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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'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(4.6
+ ,11.7
+ ,4.5
+ ,11.4
+ ,4.4
+ ,11.2
+ ,4.4
+ ,11.1
+ ,4.3
+ ,10.8
+ ,4.1
+ ,10.4
+ ,3.9
+ ,10.1
+ ,3.7
+ ,9.8
+ ,3.6
+ ,9.7
+ ,3.9
+ ,10.3
+ ,4.2
+ ,10.9
+ ,4.2
+ ,10.8
+ ,4.1
+ ,10.6
+ ,4.1
+ ,10.4
+ ,4.1
+ ,10.3
+ ,4.1
+ ,10.2
+ ,4.1
+ ,10
+ ,4
+ ,9.7
+ ,3.9
+ ,9.4
+ ,3.8
+ ,9.2
+ ,3.8
+ ,9.1
+ ,4
+ ,9.6
+ ,4.4
+ ,10.2
+ ,4.6
+ ,10.2
+ ,4.6
+ ,10
+ ,4.6
+ ,9.9
+ ,4.7
+ ,9.9
+ ,4.8
+ ,9.9
+ ,4.8
+ ,9.7
+ ,4.7
+ ,9.5
+ ,4.7
+ ,9.4
+ ,4.7
+ ,9.3
+ ,4.6
+ ,9.3
+ ,5
+ ,9.9
+ ,5.4
+ ,10.5
+ ,5.5
+ ,10.6
+ ,5.6
+ ,10.6
+ ,5.6
+ ,10.5
+ ,5.8
+ ,10.6
+ ,6
+ ,10.8
+ ,6.1
+ ,10.8
+ ,6.1
+ ,10.7
+ ,6
+ ,10.6
+ ,6
+ ,10.6
+ ,6.1
+ ,10.8
+ ,6.5
+ ,11.4
+ ,7.1
+ ,12.2
+ ,7.4
+ ,12.4
+ ,7.4
+ ,12.4
+ ,7.5
+ ,12.3
+ ,7.6
+ ,12.4
+ ,7.8
+ ,12.5
+ ,7.8
+ ,12.5
+ ,7.7
+ ,12.4
+ ,7.6
+ ,12.3
+ ,7.5
+ ,12.2
+ ,7.3
+ ,12.1
+ ,7.6
+ ,12.6
+ ,8
+ ,13.2
+ ,8
+ ,13.4
+ ,7.9
+ ,13.2
+ ,7.8
+ ,12.9
+ ,7.7
+ ,12.8
+ ,7.8
+ ,12.7
+ ,7.7
+ ,12.6
+ ,7.5
+ ,12.4
+ ,7.3
+ ,12.1
+ ,7.1
+ ,12
+ ,7
+ ,11.9
+ ,7.3
+ ,12.5
+ ,7.8
+ ,13.2
+ ,7.9
+ ,13.4
+ ,7.9
+ ,13.3
+ ,7.8
+ ,13
+ ,7.8
+ ,12.9
+ ,7.9
+ ,13
+ ,7.8
+ ,12.9
+ ,7.6
+ ,12.6
+ ,7.4
+ ,12.4
+ ,7.2
+ ,12.1
+ ,6.9
+ ,11.9
+ ,7.1
+ ,12.3
+ ,7.5
+ ,13
+ ,7.6
+ ,13
+ ,7.4
+ ,12.6
+ ,7.3
+ ,12.2
+ ,7.2
+ ,12.1
+ ,7.3
+ ,12
+ ,7.2
+ ,11.8
+ ,7.1
+ ,11.6
+ ,7
+ ,11.4
+ ,6.9
+ ,11.2
+ ,6.8
+ ,11.2
+ ,7.2
+ ,11.8
+ ,7.6
+ ,12.5
+ ,7.7
+ ,12.6
+ ,7.6
+ ,12.4
+ ,7.5
+ ,12.1
+ ,7.5
+ ,12
+ ,7.6
+ ,12
+ ,7.6
+ ,11.9
+ ,7.6
+ ,11.8
+ ,7.5
+ ,11.5
+ ,7.3
+ ,11.3
+ ,7.2
+ ,11.2
+ ,7.4
+ ,11.6
+ ,8
+ ,12.2
+ ,8.2
+ ,12.2
+ ,8
+ ,11.7
+ ,7.7
+ ,11.2
+ ,7.7
+ ,11
+ ,7.8
+ ,10.9
+ ,7.8
+ ,10.8
+ ,7.7
+ ,10.5
+ ,7.5
+ ,10.2
+ ,7.3
+ ,10
+ ,7.1
+ ,9.9
+ ,7.1
+ ,10.3
+ ,7.2
+ ,10.7
+ ,6.8
+ ,10.4
+ ,6.6
+ ,10.1
+ ,6.4
+ ,9.7
+ ,6.4
+ ,9.4
+ ,6.5
+ ,8.9
+ ,6.3
+ ,8.4
+ ,5.9
+ ,8.1
+ ,5.5
+ ,8.3
+ ,5.2
+ ,8.1
+ ,4.9
+ ,8
+ ,5.4
+ ,8.7
+ ,5.8
+ ,9.2
+ ,5.7
+ ,9
+ ,5.6
+ ,8.9
+ ,5.5
+ ,8.5
+ ,5.4
+ ,8.1
+ ,5.4
+ ,7.5
+ ,5.4
+ ,7.1
+ ,5.5
+ ,6.9
+ ,5.8
+ ,7.1
+ ,5.7
+ ,7
+ ,5.4
+ ,6.7
+ ,5.6
+ ,7
+ ,5.8
+ ,7.3
+ ,6.2
+ ,7.7
+ ,6.8
+ ,8.4
+ ,6.7
+ ,8.4
+ ,6.7
+ ,8.8
+ ,6.4
+ ,9.1
+ ,6.3
+ ,9
+ ,6.3
+ ,8.6
+ ,6.4
+ ,7.9
+ ,6.3
+ ,7.7
+ ,6
+ ,7.8
+ ,6.3
+ ,9.2
+ ,6.3
+ ,9.4
+ ,6.6
+ ,9.2
+ ,7.5
+ ,8.7
+ ,7.8
+ ,8.4
+ ,7.9
+ ,8.6
+ ,7.8
+ ,9
+ ,7.6
+ ,9.1
+ ,7.5
+ ,8.7
+ ,7.6
+ ,8.2
+ ,7.5
+ ,7.9
+ ,7.3
+ ,7.9
+ ,7.6
+ ,9.1
+ ,7.5
+ ,9.4
+ ,7.6
+ ,9.4
+ ,7.9
+ ,9.1
+ ,7.9
+ ,9
+ ,8.1
+ ,9.3
+ ,8.2
+ ,9.9
+ ,8
+ ,9.8
+ ,7.5
+ ,9.3
+ ,6.8
+ ,8.3
+ ,6.5
+ ,8
+ ,6.6
+ ,8.5
+ ,7.6
+ ,10.4
+ ,8
+ ,11.1
+ ,8.1
+ ,10.9
+ ,7.7
+ ,10
+ ,7.5
+ ,9.2
+ ,7.6
+ ,9.2
+ ,7.8
+ ,9.5
+ ,7.8
+ ,9.6
+ ,7.8
+ ,9.5
+ ,7.5
+ ,9.1
+ ,7.5
+ ,8.9
+ ,7.1
+ ,9
+ ,7.5
+ ,10.1
+ ,7.5
+ ,10.3
+ ,7.6
+ ,10.2
+ ,7.7
+ ,9.6
+ ,7.7
+ ,9.2
+ ,7.9
+ ,9.3
+ ,8.1
+ ,9.4
+ ,8.2
+ ,9.4
+ ,8.2
+ ,9.2
+ ,8.2
+ ,9
+ ,7.9
+ ,9
+ ,7.3
+ ,9
+ ,6.9
+ ,9.8
+ ,6.6
+ ,10
+ ,6.7
+ ,9.8
+ ,6.9
+ ,9.3
+ ,7
+ ,9
+ ,7.1
+ ,9
+ ,7.2
+ ,9.1
+ ,7.1
+ ,9.1
+ ,6.9
+ ,9.1
+ ,7
+ ,9.2
+ ,6.8
+ ,8.8
+ ,6.4
+ ,8.3
+ ,6.7
+ ,8.4
+ ,6.6
+ ,8.1
+ ,6.4
+ ,7.7
+ ,6.3
+ ,7.9
+ ,6.2
+ ,7.9
+ ,6.5
+ ,8
+ ,6.8
+ ,7.9
+ ,6.8
+ ,7.6
+ ,6.4
+ ,7.1
+ ,6.1
+ ,6.8
+ ,5.8
+ ,6.5
+ ,6.1
+ ,6.9
+ ,7.2
+ ,8.2
+ ,7.3
+ ,8.7
+ ,6.9
+ ,8.3
+ ,6.1
+ ,7.9
+ ,5.8
+ ,7.5
+ ,6.2
+ ,7.8
+ ,7.1
+ ,8.3
+ ,7.7
+ ,8.4
+ ,7.9
+ ,8.2
+ ,7.7
+ ,7.7
+ ,7.4
+ ,7.2
+ ,7.5
+ ,7.3
+ ,8
+ ,8.1
+ ,8.1
+ ,8.5
+ ,8
+ ,8.4)
+ ,dim=c(2
+ ,240)
+ ,dimnames=list(c('Y'
+ ,'X')
+ ,1:240))
> y <- array(NA,dim=c(2,240),dimnames=list(c('Y','X'),1:240))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 4.6 11.7 1 0 0 0 0 0 0 0 0 0 0 1
2 4.5 11.4 0 1 0 0 0 0 0 0 0 0 0 2
3 4.4 11.2 0 0 1 0 0 0 0 0 0 0 0 3
4 4.4 11.1 0 0 0 1 0 0 0 0 0 0 0 4
5 4.3 10.8 0 0 0 0 1 0 0 0 0 0 0 5
6 4.1 10.4 0 0 0 0 0 1 0 0 0 0 0 6
7 3.9 10.1 0 0 0 0 0 0 1 0 0 0 0 7
8 3.7 9.8 0 0 0 0 0 0 0 1 0 0 0 8
9 3.6 9.7 0 0 0 0 0 0 0 0 1 0 0 9
10 3.9 10.3 0 0 0 0 0 0 0 0 0 1 0 10
11 4.2 10.9 0 0 0 0 0 0 0 0 0 0 1 11
12 4.2 10.8 0 0 0 0 0 0 0 0 0 0 0 12
13 4.1 10.6 1 0 0 0 0 0 0 0 0 0 0 13
14 4.1 10.4 0 1 0 0 0 0 0 0 0 0 0 14
15 4.1 10.3 0 0 1 0 0 0 0 0 0 0 0 15
16 4.1 10.2 0 0 0 1 0 0 0 0 0 0 0 16
17 4.1 10.0 0 0 0 0 1 0 0 0 0 0 0 17
18 4.0 9.7 0 0 0 0 0 1 0 0 0 0 0 18
19 3.9 9.4 0 0 0 0 0 0 1 0 0 0 0 19
20 3.8 9.2 0 0 0 0 0 0 0 1 0 0 0 20
21 3.8 9.1 0 0 0 0 0 0 0 0 1 0 0 21
22 4.0 9.6 0 0 0 0 0 0 0 0 0 1 0 22
23 4.4 10.2 0 0 0 0 0 0 0 0 0 0 1 23
24 4.6 10.2 0 0 0 0 0 0 0 0 0 0 0 24
25 4.6 10.0 1 0 0 0 0 0 0 0 0 0 0 25
26 4.6 9.9 0 1 0 0 0 0 0 0 0 0 0 26
27 4.7 9.9 0 0 1 0 0 0 0 0 0 0 0 27
28 4.8 9.9 0 0 0 1 0 0 0 0 0 0 0 28
29 4.8 9.7 0 0 0 0 1 0 0 0 0 0 0 29
30 4.7 9.5 0 0 0 0 0 1 0 0 0 0 0 30
31 4.7 9.4 0 0 0 0 0 0 1 0 0 0 0 31
32 4.7 9.3 0 0 0 0 0 0 0 1 0 0 0 32
33 4.6 9.3 0 0 0 0 0 0 0 0 1 0 0 33
34 5.0 9.9 0 0 0 0 0 0 0 0 0 1 0 34
35 5.4 10.5 0 0 0 0 0 0 0 0 0 0 1 35
36 5.5 10.6 0 0 0 0 0 0 0 0 0 0 0 36
37 5.6 10.6 1 0 0 0 0 0 0 0 0 0 0 37
38 5.6 10.5 0 1 0 0 0 0 0 0 0 0 0 38
39 5.8 10.6 0 0 1 0 0 0 0 0 0 0 0 39
40 6.0 10.8 0 0 0 1 0 0 0 0 0 0 0 40
41 6.1 10.8 0 0 0 0 1 0 0 0 0 0 0 41
42 6.1 10.7 0 0 0 0 0 1 0 0 0 0 0 42
43 6.0 10.6 0 0 0 0 0 0 1 0 0 0 0 43
44 6.0 10.6 0 0 0 0 0 0 0 1 0 0 0 44
45 6.1 10.8 0 0 0 0 0 0 0 0 1 0 0 45
46 6.5 11.4 0 0 0 0 0 0 0 0 0 1 0 46
47 7.1 12.2 0 0 0 0 0 0 0 0 0 0 1 47
48 7.4 12.4 0 0 0 0 0 0 0 0 0 0 0 48
49 7.4 12.4 1 0 0 0 0 0 0 0 0 0 0 49
50 7.5 12.3 0 1 0 0 0 0 0 0 0 0 0 50
51 7.6 12.4 0 0 1 0 0 0 0 0 0 0 0 51
52 7.8 12.5 0 0 0 1 0 0 0 0 0 0 0 52
53 7.8 12.5 0 0 0 0 1 0 0 0 0 0 0 53
54 7.7 12.4 0 0 0 0 0 1 0 0 0 0 0 54
55 7.6 12.3 0 0 0 0 0 0 1 0 0 0 0 55
56 7.5 12.2 0 0 0 0 0 0 0 1 0 0 0 56
57 7.3 12.1 0 0 0 0 0 0 0 0 1 0 0 57
58 7.6 12.6 0 0 0 0 0 0 0 0 0 1 0 58
59 8.0 13.2 0 0 0 0 0 0 0 0 0 0 1 59
60 8.0 13.4 0 0 0 0 0 0 0 0 0 0 0 60
61 7.9 13.2 1 0 0 0 0 0 0 0 0 0 0 61
62 7.8 12.9 0 1 0 0 0 0 0 0 0 0 0 62
63 7.7 12.8 0 0 1 0 0 0 0 0 0 0 0 63
64 7.8 12.7 0 0 0 1 0 0 0 0 0 0 0 64
65 7.7 12.6 0 0 0 0 1 0 0 0 0 0 0 65
66 7.5 12.4 0 0 0 0 0 1 0 0 0 0 0 66
67 7.3 12.1 0 0 0 0 0 0 1 0 0 0 0 67
68 7.1 12.0 0 0 0 0 0 0 0 1 0 0 0 68
69 7.0 11.9 0 0 0 0 0 0 0 0 1 0 0 69
70 7.3 12.5 0 0 0 0 0 0 0 0 0 1 0 70
71 7.8 13.2 0 0 0 0 0 0 0 0 0 0 1 71
72 7.9 13.4 0 0 0 0 0 0 0 0 0 0 0 72
73 7.9 13.3 1 0 0 0 0 0 0 0 0 0 0 73
74 7.8 13.0 0 1 0 0 0 0 0 0 0 0 0 74
75 7.8 12.9 0 0 1 0 0 0 0 0 0 0 0 75
76 7.9 13.0 0 0 0 1 0 0 0 0 0 0 0 76
77 7.8 12.9 0 0 0 0 1 0 0 0 0 0 0 77
78 7.6 12.6 0 0 0 0 0 1 0 0 0 0 0 78
79 7.4 12.4 0 0 0 0 0 0 1 0 0 0 0 79
80 7.2 12.1 0 0 0 0 0 0 0 1 0 0 0 80
81 6.9 11.9 0 0 0 0 0 0 0 0 1 0 0 81
82 7.1 12.3 0 0 0 0 0 0 0 0 0 1 0 82
83 7.5 13.0 0 0 0 0 0 0 0 0 0 0 1 83
84 7.6 13.0 0 0 0 0 0 0 0 0 0 0 0 84
85 7.4 12.6 1 0 0 0 0 0 0 0 0 0 0 85
86 7.3 12.2 0 1 0 0 0 0 0 0 0 0 0 86
87 7.2 12.1 0 0 1 0 0 0 0 0 0 0 0 87
88 7.3 12.0 0 0 0 1 0 0 0 0 0 0 0 88
89 7.2 11.8 0 0 0 0 1 0 0 0 0 0 0 89
90 7.1 11.6 0 0 0 0 0 1 0 0 0 0 0 90
91 7.0 11.4 0 0 0 0 0 0 1 0 0 0 0 91
92 6.9 11.2 0 0 0 0 0 0 0 1 0 0 0 92
93 6.8 11.2 0 0 0 0 0 0 0 0 1 0 0 93
94 7.2 11.8 0 0 0 0 0 0 0 0 0 1 0 94
95 7.6 12.5 0 0 0 0 0 0 0 0 0 0 1 95
96 7.7 12.6 0 0 0 0 0 0 0 0 0 0 0 96
97 7.6 12.4 1 0 0 0 0 0 0 0 0 0 0 97
98 7.5 12.1 0 1 0 0 0 0 0 0 0 0 0 98
99 7.5 12.0 0 0 1 0 0 0 0 0 0 0 0 99
100 7.6 12.0 0 0 0 1 0 0 0 0 0 0 0 100
101 7.6 11.9 0 0 0 0 1 0 0 0 0 0 0 101
102 7.6 11.8 0 0 0 0 0 1 0 0 0 0 0 102
103 7.5 11.5 0 0 0 0 0 0 1 0 0 0 0 103
104 7.3 11.3 0 0 0 0 0 0 0 1 0 0 0 104
105 7.2 11.2 0 0 0 0 0 0 0 0 1 0 0 105
106 7.4 11.6 0 0 0 0 0 0 0 0 0 1 0 106
107 8.0 12.2 0 0 0 0 0 0 0 0 0 0 1 107
108 8.2 12.2 0 0 0 0 0 0 0 0 0 0 0 108
109 8.0 11.7 1 0 0 0 0 0 0 0 0 0 0 109
110 7.7 11.2 0 1 0 0 0 0 0 0 0 0 0 110
111 7.7 11.0 0 0 1 0 0 0 0 0 0 0 0 111
112 7.8 10.9 0 0 0 1 0 0 0 0 0 0 0 112
113 7.8 10.8 0 0 0 0 1 0 0 0 0 0 0 113
114 7.7 10.5 0 0 0 0 0 1 0 0 0 0 0 114
115 7.5 10.2 0 0 0 0 0 0 1 0 0 0 0 115
116 7.3 10.0 0 0 0 0 0 0 0 1 0 0 0 116
117 7.1 9.9 0 0 0 0 0 0 0 0 1 0 0 117
118 7.1 10.3 0 0 0 0 0 0 0 0 0 1 0 118
119 7.2 10.7 0 0 0 0 0 0 0 0 0 0 1 119
120 6.8 10.4 0 0 0 0 0 0 0 0 0 0 0 120
121 6.6 10.1 1 0 0 0 0 0 0 0 0 0 0 121
122 6.4 9.7 0 1 0 0 0 0 0 0 0 0 0 122
123 6.4 9.4 0 0 1 0 0 0 0 0 0 0 0 123
124 6.5 8.9 0 0 0 1 0 0 0 0 0 0 0 124
125 6.3 8.4 0 0 0 0 1 0 0 0 0 0 0 125
126 5.9 8.1 0 0 0 0 0 1 0 0 0 0 0 126
127 5.5 8.3 0 0 0 0 0 0 1 0 0 0 0 127
128 5.2 8.1 0 0 0 0 0 0 0 1 0 0 0 128
129 4.9 8.0 0 0 0 0 0 0 0 0 1 0 0 129
130 5.4 8.7 0 0 0 0 0 0 0 0 0 1 0 130
131 5.8 9.2 0 0 0 0 0 0 0 0 0 0 1 131
132 5.7 9.0 0 0 0 0 0 0 0 0 0 0 0 132
133 5.6 8.9 1 0 0 0 0 0 0 0 0 0 0 133
134 5.5 8.5 0 1 0 0 0 0 0 0 0 0 0 134
135 5.4 8.1 0 0 1 0 0 0 0 0 0 0 0 135
136 5.4 7.5 0 0 0 1 0 0 0 0 0 0 0 136
137 5.4 7.1 0 0 0 0 1 0 0 0 0 0 0 137
138 5.5 6.9 0 0 0 0 0 1 0 0 0 0 0 138
139 5.8 7.1 0 0 0 0 0 0 1 0 0 0 0 139
140 5.7 7.0 0 0 0 0 0 0 0 1 0 0 0 140
141 5.4 6.7 0 0 0 0 0 0 0 0 1 0 0 141
142 5.6 7.0 0 0 0 0 0 0 0 0 0 1 0 142
143 5.8 7.3 0 0 0 0 0 0 0 0 0 0 1 143
144 6.2 7.7 0 0 0 0 0 0 0 0 0 0 0 144
145 6.8 8.4 1 0 0 0 0 0 0 0 0 0 0 145
146 6.7 8.4 0 1 0 0 0 0 0 0 0 0 0 146
147 6.7 8.8 0 0 1 0 0 0 0 0 0 0 0 147
148 6.4 9.1 0 0 0 1 0 0 0 0 0 0 0 148
149 6.3 9.0 0 0 0 0 1 0 0 0 0 0 0 149
150 6.3 8.6 0 0 0 0 0 1 0 0 0 0 0 150
151 6.4 7.9 0 0 0 0 0 0 1 0 0 0 0 151
152 6.3 7.7 0 0 0 0 0 0 0 1 0 0 0 152
153 6.0 7.8 0 0 0 0 0 0 0 0 1 0 0 153
154 6.3 9.2 0 0 0 0 0 0 0 0 0 1 0 154
155 6.3 9.4 0 0 0 0 0 0 0 0 0 0 1 155
156 6.6 9.2 0 0 0 0 0 0 0 0 0 0 0 156
157 7.5 8.7 1 0 0 0 0 0 0 0 0 0 0 157
158 7.8 8.4 0 1 0 0 0 0 0 0 0 0 0 158
159 7.9 8.6 0 0 1 0 0 0 0 0 0 0 0 159
160 7.8 9.0 0 0 0 1 0 0 0 0 0 0 0 160
161 7.6 9.1 0 0 0 0 1 0 0 0 0 0 0 161
162 7.5 8.7 0 0 0 0 0 1 0 0 0 0 0 162
163 7.6 8.2 0 0 0 0 0 0 1 0 0 0 0 163
164 7.5 7.9 0 0 0 0 0 0 0 1 0 0 0 164
165 7.3 7.9 0 0 0 0 0 0 0 0 1 0 0 165
166 7.6 9.1 0 0 0 0 0 0 0 0 0 1 0 166
167 7.5 9.4 0 0 0 0 0 0 0 0 0 0 1 167
168 7.6 9.4 0 0 0 0 0 0 0 0 0 0 0 168
169 7.9 9.1 1 0 0 0 0 0 0 0 0 0 0 169
170 7.9 9.0 0 1 0 0 0 0 0 0 0 0 0 170
171 8.1 9.3 0 0 1 0 0 0 0 0 0 0 0 171
172 8.2 9.9 0 0 0 1 0 0 0 0 0 0 0 172
173 8.0 9.8 0 0 0 0 1 0 0 0 0 0 0 173
174 7.5 9.3 0 0 0 0 0 1 0 0 0 0 0 174
175 6.8 8.3 0 0 0 0 0 0 1 0 0 0 0 175
176 6.5 8.0 0 0 0 0 0 0 0 1 0 0 0 176
177 6.6 8.5 0 0 0 0 0 0 0 0 1 0 0 177
178 7.6 10.4 0 0 0 0 0 0 0 0 0 1 0 178
179 8.0 11.1 0 0 0 0 0 0 0 0 0 0 1 179
180 8.1 10.9 0 0 0 0 0 0 0 0 0 0 0 180
181 7.7 10.0 1 0 0 0 0 0 0 0 0 0 0 181
182 7.5 9.2 0 1 0 0 0 0 0 0 0 0 0 182
183 7.6 9.2 0 0 1 0 0 0 0 0 0 0 0 183
184 7.8 9.5 0 0 0 1 0 0 0 0 0 0 0 184
185 7.8 9.6 0 0 0 0 1 0 0 0 0 0 0 185
186 7.8 9.5 0 0 0 0 0 1 0 0 0 0 0 186
187 7.5 9.1 0 0 0 0 0 0 1 0 0 0 0 187
188 7.5 8.9 0 0 0 0 0 0 0 1 0 0 0 188
189 7.1 9.0 0 0 0 0 0 0 0 0 1 0 0 189
190 7.5 10.1 0 0 0 0 0 0 0 0 0 1 0 190
191 7.5 10.3 0 0 0 0 0 0 0 0 0 0 1 191
192 7.6 10.2 0 0 0 0 0 0 0 0 0 0 0 192
193 7.7 9.6 1 0 0 0 0 0 0 0 0 0 0 193
194 7.7 9.2 0 1 0 0 0 0 0 0 0 0 0 194
195 7.9 9.3 0 0 1 0 0 0 0 0 0 0 0 195
196 8.1 9.4 0 0 0 1 0 0 0 0 0 0 0 196
197 8.2 9.4 0 0 0 0 1 0 0 0 0 0 0 197
198 8.2 9.2 0 0 0 0 0 1 0 0 0 0 0 198
199 8.2 9.0 0 0 0 0 0 0 1 0 0 0 0 199
200 7.9 9.0 0 0 0 0 0 0 0 1 0 0 0 200
201 7.3 9.0 0 0 0 0 0 0 0 0 1 0 0 201
202 6.9 9.8 0 0 0 0 0 0 0 0 0 1 0 202
203 6.6 10.0 0 0 0 0 0 0 0 0 0 0 1 203
204 6.7 9.8 0 0 0 0 0 0 0 0 0 0 0 204
205 6.9 9.3 1 0 0 0 0 0 0 0 0 0 0 205
206 7.0 9.0 0 1 0 0 0 0 0 0 0 0 0 206
207 7.1 9.0 0 0 1 0 0 0 0 0 0 0 0 207
208 7.2 9.1 0 0 0 1 0 0 0 0 0 0 0 208
209 7.1 9.1 0 0 0 0 1 0 0 0 0 0 0 209
210 6.9 9.1 0 0 0 0 0 1 0 0 0 0 0 210
211 7.0 9.2 0 0 0 0 0 0 1 0 0 0 0 211
212 6.8 8.8 0 0 0 0 0 0 0 1 0 0 0 212
213 6.4 8.3 0 0 0 0 0 0 0 0 1 0 0 213
214 6.7 8.4 0 0 0 0 0 0 0 0 0 1 0 214
215 6.6 8.1 0 0 0 0 0 0 0 0 0 0 1 215
216 6.4 7.7 0 0 0 0 0 0 0 0 0 0 0 216
217 6.3 7.9 1 0 0 0 0 0 0 0 0 0 0 217
218 6.2 7.9 0 1 0 0 0 0 0 0 0 0 0 218
219 6.5 8.0 0 0 1 0 0 0 0 0 0 0 0 219
220 6.8 7.9 0 0 0 1 0 0 0 0 0 0 0 220
221 6.8 7.6 0 0 0 0 1 0 0 0 0 0 0 221
222 6.4 7.1 0 0 0 0 0 1 0 0 0 0 0 222
223 6.1 6.8 0 0 0 0 0 0 1 0 0 0 0 223
224 5.8 6.5 0 0 0 0 0 0 0 1 0 0 0 224
225 6.1 6.9 0 0 0 0 0 0 0 0 1 0 0 225
226 7.2 8.2 0 0 0 0 0 0 0 0 0 1 0 226
227 7.3 8.7 0 0 0 0 0 0 0 0 0 0 1 227
228 6.9 8.3 0 0 0 0 0 0 0 0 0 0 0 228
229 6.1 7.9 1 0 0 0 0 0 0 0 0 0 0 229
230 5.8 7.5 0 1 0 0 0 0 0 0 0 0 0 230
231 6.2 7.8 0 0 1 0 0 0 0 0 0 0 0 231
232 7.1 8.3 0 0 0 1 0 0 0 0 0 0 0 232
233 7.7 8.4 0 0 0 0 1 0 0 0 0 0 0 233
234 7.9 8.2 0 0 0 0 0 1 0 0 0 0 0 234
235 7.7 7.7 0 0 0 0 0 0 1 0 0 0 0 235
236 7.4 7.2 0 0 0 0 0 0 0 1 0 0 0 236
237 7.5 7.3 0 0 0 0 0 0 0 0 1 0 0 237
238 8.0 8.1 0 0 0 0 0 0 0 0 0 1 0 238
239 8.1 8.5 0 0 0 0 0 0 0 0 0 0 1 239
240 8.0 8.4 0 0 0 0 0 0 0 0 0 0 0 240
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
-2.95770 0.70358 0.08278 0.19870 0.24910 0.31933
M5 M6 M7 M8 M9 M10
0.36416 0.40046 0.43731 0.40047 0.23643 0.04118
M11 t
-0.06558 0.01960
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.39064 -0.30849 -0.01396 0.35619 1.55265
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.9576987 0.3710369 -7.971 7.72e-14 ***
X 0.7035804 0.0276808 25.418 < 2e-16 ***
M1 0.0827778 0.1733195 0.478 0.6334
M2 0.1987016 0.1737388 1.144 0.2540
M3 0.2491050 0.1736907 1.434 0.1529
M4 0.3193293 0.1735472 1.840 0.0671 .
M5 0.3641623 0.1737533 2.096 0.0372 *
M6 0.4004608 0.1744051 2.296 0.0226 *
M7 0.4373130 0.1753619 2.494 0.0134 *
M8 0.4004682 0.1762916 2.272 0.0241 *
M9 0.2364253 0.1762836 1.341 0.1812
M10 0.0411791 0.1735532 0.237 0.8127
M11 -0.0655824 0.1731138 -0.379 0.7052
t 0.0195966 0.0006677 29.348 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5474 on 226 degrees of freedom
Multiple R-squared: 0.8094, Adjusted R-squared: 0.7985
F-statistic: 73.84 on 13 and 226 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,] 1.037733e-43 2.075465e-43 1.0000000000
[2,] 1.066081e-57 2.132162e-57 1.0000000000
[3,] 9.982459e-07 1.996492e-06 0.9999990018
[4,] 8.863333e-08 1.772667e-07 0.9999999114
[5,] 1.064322e-07 2.128644e-07 0.9999998936
[6,] 5.572393e-08 1.114479e-07 0.9999999443
[7,] 1.415796e-07 2.831591e-07 0.9999998584
[8,] 2.106021e-07 4.212041e-07 0.9999997894
[9,] 1.288633e-06 2.577266e-06 0.9999987114
[10,] 3.113680e-07 6.227359e-07 0.9999996886
[11,] 6.474127e-08 1.294825e-07 0.9999999353
[12,] 1.505212e-08 3.010423e-08 0.9999999849
[13,] 3.474344e-09 6.948689e-09 0.9999999965
[14,] 2.453454e-09 4.906908e-09 0.9999999975
[15,] 2.274233e-09 4.548466e-09 0.9999999977
[16,] 1.061883e-09 2.123767e-09 0.9999999989
[17,] 1.176322e-09 2.352643e-09 0.9999999988
[18,] 3.289307e-10 6.578615e-10 0.9999999997
[19,] 7.440376e-11 1.488075e-10 0.9999999999
[20,] 3.075379e-11 6.150759e-11 1.0000000000
[21,] 1.620926e-11 3.241852e-11 1.0000000000
[22,] 1.027029e-11 2.054057e-11 1.0000000000
[23,] 3.827032e-12 7.654063e-12 1.0000000000
[24,] 1.793584e-12 3.587169e-12 1.0000000000
[25,] 9.610558e-13 1.922112e-12 1.0000000000
[26,] 4.378735e-13 8.757470e-13 1.0000000000
[27,] 3.458210e-13 6.916420e-13 1.0000000000
[28,] 2.591755e-13 5.183511e-13 1.0000000000
[29,] 1.510055e-13 3.020110e-13 1.0000000000
[30,] 4.348886e-14 8.697771e-14 1.0000000000
[31,] 1.064807e-14 2.129614e-14 1.0000000000
[32,] 2.927386e-15 5.854772e-15 1.0000000000
[33,] 7.946222e-16 1.589244e-15 1.0000000000
[34,] 1.900558e-16 3.801117e-16 1.0000000000
[35,] 4.715286e-17 9.430571e-17 1.0000000000
[36,] 1.110484e-17 2.220968e-17 1.0000000000
[37,] 3.518339e-18 7.036678e-18 1.0000000000
[38,] 2.277636e-18 4.555272e-18 1.0000000000
[39,] 2.320415e-18 4.640829e-18 1.0000000000
[40,] 3.257427e-18 6.514854e-18 1.0000000000
[41,] 1.230247e-17 2.460495e-17 1.0000000000
[42,] 1.932440e-17 3.864879e-17 1.0000000000
[43,] 2.159189e-17 4.318378e-17 1.0000000000
[44,] 3.666709e-16 7.333418e-16 1.0000000000
[45,] 7.045533e-15 1.409107e-14 1.0000000000
[46,] 5.701259e-14 1.140252e-13 1.0000000000
[47,] 7.523739e-13 1.504748e-12 1.0000000000
[48,] 2.370926e-12 4.741851e-12 1.0000000000
[49,] 1.443341e-11 2.886682e-11 1.0000000000
[50,] 1.308688e-10 2.617376e-10 0.9999999999
[51,] 8.002949e-10 1.600590e-09 0.9999999992
[52,] 8.214492e-09 1.642898e-08 0.9999999918
[53,] 4.266578e-08 8.533155e-08 0.9999999573
[54,] 1.846722e-07 3.693445e-07 0.9999998153
[55,] 4.652813e-07 9.305627e-07 0.9999995347
[56,] 1.357895e-06 2.715790e-06 0.9999986421
[57,] 3.173138e-06 6.346277e-06 0.9999968269
[58,] 6.321325e-06 1.264265e-05 0.9999936787
[59,] 1.043803e-05 2.087606e-05 0.9999895620
[60,] 1.791892e-05 3.583783e-05 0.9999820811
[61,] 3.642914e-05 7.285829e-05 0.9999635709
[62,] 7.013504e-05 1.402701e-04 0.9999298650
[63,] 1.492784e-04 2.985567e-04 0.9998507216
[64,] 2.718640e-04 5.437279e-04 0.9997281360
[65,] 5.942653e-04 1.188531e-03 0.9994057347
[66,] 1.119119e-03 2.238237e-03 0.9988808813
[67,] 2.046564e-03 4.093127e-03 0.9979534363
[68,] 3.029994e-03 6.059989e-03 0.9969700055
[69,] 3.952983e-03 7.905966e-03 0.9960470172
[70,] 4.383564e-03 8.767128e-03 0.9956164362
[71,] 5.267448e-03 1.053490e-02 0.9947325524
[72,] 5.412196e-03 1.082439e-02 0.9945878043
[73,] 5.537125e-03 1.107425e-02 0.9944628749
[74,] 5.497944e-03 1.099589e-02 0.9945020561
[75,] 5.283020e-03 1.056604e-02 0.9947169801
[76,] 4.742008e-03 9.484015e-03 0.9952579923
[77,] 4.213522e-03 8.427043e-03 0.9957864783
[78,] 3.437515e-03 6.875029e-03 0.9965624853
[79,] 2.844066e-03 5.688132e-03 0.9971559342
[80,] 2.377402e-03 4.754804e-03 0.9976225981
[81,] 1.950670e-03 3.901339e-03 0.9980493303
[82,] 1.607236e-03 3.214472e-03 0.9983927642
[83,] 1.315194e-03 2.630387e-03 0.9986848065
[84,] 1.072083e-03 2.144166e-03 0.9989279168
[85,] 8.701605e-04 1.740321e-03 0.9991298395
[86,] 6.962493e-04 1.392499e-03 0.9993037507
[87,] 5.393787e-04 1.078757e-03 0.9994606213
[88,] 4.315105e-04 8.630211e-04 0.9995684895
[89,] 3.219376e-04 6.438751e-04 0.9996780624
[90,] 2.303097e-04 4.606194e-04 0.9997696903
[91,] 1.617518e-04 3.235036e-04 0.9998382482
[92,] 1.236232e-04 2.472463e-04 0.9998763768
[93,] 1.094413e-04 2.188826e-04 0.9998905587
[94,] 8.823350e-05 1.764670e-04 0.9999117665
[95,] 7.614535e-05 1.522907e-04 0.9999238546
[96,] 6.926577e-05 1.385315e-04 0.9999307342
[97,] 6.106540e-05 1.221308e-04 0.9999389346
[98,] 5.702552e-05 1.140510e-04 0.9999429745
[99,] 4.968183e-05 9.936365e-05 0.9999503182
[100,] 3.963648e-05 7.927296e-05 0.9999603635
[101,] 2.984328e-05 5.968655e-05 0.9999701567
[102,] 1.995867e-05 3.991733e-05 0.9999800413
[103,] 1.320239e-05 2.640478e-05 0.9999867976
[104,] 9.869378e-06 1.973876e-05 0.9999901306
[105,] 7.694928e-06 1.538986e-05 0.9999923051
[106,] 6.321403e-06 1.264281e-05 0.9999936786
[107,] 4.478681e-06 8.957363e-06 0.9999955213
[108,] 2.994614e-06 5.989227e-06 0.9999970054
[109,] 2.083691e-06 4.167381e-06 0.9999979163
[110,] 1.354660e-06 2.709320e-06 0.9999986453
[111,] 2.032995e-06 4.065989e-06 0.9999979670
[112,] 4.424828e-06 8.849655e-06 0.9999955752
[113,] 1.236653e-05 2.473305e-05 0.9999876335
[114,] 2.039149e-05 4.078297e-05 0.9999796085
[115,] 2.368377e-05 4.736755e-05 0.9999763162
[116,] 2.996772e-05 5.993544e-05 0.9999700323
[117,] 5.120010e-05 1.024002e-04 0.9999487999
[118,] 8.331181e-05 1.666236e-04 0.9999166882
[119,] 1.197602e-04 2.395203e-04 0.9998802398
[120,] 1.190184e-04 2.380367e-04 0.9998809816
[121,] 1.135920e-04 2.271839e-04 0.9998864080
[122,] 1.154288e-04 2.308576e-04 0.9998845712
[123,] 1.296742e-04 2.593483e-04 0.9998703258
[124,] 1.391017e-04 2.782035e-04 0.9998608983
[125,] 1.403125e-04 2.806251e-04 0.9998596875
[126,] 1.412944e-04 2.825888e-04 0.9998587056
[127,] 1.359383e-04 2.718767e-04 0.9998640617
[128,] 1.341684e-04 2.683369e-04 0.9998658316
[129,] 1.394531e-04 2.789063e-04 0.9998605469
[130,] 1.129470e-04 2.258941e-04 0.9998870530
[131,] 8.674509e-05 1.734902e-04 0.9999132549
[132,] 1.617068e-04 3.234137e-04 0.9998382932
[133,] 4.002835e-04 8.005670e-04 0.9995997165
[134,] 6.249066e-04 1.249813e-03 0.9993750934
[135,] 5.733673e-04 1.146735e-03 0.9994266327
[136,] 5.201365e-04 1.040273e-03 0.9994798635
[137,] 5.464357e-04 1.092871e-03 0.9994535643
[138,] 1.297695e-03 2.595389e-03 0.9987023053
[139,] 3.490260e-03 6.980519e-03 0.9965097403
[140,] 4.747592e-03 9.495184e-03 0.9952524078
[141,] 5.722156e-03 1.144431e-02 0.9942778438
[142,] 1.298251e-02 2.596501e-02 0.9870174936
[143,] 2.160033e-02 4.320066e-02 0.9783996708
[144,] 1.996378e-02 3.992757e-02 0.9800362153
[145,] 1.601674e-02 3.203347e-02 0.9839832647
[146,] 1.302533e-02 2.605065e-02 0.9869746745
[147,] 1.390010e-02 2.780019e-02 0.9860999031
[148,] 1.749037e-02 3.498074e-02 0.9825096313
[149,] 2.092259e-02 4.184518e-02 0.9790774078
[150,] 1.841402e-02 3.682805e-02 0.9815859760
[151,] 1.484910e-02 2.969819e-02 0.9851509037
[152,] 1.232247e-02 2.464495e-02 0.9876775263
[153,] 1.994046e-02 3.988092e-02 0.9800595384
[154,] 3.239450e-02 6.478899e-02 0.9676055049
[155,] 4.724919e-02 9.449838e-02 0.9527508109
[156,] 4.474042e-02 8.948083e-02 0.9552595830
[157,] 3.809395e-02 7.618790e-02 0.9619060495
[158,] 3.213378e-02 6.426756e-02 0.9678662218
[159,] 2.721365e-02 5.442730e-02 0.9727863513
[160,] 2.354920e-02 4.709841e-02 0.9764507955
[161,] 2.147648e-02 4.295296e-02 0.9785235225
[162,] 2.151924e-02 4.303849e-02 0.9784807573
[163,] 2.131108e-02 4.262216e-02 0.9786889213
[164,] 1.937780e-02 3.875560e-02 0.9806222010
[165,] 1.924451e-02 3.848902e-02 0.9807554900
[166,] 2.107112e-02 4.214225e-02 0.9789288771
[167,] 2.233173e-02 4.466347e-02 0.9776682662
[168,] 2.013632e-02 4.027263e-02 0.9798636833
[169,] 1.671240e-02 3.342480e-02 0.9832875992
[170,] 1.380061e-02 2.760123e-02 0.9861993859
[171,] 1.131396e-02 2.262791e-02 0.9886860427
[172,] 9.722727e-03 1.944545e-02 0.9902772727
[173,] 8.255371e-03 1.651074e-02 0.9917446291
[174,] 7.598243e-03 1.519649e-02 0.9924017568
[175,] 7.172880e-03 1.434576e-02 0.9928271205
[176,] 6.372434e-03 1.274487e-02 0.9936275658
[177,] 8.788893e-03 1.757779e-02 0.9912111065
[178,] 1.756161e-02 3.512323e-02 0.9824383855
[179,] 3.588096e-02 7.176191e-02 0.9641190449
[180,] 6.048062e-02 1.209612e-01 0.9395193830
[181,] 9.519905e-02 1.903981e-01 0.9048009461
[182,] 1.897148e-01 3.794296e-01 0.8102852108
[183,] 4.494343e-01 8.988685e-01 0.5505657325
[184,] 6.960682e-01 6.078636e-01 0.3039317797
[185,] 7.606630e-01 4.786740e-01 0.2393369763
[186,] 7.738077e-01 4.523845e-01 0.2261922682
[187,] 8.622660e-01 2.754679e-01 0.1377339575
[188,] 8.940090e-01 2.119819e-01 0.1059909689
[189,] 9.011022e-01 1.977955e-01 0.0988977544
[190,] 9.540419e-01 9.191628e-02 0.0459581416
[191,] 9.829050e-01 3.419002e-02 0.0170950098
[192,] 9.859379e-01 2.812417e-02 0.0140620852
[193,] 9.800598e-01 3.988034e-02 0.0199401675
[194,] 9.741712e-01 5.165761e-02 0.0258288039
[195,] 9.664509e-01 6.709828e-02 0.0335491388
[196,] 9.554613e-01 8.907745e-02 0.0445387250
[197,] 9.607479e-01 7.850415e-02 0.0392520770
[198,] 9.589659e-01 8.206815e-02 0.0410340740
[199,] 9.329677e-01 1.340646e-01 0.0670322860
[200,] 9.065685e-01 1.868629e-01 0.0934314600
[201,] 9.157448e-01 1.685105e-01 0.0842552420
[202,] 9.155249e-01 1.689502e-01 0.0844750796
[203,] 9.660813e-01 6.783745e-02 0.0339187237
[204,] 9.944427e-01 1.111457e-02 0.0055572841
[205,] 9.992177e-01 1.564510e-03 0.0007822549
[206,] 9.995494e-01 9.012091e-04 0.0004506045
[207,] 9.993205e-01 1.359067e-03 0.0006795333
> postscript(file="/var/www/html/rcomp/tmp/1eflr1264434628.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2qjhq1264434628.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3vi3k1264434628.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/47cra1264434628.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5liiw1264434628.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 = 240
Frequency = 1
1 2 3 4 5
-0.7765663793 -0.8010126734 -0.8302965938 -0.8497595341 -0.8031150625
6 7 8 9 10
-0.7775780028 -0.8229526889 -0.7946304531 -0.6798261193 -0.6263248636
11 12 13 14 15
-0.6613082173 -0.6761291974 -0.7377875764 -0.7325919102 -0.7322338704
16 17 18 19 20
-0.7516968108 -0.6754103789 -0.6202313591 -0.5656060451 -0.5076418491
21 22 23 24 25
-0.2928375153 -0.2689782198 -0.2039615735 -0.0891405934 -0.0507989724
26 27 28 29 30
-0.1159613460 -0.0859613460 -0.0757823261 0.0005041057 -0.0146749142
31 32 33 34 35
-0.0007656799 0.0868404764 0.1312867704 0.2847880261 0.3498046724
36 37 38 39 40
0.2942676127 0.2918931541 0.2267307805 0.2863727407 0.2558356811
41 42 43 44 45
0.2914060333 0.3058689736 0.2197782080 0.2370263244 0.3407565389
46 47 48 49 50
0.4942577946 0.6185583613 0.6926632619 0.5902888033 0.6251264297
51 52 53 54 55
0.5847683899 0.6245893700 0.5601597222 0.4746226626 0.3885318969
56 57 58 59 60
0.3761380532 0.3909423870 0.5148016825 0.5798183288 0.3539232293
61 62 63 64 65
0.2922648503 0.2678185562 0.1681765960 0.2487136557 0.1546420477
66 67 68 69 70
0.0394630278 -0.0059116582 -0.1183055020 -0.0035011682 0.0500000875
71 72 73 74 75
0.1446586940 0.0187635946 -0.0132528243 -0.0376991183 -0.0373410785
76 77 78 79 80
-0.0975200984 -0.1915917063 -0.2364126865 -0.3521454123 -0.3238231765
81 82 83 84 85
-0.3386608029 -0.2444434676 -0.2497848611 -0.2349638810 -0.2559061805
86 87 88 89 90
-0.2099944347 -0.3096363949 -0.2290993353 -0.2528129034 -0.2679919233
91 92 93 94 95
-0.2837246492 -0.2257604532 -0.1813141591 -0.0278129034 -0.0331542969
96 97 98 99 100
-0.0886913566 -0.1503497356 -0.1747960297 -0.1744379899 -0.1642589700
101 102 103 104 105
-0.1583305780 -0.1438676376 -0.0892423237 -0.1312781277 -0.0164737939
106 107 108 109 110
0.0777435414 0.3427601877 0.4575811678 0.5069969081 0.4232666937
111 112 113 114 115
0.4939827732 0.5745198329 0.5804482249 0.6356272448 0.5902525588
116 117 118 119 120
0.5482167548 0.5630210886 0.4572384239 0.3629711497 0.0888662492
121 122 123 124 125
-0.0024340901 -0.0565223443 0.0845517751 0.4465209939 0.5338815451
126 127 128 129 130
0.2890605649 -0.3081043201 -0.4501401240 -0.5353357902 -0.3521925743
131 132 133 134 135
-0.2168178882 -0.2612808285 -0.3932972474 -0.3473855016 -0.2359533425
136 137 138 139 140
0.0963739161 0.3133764275 0.4981974076 0.6010325226 0.5886386789
141 142 143 144 145
0.6441590923 0.8087344673 0.8848252330 0.9182140539 0.9233333168
146 147 148 149 150
0.6878129034 0.3363807443 -0.2645143552 -0.3585859631 -0.1330489034
151 152 153 154 155
0.4030085696 0.4609727656 0.2350610199 -0.2743020427 -0.3278532373
156 157 158 159 160
0.0276838224 1.1770995627 1.5526532687 1.4419371891 0.9706840499
161 162 163 164 165
0.6358963624 0.7614334220 1.1567748155 1.2850970513 1.2295433454
166 167 168 169 170
0.8608963624 0.6369871280 0.6518081081 1.0605077689 0.9953453953
171 172 173 174 175
0.9142712759 0.5023020571 0.3082304491 0.1041255486 0.0512571410
176 177 178 179 180
-0.0204206232 -0.1277645281 -0.2889177896 -0.2942591831 -0.1387221234
181 182 183 184 185
-0.0078742239 0.2194696810 0.2494696810 0.1485745815 0.0137868940
186 187 188 189 190
0.0282498343 -0.0467668120 0.1111973840 -0.2147143617 -0.4130033049
191 192 193 194 195
-0.4665544995 -0.3813754796 0.0383983005 0.1843100462 0.2439520065
196 197 198 199 200
0.2837729866 0.3193433388 0.4041643189 0.4884315931 0.2056797095
201 202 203 204 205
-0.2498739964 -1.0370888203 -1.3906400149 -1.2351029552 -0.7856872149
206 207 208 209 210
-0.6101335089 -0.5801335089 -0.6403125288 -0.8047421765 -1.0606372760
211 212 213 214 215
-1.0874441212 -0.9887638456 -0.8925273527 -0.4872358981 -0.2889968937
216 217 218 219 220
-0.2927437544 -0.6358342926 -0.8713547060 -0.7117127458 -0.4311756861
221 222 223 224 225
-0.2845312145 -0.3886361150 -0.5340108011 -0.6056885653 -0.4426744304
226 227 228 229 230
-0.0816794532 -0.2463047671 -0.4500516279 -1.0709939273 -1.2250821816
231 232 233 234 235
-1.1061563009 -0.6477674800 -0.1825551675 0.1022658126 0.1976072061
236 237 238 239 240
0.2666455215 0.4407337757 0.5535189518 0.4592516777 0.3444306976
> postscript(file="/var/www/html/rcomp/tmp/6jkhh1264434628.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 = 240
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.7765663793 NA
1 -0.8010126734 -0.7765663793
2 -0.8302965938 -0.8010126734
3 -0.8497595341 -0.8302965938
4 -0.8031150625 -0.8497595341
5 -0.7775780028 -0.8031150625
6 -0.8229526889 -0.7775780028
7 -0.7946304531 -0.8229526889
8 -0.6798261193 -0.7946304531
9 -0.6263248636 -0.6798261193
10 -0.6613082173 -0.6263248636
11 -0.6761291974 -0.6613082173
12 -0.7377875764 -0.6761291974
13 -0.7325919102 -0.7377875764
14 -0.7322338704 -0.7325919102
15 -0.7516968108 -0.7322338704
16 -0.6754103789 -0.7516968108
17 -0.6202313591 -0.6754103789
18 -0.5656060451 -0.6202313591
19 -0.5076418491 -0.5656060451
20 -0.2928375153 -0.5076418491
21 -0.2689782198 -0.2928375153
22 -0.2039615735 -0.2689782198
23 -0.0891405934 -0.2039615735
24 -0.0507989724 -0.0891405934
25 -0.1159613460 -0.0507989724
26 -0.0859613460 -0.1159613460
27 -0.0757823261 -0.0859613460
28 0.0005041057 -0.0757823261
29 -0.0146749142 0.0005041057
30 -0.0007656799 -0.0146749142
31 0.0868404764 -0.0007656799
32 0.1312867704 0.0868404764
33 0.2847880261 0.1312867704
34 0.3498046724 0.2847880261
35 0.2942676127 0.3498046724
36 0.2918931541 0.2942676127
37 0.2267307805 0.2918931541
38 0.2863727407 0.2267307805
39 0.2558356811 0.2863727407
40 0.2914060333 0.2558356811
41 0.3058689736 0.2914060333
42 0.2197782080 0.3058689736
43 0.2370263244 0.2197782080
44 0.3407565389 0.2370263244
45 0.4942577946 0.3407565389
46 0.6185583613 0.4942577946
47 0.6926632619 0.6185583613
48 0.5902888033 0.6926632619
49 0.6251264297 0.5902888033
50 0.5847683899 0.6251264297
51 0.6245893700 0.5847683899
52 0.5601597222 0.6245893700
53 0.4746226626 0.5601597222
54 0.3885318969 0.4746226626
55 0.3761380532 0.3885318969
56 0.3909423870 0.3761380532
57 0.5148016825 0.3909423870
58 0.5798183288 0.5148016825
59 0.3539232293 0.5798183288
60 0.2922648503 0.3539232293
61 0.2678185562 0.2922648503
62 0.1681765960 0.2678185562
63 0.2487136557 0.1681765960
64 0.1546420477 0.2487136557
65 0.0394630278 0.1546420477
66 -0.0059116582 0.0394630278
67 -0.1183055020 -0.0059116582
68 -0.0035011682 -0.1183055020
69 0.0500000875 -0.0035011682
70 0.1446586940 0.0500000875
71 0.0187635946 0.1446586940
72 -0.0132528243 0.0187635946
73 -0.0376991183 -0.0132528243
74 -0.0373410785 -0.0376991183
75 -0.0975200984 -0.0373410785
76 -0.1915917063 -0.0975200984
77 -0.2364126865 -0.1915917063
78 -0.3521454123 -0.2364126865
79 -0.3238231765 -0.3521454123
80 -0.3386608029 -0.3238231765
81 -0.2444434676 -0.3386608029
82 -0.2497848611 -0.2444434676
83 -0.2349638810 -0.2497848611
84 -0.2559061805 -0.2349638810
85 -0.2099944347 -0.2559061805
86 -0.3096363949 -0.2099944347
87 -0.2290993353 -0.3096363949
88 -0.2528129034 -0.2290993353
89 -0.2679919233 -0.2528129034
90 -0.2837246492 -0.2679919233
91 -0.2257604532 -0.2837246492
92 -0.1813141591 -0.2257604532
93 -0.0278129034 -0.1813141591
94 -0.0331542969 -0.0278129034
95 -0.0886913566 -0.0331542969
96 -0.1503497356 -0.0886913566
97 -0.1747960297 -0.1503497356
98 -0.1744379899 -0.1747960297
99 -0.1642589700 -0.1744379899
100 -0.1583305780 -0.1642589700
101 -0.1438676376 -0.1583305780
102 -0.0892423237 -0.1438676376
103 -0.1312781277 -0.0892423237
104 -0.0164737939 -0.1312781277
105 0.0777435414 -0.0164737939
106 0.3427601877 0.0777435414
107 0.4575811678 0.3427601877
108 0.5069969081 0.4575811678
109 0.4232666937 0.5069969081
110 0.4939827732 0.4232666937
111 0.5745198329 0.4939827732
112 0.5804482249 0.5745198329
113 0.6356272448 0.5804482249
114 0.5902525588 0.6356272448
115 0.5482167548 0.5902525588
116 0.5630210886 0.5482167548
117 0.4572384239 0.5630210886
118 0.3629711497 0.4572384239
119 0.0888662492 0.3629711497
120 -0.0024340901 0.0888662492
121 -0.0565223443 -0.0024340901
122 0.0845517751 -0.0565223443
123 0.4465209939 0.0845517751
124 0.5338815451 0.4465209939
125 0.2890605649 0.5338815451
126 -0.3081043201 0.2890605649
127 -0.4501401240 -0.3081043201
128 -0.5353357902 -0.4501401240
129 -0.3521925743 -0.5353357902
130 -0.2168178882 -0.3521925743
131 -0.2612808285 -0.2168178882
132 -0.3932972474 -0.2612808285
133 -0.3473855016 -0.3932972474
134 -0.2359533425 -0.3473855016
135 0.0963739161 -0.2359533425
136 0.3133764275 0.0963739161
137 0.4981974076 0.3133764275
138 0.6010325226 0.4981974076
139 0.5886386789 0.6010325226
140 0.6441590923 0.5886386789
141 0.8087344673 0.6441590923
142 0.8848252330 0.8087344673
143 0.9182140539 0.8848252330
144 0.9233333168 0.9182140539
145 0.6878129034 0.9233333168
146 0.3363807443 0.6878129034
147 -0.2645143552 0.3363807443
148 -0.3585859631 -0.2645143552
149 -0.1330489034 -0.3585859631
150 0.4030085696 -0.1330489034
151 0.4609727656 0.4030085696
152 0.2350610199 0.4609727656
153 -0.2743020427 0.2350610199
154 -0.3278532373 -0.2743020427
155 0.0276838224 -0.3278532373
156 1.1770995627 0.0276838224
157 1.5526532687 1.1770995627
158 1.4419371891 1.5526532687
159 0.9706840499 1.4419371891
160 0.6358963624 0.9706840499
161 0.7614334220 0.6358963624
162 1.1567748155 0.7614334220
163 1.2850970513 1.1567748155
164 1.2295433454 1.2850970513
165 0.8608963624 1.2295433454
166 0.6369871280 0.8608963624
167 0.6518081081 0.6369871280
168 1.0605077689 0.6518081081
169 0.9953453953 1.0605077689
170 0.9142712759 0.9953453953
171 0.5023020571 0.9142712759
172 0.3082304491 0.5023020571
173 0.1041255486 0.3082304491
174 0.0512571410 0.1041255486
175 -0.0204206232 0.0512571410
176 -0.1277645281 -0.0204206232
177 -0.2889177896 -0.1277645281
178 -0.2942591831 -0.2889177896
179 -0.1387221234 -0.2942591831
180 -0.0078742239 -0.1387221234
181 0.2194696810 -0.0078742239
182 0.2494696810 0.2194696810
183 0.1485745815 0.2494696810
184 0.0137868940 0.1485745815
185 0.0282498343 0.0137868940
186 -0.0467668120 0.0282498343
187 0.1111973840 -0.0467668120
188 -0.2147143617 0.1111973840
189 -0.4130033049 -0.2147143617
190 -0.4665544995 -0.4130033049
191 -0.3813754796 -0.4665544995
192 0.0383983005 -0.3813754796
193 0.1843100462 0.0383983005
194 0.2439520065 0.1843100462
195 0.2837729866 0.2439520065
196 0.3193433388 0.2837729866
197 0.4041643189 0.3193433388
198 0.4884315931 0.4041643189
199 0.2056797095 0.4884315931
200 -0.2498739964 0.2056797095
201 -1.0370888203 -0.2498739964
202 -1.3906400149 -1.0370888203
203 -1.2351029552 -1.3906400149
204 -0.7856872149 -1.2351029552
205 -0.6101335089 -0.7856872149
206 -0.5801335089 -0.6101335089
207 -0.6403125288 -0.5801335089
208 -0.8047421765 -0.6403125288
209 -1.0606372760 -0.8047421765
210 -1.0874441212 -1.0606372760
211 -0.9887638456 -1.0874441212
212 -0.8925273527 -0.9887638456
213 -0.4872358981 -0.8925273527
214 -0.2889968937 -0.4872358981
215 -0.2927437544 -0.2889968937
216 -0.6358342926 -0.2927437544
217 -0.8713547060 -0.6358342926
218 -0.7117127458 -0.8713547060
219 -0.4311756861 -0.7117127458
220 -0.2845312145 -0.4311756861
221 -0.3886361150 -0.2845312145
222 -0.5340108011 -0.3886361150
223 -0.6056885653 -0.5340108011
224 -0.4426744304 -0.6056885653
225 -0.0816794532 -0.4426744304
226 -0.2463047671 -0.0816794532
227 -0.4500516279 -0.2463047671
228 -1.0709939273 -0.4500516279
229 -1.2250821816 -1.0709939273
230 -1.1061563009 -1.2250821816
231 -0.6477674800 -1.1061563009
232 -0.1825551675 -0.6477674800
233 0.1022658126 -0.1825551675
234 0.1976072061 0.1022658126
235 0.2666455215 0.1976072061
236 0.4407337757 0.2666455215
237 0.5535189518 0.4407337757
238 0.4592516777 0.5535189518
239 0.3444306976 0.4592516777
240 NA 0.3444306976
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.8010126734 -0.7765663793
[2,] -0.8302965938 -0.8010126734
[3,] -0.8497595341 -0.8302965938
[4,] -0.8031150625 -0.8497595341
[5,] -0.7775780028 -0.8031150625
[6,] -0.8229526889 -0.7775780028
[7,] -0.7946304531 -0.8229526889
[8,] -0.6798261193 -0.7946304531
[9,] -0.6263248636 -0.6798261193
[10,] -0.6613082173 -0.6263248636
[11,] -0.6761291974 -0.6613082173
[12,] -0.7377875764 -0.6761291974
[13,] -0.7325919102 -0.7377875764
[14,] -0.7322338704 -0.7325919102
[15,] -0.7516968108 -0.7322338704
[16,] -0.6754103789 -0.7516968108
[17,] -0.6202313591 -0.6754103789
[18,] -0.5656060451 -0.6202313591
[19,] -0.5076418491 -0.5656060451
[20,] -0.2928375153 -0.5076418491
[21,] -0.2689782198 -0.2928375153
[22,] -0.2039615735 -0.2689782198
[23,] -0.0891405934 -0.2039615735
[24,] -0.0507989724 -0.0891405934
[25,] -0.1159613460 -0.0507989724
[26,] -0.0859613460 -0.1159613460
[27,] -0.0757823261 -0.0859613460
[28,] 0.0005041057 -0.0757823261
[29,] -0.0146749142 0.0005041057
[30,] -0.0007656799 -0.0146749142
[31,] 0.0868404764 -0.0007656799
[32,] 0.1312867704 0.0868404764
[33,] 0.2847880261 0.1312867704
[34,] 0.3498046724 0.2847880261
[35,] 0.2942676127 0.3498046724
[36,] 0.2918931541 0.2942676127
[37,] 0.2267307805 0.2918931541
[38,] 0.2863727407 0.2267307805
[39,] 0.2558356811 0.2863727407
[40,] 0.2914060333 0.2558356811
[41,] 0.3058689736 0.2914060333
[42,] 0.2197782080 0.3058689736
[43,] 0.2370263244 0.2197782080
[44,] 0.3407565389 0.2370263244
[45,] 0.4942577946 0.3407565389
[46,] 0.6185583613 0.4942577946
[47,] 0.6926632619 0.6185583613
[48,] 0.5902888033 0.6926632619
[49,] 0.6251264297 0.5902888033
[50,] 0.5847683899 0.6251264297
[51,] 0.6245893700 0.5847683899
[52,] 0.5601597222 0.6245893700
[53,] 0.4746226626 0.5601597222
[54,] 0.3885318969 0.4746226626
[55,] 0.3761380532 0.3885318969
[56,] 0.3909423870 0.3761380532
[57,] 0.5148016825 0.3909423870
[58,] 0.5798183288 0.5148016825
[59,] 0.3539232293 0.5798183288
[60,] 0.2922648503 0.3539232293
[61,] 0.2678185562 0.2922648503
[62,] 0.1681765960 0.2678185562
[63,] 0.2487136557 0.1681765960
[64,] 0.1546420477 0.2487136557
[65,] 0.0394630278 0.1546420477
[66,] -0.0059116582 0.0394630278
[67,] -0.1183055020 -0.0059116582
[68,] -0.0035011682 -0.1183055020
[69,] 0.0500000875 -0.0035011682
[70,] 0.1446586940 0.0500000875
[71,] 0.0187635946 0.1446586940
[72,] -0.0132528243 0.0187635946
[73,] -0.0376991183 -0.0132528243
[74,] -0.0373410785 -0.0376991183
[75,] -0.0975200984 -0.0373410785
[76,] -0.1915917063 -0.0975200984
[77,] -0.2364126865 -0.1915917063
[78,] -0.3521454123 -0.2364126865
[79,] -0.3238231765 -0.3521454123
[80,] -0.3386608029 -0.3238231765
[81,] -0.2444434676 -0.3386608029
[82,] -0.2497848611 -0.2444434676
[83,] -0.2349638810 -0.2497848611
[84,] -0.2559061805 -0.2349638810
[85,] -0.2099944347 -0.2559061805
[86,] -0.3096363949 -0.2099944347
[87,] -0.2290993353 -0.3096363949
[88,] -0.2528129034 -0.2290993353
[89,] -0.2679919233 -0.2528129034
[90,] -0.2837246492 -0.2679919233
[91,] -0.2257604532 -0.2837246492
[92,] -0.1813141591 -0.2257604532
[93,] -0.0278129034 -0.1813141591
[94,] -0.0331542969 -0.0278129034
[95,] -0.0886913566 -0.0331542969
[96,] -0.1503497356 -0.0886913566
[97,] -0.1747960297 -0.1503497356
[98,] -0.1744379899 -0.1747960297
[99,] -0.1642589700 -0.1744379899
[100,] -0.1583305780 -0.1642589700
[101,] -0.1438676376 -0.1583305780
[102,] -0.0892423237 -0.1438676376
[103,] -0.1312781277 -0.0892423237
[104,] -0.0164737939 -0.1312781277
[105,] 0.0777435414 -0.0164737939
[106,] 0.3427601877 0.0777435414
[107,] 0.4575811678 0.3427601877
[108,] 0.5069969081 0.4575811678
[109,] 0.4232666937 0.5069969081
[110,] 0.4939827732 0.4232666937
[111,] 0.5745198329 0.4939827732
[112,] 0.5804482249 0.5745198329
[113,] 0.6356272448 0.5804482249
[114,] 0.5902525588 0.6356272448
[115,] 0.5482167548 0.5902525588
[116,] 0.5630210886 0.5482167548
[117,] 0.4572384239 0.5630210886
[118,] 0.3629711497 0.4572384239
[119,] 0.0888662492 0.3629711497
[120,] -0.0024340901 0.0888662492
[121,] -0.0565223443 -0.0024340901
[122,] 0.0845517751 -0.0565223443
[123,] 0.4465209939 0.0845517751
[124,] 0.5338815451 0.4465209939
[125,] 0.2890605649 0.5338815451
[126,] -0.3081043201 0.2890605649
[127,] -0.4501401240 -0.3081043201
[128,] -0.5353357902 -0.4501401240
[129,] -0.3521925743 -0.5353357902
[130,] -0.2168178882 -0.3521925743
[131,] -0.2612808285 -0.2168178882
[132,] -0.3932972474 -0.2612808285
[133,] -0.3473855016 -0.3932972474
[134,] -0.2359533425 -0.3473855016
[135,] 0.0963739161 -0.2359533425
[136,] 0.3133764275 0.0963739161
[137,] 0.4981974076 0.3133764275
[138,] 0.6010325226 0.4981974076
[139,] 0.5886386789 0.6010325226
[140,] 0.6441590923 0.5886386789
[141,] 0.8087344673 0.6441590923
[142,] 0.8848252330 0.8087344673
[143,] 0.9182140539 0.8848252330
[144,] 0.9233333168 0.9182140539
[145,] 0.6878129034 0.9233333168
[146,] 0.3363807443 0.6878129034
[147,] -0.2645143552 0.3363807443
[148,] -0.3585859631 -0.2645143552
[149,] -0.1330489034 -0.3585859631
[150,] 0.4030085696 -0.1330489034
[151,] 0.4609727656 0.4030085696
[152,] 0.2350610199 0.4609727656
[153,] -0.2743020427 0.2350610199
[154,] -0.3278532373 -0.2743020427
[155,] 0.0276838224 -0.3278532373
[156,] 1.1770995627 0.0276838224
[157,] 1.5526532687 1.1770995627
[158,] 1.4419371891 1.5526532687
[159,] 0.9706840499 1.4419371891
[160,] 0.6358963624 0.9706840499
[161,] 0.7614334220 0.6358963624
[162,] 1.1567748155 0.7614334220
[163,] 1.2850970513 1.1567748155
[164,] 1.2295433454 1.2850970513
[165,] 0.8608963624 1.2295433454
[166,] 0.6369871280 0.8608963624
[167,] 0.6518081081 0.6369871280
[168,] 1.0605077689 0.6518081081
[169,] 0.9953453953 1.0605077689
[170,] 0.9142712759 0.9953453953
[171,] 0.5023020571 0.9142712759
[172,] 0.3082304491 0.5023020571
[173,] 0.1041255486 0.3082304491
[174,] 0.0512571410 0.1041255486
[175,] -0.0204206232 0.0512571410
[176,] -0.1277645281 -0.0204206232
[177,] -0.2889177896 -0.1277645281
[178,] -0.2942591831 -0.2889177896
[179,] -0.1387221234 -0.2942591831
[180,] -0.0078742239 -0.1387221234
[181,] 0.2194696810 -0.0078742239
[182,] 0.2494696810 0.2194696810
[183,] 0.1485745815 0.2494696810
[184,] 0.0137868940 0.1485745815
[185,] 0.0282498343 0.0137868940
[186,] -0.0467668120 0.0282498343
[187,] 0.1111973840 -0.0467668120
[188,] -0.2147143617 0.1111973840
[189,] -0.4130033049 -0.2147143617
[190,] -0.4665544995 -0.4130033049
[191,] -0.3813754796 -0.4665544995
[192,] 0.0383983005 -0.3813754796
[193,] 0.1843100462 0.0383983005
[194,] 0.2439520065 0.1843100462
[195,] 0.2837729866 0.2439520065
[196,] 0.3193433388 0.2837729866
[197,] 0.4041643189 0.3193433388
[198,] 0.4884315931 0.4041643189
[199,] 0.2056797095 0.4884315931
[200,] -0.2498739964 0.2056797095
[201,] -1.0370888203 -0.2498739964
[202,] -1.3906400149 -1.0370888203
[203,] -1.2351029552 -1.3906400149
[204,] -0.7856872149 -1.2351029552
[205,] -0.6101335089 -0.7856872149
[206,] -0.5801335089 -0.6101335089
[207,] -0.6403125288 -0.5801335089
[208,] -0.8047421765 -0.6403125288
[209,] -1.0606372760 -0.8047421765
[210,] -1.0874441212 -1.0606372760
[211,] -0.9887638456 -1.0874441212
[212,] -0.8925273527 -0.9887638456
[213,] -0.4872358981 -0.8925273527
[214,] -0.2889968937 -0.4872358981
[215,] -0.2927437544 -0.2889968937
[216,] -0.6358342926 -0.2927437544
[217,] -0.8713547060 -0.6358342926
[218,] -0.7117127458 -0.8713547060
[219,] -0.4311756861 -0.7117127458
[220,] -0.2845312145 -0.4311756861
[221,] -0.3886361150 -0.2845312145
[222,] -0.5340108011 -0.3886361150
[223,] -0.6056885653 -0.5340108011
[224,] -0.4426744304 -0.6056885653
[225,] -0.0816794532 -0.4426744304
[226,] -0.2463047671 -0.0816794532
[227,] -0.4500516279 -0.2463047671
[228,] -1.0709939273 -0.4500516279
[229,] -1.2250821816 -1.0709939273
[230,] -1.1061563009 -1.2250821816
[231,] -0.6477674800 -1.1061563009
[232,] -0.1825551675 -0.6477674800
[233,] 0.1022658126 -0.1825551675
[234,] 0.1976072061 0.1022658126
[235,] 0.2666455215 0.1976072061
[236,] 0.4407337757 0.2666455215
[237,] 0.5535189518 0.4407337757
[238,] 0.4592516777 0.5535189518
[239,] 0.3444306976 0.4592516777
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.8010126734 -0.7765663793
2 -0.8302965938 -0.8010126734
3 -0.8497595341 -0.8302965938
4 -0.8031150625 -0.8497595341
5 -0.7775780028 -0.8031150625
6 -0.8229526889 -0.7775780028
7 -0.7946304531 -0.8229526889
8 -0.6798261193 -0.7946304531
9 -0.6263248636 -0.6798261193
10 -0.6613082173 -0.6263248636
11 -0.6761291974 -0.6613082173
12 -0.7377875764 -0.6761291974
13 -0.7325919102 -0.7377875764
14 -0.7322338704 -0.7325919102
15 -0.7516968108 -0.7322338704
16 -0.6754103789 -0.7516968108
17 -0.6202313591 -0.6754103789
18 -0.5656060451 -0.6202313591
19 -0.5076418491 -0.5656060451
20 -0.2928375153 -0.5076418491
21 -0.2689782198 -0.2928375153
22 -0.2039615735 -0.2689782198
23 -0.0891405934 -0.2039615735
24 -0.0507989724 -0.0891405934
25 -0.1159613460 -0.0507989724
26 -0.0859613460 -0.1159613460
27 -0.0757823261 -0.0859613460
28 0.0005041057 -0.0757823261
29 -0.0146749142 0.0005041057
30 -0.0007656799 -0.0146749142
31 0.0868404764 -0.0007656799
32 0.1312867704 0.0868404764
33 0.2847880261 0.1312867704
34 0.3498046724 0.2847880261
35 0.2942676127 0.3498046724
36 0.2918931541 0.2942676127
37 0.2267307805 0.2918931541
38 0.2863727407 0.2267307805
39 0.2558356811 0.2863727407
40 0.2914060333 0.2558356811
41 0.3058689736 0.2914060333
42 0.2197782080 0.3058689736
43 0.2370263244 0.2197782080
44 0.3407565389 0.2370263244
45 0.4942577946 0.3407565389
46 0.6185583613 0.4942577946
47 0.6926632619 0.6185583613
48 0.5902888033 0.6926632619
49 0.6251264297 0.5902888033
50 0.5847683899 0.6251264297
51 0.6245893700 0.5847683899
52 0.5601597222 0.6245893700
53 0.4746226626 0.5601597222
54 0.3885318969 0.4746226626
55 0.3761380532 0.3885318969
56 0.3909423870 0.3761380532
57 0.5148016825 0.3909423870
58 0.5798183288 0.5148016825
59 0.3539232293 0.5798183288
60 0.2922648503 0.3539232293
61 0.2678185562 0.2922648503
62 0.1681765960 0.2678185562
63 0.2487136557 0.1681765960
64 0.1546420477 0.2487136557
65 0.0394630278 0.1546420477
66 -0.0059116582 0.0394630278
67 -0.1183055020 -0.0059116582
68 -0.0035011682 -0.1183055020
69 0.0500000875 -0.0035011682
70 0.1446586940 0.0500000875
71 0.0187635946 0.1446586940
72 -0.0132528243 0.0187635946
73 -0.0376991183 -0.0132528243
74 -0.0373410785 -0.0376991183
75 -0.0975200984 -0.0373410785
76 -0.1915917063 -0.0975200984
77 -0.2364126865 -0.1915917063
78 -0.3521454123 -0.2364126865
79 -0.3238231765 -0.3521454123
80 -0.3386608029 -0.3238231765
81 -0.2444434676 -0.3386608029
82 -0.2497848611 -0.2444434676
83 -0.2349638810 -0.2497848611
84 -0.2559061805 -0.2349638810
85 -0.2099944347 -0.2559061805
86 -0.3096363949 -0.2099944347
87 -0.2290993353 -0.3096363949
88 -0.2528129034 -0.2290993353
89 -0.2679919233 -0.2528129034
90 -0.2837246492 -0.2679919233
91 -0.2257604532 -0.2837246492
92 -0.1813141591 -0.2257604532
93 -0.0278129034 -0.1813141591
94 -0.0331542969 -0.0278129034
95 -0.0886913566 -0.0331542969
96 -0.1503497356 -0.0886913566
97 -0.1747960297 -0.1503497356
98 -0.1744379899 -0.1747960297
99 -0.1642589700 -0.1744379899
100 -0.1583305780 -0.1642589700
101 -0.1438676376 -0.1583305780
102 -0.0892423237 -0.1438676376
103 -0.1312781277 -0.0892423237
104 -0.0164737939 -0.1312781277
105 0.0777435414 -0.0164737939
106 0.3427601877 0.0777435414
107 0.4575811678 0.3427601877
108 0.5069969081 0.4575811678
109 0.4232666937 0.5069969081
110 0.4939827732 0.4232666937
111 0.5745198329 0.4939827732
112 0.5804482249 0.5745198329
113 0.6356272448 0.5804482249
114 0.5902525588 0.6356272448
115 0.5482167548 0.5902525588
116 0.5630210886 0.5482167548
117 0.4572384239 0.5630210886
118 0.3629711497 0.4572384239
119 0.0888662492 0.3629711497
120 -0.0024340901 0.0888662492
121 -0.0565223443 -0.0024340901
122 0.0845517751 -0.0565223443
123 0.4465209939 0.0845517751
124 0.5338815451 0.4465209939
125 0.2890605649 0.5338815451
126 -0.3081043201 0.2890605649
127 -0.4501401240 -0.3081043201
128 -0.5353357902 -0.4501401240
129 -0.3521925743 -0.5353357902
130 -0.2168178882 -0.3521925743
131 -0.2612808285 -0.2168178882
132 -0.3932972474 -0.2612808285
133 -0.3473855016 -0.3932972474
134 -0.2359533425 -0.3473855016
135 0.0963739161 -0.2359533425
136 0.3133764275 0.0963739161
137 0.4981974076 0.3133764275
138 0.6010325226 0.4981974076
139 0.5886386789 0.6010325226
140 0.6441590923 0.5886386789
141 0.8087344673 0.6441590923
142 0.8848252330 0.8087344673
143 0.9182140539 0.8848252330
144 0.9233333168 0.9182140539
145 0.6878129034 0.9233333168
146 0.3363807443 0.6878129034
147 -0.2645143552 0.3363807443
148 -0.3585859631 -0.2645143552
149 -0.1330489034 -0.3585859631
150 0.4030085696 -0.1330489034
151 0.4609727656 0.4030085696
152 0.2350610199 0.4609727656
153 -0.2743020427 0.2350610199
154 -0.3278532373 -0.2743020427
155 0.0276838224 -0.3278532373
156 1.1770995627 0.0276838224
157 1.5526532687 1.1770995627
158 1.4419371891 1.5526532687
159 0.9706840499 1.4419371891
160 0.6358963624 0.9706840499
161 0.7614334220 0.6358963624
162 1.1567748155 0.7614334220
163 1.2850970513 1.1567748155
164 1.2295433454 1.2850970513
165 0.8608963624 1.2295433454
166 0.6369871280 0.8608963624
167 0.6518081081 0.6369871280
168 1.0605077689 0.6518081081
169 0.9953453953 1.0605077689
170 0.9142712759 0.9953453953
171 0.5023020571 0.9142712759
172 0.3082304491 0.5023020571
173 0.1041255486 0.3082304491
174 0.0512571410 0.1041255486
175 -0.0204206232 0.0512571410
176 -0.1277645281 -0.0204206232
177 -0.2889177896 -0.1277645281
178 -0.2942591831 -0.2889177896
179 -0.1387221234 -0.2942591831
180 -0.0078742239 -0.1387221234
181 0.2194696810 -0.0078742239
182 0.2494696810 0.2194696810
183 0.1485745815 0.2494696810
184 0.0137868940 0.1485745815
185 0.0282498343 0.0137868940
186 -0.0467668120 0.0282498343
187 0.1111973840 -0.0467668120
188 -0.2147143617 0.1111973840
189 -0.4130033049 -0.2147143617
190 -0.4665544995 -0.4130033049
191 -0.3813754796 -0.4665544995
192 0.0383983005 -0.3813754796
193 0.1843100462 0.0383983005
194 0.2439520065 0.1843100462
195 0.2837729866 0.2439520065
196 0.3193433388 0.2837729866
197 0.4041643189 0.3193433388
198 0.4884315931 0.4041643189
199 0.2056797095 0.4884315931
200 -0.2498739964 0.2056797095
201 -1.0370888203 -0.2498739964
202 -1.3906400149 -1.0370888203
203 -1.2351029552 -1.3906400149
204 -0.7856872149 -1.2351029552
205 -0.6101335089 -0.7856872149
206 -0.5801335089 -0.6101335089
207 -0.6403125288 -0.5801335089
208 -0.8047421765 -0.6403125288
209 -1.0606372760 -0.8047421765
210 -1.0874441212 -1.0606372760
211 -0.9887638456 -1.0874441212
212 -0.8925273527 -0.9887638456
213 -0.4872358981 -0.8925273527
214 -0.2889968937 -0.4872358981
215 -0.2927437544 -0.2889968937
216 -0.6358342926 -0.2927437544
217 -0.8713547060 -0.6358342926
218 -0.7117127458 -0.8713547060
219 -0.4311756861 -0.7117127458
220 -0.2845312145 -0.4311756861
221 -0.3886361150 -0.2845312145
222 -0.5340108011 -0.3886361150
223 -0.6056885653 -0.5340108011
224 -0.4426744304 -0.6056885653
225 -0.0816794532 -0.4426744304
226 -0.2463047671 -0.0816794532
227 -0.4500516279 -0.2463047671
228 -1.0709939273 -0.4500516279
229 -1.2250821816 -1.0709939273
230 -1.1061563009 -1.2250821816
231 -0.6477674800 -1.1061563009
232 -0.1825551675 -0.6477674800
233 0.1022658126 -0.1825551675
234 0.1976072061 0.1022658126
235 0.2666455215 0.1976072061
236 0.4407337757 0.2666455215
237 0.5535189518 0.4407337757
238 0.4592516777 0.5535189518
239 0.3444306976 0.4592516777
> 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/7vkya1264434628.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8vqj51264434628.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/99wes1264434628.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/109tkd1264434628.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11mpde1264434628.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/12kztt1264434628.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/13m7661264434628.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/143a111264434628.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/15i7h71264434628.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/16t8q11264434628.tab")
+ }
>
> try(system("convert tmp/1eflr1264434628.ps tmp/1eflr1264434628.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qjhq1264434628.ps tmp/2qjhq1264434628.png",intern=TRUE))
character(0)
> try(system("convert tmp/3vi3k1264434628.ps tmp/3vi3k1264434628.png",intern=TRUE))
character(0)
> try(system("convert tmp/47cra1264434628.ps tmp/47cra1264434628.png",intern=TRUE))
character(0)
> try(system("convert tmp/5liiw1264434628.ps tmp/5liiw1264434628.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jkhh1264434628.ps tmp/6jkhh1264434628.png",intern=TRUE))
character(0)
> try(system("convert tmp/7vkya1264434628.ps tmp/7vkya1264434628.png",intern=TRUE))
character(0)
> try(system("convert tmp/8vqj51264434628.ps tmp/8vqj51264434628.png",intern=TRUE))
character(0)
> try(system("convert tmp/99wes1264434628.ps tmp/99wes1264434628.png",intern=TRUE))
character(0)
> try(system("convert tmp/109tkd1264434628.ps tmp/109tkd1264434628.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.151 1.816 7.625