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)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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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 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> par3 <- 'No 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
1 8.64 1 0 0 0 0 0 0 0 0 0 0
2 8.89 0 1 0 0 0 0 0 0 0 0 0
3 8.87 0 0 1 0 0 0 0 0 0 0 0
4 8.81 0 0 0 1 0 0 0 0 0 0 0
5 8.87 0 0 0 0 1 0 0 0 0 0 0
6 9.06 0 0 0 0 0 1 0 0 0 0 0
7 9.12 0 0 0 0 0 0 1 0 0 0 0
8 8.66 0 0 0 0 0 0 0 1 0 0 0
9 8.17 0 0 0 0 0 0 0 0 1 0 0
10 8.04 0 0 0 0 0 0 0 0 0 1 0
11 7.71 0 0 0 0 0 0 0 0 0 0 1
12 7.55 0 0 0 0 0 0 0 0 0 0 0
13 7.52 1 0 0 0 0 0 0 0 0 0 0
14 7.38 0 1 0 0 0 0 0 0 0 0 0
15 7.52 0 0 1 0 0 0 0 0 0 0 0
16 7.31 0 0 0 1 0 0 0 0 0 0 0
17 6.92 0 0 0 0 1 0 0 0 0 0 0
18 7.09 0 0 0 0 0 1 0 0 0 0 0
19 7.05 0 0 0 0 0 0 1 0 0 0 0
20 7.37 0 0 0 0 0 0 0 1 0 0 0
21 7.05 0 0 0 0 0 0 0 0 1 0 0
22 6.79 0 0 0 0 0 0 0 0 0 1 0
23 6.35 0 0 0 0 0 0 0 0 0 0 1
24 6.44 0 0 0 0 0 0 0 0 0 0 0
25 6.89 1 0 0 0 0 0 0 0 0 0 0
26 7.16 0 1 0 0 0 0 0 0 0 0 0
27 7.46 0 0 1 0 0 0 0 0 0 0 0
28 7.91 0 0 0 1 0 0 0 0 0 0 0
29 7.86 0 0 0 0 1 0 0 0 0 0 0
30 8.02 0 0 0 0 0 1 0 0 0 0 0
31 8.38 0 0 0 0 0 0 1 0 0 0 0
32 8.50 0 0 0 0 0 0 0 1 0 0 0
33 8.40 0 0 0 0 0 0 0 0 1 0 0
34 8.24 0 0 0 0 0 0 0 0 0 1 0
35 8.33 0 0 0 0 0 0 0 0 0 0 1
36 8.28 0 0 0 0 0 0 0 0 0 0 0
37 8.15 1 0 0 0 0 0 0 0 0 0 0
38 8.06 0 1 0 0 0 0 0 0 0 0 0
39 7.79 0 0 1 0 0 0 0 0 0 0 0
40 7.28 0 0 0 1 0 0 0 0 0 0 0
41 7.52 0 0 0 0 1 0 0 0 0 0 0
42 7.23 0 0 0 0 0 1 0 0 0 0 0
43 7.13 0 0 0 0 0 0 1 0 0 0 0
44 7.21 0 0 0 0 0 0 0 1 0 0 0
45 6.99 0 0 0 0 0 0 0 0 1 0 0
46 6.77 0 0 0 0 0 0 0 0 0 1 0
47 6.69 0 0 0 0 0 0 0 0 0 0 1
48 6.39 0 0 0 0 0 0 0 0 0 0 0
49 6.85 1 0 0 0 0 0 0 0 0 0 0
50 6.74 0 1 0 0 0 0 0 0 0 0 0
51 6.56 0 0 1 0 0 0 0 0 0 0 0
52 6.62 0 0 0 1 0 0 0 0 0 0 0
53 6.71 0 0 0 0 1 0 0 0 0 0 0
54 6.67 0 0 0 0 0 1 0 0 0 0 0
55 6.54 0 0 0 0 0 0 1 0 0 0 0
56 6.14 0 0 0 0 0 0 0 1 0 0 0
57 6.13 0 0 0 0 0 0 0 0 1 0 0
58 5.86 0 0 0 0 0 0 0 0 0 1 0
59 5.88 0 0 0 0 0 0 0 0 0 0 1
60 5.75 0 0 0 0 0 0 0 0 0 0 0
61 5.53 1 0 0 0 0 0 0 0 0 0 0
62 5.86 0 1 0 0 0 0 0 0 0 0 0
63 5.90 0 0 1 0 0 0 0 0 0 0 0
64 5.95 0 0 0 1 0 0 0 0 0 0 0
65 5.69 0 0 0 0 1 0 0 0 0 0 0
66 5.53 0 0 0 0 0 1 0 0 0 0 0
67 5.71 0 0 0 0 0 0 1 0 0 0 0
68 5.60 0 0 0 0 0 0 0 1 0 0 0
69 5.73 0 0 0 0 0 0 0 0 1 0 0
70 5.60 0 0 0 0 0 0 0 0 0 1 0
71 5.41 0 0 0 0 0 0 0 0 0 0 1
72 5.13 0 0 0 0 0 0 0 0 0 0 0
73 5.00 1 0 0 0 0 0 0 0 0 0 0
74 5.04 0 1 0 0 0 0 0 0 0 0 0
75 5.10 0 0 1 0 0 0 0 0 0 0 0
76 4.96 0 0 0 1 0 0 0 0 0 0 0
77 4.90 0 0 0 0 1 0 0 0 0 0 0
78 4.80 0 0 0 0 0 1 0 0 0 0 0
79 4.48 0 0 0 0 0 0 1 0 0 0 0
80 4.29 0 0 0 0 0 0 0 1 0 0 0
81 4.27 0 0 0 0 0 0 0 0 1 0 0
82 4.18 0 0 0 0 0 0 0 0 0 1 0
83 4.02 0 0 0 0 0 0 0 0 0 0 1
84 3.82 0 0 0 0 0 0 0 0 0 0 0
85 4.13 1 0 0 0 0 0 0 0 0 0 0
86 4.16 0 1 0 0 0 0 0 0 0 0 0
87 3.98 0 0 1 0 0 0 0 0 0 0 0
88 4.26 0 0 0 1 0 0 0 0 0 0 0
89 4.70 0 0 0 0 1 0 0 0 0 0 0
90 4.96 0 0 0 0 0 1 0 0 0 0 0
91 5.13 0 0 0 0 0 0 1 0 0 0 0
92 5.35 0 0 0 0 0 0 0 1 0 0 0
93 5.41 0 0 0 0 0 0 0 0 1 0 0
94 5.42 0 0 0 0 0 0 0 0 0 1 0
95 5.51 0 0 0 0 0 0 0 0 0 0 1
96 5.75 0 0 0 0 0 0 0 0 0 0 0
97 5.67 1 0 0 0 0 0 0 0 0 0 0
98 5.46 0 1 0 0 0 0 0 0 0 0 0
99 5.56 0 0 1 0 0 0 0 0 0 0 0
100 5.56 0 0 0 1 0 0 0 0 0 0 0
101 5.54 0 0 0 0 1 0 0 0 0 0 0
102 5.53 0 0 0 0 0 1 0 0 0 0 0
103 5.65 0 0 0 0 0 0 1 0 0 0 0
104 5.58 0 0 0 0 0 0 0 1 0 0 0
105 5.57 0 0 0 0 0 0 0 0 1 0 0
106 5.36 0 0 0 0 0 0 0 0 0 1 0
107 5.23 0 0 0 0 0 0 0 0 0 0 1
108 5.11 0 0 0 0 0 0 0 0 0 0 0
109 5.07 1 0 0 0 0 0 0 0 0 0 0
110 5.04 0 1 0 0 0 0 0 0 0 0 0
111 5.34 0 0 1 0 0 0 0 0 0 0 0
112 5.43 0 0 0 1 0 0 0 0 0 0 0
113 5.31 0 0 0 0 1 0 0 0 0 0 0
114 5.12 0 0 0 0 0 1 0 0 0 0 0
115 4.97 0 0 0 0 0 0 1 0 0 0 0
116 5.00 0 0 0 0 0 0 0 1 0 0 0
117 4.64 0 0 0 0 0 0 0 0 1 0 0
118 4.80 0 0 0 0 0 0 0 0 0 1 0
119 5.10 0 0 0 0 0 0 0 0 0 0 1
120 5.11 0 0 0 0 0 0 0 0 0 0 0
121 5.12 1 0 0 0 0 0 0 0 0 0 0
122 5.36 0 1 0 0 0 0 0 0 0 0 0
123 5.26 0 0 1 0 0 0 0 0 0 0 0
124 5.27 0 0 0 1 0 0 0 0 0 0 0
125 5.10 0 0 0 0 1 0 0 0 0 0 0
126 4.94 0 0 0 0 0 1 0 0 0 0 0
127 4.68 0 0 0 0 0 0 1 0 0 0 0
128 4.41 0 0 0 0 0 0 0 1 0 0 0
129 4.60 0 0 0 0 0 0 0 0 1 0 0
130 4.53 0 0 0 0 0 0 0 0 0 1 0
131 4.18 0 0 0 0 0 0 0 0 0 0 1
132 4.00 0 0 0 0 0 0 0 0 0 0 0
133 3.87 1 0 0 0 0 0 0 0 0 0 0
134 4.09 0 1 0 0 0 0 0 0 0 0 0
135 4.13 0 0 1 0 0 0 0 0 0 0 0
136 3.74 0 0 0 1 0 0 0 0 0 0 0
137 3.81 0 0 0 0 1 0 0 0 0 0 0
138 4.11 0 0 0 0 0 1 0 0 0 0 0
139 4.14 0 0 0 0 0 0 1 0 0 0 0
140 3.99 0 0 0 0 0 0 0 1 0 0 0
141 4.28 0 0 0 0 0 0 0 0 1 0 0
142 4.37 0 0 0 0 0 0 0 0 0 1 0
143 4.24 0 0 0 0 0 0 0 0 0 0 1
144 4.19 0 0 0 0 0 0 0 0 0 0 0
145 4.01 1 0 0 0 0 0 0 0 0 0 0
146 3.95 0 1 0 0 0 0 0 0 0 0 0
147 4.30 0 0 1 0 0 0 0 0 0 0 0
148 4.37 0 0 0 1 0 0 0 0 0 0 0
149 4.40 0 0 0 0 1 0 0 0 0 0 0
150 4.29 0 0 0 0 0 1 0 0 0 0 0
151 4.12 0 0 0 0 0 0 1 0 0 0 0
152 4.07 0 0 0 0 0 0 0 1 0 0 0
153 3.93 0 0 0 0 0 0 0 0 1 0 0
154 3.79 0 0 0 0 0 0 0 0 0 1 0
155 3.67 0 0 0 0 0 0 0 0 0 0 1
156 3.53 0 0 0 0 0 0 0 0 0 0 0
157 3.69 1 0 0 0 0 0 0 0 0 0 0
158 3.69 0 1 0 0 0 0 0 0 0 0 0
159 3.48 0 0 1 0 0 0 0 0 0 0 0
160 3.31 0 0 0 1 0 0 0 0 0 0 0
161 3.16 0 0 0 0 1 0 0 0 0 0 0
162 3.25 0 0 0 0 0 1 0 0 0 0 0
163 3.14 0 0 0 0 0 0 1 0 0 0 0
164 3.19 0 0 0 0 0 0 0 1 0 0 0
165 3.43 0 0 0 0 0 0 0 0 1 0 0
166 3.45 0 0 0 0 0 0 0 0 0 1 0
167 3.31 0 0 0 0 0 0 0 0 0 0 1
168 3.51 0 0 0 0 0 0 0 0 0 0 0
169 3.53 1 0 0 0 0 0 0 0 0 0 0
170 3.83 0 1 0 0 0 0 0 0 0 0 0
171 4.02 0 0 1 0 0 0 0 0 0 0 0
172 3.99 0 0 0 1 0 0 0 0 0 0 0
173 4.11 0 0 0 0 1 0 0 0 0 0 0
174 3.96 0 0 0 0 0 1 0 0 0 0 0
175 3.83 0 0 0 0 0 0 1 0 0 0 0
176 3.71 0 0 0 0 0 0 0 1 0 0 0
177 3.81 0 0 0 0 0 0 0 0 1 0 0
178 3.73 0 0 0 0 0 0 0 0 0 1 0
179 3.99 0 0 0 0 0 0 0 0 0 0 1
180 4.17 0 0 0 0 0 0 0 0 0 0 0
181 4.00 1 0 0 0 0 0 0 0 0 0 0
182 4.10 0 1 0 0 0 0 0 0 0 0 0
183 4.24 0 0 1 0 0 0 0 0 0 0 0
184 4.45 0 0 0 1 0 0 0 0 0 0 0
185 4.62 0 0 0 0 1 0 0 0 0 0 0
186 4.49 0 0 0 0 0 1 0 0 0 0 0
187 4.45 0 0 0 0 0 0 1 0 0 0 0
188 4.49 0 0 0 0 0 0 0 1 0 0 0
189 4.36 0 0 0 0 0 0 0 0 1 0 0
190 4.32 0 0 0 0 0 0 0 0 0 1 0
191 4.45 0 0 0 0 0 0 0 0 0 0 1
192 4.13 0 0 0 0 0 0 0 0 0 0 0
193 4.14 1 0 0 0 0 0 0 0 0 0 0
194 4.30 0 1 0 0 0 0 0 0 0 0 0
195 4.42 0 0 1 0 0 0 0 0 0 0 0
196 4.67 0 0 0 1 0 0 0 0 0 0 0
197 4.96 0 0 0 0 1 0 0 0 0 0 0
198 4.73 0 0 0 0 0 1 0 0 0 0 0
199 4.52 0 0 0 0 0 0 1 0 0 0 0
200 4.36 0 0 0 0 0 0 0 1 0 0 0
201 4.15 0 0 0 0 0 0 0 0 1 0 0
202 3.92 0 0 0 0 0 0 0 0 0 1 0
203 3.88 0 0 0 0 0 0 0 0 0 0 1
204 4.20 0 0 0 0 0 0 0 0 0 0 0
205 3.95 1 0 0 0 0 0 0 0 0 0 0
206 3.78 0 1 0 0 0 0 0 0 0 0 0
207 3.69 0 0 1 0 0 0 0 0 0 0 0
208 3.77 0 0 0 1 0 0 0 0 0 0 0
209 3.66 0 0 0 0 1 0 0 0 0 0 0
210 3.53 0 0 0 0 0 1 0 0 0 0 0
211 3.50 0 0 0 0 0 0 1 0 0 0 0
212 3.14 0 0 0 0 0 0 0 1 0 0 0
213 3.42 0 0 0 0 0 0 0 0 1 0 0
214 3.30 0 0 0 0 0 0 0 0 0 1 0
215 2.81 0 0 0 0 0 0 0 0 0 0 1
216 3.15 0 0 0 0 0 0 0 0 0 0 0
217 3.37 1 0 0 0 0 0 0 0 0 0 0
218 4.05 0 1 0 0 0 0 0 0 0 0 0
219 4.00 0 0 1 0 0 0 0 0 0 0 0
220 4.20 0 0 0 1 0 0 0 0 0 0 0
221 4.21 0 0 0 0 1 0 0 0 0 0 0
222 4.24 0 0 0 0 0 1 0 0 0 0 0
223 4.24 0 0 0 0 0 0 1 0 0 0 0
224 4.17 0 0 0 0 0 0 0 1 0 0 0
225 4.12 0 0 0 0 0 0 0 0 1 0 0
226 4.35 0 0 0 0 0 0 0 0 0 1 0
227 3.98 0 0 0 0 0 0 0 0 0 0 1
228 3.62 0 0 0 0 0 0 0 0 0 0 0
229 4.39 1 0 0 0 0 0 0 0 0 0 0
230 5.01 0 1 0 0 0 0 0 0 0 0 0
231 4.07 0 0 1 0 0 0 0 0 0 0 0
232 3.70 0 0 0 1 0 0 0 0 0 0 0
233 3.59 0 0 0 0 1 0 0 0 0 0 0
234 3.44 0 0 0 0 0 1 0 0 0 0 0
235 3.33 0 0 0 0 0 0 1 0 0 0 0
236 2.98 0 0 0 0 0 0 0 1 0 0 0
237 3.14 0 0 0 0 0 0 0 0 1 0 0
238 2.55 0 0 0 0 0 0 0 0 0 1 0
239 2.49 0 0 0 0 0 0 0 0 0 0 1
240 2.53 0 0 0 0 0 0 0 0 0 0 0
241 2.43 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
4.8180 0.2272 0.4795 0.4665 0.4600 0.4640
M6 M7 M8 M9 M10 M11
0.4315 0.3875 0.2925 0.2620 0.1505 0.0435
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.6152 -1.1395 -0.5195 0.8915 3.9145
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.8180 0.3553 13.562 <2e-16 ***
M1 0.2272 0.4964 0.458 0.648
M2 0.4795 0.5024 0.954 0.341
M3 0.4665 0.5024 0.929 0.354
M4 0.4600 0.5024 0.916 0.361
M5 0.4640 0.5024 0.924 0.357
M6 0.4315 0.5024 0.859 0.391
M7 0.3875 0.5024 0.771 0.441
M8 0.2925 0.5024 0.582 0.561
M9 0.2620 0.5024 0.521 0.603
M10 0.1505 0.5024 0.300 0.765
M11 0.0435 0.5024 0.087 0.931
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.589 on 229 degrees of freedom
Multiple R-squared: 0.01103, Adjusted R-squared: -0.03648
F-statistic: 0.2321 on 11 and 229 DF, p-value: 0.9951
> 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,] 0.29327034 5.865407e-01 7.067297e-01
[2,] 0.26398695 5.279739e-01 7.360130e-01
[3,] 0.30150329 6.030066e-01 6.984967e-01
[4,] 0.33121207 6.624241e-01 6.687879e-01
[5,] 0.36774466 7.354893e-01 6.322553e-01
[6,] 0.32404643 6.480929e-01 6.759536e-01
[7,] 0.27400917 5.480183e-01 7.259908e-01
[8,] 0.23862741 4.772548e-01 7.613726e-01
[9,] 0.21310876 4.262175e-01 7.868912e-01
[10,] 0.17930046 3.586009e-01 8.206995e-01
[11,] 0.16501291 3.300258e-01 8.349871e-01
[12,] 0.14261092 2.852218e-01 8.573891e-01
[13,] 0.11787009 2.357402e-01 8.821299e-01
[14,] 0.09195507 1.839101e-01 9.080449e-01
[15,] 0.07088199 1.417640e-01 9.291180e-01
[16,] 0.05566790 1.113358e-01 9.443321e-01
[17,] 0.04685724 9.371447e-02 9.531428e-01
[18,] 0.04277591 8.555182e-02 9.572241e-01
[19,] 0.04320075 8.640150e-02 9.567993e-01
[20,] 0.04512923 9.025845e-02 9.548708e-01
[21,] 0.06190965 1.238193e-01 9.380904e-01
[22,] 0.08494148 1.698830e-01 9.150585e-01
[23,] 0.08902095 1.780419e-01 9.109790e-01
[24,] 0.08778526 1.755705e-01 9.122147e-01
[25,] 0.08452756 1.690551e-01 9.154724e-01
[26,] 0.08579226 1.715845e-01 9.142077e-01
[27,] 0.08369246 1.673849e-01 9.163075e-01
[28,] 0.09043747 1.808749e-01 9.095625e-01
[29,] 0.10773570 2.154714e-01 8.922643e-01
[30,] 0.12870069 2.574014e-01 8.712993e-01
[31,] 0.14463284 2.892657e-01 8.553672e-01
[32,] 0.16266786 3.253357e-01 8.373321e-01
[33,] 0.17677440 3.535488e-01 8.232256e-01
[34,] 0.20111524 4.022305e-01 7.988848e-01
[35,] 0.24078330 4.815666e-01 7.592167e-01
[36,] 0.28760690 5.752138e-01 7.123931e-01
[37,] 0.35642133 7.128427e-01 6.435787e-01
[38,] 0.41707408 8.341482e-01 5.829259e-01
[39,] 0.47306271 9.461254e-01 5.269373e-01
[40,] 0.54271575 9.145685e-01 4.572843e-01
[41,] 0.63097431 7.380514e-01 3.690257e-01
[42,] 0.74469768 5.106046e-01 2.553023e-01
[43,] 0.81061850 3.787630e-01 1.893815e-01
[44,] 0.86422011 2.715598e-01 1.357799e-01
[45,] 0.89854667 2.029067e-01 1.014533e-01
[46,] 0.92505981 1.498804e-01 7.494019e-02
[47,] 0.96289640 7.420719e-02 3.710360e-02
[48,] 0.97867944 4.264112e-02 2.132056e-02
[49,] 0.98798410 2.403179e-02 1.201590e-02
[50,] 0.99306048 1.387905e-02 6.939525e-03
[51,] 0.99641342 7.173164e-03 3.586582e-03
[52,] 0.99841143 3.177144e-03 1.588572e-03
[53,] 0.99929276 1.414478e-03 7.072390e-04
[54,] 0.99970474 5.905295e-04 2.952647e-04
[55,] 0.99984683 3.063433e-04 1.531716e-04
[56,] 0.99991514 1.697275e-04 8.486374e-05
[57,] 0.99995246 9.508373e-05 4.754186e-05
[58,] 0.99997324 5.352061e-05 2.676031e-05
[59,] 0.99999039 1.921973e-05 9.609867e-06
[60,] 0.99999634 7.315916e-06 3.657958e-06
[61,] 0.99999853 2.945231e-06 1.472616e-06
[62,] 0.99999943 1.148944e-06 5.744721e-07
[63,] 0.99999976 4.723995e-07 2.361998e-07
[64,] 0.99999991 1.815132e-07 9.075662e-08
[65,] 0.99999998 4.857939e-08 2.428970e-08
[66,] 0.99999999 1.205341e-08 6.026703e-09
[67,] 1.00000000 3.871469e-09 1.935734e-09
[68,] 1.00000000 1.492512e-09 7.462562e-10
[69,] 1.00000000 6.029154e-10 3.014577e-10
[70,] 1.00000000 2.340219e-10 1.170109e-10
[71,] 1.00000000 9.097889e-11 4.548944e-11
[72,] 1.00000000 3.477415e-11 1.738707e-11
[73,] 1.00000000 1.093150e-11 5.465752e-12
[74,] 1.00000000 5.501503e-12 2.750751e-12
[75,] 1.00000000 4.301587e-12 2.150793e-12
[76,] 1.00000000 3.675824e-12 1.837912e-12
[77,] 1.00000000 3.006981e-12 1.503491e-12
[78,] 1.00000000 2.162377e-12 1.081188e-12
[79,] 1.00000000 1.645501e-12 8.227503e-13
[80,] 1.00000000 1.201139e-12 6.005696e-13
[81,] 1.00000000 7.304404e-13 3.652202e-13
[82,] 1.00000000 3.151445e-13 1.575723e-13
[83,] 1.00000000 1.359944e-13 6.799719e-14
[84,] 1.00000000 1.109657e-13 5.548284e-14
[85,] 1.00000000 8.032675e-14 4.016338e-14
[86,] 1.00000000 5.703794e-14 2.851897e-14
[87,] 1.00000000 4.062508e-14 2.031254e-14
[88,] 1.00000000 2.549707e-14 1.274854e-14
[89,] 1.00000000 1.101803e-14 5.509016e-15
[90,] 1.00000000 3.987086e-15 1.993543e-15
[91,] 1.00000000 1.528654e-15 7.643268e-16
[92,] 1.00000000 7.164415e-16 3.582208e-16
[93,] 1.00000000 3.448595e-16 1.724298e-16
[94,] 1.00000000 2.007319e-16 1.003659e-16
[95,] 1.00000000 1.094923e-16 5.474614e-17
[96,] 1.00000000 1.027772e-16 5.138862e-17
[97,] 1.00000000 6.431221e-17 3.215611e-17
[98,] 1.00000000 3.333624e-17 1.666812e-17
[99,] 1.00000000 2.079742e-17 1.039871e-17
[100,] 1.00000000 1.456508e-17 7.282538e-18
[101,] 1.00000000 9.975105e-18 4.987552e-18
[102,] 1.00000000 5.126042e-18 2.563021e-18
[103,] 1.00000000 4.668425e-18 2.334213e-18
[104,] 1.00000000 3.445140e-18 1.722570e-18
[105,] 1.00000000 1.138269e-18 5.691346e-19
[106,] 1.00000000 3.515452e-19 1.757726e-19
[107,] 1.00000000 9.532299e-20 4.766150e-20
[108,] 1.00000000 3.596832e-20 1.798416e-20
[109,] 1.00000000 1.399093e-20 6.995467e-21
[110,] 1.00000000 4.738083e-21 2.369041e-21
[111,] 1.00000000 2.536394e-21 1.268197e-21
[112,] 1.00000000 1.612872e-21 8.064358e-22
[113,] 1.00000000 1.298419e-21 6.492096e-22
[114,] 1.00000000 1.192463e-21 5.962314e-22
[115,] 1.00000000 1.043214e-21 5.216069e-22
[116,] 1.00000000 8.921054e-22 4.460527e-22
[117,] 1.00000000 1.031690e-21 5.158449e-22
[118,] 1.00000000 1.459201e-21 7.296003e-22
[119,] 1.00000000 1.804486e-21 9.022431e-22
[120,] 1.00000000 2.609810e-21 1.304905e-21
[121,] 1.00000000 3.836640e-21 1.918320e-21
[122,] 1.00000000 4.008658e-21 2.004329e-21
[123,] 1.00000000 4.711879e-21 2.355940e-21
[124,] 1.00000000 7.364265e-21 3.682132e-21
[125,] 1.00000000 1.120304e-20 5.601520e-21
[126,] 1.00000000 1.677860e-20 8.389298e-21
[127,] 1.00000000 2.420887e-20 1.210443e-20
[128,] 1.00000000 2.798298e-20 1.399149e-20
[129,] 1.00000000 3.367494e-20 1.683747e-20
[130,] 1.00000000 4.722272e-20 2.361136e-20
[131,] 1.00000000 7.552343e-20 3.776171e-20
[132,] 1.00000000 1.276472e-19 6.382358e-20
[133,] 1.00000000 2.279959e-19 1.139979e-19
[134,] 1.00000000 4.087190e-19 2.043595e-19
[135,] 1.00000000 7.476773e-19 3.738386e-19
[136,] 1.00000000 1.334891e-18 6.674455e-19
[137,] 1.00000000 2.396388e-18 1.198194e-18
[138,] 1.00000000 4.095008e-18 2.047504e-18
[139,] 1.00000000 7.721996e-18 3.860998e-18
[140,] 1.00000000 1.458666e-17 7.293328e-18
[141,] 1.00000000 2.774324e-17 1.387162e-17
[142,] 1.00000000 5.252079e-17 2.626039e-17
[143,] 1.00000000 9.738221e-17 4.869111e-17
[144,] 1.00000000 1.379722e-16 6.898612e-17
[145,] 1.00000000 1.596565e-16 7.982823e-17
[146,] 1.00000000 1.251813e-16 6.259064e-17
[147,] 1.00000000 6.039011e-17 3.019505e-17
[148,] 1.00000000 4.772680e-17 2.386340e-17
[149,] 1.00000000 3.524632e-17 1.762316e-17
[150,] 1.00000000 4.045242e-17 2.022621e-17
[151,] 1.00000000 6.802944e-17 3.401472e-17
[152,] 1.00000000 1.353408e-16 6.767040e-17
[153,] 1.00000000 2.612237e-16 1.306118e-16
[154,] 1.00000000 5.911732e-16 2.955866e-16
[155,] 1.00000000 1.235818e-15 6.179088e-16
[156,] 1.00000000 2.363089e-15 1.181544e-15
[157,] 1.00000000 5.622653e-15 2.811326e-15
[158,] 1.00000000 1.301498e-14 6.507490e-15
[159,] 1.00000000 3.140549e-14 1.570275e-14
[160,] 1.00000000 7.291214e-14 3.645607e-14
[161,] 1.00000000 1.629986e-13 8.149928e-14
[162,] 1.00000000 3.661008e-13 1.830504e-13
[163,] 1.00000000 8.535683e-13 4.267842e-13
[164,] 1.00000000 1.985921e-12 9.929605e-13
[165,] 1.00000000 3.981244e-12 1.990622e-12
[166,] 1.00000000 6.872032e-12 3.436016e-12
[167,] 1.00000000 1.474075e-11 7.370377e-12
[168,] 1.00000000 3.410951e-11 1.705476e-11
[169,] 1.00000000 7.914306e-11 3.957153e-11
[170,] 1.00000000 1.731802e-10 8.659012e-11
[171,] 1.00000000 3.446844e-10 1.723422e-10
[172,] 1.00000000 6.823923e-10 3.411961e-10
[173,] 1.00000000 1.294934e-09 6.474669e-10
[174,] 1.00000000 1.843695e-09 9.218477e-10
[175,] 1.00000000 3.189226e-09 1.594613e-09
[176,] 1.00000000 4.745324e-09 2.372662e-09
[177,] 1.00000000 3.996892e-09 1.998446e-09
[178,] 1.00000000 6.157961e-09 3.078980e-09
[179,] 0.99999999 1.103932e-08 5.519662e-09
[180,] 0.99999999 2.674916e-08 1.337458e-08
[181,] 0.99999997 5.365290e-08 2.682645e-08
[182,] 0.99999996 8.390706e-08 4.195353e-08
[183,] 0.99999996 7.870210e-08 3.935105e-08
[184,] 0.99999995 9.115978e-08 4.557989e-08
[185,] 0.99999993 1.317942e-07 6.589708e-08
[186,] 0.99999992 1.634206e-07 8.171028e-08
[187,] 0.99999985 3.075349e-07 1.537674e-07
[188,] 0.99999969 6.109207e-07 3.054604e-07
[189,] 0.99999954 9.166641e-07 4.583321e-07
[190,] 0.99999958 8.493366e-07 4.246683e-07
[191,] 0.99999917 1.655333e-06 8.276664e-07
[192,] 0.99999865 2.692231e-06 1.346116e-06
[193,] 0.99999696 6.075430e-06 3.037715e-06
[194,] 0.99999274 1.451914e-05 7.259569e-06
[195,] 0.99998333 3.333033e-05 1.666516e-05
[196,] 0.99996342 7.315796e-05 3.657898e-05
[197,] 0.99991989 1.602218e-04 8.011089e-05
[198,] 0.99984000 3.199977e-04 1.599988e-04
[199,] 0.99965055 6.988964e-04 3.494482e-04
[200,] 0.99924438 1.511244e-03 7.556220e-04
[201,] 0.99854366 2.912681e-03 1.456340e-03
[202,] 0.99695656 6.086874e-03 3.043437e-03
[203,] 0.99380334 1.239333e-02 6.196664e-03
[204,] 0.99032593 1.934814e-02 9.674072e-03
[205,] 0.98083115 3.833770e-02 1.916885e-02
[206,] 0.96532564 6.934873e-02 3.467436e-02
[207,] 0.94073311 1.185338e-01 5.926689e-02
[208,] 0.90568750 1.886250e-01 9.431250e-02
[209,] 0.85700352 2.859930e-01 1.429965e-01
[210,] 0.80383290 3.923342e-01 1.961671e-01
[211,] 0.71307257 5.738549e-01 2.869274e-01
[212,] 0.68883887 6.223223e-01 3.111611e-01
> postscript(file="/var/fisher/rcomp/tmp/1iyen1355680657.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/28vxr1355680657.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3l8uk1355680657.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/45fmv1355680657.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5oz211355680657.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 7
3.5947619 3.5925000 3.5855000 3.5320000 3.5880000 3.8105000 3.9145000
8 9 10 11 12 13 14
3.5495000 3.0900000 3.0715000 2.8485000 2.7320000 2.4747619 2.0825000
15 16 17 18 19 20 21
2.2355000 2.0320000 1.6380000 1.8405000 1.8445000 2.2595000 1.9700000
22 23 24 25 26 27 28
1.8215000 1.4885000 1.6220000 1.8447619 1.8625000 2.1755000 2.6320000
29 30 31 32 33 34 35
2.5780000 2.7705000 3.1745000 3.3895000 3.3200000 3.2715000 3.4685000
36 37 38 39 40 41 42
3.4620000 3.1047619 2.7625000 2.5055000 2.0020000 2.2380000 1.9805000
43 44 45 46 47 48 49
1.9245000 2.0995000 1.9100000 1.8015000 1.8285000 1.5720000 1.8047619
50 51 52 53 54 55 56
1.4425000 1.2755000 1.3420000 1.4280000 1.4205000 1.3345000 1.0295000
57 58 59 60 61 62 63
1.0500000 0.8915000 1.0185000 0.9320000 0.4847619 0.5625000 0.6155000
64 65 66 67 68 69 70
0.6720000 0.4080000 0.2805000 0.5045000 0.4895000 0.6500000 0.6315000
71 72 73 74 75 76 77
0.5485000 0.3120000 -0.0452381 -0.2575000 -0.1845000 -0.3180000 -0.3820000
78 79 80 81 82 83 84
-0.4495000 -0.7255000 -0.8205000 -0.8100000 -0.7885000 -0.8415000 -0.9980000
85 86 87 88 89 90 91
-0.9152381 -1.1375000 -1.3045000 -1.0180000 -0.5820000 -0.2895000 -0.0755000
92 93 94 95 96 97 98
0.2395000 0.3300000 0.4515000 0.6485000 0.9320000 0.6247619 0.1625000
99 100 101 102 103 104 105
0.2755000 0.2820000 0.2580000 0.2805000 0.4445000 0.4695000 0.4900000
106 107 108 109 110 111 112
0.3915000 0.3685000 0.2920000 0.0247619 -0.2575000 0.0555000 0.1520000
113 114 115 116 117 118 119
0.0280000 -0.1295000 -0.2355000 -0.1105000 -0.4400000 -0.1685000 0.2385000
120 121 122 123 124 125 126
0.2920000 0.0747619 0.0625000 -0.0245000 -0.0080000 -0.1820000 -0.3095000
127 128 129 130 131 132 133
-0.5255000 -0.7005000 -0.4800000 -0.4385000 -0.6815000 -0.8180000 -1.1752381
134 135 136 137 138 139 140
-1.2075000 -1.1545000 -1.5380000 -1.4720000 -1.1395000 -1.0655000 -1.1205000
141 142 143 144 145 146 147
-0.8000000 -0.5985000 -0.6215000 -0.6280000 -1.0352381 -1.3475000 -0.9845000
148 149 150 151 152 153 154
-0.9080000 -0.8820000 -0.9595000 -1.0855000 -1.0405000 -1.1500000 -1.1785000
155 156 157 158 159 160 161
-1.1915000 -1.2880000 -1.3552381 -1.6075000 -1.8045000 -1.9680000 -2.1220000
162 163 164 165 166 167 168
-1.9995000 -2.0655000 -1.9205000 -1.6500000 -1.5185000 -1.5515000 -1.3080000
169 170 171 172 173 174 175
-1.5152381 -1.4675000 -1.2645000 -1.2880000 -1.1720000 -1.2895000 -1.3755000
176 177 178 179 180 181 182
-1.4005000 -1.2700000 -1.2385000 -0.8715000 -0.6480000 -1.0452381 -1.1975000
183 184 185 186 187 188 189
-1.0445000 -0.8280000 -0.6620000 -0.7595000 -0.7555000 -0.6205000 -0.7200000
190 191 192 193 194 195 196
-0.6485000 -0.4115000 -0.6880000 -0.9052381 -0.9975000 -0.8645000 -0.6080000
197 198 199 200 201 202 203
-0.3220000 -0.5195000 -0.6855000 -0.7505000 -0.9300000 -1.0485000 -0.9815000
204 205 206 207 208 209 210
-0.6180000 -1.0952381 -1.5175000 -1.5945000 -1.5080000 -1.6220000 -1.7195000
211 212 213 214 215 216 217
-1.7055000 -1.9705000 -1.6600000 -1.6685000 -2.0515000 -1.6680000 -1.6752381
218 219 220 221 222 223 224
-1.2475000 -1.2845000 -1.0780000 -1.0720000 -1.0095000 -0.9655000 -0.9405000
225 226 227 228 229 230 231
-0.9600000 -0.6185000 -0.8815000 -1.1980000 -0.6552381 -0.2875000 -1.2145000
232 233 234 235 236 237 238
-1.5780000 -1.6920000 -1.8095000 -1.8755000 -2.1305000 -1.9400000 -2.4185000
239 240 241
-2.3715000 -2.2880000 -2.6152381
> postscript(file="/var/fisher/rcomp/tmp/62bnq1355680657.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 3.5947619 NA
1 3.5925000 3.5947619
2 3.5855000 3.5925000
3 3.5320000 3.5855000
4 3.5880000 3.5320000
5 3.8105000 3.5880000
6 3.9145000 3.8105000
7 3.5495000 3.9145000
8 3.0900000 3.5495000
9 3.0715000 3.0900000
10 2.8485000 3.0715000
11 2.7320000 2.8485000
12 2.4747619 2.7320000
13 2.0825000 2.4747619
14 2.2355000 2.0825000
15 2.0320000 2.2355000
16 1.6380000 2.0320000
17 1.8405000 1.6380000
18 1.8445000 1.8405000
19 2.2595000 1.8445000
20 1.9700000 2.2595000
21 1.8215000 1.9700000
22 1.4885000 1.8215000
23 1.6220000 1.4885000
24 1.8447619 1.6220000
25 1.8625000 1.8447619
26 2.1755000 1.8625000
27 2.6320000 2.1755000
28 2.5780000 2.6320000
29 2.7705000 2.5780000
30 3.1745000 2.7705000
31 3.3895000 3.1745000
32 3.3200000 3.3895000
33 3.2715000 3.3200000
34 3.4685000 3.2715000
35 3.4620000 3.4685000
36 3.1047619 3.4620000
37 2.7625000 3.1047619
38 2.5055000 2.7625000
39 2.0020000 2.5055000
40 2.2380000 2.0020000
41 1.9805000 2.2380000
42 1.9245000 1.9805000
43 2.0995000 1.9245000
44 1.9100000 2.0995000
45 1.8015000 1.9100000
46 1.8285000 1.8015000
47 1.5720000 1.8285000
48 1.8047619 1.5720000
49 1.4425000 1.8047619
50 1.2755000 1.4425000
51 1.3420000 1.2755000
52 1.4280000 1.3420000
53 1.4205000 1.4280000
54 1.3345000 1.4205000
55 1.0295000 1.3345000
56 1.0500000 1.0295000
57 0.8915000 1.0500000
58 1.0185000 0.8915000
59 0.9320000 1.0185000
60 0.4847619 0.9320000
61 0.5625000 0.4847619
62 0.6155000 0.5625000
63 0.6720000 0.6155000
64 0.4080000 0.6720000
65 0.2805000 0.4080000
66 0.5045000 0.2805000
67 0.4895000 0.5045000
68 0.6500000 0.4895000
69 0.6315000 0.6500000
70 0.5485000 0.6315000
71 0.3120000 0.5485000
72 -0.0452381 0.3120000
73 -0.2575000 -0.0452381
74 -0.1845000 -0.2575000
75 -0.3180000 -0.1845000
76 -0.3820000 -0.3180000
77 -0.4495000 -0.3820000
78 -0.7255000 -0.4495000
79 -0.8205000 -0.7255000
80 -0.8100000 -0.8205000
81 -0.7885000 -0.8100000
82 -0.8415000 -0.7885000
83 -0.9980000 -0.8415000
84 -0.9152381 -0.9980000
85 -1.1375000 -0.9152381
86 -1.3045000 -1.1375000
87 -1.0180000 -1.3045000
88 -0.5820000 -1.0180000
89 -0.2895000 -0.5820000
90 -0.0755000 -0.2895000
91 0.2395000 -0.0755000
92 0.3300000 0.2395000
93 0.4515000 0.3300000
94 0.6485000 0.4515000
95 0.9320000 0.6485000
96 0.6247619 0.9320000
97 0.1625000 0.6247619
98 0.2755000 0.1625000
99 0.2820000 0.2755000
100 0.2580000 0.2820000
101 0.2805000 0.2580000
102 0.4445000 0.2805000
103 0.4695000 0.4445000
104 0.4900000 0.4695000
105 0.3915000 0.4900000
106 0.3685000 0.3915000
107 0.2920000 0.3685000
108 0.0247619 0.2920000
109 -0.2575000 0.0247619
110 0.0555000 -0.2575000
111 0.1520000 0.0555000
112 0.0280000 0.1520000
113 -0.1295000 0.0280000
114 -0.2355000 -0.1295000
115 -0.1105000 -0.2355000
116 -0.4400000 -0.1105000
117 -0.1685000 -0.4400000
118 0.2385000 -0.1685000
119 0.2920000 0.2385000
120 0.0747619 0.2920000
121 0.0625000 0.0747619
122 -0.0245000 0.0625000
123 -0.0080000 -0.0245000
124 -0.1820000 -0.0080000
125 -0.3095000 -0.1820000
126 -0.5255000 -0.3095000
127 -0.7005000 -0.5255000
128 -0.4800000 -0.7005000
129 -0.4385000 -0.4800000
130 -0.6815000 -0.4385000
131 -0.8180000 -0.6815000
132 -1.1752381 -0.8180000
133 -1.2075000 -1.1752381
134 -1.1545000 -1.2075000
135 -1.5380000 -1.1545000
136 -1.4720000 -1.5380000
137 -1.1395000 -1.4720000
138 -1.0655000 -1.1395000
139 -1.1205000 -1.0655000
140 -0.8000000 -1.1205000
141 -0.5985000 -0.8000000
142 -0.6215000 -0.5985000
143 -0.6280000 -0.6215000
144 -1.0352381 -0.6280000
145 -1.3475000 -1.0352381
146 -0.9845000 -1.3475000
147 -0.9080000 -0.9845000
148 -0.8820000 -0.9080000
149 -0.9595000 -0.8820000
150 -1.0855000 -0.9595000
151 -1.0405000 -1.0855000
152 -1.1500000 -1.0405000
153 -1.1785000 -1.1500000
154 -1.1915000 -1.1785000
155 -1.2880000 -1.1915000
156 -1.3552381 -1.2880000
157 -1.6075000 -1.3552381
158 -1.8045000 -1.6075000
159 -1.9680000 -1.8045000
160 -2.1220000 -1.9680000
161 -1.9995000 -2.1220000
162 -2.0655000 -1.9995000
163 -1.9205000 -2.0655000
164 -1.6500000 -1.9205000
165 -1.5185000 -1.6500000
166 -1.5515000 -1.5185000
167 -1.3080000 -1.5515000
168 -1.5152381 -1.3080000
169 -1.4675000 -1.5152381
170 -1.2645000 -1.4675000
171 -1.2880000 -1.2645000
172 -1.1720000 -1.2880000
173 -1.2895000 -1.1720000
174 -1.3755000 -1.2895000
175 -1.4005000 -1.3755000
176 -1.2700000 -1.4005000
177 -1.2385000 -1.2700000
178 -0.8715000 -1.2385000
179 -0.6480000 -0.8715000
180 -1.0452381 -0.6480000
181 -1.1975000 -1.0452381
182 -1.0445000 -1.1975000
183 -0.8280000 -1.0445000
184 -0.6620000 -0.8280000
185 -0.7595000 -0.6620000
186 -0.7555000 -0.7595000
187 -0.6205000 -0.7555000
188 -0.7200000 -0.6205000
189 -0.6485000 -0.7200000
190 -0.4115000 -0.6485000
191 -0.6880000 -0.4115000
192 -0.9052381 -0.6880000
193 -0.9975000 -0.9052381
194 -0.8645000 -0.9975000
195 -0.6080000 -0.8645000
196 -0.3220000 -0.6080000
197 -0.5195000 -0.3220000
198 -0.6855000 -0.5195000
199 -0.7505000 -0.6855000
200 -0.9300000 -0.7505000
201 -1.0485000 -0.9300000
202 -0.9815000 -1.0485000
203 -0.6180000 -0.9815000
204 -1.0952381 -0.6180000
205 -1.5175000 -1.0952381
206 -1.5945000 -1.5175000
207 -1.5080000 -1.5945000
208 -1.6220000 -1.5080000
209 -1.7195000 -1.6220000
210 -1.7055000 -1.7195000
211 -1.9705000 -1.7055000
212 -1.6600000 -1.9705000
213 -1.6685000 -1.6600000
214 -2.0515000 -1.6685000
215 -1.6680000 -2.0515000
216 -1.6752381 -1.6680000
217 -1.2475000 -1.6752381
218 -1.2845000 -1.2475000
219 -1.0780000 -1.2845000
220 -1.0720000 -1.0780000
221 -1.0095000 -1.0720000
222 -0.9655000 -1.0095000
223 -0.9405000 -0.9655000
224 -0.9600000 -0.9405000
225 -0.6185000 -0.9600000
226 -0.8815000 -0.6185000
227 -1.1980000 -0.8815000
228 -0.6552381 -1.1980000
229 -0.2875000 -0.6552381
230 -1.2145000 -0.2875000
231 -1.5780000 -1.2145000
232 -1.6920000 -1.5780000
233 -1.8095000 -1.6920000
234 -1.8755000 -1.8095000
235 -2.1305000 -1.8755000
236 -1.9400000 -2.1305000
237 -2.4185000 -1.9400000
238 -2.3715000 -2.4185000
239 -2.2880000 -2.3715000
240 -2.6152381 -2.2880000
241 NA -2.6152381
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.5925000 3.5947619
[2,] 3.5855000 3.5925000
[3,] 3.5320000 3.5855000
[4,] 3.5880000 3.5320000
[5,] 3.8105000 3.5880000
[6,] 3.9145000 3.8105000
[7,] 3.5495000 3.9145000
[8,] 3.0900000 3.5495000
[9,] 3.0715000 3.0900000
[10,] 2.8485000 3.0715000
[11,] 2.7320000 2.8485000
[12,] 2.4747619 2.7320000
[13,] 2.0825000 2.4747619
[14,] 2.2355000 2.0825000
[15,] 2.0320000 2.2355000
[16,] 1.6380000 2.0320000
[17,] 1.8405000 1.6380000
[18,] 1.8445000 1.8405000
[19,] 2.2595000 1.8445000
[20,] 1.9700000 2.2595000
[21,] 1.8215000 1.9700000
[22,] 1.4885000 1.8215000
[23,] 1.6220000 1.4885000
[24,] 1.8447619 1.6220000
[25,] 1.8625000 1.8447619
[26,] 2.1755000 1.8625000
[27,] 2.6320000 2.1755000
[28,] 2.5780000 2.6320000
[29,] 2.7705000 2.5780000
[30,] 3.1745000 2.7705000
[31,] 3.3895000 3.1745000
[32,] 3.3200000 3.3895000
[33,] 3.2715000 3.3200000
[34,] 3.4685000 3.2715000
[35,] 3.4620000 3.4685000
[36,] 3.1047619 3.4620000
[37,] 2.7625000 3.1047619
[38,] 2.5055000 2.7625000
[39,] 2.0020000 2.5055000
[40,] 2.2380000 2.0020000
[41,] 1.9805000 2.2380000
[42,] 1.9245000 1.9805000
[43,] 2.0995000 1.9245000
[44,] 1.9100000 2.0995000
[45,] 1.8015000 1.9100000
[46,] 1.8285000 1.8015000
[47,] 1.5720000 1.8285000
[48,] 1.8047619 1.5720000
[49,] 1.4425000 1.8047619
[50,] 1.2755000 1.4425000
[51,] 1.3420000 1.2755000
[52,] 1.4280000 1.3420000
[53,] 1.4205000 1.4280000
[54,] 1.3345000 1.4205000
[55,] 1.0295000 1.3345000
[56,] 1.0500000 1.0295000
[57,] 0.8915000 1.0500000
[58,] 1.0185000 0.8915000
[59,] 0.9320000 1.0185000
[60,] 0.4847619 0.9320000
[61,] 0.5625000 0.4847619
[62,] 0.6155000 0.5625000
[63,] 0.6720000 0.6155000
[64,] 0.4080000 0.6720000
[65,] 0.2805000 0.4080000
[66,] 0.5045000 0.2805000
[67,] 0.4895000 0.5045000
[68,] 0.6500000 0.4895000
[69,] 0.6315000 0.6500000
[70,] 0.5485000 0.6315000
[71,] 0.3120000 0.5485000
[72,] -0.0452381 0.3120000
[73,] -0.2575000 -0.0452381
[74,] -0.1845000 -0.2575000
[75,] -0.3180000 -0.1845000
[76,] -0.3820000 -0.3180000
[77,] -0.4495000 -0.3820000
[78,] -0.7255000 -0.4495000
[79,] -0.8205000 -0.7255000
[80,] -0.8100000 -0.8205000
[81,] -0.7885000 -0.8100000
[82,] -0.8415000 -0.7885000
[83,] -0.9980000 -0.8415000
[84,] -0.9152381 -0.9980000
[85,] -1.1375000 -0.9152381
[86,] -1.3045000 -1.1375000
[87,] -1.0180000 -1.3045000
[88,] -0.5820000 -1.0180000
[89,] -0.2895000 -0.5820000
[90,] -0.0755000 -0.2895000
[91,] 0.2395000 -0.0755000
[92,] 0.3300000 0.2395000
[93,] 0.4515000 0.3300000
[94,] 0.6485000 0.4515000
[95,] 0.9320000 0.6485000
[96,] 0.6247619 0.9320000
[97,] 0.1625000 0.6247619
[98,] 0.2755000 0.1625000
[99,] 0.2820000 0.2755000
[100,] 0.2580000 0.2820000
[101,] 0.2805000 0.2580000
[102,] 0.4445000 0.2805000
[103,] 0.4695000 0.4445000
[104,] 0.4900000 0.4695000
[105,] 0.3915000 0.4900000
[106,] 0.3685000 0.3915000
[107,] 0.2920000 0.3685000
[108,] 0.0247619 0.2920000
[109,] -0.2575000 0.0247619
[110,] 0.0555000 -0.2575000
[111,] 0.1520000 0.0555000
[112,] 0.0280000 0.1520000
[113,] -0.1295000 0.0280000
[114,] -0.2355000 -0.1295000
[115,] -0.1105000 -0.2355000
[116,] -0.4400000 -0.1105000
[117,] -0.1685000 -0.4400000
[118,] 0.2385000 -0.1685000
[119,] 0.2920000 0.2385000
[120,] 0.0747619 0.2920000
[121,] 0.0625000 0.0747619
[122,] -0.0245000 0.0625000
[123,] -0.0080000 -0.0245000
[124,] -0.1820000 -0.0080000
[125,] -0.3095000 -0.1820000
[126,] -0.5255000 -0.3095000
[127,] -0.7005000 -0.5255000
[128,] -0.4800000 -0.7005000
[129,] -0.4385000 -0.4800000
[130,] -0.6815000 -0.4385000
[131,] -0.8180000 -0.6815000
[132,] -1.1752381 -0.8180000
[133,] -1.2075000 -1.1752381
[134,] -1.1545000 -1.2075000
[135,] -1.5380000 -1.1545000
[136,] -1.4720000 -1.5380000
[137,] -1.1395000 -1.4720000
[138,] -1.0655000 -1.1395000
[139,] -1.1205000 -1.0655000
[140,] -0.8000000 -1.1205000
[141,] -0.5985000 -0.8000000
[142,] -0.6215000 -0.5985000
[143,] -0.6280000 -0.6215000
[144,] -1.0352381 -0.6280000
[145,] -1.3475000 -1.0352381
[146,] -0.9845000 -1.3475000
[147,] -0.9080000 -0.9845000
[148,] -0.8820000 -0.9080000
[149,] -0.9595000 -0.8820000
[150,] -1.0855000 -0.9595000
[151,] -1.0405000 -1.0855000
[152,] -1.1500000 -1.0405000
[153,] -1.1785000 -1.1500000
[154,] -1.1915000 -1.1785000
[155,] -1.2880000 -1.1915000
[156,] -1.3552381 -1.2880000
[157,] -1.6075000 -1.3552381
[158,] -1.8045000 -1.6075000
[159,] -1.9680000 -1.8045000
[160,] -2.1220000 -1.9680000
[161,] -1.9995000 -2.1220000
[162,] -2.0655000 -1.9995000
[163,] -1.9205000 -2.0655000
[164,] -1.6500000 -1.9205000
[165,] -1.5185000 -1.6500000
[166,] -1.5515000 -1.5185000
[167,] -1.3080000 -1.5515000
[168,] -1.5152381 -1.3080000
[169,] -1.4675000 -1.5152381
[170,] -1.2645000 -1.4675000
[171,] -1.2880000 -1.2645000
[172,] -1.1720000 -1.2880000
[173,] -1.2895000 -1.1720000
[174,] -1.3755000 -1.2895000
[175,] -1.4005000 -1.3755000
[176,] -1.2700000 -1.4005000
[177,] -1.2385000 -1.2700000
[178,] -0.8715000 -1.2385000
[179,] -0.6480000 -0.8715000
[180,] -1.0452381 -0.6480000
[181,] -1.1975000 -1.0452381
[182,] -1.0445000 -1.1975000
[183,] -0.8280000 -1.0445000
[184,] -0.6620000 -0.8280000
[185,] -0.7595000 -0.6620000
[186,] -0.7555000 -0.7595000
[187,] -0.6205000 -0.7555000
[188,] -0.7200000 -0.6205000
[189,] -0.6485000 -0.7200000
[190,] -0.4115000 -0.6485000
[191,] -0.6880000 -0.4115000
[192,] -0.9052381 -0.6880000
[193,] -0.9975000 -0.9052381
[194,] -0.8645000 -0.9975000
[195,] -0.6080000 -0.8645000
[196,] -0.3220000 -0.6080000
[197,] -0.5195000 -0.3220000
[198,] -0.6855000 -0.5195000
[199,] -0.7505000 -0.6855000
[200,] -0.9300000 -0.7505000
[201,] -1.0485000 -0.9300000
[202,] -0.9815000 -1.0485000
[203,] -0.6180000 -0.9815000
[204,] -1.0952381 -0.6180000
[205,] -1.5175000 -1.0952381
[206,] -1.5945000 -1.5175000
[207,] -1.5080000 -1.5945000
[208,] -1.6220000 -1.5080000
[209,] -1.7195000 -1.6220000
[210,] -1.7055000 -1.7195000
[211,] -1.9705000 -1.7055000
[212,] -1.6600000 -1.9705000
[213,] -1.6685000 -1.6600000
[214,] -2.0515000 -1.6685000
[215,] -1.6680000 -2.0515000
[216,] -1.6752381 -1.6680000
[217,] -1.2475000 -1.6752381
[218,] -1.2845000 -1.2475000
[219,] -1.0780000 -1.2845000
[220,] -1.0720000 -1.0780000
[221,] -1.0095000 -1.0720000
[222,] -0.9655000 -1.0095000
[223,] -0.9405000 -0.9655000
[224,] -0.9600000 -0.9405000
[225,] -0.6185000 -0.9600000
[226,] -0.8815000 -0.6185000
[227,] -1.1980000 -0.8815000
[228,] -0.6552381 -1.1980000
[229,] -0.2875000 -0.6552381
[230,] -1.2145000 -0.2875000
[231,] -1.5780000 -1.2145000
[232,] -1.6920000 -1.5780000
[233,] -1.8095000 -1.6920000
[234,] -1.8755000 -1.8095000
[235,] -2.1305000 -1.8755000
[236,] -1.9400000 -2.1305000
[237,] -2.4185000 -1.9400000
[238,] -2.3715000 -2.4185000
[239,] -2.2880000 -2.3715000
[240,] -2.6152381 -2.2880000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.5925000 3.5947619
2 3.5855000 3.5925000
3 3.5320000 3.5855000
4 3.5880000 3.5320000
5 3.8105000 3.5880000
6 3.9145000 3.8105000
7 3.5495000 3.9145000
8 3.0900000 3.5495000
9 3.0715000 3.0900000
10 2.8485000 3.0715000
11 2.7320000 2.8485000
12 2.4747619 2.7320000
13 2.0825000 2.4747619
14 2.2355000 2.0825000
15 2.0320000 2.2355000
16 1.6380000 2.0320000
17 1.8405000 1.6380000
18 1.8445000 1.8405000
19 2.2595000 1.8445000
20 1.9700000 2.2595000
21 1.8215000 1.9700000
22 1.4885000 1.8215000
23 1.6220000 1.4885000
24 1.8447619 1.6220000
25 1.8625000 1.8447619
26 2.1755000 1.8625000
27 2.6320000 2.1755000
28 2.5780000 2.6320000
29 2.7705000 2.5780000
30 3.1745000 2.7705000
31 3.3895000 3.1745000
32 3.3200000 3.3895000
33 3.2715000 3.3200000
34 3.4685000 3.2715000
35 3.4620000 3.4685000
36 3.1047619 3.4620000
37 2.7625000 3.1047619
38 2.5055000 2.7625000
39 2.0020000 2.5055000
40 2.2380000 2.0020000
41 1.9805000 2.2380000
42 1.9245000 1.9805000
43 2.0995000 1.9245000
44 1.9100000 2.0995000
45 1.8015000 1.9100000
46 1.8285000 1.8015000
47 1.5720000 1.8285000
48 1.8047619 1.5720000
49 1.4425000 1.8047619
50 1.2755000 1.4425000
51 1.3420000 1.2755000
52 1.4280000 1.3420000
53 1.4205000 1.4280000
54 1.3345000 1.4205000
55 1.0295000 1.3345000
56 1.0500000 1.0295000
57 0.8915000 1.0500000
58 1.0185000 0.8915000
59 0.9320000 1.0185000
60 0.4847619 0.9320000
61 0.5625000 0.4847619
62 0.6155000 0.5625000
63 0.6720000 0.6155000
64 0.4080000 0.6720000
65 0.2805000 0.4080000
66 0.5045000 0.2805000
67 0.4895000 0.5045000
68 0.6500000 0.4895000
69 0.6315000 0.6500000
70 0.5485000 0.6315000
71 0.3120000 0.5485000
72 -0.0452381 0.3120000
73 -0.2575000 -0.0452381
74 -0.1845000 -0.2575000
75 -0.3180000 -0.1845000
76 -0.3820000 -0.3180000
77 -0.4495000 -0.3820000
78 -0.7255000 -0.4495000
79 -0.8205000 -0.7255000
80 -0.8100000 -0.8205000
81 -0.7885000 -0.8100000
82 -0.8415000 -0.7885000
83 -0.9980000 -0.8415000
84 -0.9152381 -0.9980000
85 -1.1375000 -0.9152381
86 -1.3045000 -1.1375000
87 -1.0180000 -1.3045000
88 -0.5820000 -1.0180000
89 -0.2895000 -0.5820000
90 -0.0755000 -0.2895000
91 0.2395000 -0.0755000
92 0.3300000 0.2395000
93 0.4515000 0.3300000
94 0.6485000 0.4515000
95 0.9320000 0.6485000
96 0.6247619 0.9320000
97 0.1625000 0.6247619
98 0.2755000 0.1625000
99 0.2820000 0.2755000
100 0.2580000 0.2820000
101 0.2805000 0.2580000
102 0.4445000 0.2805000
103 0.4695000 0.4445000
104 0.4900000 0.4695000
105 0.3915000 0.4900000
106 0.3685000 0.3915000
107 0.2920000 0.3685000
108 0.0247619 0.2920000
109 -0.2575000 0.0247619
110 0.0555000 -0.2575000
111 0.1520000 0.0555000
112 0.0280000 0.1520000
113 -0.1295000 0.0280000
114 -0.2355000 -0.1295000
115 -0.1105000 -0.2355000
116 -0.4400000 -0.1105000
117 -0.1685000 -0.4400000
118 0.2385000 -0.1685000
119 0.2920000 0.2385000
120 0.0747619 0.2920000
121 0.0625000 0.0747619
122 -0.0245000 0.0625000
123 -0.0080000 -0.0245000
124 -0.1820000 -0.0080000
125 -0.3095000 -0.1820000
126 -0.5255000 -0.3095000
127 -0.7005000 -0.5255000
128 -0.4800000 -0.7005000
129 -0.4385000 -0.4800000
130 -0.6815000 -0.4385000
131 -0.8180000 -0.6815000
132 -1.1752381 -0.8180000
133 -1.2075000 -1.1752381
134 -1.1545000 -1.2075000
135 -1.5380000 -1.1545000
136 -1.4720000 -1.5380000
137 -1.1395000 -1.4720000
138 -1.0655000 -1.1395000
139 -1.1205000 -1.0655000
140 -0.8000000 -1.1205000
141 -0.5985000 -0.8000000
142 -0.6215000 -0.5985000
143 -0.6280000 -0.6215000
144 -1.0352381 -0.6280000
145 -1.3475000 -1.0352381
146 -0.9845000 -1.3475000
147 -0.9080000 -0.9845000
148 -0.8820000 -0.9080000
149 -0.9595000 -0.8820000
150 -1.0855000 -0.9595000
151 -1.0405000 -1.0855000
152 -1.1500000 -1.0405000
153 -1.1785000 -1.1500000
154 -1.1915000 -1.1785000
155 -1.2880000 -1.1915000
156 -1.3552381 -1.2880000
157 -1.6075000 -1.3552381
158 -1.8045000 -1.6075000
159 -1.9680000 -1.8045000
160 -2.1220000 -1.9680000
161 -1.9995000 -2.1220000
162 -2.0655000 -1.9995000
163 -1.9205000 -2.0655000
164 -1.6500000 -1.9205000
165 -1.5185000 -1.6500000
166 -1.5515000 -1.5185000
167 -1.3080000 -1.5515000
168 -1.5152381 -1.3080000
169 -1.4675000 -1.5152381
170 -1.2645000 -1.4675000
171 -1.2880000 -1.2645000
172 -1.1720000 -1.2880000
173 -1.2895000 -1.1720000
174 -1.3755000 -1.2895000
175 -1.4005000 -1.3755000
176 -1.2700000 -1.4005000
177 -1.2385000 -1.2700000
178 -0.8715000 -1.2385000
179 -0.6480000 -0.8715000
180 -1.0452381 -0.6480000
181 -1.1975000 -1.0452381
182 -1.0445000 -1.1975000
183 -0.8280000 -1.0445000
184 -0.6620000 -0.8280000
185 -0.7595000 -0.6620000
186 -0.7555000 -0.7595000
187 -0.6205000 -0.7555000
188 -0.7200000 -0.6205000
189 -0.6485000 -0.7200000
190 -0.4115000 -0.6485000
191 -0.6880000 -0.4115000
192 -0.9052381 -0.6880000
193 -0.9975000 -0.9052381
194 -0.8645000 -0.9975000
195 -0.6080000 -0.8645000
196 -0.3220000 -0.6080000
197 -0.5195000 -0.3220000
198 -0.6855000 -0.5195000
199 -0.7505000 -0.6855000
200 -0.9300000 -0.7505000
201 -1.0485000 -0.9300000
202 -0.9815000 -1.0485000
203 -0.6180000 -0.9815000
204 -1.0952381 -0.6180000
205 -1.5175000 -1.0952381
206 -1.5945000 -1.5175000
207 -1.5080000 -1.5945000
208 -1.6220000 -1.5080000
209 -1.7195000 -1.6220000
210 -1.7055000 -1.7195000
211 -1.9705000 -1.7055000
212 -1.6600000 -1.9705000
213 -1.6685000 -1.6600000
214 -2.0515000 -1.6685000
215 -1.6680000 -2.0515000
216 -1.6752381 -1.6680000
217 -1.2475000 -1.6752381
218 -1.2845000 -1.2475000
219 -1.0780000 -1.2845000
220 -1.0720000 -1.0780000
221 -1.0095000 -1.0720000
222 -0.9655000 -1.0095000
223 -0.9405000 -0.9655000
224 -0.9600000 -0.9405000
225 -0.6185000 -0.9600000
226 -0.8815000 -0.6185000
227 -1.1980000 -0.8815000
228 -0.6552381 -1.1980000
229 -0.2875000 -0.6552381
230 -1.2145000 -0.2875000
231 -1.5780000 -1.2145000
232 -1.6920000 -1.5780000
233 -1.8095000 -1.6920000
234 -1.8755000 -1.8095000
235 -2.1305000 -1.8755000
236 -1.9400000 -2.1305000
237 -2.4185000 -1.9400000
238 -2.3715000 -2.4185000
239 -2.2880000 -2.3715000
240 -2.6152381 -2.2880000
> 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/fisher/rcomp/tmp/7t6mb1355680657.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8k2kg1355680657.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9h1zf1355680657.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10vcqh1355680657.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/11muct1355680657.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/fisher/rcomp/tmp/12lgp51355680657.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/fisher/rcomp/tmp/13uo7v1355680658.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/fisher/rcomp/tmp/14918e1355680658.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/fisher/rcomp/tmp/15wkkg1355680658.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/fisher/rcomp/tmp/164v381355680658.tab")
+ }
>
> try(system("convert tmp/1iyen1355680657.ps tmp/1iyen1355680657.png",intern=TRUE))
character(0)
> try(system("convert tmp/28vxr1355680657.ps tmp/28vxr1355680657.png",intern=TRUE))
character(0)
> try(system("convert tmp/3l8uk1355680657.ps tmp/3l8uk1355680657.png",intern=TRUE))
character(0)
> try(system("convert tmp/45fmv1355680657.ps tmp/45fmv1355680657.png",intern=TRUE))
character(0)
> try(system("convert tmp/5oz211355680657.ps tmp/5oz211355680657.png",intern=TRUE))
character(0)
> try(system("convert tmp/62bnq1355680657.ps tmp/62bnq1355680657.png",intern=TRUE))
character(0)
> try(system("convert tmp/7t6mb1355680657.ps tmp/7t6mb1355680657.png",intern=TRUE))
character(0)
> try(system("convert tmp/8k2kg1355680657.ps tmp/8k2kg1355680657.png",intern=TRUE))
character(0)
> try(system("convert tmp/9h1zf1355680657.ps tmp/9h1zf1355680657.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vcqh1355680657.ps tmp/10vcqh1355680657.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.356 1.755 12.137