R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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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(8.64
+ ,8.89
+ ,8.87
+ ,8.81
+ ,8.87
+ ,9.06
+ ,9.12
+ ,8.66
+ ,8.17
+ ,8.04
+ ,7.71
+ ,7.55
+ ,7.52
+ ,7.38
+ ,7.52
+ ,7.31
+ ,6.92
+ ,7.09
+ ,7.05
+ ,7.37
+ ,7.05
+ ,6.79
+ ,6.35
+ ,6.44
+ ,6.89
+ ,7.16
+ ,7.46
+ ,7.91
+ ,7.86
+ ,8.02
+ ,8.38
+ ,8.50
+ ,8.40
+ ,8.24
+ ,8.33
+ ,8.28
+ ,8.15
+ ,8.06
+ ,7.79
+ ,7.28
+ ,7.52
+ ,7.23
+ ,7.13
+ ,7.21
+ ,6.99
+ ,6.77
+ ,6.69
+ ,6.39
+ ,6.85
+ ,6.74
+ ,6.56
+ ,6.62
+ ,6.71
+ ,6.67
+ ,6.54
+ ,6.14
+ ,6.13
+ ,5.86
+ ,5.88
+ ,5.75
+ ,5.53
+ ,5.86
+ ,5.90
+ ,5.95
+ ,5.69
+ ,5.53
+ ,5.71
+ ,5.60
+ ,5.73
+ ,5.60
+ ,5.41
+ ,5.13
+ ,5.00
+ ,5.04
+ ,5.10
+ ,4.96
+ ,4.90
+ ,4.80
+ ,4.48
+ ,4.29
+ ,4.27
+ ,4.18
+ ,4.02
+ ,3.82
+ ,4.13
+ ,4.16
+ ,3.98
+ ,4.26
+ ,4.70
+ ,4.96
+ ,5.13
+ ,5.35
+ ,5.41
+ ,5.42
+ ,5.51
+ ,5.75
+ ,5.67
+ ,5.46
+ ,5.56
+ ,5.56
+ ,5.54
+ ,5.53
+ ,5.65
+ ,5.58
+ ,5.57
+ ,5.36
+ ,5.23
+ ,5.11
+ ,5.07
+ ,5.04
+ ,5.34
+ ,5.43
+ ,5.31
+ ,5.12
+ ,4.97
+ ,5.00
+ ,4.64
+ ,4.80
+ ,5.10
+ ,5.11
+ ,5.12
+ ,5.36
+ ,5.26
+ ,5.27
+ ,5.10
+ ,4.94
+ ,4.68
+ ,4.41
+ ,4.60
+ ,4.53
+ ,4.18
+ ,4.00
+ ,3.87
+ ,4.09
+ ,4.13
+ ,3.74
+ ,3.81
+ ,4.11
+ ,4.14
+ ,3.99
+ ,4.28
+ ,4.37
+ ,4.24
+ ,4.19
+ ,4.01
+ ,3.95
+ ,4.30
+ ,4.37
+ ,4.40
+ ,4.29
+ ,4.12
+ ,4.07
+ ,3.93
+ ,3.79
+ ,3.67
+ ,3.53
+ ,3.69
+ ,3.69
+ ,3.48
+ ,3.31
+ ,3.16
+ ,3.25
+ ,3.14
+ ,3.19
+ ,3.43
+ ,3.45
+ ,3.31
+ ,3.51
+ ,3.53
+ ,3.83
+ ,4.02
+ ,3.99
+ ,4.11
+ ,3.96
+ ,3.83
+ ,3.71
+ ,3.81
+ ,3.73
+ ,3.99
+ ,4.17
+ ,4.00
+ ,4.10
+ ,4.24
+ ,4.45
+ ,4.62
+ ,4.49
+ ,4.45
+ ,4.49
+ ,4.36
+ ,4.32
+ ,4.45
+ ,4.13
+ ,4.14
+ ,4.30
+ ,4.42
+ ,4.67
+ ,4.96
+ ,4.73
+ ,4.52
+ ,4.36
+ ,4.15
+ ,3.92
+ ,3.88
+ ,4.20
+ ,3.95
+ ,3.78
+ ,3.69
+ ,3.77
+ ,3.66
+ ,3.53
+ ,3.50
+ ,3.14
+ ,3.42
+ ,3.30
+ ,2.81
+ ,3.15
+ ,3.37
+ ,4.05
+ ,4.00
+ ,4.20
+ ,4.21
+ ,4.24
+ ,4.24
+ ,4.17
+ ,4.12
+ ,4.35
+ ,3.98
+ ,3.62
+ ,4.39
+ ,5.01
+ ,4.07
+ ,3.70
+ ,3.59
+ ,3.44
+ ,3.33
+ ,2.98
+ ,3.14
+ ,2.55
+ ,2.49
+ ,2.53
+ ,2.43)
+ ,dim=c(1
+ ,241)
+ ,dimnames=list(c('OLO')
+ ,1:241))
> y <- array(NA,dim=c(1,241),dimnames=list(c('OLO'),1:241))
> 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'
> 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, 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
OLO M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.64 1 0 0 0 0 0 0 0 0 0 0 1
2 8.89 0 1 0 0 0 0 0 0 0 0 0 2
3 8.87 0 0 1 0 0 0 0 0 0 0 0 3
4 8.81 0 0 0 1 0 0 0 0 0 0 0 4
5 8.87 0 0 0 0 1 0 0 0 0 0 0 5
6 9.06 0 0 0 0 0 1 0 0 0 0 0 6
7 9.12 0 0 0 0 0 0 1 0 0 0 0 7
8 8.66 0 0 0 0 0 0 0 1 0 0 0 8
9 8.17 0 0 0 0 0 0 0 0 1 0 0 9
10 8.04 0 0 0 0 0 0 0 0 0 1 0 10
11 7.71 0 0 0 0 0 0 0 0 0 0 1 11
12 7.55 0 0 0 0 0 0 0 0 0 0 0 12
13 7.52 1 0 0 0 0 0 0 0 0 0 0 13
14 7.38 0 1 0 0 0 0 0 0 0 0 0 14
15 7.52 0 0 1 0 0 0 0 0 0 0 0 15
16 7.31 0 0 0 1 0 0 0 0 0 0 0 16
17 6.92 0 0 0 0 1 0 0 0 0 0 0 17
18 7.09 0 0 0 0 0 1 0 0 0 0 0 18
19 7.05 0 0 0 0 0 0 1 0 0 0 0 19
20 7.37 0 0 0 0 0 0 0 1 0 0 0 20
21 7.05 0 0 0 0 0 0 0 0 1 0 0 21
22 6.79 0 0 0 0 0 0 0 0 0 1 0 22
23 6.35 0 0 0 0 0 0 0 0 0 0 1 23
24 6.44 0 0 0 0 0 0 0 0 0 0 0 24
25 6.89 1 0 0 0 0 0 0 0 0 0 0 25
26 7.16 0 1 0 0 0 0 0 0 0 0 0 26
27 7.46 0 0 1 0 0 0 0 0 0 0 0 27
28 7.91 0 0 0 1 0 0 0 0 0 0 0 28
29 7.86 0 0 0 0 1 0 0 0 0 0 0 29
30 8.02 0 0 0 0 0 1 0 0 0 0 0 30
31 8.38 0 0 0 0 0 0 1 0 0 0 0 31
32 8.50 0 0 0 0 0 0 0 1 0 0 0 32
33 8.40 0 0 0 0 0 0 0 0 1 0 0 33
34 8.24 0 0 0 0 0 0 0 0 0 1 0 34
35 8.33 0 0 0 0 0 0 0 0 0 0 1 35
36 8.28 0 0 0 0 0 0 0 0 0 0 0 36
37 8.15 1 0 0 0 0 0 0 0 0 0 0 37
38 8.06 0 1 0 0 0 0 0 0 0 0 0 38
39 7.79 0 0 1 0 0 0 0 0 0 0 0 39
40 7.28 0 0 0 1 0 0 0 0 0 0 0 40
41 7.52 0 0 0 0 1 0 0 0 0 0 0 41
42 7.23 0 0 0 0 0 1 0 0 0 0 0 42
43 7.13 0 0 0 0 0 0 1 0 0 0 0 43
44 7.21 0 0 0 0 0 0 0 1 0 0 0 44
45 6.99 0 0 0 0 0 0 0 0 1 0 0 45
46 6.77 0 0 0 0 0 0 0 0 0 1 0 46
47 6.69 0 0 0 0 0 0 0 0 0 0 1 47
48 6.39 0 0 0 0 0 0 0 0 0 0 0 48
49 6.85 1 0 0 0 0 0 0 0 0 0 0 49
50 6.74 0 1 0 0 0 0 0 0 0 0 0 50
51 6.56 0 0 1 0 0 0 0 0 0 0 0 51
52 6.62 0 0 0 1 0 0 0 0 0 0 0 52
53 6.71 0 0 0 0 1 0 0 0 0 0 0 53
54 6.67 0 0 0 0 0 1 0 0 0 0 0 54
55 6.54 0 0 0 0 0 0 1 0 0 0 0 55
56 6.14 0 0 0 0 0 0 0 1 0 0 0 56
57 6.13 0 0 0 0 0 0 0 0 1 0 0 57
58 5.86 0 0 0 0 0 0 0 0 0 1 0 58
59 5.88 0 0 0 0 0 0 0 0 0 0 1 59
60 5.75 0 0 0 0 0 0 0 0 0 0 0 60
61 5.53 1 0 0 0 0 0 0 0 0 0 0 61
62 5.86 0 1 0 0 0 0 0 0 0 0 0 62
63 5.90 0 0 1 0 0 0 0 0 0 0 0 63
64 5.95 0 0 0 1 0 0 0 0 0 0 0 64
65 5.69 0 0 0 0 1 0 0 0 0 0 0 65
66 5.53 0 0 0 0 0 1 0 0 0 0 0 66
67 5.71 0 0 0 0 0 0 1 0 0 0 0 67
68 5.60 0 0 0 0 0 0 0 1 0 0 0 68
69 5.73 0 0 0 0 0 0 0 0 1 0 0 69
70 5.60 0 0 0 0 0 0 0 0 0 1 0 70
71 5.41 0 0 0 0 0 0 0 0 0 0 1 71
72 5.13 0 0 0 0 0 0 0 0 0 0 0 72
73 5.00 1 0 0 0 0 0 0 0 0 0 0 73
74 5.04 0 1 0 0 0 0 0 0 0 0 0 74
75 5.10 0 0 1 0 0 0 0 0 0 0 0 75
76 4.96 0 0 0 1 0 0 0 0 0 0 0 76
77 4.90 0 0 0 0 1 0 0 0 0 0 0 77
78 4.80 0 0 0 0 0 1 0 0 0 0 0 78
79 4.48 0 0 0 0 0 0 1 0 0 0 0 79
80 4.29 0 0 0 0 0 0 0 1 0 0 0 80
81 4.27 0 0 0 0 0 0 0 0 1 0 0 81
82 4.18 0 0 0 0 0 0 0 0 0 1 0 82
83 4.02 0 0 0 0 0 0 0 0 0 0 1 83
84 3.82 0 0 0 0 0 0 0 0 0 0 0 84
85 4.13 1 0 0 0 0 0 0 0 0 0 0 85
86 4.16 0 1 0 0 0 0 0 0 0 0 0 86
87 3.98 0 0 1 0 0 0 0 0 0 0 0 87
88 4.26 0 0 0 1 0 0 0 0 0 0 0 88
89 4.70 0 0 0 0 1 0 0 0 0 0 0 89
90 4.96 0 0 0 0 0 1 0 0 0 0 0 90
91 5.13 0 0 0 0 0 0 1 0 0 0 0 91
92 5.35 0 0 0 0 0 0 0 1 0 0 0 92
93 5.41 0 0 0 0 0 0 0 0 1 0 0 93
94 5.42 0 0 0 0 0 0 0 0 0 1 0 94
95 5.51 0 0 0 0 0 0 0 0 0 0 1 95
96 5.75 0 0 0 0 0 0 0 0 0 0 0 96
97 5.67 1 0 0 0 0 0 0 0 0 0 0 97
98 5.46 0 1 0 0 0 0 0 0 0 0 0 98
99 5.56 0 0 1 0 0 0 0 0 0 0 0 99
100 5.56 0 0 0 1 0 0 0 0 0 0 0 100
101 5.54 0 0 0 0 1 0 0 0 0 0 0 101
102 5.53 0 0 0 0 0 1 0 0 0 0 0 102
103 5.65 0 0 0 0 0 0 1 0 0 0 0 103
104 5.58 0 0 0 0 0 0 0 1 0 0 0 104
105 5.57 0 0 0 0 0 0 0 0 1 0 0 105
106 5.36 0 0 0 0 0 0 0 0 0 1 0 106
107 5.23 0 0 0 0 0 0 0 0 0 0 1 107
108 5.11 0 0 0 0 0 0 0 0 0 0 0 108
109 5.07 1 0 0 0 0 0 0 0 0 0 0 109
110 5.04 0 1 0 0 0 0 0 0 0 0 0 110
111 5.34 0 0 1 0 0 0 0 0 0 0 0 111
112 5.43 0 0 0 1 0 0 0 0 0 0 0 112
113 5.31 0 0 0 0 1 0 0 0 0 0 0 113
114 5.12 0 0 0 0 0 1 0 0 0 0 0 114
115 4.97 0 0 0 0 0 0 1 0 0 0 0 115
116 5.00 0 0 0 0 0 0 0 1 0 0 0 116
117 4.64 0 0 0 0 0 0 0 0 1 0 0 117
118 4.80 0 0 0 0 0 0 0 0 0 1 0 118
119 5.10 0 0 0 0 0 0 0 0 0 0 1 119
120 5.11 0 0 0 0 0 0 0 0 0 0 0 120
121 5.12 1 0 0 0 0 0 0 0 0 0 0 121
122 5.36 0 1 0 0 0 0 0 0 0 0 0 122
123 5.26 0 0 1 0 0 0 0 0 0 0 0 123
124 5.27 0 0 0 1 0 0 0 0 0 0 0 124
125 5.10 0 0 0 0 1 0 0 0 0 0 0 125
126 4.94 0 0 0 0 0 1 0 0 0 0 0 126
127 4.68 0 0 0 0 0 0 1 0 0 0 0 127
128 4.41 0 0 0 0 0 0 0 1 0 0 0 128
129 4.60 0 0 0 0 0 0 0 0 1 0 0 129
130 4.53 0 0 0 0 0 0 0 0 0 1 0 130
131 4.18 0 0 0 0 0 0 0 0 0 0 1 131
132 4.00 0 0 0 0 0 0 0 0 0 0 0 132
133 3.87 1 0 0 0 0 0 0 0 0 0 0 133
134 4.09 0 1 0 0 0 0 0 0 0 0 0 134
135 4.13 0 0 1 0 0 0 0 0 0 0 0 135
136 3.74 0 0 0 1 0 0 0 0 0 0 0 136
137 3.81 0 0 0 0 1 0 0 0 0 0 0 137
138 4.11 0 0 0 0 0 1 0 0 0 0 0 138
139 4.14 0 0 0 0 0 0 1 0 0 0 0 139
140 3.99 0 0 0 0 0 0 0 1 0 0 0 140
141 4.28 0 0 0 0 0 0 0 0 1 0 0 141
142 4.37 0 0 0 0 0 0 0 0 0 1 0 142
143 4.24 0 0 0 0 0 0 0 0 0 0 1 143
144 4.19 0 0 0 0 0 0 0 0 0 0 0 144
145 4.01 1 0 0 0 0 0 0 0 0 0 0 145
146 3.95 0 1 0 0 0 0 0 0 0 0 0 146
147 4.30 0 0 1 0 0 0 0 0 0 0 0 147
148 4.37 0 0 0 1 0 0 0 0 0 0 0 148
149 4.40 0 0 0 0 1 0 0 0 0 0 0 149
150 4.29 0 0 0 0 0 1 0 0 0 0 0 150
151 4.12 0 0 0 0 0 0 1 0 0 0 0 151
152 4.07 0 0 0 0 0 0 0 1 0 0 0 152
153 3.93 0 0 0 0 0 0 0 0 1 0 0 153
154 3.79 0 0 0 0 0 0 0 0 0 1 0 154
155 3.67 0 0 0 0 0 0 0 0 0 0 1 155
156 3.53 0 0 0 0 0 0 0 0 0 0 0 156
157 3.69 1 0 0 0 0 0 0 0 0 0 0 157
158 3.69 0 1 0 0 0 0 0 0 0 0 0 158
159 3.48 0 0 1 0 0 0 0 0 0 0 0 159
160 3.31 0 0 0 1 0 0 0 0 0 0 0 160
161 3.16 0 0 0 0 1 0 0 0 0 0 0 161
162 3.25 0 0 0 0 0 1 0 0 0 0 0 162
163 3.14 0 0 0 0 0 0 1 0 0 0 0 163
164 3.19 0 0 0 0 0 0 0 1 0 0 0 164
165 3.43 0 0 0 0 0 0 0 0 1 0 0 165
166 3.45 0 0 0 0 0 0 0 0 0 1 0 166
167 3.31 0 0 0 0 0 0 0 0 0 0 1 167
168 3.51 0 0 0 0 0 0 0 0 0 0 0 168
169 3.53 1 0 0 0 0 0 0 0 0 0 0 169
170 3.83 0 1 0 0 0 0 0 0 0 0 0 170
171 4.02 0 0 1 0 0 0 0 0 0 0 0 171
172 3.99 0 0 0 1 0 0 0 0 0 0 0 172
173 4.11 0 0 0 0 1 0 0 0 0 0 0 173
174 3.96 0 0 0 0 0 1 0 0 0 0 0 174
175 3.83 0 0 0 0 0 0 1 0 0 0 0 175
176 3.71 0 0 0 0 0 0 0 1 0 0 0 176
177 3.81 0 0 0 0 0 0 0 0 1 0 0 177
178 3.73 0 0 0 0 0 0 0 0 0 1 0 178
179 3.99 0 0 0 0 0 0 0 0 0 0 1 179
180 4.17 0 0 0 0 0 0 0 0 0 0 0 180
181 4.00 1 0 0 0 0 0 0 0 0 0 0 181
182 4.10 0 1 0 0 0 0 0 0 0 0 0 182
183 4.24 0 0 1 0 0 0 0 0 0 0 0 183
184 4.45 0 0 0 1 0 0 0 0 0 0 0 184
185 4.62 0 0 0 0 1 0 0 0 0 0 0 185
186 4.49 0 0 0 0 0 1 0 0 0 0 0 186
187 4.45 0 0 0 0 0 0 1 0 0 0 0 187
188 4.49 0 0 0 0 0 0 0 1 0 0 0 188
189 4.36 0 0 0 0 0 0 0 0 1 0 0 189
190 4.32 0 0 0 0 0 0 0 0 0 1 0 190
191 4.45 0 0 0 0 0 0 0 0 0 0 1 191
192 4.13 0 0 0 0 0 0 0 0 0 0 0 192
193 4.14 1 0 0 0 0 0 0 0 0 0 0 193
194 4.30 0 1 0 0 0 0 0 0 0 0 0 194
195 4.42 0 0 1 0 0 0 0 0 0 0 0 195
196 4.67 0 0 0 1 0 0 0 0 0 0 0 196
197 4.96 0 0 0 0 1 0 0 0 0 0 0 197
198 4.73 0 0 0 0 0 1 0 0 0 0 0 198
199 4.52 0 0 0 0 0 0 1 0 0 0 0 199
200 4.36 0 0 0 0 0 0 0 1 0 0 0 200
201 4.15 0 0 0 0 0 0 0 0 1 0 0 201
202 3.92 0 0 0 0 0 0 0 0 0 1 0 202
203 3.88 0 0 0 0 0 0 0 0 0 0 1 203
204 4.20 0 0 0 0 0 0 0 0 0 0 0 204
205 3.95 1 0 0 0 0 0 0 0 0 0 0 205
206 3.78 0 1 0 0 0 0 0 0 0 0 0 206
207 3.69 0 0 1 0 0 0 0 0 0 0 0 207
208 3.77 0 0 0 1 0 0 0 0 0 0 0 208
209 3.66 0 0 0 0 1 0 0 0 0 0 0 209
210 3.53 0 0 0 0 0 1 0 0 0 0 0 210
211 3.50 0 0 0 0 0 0 1 0 0 0 0 211
212 3.14 0 0 0 0 0 0 0 1 0 0 0 212
213 3.42 0 0 0 0 0 0 0 0 1 0 0 213
214 3.30 0 0 0 0 0 0 0 0 0 1 0 214
215 2.81 0 0 0 0 0 0 0 0 0 0 1 215
216 3.15 0 0 0 0 0 0 0 0 0 0 0 216
217 3.37 1 0 0 0 0 0 0 0 0 0 0 217
218 4.05 0 1 0 0 0 0 0 0 0 0 0 218
219 4.00 0 0 1 0 0 0 0 0 0 0 0 219
220 4.20 0 0 0 1 0 0 0 0 0 0 0 220
221 4.21 0 0 0 0 1 0 0 0 0 0 0 221
222 4.24 0 0 0 0 0 1 0 0 0 0 0 222
223 4.24 0 0 0 0 0 0 1 0 0 0 0 223
224 4.17 0 0 0 0 0 0 0 1 0 0 0 224
225 4.12 0 0 0 0 0 0 0 0 1 0 0 225
226 4.35 0 0 0 0 0 0 0 0 0 1 0 226
227 3.98 0 0 0 0 0 0 0 0 0 0 1 227
228 3.62 0 0 0 0 0 0 0 0 0 0 0 228
229 4.39 1 0 0 0 0 0 0 0 0 0 0 229
230 5.01 0 1 0 0 0 0 0 0 0 0 0 230
231 4.07 0 0 1 0 0 0 0 0 0 0 0 231
232 3.70 0 0 0 1 0 0 0 0 0 0 0 232
233 3.59 0 0 0 0 1 0 0 0 0 0 0 233
234 3.44 0 0 0 0 0 1 0 0 0 0 0 234
235 3.33 0 0 0 0 0 0 1 0 0 0 0 235
236 2.98 0 0 0 0 0 0 0 1 0 0 0 236
237 3.14 0 0 0 0 0 0 0 0 1 0 0 237
238 2.55 0 0 0 0 0 0 0 0 0 1 0 238
239 2.49 0 0 0 0 0 0 0 0 0 0 1 239
240 2.53 0 0 0 0 0 0 0 0 0 0 0 240
241 2.43 1 0 0 0 0 0 0 0 0 0 0 241
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
7.25671 0.13046 0.28595 0.29231 0.30516 0.32852
M6 M7 M8 M9 M10 M11
0.31537 0.29073 0.21508 0.20394 0.11179 0.02415
t
-0.01935
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.8851 -0.4599 -0.0133 0.5122 1.9189
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.2567078 0.1990105 36.464 <2e-16 ***
M1 0.1304640 0.2465122 0.529 0.597
M2 0.2859518 0.2495800 1.146 0.253
M3 0.2923066 0.2495597 1.171 0.243
M4 0.3051614 0.2495415 1.223 0.223
M5 0.3285162 0.2495254 1.317 0.189
M6 0.3153711 0.2495115 1.264 0.208
M7 0.2907259 0.2494997 1.165 0.245
M8 0.2150807 0.2494900 0.862 0.390
M9 0.2039355 0.2494825 0.817 0.415
M10 0.1117904 0.2494772 0.448 0.655
M11 0.0241452 0.2494740 0.097 0.923
t -0.0193548 0.0007311 -26.472 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7889 on 228 degrees of freedom
Multiple R-squared: 0.7572, Adjusted R-squared: 0.7444
F-statistic: 59.26 on 12 and 228 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.0069048133 1.380963e-02 9.930952e-01
[2,] 0.0121995813 2.439916e-02 9.878004e-01
[3,] 0.0080028758 1.600575e-02 9.919971e-01
[4,] 0.0054137620 1.082752e-02 9.945862e-01
[5,] 0.0024606206 4.921241e-03 9.975394e-01
[6,] 0.0015523189 3.104638e-03 9.984477e-01
[7,] 0.0006422223 1.284445e-03 9.993578e-01
[8,] 0.0002149240 4.298481e-04 9.997851e-01
[9,] 0.0001110518 2.221035e-04 9.998889e-01
[10,] 0.0008048385 1.609677e-03 9.991952e-01
[11,] 0.0025932964 5.186593e-03 9.974067e-01
[12,] 0.0068551486 1.371030e-02 9.931449e-01
[13,] 0.0352284754 7.045695e-02 9.647715e-01
[14,] 0.0844760379 1.689521e-01 9.155240e-01
[15,] 0.1314790454 2.629581e-01 8.685210e-01
[16,] 0.2368333652 4.736667e-01 7.631666e-01
[17,] 0.3864929533 7.729859e-01 6.135070e-01
[18,] 0.5825761381 8.348477e-01 4.174239e-01
[19,] 0.7314574468 5.370851e-01 2.685426e-01
[20,] 0.8917982928 2.164034e-01 1.082017e-01
[21,] 0.9604617711 7.907646e-02 3.953823e-02
[22,] 0.9750472854 4.990543e-02 2.495271e-02
[23,] 0.9801912777 3.961744e-02 1.980872e-02
[24,] 0.9804995659 3.900087e-02 1.950043e-02
[25,] 0.9781667863 4.366643e-02 2.183321e-02
[26,] 0.9766597617 4.668048e-02 2.334024e-02
[27,] 0.9752246210 4.955076e-02 2.477538e-02
[28,] 0.9757715904 4.845682e-02 2.422841e-02
[29,] 0.9772366416 4.552672e-02 2.276336e-02
[30,] 0.9761824853 4.763503e-02 2.381751e-02
[31,] 0.9745179902 5.096402e-02 2.548201e-02
[32,] 0.9721514080 5.569718e-02 2.784859e-02
[33,] 0.9694602833 6.107943e-02 3.053972e-02
[34,] 0.9696311864 6.073763e-02 3.036881e-02
[35,] 0.9680323390 6.393532e-02 3.196766e-02
[36,] 0.9673408322 6.531834e-02 3.265917e-02
[37,] 0.9652546236 6.949075e-02 3.474538e-02
[38,] 0.9631363503 7.372730e-02 3.686365e-02
[39,] 0.9623069939 7.538601e-02 3.769301e-02
[40,] 0.9632865158 7.342697e-02 3.671348e-02
[41,] 0.9683187925 6.336241e-02 3.168121e-02
[42,] 0.9672205820 6.555884e-02 3.277942e-02
[43,] 0.9654933838 6.901323e-02 3.450662e-02
[44,] 0.9615200247 7.695995e-02 3.847998e-02
[45,] 0.9564366582 8.712668e-02 4.356334e-02
[46,] 0.9579107967 8.417841e-02 4.208920e-02
[47,] 0.9531900838 9.361983e-02 4.680992e-02
[48,] 0.9475811003 1.048378e-01 5.241890e-02
[49,] 0.9404023846 1.191952e-01 5.959762e-02
[50,] 0.9338049150 1.323902e-01 6.619508e-02
[51,] 0.9307596467 1.384807e-01 6.924035e-02
[52,] 0.9249466376 1.501067e-01 7.505336e-02
[53,] 0.9193797749 1.612405e-01 8.062023e-02
[54,] 0.9083686212 1.832628e-01 9.163138e-02
[55,] 0.8949820114 2.100360e-01 1.050180e-01
[56,] 0.8788322232 2.423356e-01 1.211678e-01
[57,] 0.8597762696 2.804475e-01 1.402237e-01
[58,] 0.8463151258 3.073697e-01 1.536849e-01
[59,] 0.8332695967 3.334608e-01 1.667304e-01
[60,] 0.8154859239 3.690282e-01 1.845141e-01
[61,] 0.8015482769 3.969034e-01 1.984517e-01
[62,] 0.7875890657 4.248219e-01 2.124109e-01
[63,] 0.7789080603 4.421839e-01 2.210919e-01
[64,] 0.8026896171 3.946208e-01 1.973104e-01
[65,] 0.8338122150 3.323756e-01 1.661878e-01
[66,] 0.8509768607 2.980463e-01 1.490231e-01
[67,] 0.8609302813 2.781394e-01 1.390697e-01
[68,] 0.8732219951 2.535560e-01 1.267780e-01
[69,] 0.8958598301 2.082803e-01 1.041402e-01
[70,] 0.8983362171 2.033276e-01 1.016638e-01
[71,] 0.9094346144 1.811308e-01 9.056539e-02
[72,] 0.9313861911 1.372276e-01 6.861381e-02
[73,] 0.9358578929 1.282842e-01 6.414211e-02
[74,] 0.9289529096 1.420942e-01 7.104709e-02
[75,] 0.9202006610 1.595987e-01 7.979934e-02
[76,] 0.9128722710 1.742555e-01 8.712773e-02
[77,] 0.9167950022 1.664100e-01 8.320500e-02
[78,] 0.9276372497 1.447255e-01 7.236275e-02
[79,] 0.9436910593 1.126179e-01 5.630894e-02
[80,] 0.9644323780 7.113524e-02 3.556762e-02
[81,] 0.9858720688 2.825586e-02 1.412793e-02
[82,] 0.9928671660 1.426567e-02 7.132834e-03
[83,] 0.9942691388 1.146172e-02 5.730861e-03
[84,] 0.9957975692 8.404862e-03 4.202431e-03
[85,] 0.9968450934 6.309813e-03 3.154907e-03
[86,] 0.9974836845 5.032631e-03 2.516315e-03
[87,] 0.9979514814 4.097037e-03 2.048519e-03
[88,] 0.9986140794 2.771841e-03 1.385921e-03
[89,] 0.9990903214 1.819357e-03 9.096786e-04
[90,] 0.9994333497 1.133301e-03 5.666503e-04
[91,] 0.9995899376 8.201249e-04 4.100624e-04
[92,] 0.9996920489 6.159021e-04 3.079511e-04
[93,] 0.9997424760 5.150479e-04 2.575240e-04
[94,] 0.9997502452 4.995095e-04 2.497548e-04
[95,] 0.9997082290 5.835421e-04 2.917710e-04
[96,] 0.9997508322 4.983356e-04 2.491678e-04
[97,] 0.9998128834 3.742331e-04 1.871166e-04
[98,] 0.9998308994 3.382012e-04 1.691006e-04
[99,] 0.9998155814 3.688371e-04 1.844186e-04
[100,] 0.9997797220 4.405559e-04 2.202780e-04
[101,] 0.9997708736 4.582528e-04 2.291264e-04
[102,] 0.9996884729 6.230541e-04 3.115271e-04
[103,] 0.9996491013 7.017974e-04 3.508987e-04
[104,] 0.9997711100 4.577800e-04 2.288900e-04
[105,] 0.9998660310 2.679380e-04 1.339690e-04
[106,] 0.9999170203 1.659594e-04 8.297969e-05
[107,] 0.9999519853 9.602935e-05 4.801467e-05
[108,] 0.9999686605 6.267901e-05 3.133951e-05
[109,] 0.9999809296 3.814081e-05 1.907040e-05
[110,] 0.9999840847 3.183053e-05 1.591527e-05
[111,] 0.9999843095 3.138097e-05 1.569049e-05
[112,] 0.9999807066 3.858671e-05 1.929336e-05
[113,] 0.9999734943 5.301135e-05 2.650568e-05
[114,] 0.9999685567 6.288660e-05 3.144330e-05
[115,] 0.9999644251 7.114987e-05 3.557493e-05
[116,] 0.9999507792 9.844150e-05 4.922075e-05
[117,] 0.9999271708 1.456584e-04 7.282918e-05
[118,] 0.9998923855 2.152290e-04 1.076145e-04
[119,] 0.9998446544 3.106911e-04 1.553456e-04
[120,] 0.9997737586 4.524828e-04 2.262414e-04
[121,] 0.9997312296 5.375407e-04 2.687704e-04
[122,] 0.9996714285 6.571429e-04 3.285715e-04
[123,] 0.9995304514 9.390971e-04 4.695486e-04
[124,] 0.9993324735 1.335053e-03 6.675265e-04
[125,] 0.9990562531 1.887494e-03 9.437469e-04
[126,] 0.9987825036 2.434993e-03 1.217496e-03
[127,] 0.9986483304 2.703339e-03 1.351670e-03
[128,] 0.9984770188 3.045962e-03 1.522981e-03
[129,] 0.9982395474 3.520905e-03 1.760453e-03
[130,] 0.9976601398 4.679720e-03 2.339860e-03
[131,] 0.9969710668 6.057866e-03 3.028933e-03
[132,] 0.9961517040 7.696592e-03 3.848296e-03
[133,] 0.9952751638 9.449672e-03 4.724836e-03
[134,] 0.9942655871 1.146883e-02 5.734413e-03
[135,] 0.9928553085 1.428938e-02 7.144691e-03
[136,] 0.9907944866 1.841103e-02 9.205513e-03
[137,] 0.9884410059 2.311799e-02 1.155899e-02
[138,] 0.9850983517 2.980330e-02 1.490165e-02
[139,] 0.9808702772 3.825945e-02 1.912972e-02
[140,] 0.9756153737 4.876925e-02 2.438463e-02
[141,] 0.9695544073 6.089119e-02 3.044559e-02
[142,] 0.9620758930 7.584821e-02 3.792411e-02
[143,] 0.9586134838 8.277303e-02 4.138652e-02
[144,] 0.9574854431 8.502911e-02 4.251456e-02
[145,] 0.9634713515 7.305730e-02 3.652865e-02
[146,] 0.9760256751 4.794865e-02 2.397432e-02
[147,] 0.9818206215 3.635876e-02 1.817938e-02
[148,] 0.9877143007 2.457140e-02 1.228570e-02
[149,] 0.9900804867 1.983903e-02 9.919513e-03
[150,] 0.9905104765 1.897905e-02 9.489523e-03
[151,] 0.9900071154 1.998577e-02 9.992885e-03
[152,] 0.9900882260 1.982355e-02 9.911774e-03
[153,] 0.9894229430 2.115411e-02 1.057706e-02
[154,] 0.9893512151 2.129757e-02 1.064878e-02
[155,] 0.9913942483 1.721150e-02 8.605752e-03
[156,] 0.9910785279 1.784294e-02 8.921472e-03
[157,] 0.9915654369 1.686913e-02 8.434563e-03
[158,] 0.9919819928 1.603601e-02 8.018007e-03
[159,] 0.9924050613 1.518988e-02 7.594939e-03
[160,] 0.9932247869 1.355043e-02 6.775213e-03
[161,] 0.9937504280 1.249914e-02 6.249572e-03
[162,] 0.9940740167 1.185197e-02 5.925983e-03
[163,] 0.9941888171 1.162237e-02 5.811183e-03
[164,] 0.9932468305 1.350634e-02 6.753170e-03
[165,] 0.9924458057 1.510839e-02 7.554194e-03
[166,] 0.9915704381 1.685912e-02 8.429562e-03
[167,] 0.9934129484 1.317410e-02 6.587052e-03
[168,] 0.9930673912 1.386522e-02 6.932609e-03
[169,] 0.9925297263 1.494055e-02 7.470274e-03
[170,] 0.9919286183 1.614276e-02 8.071382e-03
[171,] 0.9907939216 1.841216e-02 9.206078e-03
[172,] 0.9892566622 2.148668e-02 1.074334e-02
[173,] 0.9877322768 2.453545e-02 1.226772e-02
[174,] 0.9853006784 2.939864e-02 1.469932e-02
[175,] 0.9826432307 3.471354e-02 1.735677e-02
[176,] 0.9831422110 3.371558e-02 1.685779e-02
[177,] 0.9794648585 4.107028e-02 2.053514e-02
[178,] 0.9743169780 5.136604e-02 2.568302e-02
[179,] 0.9721457684 5.570846e-02 2.785423e-02
[180,] 0.9658600737 6.827985e-02 3.413993e-02
[181,] 0.9613645012 7.727100e-02 3.863550e-02
[182,] 0.9650806217 6.983876e-02 3.491938e-02
[183,] 0.9640619740 7.187605e-02 3.593803e-02
[184,] 0.9588547690 8.229046e-02 4.114523e-02
[185,] 0.9548180457 9.036391e-02 4.518195e-02
[186,] 0.9428615070 1.142770e-01 5.713849e-02
[187,] 0.9264534406 1.470931e-01 7.354656e-02
[188,] 0.9137289288 1.725421e-01 8.627107e-02
[189,] 0.9217262646 1.565475e-01 7.827374e-02
[190,] 0.9042584643 1.914831e-01 9.574154e-02
[191,] 0.9072097121 1.855806e-01 9.279029e-02
[192,] 0.8892026545 2.215947e-01 1.107973e-01
[193,] 0.8625140273 2.749719e-01 1.374860e-01
[194,] 0.8349548749 3.300903e-01 1.650451e-01
[195,] 0.8107507270 3.784985e-01 1.892493e-01
[196,] 0.7864561408 4.270877e-01 2.135439e-01
[197,] 0.7839654799 4.320690e-01 2.160345e-01
[198,] 0.7663603539 4.672793e-01 2.336396e-01
[199,] 0.7515951325 4.968097e-01 2.484049e-01
[200,] 0.8048683204 3.902634e-01 1.951317e-01
[201,] 0.8058527187 3.882946e-01 1.941473e-01
[202,] 0.8649711492 2.700577e-01 1.350289e-01
[203,] 0.9818409073 3.631819e-02 1.815909e-02
[204,] 0.9938802533 1.223949e-02 6.119747e-03
[205,] 0.9937935570 1.241289e-02 6.206443e-03
[206,] 0.9932050283 1.358994e-02 6.794972e-03
[207,] 0.9903078047 1.938439e-02 9.692195e-03
[208,] 0.9846607825 3.067844e-02 1.533922e-02
[209,] 0.9619193962 7.616121e-02 3.808060e-02
[210,] 0.9476255945 1.047488e-01 5.237441e-02
> postscript(file="/var/wessaorg/rcomp/tmp/15vs41355684043.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2n3y21355684043.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3xtwl1355684043.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4md5w1355684043.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5e5ob1355684043.ps",horizontal=F,onefile=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 = 241
Frequency = 1
1 2 3 4 5 6
1.272183055 1.386050093 1.379050093 1.325550093 1.381550093 1.604050093
7 8 9 10 11 12
1.708050093 1.343050093 0.883550093 0.865050093 0.642050093 0.525550093
13 14 15 16 17 18
0.384440940 0.108307978 0.261307978 0.057807978 -0.336192022 -0.133692022
19 20 21 22 23 24
-0.129692022 0.285307978 -0.004192022 -0.152692022 -0.485692022 -0.352192022
25 26 27 28 29 30
-0.013301175 0.120565863 0.433565863 0.890065863 0.836065863 1.028565863
31 32 33 34 35 36
1.432565863 1.647565863 1.578065863 1.529565863 1.726565863 1.720065863
37 38 39 40 41 42
1.478956710 1.252823748 0.995823748 0.492323748 0.728323748 0.470823748
43 44 45 46 47 48
0.414823748 0.589823748 0.400323748 0.291823748 0.318823748 0.062323748
49 50 51 52 53 54
0.411214595 0.165081633 -0.001918367 0.064581633 0.150581633 0.143081633
55 56 57 58 59 60
0.057081633 -0.247918367 -0.227418367 -0.385918367 -0.258918367 -0.345418367
61 62 63 64 65 66
-0.676527520 -0.482660482 -0.429660482 -0.373160482 -0.637160482 -0.764660482
67 68 69 70 71 72
-0.540660482 -0.555660482 -0.395160482 -0.413660482 -0.496660482 -0.733160482
73 74 75 76 77 78
-0.974269635 -1.070402597 -0.997402597 -1.130902597 -1.194902597 -1.262402597
79 80 81 82 83 84
-1.538402597 -1.633402597 -1.622902597 -1.601402597 -1.654402597 -1.810902597
85 86 87 88 89 90
-1.612011750 -1.718144712 -1.885144712 -1.598644712 -1.162644712 -0.870144712
91 92 93 94 95 96
-0.656144712 -0.341144712 -0.250644712 -0.129144712 0.067855288 0.351355288
97 98 99 100 101 102
0.160246135 -0.185886827 -0.072886827 -0.066386827 -0.090386827 -0.067886827
103 104 105 106 107 108
0.096113173 0.121113173 0.141613173 0.043113173 0.020113173 -0.056386827
109 110 111 112 113 114
-0.207495980 -0.373628942 -0.060628942 0.035871058 -0.088128942 -0.245628942
115 116 117 118 119 120
-0.351628942 -0.226628942 -0.556128942 -0.284628942 0.122371058 0.175871058
121 122 123 124 125 126
0.074761905 0.178628942 0.091628942 0.108128942 -0.065871058 -0.193371058
127 128 129 130 131 132
-0.409371058 -0.584371058 -0.363871058 -0.322371058 -0.565371058 -0.701871058
133 134 135 136 137 138
-0.942980210 -0.859113173 -0.806113173 -1.189613173 -1.123613173 -0.791113173
139 140 141 142 143 144
-0.717113173 -0.772113173 -0.451613173 -0.250113173 -0.273113173 -0.279613173
145 146 147 148 149 150
-0.570722325 -0.766855288 -0.403855288 -0.327355288 -0.301355288 -0.378855288
151 152 153 154 155 156
-0.504855288 -0.459855288 -0.569355288 -0.597855288 -0.610855288 -0.707355288
157 158 159 160 161 162
-0.658464440 -0.794597403 -0.991597403 -1.155097403 -1.309097403 -1.186597403
163 164 165 166 167 168
-1.252597403 -1.107597403 -0.837097403 -0.705597403 -0.738597403 -0.495097403
169 170 171 172 173 174
-0.586206555 -0.422339518 -0.219339518 -0.242839518 -0.126839518 -0.244339518
175 176 177 178 179 180
-0.330339518 -0.355339518 -0.224839518 -0.193339518 0.173660482 0.397160482
181 182 183 184 185 186
0.116051330 0.079918367 0.232918367 0.449418367 0.615418367 0.517918367
187 188 189 190 191 192
0.521918367 0.656918367 0.557418367 0.628918367 0.865918367 0.589418367
193 194 195 196 197 198
0.488309215 0.512176252 0.645176252 0.901676252 1.187676252 0.990176252
199 200 201 202 203 204
0.824176252 0.759176252 0.579676252 0.461176252 0.528176252 0.891676252
205 206 207 208 209 210
0.530567100 0.224434137 0.147434137 0.233934137 0.119934137 0.022434137
211 212 213 214 215 216
0.036434137 -0.228565863 0.081934137 0.073434137 -0.309565863 0.073934137
217 218 219 220 221 222
0.182824985 0.726692022 0.689692022 0.896192022 0.902192022 0.964692022
223 224 225 226 227 228
1.008692022 1.033692022 1.014192022 1.355692022 1.092692022 0.776192022
229 230 231 232 233 234
1.435082870 1.918949907 0.991949907 0.628449907 0.514449907 0.396949907
235 236 237 238 239 240
0.330949907 0.075949907 0.266449907 -0.212050093 -0.165050093 -0.081550093
241
-0.292659246
> postscript(file="/var/wessaorg/rcomp/tmp/6arq11355684043.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 241
Frequency = 1
lag(myerror, k = 1) myerror
0 1.272183055 NA
1 1.386050093 1.272183055
2 1.379050093 1.386050093
3 1.325550093 1.379050093
4 1.381550093 1.325550093
5 1.604050093 1.381550093
6 1.708050093 1.604050093
7 1.343050093 1.708050093
8 0.883550093 1.343050093
9 0.865050093 0.883550093
10 0.642050093 0.865050093
11 0.525550093 0.642050093
12 0.384440940 0.525550093
13 0.108307978 0.384440940
14 0.261307978 0.108307978
15 0.057807978 0.261307978
16 -0.336192022 0.057807978
17 -0.133692022 -0.336192022
18 -0.129692022 -0.133692022
19 0.285307978 -0.129692022
20 -0.004192022 0.285307978
21 -0.152692022 -0.004192022
22 -0.485692022 -0.152692022
23 -0.352192022 -0.485692022
24 -0.013301175 -0.352192022
25 0.120565863 -0.013301175
26 0.433565863 0.120565863
27 0.890065863 0.433565863
28 0.836065863 0.890065863
29 1.028565863 0.836065863
30 1.432565863 1.028565863
31 1.647565863 1.432565863
32 1.578065863 1.647565863
33 1.529565863 1.578065863
34 1.726565863 1.529565863
35 1.720065863 1.726565863
36 1.478956710 1.720065863
37 1.252823748 1.478956710
38 0.995823748 1.252823748
39 0.492323748 0.995823748
40 0.728323748 0.492323748
41 0.470823748 0.728323748
42 0.414823748 0.470823748
43 0.589823748 0.414823748
44 0.400323748 0.589823748
45 0.291823748 0.400323748
46 0.318823748 0.291823748
47 0.062323748 0.318823748
48 0.411214595 0.062323748
49 0.165081633 0.411214595
50 -0.001918367 0.165081633
51 0.064581633 -0.001918367
52 0.150581633 0.064581633
53 0.143081633 0.150581633
54 0.057081633 0.143081633
55 -0.247918367 0.057081633
56 -0.227418367 -0.247918367
57 -0.385918367 -0.227418367
58 -0.258918367 -0.385918367
59 -0.345418367 -0.258918367
60 -0.676527520 -0.345418367
61 -0.482660482 -0.676527520
62 -0.429660482 -0.482660482
63 -0.373160482 -0.429660482
64 -0.637160482 -0.373160482
65 -0.764660482 -0.637160482
66 -0.540660482 -0.764660482
67 -0.555660482 -0.540660482
68 -0.395160482 -0.555660482
69 -0.413660482 -0.395160482
70 -0.496660482 -0.413660482
71 -0.733160482 -0.496660482
72 -0.974269635 -0.733160482
73 -1.070402597 -0.974269635
74 -0.997402597 -1.070402597
75 -1.130902597 -0.997402597
76 -1.194902597 -1.130902597
77 -1.262402597 -1.194902597
78 -1.538402597 -1.262402597
79 -1.633402597 -1.538402597
80 -1.622902597 -1.633402597
81 -1.601402597 -1.622902597
82 -1.654402597 -1.601402597
83 -1.810902597 -1.654402597
84 -1.612011750 -1.810902597
85 -1.718144712 -1.612011750
86 -1.885144712 -1.718144712
87 -1.598644712 -1.885144712
88 -1.162644712 -1.598644712
89 -0.870144712 -1.162644712
90 -0.656144712 -0.870144712
91 -0.341144712 -0.656144712
92 -0.250644712 -0.341144712
93 -0.129144712 -0.250644712
94 0.067855288 -0.129144712
95 0.351355288 0.067855288
96 0.160246135 0.351355288
97 -0.185886827 0.160246135
98 -0.072886827 -0.185886827
99 -0.066386827 -0.072886827
100 -0.090386827 -0.066386827
101 -0.067886827 -0.090386827
102 0.096113173 -0.067886827
103 0.121113173 0.096113173
104 0.141613173 0.121113173
105 0.043113173 0.141613173
106 0.020113173 0.043113173
107 -0.056386827 0.020113173
108 -0.207495980 -0.056386827
109 -0.373628942 -0.207495980
110 -0.060628942 -0.373628942
111 0.035871058 -0.060628942
112 -0.088128942 0.035871058
113 -0.245628942 -0.088128942
114 -0.351628942 -0.245628942
115 -0.226628942 -0.351628942
116 -0.556128942 -0.226628942
117 -0.284628942 -0.556128942
118 0.122371058 -0.284628942
119 0.175871058 0.122371058
120 0.074761905 0.175871058
121 0.178628942 0.074761905
122 0.091628942 0.178628942
123 0.108128942 0.091628942
124 -0.065871058 0.108128942
125 -0.193371058 -0.065871058
126 -0.409371058 -0.193371058
127 -0.584371058 -0.409371058
128 -0.363871058 -0.584371058
129 -0.322371058 -0.363871058
130 -0.565371058 -0.322371058
131 -0.701871058 -0.565371058
132 -0.942980210 -0.701871058
133 -0.859113173 -0.942980210
134 -0.806113173 -0.859113173
135 -1.189613173 -0.806113173
136 -1.123613173 -1.189613173
137 -0.791113173 -1.123613173
138 -0.717113173 -0.791113173
139 -0.772113173 -0.717113173
140 -0.451613173 -0.772113173
141 -0.250113173 -0.451613173
142 -0.273113173 -0.250113173
143 -0.279613173 -0.273113173
144 -0.570722325 -0.279613173
145 -0.766855288 -0.570722325
146 -0.403855288 -0.766855288
147 -0.327355288 -0.403855288
148 -0.301355288 -0.327355288
149 -0.378855288 -0.301355288
150 -0.504855288 -0.378855288
151 -0.459855288 -0.504855288
152 -0.569355288 -0.459855288
153 -0.597855288 -0.569355288
154 -0.610855288 -0.597855288
155 -0.707355288 -0.610855288
156 -0.658464440 -0.707355288
157 -0.794597403 -0.658464440
158 -0.991597403 -0.794597403
159 -1.155097403 -0.991597403
160 -1.309097403 -1.155097403
161 -1.186597403 -1.309097403
162 -1.252597403 -1.186597403
163 -1.107597403 -1.252597403
164 -0.837097403 -1.107597403
165 -0.705597403 -0.837097403
166 -0.738597403 -0.705597403
167 -0.495097403 -0.738597403
168 -0.586206555 -0.495097403
169 -0.422339518 -0.586206555
170 -0.219339518 -0.422339518
171 -0.242839518 -0.219339518
172 -0.126839518 -0.242839518
173 -0.244339518 -0.126839518
174 -0.330339518 -0.244339518
175 -0.355339518 -0.330339518
176 -0.224839518 -0.355339518
177 -0.193339518 -0.224839518
178 0.173660482 -0.193339518
179 0.397160482 0.173660482
180 0.116051330 0.397160482
181 0.079918367 0.116051330
182 0.232918367 0.079918367
183 0.449418367 0.232918367
184 0.615418367 0.449418367
185 0.517918367 0.615418367
186 0.521918367 0.517918367
187 0.656918367 0.521918367
188 0.557418367 0.656918367
189 0.628918367 0.557418367
190 0.865918367 0.628918367
191 0.589418367 0.865918367
192 0.488309215 0.589418367
193 0.512176252 0.488309215
194 0.645176252 0.512176252
195 0.901676252 0.645176252
196 1.187676252 0.901676252
197 0.990176252 1.187676252
198 0.824176252 0.990176252
199 0.759176252 0.824176252
200 0.579676252 0.759176252
201 0.461176252 0.579676252
202 0.528176252 0.461176252
203 0.891676252 0.528176252
204 0.530567100 0.891676252
205 0.224434137 0.530567100
206 0.147434137 0.224434137
207 0.233934137 0.147434137
208 0.119934137 0.233934137
209 0.022434137 0.119934137
210 0.036434137 0.022434137
211 -0.228565863 0.036434137
212 0.081934137 -0.228565863
213 0.073434137 0.081934137
214 -0.309565863 0.073434137
215 0.073934137 -0.309565863
216 0.182824985 0.073934137
217 0.726692022 0.182824985
218 0.689692022 0.726692022
219 0.896192022 0.689692022
220 0.902192022 0.896192022
221 0.964692022 0.902192022
222 1.008692022 0.964692022
223 1.033692022 1.008692022
224 1.014192022 1.033692022
225 1.355692022 1.014192022
226 1.092692022 1.355692022
227 0.776192022 1.092692022
228 1.435082870 0.776192022
229 1.918949907 1.435082870
230 0.991949907 1.918949907
231 0.628449907 0.991949907
232 0.514449907 0.628449907
233 0.396949907 0.514449907
234 0.330949907 0.396949907
235 0.075949907 0.330949907
236 0.266449907 0.075949907
237 -0.212050093 0.266449907
238 -0.165050093 -0.212050093
239 -0.081550093 -0.165050093
240 -0.292659246 -0.081550093
241 NA -0.292659246
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.386050093 1.272183055
[2,] 1.379050093 1.386050093
[3,] 1.325550093 1.379050093
[4,] 1.381550093 1.325550093
[5,] 1.604050093 1.381550093
[6,] 1.708050093 1.604050093
[7,] 1.343050093 1.708050093
[8,] 0.883550093 1.343050093
[9,] 0.865050093 0.883550093
[10,] 0.642050093 0.865050093
[11,] 0.525550093 0.642050093
[12,] 0.384440940 0.525550093
[13,] 0.108307978 0.384440940
[14,] 0.261307978 0.108307978
[15,] 0.057807978 0.261307978
[16,] -0.336192022 0.057807978
[17,] -0.133692022 -0.336192022
[18,] -0.129692022 -0.133692022
[19,] 0.285307978 -0.129692022
[20,] -0.004192022 0.285307978
[21,] -0.152692022 -0.004192022
[22,] -0.485692022 -0.152692022
[23,] -0.352192022 -0.485692022
[24,] -0.013301175 -0.352192022
[25,] 0.120565863 -0.013301175
[26,] 0.433565863 0.120565863
[27,] 0.890065863 0.433565863
[28,] 0.836065863 0.890065863
[29,] 1.028565863 0.836065863
[30,] 1.432565863 1.028565863
[31,] 1.647565863 1.432565863
[32,] 1.578065863 1.647565863
[33,] 1.529565863 1.578065863
[34,] 1.726565863 1.529565863
[35,] 1.720065863 1.726565863
[36,] 1.478956710 1.720065863
[37,] 1.252823748 1.478956710
[38,] 0.995823748 1.252823748
[39,] 0.492323748 0.995823748
[40,] 0.728323748 0.492323748
[41,] 0.470823748 0.728323748
[42,] 0.414823748 0.470823748
[43,] 0.589823748 0.414823748
[44,] 0.400323748 0.589823748
[45,] 0.291823748 0.400323748
[46,] 0.318823748 0.291823748
[47,] 0.062323748 0.318823748
[48,] 0.411214595 0.062323748
[49,] 0.165081633 0.411214595
[50,] -0.001918367 0.165081633
[51,] 0.064581633 -0.001918367
[52,] 0.150581633 0.064581633
[53,] 0.143081633 0.150581633
[54,] 0.057081633 0.143081633
[55,] -0.247918367 0.057081633
[56,] -0.227418367 -0.247918367
[57,] -0.385918367 -0.227418367
[58,] -0.258918367 -0.385918367
[59,] -0.345418367 -0.258918367
[60,] -0.676527520 -0.345418367
[61,] -0.482660482 -0.676527520
[62,] -0.429660482 -0.482660482
[63,] -0.373160482 -0.429660482
[64,] -0.637160482 -0.373160482
[65,] -0.764660482 -0.637160482
[66,] -0.540660482 -0.764660482
[67,] -0.555660482 -0.540660482
[68,] -0.395160482 -0.555660482
[69,] -0.413660482 -0.395160482
[70,] -0.496660482 -0.413660482
[71,] -0.733160482 -0.496660482
[72,] -0.974269635 -0.733160482
[73,] -1.070402597 -0.974269635
[74,] -0.997402597 -1.070402597
[75,] -1.130902597 -0.997402597
[76,] -1.194902597 -1.130902597
[77,] -1.262402597 -1.194902597
[78,] -1.538402597 -1.262402597
[79,] -1.633402597 -1.538402597
[80,] -1.622902597 -1.633402597
[81,] -1.601402597 -1.622902597
[82,] -1.654402597 -1.601402597
[83,] -1.810902597 -1.654402597
[84,] -1.612011750 -1.810902597
[85,] -1.718144712 -1.612011750
[86,] -1.885144712 -1.718144712
[87,] -1.598644712 -1.885144712
[88,] -1.162644712 -1.598644712
[89,] -0.870144712 -1.162644712
[90,] -0.656144712 -0.870144712
[91,] -0.341144712 -0.656144712
[92,] -0.250644712 -0.341144712
[93,] -0.129144712 -0.250644712
[94,] 0.067855288 -0.129144712
[95,] 0.351355288 0.067855288
[96,] 0.160246135 0.351355288
[97,] -0.185886827 0.160246135
[98,] -0.072886827 -0.185886827
[99,] -0.066386827 -0.072886827
[100,] -0.090386827 -0.066386827
[101,] -0.067886827 -0.090386827
[102,] 0.096113173 -0.067886827
[103,] 0.121113173 0.096113173
[104,] 0.141613173 0.121113173
[105,] 0.043113173 0.141613173
[106,] 0.020113173 0.043113173
[107,] -0.056386827 0.020113173
[108,] -0.207495980 -0.056386827
[109,] -0.373628942 -0.207495980
[110,] -0.060628942 -0.373628942
[111,] 0.035871058 -0.060628942
[112,] -0.088128942 0.035871058
[113,] -0.245628942 -0.088128942
[114,] -0.351628942 -0.245628942
[115,] -0.226628942 -0.351628942
[116,] -0.556128942 -0.226628942
[117,] -0.284628942 -0.556128942
[118,] 0.122371058 -0.284628942
[119,] 0.175871058 0.122371058
[120,] 0.074761905 0.175871058
[121,] 0.178628942 0.074761905
[122,] 0.091628942 0.178628942
[123,] 0.108128942 0.091628942
[124,] -0.065871058 0.108128942
[125,] -0.193371058 -0.065871058
[126,] -0.409371058 -0.193371058
[127,] -0.584371058 -0.409371058
[128,] -0.363871058 -0.584371058
[129,] -0.322371058 -0.363871058
[130,] -0.565371058 -0.322371058
[131,] -0.701871058 -0.565371058
[132,] -0.942980210 -0.701871058
[133,] -0.859113173 -0.942980210
[134,] -0.806113173 -0.859113173
[135,] -1.189613173 -0.806113173
[136,] -1.123613173 -1.189613173
[137,] -0.791113173 -1.123613173
[138,] -0.717113173 -0.791113173
[139,] -0.772113173 -0.717113173
[140,] -0.451613173 -0.772113173
[141,] -0.250113173 -0.451613173
[142,] -0.273113173 -0.250113173
[143,] -0.279613173 -0.273113173
[144,] -0.570722325 -0.279613173
[145,] -0.766855288 -0.570722325
[146,] -0.403855288 -0.766855288
[147,] -0.327355288 -0.403855288
[148,] -0.301355288 -0.327355288
[149,] -0.378855288 -0.301355288
[150,] -0.504855288 -0.378855288
[151,] -0.459855288 -0.504855288
[152,] -0.569355288 -0.459855288
[153,] -0.597855288 -0.569355288
[154,] -0.610855288 -0.597855288
[155,] -0.707355288 -0.610855288
[156,] -0.658464440 -0.707355288
[157,] -0.794597403 -0.658464440
[158,] -0.991597403 -0.794597403
[159,] -1.155097403 -0.991597403
[160,] -1.309097403 -1.155097403
[161,] -1.186597403 -1.309097403
[162,] -1.252597403 -1.186597403
[163,] -1.107597403 -1.252597403
[164,] -0.837097403 -1.107597403
[165,] -0.705597403 -0.837097403
[166,] -0.738597403 -0.705597403
[167,] -0.495097403 -0.738597403
[168,] -0.586206555 -0.495097403
[169,] -0.422339518 -0.586206555
[170,] -0.219339518 -0.422339518
[171,] -0.242839518 -0.219339518
[172,] -0.126839518 -0.242839518
[173,] -0.244339518 -0.126839518
[174,] -0.330339518 -0.244339518
[175,] -0.355339518 -0.330339518
[176,] -0.224839518 -0.355339518
[177,] -0.193339518 -0.224839518
[178,] 0.173660482 -0.193339518
[179,] 0.397160482 0.173660482
[180,] 0.116051330 0.397160482
[181,] 0.079918367 0.116051330
[182,] 0.232918367 0.079918367
[183,] 0.449418367 0.232918367
[184,] 0.615418367 0.449418367
[185,] 0.517918367 0.615418367
[186,] 0.521918367 0.517918367
[187,] 0.656918367 0.521918367
[188,] 0.557418367 0.656918367
[189,] 0.628918367 0.557418367
[190,] 0.865918367 0.628918367
[191,] 0.589418367 0.865918367
[192,] 0.488309215 0.589418367
[193,] 0.512176252 0.488309215
[194,] 0.645176252 0.512176252
[195,] 0.901676252 0.645176252
[196,] 1.187676252 0.901676252
[197,] 0.990176252 1.187676252
[198,] 0.824176252 0.990176252
[199,] 0.759176252 0.824176252
[200,] 0.579676252 0.759176252
[201,] 0.461176252 0.579676252
[202,] 0.528176252 0.461176252
[203,] 0.891676252 0.528176252
[204,] 0.530567100 0.891676252
[205,] 0.224434137 0.530567100
[206,] 0.147434137 0.224434137
[207,] 0.233934137 0.147434137
[208,] 0.119934137 0.233934137
[209,] 0.022434137 0.119934137
[210,] 0.036434137 0.022434137
[211,] -0.228565863 0.036434137
[212,] 0.081934137 -0.228565863
[213,] 0.073434137 0.081934137
[214,] -0.309565863 0.073434137
[215,] 0.073934137 -0.309565863
[216,] 0.182824985 0.073934137
[217,] 0.726692022 0.182824985
[218,] 0.689692022 0.726692022
[219,] 0.896192022 0.689692022
[220,] 0.902192022 0.896192022
[221,] 0.964692022 0.902192022
[222,] 1.008692022 0.964692022
[223,] 1.033692022 1.008692022
[224,] 1.014192022 1.033692022
[225,] 1.355692022 1.014192022
[226,] 1.092692022 1.355692022
[227,] 0.776192022 1.092692022
[228,] 1.435082870 0.776192022
[229,] 1.918949907 1.435082870
[230,] 0.991949907 1.918949907
[231,] 0.628449907 0.991949907
[232,] 0.514449907 0.628449907
[233,] 0.396949907 0.514449907
[234,] 0.330949907 0.396949907
[235,] 0.075949907 0.330949907
[236,] 0.266449907 0.075949907
[237,] -0.212050093 0.266449907
[238,] -0.165050093 -0.212050093
[239,] -0.081550093 -0.165050093
[240,] -0.292659246 -0.081550093
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.386050093 1.272183055
2 1.379050093 1.386050093
3 1.325550093 1.379050093
4 1.381550093 1.325550093
5 1.604050093 1.381550093
6 1.708050093 1.604050093
7 1.343050093 1.708050093
8 0.883550093 1.343050093
9 0.865050093 0.883550093
10 0.642050093 0.865050093
11 0.525550093 0.642050093
12 0.384440940 0.525550093
13 0.108307978 0.384440940
14 0.261307978 0.108307978
15 0.057807978 0.261307978
16 -0.336192022 0.057807978
17 -0.133692022 -0.336192022
18 -0.129692022 -0.133692022
19 0.285307978 -0.129692022
20 -0.004192022 0.285307978
21 -0.152692022 -0.004192022
22 -0.485692022 -0.152692022
23 -0.352192022 -0.485692022
24 -0.013301175 -0.352192022
25 0.120565863 -0.013301175
26 0.433565863 0.120565863
27 0.890065863 0.433565863
28 0.836065863 0.890065863
29 1.028565863 0.836065863
30 1.432565863 1.028565863
31 1.647565863 1.432565863
32 1.578065863 1.647565863
33 1.529565863 1.578065863
34 1.726565863 1.529565863
35 1.720065863 1.726565863
36 1.478956710 1.720065863
37 1.252823748 1.478956710
38 0.995823748 1.252823748
39 0.492323748 0.995823748
40 0.728323748 0.492323748
41 0.470823748 0.728323748
42 0.414823748 0.470823748
43 0.589823748 0.414823748
44 0.400323748 0.589823748
45 0.291823748 0.400323748
46 0.318823748 0.291823748
47 0.062323748 0.318823748
48 0.411214595 0.062323748
49 0.165081633 0.411214595
50 -0.001918367 0.165081633
51 0.064581633 -0.001918367
52 0.150581633 0.064581633
53 0.143081633 0.150581633
54 0.057081633 0.143081633
55 -0.247918367 0.057081633
56 -0.227418367 -0.247918367
57 -0.385918367 -0.227418367
58 -0.258918367 -0.385918367
59 -0.345418367 -0.258918367
60 -0.676527520 -0.345418367
61 -0.482660482 -0.676527520
62 -0.429660482 -0.482660482
63 -0.373160482 -0.429660482
64 -0.637160482 -0.373160482
65 -0.764660482 -0.637160482
66 -0.540660482 -0.764660482
67 -0.555660482 -0.540660482
68 -0.395160482 -0.555660482
69 -0.413660482 -0.395160482
70 -0.496660482 -0.413660482
71 -0.733160482 -0.496660482
72 -0.974269635 -0.733160482
73 -1.070402597 -0.974269635
74 -0.997402597 -1.070402597
75 -1.130902597 -0.997402597
76 -1.194902597 -1.130902597
77 -1.262402597 -1.194902597
78 -1.538402597 -1.262402597
79 -1.633402597 -1.538402597
80 -1.622902597 -1.633402597
81 -1.601402597 -1.622902597
82 -1.654402597 -1.601402597
83 -1.810902597 -1.654402597
84 -1.612011750 -1.810902597
85 -1.718144712 -1.612011750
86 -1.885144712 -1.718144712
87 -1.598644712 -1.885144712
88 -1.162644712 -1.598644712
89 -0.870144712 -1.162644712
90 -0.656144712 -0.870144712
91 -0.341144712 -0.656144712
92 -0.250644712 -0.341144712
93 -0.129144712 -0.250644712
94 0.067855288 -0.129144712
95 0.351355288 0.067855288
96 0.160246135 0.351355288
97 -0.185886827 0.160246135
98 -0.072886827 -0.185886827
99 -0.066386827 -0.072886827
100 -0.090386827 -0.066386827
101 -0.067886827 -0.090386827
102 0.096113173 -0.067886827
103 0.121113173 0.096113173
104 0.141613173 0.121113173
105 0.043113173 0.141613173
106 0.020113173 0.043113173
107 -0.056386827 0.020113173
108 -0.207495980 -0.056386827
109 -0.373628942 -0.207495980
110 -0.060628942 -0.373628942
111 0.035871058 -0.060628942
112 -0.088128942 0.035871058
113 -0.245628942 -0.088128942
114 -0.351628942 -0.245628942
115 -0.226628942 -0.351628942
116 -0.556128942 -0.226628942
117 -0.284628942 -0.556128942
118 0.122371058 -0.284628942
119 0.175871058 0.122371058
120 0.074761905 0.175871058
121 0.178628942 0.074761905
122 0.091628942 0.178628942
123 0.108128942 0.091628942
124 -0.065871058 0.108128942
125 -0.193371058 -0.065871058
126 -0.409371058 -0.193371058
127 -0.584371058 -0.409371058
128 -0.363871058 -0.584371058
129 -0.322371058 -0.363871058
130 -0.565371058 -0.322371058
131 -0.701871058 -0.565371058
132 -0.942980210 -0.701871058
133 -0.859113173 -0.942980210
134 -0.806113173 -0.859113173
135 -1.189613173 -0.806113173
136 -1.123613173 -1.189613173
137 -0.791113173 -1.123613173
138 -0.717113173 -0.791113173
139 -0.772113173 -0.717113173
140 -0.451613173 -0.772113173
141 -0.250113173 -0.451613173
142 -0.273113173 -0.250113173
143 -0.279613173 -0.273113173
144 -0.570722325 -0.279613173
145 -0.766855288 -0.570722325
146 -0.403855288 -0.766855288
147 -0.327355288 -0.403855288
148 -0.301355288 -0.327355288
149 -0.378855288 -0.301355288
150 -0.504855288 -0.378855288
151 -0.459855288 -0.504855288
152 -0.569355288 -0.459855288
153 -0.597855288 -0.569355288
154 -0.610855288 -0.597855288
155 -0.707355288 -0.610855288
156 -0.658464440 -0.707355288
157 -0.794597403 -0.658464440
158 -0.991597403 -0.794597403
159 -1.155097403 -0.991597403
160 -1.309097403 -1.155097403
161 -1.186597403 -1.309097403
162 -1.252597403 -1.186597403
163 -1.107597403 -1.252597403
164 -0.837097403 -1.107597403
165 -0.705597403 -0.837097403
166 -0.738597403 -0.705597403
167 -0.495097403 -0.738597403
168 -0.586206555 -0.495097403
169 -0.422339518 -0.586206555
170 -0.219339518 -0.422339518
171 -0.242839518 -0.219339518
172 -0.126839518 -0.242839518
173 -0.244339518 -0.126839518
174 -0.330339518 -0.244339518
175 -0.355339518 -0.330339518
176 -0.224839518 -0.355339518
177 -0.193339518 -0.224839518
178 0.173660482 -0.193339518
179 0.397160482 0.173660482
180 0.116051330 0.397160482
181 0.079918367 0.116051330
182 0.232918367 0.079918367
183 0.449418367 0.232918367
184 0.615418367 0.449418367
185 0.517918367 0.615418367
186 0.521918367 0.517918367
187 0.656918367 0.521918367
188 0.557418367 0.656918367
189 0.628918367 0.557418367
190 0.865918367 0.628918367
191 0.589418367 0.865918367
192 0.488309215 0.589418367
193 0.512176252 0.488309215
194 0.645176252 0.512176252
195 0.901676252 0.645176252
196 1.187676252 0.901676252
197 0.990176252 1.187676252
198 0.824176252 0.990176252
199 0.759176252 0.824176252
200 0.579676252 0.759176252
201 0.461176252 0.579676252
202 0.528176252 0.461176252
203 0.891676252 0.528176252
204 0.530567100 0.891676252
205 0.224434137 0.530567100
206 0.147434137 0.224434137
207 0.233934137 0.147434137
208 0.119934137 0.233934137
209 0.022434137 0.119934137
210 0.036434137 0.022434137
211 -0.228565863 0.036434137
212 0.081934137 -0.228565863
213 0.073434137 0.081934137
214 -0.309565863 0.073434137
215 0.073934137 -0.309565863
216 0.182824985 0.073934137
217 0.726692022 0.182824985
218 0.689692022 0.726692022
219 0.896192022 0.689692022
220 0.902192022 0.896192022
221 0.964692022 0.902192022
222 1.008692022 0.964692022
223 1.033692022 1.008692022
224 1.014192022 1.033692022
225 1.355692022 1.014192022
226 1.092692022 1.355692022
227 0.776192022 1.092692022
228 1.435082870 0.776192022
229 1.918949907 1.435082870
230 0.991949907 1.918949907
231 0.628449907 0.991949907
232 0.514449907 0.628449907
233 0.396949907 0.514449907
234 0.330949907 0.396949907
235 0.075949907 0.330949907
236 0.266449907 0.075949907
237 -0.212050093 0.266449907
238 -0.165050093 -0.212050093
239 -0.081550093 -0.165050093
240 -0.292659246 -0.081550093
> 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/wessaorg/rcomp/tmp/7cgbz1355684043.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/87z3s1355684043.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9cua71355684043.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10bn6n1355684043.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11njw41355684043.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/wessaorg/rcomp/tmp/12a9ls1355684043.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/wessaorg/rcomp/tmp/13adsn1355684043.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/wessaorg/rcomp/tmp/14na4r1355684043.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/wessaorg/rcomp/tmp/1522lw1355684043.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/wessaorg/rcomp/tmp/16hzd41355684043.tab")
+ }
>
> try(system("convert tmp/15vs41355684043.ps tmp/15vs41355684043.png",intern=TRUE))
character(0)
> try(system("convert tmp/2n3y21355684043.ps tmp/2n3y21355684043.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xtwl1355684043.ps tmp/3xtwl1355684043.png",intern=TRUE))
character(0)
> try(system("convert tmp/4md5w1355684043.ps tmp/4md5w1355684043.png",intern=TRUE))
character(0)
> try(system("convert tmp/5e5ob1355684043.ps tmp/5e5ob1355684043.png",intern=TRUE))
character(0)
> try(system("convert tmp/6arq11355684043.ps tmp/6arq11355684043.png",intern=TRUE))
character(0)
> try(system("convert tmp/7cgbz1355684043.ps tmp/7cgbz1355684043.png",intern=TRUE))
character(0)
> try(system("convert tmp/87z3s1355684043.ps tmp/87z3s1355684043.png",intern=TRUE))
character(0)
> try(system("convert tmp/9cua71355684043.ps tmp/9cua71355684043.png",intern=TRUE))
character(0)
> try(system("convert tmp/10bn6n1355684043.ps tmp/10bn6n1355684043.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.349 1.227 11.577