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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Type 'contributors()' for more information and
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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(7.6
+ ,2.16
+ ,7.6
+ ,2.23
+ ,7.8
+ ,2.40
+ ,8.0
+ ,2.84
+ ,8.0
+ ,2.77
+ ,8.0
+ ,2.93
+ ,7.9
+ ,2.91
+ ,7.9
+ ,2.69
+ ,8.0
+ ,2.38
+ ,8.5
+ ,2.58
+ ,9.2
+ ,3.19
+ ,9.4
+ ,2.82
+ ,9.5
+ ,2.72
+ ,9.5
+ ,2.53
+ ,9.6
+ ,2.70
+ ,9.7
+ ,2.42
+ ,9.7
+ ,2.50
+ ,9.6
+ ,2.31
+ ,9.5
+ ,2.41
+ ,9.4
+ ,2.56
+ ,9.3
+ ,2.76
+ ,9.6
+ ,2.71
+ ,10.2
+ ,2.44
+ ,10.2
+ ,2.46
+ ,10.1
+ ,2.12
+ ,9.9
+ ,1.99
+ ,9.8
+ ,1.86
+ ,9.8
+ ,1.88
+ ,9.7
+ ,1.82
+ ,9.5
+ ,1.74
+ ,9.3
+ ,1.71
+ ,9.1
+ ,1.38
+ ,9.0
+ ,1.27
+ ,9.5
+ ,1.19
+ ,10.0
+ ,1.28
+ ,10.2
+ ,1.19
+ ,10.1
+ ,1.22
+ ,10.0
+ ,1.47
+ ,9.9
+ ,1.46
+ ,10.0
+ ,1.96
+ ,9.9
+ ,1.88
+ ,9.7
+ ,2.03
+ ,9.5
+ ,2.04
+ ,9.2
+ ,1.90
+ ,9.0
+ ,1.80
+ ,9.3
+ ,1.92
+ ,9.8
+ ,1.92
+ ,9.8
+ ,1.97
+ ,9.6
+ ,2.46
+ ,9.4
+ ,2.36
+ ,9.3
+ ,2.53
+ ,9.2
+ ,2.31
+ ,9.2
+ ,1.98
+ ,9.0
+ ,1.46
+ ,8.8
+ ,1.26
+ ,8.7
+ ,1.58
+ ,8.7
+ ,1.74
+ ,9.1
+ ,1.89
+ ,9.7
+ ,1.85
+ ,9.8
+ ,1.62
+ ,9.6
+ ,1.30
+ ,9.4
+ ,1.42
+ ,9.4
+ ,1.15
+ ,9.5
+ ,0.42
+ ,9.4
+ ,0.74
+ ,9.3
+ ,1.02
+ ,9.2
+ ,1.51
+ ,9.0
+ ,1.86
+ ,8.9
+ ,1.59
+ ,9.2
+ ,1.03
+ ,9.8
+ ,0.44
+ ,9.9
+ ,0.82
+ ,9.6
+ ,0.86
+ ,9.2
+ ,0.58
+ ,9.1
+ ,0.59
+ ,9.1
+ ,0.95
+ ,9.0
+ ,0.98
+ ,8.9
+ ,1.23
+ ,8.7
+ ,1.17
+ ,8.5
+ ,0.84
+ ,8.3
+ ,0.74
+ ,8.5
+ ,0.65
+ ,8.7
+ ,0.91
+ ,8.4
+ ,1.19
+ ,8.1
+ ,1.30
+ ,7.8
+ ,1.53
+ ,7.7
+ ,1.94
+ ,7.5
+ ,1.79
+ ,7.2
+ ,1.95
+ ,6.8
+ ,2.26
+ ,6.7
+ ,2.04
+ ,6.4
+ ,2.16
+ ,6.3
+ ,2.75
+ ,6.8
+ ,2.79
+ ,7.3
+ ,2.88
+ ,7.1
+ ,3.36
+ ,7.0
+ ,2.97
+ ,6.8
+ ,3.10
+ ,6.6
+ ,2.49
+ ,6.3
+ ,2.20
+ ,6.1
+ ,2.25
+ ,6.1
+ ,2.09
+ ,6.3
+ ,2.79
+ ,6.3
+ ,3.14
+ ,6.0
+ ,2.93
+ ,6.2
+ ,2.65
+ ,6.4
+ ,2.67
+ ,6.8
+ ,2.26
+ ,7.5
+ ,2.35
+ ,7.5
+ ,2.13
+ ,7.6
+ ,2.18
+ ,7.6
+ ,2.90
+ ,7.4
+ ,2.63
+ ,7.3
+ ,2.67
+ ,7.1
+ ,1.81
+ ,6.9
+ ,1.33
+ ,6.8
+ ,0.88
+ ,7.5
+ ,1.28
+ ,7.6
+ ,1.26
+ ,7.8
+ ,1.26
+ ,8.0
+ ,1.29
+ ,8.1
+ ,1.10
+ ,8.2
+ ,1.37
+ ,8.3
+ ,1.21
+ ,8.2
+ ,1.74
+ ,8.0
+ ,1.76
+ ,7.9
+ ,1.48
+ ,7.6
+ ,1.04
+ ,7.6
+ ,1.62
+ ,8.3
+ ,1.49
+ ,8.4
+ ,1.79
+ ,8.4
+ ,1.80
+ ,8.4
+ ,1.58
+ ,8.4
+ ,1.86
+ ,8.6
+ ,1.74
+ ,8.9
+ ,1.59
+ ,8.8
+ ,1.26
+ ,8.3
+ ,1.13
+ ,7.5
+ ,1.92
+ ,7.2
+ ,2.61
+ ,7.4
+ ,2.26
+ ,8.8
+ ,2.41
+ ,9.3
+ ,2.26
+ ,9.3
+ ,2.03
+ ,8.7
+ ,2.86
+ ,8.2
+ ,2.55
+ ,8.3
+ ,2.27
+ ,8.5
+ ,2.26
+ ,8.6
+ ,2.57
+ ,8.5
+ ,3.07
+ ,8.2
+ ,2.76
+ ,8.1
+ ,2.51
+ ,7.9
+ ,2.87
+ ,8.6
+ ,3.14
+ ,8.7
+ ,3.11
+ ,8.7
+ ,3.16
+ ,8.5
+ ,2.47
+ ,8.4
+ ,2.57
+ ,8.5
+ ,2.89
+ ,8.7
+ ,2.63
+ ,8.7
+ ,2.38
+ ,8.6
+ ,1.69
+ ,8.5
+ ,1.96
+ ,8.3
+ ,2.19
+ ,8.0
+ ,1.87
+ ,8.2
+ ,1.6
+ ,8.1
+ ,1.63
+ ,8.1
+ ,1.22)
+ ,dim=c(2
+ ,168)
+ ,dimnames=list(c('Y'
+ ,'X')
+ ,1:168))
> y <- array(NA,dim=c(2,168),dimnames=list(c('Y','X'),1:168))
> 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.6 2.16 1 0 0 0 0 0 0 0 0 0 0 1
2 7.6 2.23 0 1 0 0 0 0 0 0 0 0 0 2
3 7.8 2.40 0 0 1 0 0 0 0 0 0 0 0 3
4 8.0 2.84 0 0 0 1 0 0 0 0 0 0 0 4
5 8.0 2.77 0 0 0 0 1 0 0 0 0 0 0 5
6 8.0 2.93 0 0 0 0 0 1 0 0 0 0 0 6
7 7.9 2.91 0 0 0 0 0 0 1 0 0 0 0 7
8 7.9 2.69 0 0 0 0 0 0 0 1 0 0 0 8
9 8.0 2.38 0 0 0 0 0 0 0 0 1 0 0 9
10 8.5 2.58 0 0 0 0 0 0 0 0 0 1 0 10
11 9.2 3.19 0 0 0 0 0 0 0 0 0 0 1 11
12 9.4 2.82 0 0 0 0 0 0 0 0 0 0 0 12
13 9.5 2.72 1 0 0 0 0 0 0 0 0 0 0 13
14 9.5 2.53 0 1 0 0 0 0 0 0 0 0 0 14
15 9.6 2.70 0 0 1 0 0 0 0 0 0 0 0 15
16 9.7 2.42 0 0 0 1 0 0 0 0 0 0 0 16
17 9.7 2.50 0 0 0 0 1 0 0 0 0 0 0 17
18 9.6 2.31 0 0 0 0 0 1 0 0 0 0 0 18
19 9.5 2.41 0 0 0 0 0 0 1 0 0 0 0 19
20 9.4 2.56 0 0 0 0 0 0 0 1 0 0 0 20
21 9.3 2.76 0 0 0 0 0 0 0 0 1 0 0 21
22 9.6 2.71 0 0 0 0 0 0 0 0 0 1 0 22
23 10.2 2.44 0 0 0 0 0 0 0 0 0 0 1 23
24 10.2 2.46 0 0 0 0 0 0 0 0 0 0 0 24
25 10.1 2.12 1 0 0 0 0 0 0 0 0 0 0 25
26 9.9 1.99 0 1 0 0 0 0 0 0 0 0 0 26
27 9.8 1.86 0 0 1 0 0 0 0 0 0 0 0 27
28 9.8 1.88 0 0 0 1 0 0 0 0 0 0 0 28
29 9.7 1.82 0 0 0 0 1 0 0 0 0 0 0 29
30 9.5 1.74 0 0 0 0 0 1 0 0 0 0 0 30
31 9.3 1.71 0 0 0 0 0 0 1 0 0 0 0 31
32 9.1 1.38 0 0 0 0 0 0 0 1 0 0 0 32
33 9.0 1.27 0 0 0 0 0 0 0 0 1 0 0 33
34 9.5 1.19 0 0 0 0 0 0 0 0 0 1 0 34
35 10.0 1.28 0 0 0 0 0 0 0 0 0 0 1 35
36 10.2 1.19 0 0 0 0 0 0 0 0 0 0 0 36
37 10.1 1.22 1 0 0 0 0 0 0 0 0 0 0 37
38 10.0 1.47 0 1 0 0 0 0 0 0 0 0 0 38
39 9.9 1.46 0 0 1 0 0 0 0 0 0 0 0 39
40 10.0 1.96 0 0 0 1 0 0 0 0 0 0 0 40
41 9.9 1.88 0 0 0 0 1 0 0 0 0 0 0 41
42 9.7 2.03 0 0 0 0 0 1 0 0 0 0 0 42
43 9.5 2.04 0 0 0 0 0 0 1 0 0 0 0 43
44 9.2 1.90 0 0 0 0 0 0 0 1 0 0 0 44
45 9.0 1.80 0 0 0 0 0 0 0 0 1 0 0 45
46 9.3 1.92 0 0 0 0 0 0 0 0 0 1 0 46
47 9.8 1.92 0 0 0 0 0 0 0 0 0 0 1 47
48 9.8 1.97 0 0 0 0 0 0 0 0 0 0 0 48
49 9.6 2.46 1 0 0 0 0 0 0 0 0 0 0 49
50 9.4 2.36 0 1 0 0 0 0 0 0 0 0 0 50
51 9.3 2.53 0 0 1 0 0 0 0 0 0 0 0 51
52 9.2 2.31 0 0 0 1 0 0 0 0 0 0 0 52
53 9.2 1.98 0 0 0 0 1 0 0 0 0 0 0 53
54 9.0 1.46 0 0 0 0 0 1 0 0 0 0 0 54
55 8.8 1.26 0 0 0 0 0 0 1 0 0 0 0 55
56 8.7 1.58 0 0 0 0 0 0 0 1 0 0 0 56
57 8.7 1.74 0 0 0 0 0 0 0 0 1 0 0 57
58 9.1 1.89 0 0 0 0 0 0 0 0 0 1 0 58
59 9.7 1.85 0 0 0 0 0 0 0 0 0 0 1 59
60 9.8 1.62 0 0 0 0 0 0 0 0 0 0 0 60
61 9.6 1.30 1 0 0 0 0 0 0 0 0 0 0 61
62 9.4 1.42 0 1 0 0 0 0 0 0 0 0 0 62
63 9.4 1.15 0 0 1 0 0 0 0 0 0 0 0 63
64 9.5 0.42 0 0 0 1 0 0 0 0 0 0 0 64
65 9.4 0.74 0 0 0 0 1 0 0 0 0 0 0 65
66 9.3 1.02 0 0 0 0 0 1 0 0 0 0 0 66
67 9.2 1.51 0 0 0 0 0 0 1 0 0 0 0 67
68 9.0 1.86 0 0 0 0 0 0 0 1 0 0 0 68
69 8.9 1.59 0 0 0 0 0 0 0 0 1 0 0 69
70 9.2 1.03 0 0 0 0 0 0 0 0 0 1 0 70
71 9.8 0.44 0 0 0 0 0 0 0 0 0 0 1 71
72 9.9 0.82 0 0 0 0 0 0 0 0 0 0 0 72
73 9.6 0.86 1 0 0 0 0 0 0 0 0 0 0 73
74 9.2 0.58 0 1 0 0 0 0 0 0 0 0 0 74
75 9.1 0.59 0 0 1 0 0 0 0 0 0 0 0 75
76 9.1 0.95 0 0 0 1 0 0 0 0 0 0 0 76
77 9.0 0.98 0 0 0 0 1 0 0 0 0 0 0 77
78 8.9 1.23 0 0 0 0 0 1 0 0 0 0 0 78
79 8.7 1.17 0 0 0 0 0 0 1 0 0 0 0 79
80 8.5 0.84 0 0 0 0 0 0 0 1 0 0 0 80
81 8.3 0.74 0 0 0 0 0 0 0 0 1 0 0 81
82 8.5 0.65 0 0 0 0 0 0 0 0 0 1 0 82
83 8.7 0.91 0 0 0 0 0 0 0 0 0 0 1 83
84 8.4 1.19 0 0 0 0 0 0 0 0 0 0 0 84
85 8.1 1.30 1 0 0 0 0 0 0 0 0 0 0 85
86 7.8 1.53 0 1 0 0 0 0 0 0 0 0 0 86
87 7.7 1.94 0 0 1 0 0 0 0 0 0 0 0 87
88 7.5 1.79 0 0 0 1 0 0 0 0 0 0 0 88
89 7.2 1.95 0 0 0 0 1 0 0 0 0 0 0 89
90 6.8 2.26 0 0 0 0 0 1 0 0 0 0 0 90
91 6.7 2.04 0 0 0 0 0 0 1 0 0 0 0 91
92 6.4 2.16 0 0 0 0 0 0 0 1 0 0 0 92
93 6.3 2.75 0 0 0 0 0 0 0 0 1 0 0 93
94 6.8 2.79 0 0 0 0 0 0 0 0 0 1 0 94
95 7.3 2.88 0 0 0 0 0 0 0 0 0 0 1 95
96 7.1 3.36 0 0 0 0 0 0 0 0 0 0 0 96
97 7.0 2.97 1 0 0 0 0 0 0 0 0 0 0 97
98 6.8 3.10 0 1 0 0 0 0 0 0 0 0 0 98
99 6.6 2.49 0 0 1 0 0 0 0 0 0 0 0 99
100 6.3 2.20 0 0 0 1 0 0 0 0 0 0 0 100
101 6.1 2.25 0 0 0 0 1 0 0 0 0 0 0 101
102 6.1 2.09 0 0 0 0 0 1 0 0 0 0 0 102
103 6.3 2.79 0 0 0 0 0 0 1 0 0 0 0 103
104 6.3 3.14 0 0 0 0 0 0 0 1 0 0 0 104
105 6.0 2.93 0 0 0 0 0 0 0 0 1 0 0 105
106 6.2 2.65 0 0 0 0 0 0 0 0 0 1 0 106
107 6.4 2.67 0 0 0 0 0 0 0 0 0 0 1 107
108 6.8 2.26 0 0 0 0 0 0 0 0 0 0 0 108
109 7.5 2.35 1 0 0 0 0 0 0 0 0 0 0 109
110 7.5 2.13 0 1 0 0 0 0 0 0 0 0 0 110
111 7.6 2.18 0 0 1 0 0 0 0 0 0 0 0 111
112 7.6 2.90 0 0 0 1 0 0 0 0 0 0 0 112
113 7.4 2.63 0 0 0 0 1 0 0 0 0 0 0 113
114 7.3 2.67 0 0 0 0 0 1 0 0 0 0 0 114
115 7.1 1.81 0 0 0 0 0 0 1 0 0 0 0 115
116 6.9 1.33 0 0 0 0 0 0 0 1 0 0 0 116
117 6.8 0.88 0 0 0 0 0 0 0 0 1 0 0 117
118 7.5 1.28 0 0 0 0 0 0 0 0 0 1 0 118
119 7.6 1.26 0 0 0 0 0 0 0 0 0 0 1 119
120 7.8 1.26 0 0 0 0 0 0 0 0 0 0 0 120
121 8.0 1.29 1 0 0 0 0 0 0 0 0 0 0 121
122 8.1 1.10 0 1 0 0 0 0 0 0 0 0 0 122
123 8.2 1.37 0 0 1 0 0 0 0 0 0 0 0 123
124 8.3 1.21 0 0 0 1 0 0 0 0 0 0 0 124
125 8.2 1.74 0 0 0 0 1 0 0 0 0 0 0 125
126 8.0 1.76 0 0 0 0 0 1 0 0 0 0 0 126
127 7.9 1.48 0 0 0 0 0 0 1 0 0 0 0 127
128 7.6 1.04 0 0 0 0 0 0 0 1 0 0 0 128
129 7.6 1.62 0 0 0 0 0 0 0 0 1 0 0 129
130 8.3 1.49 0 0 0 0 0 0 0 0 0 1 0 130
131 8.4 1.79 0 0 0 0 0 0 0 0 0 0 1 131
132 8.4 1.80 0 0 0 0 0 0 0 0 0 0 0 132
133 8.4 1.58 1 0 0 0 0 0 0 0 0 0 0 133
134 8.4 1.86 0 1 0 0 0 0 0 0 0 0 0 134
135 8.6 1.74 0 0 1 0 0 0 0 0 0 0 0 135
136 8.9 1.59 0 0 0 1 0 0 0 0 0 0 0 136
137 8.8 1.26 0 0 0 0 1 0 0 0 0 0 0 137
138 8.3 1.13 0 0 0 0 0 1 0 0 0 0 0 138
139 7.5 1.92 0 0 0 0 0 0 1 0 0 0 0 139
140 7.2 2.61 0 0 0 0 0 0 0 1 0 0 0 140
141 7.4 2.26 0 0 0 0 0 0 0 0 1 0 0 141
142 8.8 2.41 0 0 0 0 0 0 0 0 0 1 0 142
143 9.3 2.26 0 0 0 0 0 0 0 0 0 0 1 143
144 9.3 2.03 0 0 0 0 0 0 0 0 0 0 0 144
145 8.7 2.86 1 0 0 0 0 0 0 0 0 0 0 145
146 8.2 2.55 0 1 0 0 0 0 0 0 0 0 0 146
147 8.3 2.27 0 0 1 0 0 0 0 0 0 0 0 147
148 8.5 2.26 0 0 0 1 0 0 0 0 0 0 0 148
149 8.6 2.57 0 0 0 0 1 0 0 0 0 0 0 149
150 8.5 3.07 0 0 0 0 0 1 0 0 0 0 0 150
151 8.2 2.76 0 0 0 0 0 0 1 0 0 0 0 151
152 8.1 2.51 0 0 0 0 0 0 0 1 0 0 0 152
153 7.9 2.87 0 0 0 0 0 0 0 0 1 0 0 153
154 8.6 3.14 0 0 0 0 0 0 0 0 0 1 0 154
155 8.7 3.11 0 0 0 0 0 0 0 0 0 0 1 155
156 8.7 3.16 0 0 0 0 0 0 0 0 0 0 0 156
157 8.5 2.47 1 0 0 0 0 0 0 0 0 0 0 157
158 8.4 2.57 0 1 0 0 0 0 0 0 0 0 0 158
159 8.5 2.89 0 0 1 0 0 0 0 0 0 0 0 159
160 8.7 2.63 0 0 0 1 0 0 0 0 0 0 0 160
161 8.7 2.38 0 0 0 0 1 0 0 0 0 0 0 161
162 8.6 1.69 0 0 0 0 0 1 0 0 0 0 0 162
163 8.5 1.96 0 0 0 0 0 0 1 0 0 0 0 163
164 8.3 2.19 0 0 0 0 0 0 0 1 0 0 0 164
165 8.0 1.87 0 0 0 0 0 0 0 0 1 0 0 165
166 8.2 1.60 0 0 0 0 0 0 0 0 0 1 0 166
167 8.1 1.63 0 0 0 0 0 0 0 0 0 0 1 167
168 8.1 1.22 0 0 0 0 0 0 0 0 0 0 0 168
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
10.553735 -0.432711 -0.204465 -0.352280 -0.323755 -0.270643
M5 M6 M7 M8 M9 M10
-0.343972 -0.500510 -0.657733 -0.826083 -0.926680 -0.428237
M11 t
-0.045076 -0.009603
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.1663 -0.6186 0.1931 0.6600 1.3121
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.553735 0.330496 31.933 < 2e-16 ***
X -0.432711 0.100677 -4.298 3.04e-05 ***
M1 -0.204465 0.333939 -0.612 0.54125
M2 -0.352280 0.333861 -1.055 0.29300
M3 -0.323755 0.333813 -0.970 0.33363
M4 -0.270643 0.333752 -0.811 0.41867
M5 -0.343972 0.333711 -1.031 0.30427
M6 -0.500510 0.333670 -1.500 0.13566
M7 -0.657733 0.333662 -1.971 0.05049 .
M8 -0.826083 0.333637 -2.476 0.01437 *
M9 -0.926680 0.333592 -2.778 0.00615 **
M10 -0.428237 0.333572 -1.284 0.20114
M11 -0.045076 0.333578 -0.135 0.89269
t -0.009603 0.001408 -6.822 1.93e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8825 on 154 degrees of freedom
Multiple R-squared: 0.3482, Adjusted R-squared: 0.2932
F-statistic: 6.33 on 13 and 154 DF, p-value: 1.735e-09
> 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,] 5.973275e-05 1.194655e-04 9.999403e-01
[2,] 2.288855e-06 4.577710e-06 9.999977e-01
[3,] 1.203853e-07 2.407705e-07 9.999999e-01
[4,] 1.028740e-06 2.057481e-06 9.999990e-01
[5,] 1.929435e-05 3.858870e-05 9.999807e-01
[6,] 4.986421e-05 9.972842e-05 9.999501e-01
[7,] 4.181743e-05 8.363486e-05 9.999582e-01
[8,] 6.986527e-05 1.397305e-04 9.999301e-01
[9,] 8.668969e-05 1.733794e-04 9.999133e-01
[10,] 1.095222e-04 2.190443e-04 9.998905e-01
[11,] 1.107551e-04 2.215101e-04 9.998892e-01
[12,] 9.675505e-05 1.935101e-04 9.999032e-01
[13,] 6.900576e-05 1.380115e-04 9.999310e-01
[14,] 4.814952e-05 9.629903e-05 9.999519e-01
[15,] 2.950307e-05 5.900614e-05 9.999705e-01
[16,] 1.245316e-05 2.490632e-05 9.999875e-01
[17,] 5.097799e-06 1.019560e-05 9.999949e-01
[18,] 1.830984e-06 3.661967e-06 9.999982e-01
[19,] 6.462671e-07 1.292534e-06 9.999994e-01
[20,] 2.272729e-07 4.545457e-07 9.999998e-01
[21,] 1.255944e-07 2.511887e-07 9.999999e-01
[22,] 2.295754e-07 4.591508e-07 9.999998e-01
[23,] 3.715508e-07 7.431016e-07 9.999996e-01
[24,] 2.313826e-06 4.627651e-06 9.999977e-01
[25,] 6.039882e-06 1.207976e-05 9.999940e-01
[26,] 2.041914e-05 4.083829e-05 9.999796e-01
[27,] 4.651900e-05 9.303800e-05 9.999535e-01
[28,] 1.094489e-04 2.188977e-04 9.998906e-01
[29,] 2.430438e-04 4.860876e-04 9.997570e-01
[30,] 5.355407e-04 1.071081e-03 9.994645e-01
[31,] 1.011178e-03 2.022357e-03 9.989888e-01
[32,] 2.092800e-03 4.185600e-03 9.979072e-01
[33,] 3.260784e-03 6.521569e-03 9.967392e-01
[34,] 4.329930e-03 8.659861e-03 9.956701e-01
[35,] 5.527457e-03 1.105491e-02 9.944725e-01
[36,] 7.555234e-03 1.511047e-02 9.924448e-01
[37,] 9.225595e-03 1.845119e-02 9.907744e-01
[38,] 1.153824e-02 2.307647e-02 9.884618e-01
[39,] 1.405363e-02 2.810726e-02 9.859464e-01
[40,] 1.540862e-02 3.081724e-02 9.845914e-01
[41,] 1.634755e-02 3.269510e-02 9.836524e-01
[42,] 1.708956e-02 3.417912e-02 9.829104e-01
[43,] 2.043163e-02 4.086327e-02 9.795684e-01
[44,] 2.437521e-02 4.875042e-02 9.756248e-01
[45,] 2.341086e-02 4.682172e-02 9.765891e-01
[46,] 2.398392e-02 4.796784e-02 9.760161e-01
[47,] 2.361858e-02 4.723715e-02 9.763814e-01
[48,] 2.130788e-02 4.261575e-02 9.786921e-01
[49,] 1.991708e-02 3.983415e-02 9.800829e-01
[50,] 1.991702e-02 3.983405e-02 9.800830e-01
[51,] 2.441977e-02 4.883954e-02 9.755802e-01
[52,] 3.714947e-02 7.429893e-02 9.628505e-01
[53,] 5.496996e-02 1.099399e-01 9.450300e-01
[54,] 6.228980e-02 1.245796e-01 9.377102e-01
[55,] 7.207341e-02 1.441468e-01 9.279266e-01
[56,] 1.036945e-01 2.073889e-01 8.963055e-01
[57,] 1.248414e-01 2.496829e-01 8.751586e-01
[58,] 1.391561e-01 2.783122e-01 8.608439e-01
[59,] 1.547941e-01 3.095882e-01 8.452059e-01
[60,] 1.906855e-01 3.813710e-01 8.093145e-01
[61,] 2.433638e-01 4.867276e-01 7.566362e-01
[62,] 3.451920e-01 6.903840e-01 6.548080e-01
[63,] 4.837807e-01 9.675614e-01 5.162193e-01
[64,] 6.305620e-01 7.388760e-01 3.694380e-01
[65,] 7.778416e-01 4.443167e-01 2.221584e-01
[66,] 8.622814e-01 2.754373e-01 1.377186e-01
[67,] 9.508683e-01 9.826338e-02 4.913169e-02
[68,] 9.885741e-01 2.285189e-02 1.142594e-02
[69,] 9.956118e-01 8.776353e-03 4.388177e-03
[70,] 9.982803e-01 3.439370e-03 1.719685e-03
[71,] 9.992301e-01 1.539761e-03 7.698806e-04
[72,] 9.995919e-01 8.161229e-04 4.080614e-04
[73,] 9.997515e-01 4.970919e-04 2.485459e-04
[74,] 9.998207e-01 3.585549e-04 1.792774e-04
[75,] 9.998644e-01 2.712999e-04 1.356500e-04
[76,] 9.998819e-01 2.361754e-04 1.180877e-04
[77,] 9.998617e-01 2.765574e-04 1.382787e-04
[78,] 9.998036e-01 3.927519e-04 1.963759e-04
[79,] 9.997979e-01 4.042935e-04 2.021467e-04
[80,] 9.997530e-01 4.939279e-04 2.469639e-04
[81,] 9.996113e-01 7.774767e-04 3.887384e-04
[82,] 9.993898e-01 1.220330e-03 6.101650e-04
[83,] 9.992800e-01 1.439968e-03 7.199841e-04
[84,] 9.996752e-01 6.496866e-04 3.248433e-04
[85,] 9.999109e-01 1.782981e-04 8.914903e-05
[86,] 9.999704e-01 5.915872e-05 2.957936e-05
[87,] 9.999586e-01 8.277197e-05 4.138598e-05
[88,] 9.999317e-01 1.365629e-04 6.828147e-05
[89,] 9.999066e-01 1.868611e-04 9.343054e-05
[90,] 9.999690e-01 6.191161e-05 3.095581e-05
[91,] 9.999941e-01 1.188346e-05 5.941730e-06
[92,] 9.999979e-01 4.199716e-06 2.099858e-06
[93,] 9.999968e-01 6.373949e-06 3.186975e-06
[94,] 9.999946e-01 1.082750e-05 5.413751e-06
[95,] 9.999916e-01 1.681837e-05 8.409186e-06
[96,] 9.999931e-01 1.370306e-05 6.851531e-06
[97,] 9.999962e-01 7.665683e-06 3.832841e-06
[98,] 9.999980e-01 4.017934e-06 2.008967e-06
[99,] 9.999979e-01 4.152986e-06 2.076493e-06
[100,] 9.999976e-01 4.850433e-06 2.425216e-06
[101,] 9.999973e-01 5.447576e-06 2.723788e-06
[102,] 9.999976e-01 4.843740e-06 2.421870e-06
[103,] 9.999985e-01 2.929654e-06 1.464827e-06
[104,] 9.999987e-01 2.668443e-06 1.334222e-06
[105,] 9.999977e-01 4.596513e-06 2.298257e-06
[106,] 9.999951e-01 9.739444e-06 4.869722e-06
[107,] 9.999906e-01 1.878537e-05 9.392686e-06
[108,] 9.999831e-01 3.370426e-05 1.685213e-05
[109,] 9.999797e-01 4.064831e-05 2.032416e-05
[110,] 9.999748e-01 5.043984e-05 2.521992e-05
[111,] 9.999520e-01 9.596569e-05 4.798285e-05
[112,] 9.999030e-01 1.940124e-04 9.700620e-05
[113,] 9.998264e-01 3.471482e-04 1.735741e-04
[114,] 9.997127e-01 5.745426e-04 2.872713e-04
[115,] 9.995483e-01 9.034039e-04 4.517020e-04
[116,] 9.993121e-01 1.375887e-03 6.879434e-04
[117,] 9.988328e-01 2.334366e-03 1.167183e-03
[118,] 9.981995e-01 3.601026e-03 1.800513e-03
[119,] 9.974632e-01 5.073616e-03 2.536808e-03
[120,] 9.969514e-01 6.097156e-03 3.048578e-03
[121,] 9.958790e-01 8.242020e-03 4.121010e-03
[122,] 9.926366e-01 1.472679e-02 7.363397e-03
[123,] 9.938478e-01 1.230434e-02 6.152168e-03
[124,] 9.988573e-01 2.285478e-03 1.142739e-03
[125,] 9.995073e-01 9.853718e-04 4.926859e-04
[126,] 9.991076e-01 1.784865e-03 8.924323e-04
[127,] 9.995649e-01 8.701193e-04 4.350597e-04
[128,] 9.999951e-01 9.752845e-06 4.876423e-06
[129,] 9.999887e-01 2.266312e-05 1.133156e-05
[130,] 9.999475e-01 1.050187e-04 5.250934e-05
[131,] 9.998337e-01 3.326917e-04 1.663459e-04
[132,] 9.995563e-01 8.873661e-04 4.436831e-04
[133,] 9.988847e-01 2.230601e-03 1.115300e-03
[134,] 9.982827e-01 3.434558e-03 1.717279e-03
[135,] 9.952407e-01 9.518563e-03 4.759281e-03
> postscript(file="/var/www/html/rcomp/tmp/1m0es1258726599.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/23t0n1258726599.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3sqmq1258726599.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xzg11258726599.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5t5861258726599.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 = 168
Frequency = 1
1 2 3 4 5 6
-1.80501063 -1.61730297 -1.36266401 -1.01578054 -0.96313774 -0.72776380
7 8 9 10 11 12
-0.66959160 -0.58683473 -0.51077550 -0.41307243 0.17732314 0.18174682
13 14 15 16 17 18
0.45254422 0.52774705 0.68238600 0.61771761 0.73526705 0.71919217
19 20 21 22 23 24
0.82928968 0.97214958 1.06889138 0.85841672 0.96802668 0.94120763
25 26 27 28 29 30
0.90815440 0.80931989 0.63414556 0.59929045 0.55626036 0.48778368
31 32 33 34 35 36
0.44162877 0.27678744 0.23938885 0.21593286 0.38131875 0.50690150
37 38 39 40 41 42
0.63395131 0.79954694 0.67629793 0.94914406 0.89745975 0.92850658
43 44 45 46 47 48
0.89966011 0.71703385 0.58396237 0.44704857 0.57349048 0.55965275
49 50 51 52 53 54
0.78574959 0.69989640 0.65453535 0.41582961 0.35596758 0.09709809
55 56 57 58 59 60
-0.02261767 0.19380309 0.37323645 0.34930398 0.55843745 0.52344066
61 62 63 64 65 66
0.39904166 0.40838487 0.27263102 0.01324271 0.13464278 0.32194203
67 68 69 70 71 72
0.60079680 0.73019889 0.62356655 0.19240933 0.16355179 0.39250867
73 74 75 76 77 78
0.32388559 -0.03985556 -0.15445036 -0.04218376 -0.04626986 0.12804806
79 80 81 82 83 84
0.06891182 -0.09592951 -0.22900099 -0.55678409 -0.61783734 -0.83215156
85 86 87 88 89 90
-0.87048487 -0.91354345 -0.85505388 -1.16346985 -1.31130353 -1.41102296
91 92 93 94 95 96
-1.43939294 -1.50951436 -1.24401531 -1.21554598 -1.05016009 -1.07793213
97 98 99 100 101 102
-1.13262089 -1.11895057 -1.60182614 -2.07082164 -2.16625352 -2.06934708
103 104 105 106 107 108
-1.39962302 -1.07022093 -1.35089060 -1.76088877 -1.92579265 -1.73867740
109 110 111 112 113 114
-0.78566493 -0.72344343 -0.62072979 -0.35268726 -0.58658664 -0.50313801
115 116 117 118 119 120
-0.90844298 -1.13819095 -1.32271125 -0.93846600 -1.22067831 -1.05615158
121 122 123 124 125 126
-0.62910177 -0.45389894 -0.25598889 -0.26873198 -0.05646262 -0.08166821
127 128 129 130 131 132
-0.13600085 -0.44844038 -0.08726844 0.06764003 -0.07610478 -0.10725095
133 134 135 136 137 138
0.01162114 0.29019810 0.41935088 0.61093491 0.45107288 0.06096065
139 140 141 142 143 144
-0.23037131 -0.05384750 0.10490329 1.08097081 1.14250609 1.00750930
145 146 147 148 149 150
0.98072785 0.50400537 0.46392441 0.61608796 0.93316092 1.21565657
151 152 153 154 155 156
0.94834260 0.91811814 0.98409369 1.31208652 1.02554710 1.01170937
157 158 159 160 161 162
0.72720733 0.82789632 1.04744191 1.09142774 1.06618258 0.83375224
163 164 165 166 167 168
1.01741060 1.09488738 0.76661950 0.36094844 -0.09962832 -0.31251307
> postscript(file="/var/www/html/rcomp/tmp/6fwii1258726599.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 = 168
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.80501063 NA
1 -1.61730297 -1.80501063
2 -1.36266401 -1.61730297
3 -1.01578054 -1.36266401
4 -0.96313774 -1.01578054
5 -0.72776380 -0.96313774
6 -0.66959160 -0.72776380
7 -0.58683473 -0.66959160
8 -0.51077550 -0.58683473
9 -0.41307243 -0.51077550
10 0.17732314 -0.41307243
11 0.18174682 0.17732314
12 0.45254422 0.18174682
13 0.52774705 0.45254422
14 0.68238600 0.52774705
15 0.61771761 0.68238600
16 0.73526705 0.61771761
17 0.71919217 0.73526705
18 0.82928968 0.71919217
19 0.97214958 0.82928968
20 1.06889138 0.97214958
21 0.85841672 1.06889138
22 0.96802668 0.85841672
23 0.94120763 0.96802668
24 0.90815440 0.94120763
25 0.80931989 0.90815440
26 0.63414556 0.80931989
27 0.59929045 0.63414556
28 0.55626036 0.59929045
29 0.48778368 0.55626036
30 0.44162877 0.48778368
31 0.27678744 0.44162877
32 0.23938885 0.27678744
33 0.21593286 0.23938885
34 0.38131875 0.21593286
35 0.50690150 0.38131875
36 0.63395131 0.50690150
37 0.79954694 0.63395131
38 0.67629793 0.79954694
39 0.94914406 0.67629793
40 0.89745975 0.94914406
41 0.92850658 0.89745975
42 0.89966011 0.92850658
43 0.71703385 0.89966011
44 0.58396237 0.71703385
45 0.44704857 0.58396237
46 0.57349048 0.44704857
47 0.55965275 0.57349048
48 0.78574959 0.55965275
49 0.69989640 0.78574959
50 0.65453535 0.69989640
51 0.41582961 0.65453535
52 0.35596758 0.41582961
53 0.09709809 0.35596758
54 -0.02261767 0.09709809
55 0.19380309 -0.02261767
56 0.37323645 0.19380309
57 0.34930398 0.37323645
58 0.55843745 0.34930398
59 0.52344066 0.55843745
60 0.39904166 0.52344066
61 0.40838487 0.39904166
62 0.27263102 0.40838487
63 0.01324271 0.27263102
64 0.13464278 0.01324271
65 0.32194203 0.13464278
66 0.60079680 0.32194203
67 0.73019889 0.60079680
68 0.62356655 0.73019889
69 0.19240933 0.62356655
70 0.16355179 0.19240933
71 0.39250867 0.16355179
72 0.32388559 0.39250867
73 -0.03985556 0.32388559
74 -0.15445036 -0.03985556
75 -0.04218376 -0.15445036
76 -0.04626986 -0.04218376
77 0.12804806 -0.04626986
78 0.06891182 0.12804806
79 -0.09592951 0.06891182
80 -0.22900099 -0.09592951
81 -0.55678409 -0.22900099
82 -0.61783734 -0.55678409
83 -0.83215156 -0.61783734
84 -0.87048487 -0.83215156
85 -0.91354345 -0.87048487
86 -0.85505388 -0.91354345
87 -1.16346985 -0.85505388
88 -1.31130353 -1.16346985
89 -1.41102296 -1.31130353
90 -1.43939294 -1.41102296
91 -1.50951436 -1.43939294
92 -1.24401531 -1.50951436
93 -1.21554598 -1.24401531
94 -1.05016009 -1.21554598
95 -1.07793213 -1.05016009
96 -1.13262089 -1.07793213
97 -1.11895057 -1.13262089
98 -1.60182614 -1.11895057
99 -2.07082164 -1.60182614
100 -2.16625352 -2.07082164
101 -2.06934708 -2.16625352
102 -1.39962302 -2.06934708
103 -1.07022093 -1.39962302
104 -1.35089060 -1.07022093
105 -1.76088877 -1.35089060
106 -1.92579265 -1.76088877
107 -1.73867740 -1.92579265
108 -0.78566493 -1.73867740
109 -0.72344343 -0.78566493
110 -0.62072979 -0.72344343
111 -0.35268726 -0.62072979
112 -0.58658664 -0.35268726
113 -0.50313801 -0.58658664
114 -0.90844298 -0.50313801
115 -1.13819095 -0.90844298
116 -1.32271125 -1.13819095
117 -0.93846600 -1.32271125
118 -1.22067831 -0.93846600
119 -1.05615158 -1.22067831
120 -0.62910177 -1.05615158
121 -0.45389894 -0.62910177
122 -0.25598889 -0.45389894
123 -0.26873198 -0.25598889
124 -0.05646262 -0.26873198
125 -0.08166821 -0.05646262
126 -0.13600085 -0.08166821
127 -0.44844038 -0.13600085
128 -0.08726844 -0.44844038
129 0.06764003 -0.08726844
130 -0.07610478 0.06764003
131 -0.10725095 -0.07610478
132 0.01162114 -0.10725095
133 0.29019810 0.01162114
134 0.41935088 0.29019810
135 0.61093491 0.41935088
136 0.45107288 0.61093491
137 0.06096065 0.45107288
138 -0.23037131 0.06096065
139 -0.05384750 -0.23037131
140 0.10490329 -0.05384750
141 1.08097081 0.10490329
142 1.14250609 1.08097081
143 1.00750930 1.14250609
144 0.98072785 1.00750930
145 0.50400537 0.98072785
146 0.46392441 0.50400537
147 0.61608796 0.46392441
148 0.93316092 0.61608796
149 1.21565657 0.93316092
150 0.94834260 1.21565657
151 0.91811814 0.94834260
152 0.98409369 0.91811814
153 1.31208652 0.98409369
154 1.02554710 1.31208652
155 1.01170937 1.02554710
156 0.72720733 1.01170937
157 0.82789632 0.72720733
158 1.04744191 0.82789632
159 1.09142774 1.04744191
160 1.06618258 1.09142774
161 0.83375224 1.06618258
162 1.01741060 0.83375224
163 1.09488738 1.01741060
164 0.76661950 1.09488738
165 0.36094844 0.76661950
166 -0.09962832 0.36094844
167 -0.31251307 -0.09962832
168 NA -0.31251307
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.61730297 -1.80501063
[2,] -1.36266401 -1.61730297
[3,] -1.01578054 -1.36266401
[4,] -0.96313774 -1.01578054
[5,] -0.72776380 -0.96313774
[6,] -0.66959160 -0.72776380
[7,] -0.58683473 -0.66959160
[8,] -0.51077550 -0.58683473
[9,] -0.41307243 -0.51077550
[10,] 0.17732314 -0.41307243
[11,] 0.18174682 0.17732314
[12,] 0.45254422 0.18174682
[13,] 0.52774705 0.45254422
[14,] 0.68238600 0.52774705
[15,] 0.61771761 0.68238600
[16,] 0.73526705 0.61771761
[17,] 0.71919217 0.73526705
[18,] 0.82928968 0.71919217
[19,] 0.97214958 0.82928968
[20,] 1.06889138 0.97214958
[21,] 0.85841672 1.06889138
[22,] 0.96802668 0.85841672
[23,] 0.94120763 0.96802668
[24,] 0.90815440 0.94120763
[25,] 0.80931989 0.90815440
[26,] 0.63414556 0.80931989
[27,] 0.59929045 0.63414556
[28,] 0.55626036 0.59929045
[29,] 0.48778368 0.55626036
[30,] 0.44162877 0.48778368
[31,] 0.27678744 0.44162877
[32,] 0.23938885 0.27678744
[33,] 0.21593286 0.23938885
[34,] 0.38131875 0.21593286
[35,] 0.50690150 0.38131875
[36,] 0.63395131 0.50690150
[37,] 0.79954694 0.63395131
[38,] 0.67629793 0.79954694
[39,] 0.94914406 0.67629793
[40,] 0.89745975 0.94914406
[41,] 0.92850658 0.89745975
[42,] 0.89966011 0.92850658
[43,] 0.71703385 0.89966011
[44,] 0.58396237 0.71703385
[45,] 0.44704857 0.58396237
[46,] 0.57349048 0.44704857
[47,] 0.55965275 0.57349048
[48,] 0.78574959 0.55965275
[49,] 0.69989640 0.78574959
[50,] 0.65453535 0.69989640
[51,] 0.41582961 0.65453535
[52,] 0.35596758 0.41582961
[53,] 0.09709809 0.35596758
[54,] -0.02261767 0.09709809
[55,] 0.19380309 -0.02261767
[56,] 0.37323645 0.19380309
[57,] 0.34930398 0.37323645
[58,] 0.55843745 0.34930398
[59,] 0.52344066 0.55843745
[60,] 0.39904166 0.52344066
[61,] 0.40838487 0.39904166
[62,] 0.27263102 0.40838487
[63,] 0.01324271 0.27263102
[64,] 0.13464278 0.01324271
[65,] 0.32194203 0.13464278
[66,] 0.60079680 0.32194203
[67,] 0.73019889 0.60079680
[68,] 0.62356655 0.73019889
[69,] 0.19240933 0.62356655
[70,] 0.16355179 0.19240933
[71,] 0.39250867 0.16355179
[72,] 0.32388559 0.39250867
[73,] -0.03985556 0.32388559
[74,] -0.15445036 -0.03985556
[75,] -0.04218376 -0.15445036
[76,] -0.04626986 -0.04218376
[77,] 0.12804806 -0.04626986
[78,] 0.06891182 0.12804806
[79,] -0.09592951 0.06891182
[80,] -0.22900099 -0.09592951
[81,] -0.55678409 -0.22900099
[82,] -0.61783734 -0.55678409
[83,] -0.83215156 -0.61783734
[84,] -0.87048487 -0.83215156
[85,] -0.91354345 -0.87048487
[86,] -0.85505388 -0.91354345
[87,] -1.16346985 -0.85505388
[88,] -1.31130353 -1.16346985
[89,] -1.41102296 -1.31130353
[90,] -1.43939294 -1.41102296
[91,] -1.50951436 -1.43939294
[92,] -1.24401531 -1.50951436
[93,] -1.21554598 -1.24401531
[94,] -1.05016009 -1.21554598
[95,] -1.07793213 -1.05016009
[96,] -1.13262089 -1.07793213
[97,] -1.11895057 -1.13262089
[98,] -1.60182614 -1.11895057
[99,] -2.07082164 -1.60182614
[100,] -2.16625352 -2.07082164
[101,] -2.06934708 -2.16625352
[102,] -1.39962302 -2.06934708
[103,] -1.07022093 -1.39962302
[104,] -1.35089060 -1.07022093
[105,] -1.76088877 -1.35089060
[106,] -1.92579265 -1.76088877
[107,] -1.73867740 -1.92579265
[108,] -0.78566493 -1.73867740
[109,] -0.72344343 -0.78566493
[110,] -0.62072979 -0.72344343
[111,] -0.35268726 -0.62072979
[112,] -0.58658664 -0.35268726
[113,] -0.50313801 -0.58658664
[114,] -0.90844298 -0.50313801
[115,] -1.13819095 -0.90844298
[116,] -1.32271125 -1.13819095
[117,] -0.93846600 -1.32271125
[118,] -1.22067831 -0.93846600
[119,] -1.05615158 -1.22067831
[120,] -0.62910177 -1.05615158
[121,] -0.45389894 -0.62910177
[122,] -0.25598889 -0.45389894
[123,] -0.26873198 -0.25598889
[124,] -0.05646262 -0.26873198
[125,] -0.08166821 -0.05646262
[126,] -0.13600085 -0.08166821
[127,] -0.44844038 -0.13600085
[128,] -0.08726844 -0.44844038
[129,] 0.06764003 -0.08726844
[130,] -0.07610478 0.06764003
[131,] -0.10725095 -0.07610478
[132,] 0.01162114 -0.10725095
[133,] 0.29019810 0.01162114
[134,] 0.41935088 0.29019810
[135,] 0.61093491 0.41935088
[136,] 0.45107288 0.61093491
[137,] 0.06096065 0.45107288
[138,] -0.23037131 0.06096065
[139,] -0.05384750 -0.23037131
[140,] 0.10490329 -0.05384750
[141,] 1.08097081 0.10490329
[142,] 1.14250609 1.08097081
[143,] 1.00750930 1.14250609
[144,] 0.98072785 1.00750930
[145,] 0.50400537 0.98072785
[146,] 0.46392441 0.50400537
[147,] 0.61608796 0.46392441
[148,] 0.93316092 0.61608796
[149,] 1.21565657 0.93316092
[150,] 0.94834260 1.21565657
[151,] 0.91811814 0.94834260
[152,] 0.98409369 0.91811814
[153,] 1.31208652 0.98409369
[154,] 1.02554710 1.31208652
[155,] 1.01170937 1.02554710
[156,] 0.72720733 1.01170937
[157,] 0.82789632 0.72720733
[158,] 1.04744191 0.82789632
[159,] 1.09142774 1.04744191
[160,] 1.06618258 1.09142774
[161,] 0.83375224 1.06618258
[162,] 1.01741060 0.83375224
[163,] 1.09488738 1.01741060
[164,] 0.76661950 1.09488738
[165,] 0.36094844 0.76661950
[166,] -0.09962832 0.36094844
[167,] -0.31251307 -0.09962832
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.61730297 -1.80501063
2 -1.36266401 -1.61730297
3 -1.01578054 -1.36266401
4 -0.96313774 -1.01578054
5 -0.72776380 -0.96313774
6 -0.66959160 -0.72776380
7 -0.58683473 -0.66959160
8 -0.51077550 -0.58683473
9 -0.41307243 -0.51077550
10 0.17732314 -0.41307243
11 0.18174682 0.17732314
12 0.45254422 0.18174682
13 0.52774705 0.45254422
14 0.68238600 0.52774705
15 0.61771761 0.68238600
16 0.73526705 0.61771761
17 0.71919217 0.73526705
18 0.82928968 0.71919217
19 0.97214958 0.82928968
20 1.06889138 0.97214958
21 0.85841672 1.06889138
22 0.96802668 0.85841672
23 0.94120763 0.96802668
24 0.90815440 0.94120763
25 0.80931989 0.90815440
26 0.63414556 0.80931989
27 0.59929045 0.63414556
28 0.55626036 0.59929045
29 0.48778368 0.55626036
30 0.44162877 0.48778368
31 0.27678744 0.44162877
32 0.23938885 0.27678744
33 0.21593286 0.23938885
34 0.38131875 0.21593286
35 0.50690150 0.38131875
36 0.63395131 0.50690150
37 0.79954694 0.63395131
38 0.67629793 0.79954694
39 0.94914406 0.67629793
40 0.89745975 0.94914406
41 0.92850658 0.89745975
42 0.89966011 0.92850658
43 0.71703385 0.89966011
44 0.58396237 0.71703385
45 0.44704857 0.58396237
46 0.57349048 0.44704857
47 0.55965275 0.57349048
48 0.78574959 0.55965275
49 0.69989640 0.78574959
50 0.65453535 0.69989640
51 0.41582961 0.65453535
52 0.35596758 0.41582961
53 0.09709809 0.35596758
54 -0.02261767 0.09709809
55 0.19380309 -0.02261767
56 0.37323645 0.19380309
57 0.34930398 0.37323645
58 0.55843745 0.34930398
59 0.52344066 0.55843745
60 0.39904166 0.52344066
61 0.40838487 0.39904166
62 0.27263102 0.40838487
63 0.01324271 0.27263102
64 0.13464278 0.01324271
65 0.32194203 0.13464278
66 0.60079680 0.32194203
67 0.73019889 0.60079680
68 0.62356655 0.73019889
69 0.19240933 0.62356655
70 0.16355179 0.19240933
71 0.39250867 0.16355179
72 0.32388559 0.39250867
73 -0.03985556 0.32388559
74 -0.15445036 -0.03985556
75 -0.04218376 -0.15445036
76 -0.04626986 -0.04218376
77 0.12804806 -0.04626986
78 0.06891182 0.12804806
79 -0.09592951 0.06891182
80 -0.22900099 -0.09592951
81 -0.55678409 -0.22900099
82 -0.61783734 -0.55678409
83 -0.83215156 -0.61783734
84 -0.87048487 -0.83215156
85 -0.91354345 -0.87048487
86 -0.85505388 -0.91354345
87 -1.16346985 -0.85505388
88 -1.31130353 -1.16346985
89 -1.41102296 -1.31130353
90 -1.43939294 -1.41102296
91 -1.50951436 -1.43939294
92 -1.24401531 -1.50951436
93 -1.21554598 -1.24401531
94 -1.05016009 -1.21554598
95 -1.07793213 -1.05016009
96 -1.13262089 -1.07793213
97 -1.11895057 -1.13262089
98 -1.60182614 -1.11895057
99 -2.07082164 -1.60182614
100 -2.16625352 -2.07082164
101 -2.06934708 -2.16625352
102 -1.39962302 -2.06934708
103 -1.07022093 -1.39962302
104 -1.35089060 -1.07022093
105 -1.76088877 -1.35089060
106 -1.92579265 -1.76088877
107 -1.73867740 -1.92579265
108 -0.78566493 -1.73867740
109 -0.72344343 -0.78566493
110 -0.62072979 -0.72344343
111 -0.35268726 -0.62072979
112 -0.58658664 -0.35268726
113 -0.50313801 -0.58658664
114 -0.90844298 -0.50313801
115 -1.13819095 -0.90844298
116 -1.32271125 -1.13819095
117 -0.93846600 -1.32271125
118 -1.22067831 -0.93846600
119 -1.05615158 -1.22067831
120 -0.62910177 -1.05615158
121 -0.45389894 -0.62910177
122 -0.25598889 -0.45389894
123 -0.26873198 -0.25598889
124 -0.05646262 -0.26873198
125 -0.08166821 -0.05646262
126 -0.13600085 -0.08166821
127 -0.44844038 -0.13600085
128 -0.08726844 -0.44844038
129 0.06764003 -0.08726844
130 -0.07610478 0.06764003
131 -0.10725095 -0.07610478
132 0.01162114 -0.10725095
133 0.29019810 0.01162114
134 0.41935088 0.29019810
135 0.61093491 0.41935088
136 0.45107288 0.61093491
137 0.06096065 0.45107288
138 -0.23037131 0.06096065
139 -0.05384750 -0.23037131
140 0.10490329 -0.05384750
141 1.08097081 0.10490329
142 1.14250609 1.08097081
143 1.00750930 1.14250609
144 0.98072785 1.00750930
145 0.50400537 0.98072785
146 0.46392441 0.50400537
147 0.61608796 0.46392441
148 0.93316092 0.61608796
149 1.21565657 0.93316092
150 0.94834260 1.21565657
151 0.91811814 0.94834260
152 0.98409369 0.91811814
153 1.31208652 0.98409369
154 1.02554710 1.31208652
155 1.01170937 1.02554710
156 0.72720733 1.01170937
157 0.82789632 0.72720733
158 1.04744191 0.82789632
159 1.09142774 1.04744191
160 1.06618258 1.09142774
161 0.83375224 1.06618258
162 1.01741060 0.83375224
163 1.09488738 1.01741060
164 0.76661950 1.09488738
165 0.36094844 0.76661950
166 -0.09962832 0.36094844
167 -0.31251307 -0.09962832
> 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/7t3qh1258726599.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8e8231258726599.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9sto31258726599.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10wv0g1258726599.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11l6z51258726599.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/12w9871258726599.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/13p4zs1258726599.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/14n14j1258726599.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/15xs2c1258726599.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/167psm1258726599.tab")
+ }
> system("convert tmp/1m0es1258726599.ps tmp/1m0es1258726599.png")
> system("convert tmp/23t0n1258726599.ps tmp/23t0n1258726599.png")
> system("convert tmp/3sqmq1258726599.ps tmp/3sqmq1258726599.png")
> system("convert tmp/4xzg11258726599.ps tmp/4xzg11258726599.png")
> system("convert tmp/5t5861258726599.ps tmp/5t5861258726599.png")
> system("convert tmp/6fwii1258726599.ps tmp/6fwii1258726599.png")
> system("convert tmp/7t3qh1258726599.ps tmp/7t3qh1258726599.png")
> system("convert tmp/8e8231258726599.ps tmp/8e8231258726599.png")
> system("convert tmp/9sto31258726599.ps tmp/9sto31258726599.png")
> system("convert tmp/10wv0g1258726599.ps tmp/10wv0g1258726599.png")
>
>
> proc.time()
user system elapsed
4.268 1.658 4.767