R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(6.9
+ ,2.28
+ ,6.8
+ ,2.26
+ ,6.7
+ ,2.71
+ ,6.6
+ ,2.77
+ ,6.5
+ ,2.77
+ ,6.5
+ ,2.64
+ ,7.0
+ ,2.56
+ ,7.5
+ ,2.07
+ ,7.6
+ ,2.32
+ ,7.6
+ ,2.16
+ ,7.6
+ ,2.23
+ ,7.8
+ ,2.40
+ ,8.0
+ ,2.84
+ ,8.0
+ ,2.77
+ ,8.0
+ ,2.93
+ ,7.9
+ ,2.91
+ ,7.9
+ ,2.69
+ ,8.0
+ ,2.38
+ ,8.5
+ ,2.58
+ ,9.2
+ ,3.19
+ ,9.4
+ ,2.82
+ ,9.5
+ ,2.72
+ ,9.5
+ ,2.53
+ ,9.6
+ ,2.70
+ ,9.7
+ ,2.42
+ ,9.7
+ ,2.50
+ ,9.6
+ ,2.31
+ ,9.5
+ ,2.41
+ ,9.4
+ ,2.56
+ ,9.3
+ ,2.76
+ ,9.6
+ ,2.71
+ ,10.2
+ ,2.44
+ ,10.2
+ ,2.46
+ ,10.1
+ ,2.12
+ ,9.9
+ ,1.99
+ ,9.8
+ ,1.86
+ ,9.8
+ ,1.88
+ ,9.7
+ ,1.82
+ ,9.5
+ ,1.74
+ ,9.3
+ ,1.71
+ ,9.1
+ ,1.38
+ ,9.0
+ ,1.27
+ ,9.5
+ ,1.19
+ ,10.0
+ ,1.28
+ ,10.2
+ ,1.19
+ ,10.1
+ ,1.22
+ ,10.0
+ ,1.47
+ ,9.9
+ ,1.46
+ ,10.0
+ ,1.96
+ ,9.9
+ ,1.88
+ ,9.7
+ ,2.03
+ ,9.5
+ ,2.04
+ ,9.2
+ ,1.90
+ ,9.0
+ ,1.80
+ ,9.3
+ ,1.92
+ ,9.8
+ ,1.92
+ ,9.8
+ ,1.97
+ ,9.6
+ ,2.46
+ ,9.4
+ ,2.36
+ ,9.3
+ ,2.53
+ ,9.2
+ ,2.31
+ ,9.2
+ ,1.98
+ ,9.0
+ ,1.46
+ ,8.8
+ ,1.26
+ ,8.7
+ ,1.58
+ ,8.7
+ ,1.74
+ ,9.1
+ ,1.89
+ ,9.7
+ ,1.85
+ ,9.8
+ ,1.62
+ ,9.6
+ ,1.30
+ ,9.4
+ ,1.42
+ ,9.4
+ ,1.15
+ ,9.5
+ ,0.42
+ ,9.4
+ ,0.74
+ ,9.3
+ ,1.02
+ ,9.2
+ ,1.51
+ ,9.0
+ ,1.86
+ ,8.9
+ ,1.59
+ ,9.2
+ ,1.03
+ ,9.8
+ ,0.44
+ ,9.9
+ ,0.82
+ ,9.6
+ ,0.86
+ ,9.2
+ ,0.58
+ ,9.1
+ ,0.59
+ ,9.1
+ ,0.95
+ ,9.0
+ ,0.98
+ ,8.9
+ ,1.23
+ ,8.7
+ ,1.17
+ ,8.5
+ ,0.84
+ ,8.3
+ ,0.74
+ ,8.5
+ ,0.65
+ ,8.7
+ ,0.91
+ ,8.4
+ ,1.19
+ ,8.1
+ ,1.30
+ ,7.8
+ ,1.53
+ ,7.7
+ ,1.94
+ ,7.5
+ ,1.79
+ ,7.2
+ ,1.95
+ ,6.8
+ ,2.26
+ ,6.7
+ ,2.04
+ ,6.4
+ ,2.16
+ ,6.3
+ ,2.75
+ ,6.8
+ ,2.79
+ ,7.3
+ ,2.88
+ ,7.1
+ ,3.36
+ ,7.0
+ ,2.97
+ ,6.8
+ ,3.10
+ ,6.6
+ ,2.49
+ ,6.3
+ ,2.20
+ ,6.1
+ ,2.25
+ ,6.1
+ ,2.09
+ ,6.3
+ ,2.79
+ ,6.3
+ ,3.14
+ ,6.0
+ ,2.93
+ ,6.2
+ ,2.65
+ ,6.4
+ ,2.67
+ ,6.8
+ ,2.26
+ ,7.5
+ ,2.35
+ ,7.5
+ ,2.13
+ ,7.6
+ ,2.18
+ ,7.6
+ ,2.90
+ ,7.4
+ ,2.63
+ ,7.3
+ ,2.67
+ ,7.1
+ ,1.81
+ ,6.9
+ ,1.33
+ ,6.8
+ ,0.88
+ ,7.5
+ ,1.28
+ ,7.6
+ ,1.26
+ ,7.8
+ ,1.26
+ ,8.0
+ ,1.29
+ ,8.1
+ ,1.10
+ ,8.2
+ ,1.37
+ ,8.3
+ ,1.21
+ ,8.2
+ ,1.74
+ ,8.0
+ ,1.76
+ ,7.9
+ ,1.48
+ ,7.6
+ ,1.04
+ ,7.6
+ ,1.62
+ ,8.3
+ ,1.49
+ ,8.4
+ ,1.79
+ ,8.4
+ ,1.80
+ ,8.4
+ ,1.58
+ ,8.4
+ ,1.86
+ ,8.6
+ ,1.74
+ ,8.9
+ ,1.59
+ ,8.8
+ ,1.26
+ ,8.3
+ ,1.13
+ ,7.5
+ ,1.92
+ ,7.2
+ ,2.61
+ ,7.4
+ ,2.26
+ ,8.8
+ ,2.41
+ ,9.3
+ ,2.26
+ ,9.3
+ ,2.03
+ ,8.7
+ ,2.86
+ ,8.2
+ ,2.55
+ ,8.3
+ ,2.27
+ ,8.5
+ ,2.26
+ ,8.6
+ ,2.57
+ ,8.5
+ ,3.07
+ ,8.2
+ ,2.76
+ ,8.1
+ ,2.51
+ ,7.9
+ ,2.87
+ ,8.6
+ ,3.14
+ ,8.7
+ ,3.11
+ ,8.7
+ ,3.16
+ ,8.5
+ ,2.47
+ ,8.4
+ ,2.57
+ ,8.5
+ ,2.89
+ ,8.7
+ ,2.63
+ ,8.7
+ ,2.38
+ ,8.6
+ ,1.69
+ ,8.5
+ ,1.96
+ ,8.3
+ ,2.19
+ ,8.0
+ ,1.87
+ ,8.2
+ ,1.6
+ ,8.1
+ ,1.63
+ ,8.1
+ ,1.22
+ ,8.0
+ ,1.21
+ ,7.9
+ ,1.49
+ ,7.9
+ ,1.64)
+ ,dim=c(2
+ ,180)
+ ,dimnames=list(c('Y'
+ ,'X')
+ ,1:180))
> y <- array(NA,dim=c(2,180),dimnames=list(c('Y','X'),1:180))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 6.9 2.28 1 0 0 0 0 0 0 0 0 0 0
2 6.8 2.26 0 1 0 0 0 0 0 0 0 0 0
3 6.7 2.71 0 0 1 0 0 0 0 0 0 0 0
4 6.6 2.77 0 0 0 1 0 0 0 0 0 0 0
5 6.5 2.77 0 0 0 0 1 0 0 0 0 0 0
6 6.5 2.64 0 0 0 0 0 1 0 0 0 0 0
7 7.0 2.56 0 0 0 0 0 0 1 0 0 0 0
8 7.5 2.07 0 0 0 0 0 0 0 1 0 0 0
9 7.6 2.32 0 0 0 0 0 0 0 0 1 0 0
10 7.6 2.16 0 0 0 0 0 0 0 0 0 1 0
11 7.6 2.23 0 0 0 0 0 0 0 0 0 0 1
12 7.8 2.40 0 0 0 0 0 0 0 0 0 0 0
13 8.0 2.84 1 0 0 0 0 0 0 0 0 0 0
14 8.0 2.77 0 1 0 0 0 0 0 0 0 0 0
15 8.0 2.93 0 0 1 0 0 0 0 0 0 0 0
16 7.9 2.91 0 0 0 1 0 0 0 0 0 0 0
17 7.9 2.69 0 0 0 0 1 0 0 0 0 0 0
18 8.0 2.38 0 0 0 0 0 1 0 0 0 0 0
19 8.5 2.58 0 0 0 0 0 0 1 0 0 0 0
20 9.2 3.19 0 0 0 0 0 0 0 1 0 0 0
21 9.4 2.82 0 0 0 0 0 0 0 0 1 0 0
22 9.5 2.72 0 0 0 0 0 0 0 0 0 1 0
23 9.5 2.53 0 0 0 0 0 0 0 0 0 0 1
24 9.6 2.70 0 0 0 0 0 0 0 0 0 0 0
25 9.7 2.42 1 0 0 0 0 0 0 0 0 0 0
26 9.7 2.50 0 1 0 0 0 0 0 0 0 0 0
27 9.6 2.31 0 0 1 0 0 0 0 0 0 0 0
28 9.5 2.41 0 0 0 1 0 0 0 0 0 0 0
29 9.4 2.56 0 0 0 0 1 0 0 0 0 0 0
30 9.3 2.76 0 0 0 0 0 1 0 0 0 0 0
31 9.6 2.71 0 0 0 0 0 0 1 0 0 0 0
32 10.2 2.44 0 0 0 0 0 0 0 1 0 0 0
33 10.2 2.46 0 0 0 0 0 0 0 0 1 0 0
34 10.1 2.12 0 0 0 0 0 0 0 0 0 1 0
35 9.9 1.99 0 0 0 0 0 0 0 0 0 0 1
36 9.8 1.86 0 0 0 0 0 0 0 0 0 0 0
37 9.8 1.88 1 0 0 0 0 0 0 0 0 0 0
38 9.7 1.82 0 1 0 0 0 0 0 0 0 0 0
39 9.5 1.74 0 0 1 0 0 0 0 0 0 0 0
40 9.3 1.71 0 0 0 1 0 0 0 0 0 0 0
41 9.1 1.38 0 0 0 0 1 0 0 0 0 0 0
42 9.0 1.27 0 0 0 0 0 1 0 0 0 0 0
43 9.5 1.19 0 0 0 0 0 0 1 0 0 0 0
44 10.0 1.28 0 0 0 0 0 0 0 1 0 0 0
45 10.2 1.19 0 0 0 0 0 0 0 0 1 0 0
46 10.1 1.22 0 0 0 0 0 0 0 0 0 1 0
47 10.0 1.47 0 0 0 0 0 0 0 0 0 0 1
48 9.9 1.46 0 0 0 0 0 0 0 0 0 0 0
49 10.0 1.96 1 0 0 0 0 0 0 0 0 0 0
50 9.9 1.88 0 1 0 0 0 0 0 0 0 0 0
51 9.7 2.03 0 0 1 0 0 0 0 0 0 0 0
52 9.5 2.04 0 0 0 1 0 0 0 0 0 0 0
53 9.2 1.90 0 0 0 0 1 0 0 0 0 0 0
54 9.0 1.80 0 0 0 0 0 1 0 0 0 0 0
55 9.3 1.92 0 0 0 0 0 0 1 0 0 0 0
56 9.8 1.92 0 0 0 0 0 0 0 1 0 0 0
57 9.8 1.97 0 0 0 0 0 0 0 0 1 0 0
58 9.6 2.46 0 0 0 0 0 0 0 0 0 1 0
59 9.4 2.36 0 0 0 0 0 0 0 0 0 0 1
60 9.3 2.53 0 0 0 0 0 0 0 0 0 0 0
61 9.2 2.31 1 0 0 0 0 0 0 0 0 0 0
62 9.2 1.98 0 1 0 0 0 0 0 0 0 0 0
63 9.0 1.46 0 0 1 0 0 0 0 0 0 0 0
64 8.8 1.26 0 0 0 1 0 0 0 0 0 0 0
65 8.7 1.58 0 0 0 0 1 0 0 0 0 0 0
66 8.7 1.74 0 0 0 0 0 1 0 0 0 0 0
67 9.1 1.89 0 0 0 0 0 0 1 0 0 0 0
68 9.7 1.85 0 0 0 0 0 0 0 1 0 0 0
69 9.8 1.62 0 0 0 0 0 0 0 0 1 0 0
70 9.6 1.30 0 0 0 0 0 0 0 0 0 1 0
71 9.4 1.42 0 0 0 0 0 0 0 0 0 0 1
72 9.4 1.15 0 0 0 0 0 0 0 0 0 0 0
73 9.5 0.42 1 0 0 0 0 0 0 0 0 0 0
74 9.4 0.74 0 1 0 0 0 0 0 0 0 0 0
75 9.3 1.02 0 0 1 0 0 0 0 0 0 0 0
76 9.2 1.51 0 0 0 1 0 0 0 0 0 0 0
77 9.0 1.86 0 0 0 0 1 0 0 0 0 0 0
78 8.9 1.59 0 0 0 0 0 1 0 0 0 0 0
79 9.2 1.03 0 0 0 0 0 0 1 0 0 0 0
80 9.8 0.44 0 0 0 0 0 0 0 1 0 0 0
81 9.9 0.82 0 0 0 0 0 0 0 0 1 0 0
82 9.6 0.86 0 0 0 0 0 0 0 0 0 1 0
83 9.2 0.58 0 0 0 0 0 0 0 0 0 0 1
84 9.1 0.59 0 0 0 0 0 0 0 0 0 0 0
85 9.1 0.95 1 0 0 0 0 0 0 0 0 0 0
86 9.0 0.98 0 1 0 0 0 0 0 0 0 0 0
87 8.9 1.23 0 0 1 0 0 0 0 0 0 0 0
88 8.7 1.17 0 0 0 1 0 0 0 0 0 0 0
89 8.5 0.84 0 0 0 0 1 0 0 0 0 0 0
90 8.3 0.74 0 0 0 0 0 1 0 0 0 0 0
91 8.5 0.65 0 0 0 0 0 0 1 0 0 0 0
92 8.7 0.91 0 0 0 0 0 0 0 1 0 0 0
93 8.4 1.19 0 0 0 0 0 0 0 0 1 0 0
94 8.1 1.30 0 0 0 0 0 0 0 0 0 1 0
95 7.8 1.53 0 0 0 0 0 0 0 0 0 0 1
96 7.7 1.94 0 0 0 0 0 0 0 0 0 0 0
97 7.5 1.79 1 0 0 0 0 0 0 0 0 0 0
98 7.2 1.95 0 1 0 0 0 0 0 0 0 0 0
99 6.8 2.26 0 0 1 0 0 0 0 0 0 0 0
100 6.7 2.04 0 0 0 1 0 0 0 0 0 0 0
101 6.4 2.16 0 0 0 0 1 0 0 0 0 0 0
102 6.3 2.75 0 0 0 0 0 1 0 0 0 0 0
103 6.8 2.79 0 0 0 0 0 0 1 0 0 0 0
104 7.3 2.88 0 0 0 0 0 0 0 1 0 0 0
105 7.1 3.36 0 0 0 0 0 0 0 0 1 0 0
106 7.0 2.97 0 0 0 0 0 0 0 0 0 1 0
107 6.8 3.10 0 0 0 0 0 0 0 0 0 0 1
108 6.6 2.49 0 0 0 0 0 0 0 0 0 0 0
109 6.3 2.20 1 0 0 0 0 0 0 0 0 0 0
110 6.1 2.25 0 1 0 0 0 0 0 0 0 0 0
111 6.1 2.09 0 0 1 0 0 0 0 0 0 0 0
112 6.3 2.79 0 0 0 1 0 0 0 0 0 0 0
113 6.3 3.14 0 0 0 0 1 0 0 0 0 0 0
114 6.0 2.93 0 0 0 0 0 1 0 0 0 0 0
115 6.2 2.65 0 0 0 0 0 0 1 0 0 0 0
116 6.4 2.67 0 0 0 0 0 0 0 1 0 0 0
117 6.8 2.26 0 0 0 0 0 0 0 0 1 0 0
118 7.5 2.35 0 0 0 0 0 0 0 0 0 1 0
119 7.5 2.13 0 0 0 0 0 0 0 0 0 0 1
120 7.6 2.18 0 0 0 0 0 0 0 0 0 0 0
121 7.6 2.90 1 0 0 0 0 0 0 0 0 0 0
122 7.4 2.63 0 1 0 0 0 0 0 0 0 0 0
123 7.3 2.67 0 0 1 0 0 0 0 0 0 0 0
124 7.1 1.81 0 0 0 1 0 0 0 0 0 0 0
125 6.9 1.33 0 0 0 0 1 0 0 0 0 0 0
126 6.8 0.88 0 0 0 0 0 1 0 0 0 0 0
127 7.5 1.28 0 0 0 0 0 0 1 0 0 0 0
128 7.6 1.26 0 0 0 0 0 0 0 1 0 0 0
129 7.8 1.26 0 0 0 0 0 0 0 0 1 0 0
130 8.0 1.29 0 0 0 0 0 0 0 0 0 1 0
131 8.1 1.10 0 0 0 0 0 0 0 0 0 0 1
132 8.2 1.37 0 0 0 0 0 0 0 0 0 0 0
133 8.3 1.21 1 0 0 0 0 0 0 0 0 0 0
134 8.2 1.74 0 1 0 0 0 0 0 0 0 0 0
135 8.0 1.76 0 0 1 0 0 0 0 0 0 0 0
136 7.9 1.48 0 0 0 1 0 0 0 0 0 0 0
137 7.6 1.04 0 0 0 0 1 0 0 0 0 0 0
138 7.6 1.62 0 0 0 0 0 1 0 0 0 0 0
139 8.3 1.49 0 0 0 0 0 0 1 0 0 0 0
140 8.4 1.79 0 0 0 0 0 0 0 1 0 0 0
141 8.4 1.80 0 0 0 0 0 0 0 0 1 0 0
142 8.4 1.58 0 0 0 0 0 0 0 0 0 1 0
143 8.4 1.86 0 0 0 0 0 0 0 0 0 0 1
144 8.6 1.74 0 0 0 0 0 0 0 0 0 0 0
145 8.9 1.59 1 0 0 0 0 0 0 0 0 0 0
146 8.8 1.26 0 1 0 0 0 0 0 0 0 0 0
147 8.3 1.13 0 0 1 0 0 0 0 0 0 0 0
148 7.5 1.92 0 0 0 1 0 0 0 0 0 0 0
149 7.2 2.61 0 0 0 0 1 0 0 0 0 0 0
150 7.4 2.26 0 0 0 0 0 1 0 0 0 0 0
151 8.8 2.41 0 0 0 0 0 0 1 0 0 0 0
152 9.3 2.26 0 0 0 0 0 0 0 1 0 0 0
153 9.3 2.03 0 0 0 0 0 0 0 0 1 0 0
154 8.7 2.86 0 0 0 0 0 0 0 0 0 1 0
155 8.2 2.55 0 0 0 0 0 0 0 0 0 0 1
156 8.3 2.27 0 0 0 0 0 0 0 0 0 0 0
157 8.5 2.26 1 0 0 0 0 0 0 0 0 0 0
158 8.6 2.57 0 1 0 0 0 0 0 0 0 0 0
159 8.5 3.07 0 0 1 0 0 0 0 0 0 0 0
160 8.2 2.76 0 0 0 1 0 0 0 0 0 0 0
161 8.1 2.51 0 0 0 0 1 0 0 0 0 0 0
162 7.9 2.87 0 0 0 0 0 1 0 0 0 0 0
163 8.6 3.14 0 0 0 0 0 0 1 0 0 0 0
164 8.7 3.11 0 0 0 0 0 0 0 1 0 0 0
165 8.7 3.16 0 0 0 0 0 0 0 0 1 0 0
166 8.5 2.47 0 0 0 0 0 0 0 0 0 1 0
167 8.4 2.57 0 0 0 0 0 0 0 0 0 0 1
168 8.5 2.89 0 0 0 0 0 0 0 0 0 0 0
169 8.7 2.63 1 0 0 0 0 0 0 0 0 0 0
170 8.7 2.38 0 1 0 0 0 0 0 0 0 0 0
171 8.6 1.69 0 0 1 0 0 0 0 0 0 0 0
172 8.5 1.96 0 0 0 1 0 0 0 0 0 0 0
173 8.3 2.19 0 0 0 0 1 0 0 0 0 0 0
174 8.0 1.87 0 0 0 0 0 1 0 0 0 0 0
175 8.2 1.60 0 0 0 0 0 0 1 0 0 0 0
176 8.1 1.63 0 0 0 0 0 0 0 1 0 0 0
177 8.1 1.22 0 0 0 0 0 0 0 0 1 0 0
178 8.0 1.21 0 0 0 0 0 0 0 0 0 1 0
179 7.9 1.49 0 0 0 0 0 0 0 0 0 0 1
180 7.9 1.64 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
9.513443 -0.493038 -0.005866 -0.090232 -0.237413 -0.396284
M5 M6 M7 M8 M9 M10
-0.568960 -0.677413 -0.190982 0.176106 0.222208 0.122158
M11
-0.023194
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.2139 -0.8560 0.1319 0.8261 1.8248
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.513443 0.343332 27.709 < 2e-16 ***
X -0.493038 0.113454 -4.346 2.40e-05 ***
M1 -0.005866 0.371676 -0.016 0.9874
M2 -0.090232 0.371681 -0.243 0.8085
M3 -0.237413 0.371723 -0.639 0.5239
M4 -0.396284 0.371798 -1.066 0.2880
M5 -0.568960 0.371802 -1.530 0.1278
M6 -0.677413 0.371723 -1.822 0.0702 .
M7 -0.190982 0.371697 -0.514 0.6081
M8 0.176106 0.371680 0.474 0.6363
M9 0.222208 0.371667 0.598 0.5507
M10 0.122158 0.371671 0.329 0.7428
M11 -0.023194 0.371669 -0.062 0.9503
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.018 on 167 degrees of freedom
Multiple R-squared: 0.1662, Adjusted R-squared: 0.1063
F-statistic: 2.773 on 12 and 167 DF, p-value: 0.001813
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.1380592 2.761184e-01 8.619408e-01
[2,] 0.2597267 5.194533e-01 7.402733e-01
[3,] 0.4388295 8.776590e-01 5.611705e-01
[4,] 0.4446648 8.893296e-01 5.553352e-01
[5,] 0.3314444 6.628887e-01 6.685556e-01
[6,] 0.2816273 5.632546e-01 7.183727e-01
[7,] 0.2312107 4.624215e-01 7.687893e-01
[8,] 0.2287347 4.574695e-01 7.712653e-01
[9,] 0.2149755 4.299510e-01 7.850245e-01
[10,] 0.4963407 9.926813e-01 5.036593e-01
[11,] 0.6763258 6.473483e-01 3.236742e-01
[12,] 0.8916716 2.166568e-01 1.083284e-01
[13,] 0.9547729 9.045420e-02 4.522710e-02
[14,] 0.9756978 4.860437e-02 2.430219e-02
[15,] 0.9827397 3.452065e-02 1.726033e-02
[16,] 0.9864220 2.715608e-02 1.357804e-02
[17,] 0.9915438 1.691234e-02 8.456169e-03
[18,] 0.9939960 1.200799e-02 6.003996e-03
[19,] 0.9954990 9.001945e-03 4.500972e-03
[20,] 0.9960717 7.856532e-03 3.928266e-03
[21,] 0.9959493 8.101387e-03 4.050693e-03
[22,] 0.9965030 6.993938e-03 3.496969e-03
[23,] 0.9965240 6.952074e-03 3.476037e-03
[24,] 0.9959180 8.164010e-03 4.082005e-03
[25,] 0.9948409 1.031819e-02 5.159094e-03
[26,] 0.9929053 1.418935e-02 7.094675e-03
[27,] 0.9903036 1.939281e-02 9.696403e-03
[28,] 0.9868762 2.624766e-02 1.312383e-02
[29,] 0.9832245 3.355092e-02 1.677546e-02
[30,] 0.9793300 4.134001e-02 2.067000e-02
[31,] 0.9750026 4.999471e-02 2.499735e-02
[32,] 0.9719052 5.618958e-02 2.809479e-02
[33,] 0.9673671 6.526573e-02 3.263286e-02
[34,] 0.9706700 5.865999e-02 2.933000e-02
[35,] 0.9725835 5.483299e-02 2.741650e-02
[36,] 0.9748177 5.036467e-02 2.518233e-02
[37,] 0.9764157 4.716854e-02 2.358427e-02
[38,] 0.9754292 4.914159e-02 2.457079e-02
[39,] 0.9728581 5.428373e-02 2.714186e-02
[40,] 0.9690472 6.190563e-02 3.095282e-02
[41,] 0.9667520 6.649600e-02 3.324800e-02
[42,] 0.9645064 7.098722e-02 3.549361e-02
[43,] 0.9666392 6.672157e-02 3.336079e-02
[44,] 0.9668879 6.622410e-02 3.311205e-02
[45,] 0.9673305 6.533891e-02 3.266946e-02
[46,] 0.9648721 7.025577e-02 3.512788e-02
[47,] 0.9605052 7.898965e-02 3.949483e-02
[48,] 0.9539417 9.211665e-02 4.605833e-02
[49,] 0.9474044 1.051912e-01 5.259558e-02
[50,] 0.9391161 1.217679e-01 6.088394e-02
[51,] 0.9324067 1.351866e-01 6.759329e-02
[52,] 0.9250256 1.499489e-01 7.497444e-02
[53,] 0.9242854 1.514293e-01 7.571463e-02
[54,] 0.9224567 1.550866e-01 7.754332e-02
[55,] 0.9170372 1.659257e-01 8.296283e-02
[56,] 0.9120913 1.758174e-01 8.790868e-02
[57,] 0.9067952 1.864097e-01 9.320483e-02
[58,] 0.8978217 2.043566e-01 1.021783e-01
[59,] 0.8853850 2.292301e-01 1.146150e-01
[60,] 0.8744725 2.510550e-01 1.255275e-01
[61,] 0.8737461 2.525078e-01 1.262539e-01
[62,] 0.8822686 2.354628e-01 1.177314e-01
[63,] 0.8857052 2.285897e-01 1.142948e-01
[64,] 0.8745903 2.508193e-01 1.254097e-01
[65,] 0.8707627 2.584746e-01 1.292373e-01
[66,] 0.8726258 2.547485e-01 1.273742e-01
[67,] 0.8700222 2.599557e-01 1.299778e-01
[68,] 0.8660332 2.679337e-01 1.339668e-01
[69,] 0.8608744 2.782511e-01 1.391256e-01
[70,] 0.8464037 3.071926e-01 1.535963e-01
[71,] 0.8319718 3.360563e-01 1.680282e-01
[72,] 0.8216575 3.566850e-01 1.783425e-01
[73,] 0.8124480 3.751039e-01 1.875520e-01
[74,] 0.8041005 3.917991e-01 1.958995e-01
[75,] 0.7958175 4.083651e-01 2.041825e-01
[76,] 0.7828076 4.343847e-01 2.171924e-01
[77,] 0.7779022 4.441955e-01 2.220978e-01
[78,] 0.7817154 4.365691e-01 2.182846e-01
[79,] 0.7898404 4.203193e-01 2.101596e-01
[80,] 0.7993361 4.013278e-01 2.006639e-01
[81,] 0.8050228 3.899545e-01 1.949772e-01
[82,] 0.8179700 3.640601e-01 1.820300e-01
[83,] 0.8415339 3.169321e-01 1.584661e-01
[84,] 0.8727742 2.544516e-01 1.272258e-01
[85,] 0.8983222 2.033556e-01 1.016778e-01
[86,] 0.9231554 1.536891e-01 7.684455e-02
[87,] 0.9338179 1.323641e-01 6.618206e-02
[88,] 0.9422223 1.155555e-01 5.777775e-02
[89,] 0.9434068 1.131864e-01 5.659318e-02
[90,] 0.9465676 1.068649e-01 5.343243e-02
[91,] 0.9537131 9.257378e-02 4.628689e-02
[92,] 0.9608152 7.836961e-02 3.918480e-02
[93,] 0.9773432 4.531352e-02 2.265676e-02
[94,] 0.9931280 1.374406e-02 6.872029e-03
[95,] 0.9987224 2.555248e-03 1.277624e-03
[96,] 0.9997793 4.413992e-04 2.206996e-04
[97,] 0.9999019 1.962879e-04 9.814394e-05
[98,] 0.9999402 1.196470e-04 5.982350e-05
[99,] 0.9999799 4.013795e-05 2.006897e-05
[100,] 0.9999988 2.310956e-06 1.155478e-06
[101,] 1.0000000 5.005020e-08 2.502510e-08
[102,] 1.0000000 1.993066e-09 9.965329e-10
[103,] 1.0000000 1.296508e-09 6.482538e-10
[104,] 1.0000000 1.216487e-09 6.082435e-10
[105,] 1.0000000 1.195970e-09 5.979852e-10
[106,] 1.0000000 2.969208e-10 1.484604e-10
[107,] 1.0000000 3.055954e-11 1.527977e-11
[108,] 1.0000000 2.504278e-12 1.252139e-12
[109,] 1.0000000 1.054991e-12 5.274957e-13
[110,] 1.0000000 7.445198e-13 3.722599e-13
[111,] 1.0000000 6.600981e-13 3.300490e-13
[112,] 1.0000000 3.875105e-13 1.937553e-13
[113,] 1.0000000 1.387554e-13 6.937768e-14
[114,] 1.0000000 9.211567e-14 4.605783e-14
[115,] 1.0000000 2.263133e-13 1.131566e-13
[116,] 1.0000000 6.936443e-13 3.468222e-13
[117,] 1.0000000 2.278144e-12 1.139072e-12
[118,] 1.0000000 6.630479e-12 3.315239e-12
[119,] 1.0000000 1.298015e-11 6.490073e-12
[120,] 1.0000000 2.566831e-11 1.283415e-11
[121,] 1.0000000 8.500815e-11 4.250408e-11
[122,] 1.0000000 2.634882e-10 1.317441e-10
[123,] 1.0000000 8.511875e-10 4.255938e-10
[124,] 1.0000000 2.705384e-09 1.352692e-09
[125,] 1.0000000 7.648128e-09 3.824064e-09
[126,] 1.0000000 2.038345e-08 1.019173e-08
[127,] 1.0000000 5.994414e-08 2.997207e-08
[128,] 0.9999999 1.486260e-07 7.431299e-08
[129,] 0.9999999 2.748265e-07 1.374132e-07
[130,] 0.9999997 5.242771e-07 2.621385e-07
[131,] 0.9999995 1.082267e-06 5.411336e-07
[132,] 0.9999984 3.120848e-06 1.560424e-06
[133,] 0.9999985 3.010696e-06 1.505348e-06
[134,] 0.9999997 6.408039e-07 3.204020e-07
[135,] 0.9999995 1.011323e-06 5.056615e-07
[136,] 0.9999989 2.200451e-06 1.100225e-06
[137,] 0.9999998 3.886697e-07 1.943348e-07
[138,] 1.0000000 8.224550e-09 4.112275e-09
[139,] 1.0000000 3.473220e-08 1.736610e-08
[140,] 0.9999999 1.914474e-07 9.572369e-08
[141,] 0.9999995 9.929151e-07 4.964575e-07
[142,] 0.9999975 5.018904e-06 2.509452e-06
[143,] 0.9999884 2.314300e-05 1.157150e-05
[144,] 0.9999815 3.699474e-05 1.849737e-05
[145,] 0.9999869 2.625536e-05 1.312768e-05
[146,] 0.9999622 7.565387e-05 3.782693e-05
[147,] 0.9999888 2.240993e-05 1.120497e-05
[148,] 0.9999510 9.807613e-05 4.903806e-05
[149,] 0.9992823 1.435463e-03 7.177313e-04
> postscript(file="/var/www/html/rcomp/tmp/19dvn1258723633.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2xwzo1258723633.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3752n1258723633.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4rljj1258723633.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/59hsb1258723633.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 = 180
Frequency = 1
1 2 3 4 5 6
-1.483449743 -1.508944683 -1.239896518 -1.151443348 -1.078767399 -1.034409186
7 8 9 10 11 12
-1.060283036 -1.168959902 -0.991802478 -0.970638361 -0.790773794 -0.530151408
13 14 15 16 17 18
-0.107348392 -0.057495239 0.168571870 0.217581989 0.281789551 0.337400901
19 20 21 22 23 24
0.449577726 1.083242800 1.054716586 1.205462990 1.257137644 1.417760030
25 26 27 28 29 30
1.385575595 1.509384467 1.462888232 1.571062926 1.717694594 1.824755389
31 32 33 34 35 36
1.613672683 1.713464205 1.677222860 1.509640114 1.390897056 1.203608004
37 38 39 40 41 42
1.219335007 1.174118541 1.081856500 1.025936237 0.835909605 0.790128580
43 44 45 46 47 48
0.764254730 0.941539978 1.051064439 1.065905800 1.234517230 1.106392753
49 50 51 52 53 54
1.458778057 1.403700829 1.424837556 1.388638819 1.192289431 1.051438787
55 56 57 58 59 60
0.924172563 1.057084379 1.035634178 1.177273077 1.073321163 1.033943548
61 62 63 64 65 66
0.831341401 0.753004641 0.443805824 0.304069080 0.534517230 0.721856500
67 68 69 70 71 72
0.709381419 0.922571710 0.863070834 0.605348850 0.609865324 0.453550934
73 74 75 76 77 78
0.199499342 0.341637364 0.526869048 0.827328612 0.972567906 0.847900781
79 80 81 82 83 84
0.385368630 0.327387952 0.568640332 0.388412074 -0.004286703 -0.122550417
85 86 87 88 89 90
0.060809549 0.059966515 0.230407055 0.159695649 -0.030330984 -0.171181627
91 92 93 94 95 96
-0.501985858 -0.540884129 -0.748935561 -0.894651150 -0.935900482 -0.856948946
97 98 99 100 101 102
-1.125038425 -1.261786503 -1.361763675 -1.411361181 -1.479520656 -1.180174992
103 104 105 106 107 108
-1.146884267 -0.969599019 -0.979042826 -1.171277478 -1.161830623 -1.685777977
109 110 111 112 113 114
-2.122892793 -2.213875065 -2.145580156 -1.441582586 -1.096343292 -1.391428130
115 116 117 118 119 120
-1.815909605 -1.973137026 -1.821384765 -0.976961117 -0.940077606 -0.838619796
121 122 123 124 125 126
-0.477766104 -0.726520576 -0.659618043 -1.124759950 -1.388742302 -1.602156289
127 128 129 130 131 132
-1.191371838 -1.468320785 -1.314422892 -0.999581531 -0.847906877 -0.637980678
133 134 135 136 137 138
-0.611000538 -0.365324509 -0.408282738 -0.487462532 -0.831723358 -0.437308076
139 140 141 142 143 144
-0.287833832 -0.407010577 -0.448182304 -0.456600474 -0.173197901 -0.055556572
145 146 147 148 149 150
0.176353950 -0.001982810 -0.418896758 -0.670525756 -0.457653499 -0.321763675
151 152 153 154 155 156
0.665761245 0.724717342 0.565216466 0.474488328 -0.033001593 -0.094246364
157 158 159 160 161 162
0.106689495 0.443897136 0.737597208 0.443626270 0.393042688 0.478989583
163 164 165 166 167 168
0.825679077 0.543799750 0.522349549 0.082203458 0.176859169 0.411437274
169 170 171 172 173 174
0.489113602 0.450219892 0.157204593 0.349195769 0.435270487 0.085951456
175 176 177 178 179 180
-0.333599638 -0.785896678 -1.034144417 -1.039024581 -0.855622007 -0.804860384
> postscript(file="/var/www/html/rcomp/tmp/6re2v1258723633.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 = 180
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.483449743 NA
1 -1.508944683 -1.483449743
2 -1.239896518 -1.508944683
3 -1.151443348 -1.239896518
4 -1.078767399 -1.151443348
5 -1.034409186 -1.078767399
6 -1.060283036 -1.034409186
7 -1.168959902 -1.060283036
8 -0.991802478 -1.168959902
9 -0.970638361 -0.991802478
10 -0.790773794 -0.970638361
11 -0.530151408 -0.790773794
12 -0.107348392 -0.530151408
13 -0.057495239 -0.107348392
14 0.168571870 -0.057495239
15 0.217581989 0.168571870
16 0.281789551 0.217581989
17 0.337400901 0.281789551
18 0.449577726 0.337400901
19 1.083242800 0.449577726
20 1.054716586 1.083242800
21 1.205462990 1.054716586
22 1.257137644 1.205462990
23 1.417760030 1.257137644
24 1.385575595 1.417760030
25 1.509384467 1.385575595
26 1.462888232 1.509384467
27 1.571062926 1.462888232
28 1.717694594 1.571062926
29 1.824755389 1.717694594
30 1.613672683 1.824755389
31 1.713464205 1.613672683
32 1.677222860 1.713464205
33 1.509640114 1.677222860
34 1.390897056 1.509640114
35 1.203608004 1.390897056
36 1.219335007 1.203608004
37 1.174118541 1.219335007
38 1.081856500 1.174118541
39 1.025936237 1.081856500
40 0.835909605 1.025936237
41 0.790128580 0.835909605
42 0.764254730 0.790128580
43 0.941539978 0.764254730
44 1.051064439 0.941539978
45 1.065905800 1.051064439
46 1.234517230 1.065905800
47 1.106392753 1.234517230
48 1.458778057 1.106392753
49 1.403700829 1.458778057
50 1.424837556 1.403700829
51 1.388638819 1.424837556
52 1.192289431 1.388638819
53 1.051438787 1.192289431
54 0.924172563 1.051438787
55 1.057084379 0.924172563
56 1.035634178 1.057084379
57 1.177273077 1.035634178
58 1.073321163 1.177273077
59 1.033943548 1.073321163
60 0.831341401 1.033943548
61 0.753004641 0.831341401
62 0.443805824 0.753004641
63 0.304069080 0.443805824
64 0.534517230 0.304069080
65 0.721856500 0.534517230
66 0.709381419 0.721856500
67 0.922571710 0.709381419
68 0.863070834 0.922571710
69 0.605348850 0.863070834
70 0.609865324 0.605348850
71 0.453550934 0.609865324
72 0.199499342 0.453550934
73 0.341637364 0.199499342
74 0.526869048 0.341637364
75 0.827328612 0.526869048
76 0.972567906 0.827328612
77 0.847900781 0.972567906
78 0.385368630 0.847900781
79 0.327387952 0.385368630
80 0.568640332 0.327387952
81 0.388412074 0.568640332
82 -0.004286703 0.388412074
83 -0.122550417 -0.004286703
84 0.060809549 -0.122550417
85 0.059966515 0.060809549
86 0.230407055 0.059966515
87 0.159695649 0.230407055
88 -0.030330984 0.159695649
89 -0.171181627 -0.030330984
90 -0.501985858 -0.171181627
91 -0.540884129 -0.501985858
92 -0.748935561 -0.540884129
93 -0.894651150 -0.748935561
94 -0.935900482 -0.894651150
95 -0.856948946 -0.935900482
96 -1.125038425 -0.856948946
97 -1.261786503 -1.125038425
98 -1.361763675 -1.261786503
99 -1.411361181 -1.361763675
100 -1.479520656 -1.411361181
101 -1.180174992 -1.479520656
102 -1.146884267 -1.180174992
103 -0.969599019 -1.146884267
104 -0.979042826 -0.969599019
105 -1.171277478 -0.979042826
106 -1.161830623 -1.171277478
107 -1.685777977 -1.161830623
108 -2.122892793 -1.685777977
109 -2.213875065 -2.122892793
110 -2.145580156 -2.213875065
111 -1.441582586 -2.145580156
112 -1.096343292 -1.441582586
113 -1.391428130 -1.096343292
114 -1.815909605 -1.391428130
115 -1.973137026 -1.815909605
116 -1.821384765 -1.973137026
117 -0.976961117 -1.821384765
118 -0.940077606 -0.976961117
119 -0.838619796 -0.940077606
120 -0.477766104 -0.838619796
121 -0.726520576 -0.477766104
122 -0.659618043 -0.726520576
123 -1.124759950 -0.659618043
124 -1.388742302 -1.124759950
125 -1.602156289 -1.388742302
126 -1.191371838 -1.602156289
127 -1.468320785 -1.191371838
128 -1.314422892 -1.468320785
129 -0.999581531 -1.314422892
130 -0.847906877 -0.999581531
131 -0.637980678 -0.847906877
132 -0.611000538 -0.637980678
133 -0.365324509 -0.611000538
134 -0.408282738 -0.365324509
135 -0.487462532 -0.408282738
136 -0.831723358 -0.487462532
137 -0.437308076 -0.831723358
138 -0.287833832 -0.437308076
139 -0.407010577 -0.287833832
140 -0.448182304 -0.407010577
141 -0.456600474 -0.448182304
142 -0.173197901 -0.456600474
143 -0.055556572 -0.173197901
144 0.176353950 -0.055556572
145 -0.001982810 0.176353950
146 -0.418896758 -0.001982810
147 -0.670525756 -0.418896758
148 -0.457653499 -0.670525756
149 -0.321763675 -0.457653499
150 0.665761245 -0.321763675
151 0.724717342 0.665761245
152 0.565216466 0.724717342
153 0.474488328 0.565216466
154 -0.033001593 0.474488328
155 -0.094246364 -0.033001593
156 0.106689495 -0.094246364
157 0.443897136 0.106689495
158 0.737597208 0.443897136
159 0.443626270 0.737597208
160 0.393042688 0.443626270
161 0.478989583 0.393042688
162 0.825679077 0.478989583
163 0.543799750 0.825679077
164 0.522349549 0.543799750
165 0.082203458 0.522349549
166 0.176859169 0.082203458
167 0.411437274 0.176859169
168 0.489113602 0.411437274
169 0.450219892 0.489113602
170 0.157204593 0.450219892
171 0.349195769 0.157204593
172 0.435270487 0.349195769
173 0.085951456 0.435270487
174 -0.333599638 0.085951456
175 -0.785896678 -0.333599638
176 -1.034144417 -0.785896678
177 -1.039024581 -1.034144417
178 -0.855622007 -1.039024581
179 -0.804860384 -0.855622007
180 NA -0.804860384
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.508944683 -1.483449743
[2,] -1.239896518 -1.508944683
[3,] -1.151443348 -1.239896518
[4,] -1.078767399 -1.151443348
[5,] -1.034409186 -1.078767399
[6,] -1.060283036 -1.034409186
[7,] -1.168959902 -1.060283036
[8,] -0.991802478 -1.168959902
[9,] -0.970638361 -0.991802478
[10,] -0.790773794 -0.970638361
[11,] -0.530151408 -0.790773794
[12,] -0.107348392 -0.530151408
[13,] -0.057495239 -0.107348392
[14,] 0.168571870 -0.057495239
[15,] 0.217581989 0.168571870
[16,] 0.281789551 0.217581989
[17,] 0.337400901 0.281789551
[18,] 0.449577726 0.337400901
[19,] 1.083242800 0.449577726
[20,] 1.054716586 1.083242800
[21,] 1.205462990 1.054716586
[22,] 1.257137644 1.205462990
[23,] 1.417760030 1.257137644
[24,] 1.385575595 1.417760030
[25,] 1.509384467 1.385575595
[26,] 1.462888232 1.509384467
[27,] 1.571062926 1.462888232
[28,] 1.717694594 1.571062926
[29,] 1.824755389 1.717694594
[30,] 1.613672683 1.824755389
[31,] 1.713464205 1.613672683
[32,] 1.677222860 1.713464205
[33,] 1.509640114 1.677222860
[34,] 1.390897056 1.509640114
[35,] 1.203608004 1.390897056
[36,] 1.219335007 1.203608004
[37,] 1.174118541 1.219335007
[38,] 1.081856500 1.174118541
[39,] 1.025936237 1.081856500
[40,] 0.835909605 1.025936237
[41,] 0.790128580 0.835909605
[42,] 0.764254730 0.790128580
[43,] 0.941539978 0.764254730
[44,] 1.051064439 0.941539978
[45,] 1.065905800 1.051064439
[46,] 1.234517230 1.065905800
[47,] 1.106392753 1.234517230
[48,] 1.458778057 1.106392753
[49,] 1.403700829 1.458778057
[50,] 1.424837556 1.403700829
[51,] 1.388638819 1.424837556
[52,] 1.192289431 1.388638819
[53,] 1.051438787 1.192289431
[54,] 0.924172563 1.051438787
[55,] 1.057084379 0.924172563
[56,] 1.035634178 1.057084379
[57,] 1.177273077 1.035634178
[58,] 1.073321163 1.177273077
[59,] 1.033943548 1.073321163
[60,] 0.831341401 1.033943548
[61,] 0.753004641 0.831341401
[62,] 0.443805824 0.753004641
[63,] 0.304069080 0.443805824
[64,] 0.534517230 0.304069080
[65,] 0.721856500 0.534517230
[66,] 0.709381419 0.721856500
[67,] 0.922571710 0.709381419
[68,] 0.863070834 0.922571710
[69,] 0.605348850 0.863070834
[70,] 0.609865324 0.605348850
[71,] 0.453550934 0.609865324
[72,] 0.199499342 0.453550934
[73,] 0.341637364 0.199499342
[74,] 0.526869048 0.341637364
[75,] 0.827328612 0.526869048
[76,] 0.972567906 0.827328612
[77,] 0.847900781 0.972567906
[78,] 0.385368630 0.847900781
[79,] 0.327387952 0.385368630
[80,] 0.568640332 0.327387952
[81,] 0.388412074 0.568640332
[82,] -0.004286703 0.388412074
[83,] -0.122550417 -0.004286703
[84,] 0.060809549 -0.122550417
[85,] 0.059966515 0.060809549
[86,] 0.230407055 0.059966515
[87,] 0.159695649 0.230407055
[88,] -0.030330984 0.159695649
[89,] -0.171181627 -0.030330984
[90,] -0.501985858 -0.171181627
[91,] -0.540884129 -0.501985858
[92,] -0.748935561 -0.540884129
[93,] -0.894651150 -0.748935561
[94,] -0.935900482 -0.894651150
[95,] -0.856948946 -0.935900482
[96,] -1.125038425 -0.856948946
[97,] -1.261786503 -1.125038425
[98,] -1.361763675 -1.261786503
[99,] -1.411361181 -1.361763675
[100,] -1.479520656 -1.411361181
[101,] -1.180174992 -1.479520656
[102,] -1.146884267 -1.180174992
[103,] -0.969599019 -1.146884267
[104,] -0.979042826 -0.969599019
[105,] -1.171277478 -0.979042826
[106,] -1.161830623 -1.171277478
[107,] -1.685777977 -1.161830623
[108,] -2.122892793 -1.685777977
[109,] -2.213875065 -2.122892793
[110,] -2.145580156 -2.213875065
[111,] -1.441582586 -2.145580156
[112,] -1.096343292 -1.441582586
[113,] -1.391428130 -1.096343292
[114,] -1.815909605 -1.391428130
[115,] -1.973137026 -1.815909605
[116,] -1.821384765 -1.973137026
[117,] -0.976961117 -1.821384765
[118,] -0.940077606 -0.976961117
[119,] -0.838619796 -0.940077606
[120,] -0.477766104 -0.838619796
[121,] -0.726520576 -0.477766104
[122,] -0.659618043 -0.726520576
[123,] -1.124759950 -0.659618043
[124,] -1.388742302 -1.124759950
[125,] -1.602156289 -1.388742302
[126,] -1.191371838 -1.602156289
[127,] -1.468320785 -1.191371838
[128,] -1.314422892 -1.468320785
[129,] -0.999581531 -1.314422892
[130,] -0.847906877 -0.999581531
[131,] -0.637980678 -0.847906877
[132,] -0.611000538 -0.637980678
[133,] -0.365324509 -0.611000538
[134,] -0.408282738 -0.365324509
[135,] -0.487462532 -0.408282738
[136,] -0.831723358 -0.487462532
[137,] -0.437308076 -0.831723358
[138,] -0.287833832 -0.437308076
[139,] -0.407010577 -0.287833832
[140,] -0.448182304 -0.407010577
[141,] -0.456600474 -0.448182304
[142,] -0.173197901 -0.456600474
[143,] -0.055556572 -0.173197901
[144,] 0.176353950 -0.055556572
[145,] -0.001982810 0.176353950
[146,] -0.418896758 -0.001982810
[147,] -0.670525756 -0.418896758
[148,] -0.457653499 -0.670525756
[149,] -0.321763675 -0.457653499
[150,] 0.665761245 -0.321763675
[151,] 0.724717342 0.665761245
[152,] 0.565216466 0.724717342
[153,] 0.474488328 0.565216466
[154,] -0.033001593 0.474488328
[155,] -0.094246364 -0.033001593
[156,] 0.106689495 -0.094246364
[157,] 0.443897136 0.106689495
[158,] 0.737597208 0.443897136
[159,] 0.443626270 0.737597208
[160,] 0.393042688 0.443626270
[161,] 0.478989583 0.393042688
[162,] 0.825679077 0.478989583
[163,] 0.543799750 0.825679077
[164,] 0.522349549 0.543799750
[165,] 0.082203458 0.522349549
[166,] 0.176859169 0.082203458
[167,] 0.411437274 0.176859169
[168,] 0.489113602 0.411437274
[169,] 0.450219892 0.489113602
[170,] 0.157204593 0.450219892
[171,] 0.349195769 0.157204593
[172,] 0.435270487 0.349195769
[173,] 0.085951456 0.435270487
[174,] -0.333599638 0.085951456
[175,] -0.785896678 -0.333599638
[176,] -1.034144417 -0.785896678
[177,] -1.039024581 -1.034144417
[178,] -0.855622007 -1.039024581
[179,] -0.804860384 -0.855622007
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.508944683 -1.483449743
2 -1.239896518 -1.508944683
3 -1.151443348 -1.239896518
4 -1.078767399 -1.151443348
5 -1.034409186 -1.078767399
6 -1.060283036 -1.034409186
7 -1.168959902 -1.060283036
8 -0.991802478 -1.168959902
9 -0.970638361 -0.991802478
10 -0.790773794 -0.970638361
11 -0.530151408 -0.790773794
12 -0.107348392 -0.530151408
13 -0.057495239 -0.107348392
14 0.168571870 -0.057495239
15 0.217581989 0.168571870
16 0.281789551 0.217581989
17 0.337400901 0.281789551
18 0.449577726 0.337400901
19 1.083242800 0.449577726
20 1.054716586 1.083242800
21 1.205462990 1.054716586
22 1.257137644 1.205462990
23 1.417760030 1.257137644
24 1.385575595 1.417760030
25 1.509384467 1.385575595
26 1.462888232 1.509384467
27 1.571062926 1.462888232
28 1.717694594 1.571062926
29 1.824755389 1.717694594
30 1.613672683 1.824755389
31 1.713464205 1.613672683
32 1.677222860 1.713464205
33 1.509640114 1.677222860
34 1.390897056 1.509640114
35 1.203608004 1.390897056
36 1.219335007 1.203608004
37 1.174118541 1.219335007
38 1.081856500 1.174118541
39 1.025936237 1.081856500
40 0.835909605 1.025936237
41 0.790128580 0.835909605
42 0.764254730 0.790128580
43 0.941539978 0.764254730
44 1.051064439 0.941539978
45 1.065905800 1.051064439
46 1.234517230 1.065905800
47 1.106392753 1.234517230
48 1.458778057 1.106392753
49 1.403700829 1.458778057
50 1.424837556 1.403700829
51 1.388638819 1.424837556
52 1.192289431 1.388638819
53 1.051438787 1.192289431
54 0.924172563 1.051438787
55 1.057084379 0.924172563
56 1.035634178 1.057084379
57 1.177273077 1.035634178
58 1.073321163 1.177273077
59 1.033943548 1.073321163
60 0.831341401 1.033943548
61 0.753004641 0.831341401
62 0.443805824 0.753004641
63 0.304069080 0.443805824
64 0.534517230 0.304069080
65 0.721856500 0.534517230
66 0.709381419 0.721856500
67 0.922571710 0.709381419
68 0.863070834 0.922571710
69 0.605348850 0.863070834
70 0.609865324 0.605348850
71 0.453550934 0.609865324
72 0.199499342 0.453550934
73 0.341637364 0.199499342
74 0.526869048 0.341637364
75 0.827328612 0.526869048
76 0.972567906 0.827328612
77 0.847900781 0.972567906
78 0.385368630 0.847900781
79 0.327387952 0.385368630
80 0.568640332 0.327387952
81 0.388412074 0.568640332
82 -0.004286703 0.388412074
83 -0.122550417 -0.004286703
84 0.060809549 -0.122550417
85 0.059966515 0.060809549
86 0.230407055 0.059966515
87 0.159695649 0.230407055
88 -0.030330984 0.159695649
89 -0.171181627 -0.030330984
90 -0.501985858 -0.171181627
91 -0.540884129 -0.501985858
92 -0.748935561 -0.540884129
93 -0.894651150 -0.748935561
94 -0.935900482 -0.894651150
95 -0.856948946 -0.935900482
96 -1.125038425 -0.856948946
97 -1.261786503 -1.125038425
98 -1.361763675 -1.261786503
99 -1.411361181 -1.361763675
100 -1.479520656 -1.411361181
101 -1.180174992 -1.479520656
102 -1.146884267 -1.180174992
103 -0.969599019 -1.146884267
104 -0.979042826 -0.969599019
105 -1.171277478 -0.979042826
106 -1.161830623 -1.171277478
107 -1.685777977 -1.161830623
108 -2.122892793 -1.685777977
109 -2.213875065 -2.122892793
110 -2.145580156 -2.213875065
111 -1.441582586 -2.145580156
112 -1.096343292 -1.441582586
113 -1.391428130 -1.096343292
114 -1.815909605 -1.391428130
115 -1.973137026 -1.815909605
116 -1.821384765 -1.973137026
117 -0.976961117 -1.821384765
118 -0.940077606 -0.976961117
119 -0.838619796 -0.940077606
120 -0.477766104 -0.838619796
121 -0.726520576 -0.477766104
122 -0.659618043 -0.726520576
123 -1.124759950 -0.659618043
124 -1.388742302 -1.124759950
125 -1.602156289 -1.388742302
126 -1.191371838 -1.602156289
127 -1.468320785 -1.191371838
128 -1.314422892 -1.468320785
129 -0.999581531 -1.314422892
130 -0.847906877 -0.999581531
131 -0.637980678 -0.847906877
132 -0.611000538 -0.637980678
133 -0.365324509 -0.611000538
134 -0.408282738 -0.365324509
135 -0.487462532 -0.408282738
136 -0.831723358 -0.487462532
137 -0.437308076 -0.831723358
138 -0.287833832 -0.437308076
139 -0.407010577 -0.287833832
140 -0.448182304 -0.407010577
141 -0.456600474 -0.448182304
142 -0.173197901 -0.456600474
143 -0.055556572 -0.173197901
144 0.176353950 -0.055556572
145 -0.001982810 0.176353950
146 -0.418896758 -0.001982810
147 -0.670525756 -0.418896758
148 -0.457653499 -0.670525756
149 -0.321763675 -0.457653499
150 0.665761245 -0.321763675
151 0.724717342 0.665761245
152 0.565216466 0.724717342
153 0.474488328 0.565216466
154 -0.033001593 0.474488328
155 -0.094246364 -0.033001593
156 0.106689495 -0.094246364
157 0.443897136 0.106689495
158 0.737597208 0.443897136
159 0.443626270 0.737597208
160 0.393042688 0.443626270
161 0.478989583 0.393042688
162 0.825679077 0.478989583
163 0.543799750 0.825679077
164 0.522349549 0.543799750
165 0.082203458 0.522349549
166 0.176859169 0.082203458
167 0.411437274 0.176859169
168 0.489113602 0.411437274
169 0.450219892 0.489113602
170 0.157204593 0.450219892
171 0.349195769 0.157204593
172 0.435270487 0.349195769
173 0.085951456 0.435270487
174 -0.333599638 0.085951456
175 -0.785896678 -0.333599638
176 -1.034144417 -0.785896678
177 -1.039024581 -1.034144417
178 -0.855622007 -1.039024581
179 -0.804860384 -0.855622007
> 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/7hhvr1258723633.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/87tst1258723633.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9su8o1258723633.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10acd81258723633.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11zfg11258723633.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/129zex1258723633.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/135vyb1258723633.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/14e5da1258723633.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/15kek01258723633.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/1636tk1258723633.tab")
+ }
> system("convert tmp/19dvn1258723633.ps tmp/19dvn1258723633.png")
> system("convert tmp/2xwzo1258723633.ps tmp/2xwzo1258723633.png")
> system("convert tmp/3752n1258723633.ps tmp/3752n1258723633.png")
> system("convert tmp/4rljj1258723633.ps tmp/4rljj1258723633.png")
> system("convert tmp/59hsb1258723633.ps tmp/59hsb1258723633.png")
> system("convert tmp/6re2v1258723633.ps tmp/6re2v1258723633.png")
> system("convert tmp/7hhvr1258723633.ps tmp/7hhvr1258723633.png")
> system("convert tmp/87tst1258723633.ps tmp/87tst1258723633.png")
> system("convert tmp/9su8o1258723633.ps tmp/9su8o1258723633.png")
> system("convert tmp/10acd81258723633.ps tmp/10acd81258723633.png")
>
>
> proc.time()
user system elapsed
4.452 1.737 4.915