R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'license()' or 'licence()' for distribution details.
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(3.88
+ ,153.3
+ ,3.98
+ ,154.5
+ ,3.29
+ ,155.2
+ ,2.88
+ ,156.9
+ ,3.22
+ ,157
+ ,3.62
+ ,157.4
+ ,3.82
+ ,157.2
+ ,3.54
+ ,157.5
+ ,2.53
+ ,158
+ ,2.22
+ ,158.5
+ ,2.85
+ ,159
+ ,2.78
+ ,159.3
+ ,2.28
+ ,160
+ ,2.26
+ ,160.8
+ ,2.71
+ ,161.9
+ ,2.77
+ ,162.5
+ ,2.77
+ ,162.7
+ ,2.64
+ ,162.8
+ ,2.56
+ ,162.9
+ ,2.07
+ ,163
+ ,2.32
+ ,164
+ ,2.16
+ ,164.7
+ ,2.23
+ ,164.8
+ ,2.4
+ ,164.9
+ ,2.84
+ ,165
+ ,2.77
+ ,165.8
+ ,2.93
+ ,166.1
+ ,2.91
+ ,167.2
+ ,2.69
+ ,167.7
+ ,2.38
+ ,168.3
+ ,2.58
+ ,168.6
+ ,3.19
+ ,168.9
+ ,2.82
+ ,169.1
+ ,2.72
+ ,169.5
+ ,2.53
+ ,169.6
+ ,2.7
+ ,169.7
+ ,2.42
+ ,169.8
+ ,2.5
+ ,170.4
+ ,2.31
+ ,170.9
+ ,2.41
+ ,171.9
+ ,2.56
+ ,171.9
+ ,2.76
+ ,172
+ ,2.71
+ ,172
+ ,2.44
+ ,172.4
+ ,2.46
+ ,173
+ ,2.12
+ ,173.7
+ ,1.99
+ ,173.8
+ ,1.86
+ ,173.8
+ ,1.88
+ ,173.9
+ ,1.82
+ ,174.6
+ ,1.74
+ ,175
+ ,1.71
+ ,175.9
+ ,1.38
+ ,176
+ ,1.27
+ ,175.1
+ ,1.19
+ ,175.6
+ ,1.28
+ ,175.9
+ ,1.19
+ ,176.7
+ ,1.22
+ ,176.1
+ ,1.47
+ ,176.1
+ ,1.46
+ ,176.2
+ ,1.96
+ ,176.3
+ ,1.88
+ ,177.8
+ ,2.03
+ ,178.5
+ ,2.04
+ ,179.4
+ ,1.9
+ ,179.5
+ ,1.8
+ ,179.6
+ ,1.92
+ ,179.7
+ ,1.92
+ ,179.7
+ ,1.97
+ ,179.8
+ ,2.46
+ ,179.9
+ ,2.36
+ ,180.2
+ ,2.53
+ ,180.4
+ ,2.31
+ ,180.4
+ ,1.98
+ ,181.3
+ ,1.46
+ ,181.9
+ ,1.26
+ ,182.5
+ ,1.58
+ ,182.7
+ ,1.74
+ ,183.1
+ ,1.89
+ ,183.6
+ ,1.85
+ ,183.7
+ ,1.62
+ ,183.8
+ ,1.3
+ ,183.9
+ ,1.42
+ ,184.1
+ ,1.15
+ ,184.4
+ ,0.42
+ ,184.5
+ ,0.74
+ ,185.9
+ ,1.02
+ ,186.6
+ ,1.51
+ ,187.6
+ ,1.86
+ ,187.8
+ ,1.59
+ ,187.9
+ ,1.03
+ ,188
+ ,0.44
+ ,188.3
+ ,0.82
+ ,188.4
+ ,0.86
+ ,188.5
+ ,0.58
+ ,188.5
+ ,0.59
+ ,188.6
+ ,0.95
+ ,188.6
+ ,0.98
+ ,189.4
+ ,1.23
+ ,190
+ ,1.17
+ ,191.9
+ ,0.84
+ ,192.5
+ ,0.74
+ ,193
+ ,0.65
+ ,193.5
+ ,0.91
+ ,193.9
+ ,1.19
+ ,194.2
+ ,1.3
+ ,194.9
+ ,1.53
+ ,194.9
+ ,1.94
+ ,194.9
+ ,1.79
+ ,194.9
+ ,1.95
+ ,195.5
+ ,2.26
+ ,196
+ ,2.04
+ ,196.2
+ ,2.16
+ ,196.2
+ ,2.75
+ ,196.2
+ ,2.79
+ ,196.2
+ ,2.88
+ ,197
+ ,3.36
+ ,197.7
+ ,2.97
+ ,198
+ ,3.1
+ ,198.2
+ ,2.49
+ ,198.5
+ ,2.2
+ ,198.6
+ ,2.25
+ ,199.5
+ ,2.09
+ ,200
+ ,2.79
+ ,201.3
+ ,3.14
+ ,202.2
+ ,2.93
+ ,202.9
+ ,2.65
+ ,203.5
+ ,2.67
+ ,203.5
+ ,2.26
+ ,204
+ ,2.35
+ ,204.1
+ ,2.13
+ ,204.3
+ ,2.18
+ ,204.5
+ ,2.9
+ ,204.8
+ ,2.63
+ ,205.1
+ ,2.67
+ ,205.7
+ ,1.81
+ ,206.5
+ ,1.33
+ ,206.9
+ ,0.88
+ ,207.1
+ ,1.28
+ ,207.8
+ ,1.26
+ ,208
+ ,1.26
+ ,208.5
+ ,1.29
+ ,208.6
+ ,1.1
+ ,209
+ ,1.37
+ ,209.1
+ ,1.21
+ ,209.7
+ ,1.74
+ ,209.8
+ ,1.76
+ ,209.9
+ ,1.48
+ ,210
+ ,1.04
+ ,210.8
+ ,1.62
+ ,211.4
+ ,1.49
+ ,211.7
+ ,1.79
+ ,212
+ ,1.8
+ ,212.2
+ ,1.58
+ ,212.4
+ ,1.86
+ ,212.9
+ ,1.74
+ ,213.4
+ ,1.59
+ ,213.7
+ ,1.26
+ ,214
+ ,1.13
+ ,214.3
+ ,1.92
+ ,214.8
+ ,2.61
+ ,215
+ ,2.26
+ ,215.9
+ ,2.41
+ ,216.4
+ ,2.26
+ ,216.9
+ ,2.03
+ ,217.2
+ ,2.86
+ ,217.5
+ ,2.55
+ ,217.9
+ ,2.27
+ ,218.1
+ ,2.26
+ ,218.6
+ ,2.57
+ ,218.9
+ ,3.07
+ ,219.3
+ ,2.76
+ ,220.4
+ ,2.51
+ ,220.9
+ ,2.87
+ ,221
+ ,3.14
+ ,221.8
+ ,3.11
+ ,222
+ ,3.16
+ ,222.2
+ ,2.47
+ ,222.5
+ ,2.57
+ ,222.9
+ ,2.89
+ ,223.1
+ ,2.63
+ ,223.4
+ ,2.38
+ ,224
+ ,1.69
+ ,225.1
+ ,1.96
+ ,225.5
+ ,2.19
+ ,225.9
+ ,1.87
+ ,226.3
+ ,1.6
+ ,226.5
+ ,1.63
+ ,227
+ ,1.22
+ ,227.3
+ ,1.21
+ ,227.8
+ ,1.49
+ ,228.1
+ ,1.64
+ ,228.4
+ ,1.66
+ ,228.5
+ ,1.77
+ ,228.8
+ ,1.82
+ ,229
+ ,1.78
+ ,229.1
+ ,1.28
+ ,229.3
+ ,1.29
+ ,229.6
+ ,1.37
+ ,229.9
+ ,1.12
+ ,230
+ ,1.51
+ ,230.2
+ ,2.24
+ ,230.8
+ ,2.94
+ ,231
+ ,3.09
+ ,231.7
+ ,3.46
+ ,231.9
+ ,3.64
+ ,233
+ ,4.39
+ ,235.1
+ ,4.15
+ ,236
+ ,5.21
+ ,236.9
+ ,5.8
+ ,237.1
+ ,5.91
+ ,237.5
+ ,5.39
+ ,238.2
+ ,5.46
+ ,238.9
+ ,4.72
+ ,239.1
+ ,3.14
+ ,240
+ ,2.63
+ ,240.2
+ ,2.32
+ ,240.5
+ ,1.93
+ ,240.7
+ ,0.62
+ ,241.1
+ ,0.6
+ ,241.4
+ ,-0.37
+ ,242.2
+ ,-1.1
+ ,242.9
+ ,-1.68
+ ,243.2
+ ,-0.78
+ ,243.9)
+ ,dim=c(2
+ ,224)
+ ,dimnames=list(c('Y'
+ ,'X')
+ ,1:224))
> y <- array(NA,dim=c(2,224),dimnames=list(c('Y','X'),1:224))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> ylab = ''
> xlab = ''
> main = ''
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 3.88 153.3 1 0 0 0 0 0 0 0 0 0 0
2 3.98 154.5 0 1 0 0 0 0 0 0 0 0 0
3 3.29 155.2 0 0 1 0 0 0 0 0 0 0 0
4 2.88 156.9 0 0 0 1 0 0 0 0 0 0 0
5 3.22 157.0 0 0 0 0 1 0 0 0 0 0 0
6 3.62 157.4 0 0 0 0 0 1 0 0 0 0 0
7 3.82 157.2 0 0 0 0 0 0 1 0 0 0 0
8 3.54 157.5 0 0 0 0 0 0 0 1 0 0 0
9 2.53 158.0 0 0 0 0 0 0 0 0 1 0 0
10 2.22 158.5 0 0 0 0 0 0 0 0 0 1 0
11 2.85 159.0 0 0 0 0 0 0 0 0 0 0 1
12 2.78 159.3 0 0 0 0 0 0 0 0 0 0 0
13 2.28 160.0 1 0 0 0 0 0 0 0 0 0 0
14 2.26 160.8 0 1 0 0 0 0 0 0 0 0 0
15 2.71 161.9 0 0 1 0 0 0 0 0 0 0 0
16 2.77 162.5 0 0 0 1 0 0 0 0 0 0 0
17 2.77 162.7 0 0 0 0 1 0 0 0 0 0 0
18 2.64 162.8 0 0 0 0 0 1 0 0 0 0 0
19 2.56 162.9 0 0 0 0 0 0 1 0 0 0 0
20 2.07 163.0 0 0 0 0 0 0 0 1 0 0 0
21 2.32 164.0 0 0 0 0 0 0 0 0 1 0 0
22 2.16 164.7 0 0 0 0 0 0 0 0 0 1 0
23 2.23 164.8 0 0 0 0 0 0 0 0 0 0 1
24 2.40 164.9 0 0 0 0 0 0 0 0 0 0 0
25 2.84 165.0 1 0 0 0 0 0 0 0 0 0 0
26 2.77 165.8 0 1 0 0 0 0 0 0 0 0 0
27 2.93 166.1 0 0 1 0 0 0 0 0 0 0 0
28 2.91 167.2 0 0 0 1 0 0 0 0 0 0 0
29 2.69 167.7 0 0 0 0 1 0 0 0 0 0 0
30 2.38 168.3 0 0 0 0 0 1 0 0 0 0 0
31 2.58 168.6 0 0 0 0 0 0 1 0 0 0 0
32 3.19 168.9 0 0 0 0 0 0 0 1 0 0 0
33 2.82 169.1 0 0 0 0 0 0 0 0 1 0 0
34 2.72 169.5 0 0 0 0 0 0 0 0 0 1 0
35 2.53 169.6 0 0 0 0 0 0 0 0 0 0 1
36 2.70 169.7 0 0 0 0 0 0 0 0 0 0 0
37 2.42 169.8 1 0 0 0 0 0 0 0 0 0 0
38 2.50 170.4 0 1 0 0 0 0 0 0 0 0 0
39 2.31 170.9 0 0 1 0 0 0 0 0 0 0 0
40 2.41 171.9 0 0 0 1 0 0 0 0 0 0 0
41 2.56 171.9 0 0 0 0 1 0 0 0 0 0 0
42 2.76 172.0 0 0 0 0 0 1 0 0 0 0 0
43 2.71 172.0 0 0 0 0 0 0 1 0 0 0 0
44 2.44 172.4 0 0 0 0 0 0 0 1 0 0 0
45 2.46 173.0 0 0 0 0 0 0 0 0 1 0 0
46 2.12 173.7 0 0 0 0 0 0 0 0 0 1 0
47 1.99 173.8 0 0 0 0 0 0 0 0 0 0 1
48 1.86 173.8 0 0 0 0 0 0 0 0 0 0 0
49 1.88 173.9 1 0 0 0 0 0 0 0 0 0 0
50 1.82 174.6 0 1 0 0 0 0 0 0 0 0 0
51 1.74 175.0 0 0 1 0 0 0 0 0 0 0 0
52 1.71 175.9 0 0 0 1 0 0 0 0 0 0 0
53 1.38 176.0 0 0 0 0 1 0 0 0 0 0 0
54 1.27 175.1 0 0 0 0 0 1 0 0 0 0 0
55 1.19 175.6 0 0 0 0 0 0 1 0 0 0 0
56 1.28 175.9 0 0 0 0 0 0 0 1 0 0 0
57 1.19 176.7 0 0 0 0 0 0 0 0 1 0 0
58 1.22 176.1 0 0 0 0 0 0 0 0 0 1 0
59 1.47 176.1 0 0 0 0 0 0 0 0 0 0 1
60 1.46 176.2 0 0 0 0 0 0 0 0 0 0 0
61 1.96 176.3 1 0 0 0 0 0 0 0 0 0 0
62 1.88 177.8 0 1 0 0 0 0 0 0 0 0 0
63 2.03 178.5 0 0 1 0 0 0 0 0 0 0 0
64 2.04 179.4 0 0 0 1 0 0 0 0 0 0 0
65 1.90 179.5 0 0 0 0 1 0 0 0 0 0 0
66 1.80 179.6 0 0 0 0 0 1 0 0 0 0 0
67 1.92 179.7 0 0 0 0 0 0 1 0 0 0 0
68 1.92 179.7 0 0 0 0 0 0 0 1 0 0 0
69 1.97 179.8 0 0 0 0 0 0 0 0 1 0 0
70 2.46 179.9 0 0 0 0 0 0 0 0 0 1 0
71 2.36 180.2 0 0 0 0 0 0 0 0 0 0 1
72 2.53 180.4 0 0 0 0 0 0 0 0 0 0 0
73 2.31 180.4 1 0 0 0 0 0 0 0 0 0 0
74 1.98 181.3 0 1 0 0 0 0 0 0 0 0 0
75 1.46 181.9 0 0 1 0 0 0 0 0 0 0 0
76 1.26 182.5 0 0 0 1 0 0 0 0 0 0 0
77 1.58 182.7 0 0 0 0 1 0 0 0 0 0 0
78 1.74 183.1 0 0 0 0 0 1 0 0 0 0 0
79 1.89 183.6 0 0 0 0 0 0 1 0 0 0 0
80 1.85 183.7 0 0 0 0 0 0 0 1 0 0 0
81 1.62 183.8 0 0 0 0 0 0 0 0 1 0 0
82 1.30 183.9 0 0 0 0 0 0 0 0 0 1 0
83 1.42 184.1 0 0 0 0 0 0 0 0 0 0 1
84 1.15 184.4 0 0 0 0 0 0 0 0 0 0 0
85 0.42 184.5 1 0 0 0 0 0 0 0 0 0 0
86 0.74 185.9 0 1 0 0 0 0 0 0 0 0 0
87 1.02 186.6 0 0 1 0 0 0 0 0 0 0 0
88 1.51 187.6 0 0 0 1 0 0 0 0 0 0 0
89 1.86 187.8 0 0 0 0 1 0 0 0 0 0 0
90 1.59 187.9 0 0 0 0 0 1 0 0 0 0 0
91 1.03 188.0 0 0 0 0 0 0 1 0 0 0 0
92 0.44 188.3 0 0 0 0 0 0 0 1 0 0 0
93 0.82 188.4 0 0 0 0 0 0 0 0 1 0 0
94 0.86 188.5 0 0 0 0 0 0 0 0 0 1 0
95 0.58 188.5 0 0 0 0 0 0 0 0 0 0 1
96 0.59 188.6 0 0 0 0 0 0 0 0 0 0 0
97 0.95 188.6 1 0 0 0 0 0 0 0 0 0 0
98 0.98 189.4 0 1 0 0 0 0 0 0 0 0 0
99 1.23 190.0 0 0 1 0 0 0 0 0 0 0 0
100 1.17 191.9 0 0 0 1 0 0 0 0 0 0 0
101 0.84 192.5 0 0 0 0 1 0 0 0 0 0 0
102 0.74 193.0 0 0 0 0 0 1 0 0 0 0 0
103 0.65 193.5 0 0 0 0 0 0 1 0 0 0 0
104 0.91 193.9 0 0 0 0 0 0 0 1 0 0 0
105 1.19 194.2 0 0 0 0 0 0 0 0 1 0 0
106 1.30 194.9 0 0 0 0 0 0 0 0 0 1 0
107 1.53 194.9 0 0 0 0 0 0 0 0 0 0 1
108 1.94 194.9 0 0 0 0 0 0 0 0 0 0 0
109 1.79 194.9 1 0 0 0 0 0 0 0 0 0 0
110 1.95 195.5 0 1 0 0 0 0 0 0 0 0 0
111 2.26 196.0 0 0 1 0 0 0 0 0 0 0 0
112 2.04 196.2 0 0 0 1 0 0 0 0 0 0 0
113 2.16 196.2 0 0 0 0 1 0 0 0 0 0 0
114 2.75 196.2 0 0 0 0 0 1 0 0 0 0 0
115 2.79 196.2 0 0 0 0 0 0 1 0 0 0 0
116 2.88 197.0 0 0 0 0 0 0 0 1 0 0 0
117 3.36 197.7 0 0 0 0 0 0 0 0 1 0 0
118 2.97 198.0 0 0 0 0 0 0 0 0 0 1 0
119 3.10 198.2 0 0 0 0 0 0 0 0 0 0 1
120 2.49 198.5 0 0 0 0 0 0 0 0 0 0 0
121 2.20 198.6 1 0 0 0 0 0 0 0 0 0 0
122 2.25 199.5 0 1 0 0 0 0 0 0 0 0 0
123 2.09 200.0 0 0 1 0 0 0 0 0 0 0 0
124 2.79 201.3 0 0 0 1 0 0 0 0 0 0 0
125 3.14 202.2 0 0 0 0 1 0 0 0 0 0 0
126 2.93 202.9 0 0 0 0 0 1 0 0 0 0 0
127 2.65 203.5 0 0 0 0 0 0 1 0 0 0 0
128 2.67 203.5 0 0 0 0 0 0 0 1 0 0 0
129 2.26 204.0 0 0 0 0 0 0 0 0 1 0 0
130 2.35 204.1 0 0 0 0 0 0 0 0 0 1 0
131 2.13 204.3 0 0 0 0 0 0 0 0 0 0 1
132 2.18 204.5 0 0 0 0 0 0 0 0 0 0 0
133 2.90 204.8 1 0 0 0 0 0 0 0 0 0 0
134 2.63 205.1 0 1 0 0 0 0 0 0 0 0 0
135 2.67 205.7 0 0 1 0 0 0 0 0 0 0 0
136 1.81 206.5 0 0 0 1 0 0 0 0 0 0 0
137 1.33 206.9 0 0 0 0 1 0 0 0 0 0 0
138 0.88 207.1 0 0 0 0 0 1 0 0 0 0 0
139 1.28 207.8 0 0 0 0 0 0 1 0 0 0 0
140 1.26 208.0 0 0 0 0 0 0 0 1 0 0 0
141 1.26 208.5 0 0 0 0 0 0 0 0 1 0 0
142 1.29 208.6 0 0 0 0 0 0 0 0 0 1 0
143 1.10 209.0 0 0 0 0 0 0 0 0 0 0 1
144 1.37 209.1 0 0 0 0 0 0 0 0 0 0 0
145 1.21 209.7 1 0 0 0 0 0 0 0 0 0 0
146 1.74 209.8 0 1 0 0 0 0 0 0 0 0 0
147 1.76 209.9 0 0 1 0 0 0 0 0 0 0 0
148 1.48 210.0 0 0 0 1 0 0 0 0 0 0 0
149 1.04 210.8 0 0 0 0 1 0 0 0 0 0 0
150 1.62 211.4 0 0 0 0 0 1 0 0 0 0 0
151 1.49 211.7 0 0 0 0 0 0 1 0 0 0 0
152 1.79 212.0 0 0 0 0 0 0 0 1 0 0 0
153 1.80 212.2 0 0 0 0 0 0 0 0 1 0 0
154 1.58 212.4 0 0 0 0 0 0 0 0 0 1 0
155 1.86 212.9 0 0 0 0 0 0 0 0 0 0 1
156 1.74 213.4 0 0 0 0 0 0 0 0 0 0 0
157 1.59 213.7 1 0 0 0 0 0 0 0 0 0 0
158 1.26 214.0 0 1 0 0 0 0 0 0 0 0 0
159 1.13 214.3 0 0 1 0 0 0 0 0 0 0 0
160 1.92 214.8 0 0 0 1 0 0 0 0 0 0 0
161 2.61 215.0 0 0 0 0 1 0 0 0 0 0 0
162 2.26 215.9 0 0 0 0 0 1 0 0 0 0 0
163 2.41 216.4 0 0 0 0 0 0 1 0 0 0 0
164 2.26 216.9 0 0 0 0 0 0 0 1 0 0 0
165 2.03 217.2 0 0 0 0 0 0 0 0 1 0 0
166 2.86 217.5 0 0 0 0 0 0 0 0 0 1 0
167 2.55 217.9 0 0 0 0 0 0 0 0 0 0 1
168 2.27 218.1 0 0 0 0 0 0 0 0 0 0 0
169 2.26 218.6 1 0 0 0 0 0 0 0 0 0 0
170 2.57 218.9 0 1 0 0 0 0 0 0 0 0 0
171 3.07 219.3 0 0 1 0 0 0 0 0 0 0 0
172 2.76 220.4 0 0 0 1 0 0 0 0 0 0 0
173 2.51 220.9 0 0 0 0 1 0 0 0 0 0 0
174 2.87 221.0 0 0 0 0 0 1 0 0 0 0 0
175 3.14 221.8 0 0 0 0 0 0 1 0 0 0 0
176 3.11 222.0 0 0 0 0 0 0 0 1 0 0 0
177 3.16 222.2 0 0 0 0 0 0 0 0 1 0 0
178 2.47 222.5 0 0 0 0 0 0 0 0 0 1 0
179 2.57 222.9 0 0 0 0 0 0 0 0 0 0 1
180 2.89 223.1 0 0 0 0 0 0 0 0 0 0 0
181 2.63 223.4 1 0 0 0 0 0 0 0 0 0 0
182 2.38 224.0 0 1 0 0 0 0 0 0 0 0 0
183 1.69 225.1 0 0 1 0 0 0 0 0 0 0 0
184 1.96 225.5 0 0 0 1 0 0 0 0 0 0 0
185 2.19 225.9 0 0 0 0 1 0 0 0 0 0 0
186 1.87 226.3 0 0 0 0 0 1 0 0 0 0 0
187 1.60 226.5 0 0 0 0 0 0 1 0 0 0 0
188 1.63 227.0 0 0 0 0 0 0 0 1 0 0 0
189 1.22 227.3 0 0 0 0 0 0 0 0 1 0 0
190 1.21 227.8 0 0 0 0 0 0 0 0 0 1 0
191 1.49 228.1 0 0 0 0 0 0 0 0 0 0 1
192 1.64 228.4 0 0 0 0 0 0 0 0 0 0 0
193 1.66 228.5 1 0 0 0 0 0 0 0 0 0 0
194 1.77 228.8 0 1 0 0 0 0 0 0 0 0 0
195 1.82 229.0 0 0 1 0 0 0 0 0 0 0 0
196 1.78 229.1 0 0 0 1 0 0 0 0 0 0 0
197 1.28 229.3 0 0 0 0 1 0 0 0 0 0 0
198 1.29 229.6 0 0 0 0 0 1 0 0 0 0 0
199 1.37 229.9 0 0 0 0 0 0 1 0 0 0 0
200 1.12 230.0 0 0 0 0 0 0 0 1 0 0 0
201 1.51 230.2 0 0 0 0 0 0 0 0 1 0 0
202 2.24 230.8 0 0 0 0 0 0 0 0 0 1 0
203 2.94 231.0 0 0 0 0 0 0 0 0 0 0 1
204 3.09 231.7 0 0 0 0 0 0 0 0 0 0 0
205 3.46 231.9 1 0 0 0 0 0 0 0 0 0 0
206 3.64 233.0 0 1 0 0 0 0 0 0 0 0 0
207 4.39 235.1 0 0 1 0 0 0 0 0 0 0 0
208 4.15 236.0 0 0 0 1 0 0 0 0 0 0 0
209 5.21 236.9 0 0 0 0 1 0 0 0 0 0 0
210 5.80 237.1 0 0 0 0 0 1 0 0 0 0 0
211 5.91 237.5 0 0 0 0 0 0 1 0 0 0 0
212 5.39 238.2 0 0 0 0 0 0 0 1 0 0 0
213 5.46 238.9 0 0 0 0 0 0 0 0 1 0 0
214 4.72 239.1 0 0 0 0 0 0 0 0 0 1 0
215 3.14 240.0 0 0 0 0 0 0 0 0 0 0 1
216 2.63 240.2 0 0 0 0 0 0 0 0 0 0 0
217 2.32 240.5 1 0 0 0 0 0 0 0 0 0 0
218 1.93 240.7 0 1 0 0 0 0 0 0 0 0 0
219 0.62 241.1 0 0 1 0 0 0 0 0 0 0 0
220 0.60 241.4 0 0 0 1 0 0 0 0 0 0 0
221 -0.37 242.2 0 0 0 0 1 0 0 0 0 0 0
222 -1.10 242.9 0 0 0 0 0 1 0 0 0 0 0
223 -1.68 243.2 0 0 0 0 0 0 1 0 0 0 0
224 -0.78 243.9 0 0 0 0 0 0 0 1 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
2.442155 -0.001756 0.057044 0.061966 0.020425 0.007638
M5 M6 M7 M8 M9 M10
0.005662 -0.003830 -0.024328 -0.041650 0.069190 0.018040
M11
0.006842
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.67085 -0.61887 -0.05483 0.55608 3.90915
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.442155 0.610334 4.001 8.72e-05 ***
X -0.001756 0.002823 -0.622 0.535
M1 0.057044 0.344773 0.165 0.869
M2 0.061966 0.344744 0.180 0.858
M3 0.020425 0.344728 0.059 0.953
M4 0.007638 0.344721 0.022 0.982
M5 0.005662 0.344722 0.016 0.987
M6 -0.003830 0.344726 -0.011 0.991
M7 -0.024328 0.344732 -0.071 0.944
M8 -0.041650 0.344740 -0.121 0.904
M9 0.069190 0.349355 0.198 0.843
M10 0.018040 0.349351 0.052 0.959
M11 0.006842 0.349348 0.020 0.984
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.048 on 211 degrees of freedom
Multiple R-squared: 0.002933, Adjusted R-squared: -0.05377
F-statistic: 0.05172 on 12 and 211 DF, p-value: 1
> 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,] 1.417501e-01 2.835002e-01 0.8582499
[2,] 6.846849e-02 1.369370e-01 0.9315315
[3,] 2.724588e-02 5.449176e-02 0.9727541
[4,] 1.175208e-02 2.350415e-02 0.9882479
[5,] 6.292058e-03 1.258412e-02 0.9937079
[6,] 4.961065e-03 9.922131e-03 0.9950389
[7,] 4.427377e-03 8.854754e-03 0.9955726
[8,] 1.806054e-03 3.612108e-03 0.9981939
[9,] 8.173284e-04 1.634657e-03 0.9991827
[10,] 8.450740e-04 1.690148e-03 0.9991549
[11,] 5.212910e-04 1.042582e-03 0.9994787
[12,] 4.124830e-04 8.249661e-04 0.9995875
[13,] 3.705931e-04 7.411863e-04 0.9996294
[14,] 1.824207e-04 3.648414e-04 0.9998176
[15,] 7.604596e-05 1.520919e-04 0.9999240
[16,] 3.114595e-05 6.229190e-05 0.9999689
[17,] 6.037155e-05 1.207431e-04 0.9999396
[18,] 7.520190e-05 1.504038e-04 0.9999248
[19,] 9.917137e-05 1.983427e-04 0.9999008
[20,] 5.348665e-05 1.069733e-04 0.9999465
[21,] 3.180806e-05 6.361611e-05 0.9999682
[22,] 1.457651e-05 2.915302e-05 0.9999854
[23,] 6.526643e-06 1.305329e-05 0.9999935
[24,] 3.000881e-06 6.001762e-06 0.9999970
[25,] 1.295756e-06 2.591511e-06 0.9999987
[26,] 5.734328e-07 1.146866e-06 0.9999994
[27,] 2.951347e-07 5.902695e-07 0.9999997
[28,] 1.342345e-07 2.684691e-07 0.9999999
[29,] 5.705929e-08 1.141186e-07 0.9999999
[30,] 2.758435e-08 5.516869e-08 1.0000000
[31,] 1.125480e-08 2.250960e-08 1.0000000
[32,] 4.468050e-09 8.936099e-09 1.0000000
[33,] 2.152439e-09 4.304878e-09 1.0000000
[34,] 1.167565e-09 2.335130e-09 1.0000000
[35,] 7.035341e-10 1.407068e-09 1.0000000
[36,] 4.184122e-10 8.368243e-10 1.0000000
[37,] 2.250509e-10 4.501018e-10 1.0000000
[38,] 2.977661e-10 5.955323e-10 1.0000000
[39,] 6.675779e-10 1.335156e-09 1.0000000
[40,] 1.721783e-09 3.443566e-09 1.0000000
[41,] 2.007275e-09 4.014550e-09 1.0000000
[42,] 1.436599e-09 2.873198e-09 1.0000000
[43,] 7.692086e-10 1.538417e-09 1.0000000
[44,] 3.471023e-10 6.942046e-10 1.0000000
[45,] 1.605044e-10 3.210089e-10 1.0000000
[46,] 6.874359e-11 1.374872e-10 1.0000000
[47,] 2.829216e-11 5.658432e-11 1.0000000
[48,] 1.403559e-11 2.807119e-11 1.0000000
[49,] 7.334250e-12 1.466850e-11 1.0000000
[50,] 3.198475e-12 6.396950e-12 1.0000000
[51,] 1.273467e-12 2.546934e-12 1.0000000
[52,] 5.340864e-13 1.068173e-12 1.0000000
[53,] 2.303495e-13 4.606991e-13 1.0000000
[54,] 1.345395e-13 2.690790e-13 1.0000000
[55,] 4.992580e-13 9.985161e-13 1.0000000
[56,] 7.313229e-13 1.462646e-12 1.0000000
[57,] 1.539846e-12 3.079692e-12 1.0000000
[58,] 1.159956e-12 2.319912e-12 1.0000000
[59,] 5.283918e-13 1.056784e-12 1.0000000
[60,] 2.301218e-13 4.602436e-13 1.0000000
[61,] 1.187221e-13 2.374441e-13 1.0000000
[62,] 4.661143e-14 9.322285e-14 1.0000000
[63,] 1.888569e-14 3.777137e-14 1.0000000
[64,] 8.417447e-15 1.683489e-14 1.0000000
[65,] 3.716601e-15 7.433203e-15 1.0000000
[66,] 1.479809e-15 2.959619e-15 1.0000000
[67,] 5.642556e-16 1.128511e-15 1.0000000
[68,] 2.087694e-16 4.175387e-16 1.0000000
[69,] 9.745034e-17 1.949007e-16 1.0000000
[70,] 4.371828e-16 8.743656e-16 1.0000000
[71,] 4.229336e-16 8.458672e-16 1.0000000
[72,] 1.932440e-16 3.864880e-16 1.0000000
[73,] 8.133999e-17 1.626800e-16 1.0000000
[74,] 5.524095e-17 1.104819e-16 1.0000000
[75,] 2.348068e-17 4.696136e-17 1.0000000
[76,] 1.091273e-17 2.182546e-17 1.0000000
[77,] 1.703453e-17 3.406907e-17 1.0000000
[78,] 8.756590e-18 1.751318e-17 1.0000000
[79,] 3.999272e-18 7.998544e-18 1.0000000
[80,] 3.094726e-18 6.189451e-18 1.0000000
[81,] 2.341615e-18 4.683229e-18 1.0000000
[82,] 1.018128e-18 2.036255e-18 1.0000000
[83,] 4.371008e-19 8.742016e-19 1.0000000
[84,] 1.835440e-19 3.670879e-19 1.0000000
[85,] 7.757263e-20 1.551453e-19 1.0000000
[86,] 3.520682e-20 7.041364e-20 1.0000000
[87,] 1.744768e-20 3.489535e-20 1.0000000
[88,] 9.464871e-21 1.892974e-20 1.0000000
[89,] 4.324308e-21 8.648616e-21 1.0000000
[90,] 3.313907e-21 6.627815e-21 1.0000000
[91,] 3.849107e-21 7.698214e-21 1.0000000
[92,] 6.063517e-21 1.212703e-20 1.0000000
[93,] 3.067757e-20 6.135514e-20 1.0000000
[94,] 6.337991e-20 1.267598e-19 1.0000000
[95,] 1.702375e-19 3.404749e-19 1.0000000
[96,] 1.144433e-18 2.288867e-18 1.0000000
[97,] 2.353735e-18 4.707469e-18 1.0000000
[98,] 5.440474e-18 1.088095e-17 1.0000000
[99,] 8.996860e-17 1.799372e-16 1.0000000
[100,] 1.100133e-15 2.200267e-15 1.0000000
[101,] 1.671802e-14 3.343604e-14 1.0000000
[102,] 1.042331e-12 2.084661e-12 1.0000000
[103,] 9.503282e-12 1.900656e-11 1.0000000
[104,] 7.418094e-11 1.483619e-10 1.0000000
[105,] 1.109302e-10 2.218604e-10 1.0000000
[106,] 9.787103e-11 1.957421e-10 1.0000000
[107,] 8.677829e-11 1.735566e-10 1.0000000
[108,] 6.271827e-11 1.254365e-10 1.0000000
[109,] 1.241501e-10 2.483001e-10 1.0000000
[110,] 4.594045e-10 9.188090e-10 1.0000000
[111,] 9.611487e-10 1.922297e-09 1.0000000
[112,] 1.225995e-09 2.451991e-09 1.0000000
[113,] 1.625855e-09 3.251710e-09 1.0000000
[114,] 1.215565e-09 2.431131e-09 1.0000000
[115,] 1.036057e-09 2.072114e-09 1.0000000
[116,] 6.902552e-10 1.380510e-09 1.0000000
[117,] 4.744863e-10 9.489727e-10 1.0000000
[118,] 7.257400e-10 1.451480e-09 1.0000000
[119,] 7.079561e-10 1.415912e-09 1.0000000
[120,] 7.412202e-10 1.482440e-09 1.0000000
[121,] 3.985753e-10 7.971507e-10 1.0000000
[122,] 2.227942e-10 4.455884e-10 1.0000000
[123,] 1.681673e-10 3.363346e-10 1.0000000
[124,] 9.291732e-11 1.858346e-10 1.0000000
[125,] 5.081978e-11 1.016396e-10 1.0000000
[126,] 3.116125e-11 6.232249e-11 1.0000000
[127,] 1.851395e-11 3.702789e-11 1.0000000
[128,] 1.204859e-11 2.409719e-11 1.0000000
[129,] 6.664903e-12 1.332981e-11 1.0000000
[130,] 3.984193e-12 7.968386e-12 1.0000000
[131,] 2.008237e-12 4.016475e-12 1.0000000
[132,] 9.881043e-13 1.976209e-12 1.0000000
[133,] 4.949289e-13 9.898578e-13 1.0000000
[134,] 3.242540e-13 6.485079e-13 1.0000000
[135,] 1.577097e-13 3.154194e-13 1.0000000
[136,] 7.684018e-14 1.536804e-13 1.0000000
[137,] 3.764095e-14 7.528191e-14 1.0000000
[138,] 2.118398e-14 4.236796e-14 1.0000000
[139,] 1.262326e-14 2.524653e-14 1.0000000
[140,] 6.996837e-15 1.399367e-14 1.0000000
[141,] 3.765372e-15 7.530745e-15 1.0000000
[142,] 2.005242e-15 4.010485e-15 1.0000000
[143,] 1.308631e-15 2.617262e-15 1.0000000
[144,] 9.163228e-16 1.832646e-15 1.0000000
[145,] 4.634833e-16 9.269666e-16 1.0000000
[146,] 3.977195e-16 7.954389e-16 1.0000000
[147,] 2.284473e-16 4.568946e-16 1.0000000
[148,] 1.491486e-16 2.982971e-16 1.0000000
[149,] 8.471842e-17 1.694368e-16 1.0000000
[150,] 5.195536e-17 1.039107e-16 1.0000000
[151,] 6.444878e-17 1.288976e-16 1.0000000
[152,] 4.828441e-17 9.656883e-17 1.0000000
[153,] 2.832897e-17 5.665794e-17 1.0000000
[154,] 1.529568e-17 3.059135e-17 1.0000000
[155,] 9.801937e-18 1.960387e-17 1.0000000
[156,] 1.252659e-17 2.505318e-17 1.0000000
[157,] 9.498223e-18 1.899645e-17 1.0000000
[158,] 5.212034e-18 1.042407e-17 1.0000000
[159,] 4.488957e-18 8.977915e-18 1.0000000
[160,] 6.685876e-18 1.337175e-17 1.0000000
[161,] 9.474094e-18 1.894819e-17 1.0000000
[162,] 1.004599e-17 2.009197e-17 1.0000000
[163,] 5.003451e-18 1.000690e-17 1.0000000
[164,] 2.560019e-18 5.120038e-18 1.0000000
[165,] 1.817628e-18 3.635256e-18 1.0000000
[166,] 8.889819e-19 1.777964e-18 1.0000000
[167,] 3.546835e-19 7.093670e-19 1.0000000
[168,] 1.312319e-19 2.624638e-19 1.0000000
[169,] 4.469201e-20 8.938403e-20 1.0000000
[170,] 1.567635e-20 3.135271e-20 1.0000000
[171,] 5.051838e-21 1.010368e-20 1.0000000
[172,] 1.664755e-21 3.329511e-21 1.0000000
[173,] 5.292432e-22 1.058486e-21 1.0000000
[174,] 6.398638e-22 1.279728e-21 1.0000000
[175,] 8.485460e-22 1.697092e-21 1.0000000
[176,] 5.446314e-22 1.089263e-21 1.0000000
[177,] 2.749461e-22 5.498922e-22 1.0000000
[178,] 1.473081e-22 2.946163e-22 1.0000000
[179,] 7.036155e-23 1.407231e-22 1.0000000
[180,] 2.897849e-23 5.795697e-23 1.0000000
[181,] 1.214688e-23 2.429376e-23 1.0000000
[182,] 1.216703e-23 2.433406e-23 1.0000000
[183,] 1.634963e-23 3.269926e-23 1.0000000
[184,] 2.360536e-23 4.721073e-23 1.0000000
[185,] 3.260297e-22 6.520594e-22 1.0000000
[186,] 3.874523e-18 7.749047e-18 1.0000000
[187,] 1.283965e-14 2.567930e-14 1.0000000
[188,] 3.744906e-12 7.489812e-12 1.0000000
[189,] 1.549133e-09 3.098267e-09 1.0000000
[190,] 5.719593e-06 1.143919e-05 0.9999943
[191,] 3.615288e-02 7.230576e-02 0.9638471
[192,] 1.629032e-01 3.258064e-01 0.8370968
[193,] 7.438015e-01 5.123970e-01 0.2561985
> postscript(file="/var/www/html/rcomp/tmp/1s3uk1258639180.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2h80w1258639180.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/32n2h1258639180.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4szh11258639180.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5aqc21258639180.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 224
Frequency = 1
1 2 3 4 5 6
1.64994548 1.74712986 1.09990005 0.70567220 1.04782328 1.45801733
7 8 9 10 11 12
1.67816440 1.41601294 0.29605115 0.03807871 0.68015503 0.61752356
13 14 15 16 17 18
0.06170850 0.03819062 0.53166307 0.60550398 0.60783063 0.48749797
19 20 21 22 23 24
0.42817175 -0.04433085 0.09658520 -0.01103611 0.07033795 0.24735534
25 26 27 28 29 30
0.63048687 0.55696899 0.75903690 0.75375565 0.53660900 0.23715418
31 32 33 34 35 36
0.45817910 1.08602763 0.60553914 0.55739113 0.37876519 0.55578258
37 38 39 40 41 42
0.21891411 0.29504509 0.14746414 0.26200733 0.41398284 0.62365018
43 44 45 46 47 48
0.59414839 0.34217249 0.25238628 -0.03523503 -0.15386098 -0.27701915
49 50 51 52 53 54
-0.31388762 -0.37758107 -0.41533759 -0.43096998 -0.75881890 -0.86090723
55 56 57 58 59 60
-0.91953118 -0.81168265 -1.01111773 -0.93102141 -0.66982293 -0.67280553
61 62 63 64 65 66
-0.22967400 -0.31196291 -0.11919273 -0.09482511 -0.23267403 -0.32300669
67 68 69 70 71 72
-0.18233291 -0.16501108 -0.22567513 0.31565015 0.22737534 0.40456830
73 74 75 76 77 78
0.12752427 -0.20581805 -0.68322343 -0.86938252 -0.54705587 -0.37686183
79 80 81 82 83 84
-0.20548578 -0.22798838 -0.56865243 -0.83732715 -0.70577753 -0.96840900
85 86 87 88 89 90
-1.75527747 -1.43774194 -1.11497176 -0.61042858 -0.25810193 -0.51843459
91 92 93 94 95 96
-1.05776081 -1.62991228 -1.36057633 -1.26925104 -1.53805256 -1.52103516
97 98 99 100 101 102
-1.21807920 -1.19159708 -0.89900246 -0.94287918 -1.26985026 -1.35948064
103 104 105 106 107 108
-1.42810460 -1.15008050 -0.98039342 -0.81801472 -0.57681624 -0.15997441
109 110 111 112 113 114
-0.36701845 -0.21088746 0.14153159 -0.06532977 0.05664574 0.65613752
115 116 117 118 119 120
0.71663572 0.82536210 1.19575145 0.85742787 0.99897749 0.39634602
121 122 123 124 125 126
0.04947755 0.09613524 -0.02144571 0.69362417 1.04717979 0.84790054
127 128 129 130 131 132
0.58945215 0.62677398 0.10681220 0.24813748 0.03968711 0.09688007
133 134 135 136 137 138
0.76036274 0.48596701 0.56856163 -0.27724632 -0.75456854 -1.19472563
139 140 141 142 143 144
-0.77299845 -0.77532548 -0.88528726 -0.80396198 -0.98206122 -0.70504383
145 146 147 148 149 150
-0.92103446 -0.39578131 -0.33406453 -0.60110146 -1.03772141 -0.44717623
151 152 153 154 155 156
-0.55615132 -0.23830278 -0.33879127 -0.50729041 -0.21521409 -0.32749443
157 158 159 160 161 162
-0.53401176 -0.86840748 -0.95633956 -0.15267422 0.53965243 0.20072431
163 164 165 166 167 168
0.37210036 0.24030003 -0.10001289 0.78166353 0.48356429 0.21075725
169 170 171 172 173 174
0.14459105 0.45019533 0.99243881 0.69715756 0.45001091 0.81967825
175 176 177 178 179 180
1.11158100 1.09925397 1.03876548 0.40044190 0.51234266 0.83953562
181 182 183 184 185 186
0.52301829 0.26914927 -0.37737827 -0.09388850 0.13878928 -0.17101667
187 188 189 190 191 192
-0.42016733 -0.37196766 -0.89228058 -0.85025302 -0.55852783 -0.40115930
193 194 195 196 197 198
-0.43802777 -0.33242349 -0.24053114 -0.26756807 -0.76524142 -0.74522294
199 200 201 202 203 204
-0.64419803 -0.87670063 -0.59718912 0.18501401 0.89656363 1.05463443
205 206 207 208 209 210
1.36794153 1.54495035 2.34017848 2.11454609 3.17810171 3.77794462
211 212 213 214 215 216
3.90914510 3.40769590 3.36808525 2.67958611 1.11236470 0.60955766
217 218 219 220 221 222
0.24304033 -0.15153096 -1.41928748 -1.42597327 -2.39259322 -3.11187247
223 224
-3.67084756 -2.75229675
> postscript(file="/var/www/html/rcomp/tmp/65lwr1258639180.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 224
Frequency = 1
lag(myerror, k = 1) myerror
0 1.64994548 NA
1 1.74712986 1.64994548
2 1.09990005 1.74712986
3 0.70567220 1.09990005
4 1.04782328 0.70567220
5 1.45801733 1.04782328
6 1.67816440 1.45801733
7 1.41601294 1.67816440
8 0.29605115 1.41601294
9 0.03807871 0.29605115
10 0.68015503 0.03807871
11 0.61752356 0.68015503
12 0.06170850 0.61752356
13 0.03819062 0.06170850
14 0.53166307 0.03819062
15 0.60550398 0.53166307
16 0.60783063 0.60550398
17 0.48749797 0.60783063
18 0.42817175 0.48749797
19 -0.04433085 0.42817175
20 0.09658520 -0.04433085
21 -0.01103611 0.09658520
22 0.07033795 -0.01103611
23 0.24735534 0.07033795
24 0.63048687 0.24735534
25 0.55696899 0.63048687
26 0.75903690 0.55696899
27 0.75375565 0.75903690
28 0.53660900 0.75375565
29 0.23715418 0.53660900
30 0.45817910 0.23715418
31 1.08602763 0.45817910
32 0.60553914 1.08602763
33 0.55739113 0.60553914
34 0.37876519 0.55739113
35 0.55578258 0.37876519
36 0.21891411 0.55578258
37 0.29504509 0.21891411
38 0.14746414 0.29504509
39 0.26200733 0.14746414
40 0.41398284 0.26200733
41 0.62365018 0.41398284
42 0.59414839 0.62365018
43 0.34217249 0.59414839
44 0.25238628 0.34217249
45 -0.03523503 0.25238628
46 -0.15386098 -0.03523503
47 -0.27701915 -0.15386098
48 -0.31388762 -0.27701915
49 -0.37758107 -0.31388762
50 -0.41533759 -0.37758107
51 -0.43096998 -0.41533759
52 -0.75881890 -0.43096998
53 -0.86090723 -0.75881890
54 -0.91953118 -0.86090723
55 -0.81168265 -0.91953118
56 -1.01111773 -0.81168265
57 -0.93102141 -1.01111773
58 -0.66982293 -0.93102141
59 -0.67280553 -0.66982293
60 -0.22967400 -0.67280553
61 -0.31196291 -0.22967400
62 -0.11919273 -0.31196291
63 -0.09482511 -0.11919273
64 -0.23267403 -0.09482511
65 -0.32300669 -0.23267403
66 -0.18233291 -0.32300669
67 -0.16501108 -0.18233291
68 -0.22567513 -0.16501108
69 0.31565015 -0.22567513
70 0.22737534 0.31565015
71 0.40456830 0.22737534
72 0.12752427 0.40456830
73 -0.20581805 0.12752427
74 -0.68322343 -0.20581805
75 -0.86938252 -0.68322343
76 -0.54705587 -0.86938252
77 -0.37686183 -0.54705587
78 -0.20548578 -0.37686183
79 -0.22798838 -0.20548578
80 -0.56865243 -0.22798838
81 -0.83732715 -0.56865243
82 -0.70577753 -0.83732715
83 -0.96840900 -0.70577753
84 -1.75527747 -0.96840900
85 -1.43774194 -1.75527747
86 -1.11497176 -1.43774194
87 -0.61042858 -1.11497176
88 -0.25810193 -0.61042858
89 -0.51843459 -0.25810193
90 -1.05776081 -0.51843459
91 -1.62991228 -1.05776081
92 -1.36057633 -1.62991228
93 -1.26925104 -1.36057633
94 -1.53805256 -1.26925104
95 -1.52103516 -1.53805256
96 -1.21807920 -1.52103516
97 -1.19159708 -1.21807920
98 -0.89900246 -1.19159708
99 -0.94287918 -0.89900246
100 -1.26985026 -0.94287918
101 -1.35948064 -1.26985026
102 -1.42810460 -1.35948064
103 -1.15008050 -1.42810460
104 -0.98039342 -1.15008050
105 -0.81801472 -0.98039342
106 -0.57681624 -0.81801472
107 -0.15997441 -0.57681624
108 -0.36701845 -0.15997441
109 -0.21088746 -0.36701845
110 0.14153159 -0.21088746
111 -0.06532977 0.14153159
112 0.05664574 -0.06532977
113 0.65613752 0.05664574
114 0.71663572 0.65613752
115 0.82536210 0.71663572
116 1.19575145 0.82536210
117 0.85742787 1.19575145
118 0.99897749 0.85742787
119 0.39634602 0.99897749
120 0.04947755 0.39634602
121 0.09613524 0.04947755
122 -0.02144571 0.09613524
123 0.69362417 -0.02144571
124 1.04717979 0.69362417
125 0.84790054 1.04717979
126 0.58945215 0.84790054
127 0.62677398 0.58945215
128 0.10681220 0.62677398
129 0.24813748 0.10681220
130 0.03968711 0.24813748
131 0.09688007 0.03968711
132 0.76036274 0.09688007
133 0.48596701 0.76036274
134 0.56856163 0.48596701
135 -0.27724632 0.56856163
136 -0.75456854 -0.27724632
137 -1.19472563 -0.75456854
138 -0.77299845 -1.19472563
139 -0.77532548 -0.77299845
140 -0.88528726 -0.77532548
141 -0.80396198 -0.88528726
142 -0.98206122 -0.80396198
143 -0.70504383 -0.98206122
144 -0.92103446 -0.70504383
145 -0.39578131 -0.92103446
146 -0.33406453 -0.39578131
147 -0.60110146 -0.33406453
148 -1.03772141 -0.60110146
149 -0.44717623 -1.03772141
150 -0.55615132 -0.44717623
151 -0.23830278 -0.55615132
152 -0.33879127 -0.23830278
153 -0.50729041 -0.33879127
154 -0.21521409 -0.50729041
155 -0.32749443 -0.21521409
156 -0.53401176 -0.32749443
157 -0.86840748 -0.53401176
158 -0.95633956 -0.86840748
159 -0.15267422 -0.95633956
160 0.53965243 -0.15267422
161 0.20072431 0.53965243
162 0.37210036 0.20072431
163 0.24030003 0.37210036
164 -0.10001289 0.24030003
165 0.78166353 -0.10001289
166 0.48356429 0.78166353
167 0.21075725 0.48356429
168 0.14459105 0.21075725
169 0.45019533 0.14459105
170 0.99243881 0.45019533
171 0.69715756 0.99243881
172 0.45001091 0.69715756
173 0.81967825 0.45001091
174 1.11158100 0.81967825
175 1.09925397 1.11158100
176 1.03876548 1.09925397
177 0.40044190 1.03876548
178 0.51234266 0.40044190
179 0.83953562 0.51234266
180 0.52301829 0.83953562
181 0.26914927 0.52301829
182 -0.37737827 0.26914927
183 -0.09388850 -0.37737827
184 0.13878928 -0.09388850
185 -0.17101667 0.13878928
186 -0.42016733 -0.17101667
187 -0.37196766 -0.42016733
188 -0.89228058 -0.37196766
189 -0.85025302 -0.89228058
190 -0.55852783 -0.85025302
191 -0.40115930 -0.55852783
192 -0.43802777 -0.40115930
193 -0.33242349 -0.43802777
194 -0.24053114 -0.33242349
195 -0.26756807 -0.24053114
196 -0.76524142 -0.26756807
197 -0.74522294 -0.76524142
198 -0.64419803 -0.74522294
199 -0.87670063 -0.64419803
200 -0.59718912 -0.87670063
201 0.18501401 -0.59718912
202 0.89656363 0.18501401
203 1.05463443 0.89656363
204 1.36794153 1.05463443
205 1.54495035 1.36794153
206 2.34017848 1.54495035
207 2.11454609 2.34017848
208 3.17810171 2.11454609
209 3.77794462 3.17810171
210 3.90914510 3.77794462
211 3.40769590 3.90914510
212 3.36808525 3.40769590
213 2.67958611 3.36808525
214 1.11236470 2.67958611
215 0.60955766 1.11236470
216 0.24304033 0.60955766
217 -0.15153096 0.24304033
218 -1.41928748 -0.15153096
219 -1.42597327 -1.41928748
220 -2.39259322 -1.42597327
221 -3.11187247 -2.39259322
222 -3.67084756 -3.11187247
223 -2.75229675 -3.67084756
224 NA -2.75229675
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.74712986 1.64994548
[2,] 1.09990005 1.74712986
[3,] 0.70567220 1.09990005
[4,] 1.04782328 0.70567220
[5,] 1.45801733 1.04782328
[6,] 1.67816440 1.45801733
[7,] 1.41601294 1.67816440
[8,] 0.29605115 1.41601294
[9,] 0.03807871 0.29605115
[10,] 0.68015503 0.03807871
[11,] 0.61752356 0.68015503
[12,] 0.06170850 0.61752356
[13,] 0.03819062 0.06170850
[14,] 0.53166307 0.03819062
[15,] 0.60550398 0.53166307
[16,] 0.60783063 0.60550398
[17,] 0.48749797 0.60783063
[18,] 0.42817175 0.48749797
[19,] -0.04433085 0.42817175
[20,] 0.09658520 -0.04433085
[21,] -0.01103611 0.09658520
[22,] 0.07033795 -0.01103611
[23,] 0.24735534 0.07033795
[24,] 0.63048687 0.24735534
[25,] 0.55696899 0.63048687
[26,] 0.75903690 0.55696899
[27,] 0.75375565 0.75903690
[28,] 0.53660900 0.75375565
[29,] 0.23715418 0.53660900
[30,] 0.45817910 0.23715418
[31,] 1.08602763 0.45817910
[32,] 0.60553914 1.08602763
[33,] 0.55739113 0.60553914
[34,] 0.37876519 0.55739113
[35,] 0.55578258 0.37876519
[36,] 0.21891411 0.55578258
[37,] 0.29504509 0.21891411
[38,] 0.14746414 0.29504509
[39,] 0.26200733 0.14746414
[40,] 0.41398284 0.26200733
[41,] 0.62365018 0.41398284
[42,] 0.59414839 0.62365018
[43,] 0.34217249 0.59414839
[44,] 0.25238628 0.34217249
[45,] -0.03523503 0.25238628
[46,] -0.15386098 -0.03523503
[47,] -0.27701915 -0.15386098
[48,] -0.31388762 -0.27701915
[49,] -0.37758107 -0.31388762
[50,] -0.41533759 -0.37758107
[51,] -0.43096998 -0.41533759
[52,] -0.75881890 -0.43096998
[53,] -0.86090723 -0.75881890
[54,] -0.91953118 -0.86090723
[55,] -0.81168265 -0.91953118
[56,] -1.01111773 -0.81168265
[57,] -0.93102141 -1.01111773
[58,] -0.66982293 -0.93102141
[59,] -0.67280553 -0.66982293
[60,] -0.22967400 -0.67280553
[61,] -0.31196291 -0.22967400
[62,] -0.11919273 -0.31196291
[63,] -0.09482511 -0.11919273
[64,] -0.23267403 -0.09482511
[65,] -0.32300669 -0.23267403
[66,] -0.18233291 -0.32300669
[67,] -0.16501108 -0.18233291
[68,] -0.22567513 -0.16501108
[69,] 0.31565015 -0.22567513
[70,] 0.22737534 0.31565015
[71,] 0.40456830 0.22737534
[72,] 0.12752427 0.40456830
[73,] -0.20581805 0.12752427
[74,] -0.68322343 -0.20581805
[75,] -0.86938252 -0.68322343
[76,] -0.54705587 -0.86938252
[77,] -0.37686183 -0.54705587
[78,] -0.20548578 -0.37686183
[79,] -0.22798838 -0.20548578
[80,] -0.56865243 -0.22798838
[81,] -0.83732715 -0.56865243
[82,] -0.70577753 -0.83732715
[83,] -0.96840900 -0.70577753
[84,] -1.75527747 -0.96840900
[85,] -1.43774194 -1.75527747
[86,] -1.11497176 -1.43774194
[87,] -0.61042858 -1.11497176
[88,] -0.25810193 -0.61042858
[89,] -0.51843459 -0.25810193
[90,] -1.05776081 -0.51843459
[91,] -1.62991228 -1.05776081
[92,] -1.36057633 -1.62991228
[93,] -1.26925104 -1.36057633
[94,] -1.53805256 -1.26925104
[95,] -1.52103516 -1.53805256
[96,] -1.21807920 -1.52103516
[97,] -1.19159708 -1.21807920
[98,] -0.89900246 -1.19159708
[99,] -0.94287918 -0.89900246
[100,] -1.26985026 -0.94287918
[101,] -1.35948064 -1.26985026
[102,] -1.42810460 -1.35948064
[103,] -1.15008050 -1.42810460
[104,] -0.98039342 -1.15008050
[105,] -0.81801472 -0.98039342
[106,] -0.57681624 -0.81801472
[107,] -0.15997441 -0.57681624
[108,] -0.36701845 -0.15997441
[109,] -0.21088746 -0.36701845
[110,] 0.14153159 -0.21088746
[111,] -0.06532977 0.14153159
[112,] 0.05664574 -0.06532977
[113,] 0.65613752 0.05664574
[114,] 0.71663572 0.65613752
[115,] 0.82536210 0.71663572
[116,] 1.19575145 0.82536210
[117,] 0.85742787 1.19575145
[118,] 0.99897749 0.85742787
[119,] 0.39634602 0.99897749
[120,] 0.04947755 0.39634602
[121,] 0.09613524 0.04947755
[122,] -0.02144571 0.09613524
[123,] 0.69362417 -0.02144571
[124,] 1.04717979 0.69362417
[125,] 0.84790054 1.04717979
[126,] 0.58945215 0.84790054
[127,] 0.62677398 0.58945215
[128,] 0.10681220 0.62677398
[129,] 0.24813748 0.10681220
[130,] 0.03968711 0.24813748
[131,] 0.09688007 0.03968711
[132,] 0.76036274 0.09688007
[133,] 0.48596701 0.76036274
[134,] 0.56856163 0.48596701
[135,] -0.27724632 0.56856163
[136,] -0.75456854 -0.27724632
[137,] -1.19472563 -0.75456854
[138,] -0.77299845 -1.19472563
[139,] -0.77532548 -0.77299845
[140,] -0.88528726 -0.77532548
[141,] -0.80396198 -0.88528726
[142,] -0.98206122 -0.80396198
[143,] -0.70504383 -0.98206122
[144,] -0.92103446 -0.70504383
[145,] -0.39578131 -0.92103446
[146,] -0.33406453 -0.39578131
[147,] -0.60110146 -0.33406453
[148,] -1.03772141 -0.60110146
[149,] -0.44717623 -1.03772141
[150,] -0.55615132 -0.44717623
[151,] -0.23830278 -0.55615132
[152,] -0.33879127 -0.23830278
[153,] -0.50729041 -0.33879127
[154,] -0.21521409 -0.50729041
[155,] -0.32749443 -0.21521409
[156,] -0.53401176 -0.32749443
[157,] -0.86840748 -0.53401176
[158,] -0.95633956 -0.86840748
[159,] -0.15267422 -0.95633956
[160,] 0.53965243 -0.15267422
[161,] 0.20072431 0.53965243
[162,] 0.37210036 0.20072431
[163,] 0.24030003 0.37210036
[164,] -0.10001289 0.24030003
[165,] 0.78166353 -0.10001289
[166,] 0.48356429 0.78166353
[167,] 0.21075725 0.48356429
[168,] 0.14459105 0.21075725
[169,] 0.45019533 0.14459105
[170,] 0.99243881 0.45019533
[171,] 0.69715756 0.99243881
[172,] 0.45001091 0.69715756
[173,] 0.81967825 0.45001091
[174,] 1.11158100 0.81967825
[175,] 1.09925397 1.11158100
[176,] 1.03876548 1.09925397
[177,] 0.40044190 1.03876548
[178,] 0.51234266 0.40044190
[179,] 0.83953562 0.51234266
[180,] 0.52301829 0.83953562
[181,] 0.26914927 0.52301829
[182,] -0.37737827 0.26914927
[183,] -0.09388850 -0.37737827
[184,] 0.13878928 -0.09388850
[185,] -0.17101667 0.13878928
[186,] -0.42016733 -0.17101667
[187,] -0.37196766 -0.42016733
[188,] -0.89228058 -0.37196766
[189,] -0.85025302 -0.89228058
[190,] -0.55852783 -0.85025302
[191,] -0.40115930 -0.55852783
[192,] -0.43802777 -0.40115930
[193,] -0.33242349 -0.43802777
[194,] -0.24053114 -0.33242349
[195,] -0.26756807 -0.24053114
[196,] -0.76524142 -0.26756807
[197,] -0.74522294 -0.76524142
[198,] -0.64419803 -0.74522294
[199,] -0.87670063 -0.64419803
[200,] -0.59718912 -0.87670063
[201,] 0.18501401 -0.59718912
[202,] 0.89656363 0.18501401
[203,] 1.05463443 0.89656363
[204,] 1.36794153 1.05463443
[205,] 1.54495035 1.36794153
[206,] 2.34017848 1.54495035
[207,] 2.11454609 2.34017848
[208,] 3.17810171 2.11454609
[209,] 3.77794462 3.17810171
[210,] 3.90914510 3.77794462
[211,] 3.40769590 3.90914510
[212,] 3.36808525 3.40769590
[213,] 2.67958611 3.36808525
[214,] 1.11236470 2.67958611
[215,] 0.60955766 1.11236470
[216,] 0.24304033 0.60955766
[217,] -0.15153096 0.24304033
[218,] -1.41928748 -0.15153096
[219,] -1.42597327 -1.41928748
[220,] -2.39259322 -1.42597327
[221,] -3.11187247 -2.39259322
[222,] -3.67084756 -3.11187247
[223,] -2.75229675 -3.67084756
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.74712986 1.64994548
2 1.09990005 1.74712986
3 0.70567220 1.09990005
4 1.04782328 0.70567220
5 1.45801733 1.04782328
6 1.67816440 1.45801733
7 1.41601294 1.67816440
8 0.29605115 1.41601294
9 0.03807871 0.29605115
10 0.68015503 0.03807871
11 0.61752356 0.68015503
12 0.06170850 0.61752356
13 0.03819062 0.06170850
14 0.53166307 0.03819062
15 0.60550398 0.53166307
16 0.60783063 0.60550398
17 0.48749797 0.60783063
18 0.42817175 0.48749797
19 -0.04433085 0.42817175
20 0.09658520 -0.04433085
21 -0.01103611 0.09658520
22 0.07033795 -0.01103611
23 0.24735534 0.07033795
24 0.63048687 0.24735534
25 0.55696899 0.63048687
26 0.75903690 0.55696899
27 0.75375565 0.75903690
28 0.53660900 0.75375565
29 0.23715418 0.53660900
30 0.45817910 0.23715418
31 1.08602763 0.45817910
32 0.60553914 1.08602763
33 0.55739113 0.60553914
34 0.37876519 0.55739113
35 0.55578258 0.37876519
36 0.21891411 0.55578258
37 0.29504509 0.21891411
38 0.14746414 0.29504509
39 0.26200733 0.14746414
40 0.41398284 0.26200733
41 0.62365018 0.41398284
42 0.59414839 0.62365018
43 0.34217249 0.59414839
44 0.25238628 0.34217249
45 -0.03523503 0.25238628
46 -0.15386098 -0.03523503
47 -0.27701915 -0.15386098
48 -0.31388762 -0.27701915
49 -0.37758107 -0.31388762
50 -0.41533759 -0.37758107
51 -0.43096998 -0.41533759
52 -0.75881890 -0.43096998
53 -0.86090723 -0.75881890
54 -0.91953118 -0.86090723
55 -0.81168265 -0.91953118
56 -1.01111773 -0.81168265
57 -0.93102141 -1.01111773
58 -0.66982293 -0.93102141
59 -0.67280553 -0.66982293
60 -0.22967400 -0.67280553
61 -0.31196291 -0.22967400
62 -0.11919273 -0.31196291
63 -0.09482511 -0.11919273
64 -0.23267403 -0.09482511
65 -0.32300669 -0.23267403
66 -0.18233291 -0.32300669
67 -0.16501108 -0.18233291
68 -0.22567513 -0.16501108
69 0.31565015 -0.22567513
70 0.22737534 0.31565015
71 0.40456830 0.22737534
72 0.12752427 0.40456830
73 -0.20581805 0.12752427
74 -0.68322343 -0.20581805
75 -0.86938252 -0.68322343
76 -0.54705587 -0.86938252
77 -0.37686183 -0.54705587
78 -0.20548578 -0.37686183
79 -0.22798838 -0.20548578
80 -0.56865243 -0.22798838
81 -0.83732715 -0.56865243
82 -0.70577753 -0.83732715
83 -0.96840900 -0.70577753
84 -1.75527747 -0.96840900
85 -1.43774194 -1.75527747
86 -1.11497176 -1.43774194
87 -0.61042858 -1.11497176
88 -0.25810193 -0.61042858
89 -0.51843459 -0.25810193
90 -1.05776081 -0.51843459
91 -1.62991228 -1.05776081
92 -1.36057633 -1.62991228
93 -1.26925104 -1.36057633
94 -1.53805256 -1.26925104
95 -1.52103516 -1.53805256
96 -1.21807920 -1.52103516
97 -1.19159708 -1.21807920
98 -0.89900246 -1.19159708
99 -0.94287918 -0.89900246
100 -1.26985026 -0.94287918
101 -1.35948064 -1.26985026
102 -1.42810460 -1.35948064
103 -1.15008050 -1.42810460
104 -0.98039342 -1.15008050
105 -0.81801472 -0.98039342
106 -0.57681624 -0.81801472
107 -0.15997441 -0.57681624
108 -0.36701845 -0.15997441
109 -0.21088746 -0.36701845
110 0.14153159 -0.21088746
111 -0.06532977 0.14153159
112 0.05664574 -0.06532977
113 0.65613752 0.05664574
114 0.71663572 0.65613752
115 0.82536210 0.71663572
116 1.19575145 0.82536210
117 0.85742787 1.19575145
118 0.99897749 0.85742787
119 0.39634602 0.99897749
120 0.04947755 0.39634602
121 0.09613524 0.04947755
122 -0.02144571 0.09613524
123 0.69362417 -0.02144571
124 1.04717979 0.69362417
125 0.84790054 1.04717979
126 0.58945215 0.84790054
127 0.62677398 0.58945215
128 0.10681220 0.62677398
129 0.24813748 0.10681220
130 0.03968711 0.24813748
131 0.09688007 0.03968711
132 0.76036274 0.09688007
133 0.48596701 0.76036274
134 0.56856163 0.48596701
135 -0.27724632 0.56856163
136 -0.75456854 -0.27724632
137 -1.19472563 -0.75456854
138 -0.77299845 -1.19472563
139 -0.77532548 -0.77299845
140 -0.88528726 -0.77532548
141 -0.80396198 -0.88528726
142 -0.98206122 -0.80396198
143 -0.70504383 -0.98206122
144 -0.92103446 -0.70504383
145 -0.39578131 -0.92103446
146 -0.33406453 -0.39578131
147 -0.60110146 -0.33406453
148 -1.03772141 -0.60110146
149 -0.44717623 -1.03772141
150 -0.55615132 -0.44717623
151 -0.23830278 -0.55615132
152 -0.33879127 -0.23830278
153 -0.50729041 -0.33879127
154 -0.21521409 -0.50729041
155 -0.32749443 -0.21521409
156 -0.53401176 -0.32749443
157 -0.86840748 -0.53401176
158 -0.95633956 -0.86840748
159 -0.15267422 -0.95633956
160 0.53965243 -0.15267422
161 0.20072431 0.53965243
162 0.37210036 0.20072431
163 0.24030003 0.37210036
164 -0.10001289 0.24030003
165 0.78166353 -0.10001289
166 0.48356429 0.78166353
167 0.21075725 0.48356429
168 0.14459105 0.21075725
169 0.45019533 0.14459105
170 0.99243881 0.45019533
171 0.69715756 0.99243881
172 0.45001091 0.69715756
173 0.81967825 0.45001091
174 1.11158100 0.81967825
175 1.09925397 1.11158100
176 1.03876548 1.09925397
177 0.40044190 1.03876548
178 0.51234266 0.40044190
179 0.83953562 0.51234266
180 0.52301829 0.83953562
181 0.26914927 0.52301829
182 -0.37737827 0.26914927
183 -0.09388850 -0.37737827
184 0.13878928 -0.09388850
185 -0.17101667 0.13878928
186 -0.42016733 -0.17101667
187 -0.37196766 -0.42016733
188 -0.89228058 -0.37196766
189 -0.85025302 -0.89228058
190 -0.55852783 -0.85025302
191 -0.40115930 -0.55852783
192 -0.43802777 -0.40115930
193 -0.33242349 -0.43802777
194 -0.24053114 -0.33242349
195 -0.26756807 -0.24053114
196 -0.76524142 -0.26756807
197 -0.74522294 -0.76524142
198 -0.64419803 -0.74522294
199 -0.87670063 -0.64419803
200 -0.59718912 -0.87670063
201 0.18501401 -0.59718912
202 0.89656363 0.18501401
203 1.05463443 0.89656363
204 1.36794153 1.05463443
205 1.54495035 1.36794153
206 2.34017848 1.54495035
207 2.11454609 2.34017848
208 3.17810171 2.11454609
209 3.77794462 3.17810171
210 3.90914510 3.77794462
211 3.40769590 3.90914510
212 3.36808525 3.40769590
213 2.67958611 3.36808525
214 1.11236470 2.67958611
215 0.60955766 1.11236470
216 0.24304033 0.60955766
217 -0.15153096 0.24304033
218 -1.41928748 -0.15153096
219 -1.42597327 -1.41928748
220 -2.39259322 -1.42597327
221 -3.11187247 -2.39259322
222 -3.67084756 -3.11187247
223 -2.75229675 -3.67084756
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7i7051258639180.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/87p651258639180.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9keid1258639180.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10j0e41258639180.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11okdl1258639180.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12awhz1258639180.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13f3q81258639180.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/140do81258639180.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15ux6c1258639180.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16mebw1258639180.tab")
+ }
>
> system("convert tmp/1s3uk1258639180.ps tmp/1s3uk1258639180.png")
> system("convert tmp/2h80w1258639180.ps tmp/2h80w1258639180.png")
> system("convert tmp/32n2h1258639180.ps tmp/32n2h1258639180.png")
> system("convert tmp/4szh11258639180.ps tmp/4szh11258639180.png")
> system("convert tmp/5aqc21258639180.ps tmp/5aqc21258639180.png")
> system("convert tmp/65lwr1258639180.ps tmp/65lwr1258639180.png")
> system("convert tmp/7i7051258639180.ps tmp/7i7051258639180.png")
> system("convert tmp/87p651258639180.ps tmp/87p651258639180.png")
> system("convert tmp/9keid1258639180.ps tmp/9keid1258639180.png")
> system("convert tmp/10j0e41258639180.ps tmp/10j0e41258639180.png")
>
>
> proc.time()
user system elapsed
5.514 1.746 6.395