R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(3.22
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+ ,1.64
+ ,1.28
+ ,229.3
+ ,1.78
+ ,1.82
+ ,1.77
+ ,1.66
+ ,1.29
+ ,229.6
+ ,1.28
+ ,1.78
+ ,1.82
+ ,1.77
+ ,1.37
+ ,229.9
+ ,1.29
+ ,1.28
+ ,1.78
+ ,1.82
+ ,1.12
+ ,230
+ ,1.37
+ ,1.29
+ ,1.28
+ ,1.78
+ ,1.51
+ ,230.2
+ ,1.12
+ ,1.37
+ ,1.29
+ ,1.28
+ ,2.24
+ ,230.8
+ ,1.51
+ ,1.12
+ ,1.37
+ ,1.29
+ ,2.94
+ ,231
+ ,2.24
+ ,1.51
+ ,1.12
+ ,1.37
+ ,3.09
+ ,231.7
+ ,2.94
+ ,2.24
+ ,1.51
+ ,1.12
+ ,3.46
+ ,231.9
+ ,3.09
+ ,2.94
+ ,2.24
+ ,1.51
+ ,3.64
+ ,233
+ ,3.46
+ ,3.09
+ ,2.94
+ ,2.24
+ ,4.39
+ ,235.1
+ ,3.64
+ ,3.46
+ ,3.09
+ ,2.94
+ ,4.15
+ ,236
+ ,4.39
+ ,3.64
+ ,3.46
+ ,3.09
+ ,5.21
+ ,236.9
+ ,4.15
+ ,4.39
+ ,3.64
+ ,3.46
+ ,5.8
+ ,237.1
+ ,5.21
+ ,4.15
+ ,4.39
+ ,3.64
+ ,5.91
+ ,237.5
+ ,5.8
+ ,5.21
+ ,4.15
+ ,4.39
+ ,5.39
+ ,238.2
+ ,5.91
+ ,5.8
+ ,5.21
+ ,4.15
+ ,5.46
+ ,238.9
+ ,5.39
+ ,5.91
+ ,5.8
+ ,5.21
+ ,4.72
+ ,239.1
+ ,5.46
+ ,5.39
+ ,5.91
+ ,5.8
+ ,3.14
+ ,240
+ ,4.72
+ ,5.46
+ ,5.39
+ ,5.91
+ ,2.63
+ ,240.2
+ ,3.14
+ ,4.72
+ ,5.46
+ ,5.39
+ ,2.32
+ ,240.5
+ ,2.63
+ ,3.14
+ ,4.72
+ ,5.46
+ ,1.93
+ ,240.7
+ ,2.32
+ ,2.63
+ ,3.14
+ ,4.72
+ ,0.62
+ ,241.1
+ ,1.93
+ ,2.32
+ ,2.63
+ ,3.14
+ ,0.6
+ ,241.4
+ ,0.62
+ ,1.93
+ ,2.32
+ ,2.63
+ ,-0.37
+ ,242.2
+ ,0.6
+ ,0.62
+ ,1.93
+ ,2.32
+ ,-1.1
+ ,242.9
+ ,-0.37
+ ,0.6
+ ,0.62
+ ,1.93
+ ,-1.68
+ ,243.2
+ ,-1.1
+ ,-0.37
+ ,0.6
+ ,0.62
+ ,-0.78
+ ,243.9
+ ,-1.68
+ ,-1.1
+ ,-0.37
+ ,0.6)
+ ,dim=c(6
+ ,220)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:220))
> y <- array(NA,dim=c(6,220),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:220))
> 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 = '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 Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 3.22 157.0 2.88 3.29 3.98 3.88 1 0 0 0 0 0 0 0 0 0 0 1
2 3.62 157.4 3.22 2.88 3.29 3.98 0 1 0 0 0 0 0 0 0 0 0 2
3 3.82 157.2 3.62 3.22 2.88 3.29 0 0 1 0 0 0 0 0 0 0 0 3
4 3.54 157.5 3.82 3.62 3.22 2.88 0 0 0 1 0 0 0 0 0 0 0 4
5 2.53 158.0 3.54 3.82 3.62 3.22 0 0 0 0 1 0 0 0 0 0 0 5
6 2.22 158.5 2.53 3.54 3.82 3.62 0 0 0 0 0 1 0 0 0 0 0 6
7 2.85 159.0 2.22 2.53 3.54 3.82 0 0 0 0 0 0 1 0 0 0 0 7
8 2.78 159.3 2.85 2.22 2.53 3.54 0 0 0 0 0 0 0 1 0 0 0 8
9 2.28 160.0 2.78 2.85 2.22 2.53 0 0 0 0 0 0 0 0 1 0 0 9
10 2.26 160.8 2.28 2.78 2.85 2.22 0 0 0 0 0 0 0 0 0 1 0 10
11 2.71 161.9 2.26 2.28 2.78 2.85 0 0 0 0 0 0 0 0 0 0 1 11
12 2.77 162.5 2.71 2.26 2.28 2.78 0 0 0 0 0 0 0 0 0 0 0 12
13 2.77 162.7 2.77 2.71 2.26 2.28 1 0 0 0 0 0 0 0 0 0 0 13
14 2.64 162.8 2.77 2.77 2.71 2.26 0 1 0 0 0 0 0 0 0 0 0 14
15 2.56 162.9 2.64 2.77 2.77 2.71 0 0 1 0 0 0 0 0 0 0 0 15
16 2.07 163.0 2.56 2.64 2.77 2.77 0 0 0 1 0 0 0 0 0 0 0 16
17 2.32 164.0 2.07 2.56 2.64 2.77 0 0 0 0 1 0 0 0 0 0 0 17
18 2.16 164.7 2.32 2.07 2.56 2.64 0 0 0 0 0 1 0 0 0 0 0 18
19 2.23 164.8 2.16 2.32 2.07 2.56 0 0 0 0 0 0 1 0 0 0 0 19
20 2.40 164.9 2.23 2.16 2.32 2.07 0 0 0 0 0 0 0 1 0 0 0 20
21 2.84 165.0 2.40 2.23 2.16 2.32 0 0 0 0 0 0 0 0 1 0 0 21
22 2.77 165.8 2.84 2.40 2.23 2.16 0 0 0 0 0 0 0 0 0 1 0 22
23 2.93 166.1 2.77 2.84 2.40 2.23 0 0 0 0 0 0 0 0 0 0 1 23
24 2.91 167.2 2.93 2.77 2.84 2.40 0 0 0 0 0 0 0 0 0 0 0 24
25 2.69 167.7 2.91 2.93 2.77 2.84 1 0 0 0 0 0 0 0 0 0 0 25
26 2.38 168.3 2.69 2.91 2.93 2.77 0 1 0 0 0 0 0 0 0 0 0 26
27 2.58 168.6 2.38 2.69 2.91 2.93 0 0 1 0 0 0 0 0 0 0 0 27
28 3.19 168.9 2.58 2.38 2.69 2.91 0 0 0 1 0 0 0 0 0 0 0 28
29 2.82 169.1 3.19 2.58 2.38 2.69 0 0 0 0 1 0 0 0 0 0 0 29
30 2.72 169.5 2.82 3.19 2.58 2.38 0 0 0 0 0 1 0 0 0 0 0 30
31 2.53 169.6 2.72 2.82 3.19 2.58 0 0 0 0 0 0 1 0 0 0 0 31
32 2.70 169.7 2.53 2.72 2.82 3.19 0 0 0 0 0 0 0 1 0 0 0 32
33 2.42 169.8 2.70 2.53 2.72 2.82 0 0 0 0 0 0 0 0 1 0 0 33
34 2.50 170.4 2.42 2.70 2.53 2.72 0 0 0 0 0 0 0 0 0 1 0 34
35 2.31 170.9 2.50 2.42 2.70 2.53 0 0 0 0 0 0 0 0 0 0 1 35
36 2.41 171.9 2.31 2.50 2.42 2.70 0 0 0 0 0 0 0 0 0 0 0 36
37 2.56 171.9 2.41 2.31 2.50 2.42 1 0 0 0 0 0 0 0 0 0 0 37
38 2.76 172.0 2.56 2.41 2.31 2.50 0 1 0 0 0 0 0 0 0 0 0 38
39 2.71 172.0 2.76 2.56 2.41 2.31 0 0 1 0 0 0 0 0 0 0 0 39
40 2.44 172.4 2.71 2.76 2.56 2.41 0 0 0 1 0 0 0 0 0 0 0 40
41 2.46 173.0 2.44 2.71 2.76 2.56 0 0 0 0 1 0 0 0 0 0 0 41
42 2.12 173.7 2.46 2.44 2.71 2.76 0 0 0 0 0 1 0 0 0 0 0 42
43 1.99 173.8 2.12 2.46 2.44 2.71 0 0 0 0 0 0 1 0 0 0 0 43
44 1.86 173.8 1.99 2.12 2.46 2.44 0 0 0 0 0 0 0 1 0 0 0 44
45 1.88 173.9 1.86 1.99 2.12 2.46 0 0 0 0 0 0 0 0 1 0 0 45
46 1.82 174.6 1.88 1.86 1.99 2.12 0 0 0 0 0 0 0 0 0 1 0 46
47 1.74 175.0 1.82 1.88 1.86 1.99 0 0 0 0 0 0 0 0 0 0 1 47
48 1.71 175.9 1.74 1.82 1.88 1.86 0 0 0 0 0 0 0 0 0 0 0 48
49 1.38 176.0 1.71 1.74 1.82 1.88 1 0 0 0 0 0 0 0 0 0 0 49
50 1.27 175.1 1.38 1.71 1.74 1.82 0 1 0 0 0 0 0 0 0 0 0 50
51 1.19 175.6 1.27 1.38 1.71 1.74 0 0 1 0 0 0 0 0 0 0 0 51
52 1.28 175.9 1.19 1.27 1.38 1.71 0 0 0 1 0 0 0 0 0 0 0 52
53 1.19 176.7 1.28 1.19 1.27 1.38 0 0 0 0 1 0 0 0 0 0 0 53
54 1.22 176.1 1.19 1.28 1.19 1.27 0 0 0 0 0 1 0 0 0 0 0 54
55 1.47 176.1 1.22 1.19 1.28 1.19 0 0 0 0 0 0 1 0 0 0 0 55
56 1.46 176.2 1.47 1.22 1.19 1.28 0 0 0 0 0 0 0 1 0 0 0 56
57 1.96 176.3 1.46 1.47 1.22 1.19 0 0 0 0 0 0 0 0 1 0 0 57
58 1.88 177.8 1.96 1.46 1.47 1.22 0 0 0 0 0 0 0 0 0 1 0 58
59 2.03 178.5 1.88 1.96 1.46 1.47 0 0 0 0 0 0 0 0 0 0 1 59
60 2.04 179.4 2.03 1.88 1.96 1.46 0 0 0 0 0 0 0 0 0 0 0 60
61 1.90 179.5 2.04 2.03 1.88 1.96 1 0 0 0 0 0 0 0 0 0 0 61
62 1.80 179.6 1.90 2.04 2.03 1.88 0 1 0 0 0 0 0 0 0 0 0 62
63 1.92 179.7 1.80 1.90 2.04 2.03 0 0 1 0 0 0 0 0 0 0 0 63
64 1.92 179.7 1.92 1.80 1.90 2.04 0 0 0 1 0 0 0 0 0 0 0 64
65 1.97 179.8 1.92 1.92 1.80 1.90 0 0 0 0 1 0 0 0 0 0 0 65
66 2.46 179.9 1.97 1.92 1.92 1.80 0 0 0 0 0 1 0 0 0 0 0 66
67 2.36 180.2 2.46 1.97 1.92 1.92 0 0 0 0 0 0 1 0 0 0 0 67
68 2.53 180.4 2.36 2.46 1.97 1.92 0 0 0 0 0 0 0 1 0 0 0 68
69 2.31 180.4 2.53 2.36 2.46 1.97 0 0 0 0 0 0 0 0 1 0 0 69
70 1.98 181.3 2.31 2.53 2.36 2.46 0 0 0 0 0 0 0 0 0 1 0 70
71 1.46 181.9 1.98 2.31 2.53 2.36 0 0 0 0 0 0 0 0 0 0 1 71
72 1.26 182.5 1.46 1.98 2.31 2.53 0 0 0 0 0 0 0 0 0 0 0 72
73 1.58 182.7 1.26 1.46 1.98 2.31 1 0 0 0 0 0 0 0 0 0 0 73
74 1.74 183.1 1.58 1.26 1.46 1.98 0 1 0 0 0 0 0 0 0 0 0 74
75 1.89 183.6 1.74 1.58 1.26 1.46 0 0 1 0 0 0 0 0 0 0 0 75
76 1.85 183.7 1.89 1.74 1.58 1.26 0 0 0 1 0 0 0 0 0 0 0 76
77 1.62 183.8 1.85 1.89 1.74 1.58 0 0 0 0 1 0 0 0 0 0 0 77
78 1.30 183.9 1.62 1.85 1.89 1.74 0 0 0 0 0 1 0 0 0 0 0 78
79 1.42 184.1 1.30 1.62 1.85 1.89 0 0 0 0 0 0 1 0 0 0 0 79
80 1.15 184.4 1.42 1.30 1.62 1.85 0 0 0 0 0 0 0 1 0 0 0 80
81 0.42 184.5 1.15 1.42 1.30 1.62 0 0 0 0 0 0 0 0 1 0 0 81
82 0.74 185.9 0.42 1.15 1.42 1.30 0 0 0 0 0 0 0 0 0 1 0 82
83 1.02 186.6 0.74 0.42 1.15 1.42 0 0 0 0 0 0 0 0 0 0 1 83
84 1.51 187.6 1.02 0.74 0.42 1.15 0 0 0 0 0 0 0 0 0 0 0 84
85 1.86 187.8 1.51 1.02 0.74 0.42 1 0 0 0 0 0 0 0 0 0 0 85
86 1.59 187.9 1.86 1.51 1.02 0.74 0 1 0 0 0 0 0 0 0 0 0 86
87 1.03 188.0 1.59 1.86 1.51 1.02 0 0 1 0 0 0 0 0 0 0 0 87
88 0.44 188.3 1.03 1.59 1.86 1.51 0 0 0 1 0 0 0 0 0 0 0 88
89 0.82 188.4 0.44 1.03 1.59 1.86 0 0 0 0 1 0 0 0 0 0 0 89
90 0.86 188.5 0.82 0.44 1.03 1.59 0 0 0 0 0 1 0 0 0 0 0 90
91 0.58 188.5 0.86 0.82 0.44 1.03 0 0 0 0 0 0 1 0 0 0 0 91
92 0.59 188.6 0.58 0.86 0.82 0.44 0 0 0 0 0 0 0 1 0 0 0 92
93 0.95 188.6 0.59 0.58 0.86 0.82 0 0 0 0 0 0 0 0 1 0 0 93
94 0.98 189.4 0.95 0.59 0.58 0.86 0 0 0 0 0 0 0 0 0 1 0 94
95 1.23 190.0 0.98 0.95 0.59 0.58 0 0 0 0 0 0 0 0 0 0 1 95
96 1.17 191.9 1.23 0.98 0.95 0.59 0 0 0 0 0 0 0 0 0 0 0 96
97 0.84 192.5 1.17 1.23 0.98 0.95 1 0 0 0 0 0 0 0 0 0 0 97
98 0.74 193.0 0.84 1.17 1.23 0.98 0 1 0 0 0 0 0 0 0 0 0 98
99 0.65 193.5 0.74 0.84 1.17 1.23 0 0 1 0 0 0 0 0 0 0 0 99
100 0.91 193.9 0.65 0.74 0.84 1.17 0 0 0 1 0 0 0 0 0 0 0 100
101 1.19 194.2 0.91 0.65 0.74 0.84 0 0 0 0 1 0 0 0 0 0 0 101
102 1.30 194.9 1.19 0.91 0.65 0.74 0 0 0 0 0 1 0 0 0 0 0 102
103 1.53 194.9 1.30 1.19 0.91 0.65 0 0 0 0 0 0 1 0 0 0 0 103
104 1.94 194.9 1.53 1.30 1.19 0.91 0 0 0 0 0 0 0 1 0 0 0 104
105 1.79 194.9 1.94 1.53 1.30 1.19 0 0 0 0 0 0 0 0 1 0 0 105
106 1.95 195.5 1.79 1.94 1.53 1.30 0 0 0 0 0 0 0 0 0 1 0 106
107 2.26 196.0 1.95 1.79 1.94 1.53 0 0 0 0 0 0 0 0 0 0 1 107
108 2.04 196.2 2.26 1.95 1.79 1.94 0 0 0 0 0 0 0 0 0 0 0 108
109 2.16 196.2 2.04 2.26 1.95 1.79 1 0 0 0 0 0 0 0 0 0 0 109
110 2.75 196.2 2.16 2.04 2.26 1.95 0 1 0 0 0 0 0 0 0 0 0 110
111 2.79 196.2 2.75 2.16 2.04 2.26 0 0 1 0 0 0 0 0 0 0 0 111
112 2.88 197.0 2.79 2.75 2.16 2.04 0 0 0 1 0 0 0 0 0 0 0 112
113 3.36 197.7 2.88 2.79 2.75 2.16 0 0 0 0 1 0 0 0 0 0 0 113
114 2.97 198.0 3.36 2.88 2.79 2.75 0 0 0 0 0 1 0 0 0 0 0 114
115 3.10 198.2 2.97 3.36 2.88 2.79 0 0 0 0 0 0 1 0 0 0 0 115
116 2.49 198.5 3.10 2.97 3.36 2.88 0 0 0 0 0 0 0 1 0 0 0 116
117 2.20 198.6 2.49 3.10 2.97 3.36 0 0 0 0 0 0 0 0 1 0 0 117
118 2.25 199.5 2.20 2.49 3.10 2.97 0 0 0 0 0 0 0 0 0 1 0 118
119 2.09 200.0 2.25 2.20 2.49 3.10 0 0 0 0 0 0 0 0 0 0 1 119
120 2.79 201.3 2.09 2.25 2.20 2.49 0 0 0 0 0 0 0 0 0 0 0 120
121 3.14 202.2 2.79 2.09 2.25 2.20 1 0 0 0 0 0 0 0 0 0 0 121
122 2.93 202.9 3.14 2.79 2.09 2.25 0 1 0 0 0 0 0 0 0 0 0 122
123 2.65 203.5 2.93 3.14 2.79 2.09 0 0 1 0 0 0 0 0 0 0 0 123
124 2.67 203.5 2.65 2.93 3.14 2.79 0 0 0 1 0 0 0 0 0 0 0 124
125 2.26 204.0 2.67 2.65 2.93 3.14 0 0 0 0 1 0 0 0 0 0 0 125
126 2.35 204.1 2.26 2.67 2.65 2.93 0 0 0 0 0 1 0 0 0 0 0 126
127 2.13 204.3 2.35 2.26 2.67 2.65 0 0 0 0 0 0 1 0 0 0 0 127
128 2.18 204.5 2.13 2.35 2.26 2.67 0 0 0 0 0 0 0 1 0 0 0 128
129 2.90 204.8 2.18 2.13 2.35 2.26 0 0 0 0 0 0 0 0 1 0 0 129
130 2.63 205.1 2.90 2.18 2.13 2.35 0 0 0 0 0 0 0 0 0 1 0 130
131 2.67 205.7 2.63 2.90 2.18 2.13 0 0 0 0 0 0 0 0 0 0 1 131
132 1.81 206.5 2.67 2.63 2.90 2.18 0 0 0 0 0 0 0 0 0 0 0 132
133 1.33 206.9 1.81 2.67 2.63 2.90 1 0 0 0 0 0 0 0 0 0 0 133
134 0.88 207.1 1.33 1.81 2.67 2.63 0 1 0 0 0 0 0 0 0 0 0 134
135 1.28 207.8 0.88 1.33 1.81 2.67 0 0 1 0 0 0 0 0 0 0 0 135
136 1.26 208.0 1.28 0.88 1.33 1.81 0 0 0 1 0 0 0 0 0 0 0 136
137 1.26 208.5 1.26 1.28 0.88 1.33 0 0 0 0 1 0 0 0 0 0 0 137
138 1.29 208.6 1.26 1.26 1.28 0.88 0 0 0 0 0 1 0 0 0 0 0 138
139 1.10 209.0 1.29 1.26 1.26 1.28 0 0 0 0 0 0 1 0 0 0 0 139
140 1.37 209.1 1.10 1.29 1.26 1.26 0 0 0 0 0 0 0 1 0 0 0 140
141 1.21 209.7 1.37 1.10 1.29 1.26 0 0 0 0 0 0 0 0 1 0 0 141
142 1.74 209.8 1.21 1.37 1.10 1.29 0 0 0 0 0 0 0 0 0 1 0 142
143 1.76 209.9 1.74 1.21 1.37 1.10 0 0 0 0 0 0 0 0 0 0 1 143
144 1.48 210.0 1.76 1.74 1.21 1.37 0 0 0 0 0 0 0 0 0 0 0 144
145 1.04 210.8 1.48 1.76 1.74 1.21 1 0 0 0 0 0 0 0 0 0 0 145
146 1.62 211.4 1.04 1.48 1.76 1.74 0 1 0 0 0 0 0 0 0 0 0 146
147 1.49 211.7 1.62 1.04 1.48 1.76 0 0 1 0 0 0 0 0 0 0 0 147
148 1.79 212.0 1.49 1.62 1.04 1.48 0 0 0 1 0 0 0 0 0 0 0 148
149 1.80 212.2 1.79 1.49 1.62 1.04 0 0 0 0 1 0 0 0 0 0 0 149
150 1.58 212.4 1.80 1.79 1.49 1.62 0 0 0 0 0 1 0 0 0 0 0 150
151 1.86 212.9 1.58 1.80 1.79 1.49 0 0 0 0 0 0 1 0 0 0 0 151
152 1.74 213.4 1.86 1.58 1.80 1.79 0 0 0 0 0 0 0 1 0 0 0 152
153 1.59 213.7 1.74 1.86 1.58 1.80 0 0 0 0 0 0 0 0 1 0 0 153
154 1.26 214.0 1.59 1.74 1.86 1.58 0 0 0 0 0 0 0 0 0 1 0 154
155 1.13 214.3 1.26 1.59 1.74 1.86 0 0 0 0 0 0 0 0 0 0 1 155
156 1.92 214.8 1.13 1.26 1.59 1.74 0 0 0 0 0 0 0 0 0 0 0 156
157 2.61 215.0 1.92 1.13 1.26 1.59 1 0 0 0 0 0 0 0 0 0 0 157
158 2.26 215.9 2.61 1.92 1.13 1.26 0 1 0 0 0 0 0 0 0 0 0 158
159 2.41 216.4 2.26 2.61 1.92 1.13 0 0 1 0 0 0 0 0 0 0 0 159
160 2.26 216.9 2.41 2.26 2.61 1.92 0 0 0 1 0 0 0 0 0 0 0 160
161 2.03 217.2 2.26 2.41 2.26 2.61 0 0 0 0 1 0 0 0 0 0 0 161
162 2.86 217.5 2.03 2.26 2.41 2.26 0 0 0 0 0 1 0 0 0 0 0 162
163 2.55 217.9 2.86 2.03 2.26 2.41 0 0 0 0 0 0 1 0 0 0 0 163
164 2.27 218.1 2.55 2.86 2.03 2.26 0 0 0 0 0 0 0 1 0 0 0 164
165 2.26 218.6 2.27 2.55 2.86 2.03 0 0 0 0 0 0 0 0 1 0 0 165
166 2.57 218.9 2.26 2.27 2.55 2.86 0 0 0 0 0 0 0 0 0 1 0 166
167 3.07 219.3 2.57 2.26 2.27 2.55 0 0 0 0 0 0 0 0 0 0 1 167
168 2.76 220.4 3.07 2.57 2.26 2.27 0 0 0 0 0 0 0 0 0 0 0 168
169 2.51 220.9 2.76 3.07 2.57 2.26 1 0 0 0 0 0 0 0 0 0 0 169
170 2.87 221.0 2.51 2.76 3.07 2.57 0 1 0 0 0 0 0 0 0 0 0 170
171 3.14 221.8 2.87 2.51 2.76 3.07 0 0 1 0 0 0 0 0 0 0 0 171
172 3.11 222.0 3.14 2.87 2.51 2.76 0 0 0 1 0 0 0 0 0 0 0 172
173 3.16 222.2 3.11 3.14 2.87 2.51 0 0 0 0 1 0 0 0 0 0 0 173
174 2.47 222.5 3.16 3.11 3.14 2.87 0 0 0 0 0 1 0 0 0 0 0 174
175 2.57 222.9 2.47 3.16 3.11 3.14 0 0 0 0 0 0 1 0 0 0 0 175
176 2.89 223.1 2.57 2.47 3.16 3.11 0 0 0 0 0 0 0 1 0 0 0 176
177 2.63 223.4 2.89 2.57 2.47 3.16 0 0 0 0 0 0 0 0 1 0 0 177
178 2.38 224.0 2.63 2.89 2.57 2.47 0 0 0 0 0 0 0 0 0 1 0 178
179 1.69 225.1 2.38 2.63 2.89 2.57 0 0 0 0 0 0 0 0 0 0 1 179
180 1.96 225.5 1.69 2.38 2.63 2.89 0 0 0 0 0 0 0 0 0 0 0 180
181 2.19 225.9 1.96 1.69 2.38 2.63 1 0 0 0 0 0 0 0 0 0 0 181
182 1.87 226.3 2.19 1.96 1.69 2.38 0 1 0 0 0 0 0 0 0 0 0 182
183 1.60 226.5 1.87 2.19 1.96 1.69 0 0 1 0 0 0 0 0 0 0 0 183
184 1.63 227.0 1.60 1.87 2.19 1.96 0 0 0 1 0 0 0 0 0 0 0 184
185 1.22 227.3 1.63 1.60 1.87 2.19 0 0 0 0 1 0 0 0 0 0 0 185
186 1.21 227.8 1.22 1.63 1.60 1.87 0 0 0 0 0 1 0 0 0 0 0 186
187 1.49 228.1 1.21 1.22 1.63 1.60 0 0 0 0 0 0 1 0 0 0 0 187
188 1.64 228.4 1.49 1.21 1.22 1.63 0 0 0 0 0 0 0 1 0 0 0 188
189 1.66 228.5 1.64 1.49 1.21 1.22 0 0 0 0 0 0 0 0 1 0 0 189
190 1.77 228.8 1.66 1.64 1.49 1.21 0 0 0 0 0 0 0 0 0 1 0 190
191 1.82 229.0 1.77 1.66 1.64 1.49 0 0 0 0 0 0 0 0 0 0 1 191
192 1.78 229.1 1.82 1.77 1.66 1.64 0 0 0 0 0 0 0 0 0 0 0 192
193 1.28 229.3 1.78 1.82 1.77 1.66 1 0 0 0 0 0 0 0 0 0 0 193
194 1.29 229.6 1.28 1.78 1.82 1.77 0 1 0 0 0 0 0 0 0 0 0 194
195 1.37 229.9 1.29 1.28 1.78 1.82 0 0 1 0 0 0 0 0 0 0 0 195
196 1.12 230.0 1.37 1.29 1.28 1.78 0 0 0 1 0 0 0 0 0 0 0 196
197 1.51 230.2 1.12 1.37 1.29 1.28 0 0 0 0 1 0 0 0 0 0 0 197
198 2.24 230.8 1.51 1.12 1.37 1.29 0 0 0 0 0 1 0 0 0 0 0 198
199 2.94 231.0 2.24 1.51 1.12 1.37 0 0 0 0 0 0 1 0 0 0 0 199
200 3.09 231.7 2.94 2.24 1.51 1.12 0 0 0 0 0 0 0 1 0 0 0 200
201 3.46 231.9 3.09 2.94 2.24 1.51 0 0 0 0 0 0 0 0 1 0 0 201
202 3.64 233.0 3.46 3.09 2.94 2.24 0 0 0 0 0 0 0 0 0 1 0 202
203 4.39 235.1 3.64 3.46 3.09 2.94 0 0 0 0 0 0 0 0 0 0 1 203
204 4.15 236.0 4.39 3.64 3.46 3.09 0 0 0 0 0 0 0 0 0 0 0 204
205 5.21 236.9 4.15 4.39 3.64 3.46 1 0 0 0 0 0 0 0 0 0 0 205
206 5.80 237.1 5.21 4.15 4.39 3.64 0 1 0 0 0 0 0 0 0 0 0 206
207 5.91 237.5 5.80 5.21 4.15 4.39 0 0 1 0 0 0 0 0 0 0 0 207
208 5.39 238.2 5.91 5.80 5.21 4.15 0 0 0 1 0 0 0 0 0 0 0 208
209 5.46 238.9 5.39 5.91 5.80 5.21 0 0 0 0 1 0 0 0 0 0 0 209
210 4.72 239.1 5.46 5.39 5.91 5.80 0 0 0 0 0 1 0 0 0 0 0 210
211 3.14 240.0 4.72 5.46 5.39 5.91 0 0 0 0 0 0 1 0 0 0 0 211
212 2.63 240.2 3.14 4.72 5.46 5.39 0 0 0 0 0 0 0 1 0 0 0 212
213 2.32 240.5 2.63 3.14 4.72 5.46 0 0 0 0 0 0 0 0 1 0 0 213
214 1.93 240.7 2.32 2.63 3.14 4.72 0 0 0 0 0 0 0 0 0 1 0 214
215 0.62 241.1 1.93 2.32 2.63 3.14 0 0 0 0 0 0 0 0 0 0 1 215
216 0.60 241.4 0.62 1.93 2.32 2.63 0 0 0 0 0 0 0 0 0 0 0 216
217 -0.37 242.2 0.60 0.62 1.93 2.32 1 0 0 0 0 0 0 0 0 0 0 217
218 -1.10 242.9 -0.37 0.60 0.62 1.93 0 1 0 0 0 0 0 0 0 0 0 218
219 -1.68 243.2 -1.10 -0.37 0.60 0.62 0 0 1 0 0 0 0 0 0 0 0 219
220 -0.78 243.9 -1.68 -1.10 -0.37 0.60 0 0 0 1 0 0 0 0 0 0 0 220
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
2.978007 -0.017888 1.137700 -0.189411 0.013505 -0.035014
M1 M2 M3 M4 M5 M6
-0.006106 -0.016591 -0.029088 -0.026220 -0.036353 -0.052887
M7 M8 M9 M10 M11 t
-0.014495 -0.022860 -0.055448 -0.014861 -0.022211 0.006671
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.13941 -0.21215 -0.00567 0.22082 1.29044
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.978007 3.539772 0.841 0.4012
X -0.017888 0.022850 -0.783 0.4346
Y1 1.137700 0.071406 15.933 <2e-16 ***
Y2 -0.189411 0.109253 -1.734 0.0845 .
Y3 0.013505 0.108697 0.124 0.9012
Y4 -0.035014 0.075492 -0.464 0.6433
M1 -0.006106 0.123560 -0.049 0.9606
M2 -0.016591 0.123516 -0.134 0.8933
M3 -0.029088 0.123525 -0.235 0.8141
M4 -0.026220 0.123576 -0.212 0.8322
M5 -0.036353 0.125872 -0.289 0.7730
M6 -0.052887 0.126127 -0.419 0.6754
M7 -0.014495 0.126396 -0.115 0.9088
M8 -0.022860 0.126652 -0.180 0.8569
M9 -0.055448 0.127066 -0.436 0.6630
M10 -0.014861 0.125978 -0.118 0.9062
M11 -0.022211 0.125466 -0.177 0.8597
t 0.006671 0.008750 0.762 0.4467
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3753 on 202 degrees of freedom
Multiple R-squared: 0.8728, Adjusted R-squared: 0.8621
F-statistic: 81.52 on 17 and 202 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 9.215164e-01 1.569671e-01 0.07848356
[2,] 8.524446e-01 2.951108e-01 0.14755540
[3,] 7.638189e-01 4.723622e-01 0.23618111
[4,] 6.648382e-01 6.703235e-01 0.33516177
[5,] 5.560161e-01 8.879678e-01 0.44398390
[6,] 4.469333e-01 8.938665e-01 0.55306673
[7,] 3.830521e-01 7.661043e-01 0.61694785
[8,] 5.410721e-01 9.178557e-01 0.45892787
[9,] 4.546505e-01 9.093010e-01 0.54534950
[10,] 5.351371e-01 9.297258e-01 0.46486291
[11,] 4.764349e-01 9.528698e-01 0.52356510
[12,] 4.068944e-01 8.137888e-01 0.59310562
[13,] 3.895930e-01 7.791860e-01 0.61040702
[14,] 3.193338e-01 6.386677e-01 0.68066615
[15,] 3.112050e-01 6.224100e-01 0.68879498
[16,] 2.525263e-01 5.050526e-01 0.74747369
[17,] 2.071385e-01 4.142770e-01 0.79286151
[18,] 1.777349e-01 3.554699e-01 0.82226506
[19,] 1.360318e-01 2.720635e-01 0.86396823
[20,] 1.045422e-01 2.090844e-01 0.89545780
[21,] 9.823750e-02 1.964750e-01 0.90176250
[22,] 7.966160e-02 1.593232e-01 0.92033840
[23,] 6.559194e-02 1.311839e-01 0.93440806
[24,] 5.043209e-02 1.008642e-01 0.94956791
[25,] 3.641589e-02 7.283177e-02 0.96358411
[26,] 2.772028e-02 5.544055e-02 0.97227972
[27,] 2.106573e-02 4.213147e-02 0.97893427
[28,] 1.448363e-02 2.896726e-02 0.98551637
[29,] 1.371033e-02 2.742067e-02 0.98628967
[30,] 9.294820e-03 1.858964e-02 0.99070518
[31,] 6.384549e-03 1.276910e-02 0.99361545
[32,] 4.501712e-03 9.003424e-03 0.99549829
[33,] 3.251412e-03 6.502825e-03 0.99674859
[34,] 3.790614e-03 7.581229e-03 0.99620939
[35,] 3.829199e-03 7.658398e-03 0.99617080
[36,] 2.688948e-03 5.377897e-03 0.99731105
[37,] 7.644800e-03 1.528960e-02 0.99235520
[38,] 5.218458e-03 1.043692e-02 0.99478154
[39,] 4.196014e-03 8.392028e-03 0.99580399
[40,] 2.950333e-03 5.900667e-03 0.99704967
[41,] 1.951987e-03 3.903973e-03 0.99804801
[42,] 1.269188e-03 2.538375e-03 0.99873081
[43,] 8.754210e-04 1.750842e-03 0.99912458
[44,] 5.609569e-04 1.121914e-03 0.99943904
[45,] 4.748955e-04 9.497911e-04 0.99952510
[46,] 1.370917e-03 2.741835e-03 0.99862908
[47,] 9.517306e-04 1.903461e-03 0.99904827
[48,] 9.047336e-04 1.809467e-03 0.99909527
[49,] 6.398619e-04 1.279724e-03 0.99936014
[50,] 5.440856e-04 1.088171e-03 0.99945591
[51,] 9.329091e-04 1.865818e-03 0.99906709
[52,] 7.303130e-04 1.460626e-03 0.99926969
[53,] 5.827097e-04 1.165419e-03 0.99941729
[54,] 3.859842e-04 7.719684e-04 0.99961402
[55,] 2.630202e-04 5.260403e-04 0.99973698
[56,] 1.720546e-04 3.441092e-04 0.99982795
[57,] 1.144311e-04 2.288621e-04 0.99988557
[58,] 8.693333e-05 1.738667e-04 0.99991307
[59,] 5.567244e-05 1.113449e-04 0.99994433
[60,] 5.570772e-05 1.114154e-04 0.99994429
[61,] 2.534557e-04 5.069114e-04 0.99974654
[62,] 2.339372e-04 4.678744e-04 0.99976606
[63,] 1.590904e-04 3.181809e-04 0.99984091
[64,] 1.654728e-04 3.309455e-04 0.99983453
[65,] 1.506157e-04 3.012314e-04 0.99984938
[66,] 1.139466e-04 2.278932e-04 0.99988605
[67,] 1.462524e-04 2.925048e-04 0.99985375
[68,] 2.100233e-04 4.200466e-04 0.99978998
[69,] 2.322433e-04 4.644866e-04 0.99976776
[70,] 1.685832e-04 3.371664e-04 0.99983142
[71,] 1.979737e-04 3.959474e-04 0.99980203
[72,] 1.346478e-04 2.692955e-04 0.99986535
[73,] 1.159185e-04 2.318369e-04 0.99988408
[74,] 7.905568e-05 1.581114e-04 0.99992094
[75,] 6.088359e-05 1.217672e-04 0.99993912
[76,] 4.006544e-05 8.013087e-05 0.99995993
[77,] 3.604947e-05 7.209893e-05 0.99996395
[78,] 2.320680e-05 4.641360e-05 0.99997679
[79,] 1.570261e-05 3.140523e-05 0.99998430
[80,] 1.232929e-05 2.465859e-05 0.99998767
[81,] 9.996198e-06 1.999240e-05 0.99999000
[82,] 6.973981e-06 1.394796e-05 0.99999303
[83,] 6.030730e-06 1.206146e-05 0.99999397
[84,] 9.407804e-06 1.881561e-05 0.99999059
[85,] 6.159959e-06 1.231992e-05 0.99999384
[86,] 6.144760e-06 1.228952e-05 0.99999386
[87,] 6.352914e-06 1.270583e-05 0.99999365
[88,] 4.314259e-06 8.628519e-06 0.99999569
[89,] 3.693884e-06 7.387768e-06 0.99999631
[90,] 1.053063e-05 2.106126e-05 0.99998947
[91,] 6.676548e-06 1.335310e-05 0.99999332
[92,] 5.798383e-06 1.159677e-05 0.99999420
[93,] 1.166442e-05 2.332884e-05 0.99998834
[94,] 1.175080e-05 2.350159e-05 0.99998825
[95,] 9.035793e-06 1.807159e-05 0.99999096
[96,] 1.993545e-05 3.987091e-05 0.99998006
[97,] 1.410854e-05 2.821708e-05 0.99998589
[98,] 8.897703e-06 1.779541e-05 0.99999110
[99,] 6.799021e-06 1.359804e-05 0.99999320
[100,] 2.535738e-05 5.071476e-05 0.99997464
[101,] 2.141612e-05 4.283225e-05 0.99997858
[102,] 1.475755e-05 2.951510e-05 0.99998524
[103,] 9.725746e-06 1.945149e-05 0.99999027
[104,] 6.316810e-06 1.263362e-05 0.99999368
[105,] 6.352108e-06 1.270422e-05 0.99999365
[106,] 4.703610e-06 9.407220e-06 0.99999530
[107,] 3.748768e-06 7.497535e-06 0.99999625
[108,] 2.377963e-06 4.755927e-06 0.99999762
[109,] 1.092135e-05 2.184270e-05 0.99998908
[110,] 9.457311e-06 1.891462e-05 0.99999054
[111,] 6.612009e-06 1.322402e-05 0.99999339
[112,] 3.176538e-05 6.353076e-05 0.99996823
[113,] 3.189025e-05 6.378050e-05 0.99996811
[114,] 3.749702e-05 7.499404e-05 0.99996250
[115,] 4.279159e-05 8.558318e-05 0.99995721
[116,] 2.953734e-05 5.907469e-05 0.99997046
[117,] 1.894174e-05 3.788348e-05 0.99998106
[118,] 1.204219e-05 2.408437e-05 0.99998796
[119,] 8.814294e-06 1.762859e-05 0.99999119
[120,] 6.823670e-06 1.364734e-05 0.99999318
[121,] 4.709144e-06 9.418288e-06 0.99999529
[122,] 7.100393e-06 1.420079e-05 0.99999290
[123,] 4.377730e-06 8.755461e-06 0.99999562
[124,] 3.858165e-06 7.716329e-06 0.99999614
[125,] 4.646272e-06 9.292544e-06 0.99999535
[126,] 8.566624e-06 1.713325e-05 0.99999143
[127,] 6.193877e-06 1.238775e-05 0.99999381
[128,] 4.869835e-06 9.739670e-06 0.99999513
[129,] 3.139021e-06 6.278042e-06 0.99999686
[130,] 2.236198e-06 4.472396e-06 0.99999776
[131,] 1.788901e-06 3.577802e-06 0.99999821
[132,] 1.162912e-06 2.325824e-06 0.99999884
[133,] 7.074572e-07 1.414914e-06 0.99999929
[134,] 6.646721e-07 1.329344e-06 0.99999934
[135,] 4.104157e-07 8.208315e-07 0.99999959
[136,] 1.290632e-06 2.581264e-06 0.99999871
[137,] 1.910952e-06 3.821904e-06 0.99999809
[138,] 2.207950e-06 4.415900e-06 0.99999779
[139,] 1.654179e-06 3.308358e-06 0.99999835
[140,] 1.147785e-06 2.295571e-06 0.99999885
[141,] 7.409167e-07 1.481833e-06 0.99999926
[142,] 6.316445e-06 1.263289e-05 0.99999368
[143,] 5.927213e-06 1.185443e-05 0.99999407
[144,] 4.162599e-06 8.325199e-06 0.99999584
[145,] 2.482037e-06 4.964075e-06 0.99999752
[146,] 2.004902e-06 4.009805e-06 0.99999800
[147,] 3.513318e-06 7.026636e-06 0.99999649
[148,] 3.222954e-06 6.445908e-06 0.99999678
[149,] 2.063328e-06 4.126657e-06 0.99999794
[150,] 2.081982e-06 4.163965e-06 0.99999792
[151,] 1.994982e-06 3.989965e-06 0.99999801
[152,] 1.085912e-06 2.171825e-06 0.99999891
[153,] 6.270475e-07 1.254095e-06 0.99999937
[154,] 1.269368e-06 2.538737e-06 0.99999873
[155,] 9.745766e-07 1.949153e-06 0.99999903
[156,] 8.844481e-07 1.768896e-06 0.99999912
[157,] 5.526779e-07 1.105356e-06 0.99999945
[158,] 3.200865e-07 6.401730e-07 0.99999968
[159,] 4.477999e-07 8.955998e-07 0.99999955
[160,] 4.803718e-07 9.607436e-07 0.99999952
[161,] 4.006159e-07 8.012318e-07 0.99999960
[162,] 2.682561e-07 5.365122e-07 0.99999973
[163,] 1.451402e-07 2.902804e-07 0.99999985
[164,] 6.713663e-08 1.342733e-07 0.99999993
[165,] 1.076266e-07 2.152532e-07 0.99999989
[166,] 5.198025e-08 1.039605e-07 0.99999995
[167,] 3.710278e-08 7.420556e-08 0.99999996
[168,] 1.538763e-08 3.077525e-08 0.99999998
[169,] 1.647045e-08 3.294090e-08 0.99999998
[170,] 1.878054e-08 3.756109e-08 0.99999998
[171,] 1.210138e-08 2.420275e-08 0.99999999
[172,] 1.604460e-08 3.208919e-08 0.99999998
[173,] 2.467492e-07 4.934984e-07 0.99999975
[174,] 1.590370e-07 3.180740e-07 0.99999984
[175,] 5.773847e-08 1.154769e-07 0.99999994
[176,] 2.562947e-07 5.125894e-07 0.99999974
[177,] 9.433119e-06 1.886624e-05 0.99999057
[178,] 4.126375e-04 8.252751e-04 0.99958736
[179,] 2.472762e-04 4.945523e-04 0.99975272
> postscript(file="/var/www/html/rcomp/tmp/1vcr01258639561.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2t65j1258639561.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3i7621258639561.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4p4yi1258639561.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5zvx01258639561.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 = 220
Frequency = 1
1 2 3 4 5
0.4786082798 0.4379216100 0.2308670056 -0.2240274515 -0.8586806524
6 7 8 9 10
-0.0425255659 0.7235220988 -0.1110499077 -0.4048200337 0.0784618697
11 12 13 14 15
0.4998713105 0.0302709340 0.0330199882 -0.0867893506 0.0036722789
16 17 18 19 20
-0.4255834672 0.3898425431 -0.1284792149 0.1314481349 0.1744529938
21 22 23 24 25
0.4729230946 -0.1049597255 0.2242214120 -0.0002636460 -0.1424730540
26 27 28 29 30
-0.1960311979 0.3320501675 0.6538920639 -0.3687000396 0.0712540839
31 32 33 34 35
-0.1195671335 0.2774927113 -0.2158028822 0.1774929414 -0.1558827873
36 37 38 39 40
0.1741736971 0.1629665001 0.2222231682 -0.0290824855 -0.2052222627
41 42 43 44 45
0.1292320210 -0.2545988605 -0.0353706977 -0.0898994316 0.0863762937
46 47 48 49 50
-0.0658860314 -0.0687969179 -0.0367494958 -0.3450367641 -0.0985833541
51 52 53 54 55
-0.1035678113 0.0558477887 -0.1439946954 0.0018052968 0.1515480637
56 57 58 59 60
-0.1293453414 0.4535341632 -0.2199610235 0.1378505532 -0.0578420850
61 62 63 64 65
-0.1609961276 -0.0990472884 0.1209369168 -0.0418253819 0.0326029931
66 67 68 69 70
0.4722490144 -0.2112480554 0.1699296176 -0.2413700378 -0.3015270329
71 72 73 74 75
-0.4821409001 -0.1622675424 0.2865452936 0.0510371781 0.0788806301
76 77 78 79 80
-0.1205421698 -0.2623284432 -0.3130043631 0.0918024699 -0.3665671457
81 82 83 84 85
-0.7426841594 0.3216571263 0.1203725430 0.3418397703 0.1605329494
86 87 88 89 90
-0.4018242096 -0.5775501869 -0.5733205062 0.3930041227 -0.1013123614
91 92 93 94 95
-0.4115465729 -0.0977214401 0.2365487715 -0.1688942751 0.1166364923
96 97 98 99 100
-0.2215115002 -0.4135286608 -0.1390191433 -0.1534209159 0.1900042327
101 102 103 104 105
0.1557793014 0.0165702752 0.1227331069 0.2989132102 -0.2397437895
106 107 108 109 110
0.1327904114 0.2444866555 -0.3068172567 0.1142165366 0.5312524076
111 112 113 114 115
-0.0576101974 0.0940834487 0.4914840905 -0.3922164316 0.2311040881
116 117 118 119 120
-0.5969381024 -0.1185377984 0.0992853606 -0.1501151318 0.7183188860
121 122 123 124 125
0.2463284963 -0.2090307471 -0.1823166109 0.1267079969 -0.3315843665
126 127 128 129 130
0.2367425126 -0.2148679689 0.1139819216 0.7511392165 -0.3643033438
131 132 133 134 135
0.1222861031 -0.8569067356 -0.3154612222 -0.3848600978 0.4675504220
136 137 138 139 140
-0.1223543626 -0.0221594061 -0.0054528166 -0.2532155124 0.2414122389
141 142 143 144 145
-0.2255097148 0.4958107270 -0.1253066993 -0.3431511686 -0.4598207835
146 147 148 149 150
0.6005672188 -0.2569657034 0.2927606451 -0.0793725333 -0.2184207329
151 152 153 154 155
0.2690456981 -0.1901737021 -0.1160095029 -0.3514594828 -0.1169594329
156 157 158 159 160
0.7363226286 0.5051334543 -0.4701295699 0.2083084677 -0.1608918313
161 162 163 164 165
-0.1541107167 0.9100988351 -0.4183864021 -0.1853625655 0.0800752600
166 167 168 169 170
0.3397738874 0.4859548502 -0.3530510210 -0.1518157004 0.4435972305
171 172 173 174 175
0.2985024544 0.0160730434 0.1447690945 -0.5836089549 0.2828263660
176 177 178 179 180
0.3619085479 -0.2008614384 -0.1564825392 -0.5917676403 0.4088822161
181 182 183 184 185
0.2018727863 -0.3171221587 -0.1978958199 0.0844255058 -0.3896435393
186 187 188 189 190
0.0837462787 0.2479097599 0.0911073923 0.0069729870 0.0766078258
191 192 193 194 195
0.0172844174 -0.0808762542 -0.5236700959 0.0599611042 0.0473618791
196 197 198 199 200
-0.2941572154 0.3848181320 0.6436292519 0.5516707327 0.0437459160
201 202 203 204 205
0.4089702516 0.1849588243 0.8609841391 -0.2107239414 1.2904413895
206 207 208 209 210
0.6325865250 0.3146018338 -0.2385274105 0.4890420944 -0.3964762467
211 212 213 214 215
-1.1394081763 -0.0058869129 0.0087993192 -0.1733655199 -1.1389789665
216 217 218 219 220
0.2203525146 -0.9668632658 -0.5767093253 -0.5443223246 0.8926573339
> postscript(file="/var/www/html/rcomp/tmp/6e46o1258639561.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 = 220
Frequency = 1
lag(myerror, k = 1) myerror
0 0.4786082798 NA
1 0.4379216100 0.4786082798
2 0.2308670056 0.4379216100
3 -0.2240274515 0.2308670056
4 -0.8586806524 -0.2240274515
5 -0.0425255659 -0.8586806524
6 0.7235220988 -0.0425255659
7 -0.1110499077 0.7235220988
8 -0.4048200337 -0.1110499077
9 0.0784618697 -0.4048200337
10 0.4998713105 0.0784618697
11 0.0302709340 0.4998713105
12 0.0330199882 0.0302709340
13 -0.0867893506 0.0330199882
14 0.0036722789 -0.0867893506
15 -0.4255834672 0.0036722789
16 0.3898425431 -0.4255834672
17 -0.1284792149 0.3898425431
18 0.1314481349 -0.1284792149
19 0.1744529938 0.1314481349
20 0.4729230946 0.1744529938
21 -0.1049597255 0.4729230946
22 0.2242214120 -0.1049597255
23 -0.0002636460 0.2242214120
24 -0.1424730540 -0.0002636460
25 -0.1960311979 -0.1424730540
26 0.3320501675 -0.1960311979
27 0.6538920639 0.3320501675
28 -0.3687000396 0.6538920639
29 0.0712540839 -0.3687000396
30 -0.1195671335 0.0712540839
31 0.2774927113 -0.1195671335
32 -0.2158028822 0.2774927113
33 0.1774929414 -0.2158028822
34 -0.1558827873 0.1774929414
35 0.1741736971 -0.1558827873
36 0.1629665001 0.1741736971
37 0.2222231682 0.1629665001
38 -0.0290824855 0.2222231682
39 -0.2052222627 -0.0290824855
40 0.1292320210 -0.2052222627
41 -0.2545988605 0.1292320210
42 -0.0353706977 -0.2545988605
43 -0.0898994316 -0.0353706977
44 0.0863762937 -0.0898994316
45 -0.0658860314 0.0863762937
46 -0.0687969179 -0.0658860314
47 -0.0367494958 -0.0687969179
48 -0.3450367641 -0.0367494958
49 -0.0985833541 -0.3450367641
50 -0.1035678113 -0.0985833541
51 0.0558477887 -0.1035678113
52 -0.1439946954 0.0558477887
53 0.0018052968 -0.1439946954
54 0.1515480637 0.0018052968
55 -0.1293453414 0.1515480637
56 0.4535341632 -0.1293453414
57 -0.2199610235 0.4535341632
58 0.1378505532 -0.2199610235
59 -0.0578420850 0.1378505532
60 -0.1609961276 -0.0578420850
61 -0.0990472884 -0.1609961276
62 0.1209369168 -0.0990472884
63 -0.0418253819 0.1209369168
64 0.0326029931 -0.0418253819
65 0.4722490144 0.0326029931
66 -0.2112480554 0.4722490144
67 0.1699296176 -0.2112480554
68 -0.2413700378 0.1699296176
69 -0.3015270329 -0.2413700378
70 -0.4821409001 -0.3015270329
71 -0.1622675424 -0.4821409001
72 0.2865452936 -0.1622675424
73 0.0510371781 0.2865452936
74 0.0788806301 0.0510371781
75 -0.1205421698 0.0788806301
76 -0.2623284432 -0.1205421698
77 -0.3130043631 -0.2623284432
78 0.0918024699 -0.3130043631
79 -0.3665671457 0.0918024699
80 -0.7426841594 -0.3665671457
81 0.3216571263 -0.7426841594
82 0.1203725430 0.3216571263
83 0.3418397703 0.1203725430
84 0.1605329494 0.3418397703
85 -0.4018242096 0.1605329494
86 -0.5775501869 -0.4018242096
87 -0.5733205062 -0.5775501869
88 0.3930041227 -0.5733205062
89 -0.1013123614 0.3930041227
90 -0.4115465729 -0.1013123614
91 -0.0977214401 -0.4115465729
92 0.2365487715 -0.0977214401
93 -0.1688942751 0.2365487715
94 0.1166364923 -0.1688942751
95 -0.2215115002 0.1166364923
96 -0.4135286608 -0.2215115002
97 -0.1390191433 -0.4135286608
98 -0.1534209159 -0.1390191433
99 0.1900042327 -0.1534209159
100 0.1557793014 0.1900042327
101 0.0165702752 0.1557793014
102 0.1227331069 0.0165702752
103 0.2989132102 0.1227331069
104 -0.2397437895 0.2989132102
105 0.1327904114 -0.2397437895
106 0.2444866555 0.1327904114
107 -0.3068172567 0.2444866555
108 0.1142165366 -0.3068172567
109 0.5312524076 0.1142165366
110 -0.0576101974 0.5312524076
111 0.0940834487 -0.0576101974
112 0.4914840905 0.0940834487
113 -0.3922164316 0.4914840905
114 0.2311040881 -0.3922164316
115 -0.5969381024 0.2311040881
116 -0.1185377984 -0.5969381024
117 0.0992853606 -0.1185377984
118 -0.1501151318 0.0992853606
119 0.7183188860 -0.1501151318
120 0.2463284963 0.7183188860
121 -0.2090307471 0.2463284963
122 -0.1823166109 -0.2090307471
123 0.1267079969 -0.1823166109
124 -0.3315843665 0.1267079969
125 0.2367425126 -0.3315843665
126 -0.2148679689 0.2367425126
127 0.1139819216 -0.2148679689
128 0.7511392165 0.1139819216
129 -0.3643033438 0.7511392165
130 0.1222861031 -0.3643033438
131 -0.8569067356 0.1222861031
132 -0.3154612222 -0.8569067356
133 -0.3848600978 -0.3154612222
134 0.4675504220 -0.3848600978
135 -0.1223543626 0.4675504220
136 -0.0221594061 -0.1223543626
137 -0.0054528166 -0.0221594061
138 -0.2532155124 -0.0054528166
139 0.2414122389 -0.2532155124
140 -0.2255097148 0.2414122389
141 0.4958107270 -0.2255097148
142 -0.1253066993 0.4958107270
143 -0.3431511686 -0.1253066993
144 -0.4598207835 -0.3431511686
145 0.6005672188 -0.4598207835
146 -0.2569657034 0.6005672188
147 0.2927606451 -0.2569657034
148 -0.0793725333 0.2927606451
149 -0.2184207329 -0.0793725333
150 0.2690456981 -0.2184207329
151 -0.1901737021 0.2690456981
152 -0.1160095029 -0.1901737021
153 -0.3514594828 -0.1160095029
154 -0.1169594329 -0.3514594828
155 0.7363226286 -0.1169594329
156 0.5051334543 0.7363226286
157 -0.4701295699 0.5051334543
158 0.2083084677 -0.4701295699
159 -0.1608918313 0.2083084677
160 -0.1541107167 -0.1608918313
161 0.9100988351 -0.1541107167
162 -0.4183864021 0.9100988351
163 -0.1853625655 -0.4183864021
164 0.0800752600 -0.1853625655
165 0.3397738874 0.0800752600
166 0.4859548502 0.3397738874
167 -0.3530510210 0.4859548502
168 -0.1518157004 -0.3530510210
169 0.4435972305 -0.1518157004
170 0.2985024544 0.4435972305
171 0.0160730434 0.2985024544
172 0.1447690945 0.0160730434
173 -0.5836089549 0.1447690945
174 0.2828263660 -0.5836089549
175 0.3619085479 0.2828263660
176 -0.2008614384 0.3619085479
177 -0.1564825392 -0.2008614384
178 -0.5917676403 -0.1564825392
179 0.4088822161 -0.5917676403
180 0.2018727863 0.4088822161
181 -0.3171221587 0.2018727863
182 -0.1978958199 -0.3171221587
183 0.0844255058 -0.1978958199
184 -0.3896435393 0.0844255058
185 0.0837462787 -0.3896435393
186 0.2479097599 0.0837462787
187 0.0911073923 0.2479097599
188 0.0069729870 0.0911073923
189 0.0766078258 0.0069729870
190 0.0172844174 0.0766078258
191 -0.0808762542 0.0172844174
192 -0.5236700959 -0.0808762542
193 0.0599611042 -0.5236700959
194 0.0473618791 0.0599611042
195 -0.2941572154 0.0473618791
196 0.3848181320 -0.2941572154
197 0.6436292519 0.3848181320
198 0.5516707327 0.6436292519
199 0.0437459160 0.5516707327
200 0.4089702516 0.0437459160
201 0.1849588243 0.4089702516
202 0.8609841391 0.1849588243
203 -0.2107239414 0.8609841391
204 1.2904413895 -0.2107239414
205 0.6325865250 1.2904413895
206 0.3146018338 0.6325865250
207 -0.2385274105 0.3146018338
208 0.4890420944 -0.2385274105
209 -0.3964762467 0.4890420944
210 -1.1394081763 -0.3964762467
211 -0.0058869129 -1.1394081763
212 0.0087993192 -0.0058869129
213 -0.1733655199 0.0087993192
214 -1.1389789665 -0.1733655199
215 0.2203525146 -1.1389789665
216 -0.9668632658 0.2203525146
217 -0.5767093253 -0.9668632658
218 -0.5443223246 -0.5767093253
219 0.8926573339 -0.5443223246
220 NA 0.8926573339
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.4379216100 0.4786082798
[2,] 0.2308670056 0.4379216100
[3,] -0.2240274515 0.2308670056
[4,] -0.8586806524 -0.2240274515
[5,] -0.0425255659 -0.8586806524
[6,] 0.7235220988 -0.0425255659
[7,] -0.1110499077 0.7235220988
[8,] -0.4048200337 -0.1110499077
[9,] 0.0784618697 -0.4048200337
[10,] 0.4998713105 0.0784618697
[11,] 0.0302709340 0.4998713105
[12,] 0.0330199882 0.0302709340
[13,] -0.0867893506 0.0330199882
[14,] 0.0036722789 -0.0867893506
[15,] -0.4255834672 0.0036722789
[16,] 0.3898425431 -0.4255834672
[17,] -0.1284792149 0.3898425431
[18,] 0.1314481349 -0.1284792149
[19,] 0.1744529938 0.1314481349
[20,] 0.4729230946 0.1744529938
[21,] -0.1049597255 0.4729230946
[22,] 0.2242214120 -0.1049597255
[23,] -0.0002636460 0.2242214120
[24,] -0.1424730540 -0.0002636460
[25,] -0.1960311979 -0.1424730540
[26,] 0.3320501675 -0.1960311979
[27,] 0.6538920639 0.3320501675
[28,] -0.3687000396 0.6538920639
[29,] 0.0712540839 -0.3687000396
[30,] -0.1195671335 0.0712540839
[31,] 0.2774927113 -0.1195671335
[32,] -0.2158028822 0.2774927113
[33,] 0.1774929414 -0.2158028822
[34,] -0.1558827873 0.1774929414
[35,] 0.1741736971 -0.1558827873
[36,] 0.1629665001 0.1741736971
[37,] 0.2222231682 0.1629665001
[38,] -0.0290824855 0.2222231682
[39,] -0.2052222627 -0.0290824855
[40,] 0.1292320210 -0.2052222627
[41,] -0.2545988605 0.1292320210
[42,] -0.0353706977 -0.2545988605
[43,] -0.0898994316 -0.0353706977
[44,] 0.0863762937 -0.0898994316
[45,] -0.0658860314 0.0863762937
[46,] -0.0687969179 -0.0658860314
[47,] -0.0367494958 -0.0687969179
[48,] -0.3450367641 -0.0367494958
[49,] -0.0985833541 -0.3450367641
[50,] -0.1035678113 -0.0985833541
[51,] 0.0558477887 -0.1035678113
[52,] -0.1439946954 0.0558477887
[53,] 0.0018052968 -0.1439946954
[54,] 0.1515480637 0.0018052968
[55,] -0.1293453414 0.1515480637
[56,] 0.4535341632 -0.1293453414
[57,] -0.2199610235 0.4535341632
[58,] 0.1378505532 -0.2199610235
[59,] -0.0578420850 0.1378505532
[60,] -0.1609961276 -0.0578420850
[61,] -0.0990472884 -0.1609961276
[62,] 0.1209369168 -0.0990472884
[63,] -0.0418253819 0.1209369168
[64,] 0.0326029931 -0.0418253819
[65,] 0.4722490144 0.0326029931
[66,] -0.2112480554 0.4722490144
[67,] 0.1699296176 -0.2112480554
[68,] -0.2413700378 0.1699296176
[69,] -0.3015270329 -0.2413700378
[70,] -0.4821409001 -0.3015270329
[71,] -0.1622675424 -0.4821409001
[72,] 0.2865452936 -0.1622675424
[73,] 0.0510371781 0.2865452936
[74,] 0.0788806301 0.0510371781
[75,] -0.1205421698 0.0788806301
[76,] -0.2623284432 -0.1205421698
[77,] -0.3130043631 -0.2623284432
[78,] 0.0918024699 -0.3130043631
[79,] -0.3665671457 0.0918024699
[80,] -0.7426841594 -0.3665671457
[81,] 0.3216571263 -0.7426841594
[82,] 0.1203725430 0.3216571263
[83,] 0.3418397703 0.1203725430
[84,] 0.1605329494 0.3418397703
[85,] -0.4018242096 0.1605329494
[86,] -0.5775501869 -0.4018242096
[87,] -0.5733205062 -0.5775501869
[88,] 0.3930041227 -0.5733205062
[89,] -0.1013123614 0.3930041227
[90,] -0.4115465729 -0.1013123614
[91,] -0.0977214401 -0.4115465729
[92,] 0.2365487715 -0.0977214401
[93,] -0.1688942751 0.2365487715
[94,] 0.1166364923 -0.1688942751
[95,] -0.2215115002 0.1166364923
[96,] -0.4135286608 -0.2215115002
[97,] -0.1390191433 -0.4135286608
[98,] -0.1534209159 -0.1390191433
[99,] 0.1900042327 -0.1534209159
[100,] 0.1557793014 0.1900042327
[101,] 0.0165702752 0.1557793014
[102,] 0.1227331069 0.0165702752
[103,] 0.2989132102 0.1227331069
[104,] -0.2397437895 0.2989132102
[105,] 0.1327904114 -0.2397437895
[106,] 0.2444866555 0.1327904114
[107,] -0.3068172567 0.2444866555
[108,] 0.1142165366 -0.3068172567
[109,] 0.5312524076 0.1142165366
[110,] -0.0576101974 0.5312524076
[111,] 0.0940834487 -0.0576101974
[112,] 0.4914840905 0.0940834487
[113,] -0.3922164316 0.4914840905
[114,] 0.2311040881 -0.3922164316
[115,] -0.5969381024 0.2311040881
[116,] -0.1185377984 -0.5969381024
[117,] 0.0992853606 -0.1185377984
[118,] -0.1501151318 0.0992853606
[119,] 0.7183188860 -0.1501151318
[120,] 0.2463284963 0.7183188860
[121,] -0.2090307471 0.2463284963
[122,] -0.1823166109 -0.2090307471
[123,] 0.1267079969 -0.1823166109
[124,] -0.3315843665 0.1267079969
[125,] 0.2367425126 -0.3315843665
[126,] -0.2148679689 0.2367425126
[127,] 0.1139819216 -0.2148679689
[128,] 0.7511392165 0.1139819216
[129,] -0.3643033438 0.7511392165
[130,] 0.1222861031 -0.3643033438
[131,] -0.8569067356 0.1222861031
[132,] -0.3154612222 -0.8569067356
[133,] -0.3848600978 -0.3154612222
[134,] 0.4675504220 -0.3848600978
[135,] -0.1223543626 0.4675504220
[136,] -0.0221594061 -0.1223543626
[137,] -0.0054528166 -0.0221594061
[138,] -0.2532155124 -0.0054528166
[139,] 0.2414122389 -0.2532155124
[140,] -0.2255097148 0.2414122389
[141,] 0.4958107270 -0.2255097148
[142,] -0.1253066993 0.4958107270
[143,] -0.3431511686 -0.1253066993
[144,] -0.4598207835 -0.3431511686
[145,] 0.6005672188 -0.4598207835
[146,] -0.2569657034 0.6005672188
[147,] 0.2927606451 -0.2569657034
[148,] -0.0793725333 0.2927606451
[149,] -0.2184207329 -0.0793725333
[150,] 0.2690456981 -0.2184207329
[151,] -0.1901737021 0.2690456981
[152,] -0.1160095029 -0.1901737021
[153,] -0.3514594828 -0.1160095029
[154,] -0.1169594329 -0.3514594828
[155,] 0.7363226286 -0.1169594329
[156,] 0.5051334543 0.7363226286
[157,] -0.4701295699 0.5051334543
[158,] 0.2083084677 -0.4701295699
[159,] -0.1608918313 0.2083084677
[160,] -0.1541107167 -0.1608918313
[161,] 0.9100988351 -0.1541107167
[162,] -0.4183864021 0.9100988351
[163,] -0.1853625655 -0.4183864021
[164,] 0.0800752600 -0.1853625655
[165,] 0.3397738874 0.0800752600
[166,] 0.4859548502 0.3397738874
[167,] -0.3530510210 0.4859548502
[168,] -0.1518157004 -0.3530510210
[169,] 0.4435972305 -0.1518157004
[170,] 0.2985024544 0.4435972305
[171,] 0.0160730434 0.2985024544
[172,] 0.1447690945 0.0160730434
[173,] -0.5836089549 0.1447690945
[174,] 0.2828263660 -0.5836089549
[175,] 0.3619085479 0.2828263660
[176,] -0.2008614384 0.3619085479
[177,] -0.1564825392 -0.2008614384
[178,] -0.5917676403 -0.1564825392
[179,] 0.4088822161 -0.5917676403
[180,] 0.2018727863 0.4088822161
[181,] -0.3171221587 0.2018727863
[182,] -0.1978958199 -0.3171221587
[183,] 0.0844255058 -0.1978958199
[184,] -0.3896435393 0.0844255058
[185,] 0.0837462787 -0.3896435393
[186,] 0.2479097599 0.0837462787
[187,] 0.0911073923 0.2479097599
[188,] 0.0069729870 0.0911073923
[189,] 0.0766078258 0.0069729870
[190,] 0.0172844174 0.0766078258
[191,] -0.0808762542 0.0172844174
[192,] -0.5236700959 -0.0808762542
[193,] 0.0599611042 -0.5236700959
[194,] 0.0473618791 0.0599611042
[195,] -0.2941572154 0.0473618791
[196,] 0.3848181320 -0.2941572154
[197,] 0.6436292519 0.3848181320
[198,] 0.5516707327 0.6436292519
[199,] 0.0437459160 0.5516707327
[200,] 0.4089702516 0.0437459160
[201,] 0.1849588243 0.4089702516
[202,] 0.8609841391 0.1849588243
[203,] -0.2107239414 0.8609841391
[204,] 1.2904413895 -0.2107239414
[205,] 0.6325865250 1.2904413895
[206,] 0.3146018338 0.6325865250
[207,] -0.2385274105 0.3146018338
[208,] 0.4890420944 -0.2385274105
[209,] -0.3964762467 0.4890420944
[210,] -1.1394081763 -0.3964762467
[211,] -0.0058869129 -1.1394081763
[212,] 0.0087993192 -0.0058869129
[213,] -0.1733655199 0.0087993192
[214,] -1.1389789665 -0.1733655199
[215,] 0.2203525146 -1.1389789665
[216,] -0.9668632658 0.2203525146
[217,] -0.5767093253 -0.9668632658
[218,] -0.5443223246 -0.5767093253
[219,] 0.8926573339 -0.5443223246
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.4379216100 0.4786082798
2 0.2308670056 0.4379216100
3 -0.2240274515 0.2308670056
4 -0.8586806524 -0.2240274515
5 -0.0425255659 -0.8586806524
6 0.7235220988 -0.0425255659
7 -0.1110499077 0.7235220988
8 -0.4048200337 -0.1110499077
9 0.0784618697 -0.4048200337
10 0.4998713105 0.0784618697
11 0.0302709340 0.4998713105
12 0.0330199882 0.0302709340
13 -0.0867893506 0.0330199882
14 0.0036722789 -0.0867893506
15 -0.4255834672 0.0036722789
16 0.3898425431 -0.4255834672
17 -0.1284792149 0.3898425431
18 0.1314481349 -0.1284792149
19 0.1744529938 0.1314481349
20 0.4729230946 0.1744529938
21 -0.1049597255 0.4729230946
22 0.2242214120 -0.1049597255
23 -0.0002636460 0.2242214120
24 -0.1424730540 -0.0002636460
25 -0.1960311979 -0.1424730540
26 0.3320501675 -0.1960311979
27 0.6538920639 0.3320501675
28 -0.3687000396 0.6538920639
29 0.0712540839 -0.3687000396
30 -0.1195671335 0.0712540839
31 0.2774927113 -0.1195671335
32 -0.2158028822 0.2774927113
33 0.1774929414 -0.2158028822
34 -0.1558827873 0.1774929414
35 0.1741736971 -0.1558827873
36 0.1629665001 0.1741736971
37 0.2222231682 0.1629665001
38 -0.0290824855 0.2222231682
39 -0.2052222627 -0.0290824855
40 0.1292320210 -0.2052222627
41 -0.2545988605 0.1292320210
42 -0.0353706977 -0.2545988605
43 -0.0898994316 -0.0353706977
44 0.0863762937 -0.0898994316
45 -0.0658860314 0.0863762937
46 -0.0687969179 -0.0658860314
47 -0.0367494958 -0.0687969179
48 -0.3450367641 -0.0367494958
49 -0.0985833541 -0.3450367641
50 -0.1035678113 -0.0985833541
51 0.0558477887 -0.1035678113
52 -0.1439946954 0.0558477887
53 0.0018052968 -0.1439946954
54 0.1515480637 0.0018052968
55 -0.1293453414 0.1515480637
56 0.4535341632 -0.1293453414
57 -0.2199610235 0.4535341632
58 0.1378505532 -0.2199610235
59 -0.0578420850 0.1378505532
60 -0.1609961276 -0.0578420850
61 -0.0990472884 -0.1609961276
62 0.1209369168 -0.0990472884
63 -0.0418253819 0.1209369168
64 0.0326029931 -0.0418253819
65 0.4722490144 0.0326029931
66 -0.2112480554 0.4722490144
67 0.1699296176 -0.2112480554
68 -0.2413700378 0.1699296176
69 -0.3015270329 -0.2413700378
70 -0.4821409001 -0.3015270329
71 -0.1622675424 -0.4821409001
72 0.2865452936 -0.1622675424
73 0.0510371781 0.2865452936
74 0.0788806301 0.0510371781
75 -0.1205421698 0.0788806301
76 -0.2623284432 -0.1205421698
77 -0.3130043631 -0.2623284432
78 0.0918024699 -0.3130043631
79 -0.3665671457 0.0918024699
80 -0.7426841594 -0.3665671457
81 0.3216571263 -0.7426841594
82 0.1203725430 0.3216571263
83 0.3418397703 0.1203725430
84 0.1605329494 0.3418397703
85 -0.4018242096 0.1605329494
86 -0.5775501869 -0.4018242096
87 -0.5733205062 -0.5775501869
88 0.3930041227 -0.5733205062
89 -0.1013123614 0.3930041227
90 -0.4115465729 -0.1013123614
91 -0.0977214401 -0.4115465729
92 0.2365487715 -0.0977214401
93 -0.1688942751 0.2365487715
94 0.1166364923 -0.1688942751
95 -0.2215115002 0.1166364923
96 -0.4135286608 -0.2215115002
97 -0.1390191433 -0.4135286608
98 -0.1534209159 -0.1390191433
99 0.1900042327 -0.1534209159
100 0.1557793014 0.1900042327
101 0.0165702752 0.1557793014
102 0.1227331069 0.0165702752
103 0.2989132102 0.1227331069
104 -0.2397437895 0.2989132102
105 0.1327904114 -0.2397437895
106 0.2444866555 0.1327904114
107 -0.3068172567 0.2444866555
108 0.1142165366 -0.3068172567
109 0.5312524076 0.1142165366
110 -0.0576101974 0.5312524076
111 0.0940834487 -0.0576101974
112 0.4914840905 0.0940834487
113 -0.3922164316 0.4914840905
114 0.2311040881 -0.3922164316
115 -0.5969381024 0.2311040881
116 -0.1185377984 -0.5969381024
117 0.0992853606 -0.1185377984
118 -0.1501151318 0.0992853606
119 0.7183188860 -0.1501151318
120 0.2463284963 0.7183188860
121 -0.2090307471 0.2463284963
122 -0.1823166109 -0.2090307471
123 0.1267079969 -0.1823166109
124 -0.3315843665 0.1267079969
125 0.2367425126 -0.3315843665
126 -0.2148679689 0.2367425126
127 0.1139819216 -0.2148679689
128 0.7511392165 0.1139819216
129 -0.3643033438 0.7511392165
130 0.1222861031 -0.3643033438
131 -0.8569067356 0.1222861031
132 -0.3154612222 -0.8569067356
133 -0.3848600978 -0.3154612222
134 0.4675504220 -0.3848600978
135 -0.1223543626 0.4675504220
136 -0.0221594061 -0.1223543626
137 -0.0054528166 -0.0221594061
138 -0.2532155124 -0.0054528166
139 0.2414122389 -0.2532155124
140 -0.2255097148 0.2414122389
141 0.4958107270 -0.2255097148
142 -0.1253066993 0.4958107270
143 -0.3431511686 -0.1253066993
144 -0.4598207835 -0.3431511686
145 0.6005672188 -0.4598207835
146 -0.2569657034 0.6005672188
147 0.2927606451 -0.2569657034
148 -0.0793725333 0.2927606451
149 -0.2184207329 -0.0793725333
150 0.2690456981 -0.2184207329
151 -0.1901737021 0.2690456981
152 -0.1160095029 -0.1901737021
153 -0.3514594828 -0.1160095029
154 -0.1169594329 -0.3514594828
155 0.7363226286 -0.1169594329
156 0.5051334543 0.7363226286
157 -0.4701295699 0.5051334543
158 0.2083084677 -0.4701295699
159 -0.1608918313 0.2083084677
160 -0.1541107167 -0.1608918313
161 0.9100988351 -0.1541107167
162 -0.4183864021 0.9100988351
163 -0.1853625655 -0.4183864021
164 0.0800752600 -0.1853625655
165 0.3397738874 0.0800752600
166 0.4859548502 0.3397738874
167 -0.3530510210 0.4859548502
168 -0.1518157004 -0.3530510210
169 0.4435972305 -0.1518157004
170 0.2985024544 0.4435972305
171 0.0160730434 0.2985024544
172 0.1447690945 0.0160730434
173 -0.5836089549 0.1447690945
174 0.2828263660 -0.5836089549
175 0.3619085479 0.2828263660
176 -0.2008614384 0.3619085479
177 -0.1564825392 -0.2008614384
178 -0.5917676403 -0.1564825392
179 0.4088822161 -0.5917676403
180 0.2018727863 0.4088822161
181 -0.3171221587 0.2018727863
182 -0.1978958199 -0.3171221587
183 0.0844255058 -0.1978958199
184 -0.3896435393 0.0844255058
185 0.0837462787 -0.3896435393
186 0.2479097599 0.0837462787
187 0.0911073923 0.2479097599
188 0.0069729870 0.0911073923
189 0.0766078258 0.0069729870
190 0.0172844174 0.0766078258
191 -0.0808762542 0.0172844174
192 -0.5236700959 -0.0808762542
193 0.0599611042 -0.5236700959
194 0.0473618791 0.0599611042
195 -0.2941572154 0.0473618791
196 0.3848181320 -0.2941572154
197 0.6436292519 0.3848181320
198 0.5516707327 0.6436292519
199 0.0437459160 0.5516707327
200 0.4089702516 0.0437459160
201 0.1849588243 0.4089702516
202 0.8609841391 0.1849588243
203 -0.2107239414 0.8609841391
204 1.2904413895 -0.2107239414
205 0.6325865250 1.2904413895
206 0.3146018338 0.6325865250
207 -0.2385274105 0.3146018338
208 0.4890420944 -0.2385274105
209 -0.3964762467 0.4890420944
210 -1.1394081763 -0.3964762467
211 -0.0058869129 -1.1394081763
212 0.0087993192 -0.0058869129
213 -0.1733655199 0.0087993192
214 -1.1389789665 -0.1733655199
215 0.2203525146 -1.1389789665
216 -0.9668632658 0.2203525146
217 -0.5767093253 -0.9668632658
218 -0.5443223246 -0.5767093253
219 0.8926573339 -0.5443223246
> 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/7gewl1258639561.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8i09r1258639561.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9okyd1258639561.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10wruo1258639561.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11qq5g1258639561.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/12w6oz1258639561.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/13auaz1258639561.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/14uzkn1258639561.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/15c7bh1258639561.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/168b4s1258639561.tab")
+ }
>
> system("convert tmp/1vcr01258639561.ps tmp/1vcr01258639561.png")
> system("convert tmp/2t65j1258639561.ps tmp/2t65j1258639561.png")
> system("convert tmp/3i7621258639561.ps tmp/3i7621258639561.png")
> system("convert tmp/4p4yi1258639561.ps tmp/4p4yi1258639561.png")
> system("convert tmp/5zvx01258639561.ps tmp/5zvx01258639561.png")
> system("convert tmp/6e46o1258639561.ps tmp/6e46o1258639561.png")
> system("convert tmp/7gewl1258639561.ps tmp/7gewl1258639561.png")
> system("convert tmp/8i09r1258639561.ps tmp/8i09r1258639561.png")
> system("convert tmp/9okyd1258639561.ps tmp/9okyd1258639561.png")
> system("convert tmp/10wruo1258639561.ps tmp/10wruo1258639561.png")
>
>
> proc.time()
user system elapsed
6.014 1.824 8.758