228625 1.351 0.1808
M7 0.129615 0.208424 0.622 0.5359
M8 0.324046 0.225254 1.439 0.1544
M9 0.429470 0.222787 1.928 0.0577 .
M10 0.248536 0.222361 1.118 0.2673
M11 0.138288 0.224110 0.617 0.5391
t -0.005054 0.002206 -2.291 0.0247 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4021 on 75 degrees of freedom
Multiple R-squared: 0.7601, Adjusted R-squared: 0.7058
F-statistic: 13.98 on 17 and 75 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,] 0.18377871 3.675574e-01 8.162213e-01
[2,] 0.27649518 5.529904e-01 7.235048e-01
[3,] 0.23128256 4.625651e-01 7.687174e-01
[4,] 0.22827655 4.565531e-01 7.717235e-01
[5,] 0.21218695 4.243739e-01 7.878131e-01
[6,] 0.15324404 3.064881e-01 8.467560e-01
[7,] 0.10717570 2.143514e-01 8.928243e-01
[8,] 0.08141971 1.628394e-01 9.185803e-01
[9,] 0.05090792 1.018158e-01 9.490921e-01
[10,] 0.22789864 4.557973e-01 7.721014e-01
[11,] 0.32102583 6.420517e-01 6.789742e-01
[12,] 0.59146158 8.170768e-01 4.085384e-01
[13,] 0.51073883 9.785223e-01 4.892612e-01
[14,] 0.45158122 9.031624e-01 5.484188e-01
[15,] 0.53515497 9.296901e-01 4.648450e-01
[16,] 0.96892348 6.215304e-02 3.107652e-02
[17,] 0.99629234 7.415316e-03 3.707658e-03
[18,] 0.99881410 2.371808e-03 1.185904e-03
[19,] 0.99862189 2.756221e-03 1.378110e-03
[20,] 0.99866627 2.667457e-03 1.333729e-03
[21,] 0.99906707 1.865855e-03 9.329273e-04
[22,] 0.99845112 3.097753e-03 1.548877e-03
[23,] 0.99939592 1.208163e-03 6.040816e-04
[24,] 0.99990064 1.987288e-04 9.936440e-05
[25,] 0.99997157 5.685113e-05 2.842556e-05
[26,] 0.99994976 1.004861e-04 5.024307e-05
[27,] 0.99991238 1.752489e-04 8.762446e-05
[28,] 0.99988516 2.296765e-04 1.148382e-04
[29,] 0.99981572 3.685651e-04 1.842825e-04
[30,] 0.99984669 3.066152e-04 1.533076e-04
[31,] 0.99968860 6.228000e-04 3.114000e-04
[32,] 0.99955515 8.896924e-04 4.448462e-04
[33,] 0.99941727 1.165467e-03 5.827336e-04
[34,] 0.99938905 1.221905e-03 6.109523e-04
[35,] 0.99936336 1.273281e-03 6.366405e-04
[36,] 0.99906277 1.874465e-03 9.372326e-04
[37,] 0.99833630 3.327390e-03 1.663695e-03
[38,] 0.99686439 6.271217e-03 3.135608e-03
[39,] 0.99517039 9.659220e-03 4.829610e-03
[40,] 0.99133850 1.732301e-02 8.661504e-03
[41,] 0.99045042 1.909915e-02 9.549576e-03
[42,] 0.98519489 2.961022e-02 1.480511e-02
[43,] 0.99013646 1.972708e-02 9.863538e-03
[44,] 0.99256917 1.486165e-02 7.430826e-03
[45,] 0.98983016 2.033968e-02 1.016984e-02
[46,] 0.99078926 1.842148e-02 9.210742e-03
[47,] 0.98798902 2.402196e-02 1.201098e-02
[48,] 0.97570954 4.858092e-02 2.429046e-02
[49,] 0.95343803 9.312394e-02 4.656197e-02
[50,] 0.90554425 1.889115e-01 9.445575e-02
[51,] 0.93061516 1.387697e-01 6.938484e-02
[52,] 0.90273690 1.945262e-01 9.726310e-02
> postscript(file="/var/www/html/rcomp/tmp/1zloc1258718185.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2klq71258718185.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/31ce51258718185.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4q43b1258718185.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5lf7p1258718185.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 = 93
Frequency = 1
1 2 3 4 5 6
1.956391196 -0.924348989 -0.211765514 0.142430278 -0.503109261 0.017843397
7 8 9 10 11 12
-0.201761925 -0.124167909 -0.361019999 -0.498575340 -0.363406543 -0.413893512
13 14 15 16 17 18
-0.642269372 -0.231547868 -0.376247145 -0.408041956 -0.063130359 0.055583759
19 20 21 22 23 24
0.130559101 0.028619081 -0.062400168 -0.058450987 -0.085913199 0.059897457
25 26 27 28 29 30
-0.370447389 0.199612994 -0.016036844 -0.041229240 0.117702161 0.161105496
31 32 33 34 35 36
0.230543520 0.223794569 0.289908465 0.213361918 -0.057083346 -0.402428955
37 38 39 40 41 42
-0.463610176 0.110147650 0.506761567 0.580831527 0.646094070 0.184109099
43 44 45 46 47 48
0.019965304 0.030345323 -0.067545061 0.208133360 0.292819304 0.220061772
49 50 51 52 53 54
0.138403108 0.264497686 0.239669156 0.190098424 0.303901326 0.209652721
55 56 57 58 59 60
0.281496435 0.206176549 0.212203450 0.319011035 0.374897730 0.506494051
61 62 63 64 65 66
0.152824936 0.340881287 -0.139256542 -0.116032793 0.039696663 -0.021477739
67 68 69 70 71 72
0.093161942 -0.006417582 -0.043556873 0.092234236 0.122963191 0.466957278
73 74 75 76 77 78
-0.089930397 0.007662128 -0.260588707 -0.492752229 -0.518337738 -0.268906915
79 80 81 82 83 84
-0.207866230 -0.138205071 -0.236189324 -0.275714221 -0.284277138 -0.437088091
85 86 87 88 89 90
-0.681361905 0.233095113 0.257464028 0.144695990 -0.022816862 -0.337909818
91 92 93
-0.346098146 -0.220144961 0.268599509
> postscript(file="/var/www/html/rcomp/tmp/6lgf31258718185.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 = 93
Frequency = 1
lag(myerror, k = 1) myerror
0 1.956391196 NA
1 -0.924348989 1.956391196
2 -0.211765514 -0.924348989
3 0.142430278 -0.211765514
4 -0.503109261 0.142430278
5 0.017843397 -0.503109261
6 -0.201761925 0.017843397
7 -0.124167909 -0.201761925
8 -0.361019999 -0.124167909
9 -0.498575340 -0.361019999
10 -0.363406543 -0.498575340
11 -0.413893512 -0.363406543
12 -0.642269372 -0.413893512
13 -0.231547868 -0.642269372
14 -0.376247145 -0.231547868
15 -0.408041956 -0.376247145
16 -0.063130359 -0.408041956
17 0.055583759 -0.063130359
18 0.130559101 0.055583759
19 0.028619081 0.130559101
20 -0.062400168 0.028619081
21 -0.058450987 -0.062400168
22 -0.085913199 -0.058450987
23 0.059897457 -0.085913199
24 -0.370447389 0.059897457
25 0.199612994 -0.370447389
26 -0.016036844 0.199612994
27 -0.041229240 -0.016036844
28 0.117702161 -0.041229240
29 0.161105496 0.117702161
30 0.230543520 0.161105496
31 0.223794569 0.230543520
32 0.289908465 0.223794569
33 0.213361918 0.289908465
34 -0.057083346 0.213361918
35 -0.402428955 -0.057083346
36 -0.463610176 -0.402428955
37 0.110147650 -0.463610176
38 0.506761567 0.110147650
39 0.580831527 0.506761567
40 0.646094070 0.580831527
41 0.184109099 0.646094070
42 0.019965304 0.184109099
43 0.030345323 0.019965304
44 -0.067545061 0.030345323
45 0.208133360 -0.067545061
46 0.292819304 0.208133360
47 0.220061772 0.292819304
48 0.138403108 0.220061772
49 0.264497686 0.138403108
50 0.239669156 0.264497686
51 0.190098424 0.239669156
52 0.303901326 0.190098424
53 0.209652721 0.303901326
54 0.281496435 0.209652721
55 0.206176549 0.281496435
56 0.212203450 0.206176549
57 0.319011035 0.212203450
58 0.374897730 0.319011035
59 0.506494051 0.374897730
60 0.152824936 0.506494051
61 0.340881287 0.152824936
62 -0.139256542 0.340881287
63 -0.116032793 -0.139256542
64 0.039696663 -0.116032793
65 -0.021477739 0.039696663
66 0.093161942 -0.021477739
67 -0.006417582 0.093161942
68 -0.043556873 -0.006417582
69 0.092234236 -0.043556873
70 0.122963191 0.092234236
71 0.466957278 0.122963191
72 -0.089930397 0.466957278
73 0.007662128 -0.089930397
74 -0.260588707 0.007662128
75 -0.492752229 -0.260588707
76 -0.518337738 -0.492752229
77 -0.268906915 -0.518337738
78 -0.207866230 -0.268906915
79 -0.138205071 -0.207866230
80 -0.236189324 -0.138205071
81 -0.275714221 -0.236189324
82 -0.284277138 -0.275714221
83 -0.437088091 -0.284277138
84 -0.681361905 -0.437088091
85 0.233095113 -0.681361905
86 0.257464028 0.233095113
87 0.144695990 0.257464028
88 -0.022816862 0.144695990
89 -0.337909818 -0.022816862
90 -0.346098146 -0.337909818
91 -0.220144961 -0.346098146
92 0.268599509 -0.220144961
93 NA 0.268599509
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.924348989 1.956391196
[2,] -0.211765514 -0.924348989
[3,] 0.142430278 -0.211765514
[4,] -0.503109261 0.142430278
[5,] 0.017843397 -0.503109261
[6,] -0.201761925 0.017843397
[7,] -0.124167909 -0.201761925
[8,] -0.361019999 -0.124167909
[9,] -0.498575340 -0.361019999
[10,] -0.363406543 -0.498575340
[11,] -0.413893512 -0.363406543
[12,] -0.642269372 -0.413893512
[13,] -0.231547868 -0.642269372
[14,] -0.376247145 -0.231547868
[15,] -0.408041956 -0.376247145
[16,] -0.063130359 -0.408041956
[17,] 0.055583759 -0.063130359
[18,] 0.130559101 0.055583759
[19,] 0.028619081 0.130559101
[20,] -0.062400168 0.028619081
[21,] -0.058450987 -0.062400168
[22,] -0.085913199 -0.058450987
[23,] 0.059897457 -0.085913199
[24,] -0.370447389 0.059897457
[25,] 0.199612994 -0.370447389
[26,] -0.016036844 0.199612994
[27,] -0.041229240 -0.016036844
[28,] 0.117702161 -0.041229240
[29,] 0.161105496 0.117702161
[30,] 0.230543520 0.161105496
[31,] 0.223794569 0.230543520
[32,] 0.289908465 0.223794569
[33,] 0.213361918 0.289908465
[34,] -0.057083346 0.213361918
[35,] -0.402428955 -0.057083346
[36,] -0.463610176 -0.402428955
[37,] 0.110147650 -0.463610176
[38,] 0.506761567 0.110147650
[39,] 0.580831527 0.506761567
[40,] 0.646094070 0.580831527
[41,] 0.184109099 0.646094070
[42,] 0.019965304 0.184109099
[43,] 0.030345323 0.019965304
[44,] -0.067545061 0.030345323
[45,] 0.208133360 -0.067545061
[46,] 0.292819304 0.208133360
[47,] 0.220061772 0.292819304
[48,] 0.138403108 0.220061772
[49,] 0.264497686 0.138403108
[50,] 0.239669156 0.264497686
[51,] 0.190098424 0.239669156
[52,] 0.303901326 0.190098424
[53,] 0.209652721 0.303901326
[54,] 0.281496435 0.209652721
[55,] 0.206176549 0.281496435
[56,] 0.212203450 0.206176549
[57,] 0.319011035 0.212203450
[58,] 0.374897730 0.319011035
[59,] 0.506494051 0.374897730
[60,] 0.152824936 0.506494051
[61,] 0.340881287 0.152824936
[62,] -0.139256542 0.340881287
[63,] -0.116032793 -0.139256542
[64,] 0.039696663 -0.116032793
[65,] -0.021477739 0.039696663
[66,] 0.093161942 -0.021477739
[67,] -0.006417582 0.093161942
[68,] -0.043556873 -0.006417582
[69,] 0.092234236 -0.043556873
[70,] 0.122963191 0.092234236
[71,] 0.466957278 0.122963191
[72,] -0.089930397 0.466957278
[73,] 0.007662128 -0.089930397
[74,] -0.260588707 0.007662128
[75,] -0.492752229 -0.260588707
[76,] -0.518337738 -0.492752229
[77,] -0.268906915 -0.518337738
[78,] -0.207866230 -0.268906915
[79,] -0.138205071 -0.207866230
[80,] -0.236189324 -0.138205071
[81,] -0.275714221 -0.236189324
[82,] -0.284277138 -0.275714221
[83,] -0.437088091 -0.284277138
[84,] -0.681361905 -0.437088091
[85,] 0.233095113 -0.681361905
[86,] 0.257464028 0.233095113
[87,] 0.144695990 0.257464028
[88,] -0.022816862 0.144695990
[89,] -0.337909818 -0.022816862
[90,] -0.346098146 -0.337909818
[91,] -0.220144961 -0.346098146
[92,] 0.268599509 -0.220144961
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.924348989 1.956391196
2 -0.211765514 -0.924348989
3 0.142430278 -0.211765514
4 -0.503109261 0.142430278
5 0.017843397 -0.503109261
6 -0.201761925 0.017843397
7 -0.124167909 -0.201761925
8 -0.361019999 -0.124167909
9 -0.498575340 -0.361019999
10 -0.363406543 -0.498575340
11 -0.413893512 -0.363406543
12 -0.642269372 -0.413893512
13 -0.231547868 -0.642269372
14 -0.376247145 -0.231547868
15 -0.408041956 -0.376247145
16 -0.063130359 -0.408041956
17 0.055583759 -0.063130359
18 0.130559101 0.055583759
19 0.028619081 0.130559101
20 -0.062400168 0.028619081
21 -0.058450987 -0.062400168
22 -0.085913199 -0.058450987
23 0.059897457 -0.085913199
24 -0.370447389 0.059897457
25 0.199612994 -0.370447389
26 -0.016036844 0.199612994
27 -0.041229240 -0.016036844
28 0.117702161 -0.041229240
29 0.161105496 0.117702161
30 0.230543520 0.161105496
31 0.223794569 0.230543520
32 0.289908465 0.223794569
33 0.213361918 0.289908465
34 -0.057083346 0.213361918
35 -0.402428955 -0.057083346
36 -0.463610176 -0.402428955
37 0.110147650 -0.463610176
38 0.506761567 0.110147650
39 0.580831527 0.506761567
40 0.646094070 0.580831527
41 0.184109099 0.646094070
42 0.019965304 0.184109099
43 0.030345323 0.019965304
44 -0.067545061 0.030345323
45 0.208133360 -0.067545061
46 0.292819304 0.208133360
47 0.220061772 0.292819304
48 0.138403108 0.220061772
49 0.264497686 0.138403108
50 0.239669156 0.264497686
51 0.190098424 0.239669156
52 0.303901326 0.190098424
53 0.209652721 0.303901326
54 0.281496435 0.209652721
55 0.206176549 0.281496435
56 0.212203450 0.206176549
57 0.319011035 0.212203450
58 0.374897730 0.319011035
59 0.506494051 0.374897730
60 0.152824936 0.506494051
61 0.340881287 0.152824936
62 -0.139256542 0.340881287
63 -0.116032793 -0.139256542
64 0.039696663 -0.116032793
65 -0.021477739 0.039696663
66 0.093161942 -0.021477739
67 -0.006417582 0.093161942
68 -0.043556873 -0.006417582
69 0.092234236 -0.043556873
70 0.122963191 0.092234236
71 0.466957278 0.122963191
72 -0.089930397 0.466957278
73 0.007662128 -0.089930397
74 -0.260588707 0.007662128
75 -0.492752229 -0.260588707
76 -0.518337738 -0.492752229
77 -0.268906915 -0.518337738
78 -0.207866230 -0.268906915
79 -0.138205071 -0.207866230
80 -0.236189324 -0.138205071
81 -0.275714221 -0.236189324
82 -0.284277138 -0.275714221
83 -0.437088091 -0.284277138
84 -0.681361905 -0.437088091
85 0.233095113 -0.681361905
86 0.257464028 0.233095113
87 0.144695990 0.257464028
88 -0.022816862 0.144695990
89 -0.337909818 -0.022816862
90 -0.346098146 -0.337909818
91 -0.220144961 -0.346098146
92 0.268599509 -0.220144961
> 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/7iyyt1258718185.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8k37o1258718185.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9e8i51258718185.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10czb01258718185.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11fc9h1258718185.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/12di7p1258718185.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/13hm1l1258718186.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/14p7ql1258718186.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/152kn21258718186.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/16f6t11258718186.tab")
+ }
>
> system("convert tmp/1zloc1258718185.ps tmp/1zloc1258718185.png")
> system("convert tmp/2klq71258718185.ps tmp/2klq71258718185.png")
> system("convert tmp/31ce51258718185.ps tmp/31ce51258718185.png")
> system("convert tmp/4q43b1258718185.ps tmp/4q43b1258718185.png")
> system("convert tmp/5lf7p1258718185.ps tmp/5lf7p1258718185.png")
> system("convert tmp/6lgf31258718185.ps tmp/6lgf31258718185.png")
> system("convert tmp/7iyyt1258718185.ps tmp/7iyyt1258718185.png")
> system("convert tmp/8k37o1258718185.ps tmp/8k37o1258718185.png")
> system("convert tmp/9e8i51258718185.ps tmp/9e8i51258718185.png")
> system("convert tmp/10czb01258718185.ps tmp/10czb01258718185.png")
>
>
> proc.time()
user system elapsed
2.938 1.610 3.330