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(97.7
+ ,0
+ ,98.3
+ ,91.6
+ ,104.6
+ ,111.6
+ ,106.3
+ ,0
+ ,97.7
+ ,98.3
+ ,91.6
+ ,104.6
+ ,102.3
+ ,0
+ ,106.3
+ ,97.7
+ ,98.3
+ ,91.6
+ ,106.6
+ ,0
+ ,102.3
+ ,106.3
+ ,97.7
+ ,98.3
+ ,108.1
+ ,0
+ ,106.6
+ ,102.3
+ ,106.3
+ ,97.7
+ ,93.8
+ ,0
+ ,108.1
+ ,106.6
+ ,102.3
+ ,106.3
+ ,88.2
+ ,0
+ ,93.8
+ ,108.1
+ ,106.6
+ ,102.3
+ ,108.9
+ ,0
+ ,88.2
+ ,93.8
+ ,108.1
+ ,106.6
+ ,114.2
+ ,0
+ ,108.9
+ ,88.2
+ ,93.8
+ ,108.1
+ ,102.5
+ ,0
+ ,114.2
+ ,108.9
+ ,88.2
+ ,93.8
+ ,94.2
+ ,0
+ ,102.5
+ ,114.2
+ ,108.9
+ ,88.2
+ ,97.4
+ ,0
+ ,94.2
+ ,102.5
+ ,114.2
+ ,108.9
+ ,98.5
+ ,0
+ ,97.4
+ ,94.2
+ ,102.5
+ ,114.2
+ ,106.5
+ ,0
+ ,98.5
+ ,97.4
+ ,94.2
+ ,102.5
+ ,102.9
+ ,0
+ ,106.5
+ ,98.5
+ ,97.4
+ ,94.2
+ ,97.1
+ ,0
+ ,102.9
+ ,106.5
+ ,98.5
+ ,97.4
+ ,103.7
+ ,0
+ ,97.1
+ ,102.9
+ ,106.5
+ ,98.5
+ ,93.4
+ ,0
+ ,103.7
+ ,97.1
+ ,102.9
+ ,106.5
+ ,85.8
+ ,0
+ ,93.4
+ ,103.7
+ ,97.1
+ ,102.9
+ ,108.6
+ ,0
+ ,85.8
+ ,93.4
+ ,103.7
+ ,97.1
+ ,110.2
+ ,0
+ ,108.6
+ ,85.8
+ ,93.4
+ ,103.7
+ ,101.2
+ ,0
+ ,110.2
+ ,108.6
+ ,85.8
+ ,93.4
+ ,101.2
+ ,0
+ ,101.2
+ ,110.2
+ ,108.6
+ ,85.8
+ ,96.9
+ ,0
+ ,101.2
+ ,101.2
+ ,110.2
+ ,108.6
+ ,99.4
+ ,0
+ ,96.9
+ ,101.2
+ ,101.2
+ ,110.2
+ ,118.7
+ ,0
+ ,99.4
+ ,96.9
+ ,101.2
+ ,101.2
+ ,108.0
+ ,0
+ ,118.7
+ ,99.4
+ ,96.9
+ ,101.2
+ ,101.2
+ ,0
+ ,108.0
+ ,118.7
+ ,99.4
+ ,96.9
+ ,119.9
+ ,0
+ ,101.2
+ ,108.0
+ ,118.7
+ ,99.4
+ ,94.8
+ ,0
+ ,119.9
+ ,101.2
+ ,108.0
+ ,118.7
+ ,95.3
+ ,0
+ ,94.8
+ ,119.9
+ ,101.2
+ ,108.0
+ ,118.0
+ ,0
+ ,95.3
+ ,94.8
+ ,119.9
+ ,101.2
+ ,115.9
+ ,0
+ ,118.0
+ ,95.3
+ ,94.8
+ ,119.9
+ ,111.4
+ ,0
+ ,115.9
+ ,118.0
+ ,95.3
+ ,94.8
+ ,108.2
+ ,0
+ ,111.4
+ ,115.9
+ ,118.0
+ ,95.3
+ ,108.8
+ ,0
+ ,108.2
+ ,111.4
+ ,115.9
+ ,118.0
+ ,109.5
+ ,0
+ ,108.8
+ ,108.2
+ ,111.4
+ ,115.9
+ ,124.8
+ ,0
+ ,109.5
+ ,108.8
+ ,108.2
+ ,111.4
+ ,115.3
+ ,0
+ ,124.8
+ ,109.5
+ ,108.8
+ ,108.2
+ ,109.5
+ ,0
+ ,115.3
+ ,124.8
+ ,109.5
+ ,108.8
+ ,124.2
+ ,0
+ ,109.5
+ ,115.3
+ ,124.8
+ ,109.5
+ ,92.9
+ ,0
+ ,124.2
+ ,109.5
+ ,115.3
+ ,124.8
+ ,98.4
+ ,0
+ ,92.9
+ ,124.2
+ ,109.5
+ ,115.3
+ ,120.9
+ ,0
+ ,98.4
+ ,92.9
+ ,124.2
+ ,109.5
+ ,111.7
+ ,0
+ ,120.9
+ ,98.4
+ ,92.9
+ ,124.2
+ ,116.1
+ ,0
+ ,111.7
+ ,120.9
+ ,98.4
+ ,92.9
+ ,109.4
+ ,0
+ ,116.1
+ ,111.7
+ ,120.9
+ ,98.4
+ ,111.7
+ ,0
+ ,109.4
+ ,116.1
+ ,111.7
+ ,120.9
+ ,114.3
+ ,0
+ ,111.7
+ ,109.4
+ ,116.1
+ ,111.7
+ ,133.7
+ ,0
+ ,114.3
+ ,111.7
+ ,109.4
+ ,116.1
+ ,114.3
+ ,0
+ ,133.7
+ ,114.3
+ ,111.7
+ ,109.4
+ ,126.5
+ ,0
+ ,114.3
+ ,133.7
+ ,114.3
+ ,111.7
+ ,131.0
+ ,0
+ ,126.5
+ ,114.3
+ ,133.7
+ ,114.3
+ ,104.0
+ ,0
+ ,131.0
+ ,126.5
+ ,114.3
+ ,133.7
+ ,108.9
+ ,0
+ ,104.0
+ ,131.0
+ ,126.5
+ ,114.3
+ ,128.5
+ ,0
+ ,108.9
+ ,104.0
+ ,131.0
+ ,126.5
+ ,132.4
+ ,0
+ ,128.5
+ ,108.9
+ ,104.0
+ ,131.0
+ ,128.0
+ ,0
+ ,132.4
+ ,128.5
+ ,108.9
+ ,104.0
+ ,116.4
+ ,0
+ ,128.0
+ ,132.4
+ ,128.5
+ ,108.9
+ ,120.9
+ ,0
+ ,116.4
+ ,128.0
+ ,132.4
+ ,128.5
+ ,118.6
+ ,0
+ ,120.9
+ ,116.4
+ ,128.0
+ ,132.4
+ ,133.1
+ ,0
+ ,118.6
+ ,120.9
+ ,116.4
+ ,128.0
+ ,121.1
+ ,0
+ ,133.1
+ ,118.6
+ ,120.9
+ ,116.4
+ ,127.6
+ ,0
+ ,121.1
+ ,133.1
+ ,118.6
+ ,120.9
+ ,135.4
+ ,0
+ ,127.6
+ ,121.1
+ ,133.1
+ ,118.6
+ ,114.9
+ ,0
+ ,135.4
+ ,127.6
+ ,121.1
+ ,133.1
+ ,114.3
+ ,0
+ ,114.9
+ ,135.4
+ ,127.6
+ ,121.1
+ ,128.9
+ ,0
+ ,114.3
+ ,114.9
+ ,135.4
+ ,127.6
+ ,138.9
+ ,0
+ ,128.9
+ ,114.3
+ ,114.9
+ ,135.4
+ ,129.4
+ ,0
+ ,138.9
+ ,128.9
+ ,114.3
+ ,114.9
+ ,115.0
+ ,0
+ ,129.4
+ ,138.9
+ ,128.9
+ ,114.3
+ ,128.0
+ ,1
+ ,115.0
+ ,129.4
+ ,138.9
+ ,128.9
+ ,127.0
+ ,1
+ ,128.0
+ ,115.0
+ ,129.4
+ ,138.9
+ ,128.8
+ ,1
+ ,127.0
+ ,128.0
+ ,115.0
+ ,129.4
+ ,137.9
+ ,1
+ ,128.8
+ ,127.0
+ ,128.0
+ ,115.0
+ ,128.4
+ ,1
+ ,137.9
+ ,128.8
+ ,127.0
+ ,128.0
+ ,135.9
+ ,1
+ ,128.4
+ ,137.9
+ ,128.8
+ ,127.0
+ ,122.2
+ ,1
+ ,135.9
+ ,128.4
+ ,137.9
+ ,128.8
+ ,113.1
+ ,1
+ ,122.2
+ ,135.9
+ ,128.4
+ ,137.9
+ ,136.2
+ ,1
+ ,113.1
+ ,122.2
+ ,135.9
+ ,128.4
+ ,138.0
+ ,1
+ ,136.2
+ ,113.1
+ ,122.2
+ ,135.9
+ ,115.2
+ ,1
+ ,138.0
+ ,136.2
+ ,113.1
+ ,122.2
+ ,111.0
+ ,1
+ ,115.2
+ ,138.0
+ ,136.2
+ ,113.1
+ ,99.2
+ ,1
+ ,111.0
+ ,115.2
+ ,138.0
+ ,136.2
+ ,102.4
+ ,1
+ ,99.2
+ ,111.0
+ ,115.2
+ ,138.0
+ ,112.7
+ ,1
+ ,102.4
+ ,99.2
+ ,111.0
+ ,115.2
+ ,105.5
+ ,1
+ ,112.7
+ ,102.4
+ ,99.2
+ ,111.0
+ ,98.3
+ ,1
+ ,105.5
+ ,112.7
+ ,102.4
+ ,99.2
+ ,116.4
+ ,1
+ ,98.3
+ ,105.5
+ ,112.7
+ ,102.4
+ ,97.4
+ ,1
+ ,116.4
+ ,98.3
+ ,105.5
+ ,112.7
+ ,93.3
+ ,1
+ ,97.4
+ ,116.4
+ ,98.3
+ ,105.5
+ ,117.4
+ ,1
+ ,93.3
+ ,97.4
+ ,116.4
+ ,98.3)
+ ,dim=c(6
+ ,92)
+ ,dimnames=list(c('y'
+ ,'x'
+ ,'y1'
+ ,'y2'
+ ,'y3'
+ ,'y4')
+ ,1:92))
> y <- array(NA,dim=c(6,92),dimnames=list(c('y','x','y1','y2','y3','y4'),1:92))
> 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 97.7 0 98.3 91.6 104.6 111.6 1 0 0 0 0 0 0 0 0 0 0 1
2 106.3 0 97.7 98.3 91.6 104.6 0 1 0 0 0 0 0 0 0 0 0 2
3 102.3 0 106.3 97.7 98.3 91.6 0 0 1 0 0 0 0 0 0 0 0 3
4 106.6 0 102.3 106.3 97.7 98.3 0 0 0 1 0 0 0 0 0 0 0 4
5 108.1 0 106.6 102.3 106.3 97.7 0 0 0 0 1 0 0 0 0 0 0 5
6 93.8 0 108.1 106.6 102.3 106.3 0 0 0 0 0 1 0 0 0 0 0 6
7 88.2 0 93.8 108.1 106.6 102.3 0 0 0 0 0 0 1 0 0 0 0 7
8 108.9 0 88.2 93.8 108.1 106.6 0 0 0 0 0 0 0 1 0 0 0 8
9 114.2 0 108.9 88.2 93.8 108.1 0 0 0 0 0 0 0 0 1 0 0 9
10 102.5 0 114.2 108.9 88.2 93.8 0 0 0 0 0 0 0 0 0 1 0 10
11 94.2 0 102.5 114.2 108.9 88.2 0 0 0 0 0 0 0 0 0 0 1 11
12 97.4 0 94.2 102.5 114.2 108.9 0 0 0 0 0 0 0 0 0 0 0 12
13 98.5 0 97.4 94.2 102.5 114.2 1 0 0 0 0 0 0 0 0 0 0 13
14 106.5 0 98.5 97.4 94.2 102.5 0 1 0 0 0 0 0 0 0 0 0 14
15 102.9 0 106.5 98.5 97.4 94.2 0 0 1 0 0 0 0 0 0 0 0 15
16 97.1 0 102.9 106.5 98.5 97.4 0 0 0 1 0 0 0 0 0 0 0 16
17 103.7 0 97.1 102.9 106.5 98.5 0 0 0 0 1 0 0 0 0 0 0 17
18 93.4 0 103.7 97.1 102.9 106.5 0 0 0 0 0 1 0 0 0 0 0 18
19 85.8 0 93.4 103.7 97.1 102.9 0 0 0 0 0 0 1 0 0 0 0 19
20 108.6 0 85.8 93.4 103.7 97.1 0 0 0 0 0 0 0 1 0 0 0 20
21 110.2 0 108.6 85.8 93.4 103.7 0 0 0 0 0 0 0 0 1 0 0 21
22 101.2 0 110.2 108.6 85.8 93.4 0 0 0 0 0 0 0 0 0 1 0 22
23 101.2 0 101.2 110.2 108.6 85.8 0 0 0 0 0 0 0 0 0 0 1 23
24 96.9 0 101.2 101.2 110.2 108.6 0 0 0 0 0 0 0 0 0 0 0 24
25 99.4 0 96.9 101.2 101.2 110.2 1 0 0 0 0 0 0 0 0 0 0 25
26 118.7 0 99.4 96.9 101.2 101.2 0 1 0 0 0 0 0 0 0 0 0 26
27 108.0 0 118.7 99.4 96.9 101.2 0 0 1 0 0 0 0 0 0 0 0 27
28 101.2 0 108.0 118.7 99.4 96.9 0 0 0 1 0 0 0 0 0 0 0 28
29 119.9 0 101.2 108.0 118.7 99.4 0 0 0 0 1 0 0 0 0 0 0 29
30 94.8 0 119.9 101.2 108.0 118.7 0 0 0 0 0 1 0 0 0 0 0 30
31 95.3 0 94.8 119.9 101.2 108.0 0 0 0 0 0 0 1 0 0 0 0 31
32 118.0 0 95.3 94.8 119.9 101.2 0 0 0 0 0 0 0 1 0 0 0 32
33 115.9 0 118.0 95.3 94.8 119.9 0 0 0 0 0 0 0 0 1 0 0 33
34 111.4 0 115.9 118.0 95.3 94.8 0 0 0 0 0 0 0 0 0 1 0 34
35 108.2 0 111.4 115.9 118.0 95.3 0 0 0 0 0 0 0 0 0 0 1 35
36 108.8 0 108.2 111.4 115.9 118.0 0 0 0 0 0 0 0 0 0 0 0 36
37 109.5 0 108.8 108.2 111.4 115.9 1 0 0 0 0 0 0 0 0 0 0 37
38 124.8 0 109.5 108.8 108.2 111.4 0 1 0 0 0 0 0 0 0 0 0 38
39 115.3 0 124.8 109.5 108.8 108.2 0 0 1 0 0 0 0 0 0 0 0 39
40 109.5 0 115.3 124.8 109.5 108.8 0 0 0 1 0 0 0 0 0 0 0 40
41 124.2 0 109.5 115.3 124.8 109.5 0 0 0 0 1 0 0 0 0 0 0 41
42 92.9 0 124.2 109.5 115.3 124.8 0 0 0 0 0 1 0 0 0 0 0 42
43 98.4 0 92.9 124.2 109.5 115.3 0 0 0 0 0 0 1 0 0 0 0 43
44 120.9 0 98.4 92.9 124.2 109.5 0 0 0 0 0 0 0 1 0 0 0 44
45 111.7 0 120.9 98.4 92.9 124.2 0 0 0 0 0 0 0 0 1 0 0 45
46 116.1 0 111.7 120.9 98.4 92.9 0 0 0 0 0 0 0 0 0 1 0 46
47 109.4 0 116.1 111.7 120.9 98.4 0 0 0 0 0 0 0 0 0 0 1 47
48 111.7 0 109.4 116.1 111.7 120.9 0 0 0 0 0 0 0 0 0 0 0 48
49 114.3 0 111.7 109.4 116.1 111.7 1 0 0 0 0 0 0 0 0 0 0 49
50 133.7 0 114.3 111.7 109.4 116.1 0 1 0 0 0 0 0 0 0 0 0 50
51 114.3 0 133.7 114.3 111.7 109.4 0 0 1 0 0 0 0 0 0 0 0 51
52 126.5 0 114.3 133.7 114.3 111.7 0 0 0 1 0 0 0 0 0 0 0 52
53 131.0 0 126.5 114.3 133.7 114.3 0 0 0 0 1 0 0 0 0 0 0 53
54 104.0 0 131.0 126.5 114.3 133.7 0 0 0 0 0 1 0 0 0 0 0 54
55 108.9 0 104.0 131.0 126.5 114.3 0 0 0 0 0 0 1 0 0 0 0 55
56 128.5 0 108.9 104.0 131.0 126.5 0 0 0 0 0 0 0 1 0 0 0 56
57 132.4 0 128.5 108.9 104.0 131.0 0 0 0 0 0 0 0 0 1 0 0 57
58 128.0 0 132.4 128.5 108.9 104.0 0 0 0 0 0 0 0 0 0 1 0 58
59 116.4 0 128.0 132.4 128.5 108.9 0 0 0 0 0 0 0 0 0 0 1 59
60 120.9 0 116.4 128.0 132.4 128.5 0 0 0 0 0 0 0 0 0 0 0 60
61 118.6 0 120.9 116.4 128.0 132.4 1 0 0 0 0 0 0 0 0 0 0 61
62 133.1 0 118.6 120.9 116.4 128.0 0 1 0 0 0 0 0 0 0 0 0 62
63 121.1 0 133.1 118.6 120.9 116.4 0 0 1 0 0 0 0 0 0 0 0 63
64 127.6 0 121.1 133.1 118.6 120.9 0 0 0 1 0 0 0 0 0 0 0 64
65 135.4 0 127.6 121.1 133.1 118.6 0 0 0 0 1 0 0 0 0 0 0 65
66 114.9 0 135.4 127.6 121.1 133.1 0 0 0 0 0 1 0 0 0 0 0 66
67 114.3 0 114.9 135.4 127.6 121.1 0 0 0 0 0 0 1 0 0 0 0 67
68 128.9 0 114.3 114.9 135.4 127.6 0 0 0 0 0 0 0 1 0 0 0 68
69 138.9 0 128.9 114.3 114.9 135.4 0 0 0 0 0 0 0 0 1 0 0 69
70 129.4 0 138.9 128.9 114.3 114.9 0 0 0 0 0 0 0 0 0 1 0 70
71 115.0 0 129.4 138.9 128.9 114.3 0 0 0 0 0 0 0 0 0 0 1 71
72 128.0 1 115.0 129.4 138.9 128.9 0 0 0 0 0 0 0 0 0 0 0 72
73 127.0 1 128.0 115.0 129.4 138.9 1 0 0 0 0 0 0 0 0 0 0 73
74 128.8 1 127.0 128.0 115.0 129.4 0 1 0 0 0 0 0 0 0 0 0 74
75 137.9 1 128.8 127.0 128.0 115.0 0 0 1 0 0 0 0 0 0 0 0 75
76 128.4 1 137.9 128.8 127.0 128.0 0 0 0 1 0 0 0 0 0 0 0 76
77 135.9 1 128.4 137.9 128.8 127.0 0 0 0 0 1 0 0 0 0 0 0 77
78 122.2 1 135.9 128.4 137.9 128.8 0 0 0 0 0 1 0 0 0 0 0 78
79 113.1 1 122.2 135.9 128.4 137.9 0 0 0 0 0 0 1 0 0 0 0 79
80 136.2 1 113.1 122.2 135.9 128.4 0 0 0 0 0 0 0 1 0 0 0 80
81 138.0 1 136.2 113.1 122.2 135.9 0 0 0 0 0 0 0 0 1 0 0 81
82 115.2 1 138.0 136.2 113.1 122.2 0 0 0 0 0 0 0 0 0 1 0 82
83 111.0 1 115.2 138.0 136.2 113.1 0 0 0 0 0 0 0 0 0 0 1 83
84 99.2 1 111.0 115.2 138.0 136.2 0 0 0 0 0 0 0 0 0 0 0 84
85 102.4 1 99.2 111.0 115.2 138.0 1 0 0 0 0 0 0 0 0 0 0 85
86 112.7 1 102.4 99.2 111.0 115.2 0 1 0 0 0 0 0 0 0 0 0 86
87 105.5 1 112.7 102.4 99.2 111.0 0 0 1 0 0 0 0 0 0 0 0 87
88 98.3 1 105.5 112.7 102.4 99.2 0 0 0 1 0 0 0 0 0 0 0 88
89 116.4 1 98.3 105.5 112.7 102.4 0 0 0 0 1 0 0 0 0 0 0 89
90 97.4 1 116.4 98.3 105.5 112.7 0 0 0 0 0 1 0 0 0 0 0 90
91 93.3 1 97.4 116.4 98.3 105.5 0 0 0 0 0 0 1 0 0 0 0 91
92 117.4 1 93.3 97.4 116.4 98.3 0 0 0 0 0 0 0 1 0 0 0 92
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x y1 y2 y3 y4
6.22105 -2.97335 0.30329 0.43052 0.49388 -0.34294
M1 M2 M3 M4 M5 M6
8.26140 20.38931 5.65106 1.25758 8.94166 -5.91155
M7 M8 M9 M10 M11 t
-7.84346 17.23558 26.47015 2.61838 -13.43681 0.04841
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.8216 -2.7300 0.4627 2.5680 7.8527
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.22105 7.90507 0.787 0.43381
x -2.97335 1.82373 -1.630 0.10727
y1 0.30329 0.11059 2.742 0.00764 **
y2 0.43052 0.09920 4.340 4.45e-05 ***
y3 0.49388 0.10021 4.928 4.92e-06 ***
y4 -0.34294 0.11127 -3.082 0.00289 **
M1 8.26140 2.58299 3.198 0.00203 **
M2 20.38931 2.87494 7.092 6.62e-10 ***
M3 5.65106 3.90391 1.448 0.15197
M4 1.25758 3.53957 0.355 0.72338
M5 8.94166 2.87966 3.105 0.00270 **
M6 -5.91155 3.17459 -1.862 0.06655 .
M7 -7.84346 2.86540 -2.737 0.00775 **
M8 17.23558 2.63143 6.550 6.71e-09 ***
M9 26.47015 3.95833 6.687 3.74e-09 ***
M10 2.61838 4.94999 0.529 0.59841
M11 -13.43681 3.87583 -3.467 0.00088 ***
t 0.04841 0.03783 1.280 0.20471
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.55 on 74 degrees of freedom
Multiple R-squared: 0.9018, Adjusted R-squared: 0.8793
F-statistic: 39.99 on 17 and 74 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,] 0.379275916 0.758551833 0.6207241
[2,] 0.222053440 0.444106880 0.7779466
[3,] 0.317941491 0.635882981 0.6820585
[4,] 0.204646399 0.409292798 0.7953536
[5,] 0.153306251 0.306612503 0.8466937
[6,] 0.230074849 0.460149698 0.7699252
[7,] 0.154142817 0.308285634 0.8458572
[8,] 0.134234864 0.268469727 0.8657651
[9,] 0.110934715 0.221869431 0.8890653
[10,] 0.121348362 0.242696724 0.8786516
[11,] 0.130828850 0.261657699 0.8691712
[12,] 0.090570387 0.181140773 0.9094296
[13,] 0.059685195 0.119370390 0.9403148
[14,] 0.037656232 0.075312463 0.9623438
[15,] 0.026018145 0.052036289 0.9739819
[16,] 0.023350260 0.046700519 0.9766497
[17,] 0.014111910 0.028223820 0.9858881
[18,] 0.008042081 0.016084161 0.9919579
[19,] 0.004832367 0.009664734 0.9951676
[20,] 0.005832303 0.011664605 0.9941677
[21,] 0.003274081 0.006548162 0.9967259
[22,] 0.025507897 0.051015794 0.9744921
[23,] 0.015788172 0.031576344 0.9842118
[24,] 0.009469560 0.018939121 0.9905304
[25,] 0.015797468 0.031594936 0.9842025
[26,] 0.009885945 0.019771890 0.9901141
[27,] 0.007030639 0.014061277 0.9929694
[28,] 0.011044017 0.022088033 0.9889560
[29,] 0.008493926 0.016987851 0.9915061
[30,] 0.018350409 0.036700818 0.9816496
[31,] 0.038698678 0.077397355 0.9613013
[32,] 0.044592215 0.089184430 0.9554078
[33,] 0.037215081 0.074430161 0.9627849
[34,] 0.048181312 0.096362624 0.9518187
[35,] 0.063647889 0.127295778 0.9363521
[36,] 0.043007409 0.086014818 0.9569926
[37,] 0.053580901 0.107161802 0.9464191
[38,] 0.044900338 0.089800677 0.9550997
[39,] 0.069574429 0.139148858 0.9304256
[40,] 0.047823992 0.095647983 0.9521760
[41,] 0.069249771 0.138499541 0.9307502
[42,] 0.049122085 0.098244169 0.9508779
[43,] 0.166153928 0.332307855 0.8338461
[44,] 0.171813505 0.343627009 0.8281865
[45,] 0.167396798 0.334793596 0.8326032
[46,] 0.120081538 0.240163075 0.8799185
[47,] 0.122201699 0.244403397 0.8777983
[48,] 0.561366632 0.877266735 0.4386334
[49,] 0.548245993 0.903508014 0.4517540
[50,] 0.414847251 0.829694501 0.5851527
[51,] 0.283738509 0.567477018 0.7162615
> postscript(file="/var/www/html/rcomp/tmp/1ly2i1262009908.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ynxy1262009908.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32mhi1262009908.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4vyls1262009908.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5aptv1262009908.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 = 92
Frequency = 1
1 2 3 4 5 6
0.53271244 -1.72630314 -1.15369111 7.59609540 -2.67157448 0.45187756
7 8 9 10 11 12
-3.06882430 1.09246271 0.81921991 0.26495944 -2.90533165 -1.15476330
13 14 15 16 17 18
1.83423113 -3.96661579 -0.20350489 -3.45661874 -4.85394785 4.66769092
19 20 21 22 23 24
0.86352851 0.02678542 -3.94882341 0.77452162 4.95524967 -1.92635467
25 26 27 28 29 30
-1.43839766 3.69181901 2.87556340 -7.35261424 1.60933919 0.47351412
31 32 33 34 35 36
2.10768244 -1.23285167 -0.90632862 0.40624580 4.44234007 3.28696817
37 38 39 40 41 42
-1.62486766 1.06536966 -0.08021929 -5.38084566 0.11941084 -8.39821180
43 44 45 46 47 48
1.75609097 1.68681413 -5.48834041 2.36804076 5.07506917 6.28751281
49 50 51 52 53 54
-2.56351322 7.69936469 -7.44759992 6.13382447 -1.13625789 -3.71426422
55 56 57 58 59 60
-3.35774716 3.21426094 4.65524937 2.75804768 -1.17934343 0.04345645
61 62 63 64 65 66
-3.42654154 1.87740625 -5.04084159 5.88029147 1.19267344 1.23265993
67 68 69 70 71 72
-1.95002827 -5.09290995 4.25389504 2.50471579 -4.72888329 6.28475324
73 74 75 76 77 78
7.35300539 -4.46291512 7.85270838 4.11506178 1.61413545 0.65722710
79 80 81 82 83 84
2.17951667 1.84810014 0.61512811 -9.07653108 -5.65910055 -12.82157272
85 86 87 88 89 90
-0.66662888 -4.17812556 3.19758502 -7.53519448 4.12622131 4.62950639
91 92
1.46978114 -1.54266172
> postscript(file="/var/www/html/rcomp/tmp/686h11262009908.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 = 92
Frequency = 1
lag(myerror, k = 1) myerror
0 0.53271244 NA
1 -1.72630314 0.53271244
2 -1.15369111 -1.72630314
3 7.59609540 -1.15369111
4 -2.67157448 7.59609540
5 0.45187756 -2.67157448
6 -3.06882430 0.45187756
7 1.09246271 -3.06882430
8 0.81921991 1.09246271
9 0.26495944 0.81921991
10 -2.90533165 0.26495944
11 -1.15476330 -2.90533165
12 1.83423113 -1.15476330
13 -3.96661579 1.83423113
14 -0.20350489 -3.96661579
15 -3.45661874 -0.20350489
16 -4.85394785 -3.45661874
17 4.66769092 -4.85394785
18 0.86352851 4.66769092
19 0.02678542 0.86352851
20 -3.94882341 0.02678542
21 0.77452162 -3.94882341
22 4.95524967 0.77452162
23 -1.92635467 4.95524967
24 -1.43839766 -1.92635467
25 3.69181901 -1.43839766
26 2.87556340 3.69181901
27 -7.35261424 2.87556340
28 1.60933919 -7.35261424
29 0.47351412 1.60933919
30 2.10768244 0.47351412
31 -1.23285167 2.10768244
32 -0.90632862 -1.23285167
33 0.40624580 -0.90632862
34 4.44234007 0.40624580
35 3.28696817 4.44234007
36 -1.62486766 3.28696817
37 1.06536966 -1.62486766
38 -0.08021929 1.06536966
39 -5.38084566 -0.08021929
40 0.11941084 -5.38084566
41 -8.39821180 0.11941084
42 1.75609097 -8.39821180
43 1.68681413 1.75609097
44 -5.48834041 1.68681413
45 2.36804076 -5.48834041
46 5.07506917 2.36804076
47 6.28751281 5.07506917
48 -2.56351322 6.28751281
49 7.69936469 -2.56351322
50 -7.44759992 7.69936469
51 6.13382447 -7.44759992
52 -1.13625789 6.13382447
53 -3.71426422 -1.13625789
54 -3.35774716 -3.71426422
55 3.21426094 -3.35774716
56 4.65524937 3.21426094
57 2.75804768 4.65524937
58 -1.17934343 2.75804768
59 0.04345645 -1.17934343
60 -3.42654154 0.04345645
61 1.87740625 -3.42654154
62 -5.04084159 1.87740625
63 5.88029147 -5.04084159
64 1.19267344 5.88029147
65 1.23265993 1.19267344
66 -1.95002827 1.23265993
67 -5.09290995 -1.95002827
68 4.25389504 -5.09290995
69 2.50471579 4.25389504
70 -4.72888329 2.50471579
71 6.28475324 -4.72888329
72 7.35300539 6.28475324
73 -4.46291512 7.35300539
74 7.85270838 -4.46291512
75 4.11506178 7.85270838
76 1.61413545 4.11506178
77 0.65722710 1.61413545
78 2.17951667 0.65722710
79 1.84810014 2.17951667
80 0.61512811 1.84810014
81 -9.07653108 0.61512811
82 -5.65910055 -9.07653108
83 -12.82157272 -5.65910055
84 -0.66662888 -12.82157272
85 -4.17812556 -0.66662888
86 3.19758502 -4.17812556
87 -7.53519448 3.19758502
88 4.12622131 -7.53519448
89 4.62950639 4.12622131
90 1.46978114 4.62950639
91 -1.54266172 1.46978114
92 NA -1.54266172
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.72630314 0.53271244
[2,] -1.15369111 -1.72630314
[3,] 7.59609540 -1.15369111
[4,] -2.67157448 7.59609540
[5,] 0.45187756 -2.67157448
[6,] -3.06882430 0.45187756
[7,] 1.09246271 -3.06882430
[8,] 0.81921991 1.09246271
[9,] 0.26495944 0.81921991
[10,] -2.90533165 0.26495944
[11,] -1.15476330 -2.90533165
[12,] 1.83423113 -1.15476330
[13,] -3.96661579 1.83423113
[14,] -0.20350489 -3.96661579
[15,] -3.45661874 -0.20350489
[16,] -4.85394785 -3.45661874
[17,] 4.66769092 -4.85394785
[18,] 0.86352851 4.66769092
[19,] 0.02678542 0.86352851
[20,] -3.94882341 0.02678542
[21,] 0.77452162 -3.94882341
[22,] 4.95524967 0.77452162
[23,] -1.92635467 4.95524967
[24,] -1.43839766 -1.92635467
[25,] 3.69181901 -1.43839766
[26,] 2.87556340 3.69181901
[27,] -7.35261424 2.87556340
[28,] 1.60933919 -7.35261424
[29,] 0.47351412 1.60933919
[30,] 2.10768244 0.47351412
[31,] -1.23285167 2.10768244
[32,] -0.90632862 -1.23285167
[33,] 0.40624580 -0.90632862
[34,] 4.44234007 0.40624580
[35,] 3.28696817 4.44234007
[36,] -1.62486766 3.28696817
[37,] 1.06536966 -1.62486766
[38,] -0.08021929 1.06536966
[39,] -5.38084566 -0.08021929
[40,] 0.11941084 -5.38084566
[41,] -8.39821180 0.11941084
[42,] 1.75609097 -8.39821180
[43,] 1.68681413 1.75609097
[44,] -5.48834041 1.68681413
[45,] 2.36804076 -5.48834041
[46,] 5.07506917 2.36804076
[47,] 6.28751281 5.07506917
[48,] -2.56351322 6.28751281
[49,] 7.69936469 -2.56351322
[50,] -7.44759992 7.69936469
[51,] 6.13382447 -7.44759992
[52,] -1.13625789 6.13382447
[53,] -3.71426422 -1.13625789
[54,] -3.35774716 -3.71426422
[55,] 3.21426094 -3.35774716
[56,] 4.65524937 3.21426094
[57,] 2.75804768 4.65524937
[58,] -1.17934343 2.75804768
[59,] 0.04345645 -1.17934343
[60,] -3.42654154 0.04345645
[61,] 1.87740625 -3.42654154
[62,] -5.04084159 1.87740625
[63,] 5.88029147 -5.04084159
[64,] 1.19267344 5.88029147
[65,] 1.23265993 1.19267344
[66,] -1.95002827 1.23265993
[67,] -5.09290995 -1.95002827
[68,] 4.25389504 -5.09290995
[69,] 2.50471579 4.25389504
[70,] -4.72888329 2.50471579
[71,] 6.28475324 -4.72888329
[72,] 7.35300539 6.28475324
[73,] -4.46291512 7.35300539
[74,] 7.85270838 -4.46291512
[75,] 4.11506178 7.85270838
[76,] 1.61413545 4.11506178
[77,] 0.65722710 1.61413545
[78,] 2.17951667 0.65722710
[79,] 1.84810014 2.17951667
[80,] 0.61512811 1.84810014
[81,] -9.07653108 0.61512811
[82,] -5.65910055 -9.07653108
[83,] -12.82157272 -5.65910055
[84,] -0.66662888 -12.82157272
[85,] -4.17812556 -0.66662888
[86,] 3.19758502 -4.17812556
[87,] -7.53519448 3.19758502
[88,] 4.12622131 -7.53519448
[89,] 4.62950639 4.12622131
[90,] 1.46978114 4.62950639
[91,] -1.54266172 1.46978114
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.72630314 0.53271244
2 -1.15369111 -1.72630314
3 7.59609540 -1.15369111
4 -2.67157448 7.59609540
5 0.45187756 -2.67157448
6 -3.06882430 0.45187756
7 1.09246271 -3.06882430
8 0.81921991 1.09246271
9 0.26495944 0.81921991
10 -2.90533165 0.26495944
11 -1.15476330 -2.90533165
12 1.83423113 -1.15476330
13 -3.96661579 1.83423113
14 -0.20350489 -3.96661579
15 -3.45661874 -0.20350489
16 -4.85394785 -3.45661874
17 4.66769092 -4.85394785
18 0.86352851 4.66769092
19 0.02678542 0.86352851
20 -3.94882341 0.02678542
21 0.77452162 -3.94882341
22 4.95524967 0.77452162
23 -1.92635467 4.95524967
24 -1.43839766 -1.92635467
25 3.69181901 -1.43839766
26 2.87556340 3.69181901
27 -7.35261424 2.87556340
28 1.60933919 -7.35261424
29 0.47351412 1.60933919
30 2.10768244 0.47351412
31 -1.23285167 2.10768244
32 -0.90632862 -1.23285167
33 0.40624580 -0.90632862
34 4.44234007 0.40624580
35 3.28696817 4.44234007
36 -1.62486766 3.28696817
37 1.06536966 -1.62486766
38 -0.08021929 1.06536966
39 -5.38084566 -0.08021929
40 0.11941084 -5.38084566
41 -8.39821180 0.11941084
42 1.75609097 -8.39821180
43 1.68681413 1.75609097
44 -5.48834041 1.68681413
45 2.36804076 -5.48834041
46 5.07506917 2.36804076
47 6.28751281 5.07506917
48 -2.56351322 6.28751281
49 7.69936469 -2.56351322
50 -7.44759992 7.69936469
51 6.13382447 -7.44759992
52 -1.13625789 6.13382447
53 -3.71426422 -1.13625789
54 -3.35774716 -3.71426422
55 3.21426094 -3.35774716
56 4.65524937 3.21426094
57 2.75804768 4.65524937
58 -1.17934343 2.75804768
59 0.04345645 -1.17934343
60 -3.42654154 0.04345645
61 1.87740625 -3.42654154
62 -5.04084159 1.87740625
63 5.88029147 -5.04084159
64 1.19267344 5.88029147
65 1.23265993 1.19267344
66 -1.95002827 1.23265993
67 -5.09290995 -1.95002827
68 4.25389504 -5.09290995
69 2.50471579 4.25389504
70 -4.72888329 2.50471579
71 6.28475324 -4.72888329
72 7.35300539 6.28475324
73 -4.46291512 7.35300539
74 7.85270838 -4.46291512
75 4.11506178 7.85270838
76 1.61413545 4.11506178
77 0.65722710 1.61413545
78 2.17951667 0.65722710
79 1.84810014 2.17951667
80 0.61512811 1.84810014
81 -9.07653108 0.61512811
82 -5.65910055 -9.07653108
83 -12.82157272 -5.65910055
84 -0.66662888 -12.82157272
85 -4.17812556 -0.66662888
86 3.19758502 -4.17812556
87 -7.53519448 3.19758502
88 4.12622131 -7.53519448
89 4.62950639 4.12622131
90 1.46978114 4.62950639
91 -1.54266172 1.46978114
> 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/7esam1262009908.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/893lv1262009908.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9m0rv1262009908.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/105mdy1262009908.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11htco1262009908.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/12iegb1262009908.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/137rp81262009908.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/1457up1262009908.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/15myfr1262009908.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/16vs5e1262009908.tab")
+ }
>
> try(system("convert tmp/1ly2i1262009908.ps tmp/1ly2i1262009908.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ynxy1262009908.ps tmp/2ynxy1262009908.png",intern=TRUE))
character(0)
> try(system("convert tmp/32mhi1262009908.ps tmp/32mhi1262009908.png",intern=TRUE))
character(0)
> try(system("convert tmp/4vyls1262009908.ps tmp/4vyls1262009908.png",intern=TRUE))
character(0)
> try(system("convert tmp/5aptv1262009908.ps tmp/5aptv1262009908.png",intern=TRUE))
character(0)
> try(system("convert tmp/686h11262009908.ps tmp/686h11262009908.png",intern=TRUE))
character(0)
> try(system("convert tmp/7esam1262009908.ps tmp/7esam1262009908.png",intern=TRUE))
character(0)
> try(system("convert tmp/893lv1262009908.ps tmp/893lv1262009908.png",intern=TRUE))
character(0)
> try(system("convert tmp/9m0rv1262009908.ps tmp/9m0rv1262009908.png",intern=TRUE))
character(0)
> try(system("convert tmp/105mdy1262009908.ps tmp/105mdy1262009908.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.916 1.629 4.035