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
+ ,0
+ ,115.0
+ ,129.4
+ ,138.9
+ ,128.9
+ ,127.0
+ ,0
+ ,128.0
+ ,115.0
+ ,129.4
+ ,138.9
+ ,128.8
+ ,0
+ ,127.0
+ ,128.0
+ ,115.0
+ ,129.4
+ ,137.9
+ ,0
+ ,128.8
+ ,127.0
+ ,128.0
+ ,115.0
+ ,128.4
+ ,0
+ ,137.9
+ ,128.8
+ ,127.0
+ ,128.0
+ ,135.9
+ ,0
+ ,128.4
+ ,137.9
+ ,128.8
+ ,127.0
+ ,122.2
+ ,0
+ ,135.9
+ ,128.4
+ ,137.9
+ ,128.8
+ ,113.1
+ ,0
+ ,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'
+ ,'dummy'
+ ,'y1'
+ ,'y2'
+ ,'y3'
+ ,'y4')
+ ,1:92))
> y <- array(NA,dim=c(6,92),dimnames=list(c('y','dummy','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 dummy 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 0 115.0 129.4 138.9 128.9 0 0 0 0 0 0 0 0 0 0 0 72
73 127.0 0 128.0 115.0 129.4 138.9 1 0 0 0 0 0 0 0 0 0 0 73
74 128.8 0 127.0 128.0 115.0 129.4 0 1 0 0 0 0 0 0 0 0 0 74
75 137.9 0 128.8 127.0 128.0 115.0 0 0 1 0 0 0 0 0 0 0 0 75
76 128.4 0 137.9 128.8 127.0 128.0 0 0 0 1 0 0 0 0 0 0 0 76
77 135.9 0 128.4 137.9 128.8 127.0 0 0 0 0 1 0 0 0 0 0 0 77
78 122.2 0 135.9 128.4 137.9 128.8 0 0 0 0 0 1 0 0 0 0 0 78
79 113.1 0 122.2 135.9 128.4 137.9 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) dummy y1 y2 y3 y4
26.0462 -10.2214 0.1254 0.3379 0.4679 -0.2704
M1 M2 M3 M4 M5 M6
7.5710 20.2818 8.2752 3.5202 10.4267 -3.9730
M7 M8 M9 M10 M11 t
-8.1946 16.2001 27.6669 7.3593 -9.4455 0.1530
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.04819 -1.91824 -0.02218 2.68156 8.55368
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 26.04616 8.70639 2.992 0.003768 **
dummy -10.22138 2.34866 -4.352 4.26e-05 ***
y1 0.12538 0.10991 1.141 0.257676
y2 0.33785 0.09306 3.631 0.000518 ***
y3 0.46787 0.09099 5.142 2.15e-06 ***
y4 -0.27042 0.10260 -2.636 0.010222 *
M1 7.57100 2.35162 3.219 0.001908 **
M2 20.28176 2.60727 7.779 3.39e-11 ***
M3 8.27516 3.60061 2.298 0.024373 *
M4 3.52016 3.26025 1.080 0.283773
M5 10.42666 2.64137 3.947 0.000178 ***
M6 -3.97298 2.92313 -1.359 0.178226
M7 -8.19458 2.60355 -3.147 0.002374 **
M8 16.20007 2.39496 6.764 2.70e-09 ***
M9 27.66695 3.60722 7.670 5.44e-11 ***
M10 7.35927 4.65111 1.582 0.117855
M11 -9.44548 3.65983 -2.581 0.011836 *
t 0.15300 0.04192 3.649 0.000487 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.133 on 74 degrees of freedom
Multiple R-squared: 0.919, Adjusted R-squared: 0.9004
F-statistic: 49.41 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,] 0.47146300 0.94292601 0.5285370
[2,] 0.30364160 0.60728319 0.6963584
[3,] 0.43232685 0.86465369 0.5676732
[4,] 0.30378258 0.60756516 0.6962174
[5,] 0.24411569 0.48823138 0.7558843
[6,] 0.35882726 0.71765453 0.6411727
[7,] 0.26798508 0.53597016 0.7320149
[8,] 0.23854101 0.47708202 0.7614590
[9,] 0.20847295 0.41694589 0.7915271
[10,] 0.23300105 0.46600209 0.7669990
[11,] 0.25687132 0.51374264 0.7431287
[12,] 0.19497424 0.38994849 0.8050258
[13,] 0.14253965 0.28507929 0.8574604
[14,] 0.09970377 0.19940755 0.9002962
[15,] 0.07587348 0.15174697 0.9241265
[16,] 0.07437826 0.14875653 0.9256217
[17,] 0.04876700 0.09753399 0.9512330
[18,] 0.03180300 0.06360599 0.9681970
[19,] 0.02186986 0.04373972 0.9781301
[20,] 0.02313347 0.04626694 0.9768665
[21,] 0.01405926 0.02811852 0.9859407
[22,] 0.07481329 0.14962658 0.9251867
[23,] 0.05070214 0.10140428 0.9492979
[24,] 0.03363067 0.06726134 0.9663693
[25,] 0.06498729 0.12997457 0.9350127
[26,] 0.04806548 0.09613097 0.9519345
[27,] 0.03732002 0.07464005 0.9626800
[28,] 0.05932418 0.11864836 0.9406758
[29,] 0.04414655 0.08829309 0.9558535
[30,] 0.11258334 0.22516669 0.8874167
[31,] 0.13630984 0.27261968 0.8636902
[32,] 0.17048724 0.34097447 0.8295128
[33,] 0.12884021 0.25768042 0.8711598
[34,] 0.11717114 0.23434229 0.8828289
[35,] 0.10836831 0.21673661 0.8916317
[36,] 0.07722946 0.15445892 0.9227705
[37,] 0.07440610 0.14881221 0.9255939
[38,] 0.05677142 0.11354285 0.9432286
[39,] 0.04097887 0.08195773 0.9590211
[40,] 0.02856111 0.05712223 0.9714389
[41,] 0.02772639 0.05545279 0.9722736
[42,] 0.02501816 0.05003631 0.9749818
[43,] 0.04918329 0.09836658 0.9508167
[44,] 0.07169407 0.14338813 0.9283059
[45,] 0.04933384 0.09866768 0.9506662
[46,] 0.03111741 0.06223482 0.9688826
[47,] 0.02739036 0.05478072 0.9726096
[48,] 0.13570402 0.27140804 0.8642960
[49,] 0.10709952 0.21419905 0.8929005
[50,] 0.06125072 0.12250143 0.9387493
[51,] 0.30179620 0.60359239 0.6982038
> postscript(file="/var/www/html/rcomp/tmp/1oa5m1262013698.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2tiea1262013698.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3i6221262013698.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ocl11262013698.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/54swh1262013698.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
1.898088578 -0.364731359 -0.036836358 8.553682294 -0.379427845
6 7 8 9 10
2.123468617 -1.215339096 0.931436948 1.004382826 0.554047417
11 12 13 14 15
-2.617129463 -0.904082140 1.782176319 -3.581251661 -0.443967078
16 17 18 19 20
-3.542735075 -5.504251506 3.422145495 0.692408875 -1.278852126
21 22 23 24 25
-4.985810701 -0.964327491 3.552673912 -1.888111390 -1.929514032
26 27 28 29 30
3.212270223 3.113309467 -6.596202820 1.158105236 0.482920374
31 32 33 34 35
2.168602716 -1.849590542 -1.784092841 -0.556889661 3.683174164
36 37 38 39 40
3.727319211 -0.753256484 1.672793756 0.725576455 -4.615749523
41 42 43 44 45
-0.007530663 -8.362189475 0.308876109 -0.299617298 -7.179218547
46 47 48 49 50
-0.110221287 3.358432512 5.802272679 -1.892964067 7.864804143
51 52 53 54 55
-5.880224688 6.205242564 0.296983442 -2.819586244 -2.940313366
56 57 58 59 60
1.813475452 3.830052498 2.879928294 -0.679423983 0.638586345
61 62 63 64 65
-2.917248547 1.724428961 -4.705147084 5.295493886 1.869250162
66 67 68 69 70
1.977422174 -0.905206184 -5.743257333 2.709646181 1.915001107
71 72 73 74 75
-5.013818105 2.672201544 4.332350508 -6.829833431 4.259607438
76 77 78 79 80
1.595901272 -0.959567964 -1.914476896 -0.856545798 7.608704492
81 82 83 84 85
6.405040584 -3.717538378 -2.283909037 -10.048186250 -0.519632275
86 87 88 89 90
-3.698480632 2.967681848 -6.895632598 3.526439139 5.090295955
91 92
2.747516745 -1.182299594
> postscript(file="/var/www/html/rcomp/tmp/6066m1262013698.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 1.898088578 NA
1 -0.364731359 1.898088578
2 -0.036836358 -0.364731359
3 8.553682294 -0.036836358
4 -0.379427845 8.553682294
5 2.123468617 -0.379427845
6 -1.215339096 2.123468617
7 0.931436948 -1.215339096
8 1.004382826 0.931436948
9 0.554047417 1.004382826
10 -2.617129463 0.554047417
11 -0.904082140 -2.617129463
12 1.782176319 -0.904082140
13 -3.581251661 1.782176319
14 -0.443967078 -3.581251661
15 -3.542735075 -0.443967078
16 -5.504251506 -3.542735075
17 3.422145495 -5.504251506
18 0.692408875 3.422145495
19 -1.278852126 0.692408875
20 -4.985810701 -1.278852126
21 -0.964327491 -4.985810701
22 3.552673912 -0.964327491
23 -1.888111390 3.552673912
24 -1.929514032 -1.888111390
25 3.212270223 -1.929514032
26 3.113309467 3.212270223
27 -6.596202820 3.113309467
28 1.158105236 -6.596202820
29 0.482920374 1.158105236
30 2.168602716 0.482920374
31 -1.849590542 2.168602716
32 -1.784092841 -1.849590542
33 -0.556889661 -1.784092841
34 3.683174164 -0.556889661
35 3.727319211 3.683174164
36 -0.753256484 3.727319211
37 1.672793756 -0.753256484
38 0.725576455 1.672793756
39 -4.615749523 0.725576455
40 -0.007530663 -4.615749523
41 -8.362189475 -0.007530663
42 0.308876109 -8.362189475
43 -0.299617298 0.308876109
44 -7.179218547 -0.299617298
45 -0.110221287 -7.179218547
46 3.358432512 -0.110221287
47 5.802272679 3.358432512
48 -1.892964067 5.802272679
49 7.864804143 -1.892964067
50 -5.880224688 7.864804143
51 6.205242564 -5.880224688
52 0.296983442 6.205242564
53 -2.819586244 0.296983442
54 -2.940313366 -2.819586244
55 1.813475452 -2.940313366
56 3.830052498 1.813475452
57 2.879928294 3.830052498
58 -0.679423983 2.879928294
59 0.638586345 -0.679423983
60 -2.917248547 0.638586345
61 1.724428961 -2.917248547
62 -4.705147084 1.724428961
63 5.295493886 -4.705147084
64 1.869250162 5.295493886
65 1.977422174 1.869250162
66 -0.905206184 1.977422174
67 -5.743257333 -0.905206184
68 2.709646181 -5.743257333
69 1.915001107 2.709646181
70 -5.013818105 1.915001107
71 2.672201544 -5.013818105
72 4.332350508 2.672201544
73 -6.829833431 4.332350508
74 4.259607438 -6.829833431
75 1.595901272 4.259607438
76 -0.959567964 1.595901272
77 -1.914476896 -0.959567964
78 -0.856545798 -1.914476896
79 7.608704492 -0.856545798
80 6.405040584 7.608704492
81 -3.717538378 6.405040584
82 -2.283909037 -3.717538378
83 -10.048186250 -2.283909037
84 -0.519632275 -10.048186250
85 -3.698480632 -0.519632275
86 2.967681848 -3.698480632
87 -6.895632598 2.967681848
88 3.526439139 -6.895632598
89 5.090295955 3.526439139
90 2.747516745 5.090295955
91 -1.182299594 2.747516745
92 NA -1.182299594
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.364731359 1.898088578
[2,] -0.036836358 -0.364731359
[3,] 8.553682294 -0.036836358
[4,] -0.379427845 8.553682294
[5,] 2.123468617 -0.379427845
[6,] -1.215339096 2.123468617
[7,] 0.931436948 -1.215339096
[8,] 1.004382826 0.931436948
[9,] 0.554047417 1.004382826
[10,] -2.617129463 0.554047417
[11,] -0.904082140 -2.617129463
[12,] 1.782176319 -0.904082140
[13,] -3.581251661 1.782176319
[14,] -0.443967078 -3.581251661
[15,] -3.542735075 -0.443967078
[16,] -5.504251506 -3.542735075
[17,] 3.422145495 -5.504251506
[18,] 0.692408875 3.422145495
[19,] -1.278852126 0.692408875
[20,] -4.985810701 -1.278852126
[21,] -0.964327491 -4.985810701
[22,] 3.552673912 -0.964327491
[23,] -1.888111390 3.552673912
[24,] -1.929514032 -1.888111390
[25,] 3.212270223 -1.929514032
[26,] 3.113309467 3.212270223
[27,] -6.596202820 3.113309467
[28,] 1.158105236 -6.596202820
[29,] 0.482920374 1.158105236
[30,] 2.168602716 0.482920374
[31,] -1.849590542 2.168602716
[32,] -1.784092841 -1.849590542
[33,] -0.556889661 -1.784092841
[34,] 3.683174164 -0.556889661
[35,] 3.727319211 3.683174164
[36,] -0.753256484 3.727319211
[37,] 1.672793756 -0.753256484
[38,] 0.725576455 1.672793756
[39,] -4.615749523 0.725576455
[40,] -0.007530663 -4.615749523
[41,] -8.362189475 -0.007530663
[42,] 0.308876109 -8.362189475
[43,] -0.299617298 0.308876109
[44,] -7.179218547 -0.299617298
[45,] -0.110221287 -7.179218547
[46,] 3.358432512 -0.110221287
[47,] 5.802272679 3.358432512
[48,] -1.892964067 5.802272679
[49,] 7.864804143 -1.892964067
[50,] -5.880224688 7.864804143
[51,] 6.205242564 -5.880224688
[52,] 0.296983442 6.205242564
[53,] -2.819586244 0.296983442
[54,] -2.940313366 -2.819586244
[55,] 1.813475452 -2.940313366
[56,] 3.830052498 1.813475452
[57,] 2.879928294 3.830052498
[58,] -0.679423983 2.879928294
[59,] 0.638586345 -0.679423983
[60,] -2.917248547 0.638586345
[61,] 1.724428961 -2.917248547
[62,] -4.705147084 1.724428961
[63,] 5.295493886 -4.705147084
[64,] 1.869250162 5.295493886
[65,] 1.977422174 1.869250162
[66,] -0.905206184 1.977422174
[67,] -5.743257333 -0.905206184
[68,] 2.709646181 -5.743257333
[69,] 1.915001107 2.709646181
[70,] -5.013818105 1.915001107
[71,] 2.672201544 -5.013818105
[72,] 4.332350508 2.672201544
[73,] -6.829833431 4.332350508
[74,] 4.259607438 -6.829833431
[75,] 1.595901272 4.259607438
[76,] -0.959567964 1.595901272
[77,] -1.914476896 -0.959567964
[78,] -0.856545798 -1.914476896
[79,] 7.608704492 -0.856545798
[80,] 6.405040584 7.608704492
[81,] -3.717538378 6.405040584
[82,] -2.283909037 -3.717538378
[83,] -10.048186250 -2.283909037
[84,] -0.519632275 -10.048186250
[85,] -3.698480632 -0.519632275
[86,] 2.967681848 -3.698480632
[87,] -6.895632598 2.967681848
[88,] 3.526439139 -6.895632598
[89,] 5.090295955 3.526439139
[90,] 2.747516745 5.090295955
[91,] -1.182299594 2.747516745
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.364731359 1.898088578
2 -0.036836358 -0.364731359
3 8.553682294 -0.036836358
4 -0.379427845 8.553682294
5 2.123468617 -0.379427845
6 -1.215339096 2.123468617
7 0.931436948 -1.215339096
8 1.004382826 0.931436948
9 0.554047417 1.004382826
10 -2.617129463 0.554047417
11 -0.904082140 -2.617129463
12 1.782176319 -0.904082140
13 -3.581251661 1.782176319
14 -0.443967078 -3.581251661
15 -3.542735075 -0.443967078
16 -5.504251506 -3.542735075
17 3.422145495 -5.504251506
18 0.692408875 3.422145495
19 -1.278852126 0.692408875
20 -4.985810701 -1.278852126
21 -0.964327491 -4.985810701
22 3.552673912 -0.964327491
23 -1.888111390 3.552673912
24 -1.929514032 -1.888111390
25 3.212270223 -1.929514032
26 3.113309467 3.212270223
27 -6.596202820 3.113309467
28 1.158105236 -6.596202820
29 0.482920374 1.158105236
30 2.168602716 0.482920374
31 -1.849590542 2.168602716
32 -1.784092841 -1.849590542
33 -0.556889661 -1.784092841
34 3.683174164 -0.556889661
35 3.727319211 3.683174164
36 -0.753256484 3.727319211
37 1.672793756 -0.753256484
38 0.725576455 1.672793756
39 -4.615749523 0.725576455
40 -0.007530663 -4.615749523
41 -8.362189475 -0.007530663
42 0.308876109 -8.362189475
43 -0.299617298 0.308876109
44 -7.179218547 -0.299617298
45 -0.110221287 -7.179218547
46 3.358432512 -0.110221287
47 5.802272679 3.358432512
48 -1.892964067 5.802272679
49 7.864804143 -1.892964067
50 -5.880224688 7.864804143
51 6.205242564 -5.880224688
52 0.296983442 6.205242564
53 -2.819586244 0.296983442
54 -2.940313366 -2.819586244
55 1.813475452 -2.940313366
56 3.830052498 1.813475452
57 2.879928294 3.830052498
58 -0.679423983 2.879928294
59 0.638586345 -0.679423983
60 -2.917248547 0.638586345
61 1.724428961 -2.917248547
62 -4.705147084 1.724428961
63 5.295493886 -4.705147084
64 1.869250162 5.295493886
65 1.977422174 1.869250162
66 -0.905206184 1.977422174
67 -5.743257333 -0.905206184
68 2.709646181 -5.743257333
69 1.915001107 2.709646181
70 -5.013818105 1.915001107
71 2.672201544 -5.013818105
72 4.332350508 2.672201544
73 -6.829833431 4.332350508
74 4.259607438 -6.829833431
75 1.595901272 4.259607438
76 -0.959567964 1.595901272
77 -1.914476896 -0.959567964
78 -0.856545798 -1.914476896
79 7.608704492 -0.856545798
80 6.405040584 7.608704492
81 -3.717538378 6.405040584
82 -2.283909037 -3.717538378
83 -10.048186250 -2.283909037
84 -0.519632275 -10.048186250
85 -3.698480632 -0.519632275
86 2.967681848 -3.698480632
87 -6.895632598 2.967681848
88 3.526439139 -6.895632598
89 5.090295955 3.526439139
90 2.747516745 5.090295955
91 -1.182299594 2.747516745
> 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/78fum1262013699.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8gsmh1262013699.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/95fd41262013699.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10jllv1262013699.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11p2er1262013699.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/12yxls1262013699.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/134srf1262013699.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/14sgpk1262013699.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/159wha1262013699.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/16gak41262013699.tab")
+ }
>
> try(system("convert tmp/1oa5m1262013698.ps tmp/1oa5m1262013698.png",intern=TRUE))
character(0)
> try(system("convert tmp/2tiea1262013698.ps tmp/2tiea1262013698.png",intern=TRUE))
character(0)
> try(system("convert tmp/3i6221262013698.ps tmp/3i6221262013698.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ocl11262013698.ps tmp/4ocl11262013698.png",intern=TRUE))
character(0)
> try(system("convert tmp/54swh1262013698.ps tmp/54swh1262013698.png",intern=TRUE))
character(0)
> try(system("convert tmp/6066m1262013698.ps tmp/6066m1262013698.png",intern=TRUE))
character(0)
> try(system("convert tmp/78fum1262013699.ps tmp/78fum1262013699.png",intern=TRUE))
character(0)
> try(system("convert tmp/8gsmh1262013699.ps tmp/8gsmh1262013699.png",intern=TRUE))
character(0)
> try(system("convert tmp/95fd41262013699.ps tmp/95fd41262013699.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jllv1262013699.ps tmp/10jllv1262013699.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.868 1.572 4.755