R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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.
Natural language support but running in an English locale
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(9
+ ,26
+ ,24
+ ,14
+ ,11
+ ,12
+ ,24
+ ,9
+ ,23
+ ,25
+ ,11
+ ,7
+ ,8
+ ,25
+ ,9
+ ,25
+ ,17
+ ,6
+ ,17
+ ,8
+ ,30
+ ,9
+ ,23
+ ,18
+ ,12
+ ,10
+ ,8
+ ,19
+ ,9
+ ,19
+ ,18
+ ,8
+ ,12
+ ,9
+ ,22
+ ,9
+ ,29
+ ,16
+ ,10
+ ,12
+ ,7
+ ,22
+ ,10
+ ,25
+ ,20
+ ,10
+ ,11
+ ,4
+ ,25
+ ,10
+ ,21
+ ,16
+ ,11
+ ,11
+ ,11
+ ,23
+ ,10
+ ,22
+ ,18
+ ,16
+ ,12
+ ,7
+ ,17
+ ,10
+ ,25
+ ,17
+ ,11
+ ,13
+ ,7
+ ,21
+ ,10
+ ,24
+ ,23
+ ,13
+ ,14
+ ,12
+ ,19
+ ,10
+ ,18
+ ,30
+ ,12
+ ,16
+ ,10
+ ,19
+ ,10
+ ,22
+ ,23
+ ,8
+ ,11
+ ,10
+ ,15
+ ,10
+ ,15
+ ,18
+ ,12
+ ,10
+ ,8
+ ,16
+ ,10
+ ,22
+ ,15
+ ,11
+ ,11
+ ,8
+ ,23
+ ,10
+ ,28
+ ,12
+ ,4
+ ,15
+ ,4
+ ,27
+ ,10
+ ,20
+ ,21
+ ,9
+ ,9
+ ,9
+ ,22
+ ,10
+ ,12
+ ,15
+ ,8
+ ,11
+ ,8
+ ,14
+ ,10
+ ,24
+ ,20
+ ,8
+ ,17
+ ,7
+ ,22
+ ,10
+ ,20
+ ,31
+ ,14
+ ,17
+ ,11
+ ,23
+ ,10
+ ,21
+ ,27
+ ,15
+ ,11
+ ,9
+ ,23
+ ,10
+ ,20
+ ,34
+ ,16
+ ,18
+ ,11
+ ,21
+ ,10
+ ,21
+ ,21
+ ,9
+ ,14
+ ,13
+ ,19
+ ,10
+ ,23
+ ,31
+ ,14
+ ,10
+ ,8
+ ,18
+ ,10
+ ,28
+ ,19
+ ,11
+ ,11
+ ,8
+ ,20
+ ,10
+ ,24
+ ,16
+ ,8
+ ,15
+ ,9
+ ,23
+ ,10
+ ,24
+ ,20
+ ,9
+ ,15
+ ,6
+ ,25
+ ,10
+ ,24
+ ,21
+ ,9
+ ,13
+ ,9
+ ,19
+ ,10
+ ,23
+ ,22
+ ,9
+ ,16
+ ,9
+ ,24
+ ,10
+ ,23
+ ,17
+ ,9
+ ,13
+ ,6
+ ,22
+ ,10
+ ,29
+ ,24
+ ,10
+ ,9
+ ,6
+ ,25
+ ,10
+ ,24
+ ,25
+ ,16
+ ,18
+ ,16
+ ,26
+ ,10
+ ,18
+ ,26
+ ,11
+ ,18
+ ,5
+ ,29
+ ,10
+ ,25
+ ,25
+ ,8
+ ,12
+ ,7
+ ,32
+ ,10
+ ,21
+ ,17
+ ,9
+ ,17
+ ,9
+ ,25
+ ,10
+ ,26
+ ,32
+ ,16
+ ,9
+ ,6
+ ,29
+ ,10
+ ,22
+ ,33
+ ,11
+ ,9
+ ,6
+ ,28
+ ,10
+ ,22
+ ,13
+ ,16
+ ,12
+ ,5
+ ,17
+ ,10
+ ,22
+ ,32
+ ,12
+ ,18
+ ,12
+ ,28
+ ,10
+ ,23
+ ,25
+ ,12
+ ,12
+ ,7
+ ,29
+ ,10
+ ,30
+ ,29
+ ,14
+ ,18
+ ,10
+ ,26
+ ,10
+ ,23
+ ,22
+ ,9
+ ,14
+ ,9
+ ,25
+ ,10
+ ,17
+ ,18
+ ,10
+ ,15
+ ,8
+ ,14
+ ,10
+ ,23
+ ,17
+ ,9
+ ,16
+ ,5
+ ,25
+ ,10
+ ,23
+ ,20
+ ,10
+ ,10
+ ,8
+ ,26
+ ,10
+ ,25
+ ,15
+ ,12
+ ,11
+ ,8
+ ,20
+ ,10
+ ,24
+ ,20
+ ,14
+ ,14
+ ,10
+ ,18
+ ,10
+ ,24
+ ,33
+ ,14
+ ,9
+ ,6
+ ,32
+ ,10
+ ,23
+ ,29
+ ,10
+ ,12
+ ,8
+ ,25
+ ,10
+ ,21
+ ,23
+ ,14
+ ,17
+ ,7
+ ,25
+ ,10
+ ,24
+ ,26
+ ,16
+ ,5
+ ,4
+ ,23
+ ,10
+ ,24
+ ,18
+ ,9
+ ,12
+ ,8
+ ,21
+ ,10
+ ,28
+ ,20
+ ,10
+ ,12
+ ,8
+ ,20
+ ,10
+ ,16
+ ,11
+ ,6
+ ,6
+ ,4
+ ,15
+ ,10
+ ,20
+ ,28
+ ,8
+ ,24
+ ,20
+ ,30
+ ,10
+ ,29
+ ,26
+ ,13
+ ,12
+ ,8
+ ,24
+ ,10
+ ,27
+ ,22
+ ,10
+ ,12
+ ,8
+ ,26
+ ,10
+ ,22
+ ,17
+ ,8
+ ,14
+ ,6
+ ,24
+ ,10
+ ,28
+ ,12
+ ,7
+ ,7
+ ,4
+ ,22
+ ,10
+ ,16
+ ,14
+ ,15
+ ,13
+ ,8
+ ,14
+ ,10
+ ,25
+ ,17
+ ,9
+ ,12
+ ,9
+ ,24
+ ,10
+ ,24
+ ,21
+ ,10
+ ,13
+ ,6
+ ,24
+ ,10
+ ,28
+ ,19
+ ,12
+ ,14
+ ,7
+ ,24
+ ,10
+ ,24
+ ,18
+ ,13
+ ,8
+ ,9
+ ,24
+ ,10
+ ,23
+ ,10
+ ,10
+ ,11
+ ,5
+ ,19
+ ,10
+ ,30
+ ,29
+ ,11
+ ,9
+ ,5
+ ,31
+ ,10
+ ,24
+ ,31
+ ,8
+ ,11
+ ,8
+ ,22
+ ,10
+ ,21
+ ,19
+ ,9
+ ,13
+ ,8
+ ,27
+ ,10
+ ,25
+ ,9
+ ,13
+ ,10
+ ,6
+ ,19
+ ,10
+ ,25
+ ,20
+ ,11
+ ,11
+ ,8
+ ,25
+ ,10
+ ,22
+ ,28
+ ,8
+ ,12
+ ,7
+ ,20
+ ,10
+ ,23
+ ,19
+ ,9
+ ,9
+ ,7
+ ,21
+ ,10
+ ,26
+ ,30
+ ,9
+ ,15
+ ,9
+ ,27
+ ,10
+ ,23
+ ,29
+ ,15
+ ,18
+ ,11
+ ,23
+ ,10
+ ,25
+ ,26
+ ,9
+ ,15
+ ,6
+ ,25
+ ,10
+ ,21
+ ,23
+ ,10
+ ,12
+ ,8
+ ,20
+ ,10
+ ,25
+ ,13
+ ,14
+ ,13
+ ,6
+ ,21
+ ,10
+ ,24
+ ,21
+ ,12
+ ,14
+ ,9
+ ,22
+ ,10
+ ,29
+ ,19
+ ,12
+ ,10
+ ,8
+ ,23
+ ,10
+ ,22
+ ,28
+ ,11
+ ,13
+ ,6
+ ,25
+ ,10
+ ,27
+ ,23
+ ,14
+ ,13
+ ,10
+ ,25
+ ,10
+ ,26
+ ,18
+ ,6
+ ,11
+ ,8
+ ,17
+ ,10
+ ,22
+ ,21
+ ,12
+ ,13
+ ,8
+ ,19
+ ,10
+ ,24
+ ,20
+ ,8
+ ,16
+ ,10
+ ,25
+ ,10
+ ,27
+ ,23
+ ,14
+ ,8
+ ,5
+ ,19
+ ,10
+ ,24
+ ,21
+ ,11
+ ,16
+ ,7
+ ,20
+ ,10
+ ,24
+ ,21
+ ,10
+ ,11
+ ,5
+ ,26
+ ,10
+ ,29
+ ,15
+ ,14
+ ,9
+ ,8
+ ,23
+ ,10
+ ,22
+ ,28
+ ,12
+ ,16
+ ,14
+ ,27
+ ,10
+ ,21
+ ,19
+ ,10
+ ,12
+ ,7
+ ,17
+ ,10
+ ,24
+ ,26
+ ,14
+ ,14
+ ,8
+ ,17
+ ,10
+ ,24
+ ,10
+ ,5
+ ,8
+ ,6
+ ,19
+ ,10
+ ,23
+ ,16
+ ,11
+ ,9
+ ,5
+ ,17
+ ,10
+ ,20
+ ,22
+ ,10
+ ,15
+ ,6
+ ,22
+ ,10
+ ,27
+ ,19
+ ,9
+ ,11
+ ,10
+ ,21
+ ,10
+ ,26
+ ,31
+ ,10
+ ,21
+ ,12
+ ,32
+ ,10
+ ,25
+ ,31
+ ,16
+ ,14
+ ,9
+ ,21
+ ,10
+ ,21
+ ,29
+ ,13
+ ,18
+ ,12
+ ,21
+ ,10
+ ,21
+ ,19
+ ,9
+ ,12
+ ,7
+ ,18
+ ,10
+ ,19
+ ,22
+ ,10
+ ,13
+ ,8
+ ,18
+ ,10
+ ,21
+ ,23
+ ,10
+ ,15
+ ,10
+ ,23
+ ,10
+ ,21
+ ,15
+ ,7
+ ,12
+ ,6
+ ,19
+ ,10
+ ,16
+ ,20
+ ,9
+ ,19
+ ,10
+ ,20
+ ,10
+ ,22
+ ,18
+ ,8
+ ,15
+ ,10
+ ,21
+ ,10
+ ,29
+ ,23
+ ,14
+ ,11
+ ,10
+ ,20
+ ,10
+ ,15
+ ,25
+ ,14
+ ,11
+ ,5
+ ,17
+ ,10
+ ,17
+ ,21
+ ,8
+ ,10
+ ,7
+ ,18
+ ,10
+ ,15
+ ,24
+ ,9
+ ,13
+ ,10
+ ,19
+ ,10
+ ,21
+ ,25
+ ,14
+ ,15
+ ,11
+ ,22
+ ,10
+ ,21
+ ,17
+ ,14
+ ,12
+ ,6
+ ,15
+ ,10
+ ,19
+ ,13
+ ,8
+ ,12
+ ,7
+ ,14
+ ,10
+ ,24
+ ,28
+ ,8
+ ,16
+ ,12
+ ,18
+ ,10
+ ,20
+ ,21
+ ,8
+ ,9
+ ,11
+ ,24
+ ,10
+ ,17
+ ,25
+ ,7
+ ,18
+ ,11
+ ,35
+ ,10
+ ,23
+ ,9
+ ,6
+ ,8
+ ,11
+ ,29
+ ,10
+ ,24
+ ,16
+ ,8
+ ,13
+ ,5
+ ,21
+ ,10
+ ,14
+ ,19
+ ,6
+ ,17
+ ,8
+ ,25
+ ,10
+ ,19
+ ,17
+ ,11
+ ,9
+ ,6
+ ,20
+ ,10
+ ,24
+ ,25
+ ,14
+ ,15
+ ,9
+ ,22
+ ,10
+ ,13
+ ,20
+ ,11
+ ,8
+ ,4
+ ,13
+ ,10
+ ,22
+ ,29
+ ,11
+ ,7
+ ,4
+ ,26
+ ,10
+ ,16
+ ,14
+ ,11
+ ,12
+ ,7
+ ,17
+ ,10
+ ,19
+ ,22
+ ,14
+ ,14
+ ,11
+ ,25
+ ,10
+ ,25
+ ,15
+ ,8
+ ,6
+ ,6
+ ,20
+ ,10
+ ,25
+ ,19
+ ,20
+ ,8
+ ,7
+ ,19
+ ,10
+ ,23
+ ,20
+ ,11
+ ,17
+ ,8
+ ,21
+ ,10
+ ,24
+ ,15
+ ,8
+ ,10
+ ,4
+ ,22
+ ,10
+ ,26
+ ,20
+ ,11
+ ,11
+ ,8
+ ,24
+ ,10
+ ,26
+ ,18
+ ,10
+ ,14
+ ,9
+ ,21
+ ,10
+ ,25
+ ,33
+ ,14
+ ,11
+ ,8
+ ,26
+ ,10
+ ,18
+ ,22
+ ,11
+ ,13
+ ,11
+ ,24
+ ,10
+ ,21
+ ,16
+ ,9
+ ,12
+ ,8
+ ,16
+ ,10
+ ,26
+ ,17
+ ,9
+ ,11
+ ,5
+ ,23
+ ,10
+ ,23
+ ,16
+ ,8
+ ,9
+ ,4
+ ,18
+ ,10
+ ,23
+ ,21
+ ,10
+ ,12
+ ,8
+ ,16
+ ,10
+ ,22
+ ,26
+ ,13
+ ,20
+ ,10
+ ,26
+ ,10
+ ,20
+ ,18
+ ,13
+ ,12
+ ,6
+ ,19
+ ,10
+ ,13
+ ,18
+ ,12
+ ,13
+ ,9
+ ,21
+ ,10
+ ,24
+ ,17
+ ,8
+ ,12
+ ,9
+ ,21
+ ,10
+ ,15
+ ,22
+ ,13
+ ,12
+ ,13
+ ,22
+ ,10
+ ,14
+ ,30
+ ,14
+ ,9
+ ,9
+ ,23
+ ,10
+ ,22
+ ,30
+ ,12
+ ,15
+ ,10
+ ,29
+ ,10
+ ,10
+ ,24
+ ,14
+ ,24
+ ,20
+ ,21
+ ,10
+ ,24
+ ,21
+ ,15
+ ,7
+ ,5
+ ,21
+ ,10
+ ,22
+ ,21
+ ,13
+ ,17
+ ,11
+ ,23
+ ,10
+ ,24
+ ,29
+ ,16
+ ,11
+ ,6
+ ,27
+ ,10
+ ,19
+ ,31
+ ,9
+ ,17
+ ,9
+ ,25
+ ,10
+ ,20
+ ,20
+ ,9
+ ,11
+ ,7
+ ,21
+ ,10
+ ,13
+ ,16
+ ,9
+ ,12
+ ,9
+ ,10
+ ,10
+ ,20
+ ,22
+ ,8
+ ,14
+ ,10
+ ,20
+ ,10
+ ,22
+ ,20
+ ,7
+ ,11
+ ,9
+ ,26
+ ,10
+ ,24
+ ,28
+ ,16
+ ,16
+ ,8
+ ,24
+ ,10
+ ,29
+ ,38
+ ,11
+ ,21
+ ,7
+ ,29
+ ,10
+ ,12
+ ,22
+ ,9
+ ,14
+ ,6
+ ,19
+ ,10
+ ,20
+ ,20
+ ,11
+ ,20
+ ,13
+ ,24
+ ,10
+ ,21
+ ,17
+ ,9
+ ,13
+ ,6
+ ,19
+ ,10
+ ,24
+ ,28
+ ,14
+ ,11
+ ,8
+ ,24
+ ,10
+ ,22
+ ,22
+ ,13
+ ,15
+ ,10
+ ,22
+ ,10
+ ,20
+ ,31
+ ,16
+ ,19
+ ,16
+ ,17)
+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('Month'
+ ,'O'
+ ,'CM'
+ ,'D'
+ ,'PE'
+ ,'PC'
+ ,'PS')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('Month','O','CM','D','PE','PC','PS'),1:159))
> 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 = 'Do not include Seasonal Dummies'
> par1 = '2'
> #'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
O Month CM D PE PC PS t
1 26 9 24 14 11 12 24 1
2 23 9 25 11 7 8 25 2
3 25 9 17 6 17 8 30 3
4 23 9 18 12 10 8 19 4
5 19 9 18 8 12 9 22 5
6 29 9 16 10 12 7 22 6
7 25 10 20 10 11 4 25 7
8 21 10 16 11 11 11 23 8
9 22 10 18 16 12 7 17 9
10 25 10 17 11 13 7 21 10
11 24 10 23 13 14 12 19 11
12 18 10 30 12 16 10 19 12
13 22 10 23 8 11 10 15 13
14 15 10 18 12 10 8 16 14
15 22 10 15 11 11 8 23 15
16 28 10 12 4 15 4 27 16
17 20 10 21 9 9 9 22 17
18 12 10 15 8 11 8 14 18
19 24 10 20 8 17 7 22 19
20 20 10 31 14 17 11 23 20
21 21 10 27 15 11 9 23 21
22 20 10 34 16 18 11 21 22
23 21 10 21 9 14 13 19 23
24 23 10 31 14 10 8 18 24
25 28 10 19 11 11 8 20 25
26 24 10 16 8 15 9 23 26
27 24 10 20 9 15 6 25 27
28 24 10 21 9 13 9 19 28
29 23 10 22 9 16 9 24 29
30 23 10 17 9 13 6 22 30
31 29 10 24 10 9 6 25 31
32 24 10 25 16 18 16 26 32
33 18 10 26 11 18 5 29 33
34 25 10 25 8 12 7 32 34
35 21 10 17 9 17 9 25 35
36 26 10 32 16 9 6 29 36
37 22 10 33 11 9 6 28 37
38 22 10 13 16 12 5 17 38
39 22 10 32 12 18 12 28 39
40 23 10 25 12 12 7 29 40
41 30 10 29 14 18 10 26 41
42 23 10 22 9 14 9 25 42
43 17 10 18 10 15 8 14 43
44 23 10 17 9 16 5 25 44
45 23 10 20 10 10 8 26 45
46 25 10 15 12 11 8 20 46
47 24 10 20 14 14 10 18 47
48 24 10 33 14 9 6 32 48
49 23 10 29 10 12 8 25 49
50 21 10 23 14 17 7 25 50
51 24 10 26 16 5 4 23 51
52 24 10 18 9 12 8 21 52
53 28 10 20 10 12 8 20 53
54 16 10 11 6 6 4 15 54
55 20 10 28 8 24 20 30 55
56 29 10 26 13 12 8 24 56
57 27 10 22 10 12 8 26 57
58 22 10 17 8 14 6 24 58
59 28 10 12 7 7 4 22 59
60 16 10 14 15 13 8 14 60
61 25 10 17 9 12 9 24 61
62 24 10 21 10 13 6 24 62
63 28 10 19 12 14 7 24 63
64 24 10 18 13 8 9 24 64
65 23 10 10 10 11 5 19 65
66 30 10 29 11 9 5 31 66
67 24 10 31 8 11 8 22 67
68 21 10 19 9 13 8 27 68
69 25 10 9 13 10 6 19 69
70 25 10 20 11 11 8 25 70
71 22 10 28 8 12 7 20 71
72 23 10 19 9 9 7 21 72
73 26 10 30 9 15 9 27 73
74 23 10 29 15 18 11 23 74
75 25 10 26 9 15 6 25 75
76 21 10 23 10 12 8 20 76
77 25 10 13 14 13 6 21 77
78 24 10 21 12 14 9 22 78
79 29 10 19 12 10 8 23 79
80 22 10 28 11 13 6 25 80
81 27 10 23 14 13 10 25 81
82 26 10 18 6 11 8 17 82
83 22 10 21 12 13 8 19 83
84 24 10 20 8 16 10 25 84
85 27 10 23 14 8 5 19 85
86 24 10 21 11 16 7 20 86
87 24 10 21 10 11 5 26 87
88 29 10 15 14 9 8 23 88
89 22 10 28 12 16 14 27 89
90 21 10 19 10 12 7 17 90
91 24 10 26 14 14 8 17 91
92 24 10 10 5 8 6 19 92
93 23 10 16 11 9 5 17 93
94 20 10 22 10 15 6 22 94
95 27 10 19 9 11 10 21 95
96 26 10 31 10 21 12 32 96
97 25 10 31 16 14 9 21 97
98 21 10 29 13 18 12 21 98
99 21 10 19 9 12 7 18 99
100 19 10 22 10 13 8 18 100
101 21 10 23 10 15 10 23 101
102 21 10 15 7 12 6 19 102
103 16 10 20 9 19 10 20 103
104 22 10 18 8 15 10 21 104
105 29 10 23 14 11 10 20 105
106 15 10 25 14 11 5 17 106
107 17 10 21 8 10 7 18 107
108 15 10 24 9 13 10 19 108
109 21 10 25 14 15 11 22 109
110 21 10 17 14 12 6 15 110
111 19 10 13 8 12 7 14 111
112 24 10 28 8 16 12 18 112
113 20 10 21 8 9 11 24 113
114 17 10 25 7 18 11 35 114
115 23 10 9 6 8 11 29 115
116 24 10 16 8 13 5 21 116
117 14 10 19 6 17 8 25 117
118 19 10 17 11 9 6 20 118
119 24 10 25 14 15 9 22 119
120 13 10 20 11 8 4 13 120
121 22 10 29 11 7 4 26 121
122 16 10 14 11 12 7 17 122
123 19 10 22 14 14 11 25 123
124 25 10 15 8 6 6 20 124
125 25 10 19 20 8 7 19 125
126 23 10 20 11 17 8 21 126
127 24 10 15 8 10 4 22 127
128 26 10 20 11 11 8 24 128
129 26 10 18 10 14 9 21 129
130 25 10 33 14 11 8 26 130
131 18 10 22 11 13 11 24 131
132 21 10 16 9 12 8 16 132
133 26 10 17 9 11 5 23 133
134 23 10 16 8 9 4 18 134
135 23 10 21 10 12 8 16 135
136 22 10 26 13 20 10 26 136
137 20 10 18 13 12 6 19 137
138 13 10 18 12 13 9 21 138
139 24 10 17 8 12 9 21 139
140 15 10 22 13 12 13 22 140
141 14 10 30 14 9 9 23 141
142 22 10 30 12 15 10 29 142
143 10 10 24 14 24 20 21 143
144 24 10 21 15 7 5 21 144
145 22 10 21 13 17 11 23 145
146 24 10 29 16 11 6 27 146
147 19 10 31 9 17 9 25 147
148 20 10 20 9 11 7 21 148
149 13 10 16 9 12 9 10 149
150 20 10 22 8 14 10 20 150
151 22 10 20 7 11 9 26 151
152 24 10 28 16 16 8 24 152
153 29 10 38 11 21 7 29 153
154 12 10 22 9 14 6 19 154
155 20 10 20 11 20 13 24 155
156 21 10 17 9 13 6 19 156
157 24 10 28 14 11 8 24 157
158 22 10 22 13 15 10 22 158
159 20 10 31 16 19 16 17 159
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Month CM D PE PC
18.26518 -0.08082 -0.05915 0.21692 -0.13256 -0.25400
PS t
0.39567 -0.01477
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.2587 -1.9245 0.2826 2.1639 7.5018
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.265180 15.349762 1.190 0.2359
Month -0.080824 1.543240 -0.052 0.9583
CM -0.059154 0.062410 -0.948 0.3447
D 0.216924 0.111196 1.951 0.0529 .
PE -0.132556 0.103796 -1.277 0.2035
PC -0.254001 0.129774 -1.957 0.0522 .
PS 0.395674 0.075665 5.229 5.58e-07 ***
t -0.014766 0.006382 -2.314 0.0220 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.454 on 151 degrees of freedom
Multiple R-squared: 0.2523, Adjusted R-squared: 0.2176
F-statistic: 7.278 on 7 and 151 DF, p-value: 1.633e-07
> 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,] 0.388811921 0.777623841 0.6111881
[2,] 0.327160951 0.654321901 0.6728390
[3,] 0.605495953 0.789008095 0.3945040
[4,] 0.798945740 0.402108520 0.2010543
[5,] 0.735893075 0.528213850 0.2641069
[6,] 0.728799730 0.542400540 0.2712003
[7,] 0.646254838 0.707490324 0.3537452
[8,] 0.770551049 0.458897903 0.2294490
[9,] 0.712553678 0.574892645 0.2874463
[10,] 0.658537074 0.682925853 0.3414629
[11,] 0.590212787 0.819574426 0.4097872
[12,] 0.514603674 0.970792652 0.4853963
[13,] 0.501911471 0.996177058 0.4980885
[14,] 0.541661035 0.916677930 0.4583390
[15,] 0.675128752 0.649742495 0.3248712
[16,] 0.608206676 0.783586648 0.3917933
[17,] 0.549677876 0.900644248 0.4503221
[18,] 0.513206161 0.973587678 0.4867938
[19,] 0.451042831 0.902085661 0.5489572
[20,] 0.394921653 0.789843306 0.6050783
[21,] 0.391136589 0.782273177 0.6088634
[22,] 0.330310656 0.660621311 0.6696893
[23,] 0.621934224 0.756131552 0.3780658
[24,] 0.577676871 0.844646258 0.4223231
[25,] 0.553222368 0.893555264 0.4467776
[26,] 0.497071700 0.994143400 0.5029283
[27,] 0.483014894 0.966029788 0.5169851
[28,] 0.431904453 0.863808906 0.5680955
[29,] 0.382554842 0.765109683 0.6174452
[30,] 0.359526273 0.719052547 0.6404737
[31,] 0.509702399 0.980595201 0.4902976
[32,] 0.455954760 0.911909519 0.5440452
[33,] 0.437020250 0.874040501 0.5629797
[34,] 0.390414503 0.780829006 0.6095855
[35,] 0.350904247 0.701808494 0.6490958
[36,] 0.320169561 0.640339122 0.6798304
[37,] 0.290611801 0.581223601 0.7093882
[38,] 0.287170565 0.574341130 0.7128294
[39,] 0.248734757 0.497469514 0.7512652
[40,] 0.252832407 0.505664814 0.7471676
[41,] 0.232068095 0.464136190 0.7679319
[42,] 0.199720959 0.399441918 0.8002790
[43,] 0.258190266 0.516380532 0.7418097
[44,] 0.344858192 0.689716384 0.6551418
[45,] 0.330972549 0.661945099 0.6690275
[46,] 0.383485769 0.766971537 0.6165142
[47,] 0.357941919 0.715883839 0.6420581
[48,] 0.329596310 0.659192620 0.6704037
[49,] 0.328065181 0.656130361 0.6719348
[50,] 0.417917511 0.835835023 0.5820825
[51,] 0.373373721 0.746747442 0.6266263
[52,] 0.330689568 0.661379137 0.6693104
[53,] 0.328468945 0.656937889 0.6715311
[54,] 0.294505633 0.589011267 0.7054944
[55,] 0.256216239 0.512432478 0.7437838
[56,] 0.238331788 0.476663577 0.7616682
[57,] 0.207879658 0.415759316 0.7921203
[58,] 0.231608170 0.463216340 0.7683918
[59,] 0.198505794 0.397011588 0.8014942
[60,] 0.166937051 0.333874102 0.8330629
[61,] 0.138905493 0.277810985 0.8610945
[62,] 0.115186507 0.230373015 0.8848135
[63,] 0.098653574 0.197307149 0.9013464
[64,] 0.079644636 0.159289272 0.9203554
[65,] 0.063938468 0.127876936 0.9360615
[66,] 0.053774558 0.107549116 0.9462254
[67,] 0.042184184 0.084368369 0.9578158
[68,] 0.032571494 0.065142987 0.9674285
[69,] 0.036777262 0.073554524 0.9632227
[70,] 0.034359477 0.068718953 0.9656405
[71,] 0.028479770 0.056959539 0.9715202
[72,] 0.036990750 0.073981500 0.9630093
[73,] 0.028914791 0.057829581 0.9710852
[74,] 0.022521125 0.045042250 0.9774789
[75,] 0.020172501 0.040345002 0.9798275
[76,] 0.015670463 0.031340927 0.9843295
[77,] 0.012816537 0.025633075 0.9871835
[78,] 0.013111807 0.026223614 0.9868882
[79,] 0.011096811 0.022193621 0.9889032
[80,] 0.008435633 0.016871265 0.9915644
[81,] 0.007009882 0.014019765 0.9929901
[82,] 0.005684286 0.011368571 0.9943157
[83,] 0.004172386 0.008344771 0.9958276
[84,] 0.004107159 0.008214318 0.9958928
[85,] 0.006095033 0.012190066 0.9939050
[86,] 0.004923541 0.009847082 0.9950765
[87,] 0.004132592 0.008265184 0.9958674
[88,] 0.003183520 0.006367039 0.9968165
[89,] 0.002416003 0.004832006 0.9975840
[90,] 0.002045477 0.004090953 0.9979545
[91,] 0.001621383 0.003242767 0.9983786
[92,] 0.001194825 0.002389650 0.9988052
[93,] 0.001514341 0.003028683 0.9984857
[94,] 0.001178049 0.002356098 0.9988220
[95,] 0.005144788 0.010289576 0.9948552
[96,] 0.012975228 0.025950456 0.9870248
[97,] 0.013762186 0.027524372 0.9862378
[98,] 0.018845327 0.037690654 0.9811547
[99,] 0.014686960 0.029373920 0.9853130
[100,] 0.010639088 0.021278176 0.9893609
[101,] 0.007680369 0.015360738 0.9923196
[102,] 0.022127754 0.044255508 0.9778722
[103,] 0.022118259 0.044236518 0.9778817
[104,] 0.055328486 0.110656972 0.9446715
[105,] 0.047136392 0.094272783 0.9528636
[106,] 0.039427480 0.078854960 0.9605725
[107,] 0.103765893 0.207531787 0.8962341
[108,] 0.097924799 0.195849599 0.9020752
[109,] 0.087245832 0.174491663 0.9127542
[110,] 0.152343613 0.304687227 0.8476564
[111,] 0.149510112 0.299020225 0.8504899
[112,] 0.189821231 0.379642463 0.8101788
[113,] 0.189201762 0.378403525 0.8107982
[114,] 0.179172697 0.358345394 0.8208273
[115,] 0.155186382 0.310372764 0.8448136
[116,] 0.124362819 0.248725638 0.8756372
[117,] 0.097319045 0.194638090 0.9026810
[118,] 0.094740436 0.189480872 0.9052596
[119,] 0.132633761 0.265267522 0.8673662
[120,] 0.114656101 0.229312202 0.8853439
[121,] 0.096107012 0.192214025 0.9038930
[122,] 0.085267336 0.170534672 0.9147327
[123,] 0.083324994 0.166649988 0.9166750
[124,] 0.074095553 0.148191106 0.9259044
[125,] 0.173101014 0.346202027 0.8268990
[126,] 0.138030968 0.276061936 0.8619690
[127,] 0.115666778 0.231333557 0.8843332
[128,] 0.163171769 0.326343537 0.8368282
[129,] 0.394903450 0.789806901 0.6050965
[130,] 0.340483059 0.680966118 0.6595169
[131,] 0.484493738 0.968987476 0.5155063
[132,] 0.393268068 0.786536136 0.6067319
[133,] 0.556428664 0.887142671 0.4435713
[134,] 0.502585613 0.994828774 0.4974144
[135,] 0.405643386 0.811286772 0.5943566
[136,] 0.300847635 0.601695271 0.6991524
[137,] 0.292262080 0.584524160 0.7077379
[138,] 0.174421109 0.348842218 0.8255789
> postscript(file="/var/www/html/freestat/rcomp/tmp/1kdfg1290521263.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2dne11290521263.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3dne11290521263.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4dne11290521263.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5dne11290521263.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 = 159
Frequency = 1
1 2 3 4 5 6
1.86971023 -2.34750045 -0.37415319 -0.17725791 -3.96270253 4.99190386
7 8 9 10 11 12
-0.75747263 -2.62688953 -1.08784218 1.50225196 2.63200470 -2.96511662
13 14 15 16 17 18
2.42318440 -6.76175059 -2.34468471 2.94261336 -3.14181607 -8.08854603
19 20 21 22 23 24
1.59793335 -2.41782127 -3.15993564 -1.72077134 0.81258936 0.92971643
25 26 27 28 29 30
5.22661518 1.31189628 -0.20699806 2.73785843 0.23107668 -0.41825190
31 32 33 34 35 36
4.07642117 1.18613970 -7.63635500 -1.50432626 -2.23921474 -1.26075882
37 38 39 40 41 42
-3.70654224 -1.46339731 -1.23607472 -3.09640498 6.46549195 -0.23775005
43 44 45 46 47 48
-2.44555468 -1.25488114 -1.70858442 2.08316328 2.65687027 -3.77758454
49 50 51 52 53 54
-0.45634686 -3.25542336 -2.05837402 1.73687772 6.04870162 -5.43419298
55 56 57 58 59 60
-0.33273527 5.21445462 2.85202968 -1.44666772 3.84470477 -4.78088130
61 62 63 64 65 66
1.87759791 0.28260796 4.13177460 -0.41687218 0.13546847 3.04403574
67 68 69 70 71 72
2.41606670 -3.20919850 1.60605074 0.97187475 0.96757208 0.43968455
73 74 75 76 77 78
3.03444057 1.17687356 1.85670073 -0.43421512 1.35019195 1.77092563
79 80 81 82 83 84
5.48748346 -1.65012176 3.43410611 6.28077532 0.64522107 2.00015762
85 86 87 88 89 90
3.93442757 2.65443702 -0.65870062 4.81735732 -0.09581984 0.46891546
91 92 93 94 95 96
3.54917592 2.47510917 1.21315596 -2.12926113 5.80642206 2.79526190
97 98 99 100 101 102
3.17099906 1.01045924 0.42306102 -1.21507758 -0.34641282 0.01491663
103 104 105 106 107 108
-3.56017124 1.62731239 7.50175146 -6.44815875 -3.38868970 -4.64938733
109 110 111 112 113 114
-0.32799800 0.31557903 0.04495112 6.16456327 -1.79068827 -7.48179089
115 116 117 118 119 120
-1.14808252 2.15107874 -7.51331164 -3.29155758 2.31166063 -6.95540331
121 122 123 124 125 126
-2.68457012 -4.57126302 -3.61831280 2.93183592 1.49491264 2.17681175
127 128 129 130 131 132
1.20700815 3.22398461 5.17605928 1.58039810 -3.58629244 1.77823080
133 134 135 136 137 138
3.18787205 1.81966536 3.90137505 0.17284929 -1.59235324 -8.25745061
139 140 141 142 143 144
3.43330311 -5.72045143 -8.25872532 -1.13481642 -7.01041158 0.54649249
145 146 147 148 149 150
1.05332929 -0.75748572 -1.75725091 -1.11382285 -3.34269897 0.80628870
151 152 153 154 155 156
-0.10604334 1.62976295 6.75110104 -7.97190253 0.08568264 0.62930325
157 158 159
1.47466161 1.18100347 3.10998615
> postscript(file="/var/www/html/freestat/rcomp/tmp/66ed41290521263.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 1.86971023 NA
1 -2.34750045 1.86971023
2 -0.37415319 -2.34750045
3 -0.17725791 -0.37415319
4 -3.96270253 -0.17725791
5 4.99190386 -3.96270253
6 -0.75747263 4.99190386
7 -2.62688953 -0.75747263
8 -1.08784218 -2.62688953
9 1.50225196 -1.08784218
10 2.63200470 1.50225196
11 -2.96511662 2.63200470
12 2.42318440 -2.96511662
13 -6.76175059 2.42318440
14 -2.34468471 -6.76175059
15 2.94261336 -2.34468471
16 -3.14181607 2.94261336
17 -8.08854603 -3.14181607
18 1.59793335 -8.08854603
19 -2.41782127 1.59793335
20 -3.15993564 -2.41782127
21 -1.72077134 -3.15993564
22 0.81258936 -1.72077134
23 0.92971643 0.81258936
24 5.22661518 0.92971643
25 1.31189628 5.22661518
26 -0.20699806 1.31189628
27 2.73785843 -0.20699806
28 0.23107668 2.73785843
29 -0.41825190 0.23107668
30 4.07642117 -0.41825190
31 1.18613970 4.07642117
32 -7.63635500 1.18613970
33 -1.50432626 -7.63635500
34 -2.23921474 -1.50432626
35 -1.26075882 -2.23921474
36 -3.70654224 -1.26075882
37 -1.46339731 -3.70654224
38 -1.23607472 -1.46339731
39 -3.09640498 -1.23607472
40 6.46549195 -3.09640498
41 -0.23775005 6.46549195
42 -2.44555468 -0.23775005
43 -1.25488114 -2.44555468
44 -1.70858442 -1.25488114
45 2.08316328 -1.70858442
46 2.65687027 2.08316328
47 -3.77758454 2.65687027
48 -0.45634686 -3.77758454
49 -3.25542336 -0.45634686
50 -2.05837402 -3.25542336
51 1.73687772 -2.05837402
52 6.04870162 1.73687772
53 -5.43419298 6.04870162
54 -0.33273527 -5.43419298
55 5.21445462 -0.33273527
56 2.85202968 5.21445462
57 -1.44666772 2.85202968
58 3.84470477 -1.44666772
59 -4.78088130 3.84470477
60 1.87759791 -4.78088130
61 0.28260796 1.87759791
62 4.13177460 0.28260796
63 -0.41687218 4.13177460
64 0.13546847 -0.41687218
65 3.04403574 0.13546847
66 2.41606670 3.04403574
67 -3.20919850 2.41606670
68 1.60605074 -3.20919850
69 0.97187475 1.60605074
70 0.96757208 0.97187475
71 0.43968455 0.96757208
72 3.03444057 0.43968455
73 1.17687356 3.03444057
74 1.85670073 1.17687356
75 -0.43421512 1.85670073
76 1.35019195 -0.43421512
77 1.77092563 1.35019195
78 5.48748346 1.77092563
79 -1.65012176 5.48748346
80 3.43410611 -1.65012176
81 6.28077532 3.43410611
82 0.64522107 6.28077532
83 2.00015762 0.64522107
84 3.93442757 2.00015762
85 2.65443702 3.93442757
86 -0.65870062 2.65443702
87 4.81735732 -0.65870062
88 -0.09581984 4.81735732
89 0.46891546 -0.09581984
90 3.54917592 0.46891546
91 2.47510917 3.54917592
92 1.21315596 2.47510917
93 -2.12926113 1.21315596
94 5.80642206 -2.12926113
95 2.79526190 5.80642206
96 3.17099906 2.79526190
97 1.01045924 3.17099906
98 0.42306102 1.01045924
99 -1.21507758 0.42306102
100 -0.34641282 -1.21507758
101 0.01491663 -0.34641282
102 -3.56017124 0.01491663
103 1.62731239 -3.56017124
104 7.50175146 1.62731239
105 -6.44815875 7.50175146
106 -3.38868970 -6.44815875
107 -4.64938733 -3.38868970
108 -0.32799800 -4.64938733
109 0.31557903 -0.32799800
110 0.04495112 0.31557903
111 6.16456327 0.04495112
112 -1.79068827 6.16456327
113 -7.48179089 -1.79068827
114 -1.14808252 -7.48179089
115 2.15107874 -1.14808252
116 -7.51331164 2.15107874
117 -3.29155758 -7.51331164
118 2.31166063 -3.29155758
119 -6.95540331 2.31166063
120 -2.68457012 -6.95540331
121 -4.57126302 -2.68457012
122 -3.61831280 -4.57126302
123 2.93183592 -3.61831280
124 1.49491264 2.93183592
125 2.17681175 1.49491264
126 1.20700815 2.17681175
127 3.22398461 1.20700815
128 5.17605928 3.22398461
129 1.58039810 5.17605928
130 -3.58629244 1.58039810
131 1.77823080 -3.58629244
132 3.18787205 1.77823080
133 1.81966536 3.18787205
134 3.90137505 1.81966536
135 0.17284929 3.90137505
136 -1.59235324 0.17284929
137 -8.25745061 -1.59235324
138 3.43330311 -8.25745061
139 -5.72045143 3.43330311
140 -8.25872532 -5.72045143
141 -1.13481642 -8.25872532
142 -7.01041158 -1.13481642
143 0.54649249 -7.01041158
144 1.05332929 0.54649249
145 -0.75748572 1.05332929
146 -1.75725091 -0.75748572
147 -1.11382285 -1.75725091
148 -3.34269897 -1.11382285
149 0.80628870 -3.34269897
150 -0.10604334 0.80628870
151 1.62976295 -0.10604334
152 6.75110104 1.62976295
153 -7.97190253 6.75110104
154 0.08568264 -7.97190253
155 0.62930325 0.08568264
156 1.47466161 0.62930325
157 1.18100347 1.47466161
158 3.10998615 1.18100347
159 NA 3.10998615
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.34750045 1.86971023
[2,] -0.37415319 -2.34750045
[3,] -0.17725791 -0.37415319
[4,] -3.96270253 -0.17725791
[5,] 4.99190386 -3.96270253
[6,] -0.75747263 4.99190386
[7,] -2.62688953 -0.75747263
[8,] -1.08784218 -2.62688953
[9,] 1.50225196 -1.08784218
[10,] 2.63200470 1.50225196
[11,] -2.96511662 2.63200470
[12,] 2.42318440 -2.96511662
[13,] -6.76175059 2.42318440
[14,] -2.34468471 -6.76175059
[15,] 2.94261336 -2.34468471
[16,] -3.14181607 2.94261336
[17,] -8.08854603 -3.14181607
[18,] 1.59793335 -8.08854603
[19,] -2.41782127 1.59793335
[20,] -3.15993564 -2.41782127
[21,] -1.72077134 -3.15993564
[22,] 0.81258936 -1.72077134
[23,] 0.92971643 0.81258936
[24,] 5.22661518 0.92971643
[25,] 1.31189628 5.22661518
[26,] -0.20699806 1.31189628
[27,] 2.73785843 -0.20699806
[28,] 0.23107668 2.73785843
[29,] -0.41825190 0.23107668
[30,] 4.07642117 -0.41825190
[31,] 1.18613970 4.07642117
[32,] -7.63635500 1.18613970
[33,] -1.50432626 -7.63635500
[34,] -2.23921474 -1.50432626
[35,] -1.26075882 -2.23921474
[36,] -3.70654224 -1.26075882
[37,] -1.46339731 -3.70654224
[38,] -1.23607472 -1.46339731
[39,] -3.09640498 -1.23607472
[40,] 6.46549195 -3.09640498
[41,] -0.23775005 6.46549195
[42,] -2.44555468 -0.23775005
[43,] -1.25488114 -2.44555468
[44,] -1.70858442 -1.25488114
[45,] 2.08316328 -1.70858442
[46,] 2.65687027 2.08316328
[47,] -3.77758454 2.65687027
[48,] -0.45634686 -3.77758454
[49,] -3.25542336 -0.45634686
[50,] -2.05837402 -3.25542336
[51,] 1.73687772 -2.05837402
[52,] 6.04870162 1.73687772
[53,] -5.43419298 6.04870162
[54,] -0.33273527 -5.43419298
[55,] 5.21445462 -0.33273527
[56,] 2.85202968 5.21445462
[57,] -1.44666772 2.85202968
[58,] 3.84470477 -1.44666772
[59,] -4.78088130 3.84470477
[60,] 1.87759791 -4.78088130
[61,] 0.28260796 1.87759791
[62,] 4.13177460 0.28260796
[63,] -0.41687218 4.13177460
[64,] 0.13546847 -0.41687218
[65,] 3.04403574 0.13546847
[66,] 2.41606670 3.04403574
[67,] -3.20919850 2.41606670
[68,] 1.60605074 -3.20919850
[69,] 0.97187475 1.60605074
[70,] 0.96757208 0.97187475
[71,] 0.43968455 0.96757208
[72,] 3.03444057 0.43968455
[73,] 1.17687356 3.03444057
[74,] 1.85670073 1.17687356
[75,] -0.43421512 1.85670073
[76,] 1.35019195 -0.43421512
[77,] 1.77092563 1.35019195
[78,] 5.48748346 1.77092563
[79,] -1.65012176 5.48748346
[80,] 3.43410611 -1.65012176
[81,] 6.28077532 3.43410611
[82,] 0.64522107 6.28077532
[83,] 2.00015762 0.64522107
[84,] 3.93442757 2.00015762
[85,] 2.65443702 3.93442757
[86,] -0.65870062 2.65443702
[87,] 4.81735732 -0.65870062
[88,] -0.09581984 4.81735732
[89,] 0.46891546 -0.09581984
[90,] 3.54917592 0.46891546
[91,] 2.47510917 3.54917592
[92,] 1.21315596 2.47510917
[93,] -2.12926113 1.21315596
[94,] 5.80642206 -2.12926113
[95,] 2.79526190 5.80642206
[96,] 3.17099906 2.79526190
[97,] 1.01045924 3.17099906
[98,] 0.42306102 1.01045924
[99,] -1.21507758 0.42306102
[100,] -0.34641282 -1.21507758
[101,] 0.01491663 -0.34641282
[102,] -3.56017124 0.01491663
[103,] 1.62731239 -3.56017124
[104,] 7.50175146 1.62731239
[105,] -6.44815875 7.50175146
[106,] -3.38868970 -6.44815875
[107,] -4.64938733 -3.38868970
[108,] -0.32799800 -4.64938733
[109,] 0.31557903 -0.32799800
[110,] 0.04495112 0.31557903
[111,] 6.16456327 0.04495112
[112,] -1.79068827 6.16456327
[113,] -7.48179089 -1.79068827
[114,] -1.14808252 -7.48179089
[115,] 2.15107874 -1.14808252
[116,] -7.51331164 2.15107874
[117,] -3.29155758 -7.51331164
[118,] 2.31166063 -3.29155758
[119,] -6.95540331 2.31166063
[120,] -2.68457012 -6.95540331
[121,] -4.57126302 -2.68457012
[122,] -3.61831280 -4.57126302
[123,] 2.93183592 -3.61831280
[124,] 1.49491264 2.93183592
[125,] 2.17681175 1.49491264
[126,] 1.20700815 2.17681175
[127,] 3.22398461 1.20700815
[128,] 5.17605928 3.22398461
[129,] 1.58039810 5.17605928
[130,] -3.58629244 1.58039810
[131,] 1.77823080 -3.58629244
[132,] 3.18787205 1.77823080
[133,] 1.81966536 3.18787205
[134,] 3.90137505 1.81966536
[135,] 0.17284929 3.90137505
[136,] -1.59235324 0.17284929
[137,] -8.25745061 -1.59235324
[138,] 3.43330311 -8.25745061
[139,] -5.72045143 3.43330311
[140,] -8.25872532 -5.72045143
[141,] -1.13481642 -8.25872532
[142,] -7.01041158 -1.13481642
[143,] 0.54649249 -7.01041158
[144,] 1.05332929 0.54649249
[145,] -0.75748572 1.05332929
[146,] -1.75725091 -0.75748572
[147,] -1.11382285 -1.75725091
[148,] -3.34269897 -1.11382285
[149,] 0.80628870 -3.34269897
[150,] -0.10604334 0.80628870
[151,] 1.62976295 -0.10604334
[152,] 6.75110104 1.62976295
[153,] -7.97190253 6.75110104
[154,] 0.08568264 -7.97190253
[155,] 0.62930325 0.08568264
[156,] 1.47466161 0.62930325
[157,] 1.18100347 1.47466161
[158,] 3.10998615 1.18100347
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.34750045 1.86971023
2 -0.37415319 -2.34750045
3 -0.17725791 -0.37415319
4 -3.96270253 -0.17725791
5 4.99190386 -3.96270253
6 -0.75747263 4.99190386
7 -2.62688953 -0.75747263
8 -1.08784218 -2.62688953
9 1.50225196 -1.08784218
10 2.63200470 1.50225196
11 -2.96511662 2.63200470
12 2.42318440 -2.96511662
13 -6.76175059 2.42318440
14 -2.34468471 -6.76175059
15 2.94261336 -2.34468471
16 -3.14181607 2.94261336
17 -8.08854603 -3.14181607
18 1.59793335 -8.08854603
19 -2.41782127 1.59793335
20 -3.15993564 -2.41782127
21 -1.72077134 -3.15993564
22 0.81258936 -1.72077134
23 0.92971643 0.81258936
24 5.22661518 0.92971643
25 1.31189628 5.22661518
26 -0.20699806 1.31189628
27 2.73785843 -0.20699806
28 0.23107668 2.73785843
29 -0.41825190 0.23107668
30 4.07642117 -0.41825190
31 1.18613970 4.07642117
32 -7.63635500 1.18613970
33 -1.50432626 -7.63635500
34 -2.23921474 -1.50432626
35 -1.26075882 -2.23921474
36 -3.70654224 -1.26075882
37 -1.46339731 -3.70654224
38 -1.23607472 -1.46339731
39 -3.09640498 -1.23607472
40 6.46549195 -3.09640498
41 -0.23775005 6.46549195
42 -2.44555468 -0.23775005
43 -1.25488114 -2.44555468
44 -1.70858442 -1.25488114
45 2.08316328 -1.70858442
46 2.65687027 2.08316328
47 -3.77758454 2.65687027
48 -0.45634686 -3.77758454
49 -3.25542336 -0.45634686
50 -2.05837402 -3.25542336
51 1.73687772 -2.05837402
52 6.04870162 1.73687772
53 -5.43419298 6.04870162
54 -0.33273527 -5.43419298
55 5.21445462 -0.33273527
56 2.85202968 5.21445462
57 -1.44666772 2.85202968
58 3.84470477 -1.44666772
59 -4.78088130 3.84470477
60 1.87759791 -4.78088130
61 0.28260796 1.87759791
62 4.13177460 0.28260796
63 -0.41687218 4.13177460
64 0.13546847 -0.41687218
65 3.04403574 0.13546847
66 2.41606670 3.04403574
67 -3.20919850 2.41606670
68 1.60605074 -3.20919850
69 0.97187475 1.60605074
70 0.96757208 0.97187475
71 0.43968455 0.96757208
72 3.03444057 0.43968455
73 1.17687356 3.03444057
74 1.85670073 1.17687356
75 -0.43421512 1.85670073
76 1.35019195 -0.43421512
77 1.77092563 1.35019195
78 5.48748346 1.77092563
79 -1.65012176 5.48748346
80 3.43410611 -1.65012176
81 6.28077532 3.43410611
82 0.64522107 6.28077532
83 2.00015762 0.64522107
84 3.93442757 2.00015762
85 2.65443702 3.93442757
86 -0.65870062 2.65443702
87 4.81735732 -0.65870062
88 -0.09581984 4.81735732
89 0.46891546 -0.09581984
90 3.54917592 0.46891546
91 2.47510917 3.54917592
92 1.21315596 2.47510917
93 -2.12926113 1.21315596
94 5.80642206 -2.12926113
95 2.79526190 5.80642206
96 3.17099906 2.79526190
97 1.01045924 3.17099906
98 0.42306102 1.01045924
99 -1.21507758 0.42306102
100 -0.34641282 -1.21507758
101 0.01491663 -0.34641282
102 -3.56017124 0.01491663
103 1.62731239 -3.56017124
104 7.50175146 1.62731239
105 -6.44815875 7.50175146
106 -3.38868970 -6.44815875
107 -4.64938733 -3.38868970
108 -0.32799800 -4.64938733
109 0.31557903 -0.32799800
110 0.04495112 0.31557903
111 6.16456327 0.04495112
112 -1.79068827 6.16456327
113 -7.48179089 -1.79068827
114 -1.14808252 -7.48179089
115 2.15107874 -1.14808252
116 -7.51331164 2.15107874
117 -3.29155758 -7.51331164
118 2.31166063 -3.29155758
119 -6.95540331 2.31166063
120 -2.68457012 -6.95540331
121 -4.57126302 -2.68457012
122 -3.61831280 -4.57126302
123 2.93183592 -3.61831280
124 1.49491264 2.93183592
125 2.17681175 1.49491264
126 1.20700815 2.17681175
127 3.22398461 1.20700815
128 5.17605928 3.22398461
129 1.58039810 5.17605928
130 -3.58629244 1.58039810
131 1.77823080 -3.58629244
132 3.18787205 1.77823080
133 1.81966536 3.18787205
134 3.90137505 1.81966536
135 0.17284929 3.90137505
136 -1.59235324 0.17284929
137 -8.25745061 -1.59235324
138 3.43330311 -8.25745061
139 -5.72045143 3.43330311
140 -8.25872532 -5.72045143
141 -1.13481642 -8.25872532
142 -7.01041158 -1.13481642
143 0.54649249 -7.01041158
144 1.05332929 0.54649249
145 -0.75748572 1.05332929
146 -1.75725091 -0.75748572
147 -1.11382285 -1.75725091
148 -3.34269897 -1.11382285
149 0.80628870 -3.34269897
150 -0.10604334 0.80628870
151 1.62976295 -0.10604334
152 6.75110104 1.62976295
153 -7.97190253 6.75110104
154 0.08568264 -7.97190253
155 0.62930325 0.08568264
156 1.47466161 0.62930325
157 1.18100347 1.47466161
158 3.10998615 1.18100347
> 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/freestat/rcomp/tmp/7yndp1290521263.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8yndp1290521263.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9rwus1290521263.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10rwus1290521263.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11cxag1290521263.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/freestat/rcomp/tmp/12gx9m1290521263.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/freestat/rcomp/tmp/13nzog1290521263.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/freestat/rcomp/tmp/14qh4m1290521263.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/freestat/rcomp/tmp/150qlo1290521263.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/freestat/rcomp/tmp/16fijf1290521263.tab")
+ }
> try(system("convert tmp/1kdfg1290521263.ps tmp/1kdfg1290521263.png",intern=TRUE))
character(0)
> try(system("convert tmp/2dne11290521263.ps tmp/2dne11290521263.png",intern=TRUE))
character(0)
> try(system("convert tmp/3dne11290521263.ps tmp/3dne11290521263.png",intern=TRUE))
character(0)
> try(system("convert tmp/4dne11290521263.ps tmp/4dne11290521263.png",intern=TRUE))
character(0)
> try(system("convert tmp/5dne11290521263.ps tmp/5dne11290521263.png",intern=TRUE))
character(0)
> try(system("convert tmp/66ed41290521263.ps tmp/66ed41290521263.png",intern=TRUE))
character(0)
> try(system("convert tmp/7yndp1290521263.ps tmp/7yndp1290521263.png",intern=TRUE))
character(0)
> try(system("convert tmp/8yndp1290521263.ps tmp/8yndp1290521263.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rwus1290521263.ps tmp/9rwus1290521263.png",intern=TRUE))
character(0)
> try(system("convert tmp/10rwus1290521263.ps tmp/10rwus1290521263.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.957 2.673 20.280