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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You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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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 = '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 6.9 2.28 1 0 0 0 0 0 0 0 0 0 0 1
2 6.8 2.26 0 1 0 0 0 0 0 0 0 0 0 2
3 6.7 2.71 0 0 1 0 0 0 0 0 0 0 0 3
4 6.6 2.77 0 0 0 1 0 0 0 0 0 0 0 4
5 6.5 2.77 0 0 0 0 1 0 0 0 0 0 0 5
6 6.5 2.64 0 0 0 0 0 1 0 0 0 0 0 6
7 7.0 2.56 0 0 0 0 0 0 1 0 0 0 0 7
8 7.5 2.07 0 0 0 0 0 0 0 1 0 0 0 8
9 7.6 2.32 0 0 0 0 0 0 0 0 1 0 0 9
10 7.6 2.16 0 0 0 0 0 0 0 0 0 1 0 10
11 7.6 2.23 0 0 0 0 0 0 0 0 0 0 1 11
12 7.8 2.40 0 0 0 0 0 0 0 0 0 0 0 12
13 8.0 2.84 1 0 0 0 0 0 0 0 0 0 0 13
14 8.0 2.77 0 1 0 0 0 0 0 0 0 0 0 14
15 8.0 2.93 0 0 1 0 0 0 0 0 0 0 0 15
16 7.9 2.91 0 0 0 1 0 0 0 0 0 0 0 16
17 7.9 2.69 0 0 0 0 1 0 0 0 0 0 0 17
18 8.0 2.38 0 0 0 0 0 1 0 0 0 0 0 18
19 8.5 2.58 0 0 0 0 0 0 1 0 0 0 0 19
20 9.2 3.19 0 0 0 0 0 0 0 1 0 0 0 20
21 9.4 2.82 0 0 0 0 0 0 0 0 1 0 0 21
22 9.5 2.72 0 0 0 0 0 0 0 0 0 1 0 22
23 9.5 2.53 0 0 0 0 0 0 0 0 0 0 1 23
24 9.6 2.70 0 0 0 0 0 0 0 0 0 0 0 24
25 9.7 2.42 1 0 0 0 0 0 0 0 0 0 0 25
26 9.7 2.50 0 1 0 0 0 0 0 0 0 0 0 26
27 9.6 2.31 0 0 1 0 0 0 0 0 0 0 0 27
28 9.5 2.41 0 0 0 1 0 0 0 0 0 0 0 28
29 9.4 2.56 0 0 0 0 1 0 0 0 0 0 0 29
30 9.3 2.76 0 0 0 0 0 1 0 0 0 0 0 30
31 9.6 2.71 0 0 0 0 0 0 1 0 0 0 0 31
32 10.2 2.44 0 0 0 0 0 0 0 1 0 0 0 32
33 10.2 2.46 0 0 0 0 0 0 0 0 1 0 0 33
34 10.1 2.12 0 0 0 0 0 0 0 0 0 1 0 34
35 9.9 1.99 0 0 0 0 0 0 0 0 0 0 1 35
36 9.8 1.86 0 0 0 0 0 0 0 0 0 0 0 36
37 9.8 1.88 1 0 0 0 0 0 0 0 0 0 0 37
38 9.7 1.82 0 1 0 0 0 0 0 0 0 0 0 38
39 9.5 1.74 0 0 1 0 0 0 0 0 0 0 0 39
40 9.3 1.71 0 0 0 1 0 0 0 0 0 0 0 40
41 9.1 1.38 0 0 0 0 1 0 0 0 0 0 0 41
42 9.0 1.27 0 0 0 0 0 1 0 0 0 0 0 42
43 9.5 1.19 0 0 0 0 0 0 1 0 0 0 0 43
44 10.0 1.28 0 0 0 0 0 0 0 1 0 0 0 44
45 10.2 1.19 0 0 0 0 0 0 0 0 1 0 0 45
46 10.1 1.22 0 0 0 0 0 0 0 0 0 1 0 46
47 10.0 1.47 0 0 0 0 0 0 0 0 0 0 1 47
48 9.9 1.46 0 0 0 0 0 0 0 0 0 0 0 48
49 10.0 1.96 1 0 0 0 0 0 0 0 0 0 0 49
50 9.9 1.88 0 1 0 0 0 0 0 0 0 0 0 50
51 9.7 2.03 0 0 1 0 0 0 0 0 0 0 0 51
52 9.5 2.04 0 0 0 1 0 0 0 0 0 0 0 52
53 9.2 1.90 0 0 0 0 1 0 0 0 0 0 0 53
54 9.0 1.80 0 0 0 0 0 1 0 0 0 0 0 54
55 9.3 1.92 0 0 0 0 0 0 1 0 0 0 0 55
56 9.8 1.92 0 0 0 0 0 0 0 1 0 0 0 56
57 9.8 1.97 0 0 0 0 0 0 0 0 1 0 0 57
58 9.6 2.46 0 0 0 0 0 0 0 0 0 1 0 58
59 9.4 2.36 0 0 0 0 0 0 0 0 0 0 1 59
60 9.3 2.53 0 0 0 0 0 0 0 0 0 0 0 60
61 9.2 2.31 1 0 0 0 0 0 0 0 0 0 0 61
62 9.2 1.98 0 1 0 0 0 0 0 0 0 0 0 62
63 9.0 1.46 0 0 1 0 0 0 0 0 0 0 0 63
64 8.8 1.26 0 0 0 1 0 0 0 0 0 0 0 64
65 8.7 1.58 0 0 0 0 1 0 0 0 0 0 0 65
66 8.7 1.74 0 0 0 0 0 1 0 0 0 0 0 66
67 9.1 1.89 0 0 0 0 0 0 1 0 0 0 0 67
68 9.7 1.85 0 0 0 0 0 0 0 1 0 0 0 68
69 9.8 1.62 0 0 0 0 0 0 0 0 1 0 0 69
70 9.6 1.30 0 0 0 0 0 0 0 0 0 1 0 70
71 9.4 1.42 0 0 0 0 0 0 0 0 0 0 1 71
72 9.4 1.15 0 0 0 0 0 0 0 0 0 0 0 72
73 9.5 0.42 1 0 0 0 0 0 0 0 0 0 0 73
74 9.4 0.74 0 1 0 0 0 0 0 0 0 0 0 74
75 9.3 1.02 0 0 1 0 0 0 0 0 0 0 0 75
76 9.2 1.51 0 0 0 1 0 0 0 0 0 0 0 76
77 9.0 1.86 0 0 0 0 1 0 0 0 0 0 0 77
78 8.9 1.59 0 0 0 0 0 1 0 0 0 0 0 78
79 9.2 1.03 0 0 0 0 0 0 1 0 0 0 0 79
80 9.8 0.44 0 0 0 0 0 0 0 1 0 0 0 80
81 9.9 0.82 0 0 0 0 0 0 0 0 1 0 0 81
82 9.6 0.86 0 0 0 0 0 0 0 0 0 1 0 82
83 9.2 0.58 0 0 0 0 0 0 0 0 0 0 1 83
84 9.1 0.59 0 0 0 0 0 0 0 0 0 0 0 84
85 9.1 0.95 1 0 0 0 0 0 0 0 0 0 0 85
86 9.0 0.98 0 1 0 0 0 0 0 0 0 0 0 86
87 8.9 1.23 0 0 1 0 0 0 0 0 0 0 0 87
88 8.7 1.17 0 0 0 1 0 0 0 0 0 0 0 88
89 8.5 0.84 0 0 0 0 1 0 0 0 0 0 0 89
90 8.3 0.74 0 0 0 0 0 1 0 0 0 0 0 90
91 8.5 0.65 0 0 0 0 0 0 1 0 0 0 0 91
92 8.7 0.91 0 0 0 0 0 0 0 1 0 0 0 92
93 8.4 1.19 0 0 0 0 0 0 0 0 1 0 0 93
94 8.1 1.30 0 0 0 0 0 0 0 0 0 1 0 94
95 7.8 1.53 0 0 0 0 0 0 0 0 0 0 1 95
96 7.7 1.94 0 0 0 0 0 0 0 0 0 0 0 96
97 7.5 1.79 1 0 0 0 0 0 0 0 0 0 0 97
98 7.2 1.95 0 1 0 0 0 0 0 0 0 0 0 98
99 6.8 2.26 0 0 1 0 0 0 0 0 0 0 0 99
100 6.7 2.04 0 0 0 1 0 0 0 0 0 0 0 100
101 6.4 2.16 0 0 0 0 1 0 0 0 0 0 0 101
102 6.3 2.75 0 0 0 0 0 1 0 0 0 0 0 102
103 6.8 2.79 0 0 0 0 0 0 1 0 0 0 0 103
104 7.3 2.88 0 0 0 0 0 0 0 1 0 0 0 104
105 7.1 3.36 0 0 0 0 0 0 0 0 1 0 0 105
106 7.0 2.97 0 0 0 0 0 0 0 0 0 1 0 106
107 6.8 3.10 0 0 0 0 0 0 0 0 0 0 1 107
108 6.6 2.49 0 0 0 0 0 0 0 0 0 0 0 108
109 6.3 2.20 1 0 0 0 0 0 0 0 0 0 0 109
110 6.1 2.25 0 1 0 0 0 0 0 0 0 0 0 110
111 6.1 2.09 0 0 1 0 0 0 0 0 0 0 0 111
112 6.3 2.79 0 0 0 1 0 0 0 0 0 0 0 112
113 6.3 3.14 0 0 0 0 1 0 0 0 0 0 0 113
114 6.0 2.93 0 0 0 0 0 1 0 0 0 0 0 114
115 6.2 2.65 0 0 0 0 0 0 1 0 0 0 0 115
116 6.4 2.67 0 0 0 0 0 0 0 1 0 0 0 116
117 6.8 2.26 0 0 0 0 0 0 0 0 1 0 0 117
118 7.5 2.35 0 0 0 0 0 0 0 0 0 1 0 118
119 7.5 2.13 0 0 0 0 0 0 0 0 0 0 1 119
120 7.6 2.18 0 0 0 0 0 0 0 0 0 0 0 120
121 7.6 2.90 1 0 0 0 0 0 0 0 0 0 0 121
122 7.4 2.63 0 1 0 0 0 0 0 0 0 0 0 122
123 7.3 2.67 0 0 1 0 0 0 0 0 0 0 0 123
124 7.1 1.81 0 0 0 1 0 0 0 0 0 0 0 124
125 6.9 1.33 0 0 0 0 1 0 0 0 0 0 0 125
126 6.8 0.88 0 0 0 0 0 1 0 0 0 0 0 126
127 7.5 1.28 0 0 0 0 0 0 1 0 0 0 0 127
128 7.6 1.26 0 0 0 0 0 0 0 1 0 0 0 128
129 7.8 1.26 0 0 0 0 0 0 0 0 1 0 0 129
130 8.0 1.29 0 0 0 0 0 0 0 0 0 1 0 130
131 8.1 1.10 0 0 0 0 0 0 0 0 0 0 1 131
132 8.2 1.37 0 0 0 0 0 0 0 0 0 0 0 132
133 8.3 1.21 1 0 0 0 0 0 0 0 0 0 0 133
134 8.2 1.74 0 1 0 0 0 0 0 0 0 0 0 134
135 8.0 1.76 0 0 1 0 0 0 0 0 0 0 0 135
136 7.9 1.48 0 0 0 1 0 0 0 0 0 0 0 136
137 7.6 1.04 0 0 0 0 1 0 0 0 0 0 0 137
138 7.6 1.62 0 0 0 0 0 1 0 0 0 0 0 138
139 8.3 1.49 0 0 0 0 0 0 1 0 0 0 0 139
140 8.4 1.79 0 0 0 0 0 0 0 1 0 0 0 140
141 8.4 1.80 0 0 0 0 0 0 0 0 1 0 0 141
142 8.4 1.58 0 0 0 0 0 0 0 0 0 1 0 142
143 8.4 1.86 0 0 0 0 0 0 0 0 0 0 1 143
144 8.6 1.74 0 0 0 0 0 0 0 0 0 0 0 144
145 8.9 1.59 1 0 0 0 0 0 0 0 0 0 0 145
146 8.8 1.26 0 1 0 0 0 0 0 0 0 0 0 146
147 8.3 1.13 0 0 1 0 0 0 0 0 0 0 0 147
148 7.5 1.92 0 0 0 1 0 0 0 0 0 0 0 148
149 7.2 2.61 0 0 0 0 1 0 0 0 0 0 0 149
150 7.4 2.26 0 0 0 0 0 1 0 0 0 0 0 150
151 8.8 2.41 0 0 0 0 0 0 1 0 0 0 0 151
152 9.3 2.26 0 0 0 0 0 0 0 1 0 0 0 152
153 9.3 2.03 0 0 0 0 0 0 0 0 1 0 0 153
154 8.7 2.86 0 0 0 0 0 0 0 0 0 1 0 154
155 8.2 2.55 0 0 0 0 0 0 0 0 0 0 1 155
156 8.3 2.27 0 0 0 0 0 0 0 0 0 0 0 156
157 8.5 2.26 1 0 0 0 0 0 0 0 0 0 0 157
158 8.6 2.57 0 1 0 0 0 0 0 0 0 0 0 158
159 8.5 3.07 0 0 1 0 0 0 0 0 0 0 0 159
160 8.2 2.76 0 0 0 1 0 0 0 0 0 0 0 160
161 8.1 2.51 0 0 0 0 1 0 0 0 0 0 0 161
162 7.9 2.87 0 0 0 0 0 1 0 0 0 0 0 162
163 8.6 3.14 0 0 0 0 0 0 1 0 0 0 0 163
164 8.7 3.11 0 0 0 0 0 0 0 1 0 0 0 164
165 8.7 3.16 0 0 0 0 0 0 0 0 1 0 0 165
166 8.5 2.47 0 0 0 0 0 0 0 0 0 1 0 166
167 8.4 2.57 0 0 0 0 0 0 0 0 0 0 1 167
168 8.5 2.89 0 0 0 0 0 0 0 0 0 0 0 168
169 8.7 2.63 1 0 0 0 0 0 0 0 0 0 0 169
170 8.7 2.38 0 1 0 0 0 0 0 0 0 0 0 170
171 8.6 1.69 0 0 1 0 0 0 0 0 0 0 0 171
172 8.5 1.96 0 0 0 1 0 0 0 0 0 0 0 172
173 8.3 2.19 0 0 0 0 1 0 0 0 0 0 0 173
174 8.0 1.87 0 0 0 0 0 1 0 0 0 0 0 174
175 8.2 1.60 0 0 0 0 0 0 1 0 0 0 0 175
176 8.1 1.63 0 0 0 0 0 0 0 1 0 0 0 176
177 8.1 1.22 0 0 0 0 0 0 0 0 1 0 0 177
178 8.0 1.21 0 0 0 0 0 0 0 0 0 1 0 178
179 7.9 1.49 0 0 0 0 0 0 0 0 0 0 1 179
180 7.9 1.64 0 0 0 0 0 0 0 0 0 0 0 180
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
10.192427 -0.529301 -0.074536 -0.152395 -0.292296 -0.443766
M5 M6 M7 M8 M9 M10
-0.610056 -0.713284 -0.221024 0.151942 0.203849 0.108662
M11 t
-0.030257 -0.006337
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.0520 -0.7779 0.1565 0.8097 1.4718
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.192427 0.356970 28.553 < 2e-16 ***
X -0.529301 0.107545 -4.922 2.05e-06 ***
M1 -0.074536 0.351679 -0.212 0.8324
M2 -0.152395 0.351625 -0.433 0.6653
M3 -0.292296 0.351607 -0.831 0.4070
M4 -0.443766 0.351626 -1.262 0.2087
M5 -0.610056 0.351592 -1.735 0.0846 .
M6 -0.713284 0.351489 -2.029 0.0440 *
M7 -0.221024 0.351439 -0.629 0.5303
M8 0.151942 0.351401 0.432 0.6660
M9 0.203849 0.351372 0.580 0.5626
M10 0.108662 0.351365 0.309 0.7575
M11 -0.030257 0.351354 -0.086 0.9315
t -0.006337 0.001387 -4.569 9.53e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9622 on 166 degrees of freedom
Multiple R-squared: 0.2593, Adjusted R-squared: 0.2013
F-statistic: 4.47 on 13 and 166 DF, p-value: 1.695e-06
> 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,] 3.587705e-05 7.175410e-05 9.999641e-01
[2,] 9.680407e-07 1.936081e-06 9.999990e-01
[3,] 9.561765e-07 1.912353e-06 9.999990e-01
[4,] 1.095119e-04 2.190237e-04 9.998905e-01
[5,] 8.037703e-05 1.607541e-04 9.999196e-01
[6,] 5.850431e-05 1.170086e-04 9.999415e-01
[7,] 3.645636e-05 7.291272e-05 9.999635e-01
[8,] 1.296132e-05 2.592263e-05 9.999870e-01
[9,] 2.891217e-06 5.782435e-06 9.999971e-01
[10,] 6.310909e-07 1.262182e-06 9.999994e-01
[11,] 1.325261e-07 2.650522e-07 9.999999e-01
[12,] 2.664925e-08 5.329850e-08 1.000000e+00
[13,] 5.377024e-09 1.075405e-08 1.000000e+00
[14,] 1.916033e-09 3.832066e-09 1.000000e+00
[15,] 1.838254e-09 3.676507e-09 1.000000e+00
[16,] 1.060032e-09 2.120065e-09 1.000000e+00
[17,] 1.288017e-09 2.576035e-09 1.000000e+00
[18,] 2.077549e-09 4.155098e-09 1.000000e+00
[19,] 5.699517e-09 1.139903e-08 1.000000e+00
[20,] 1.472824e-08 2.945649e-08 1.000000e+00
[21,] 4.779546e-08 9.559091e-08 1.000000e+00
[22,] 7.006409e-08 1.401282e-07 9.999999e-01
[23,] 5.679454e-08 1.135891e-07 9.999999e-01
[24,] 3.781441e-08 7.562882e-08 1.000000e+00
[25,] 1.571247e-08 3.142493e-08 1.000000e+00
[26,] 6.661117e-09 1.332223e-08 1.000000e+00
[27,] 2.247867e-09 4.495734e-09 1.000000e+00
[28,] 9.868962e-10 1.973792e-09 1.000000e+00
[29,] 3.491074e-10 6.982148e-10 1.000000e+00
[30,] 2.138033e-10 4.276067e-10 1.000000e+00
[31,] 4.987073e-10 9.974145e-10 1.000000e+00
[32,] 1.218957e-09 2.437914e-09 1.000000e+00
[33,] 2.961150e-08 5.922300e-08 1.000000e+00
[34,] 1.894194e-07 3.788389e-07 9.999998e-01
[35,] 1.127960e-06 2.255921e-06 9.999989e-01
[36,] 4.480710e-06 8.961421e-06 9.999955e-01
[37,] 1.728570e-05 3.457139e-05 9.999827e-01
[38,] 6.416993e-05 1.283399e-04 9.999358e-01
[39,] 2.620519e-04 5.241038e-04 9.997379e-01
[40,] 8.571926e-04 1.714385e-03 9.991428e-01
[41,] 2.689817e-03 5.379634e-03 9.973102e-01
[42,] 1.214881e-02 2.429761e-02 9.878512e-01
[43,] 3.135802e-02 6.271605e-02 9.686420e-01
[44,] 6.477126e-02 1.295425e-01 9.352287e-01
[45,] 9.943331e-02 1.988666e-01 9.005667e-01
[46,] 1.277801e-01 2.555601e-01 8.722199e-01
[47,] 1.521574e-01 3.043147e-01 8.478426e-01
[48,] 1.723025e-01 3.446049e-01 8.276975e-01
[49,] 1.930716e-01 3.861433e-01 8.069284e-01
[50,] 2.161277e-01 4.322554e-01 7.838723e-01
[51,] 2.397712e-01 4.795423e-01 7.602288e-01
[52,] 2.778257e-01 5.556514e-01 7.221743e-01
[53,] 3.146020e-01 6.292040e-01 6.853980e-01
[54,] 3.442557e-01 6.885114e-01 6.557443e-01
[55,] 3.858800e-01 7.717601e-01 6.141200e-01
[56,] 4.127705e-01 8.255411e-01 5.872295e-01
[57,] 3.934723e-01 7.869445e-01 6.065277e-01
[58,] 3.860954e-01 7.721907e-01 6.139046e-01
[59,] 3.940937e-01 7.881874e-01 6.059063e-01
[60,] 4.395757e-01 8.791515e-01 5.604243e-01
[61,] 5.261840e-01 9.476320e-01 4.738160e-01
[62,] 6.066078e-01 7.867845e-01 3.933922e-01
[63,] 6.319303e-01 7.361395e-01 3.680697e-01
[64,] 6.545159e-01 6.909681e-01 3.454841e-01
[65,] 7.199940e-01 5.600121e-01 2.800060e-01
[66,] 7.797998e-01 4.404004e-01 2.202002e-01
[67,] 8.156732e-01 3.686537e-01 1.843268e-01
[68,] 8.465410e-01 3.069179e-01 1.534590e-01
[69,] 8.780208e-01 2.439585e-01 1.219792e-01
[70,] 9.097927e-01 1.804146e-01 9.020731e-02
[71,] 9.473829e-01 1.052341e-01 5.261707e-02
[72,] 9.735325e-01 5.293491e-02 2.646745e-02
[73,] 9.875047e-01 2.499061e-02 1.249530e-02
[74,] 9.950888e-01 9.822426e-03 4.911213e-03
[75,] 9.975858e-01 4.828364e-03 2.414182e-03
[76,] 9.993900e-01 1.219978e-03 6.099890e-04
[77,] 9.998840e-01 2.319991e-04 1.159995e-04
[78,] 9.999774e-01 4.528798e-05 2.264399e-05
[79,] 9.999956e-01 8.880215e-06 4.440108e-06
[80,] 9.999990e-01 1.973438e-06 9.867188e-07
[81,] 9.999995e-01 9.690844e-07 4.845422e-07
[82,] 9.999997e-01 5.619533e-07 2.809766e-07
[83,] 9.999998e-01 3.735349e-07 1.867674e-07
[84,] 9.999999e-01 2.921562e-07 1.460781e-07
[85,] 9.999999e-01 2.599247e-07 1.299624e-07
[86,] 9.999998e-01 3.269405e-07 1.634702e-07
[87,] 9.999997e-01 5.078696e-07 2.539348e-07
[88,] 9.999996e-01 7.169996e-07 3.584998e-07
[89,] 9.999994e-01 1.202744e-06 6.013719e-07
[90,] 9.999990e-01 2.001594e-06 1.000797e-06
[91,] 9.999983e-01 3.463231e-06 1.731615e-06
[92,] 9.999979e-01 4.194940e-06 2.097470e-06
[93,] 9.999992e-01 1.674463e-06 8.372314e-07
[94,] 9.999998e-01 3.951225e-07 1.975613e-07
[95,] 9.999999e-01 1.152499e-07 5.762497e-08
[96,] 9.999999e-01 1.391480e-07 6.957398e-08
[97,] 9.999999e-01 2.269992e-07 1.134996e-07
[98,] 9.999999e-01 2.435928e-07 1.217964e-07
[99,] 1.000000e+00 4.272441e-08 2.136220e-08
[100,] 1.000000e+00 3.811924e-09 1.905962e-09
[101,] 1.000000e+00 7.370993e-10 3.685497e-10
[102,] 1.000000e+00 1.403953e-09 7.019763e-10
[103,] 1.000000e+00 3.044034e-09 1.522017e-09
[104,] 1.000000e+00 6.439478e-09 3.219739e-09
[105,] 1.000000e+00 4.334131e-09 2.167065e-09
[106,] 1.000000e+00 1.471663e-09 7.358313e-10
[107,] 1.000000e+00 4.057423e-10 2.028711e-10
[108,] 1.000000e+00 3.358614e-10 1.679307e-10
[109,] 1.000000e+00 3.750214e-10 1.875107e-10
[110,] 1.000000e+00 4.568483e-10 2.284242e-10
[111,] 1.000000e+00 3.801548e-10 1.900774e-10
[112,] 1.000000e+00 1.991194e-10 9.955971e-11
[113,] 1.000000e+00 1.617809e-10 8.089043e-11
[114,] 1.000000e+00 3.859069e-10 1.929535e-10
[115,] 1.000000e+00 1.088365e-09 5.441824e-10
[116,] 1.000000e+00 3.073992e-09 1.536996e-09
[117,] 1.000000e+00 7.514994e-09 3.757497e-09
[118,] 1.000000e+00 1.107820e-08 5.539098e-09
[119,] 1.000000e+00 1.596079e-08 7.980396e-09
[120,] 1.000000e+00 4.249918e-08 2.124959e-08
[121,] 9.999999e-01 1.144619e-07 5.723096e-08
[122,] 9.999999e-01 2.870658e-07 1.435329e-07
[123,] 9.999997e-01 6.720747e-07 3.360373e-07
[124,] 9.999993e-01 1.338136e-06 6.690679e-07
[125,] 9.999988e-01 2.423824e-06 1.211912e-06
[126,] 9.999970e-01 6.007660e-06 3.003830e-06
[127,] 9.999933e-01 1.343787e-05 6.718934e-06
[128,] 9.999879e-01 2.415842e-05 1.207921e-05
[129,] 9.999808e-01 3.848916e-05 1.924458e-05
[130,] 9.999675e-01 6.499299e-05 3.249650e-05
[131,] 9.999237e-01 1.526303e-04 7.631517e-05
[132,] 9.999371e-01 1.257690e-04 6.288449e-05
[133,] 9.999940e-01 1.209945e-05 6.049724e-06
[134,] 9.999980e-01 3.927699e-06 1.963850e-06
[135,] 9.999948e-01 1.034646e-05 5.173230e-06
[136,] 9.999971e-01 5.826810e-06 2.913405e-06
[137,] 9.999999e-01 2.259538e-07 1.129769e-07
[138,] 9.999997e-01 6.189145e-07 3.094572e-07
[139,] 9.999985e-01 3.000614e-06 1.500307e-06
[140,] 9.999967e-01 6.576063e-06 3.288032e-06
[141,] 9.999887e-01 2.267986e-05 1.133993e-05
[142,] 9.999569e-01 8.629952e-05 4.314976e-05
[143,] 9.999554e-01 8.927298e-05 4.463649e-05
[144,] 9.999287e-01 1.426070e-04 7.130349e-05
[145,] 9.995975e-01 8.050812e-04 4.025406e-04
[146,] 9.999645e-01 7.107120e-05 3.553560e-05
[147,] 9.999390e-01 1.220257e-04 6.101286e-05
> postscript(file="/var/www/html/rcomp/tmp/144dj1258725386.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2zbcl1258725386.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3656v1258725386.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4gja71258725386.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5so031258725386.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
-2.004747878 -2.031137300 -1.746713718 -1.657148490 -1.584520892 -1.543764781
7 8 9 10 11 12
-1.572031974 -1.698018270 -1.511263299 -1.494426539 -1.312120279 -1.046058477
13 14 15 16 17 18
-0.532293371 -0.485147838 -0.254221518 -0.207000362 -0.150818961 -0.105337013
19 20 21 22 23 24
0.014600047 0.670844741 0.629433154 0.778027967 0.822715994 0.988777796
25 26 27 28 29 30
1.021446254 1.147986921 1.093657927 1.204395191 1.356417924 1.471843332
31 32 33 34 35 36
1.259455166 1.349915068 1.314930833 1.136493430 1.012939510 0.820211042
37 38 39 40 41 42
0.911669770 0.864108312 0.768002416 0.709930563 0.507888865 0.459230993
43 44 45 46 47 48
0.430963801 0.611972026 0.718764692 0.736168622 0.913749045 0.784536685
49 50 51 52 53 54
1.230059845 1.171912368 1.197545680 1.160645863 0.959171335 0.815806473
55 56 57 58 59 60
0.693399460 0.826770605 0.807665397 0.968547741 0.860872848 0.826934650
61 62 63 64 65 66
0.691361162 0.600888461 0.271890170 0.123837163 0.365841050 0.560094422
67 68 69 70 71 72
0.553566436 0.765765545 0.698456084 0.430604699 0.439376005 0.272545411
73 74 75 76 77 78
0.067028464 0.220601347 0.415043776 0.732208391 0.890091305 0.756745289
79 80 81 82 83 84
0.274413664 0.195497278 0.451061367 0.273758306 -0.129190749 -0.247817091
85 86 87 88 89 90
0.023603943 0.023679566 0.202242968 0.128292087 -0.073749611 -0.217114473
91 92 93 94 95 96
-0.550674675 -0.579685296 -0.777051298 -0.917303296 -0.950308891 -0.857214873
97 98 99 100 101 102
-1.055737298 -1.186852558 -1.276531103 -1.335170127 -1.399026420 -1.077173662
103 104 105 106 107 108
-1.041924746 -0.860916521 -0.852422342 -1.057324790 -1.043260475 -1.590053376
109 110 111 112 113 114
-1.962677927 -2.052016286 -1.990466253 -1.262148450 -0.904265536 -1.205853497
115 116 117 118 119 120
-1.639980870 -1.796023707 -1.658607330 -0.809445345 -0.780636346 -0.678090652
121 122 123 124 125 126
-0.216121294 -0.474835941 -0.407425729 -0.904817329 -1.186254162 -1.414874340
127 128 129 130 131 132
-0.989077100 -1.266291974 -1.111862227 -0.794458297 -0.649770270 -0.430778379
133 134 135 136 137 138
-0.334593813 -0.069867740 -0.113043545 -0.203440624 -0.563705421 -0.147145671
139 140 141 142 143 144
-0.001877909 -0.109716494 -0.149993739 -0.164915033 0.128544416 0.241108957
145 146 147 148 149 150
0.542586532 0.352113831 -0.070457110 -0.294502225 -0.056657005 0.067652907
151 152 153 154 155 156
1.061124922 1.115100931 0.947791471 0.888636121 0.369808040 0.297684436
157 158 159 160 161 162
0.573264138 0.921544012 1.232432639 0.926156534 0.866458907 0.966572459
163 164 165 166 167 168
1.323560581 1.041052699 1.021947491 0.558254773 0.656440060 0.901896997
169 170 171 172 173 174
1.045151473 0.997022844 0.678043399 0.878761816 0.973128622 0.613317561
175 176 177 178 179 180
0.184483198 -0.266266631 -0.528850253 -0.532618359 -0.339158909 -0.283683125
> postscript(file="/var/www/html/rcomp/tmp/6a7pi1258725386.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 -2.004747878 NA
1 -2.031137300 -2.004747878
2 -1.746713718 -2.031137300
3 -1.657148490 -1.746713718
4 -1.584520892 -1.657148490
5 -1.543764781 -1.584520892
6 -1.572031974 -1.543764781
7 -1.698018270 -1.572031974
8 -1.511263299 -1.698018270
9 -1.494426539 -1.511263299
10 -1.312120279 -1.494426539
11 -1.046058477 -1.312120279
12 -0.532293371 -1.046058477
13 -0.485147838 -0.532293371
14 -0.254221518 -0.485147838
15 -0.207000362 -0.254221518
16 -0.150818961 -0.207000362
17 -0.105337013 -0.150818961
18 0.014600047 -0.105337013
19 0.670844741 0.014600047
20 0.629433154 0.670844741
21 0.778027967 0.629433154
22 0.822715994 0.778027967
23 0.988777796 0.822715994
24 1.021446254 0.988777796
25 1.147986921 1.021446254
26 1.093657927 1.147986921
27 1.204395191 1.093657927
28 1.356417924 1.204395191
29 1.471843332 1.356417924
30 1.259455166 1.471843332
31 1.349915068 1.259455166
32 1.314930833 1.349915068
33 1.136493430 1.314930833
34 1.012939510 1.136493430
35 0.820211042 1.012939510
36 0.911669770 0.820211042
37 0.864108312 0.911669770
38 0.768002416 0.864108312
39 0.709930563 0.768002416
40 0.507888865 0.709930563
41 0.459230993 0.507888865
42 0.430963801 0.459230993
43 0.611972026 0.430963801
44 0.718764692 0.611972026
45 0.736168622 0.718764692
46 0.913749045 0.736168622
47 0.784536685 0.913749045
48 1.230059845 0.784536685
49 1.171912368 1.230059845
50 1.197545680 1.171912368
51 1.160645863 1.197545680
52 0.959171335 1.160645863
53 0.815806473 0.959171335
54 0.693399460 0.815806473
55 0.826770605 0.693399460
56 0.807665397 0.826770605
57 0.968547741 0.807665397
58 0.860872848 0.968547741
59 0.826934650 0.860872848
60 0.691361162 0.826934650
61 0.600888461 0.691361162
62 0.271890170 0.600888461
63 0.123837163 0.271890170
64 0.365841050 0.123837163
65 0.560094422 0.365841050
66 0.553566436 0.560094422
67 0.765765545 0.553566436
68 0.698456084 0.765765545
69 0.430604699 0.698456084
70 0.439376005 0.430604699
71 0.272545411 0.439376005
72 0.067028464 0.272545411
73 0.220601347 0.067028464
74 0.415043776 0.220601347
75 0.732208391 0.415043776
76 0.890091305 0.732208391
77 0.756745289 0.890091305
78 0.274413664 0.756745289
79 0.195497278 0.274413664
80 0.451061367 0.195497278
81 0.273758306 0.451061367
82 -0.129190749 0.273758306
83 -0.247817091 -0.129190749
84 0.023603943 -0.247817091
85 0.023679566 0.023603943
86 0.202242968 0.023679566
87 0.128292087 0.202242968
88 -0.073749611 0.128292087
89 -0.217114473 -0.073749611
90 -0.550674675 -0.217114473
91 -0.579685296 -0.550674675
92 -0.777051298 -0.579685296
93 -0.917303296 -0.777051298
94 -0.950308891 -0.917303296
95 -0.857214873 -0.950308891
96 -1.055737298 -0.857214873
97 -1.186852558 -1.055737298
98 -1.276531103 -1.186852558
99 -1.335170127 -1.276531103
100 -1.399026420 -1.335170127
101 -1.077173662 -1.399026420
102 -1.041924746 -1.077173662
103 -0.860916521 -1.041924746
104 -0.852422342 -0.860916521
105 -1.057324790 -0.852422342
106 -1.043260475 -1.057324790
107 -1.590053376 -1.043260475
108 -1.962677927 -1.590053376
109 -2.052016286 -1.962677927
110 -1.990466253 -2.052016286
111 -1.262148450 -1.990466253
112 -0.904265536 -1.262148450
113 -1.205853497 -0.904265536
114 -1.639980870 -1.205853497
115 -1.796023707 -1.639980870
116 -1.658607330 -1.796023707
117 -0.809445345 -1.658607330
118 -0.780636346 -0.809445345
119 -0.678090652 -0.780636346
120 -0.216121294 -0.678090652
121 -0.474835941 -0.216121294
122 -0.407425729 -0.474835941
123 -0.904817329 -0.407425729
124 -1.186254162 -0.904817329
125 -1.414874340 -1.186254162
126 -0.989077100 -1.414874340
127 -1.266291974 -0.989077100
128 -1.111862227 -1.266291974
129 -0.794458297 -1.111862227
130 -0.649770270 -0.794458297
131 -0.430778379 -0.649770270
132 -0.334593813 -0.430778379
133 -0.069867740 -0.334593813
134 -0.113043545 -0.069867740
135 -0.203440624 -0.113043545
136 -0.563705421 -0.203440624
137 -0.147145671 -0.563705421
138 -0.001877909 -0.147145671
139 -0.109716494 -0.001877909
140 -0.149993739 -0.109716494
141 -0.164915033 -0.149993739
142 0.128544416 -0.164915033
143 0.241108957 0.128544416
144 0.542586532 0.241108957
145 0.352113831 0.542586532
146 -0.070457110 0.352113831
147 -0.294502225 -0.070457110
148 -0.056657005 -0.294502225
149 0.067652907 -0.056657005
150 1.061124922 0.067652907
151 1.115100931 1.061124922
152 0.947791471 1.115100931
153 0.888636121 0.947791471
154 0.369808040 0.888636121
155 0.297684436 0.369808040
156 0.573264138 0.297684436
157 0.921544012 0.573264138
158 1.232432639 0.921544012
159 0.926156534 1.232432639
160 0.866458907 0.926156534
161 0.966572459 0.866458907
162 1.323560581 0.966572459
163 1.041052699 1.323560581
164 1.021947491 1.041052699
165 0.558254773 1.021947491
166 0.656440060 0.558254773
167 0.901896997 0.656440060
168 1.045151473 0.901896997
169 0.997022844 1.045151473
170 0.678043399 0.997022844
171 0.878761816 0.678043399
172 0.973128622 0.878761816
173 0.613317561 0.973128622
174 0.184483198 0.613317561
175 -0.266266631 0.184483198
176 -0.528850253 -0.266266631
177 -0.532618359 -0.528850253
178 -0.339158909 -0.532618359
179 -0.283683125 -0.339158909
180 NA -0.283683125
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.031137300 -2.004747878
[2,] -1.746713718 -2.031137300
[3,] -1.657148490 -1.746713718
[4,] -1.584520892 -1.657148490
[5,] -1.543764781 -1.584520892
[6,] -1.572031974 -1.543764781
[7,] -1.698018270 -1.572031974
[8,] -1.511263299 -1.698018270
[9,] -1.494426539 -1.511263299
[10,] -1.312120279 -1.494426539
[11,] -1.046058477 -1.312120279
[12,] -0.532293371 -1.046058477
[13,] -0.485147838 -0.532293371
[14,] -0.254221518 -0.485147838
[15,] -0.207000362 -0.254221518
[16,] -0.150818961 -0.207000362
[17,] -0.105337013 -0.150818961
[18,] 0.014600047 -0.105337013
[19,] 0.670844741 0.014600047
[20,] 0.629433154 0.670844741
[21,] 0.778027967 0.629433154
[22,] 0.822715994 0.778027967
[23,] 0.988777796 0.822715994
[24,] 1.021446254 0.988777796
[25,] 1.147986921 1.021446254
[26,] 1.093657927 1.147986921
[27,] 1.204395191 1.093657927
[28,] 1.356417924 1.204395191
[29,] 1.471843332 1.356417924
[30,] 1.259455166 1.471843332
[31,] 1.349915068 1.259455166
[32,] 1.314930833 1.349915068
[33,] 1.136493430 1.314930833
[34,] 1.012939510 1.136493430
[35,] 0.820211042 1.012939510
[36,] 0.911669770 0.820211042
[37,] 0.864108312 0.911669770
[38,] 0.768002416 0.864108312
[39,] 0.709930563 0.768002416
[40,] 0.507888865 0.709930563
[41,] 0.459230993 0.507888865
[42,] 0.430963801 0.459230993
[43,] 0.611972026 0.430963801
[44,] 0.718764692 0.611972026
[45,] 0.736168622 0.718764692
[46,] 0.913749045 0.736168622
[47,] 0.784536685 0.913749045
[48,] 1.230059845 0.784536685
[49,] 1.171912368 1.230059845
[50,] 1.197545680 1.171912368
[51,] 1.160645863 1.197545680
[52,] 0.959171335 1.160645863
[53,] 0.815806473 0.959171335
[54,] 0.693399460 0.815806473
[55,] 0.826770605 0.693399460
[56,] 0.807665397 0.826770605
[57,] 0.968547741 0.807665397
[58,] 0.860872848 0.968547741
[59,] 0.826934650 0.860872848
[60,] 0.691361162 0.826934650
[61,] 0.600888461 0.691361162
[62,] 0.271890170 0.600888461
[63,] 0.123837163 0.271890170
[64,] 0.365841050 0.123837163
[65,] 0.560094422 0.365841050
[66,] 0.553566436 0.560094422
[67,] 0.765765545 0.553566436
[68,] 0.698456084 0.765765545
[69,] 0.430604699 0.698456084
[70,] 0.439376005 0.430604699
[71,] 0.272545411 0.439376005
[72,] 0.067028464 0.272545411
[73,] 0.220601347 0.067028464
[74,] 0.415043776 0.220601347
[75,] 0.732208391 0.415043776
[76,] 0.890091305 0.732208391
[77,] 0.756745289 0.890091305
[78,] 0.274413664 0.756745289
[79,] 0.195497278 0.274413664
[80,] 0.451061367 0.195497278
[81,] 0.273758306 0.451061367
[82,] -0.129190749 0.273758306
[83,] -0.247817091 -0.129190749
[84,] 0.023603943 -0.247817091
[85,] 0.023679566 0.023603943
[86,] 0.202242968 0.023679566
[87,] 0.128292087 0.202242968
[88,] -0.073749611 0.128292087
[89,] -0.217114473 -0.073749611
[90,] -0.550674675 -0.217114473
[91,] -0.579685296 -0.550674675
[92,] -0.777051298 -0.579685296
[93,] -0.917303296 -0.777051298
[94,] -0.950308891 -0.917303296
[95,] -0.857214873 -0.950308891
[96,] -1.055737298 -0.857214873
[97,] -1.186852558 -1.055737298
[98,] -1.276531103 -1.186852558
[99,] -1.335170127 -1.276531103
[100,] -1.399026420 -1.335170127
[101,] -1.077173662 -1.399026420
[102,] -1.041924746 -1.077173662
[103,] -0.860916521 -1.041924746
[104,] -0.852422342 -0.860916521
[105,] -1.057324790 -0.852422342
[106,] -1.043260475 -1.057324790
[107,] -1.590053376 -1.043260475
[108,] -1.962677927 -1.590053376
[109,] -2.052016286 -1.962677927
[110,] -1.990466253 -2.052016286
[111,] -1.262148450 -1.990466253
[112,] -0.904265536 -1.262148450
[113,] -1.205853497 -0.904265536
[114,] -1.639980870 -1.205853497
[115,] -1.796023707 -1.639980870
[116,] -1.658607330 -1.796023707
[117,] -0.809445345 -1.658607330
[118,] -0.780636346 -0.809445345
[119,] -0.678090652 -0.780636346
[120,] -0.216121294 -0.678090652
[121,] -0.474835941 -0.216121294
[122,] -0.407425729 -0.474835941
[123,] -0.904817329 -0.407425729
[124,] -1.186254162 -0.904817329
[125,] -1.414874340 -1.186254162
[126,] -0.989077100 -1.414874340
[127,] -1.266291974 -0.989077100
[128,] -1.111862227 -1.266291974
[129,] -0.794458297 -1.111862227
[130,] -0.649770270 -0.794458297
[131,] -0.430778379 -0.649770270
[132,] -0.334593813 -0.430778379
[133,] -0.069867740 -0.334593813
[134,] -0.113043545 -0.069867740
[135,] -0.203440624 -0.113043545
[136,] -0.563705421 -0.203440624
[137,] -0.147145671 -0.563705421
[138,] -0.001877909 -0.147145671
[139,] -0.109716494 -0.001877909
[140,] -0.149993739 -0.109716494
[141,] -0.164915033 -0.149993739
[142,] 0.128544416 -0.164915033
[143,] 0.241108957 0.128544416
[144,] 0.542586532 0.241108957
[145,] 0.352113831 0.542586532
[146,] -0.070457110 0.352113831
[147,] -0.294502225 -0.070457110
[148,] -0.056657005 -0.294502225
[149,] 0.067652907 -0.056657005
[150,] 1.061124922 0.067652907
[151,] 1.115100931 1.061124922
[152,] 0.947791471 1.115100931
[153,] 0.888636121 0.947791471
[154,] 0.369808040 0.888636121
[155,] 0.297684436 0.369808040
[156,] 0.573264138 0.297684436
[157,] 0.921544012 0.573264138
[158,] 1.232432639 0.921544012
[159,] 0.926156534 1.232432639
[160,] 0.866458907 0.926156534
[161,] 0.966572459 0.866458907
[162,] 1.323560581 0.966572459
[163,] 1.041052699 1.323560581
[164,] 1.021947491 1.041052699
[165,] 0.558254773 1.021947491
[166,] 0.656440060 0.558254773
[167,] 0.901896997 0.656440060
[168,] 1.045151473 0.901896997
[169,] 0.997022844 1.045151473
[170,] 0.678043399 0.997022844
[171,] 0.878761816 0.678043399
[172,] 0.973128622 0.878761816
[173,] 0.613317561 0.973128622
[174,] 0.184483198 0.613317561
[175,] -0.266266631 0.184483198
[176,] -0.528850253 -0.266266631
[177,] -0.532618359 -0.528850253
[178,] -0.339158909 -0.532618359
[179,] -0.283683125 -0.339158909
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.031137300 -2.004747878
2 -1.746713718 -2.031137300
3 -1.657148490 -1.746713718
4 -1.584520892 -1.657148490
5 -1.543764781 -1.584520892
6 -1.572031974 -1.543764781
7 -1.698018270 -1.572031974
8 -1.511263299 -1.698018270
9 -1.494426539 -1.511263299
10 -1.312120279 -1.494426539
11 -1.046058477 -1.312120279
12 -0.532293371 -1.046058477
13 -0.485147838 -0.532293371
14 -0.254221518 -0.485147838
15 -0.207000362 -0.254221518
16 -0.150818961 -0.207000362
17 -0.105337013 -0.150818961
18 0.014600047 -0.105337013
19 0.670844741 0.014600047
20 0.629433154 0.670844741
21 0.778027967 0.629433154
22 0.822715994 0.778027967
23 0.988777796 0.822715994
24 1.021446254 0.988777796
25 1.147986921 1.021446254
26 1.093657927 1.147986921
27 1.204395191 1.093657927
28 1.356417924 1.204395191
29 1.471843332 1.356417924
30 1.259455166 1.471843332
31 1.349915068 1.259455166
32 1.314930833 1.349915068
33 1.136493430 1.314930833
34 1.012939510 1.136493430
35 0.820211042 1.012939510
36 0.911669770 0.820211042
37 0.864108312 0.911669770
38 0.768002416 0.864108312
39 0.709930563 0.768002416
40 0.507888865 0.709930563
41 0.459230993 0.507888865
42 0.430963801 0.459230993
43 0.611972026 0.430963801
44 0.718764692 0.611972026
45 0.736168622 0.718764692
46 0.913749045 0.736168622
47 0.784536685 0.913749045
48 1.230059845 0.784536685
49 1.171912368 1.230059845
50 1.197545680 1.171912368
51 1.160645863 1.197545680
52 0.959171335 1.160645863
53 0.815806473 0.959171335
54 0.693399460 0.815806473
55 0.826770605 0.693399460
56 0.807665397 0.826770605
57 0.968547741 0.807665397
58 0.860872848 0.968547741
59 0.826934650 0.860872848
60 0.691361162 0.826934650
61 0.600888461 0.691361162
62 0.271890170 0.600888461
63 0.123837163 0.271890170
64 0.365841050 0.123837163
65 0.560094422 0.365841050
66 0.553566436 0.560094422
67 0.765765545 0.553566436
68 0.698456084 0.765765545
69 0.430604699 0.698456084
70 0.439376005 0.430604699
71 0.272545411 0.439376005
72 0.067028464 0.272545411
73 0.220601347 0.067028464
74 0.415043776 0.220601347
75 0.732208391 0.415043776
76 0.890091305 0.732208391
77 0.756745289 0.890091305
78 0.274413664 0.756745289
79 0.195497278 0.274413664
80 0.451061367 0.195497278
81 0.273758306 0.451061367
82 -0.129190749 0.273758306
83 -0.247817091 -0.129190749
84 0.023603943 -0.247817091
85 0.023679566 0.023603943
86 0.202242968 0.023679566
87 0.128292087 0.202242968
88 -0.073749611 0.128292087
89 -0.217114473 -0.073749611
90 -0.550674675 -0.217114473
91 -0.579685296 -0.550674675
92 -0.777051298 -0.579685296
93 -0.917303296 -0.777051298
94 -0.950308891 -0.917303296
95 -0.857214873 -0.950308891
96 -1.055737298 -0.857214873
97 -1.186852558 -1.055737298
98 -1.276531103 -1.186852558
99 -1.335170127 -1.276531103
100 -1.399026420 -1.335170127
101 -1.077173662 -1.399026420
102 -1.041924746 -1.077173662
103 -0.860916521 -1.041924746
104 -0.852422342 -0.860916521
105 -1.057324790 -0.852422342
106 -1.043260475 -1.057324790
107 -1.590053376 -1.043260475
108 -1.962677927 -1.590053376
109 -2.052016286 -1.962677927
110 -1.990466253 -2.052016286
111 -1.262148450 -1.990466253
112 -0.904265536 -1.262148450
113 -1.205853497 -0.904265536
114 -1.639980870 -1.205853497
115 -1.796023707 -1.639980870
116 -1.658607330 -1.796023707
117 -0.809445345 -1.658607330
118 -0.780636346 -0.809445345
119 -0.678090652 -0.780636346
120 -0.216121294 -0.678090652
121 -0.474835941 -0.216121294
122 -0.407425729 -0.474835941
123 -0.904817329 -0.407425729
124 -1.186254162 -0.904817329
125 -1.414874340 -1.186254162
126 -0.989077100 -1.414874340
127 -1.266291974 -0.989077100
128 -1.111862227 -1.266291974
129 -0.794458297 -1.111862227
130 -0.649770270 -0.794458297
131 -0.430778379 -0.649770270
132 -0.334593813 -0.430778379
133 -0.069867740 -0.334593813
134 -0.113043545 -0.069867740
135 -0.203440624 -0.113043545
136 -0.563705421 -0.203440624
137 -0.147145671 -0.563705421
138 -0.001877909 -0.147145671
139 -0.109716494 -0.001877909
140 -0.149993739 -0.109716494
141 -0.164915033 -0.149993739
142 0.128544416 -0.164915033
143 0.241108957 0.128544416
144 0.542586532 0.241108957
145 0.352113831 0.542586532
146 -0.070457110 0.352113831
147 -0.294502225 -0.070457110
148 -0.056657005 -0.294502225
149 0.067652907 -0.056657005
150 1.061124922 0.067652907
151 1.115100931 1.061124922
152 0.947791471 1.115100931
153 0.888636121 0.947791471
154 0.369808040 0.888636121
155 0.297684436 0.369808040
156 0.573264138 0.297684436
157 0.921544012 0.573264138
158 1.232432639 0.921544012
159 0.926156534 1.232432639
160 0.866458907 0.926156534
161 0.966572459 0.866458907
162 1.323560581 0.966572459
163 1.041052699 1.323560581
164 1.021947491 1.041052699
165 0.558254773 1.021947491
166 0.656440060 0.558254773
167 0.901896997 0.656440060
168 1.045151473 0.901896997
169 0.997022844 1.045151473
170 0.678043399 0.997022844
171 0.878761816 0.678043399
172 0.973128622 0.878761816
173 0.613317561 0.973128622
174 0.184483198 0.613317561
175 -0.266266631 0.184483198
176 -0.528850253 -0.266266631
177 -0.532618359 -0.528850253
178 -0.339158909 -0.532618359
179 -0.283683125 -0.339158909
> 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/7c6251258725386.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8o4ny1258725386.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9r53b1258725386.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/102xo11258725386.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11263b1258725386.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/12d8f41258725386.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/1350sl1258725387.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/147iyy1258725387.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/15a2co1258725387.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/166z8b1258725387.tab")
+ }
> system("convert tmp/144dj1258725386.ps tmp/144dj1258725386.png")
> system("convert tmp/2zbcl1258725386.ps tmp/2zbcl1258725386.png")
> system("convert tmp/3656v1258725386.ps tmp/3656v1258725386.png")
> system("convert tmp/4gja71258725386.ps tmp/4gja71258725386.png")
> system("convert tmp/5so031258725386.ps tmp/5so031258725386.png")
> system("convert tmp/6a7pi1258725386.ps tmp/6a7pi1258725386.png")
> system("convert tmp/7c6251258725386.ps tmp/7c6251258725386.png")
> system("convert tmp/8o4ny1258725386.ps tmp/8o4ny1258725386.png")
> system("convert tmp/9r53b1258725386.ps tmp/9r53b1258725386.png")
> system("convert tmp/102xo11258725386.ps tmp/102xo11258725386.png")
>
>
> proc.time()
user system elapsed
4.525 1.769 7.666