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 '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(17.9
+ ,2.1
+ ,17.4
+ ,2.1
+ ,17.4
+ ,2.6
+ ,20.1
+ ,2.6
+ ,23.2
+ ,2.7
+ ,24.2
+ ,2.5
+ ,24.2
+ ,2.4
+ ,23.9
+ ,1.9
+ ,23.8
+ ,2.2
+ ,23.8
+ ,1.9
+ ,23.3
+ ,2
+ ,22.4
+ ,2.2
+ ,21.5
+ ,2.5
+ ,20.5
+ ,2.5
+ ,19.9
+ ,2.7
+ ,22
+ ,2.6
+ ,24.9
+ ,2.3
+ ,25.7
+ ,2
+ ,25.3
+ ,2.3
+ ,24.4
+ ,2.9
+ ,23.8
+ ,2.5
+ ,23.5
+ ,2.5
+ ,23
+ ,2.3
+ ,22.2
+ ,2.5
+ ,21.4
+ ,2.3
+ ,20.3
+ ,2.4
+ ,19.5
+ ,2.2
+ ,21.7
+ ,2.4
+ ,24.7
+ ,2.6
+ ,25.3
+ ,2.8
+ ,24.9
+ ,2.8
+ ,24.1
+ ,2.5
+ ,23.4
+ ,2.5
+ ,23.1
+ ,2.2
+ ,22.4
+ ,2.1
+ ,21.3
+ ,1.9
+ ,20.3
+ ,1.9
+ ,19.3
+ ,1.7
+ ,18.7
+ ,1.7
+ ,21
+ ,1.6
+ ,24
+ ,1.4
+ ,24.8
+ ,1.1
+ ,24.2
+ ,0.8
+ ,23.3
+ ,0.9
+ ,22.7
+ ,1
+ ,22.3
+ ,1
+ ,21.8
+ ,1.1
+ ,21.2
+ ,1.3
+ ,20.5
+ ,1.4
+ ,19.7
+ ,1.4
+ ,19.2
+ ,1.6
+ ,21.2
+ ,2
+ ,23.9
+ ,2.1
+ ,24.8
+ ,1.9
+ ,24.2
+ ,1.5
+ ,23
+ ,1.2
+ ,22.2
+ ,1.5
+ ,21.8
+ ,2.2
+ ,21.2
+ ,2.1
+ ,20.5
+ ,2.1
+ ,19.7
+ ,2.1
+ ,19
+ ,1.9
+ ,18.4
+ ,1.3
+ ,20.7
+ ,1.1
+ ,24.5
+ ,1.4
+ ,26
+ ,1.6
+ ,25.2
+ ,1.9
+ ,24.1
+ ,1.7
+ ,23.7
+ ,1.6
+ ,23.5
+ ,1.2
+ ,23.1
+ ,1.3
+ ,22.7
+ ,0.9
+ ,22.5
+ ,0.5
+ ,21.7
+ ,0.8
+ ,20.5
+ ,1
+ ,21.9
+ ,1.3
+ ,22.9
+ ,1.3
+ ,21.5
+ ,1.2
+ ,19
+ ,1.2
+ ,17
+ ,1
+ ,16.1
+ ,0.8
+ ,15.9
+ ,0.7
+ ,15.7
+ ,0.6
+ ,15.1
+ ,0.7
+ ,14.8
+ ,1
+ ,14.3
+ ,1
+ ,14.5
+ ,1.3
+ ,18.9
+ ,1.1
+ ,21.6
+ ,0.8
+ ,20.4
+ ,0.7
+ ,17.9
+ ,0.7
+ ,15.7
+ ,0.9
+ ,14.5
+ ,1.3
+ ,14
+ ,1.4
+ ,13.9
+ ,1.6
+ ,14.4
+ ,2.1
+ ,15.8
+ ,0.3
+ ,15.6
+ ,2.1
+ ,14.7
+ ,2.5
+ ,16.7
+ ,2.3
+ ,17.9
+ ,2.4
+ ,18.7
+ ,3
+ ,20.1
+ ,1.7
+ ,19.5
+ ,3.5
+ ,19.4
+ ,4
+ ,18.6
+ ,3.7
+ ,17.8
+ ,3.7
+ ,17.1
+ ,3
+ ,16.5
+ ,2.7
+ ,15.5
+ ,2.5
+ ,14.9
+ ,2.2
+ ,18.6
+ ,2.9
+ ,19.1
+ ,3.1
+ ,18.8
+ ,3
+ ,18.2
+ ,2.8
+ ,18
+ ,2.5
+ ,19
+ ,1.9
+ ,20.7
+ ,1.9
+ ,21.2
+ ,1.8
+ ,20.7
+ ,2
+ ,19.6
+ ,2.6
+ ,18.6
+ ,2.5
+ ,18.7
+ ,2.5
+ ,23.8
+ ,1.6
+ ,24.9
+ ,1.4
+ ,24.8
+ ,0.8
+ ,23.8
+ ,1.1
+ ,22.3
+ ,1.3
+ ,21.7
+ ,1.2
+ ,20.7
+ ,1.3
+ ,19.7
+ ,1.1
+ ,18.4
+ ,1.3
+ ,17.4
+ ,1.2
+ ,17
+ ,1.6
+ ,18
+ ,1.7
+ ,23.8
+ ,1.5
+ ,25.5
+ ,0.9
+ ,25.6
+ ,1.5
+ ,23.7
+ ,1.4
+ ,22
+ ,1.6
+ ,21.3
+ ,1.7
+ ,20.7
+ ,1.4
+ ,20.4
+ ,1.8
+ ,20.3
+ ,1.7
+ ,20.4
+ ,1.4
+ ,19.8
+ ,1.2
+ ,19.5
+ ,1
+ ,23.1
+ ,1.7
+ ,23.5
+ ,2.4
+ ,23.5
+ ,2
+ ,22.9
+ ,2.1
+ ,21.9
+ ,2
+ ,21.5
+ ,1.8
+ ,20.5
+ ,2.7
+ ,20.2
+ ,2.3
+ ,19.4
+ ,1.9
+ ,19.2
+ ,2
+ ,18.8
+ ,2.3
+ ,18.8
+ ,2.8
+ ,22.6
+ ,2.4
+ ,23.3
+ ,2.3
+ ,23
+ ,2.7
+ ,21.4
+ ,2.7
+ ,19.9
+ ,2.9
+ ,18.8
+ ,3
+ ,18.6
+ ,2.2
+ ,18.4
+ ,2.3
+ ,18.6
+ ,2.8
+ ,19.9
+ ,2.8
+ ,19.2
+ ,2.8
+ ,18.4
+ ,2.2
+ ,21.1
+ ,2.6
+ ,20.5
+ ,2.8
+ ,19.1
+ ,2.5
+ ,18.1
+ ,2.4
+ ,17
+ ,2.3
+ ,17.1
+ ,1.9
+ ,17.4
+ ,1.7
+ ,16.8
+ ,2
+ ,15.3
+ ,2.1
+ ,14.3
+ ,1.7
+ ,13.4
+ ,1.8
+ ,15.3
+ ,1.8
+ ,22.1
+ ,1.8
+ ,23.7
+ ,1.3
+ ,22.2
+ ,1.3
+ ,19.5
+ ,1.3
+ ,16.6
+ ,1.2
+ ,17.3
+ ,1.4
+ ,19.8
+ ,2.2
+ ,21.2
+ ,2.9
+ ,21.5
+ ,3.1
+ ,20.6
+ ,3.5
+ ,19.1
+ ,3.6
+ ,19.6
+ ,4.4
+ ,23.5
+ ,4.1
+ ,24
+ ,5.1
+ ,23.2
+ ,5.8
+ ,21.2
+ ,5.9)
+ ,dim=c(2
+ ,199)
+ ,dimnames=list(c('Y[t-16](Werkloosheid)'
+ ,'X(inflatie)')
+ ,1:199))
> y <- array(NA,dim=c(2,199),dimnames=list(c('Y[t-16](Werkloosheid)','X(inflatie)'),1:199))
> 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[t-16](Werkloosheid) X(inflatie) M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 17.9 2.1 1 0 0 0 0 0 0 0 0 0 0 1
2 17.4 2.1 0 1 0 0 0 0 0 0 0 0 0 2
3 17.4 2.6 0 0 1 0 0 0 0 0 0 0 0 3
4 20.1 2.6 0 0 0 1 0 0 0 0 0 0 0 4
5 23.2 2.7 0 0 0 0 1 0 0 0 0 0 0 5
6 24.2 2.5 0 0 0 0 0 1 0 0 0 0 0 6
7 24.2 2.4 0 0 0 0 0 0 1 0 0 0 0 7
8 23.9 1.9 0 0 0 0 0 0 0 1 0 0 0 8
9 23.8 2.2 0 0 0 0 0 0 0 0 1 0 0 9
10 23.8 1.9 0 0 0 0 0 0 0 0 0 1 0 10
11 23.3 2.0 0 0 0 0 0 0 0 0 0 0 1 11
12 22.4 2.2 0 0 0 0 0 0 0 0 0 0 0 12
13 21.5 2.5 1 0 0 0 0 0 0 0 0 0 0 13
14 20.5 2.5 0 1 0 0 0 0 0 0 0 0 0 14
15 19.9 2.7 0 0 1 0 0 0 0 0 0 0 0 15
16 22.0 2.6 0 0 0 1 0 0 0 0 0 0 0 16
17 24.9 2.3 0 0 0 0 1 0 0 0 0 0 0 17
18 25.7 2.0 0 0 0 0 0 1 0 0 0 0 0 18
19 25.3 2.3 0 0 0 0 0 0 1 0 0 0 0 19
20 24.4 2.9 0 0 0 0 0 0 0 1 0 0 0 20
21 23.8 2.5 0 0 0 0 0 0 0 0 1 0 0 21
22 23.5 2.5 0 0 0 0 0 0 0 0 0 1 0 22
23 23.0 2.3 0 0 0 0 0 0 0 0 0 0 1 23
24 22.2 2.5 0 0 0 0 0 0 0 0 0 0 0 24
25 21.4 2.3 1 0 0 0 0 0 0 0 0 0 0 25
26 20.3 2.4 0 1 0 0 0 0 0 0 0 0 0 26
27 19.5 2.2 0 0 1 0 0 0 0 0 0 0 0 27
28 21.7 2.4 0 0 0 1 0 0 0 0 0 0 0 28
29 24.7 2.6 0 0 0 0 1 0 0 0 0 0 0 29
30 25.3 2.8 0 0 0 0 0 1 0 0 0 0 0 30
31 24.9 2.8 0 0 0 0 0 0 1 0 0 0 0 31
32 24.1 2.5 0 0 0 0 0 0 0 1 0 0 0 32
33 23.4 2.5 0 0 0 0 0 0 0 0 1 0 0 33
34 23.1 2.2 0 0 0 0 0 0 0 0 0 1 0 34
35 22.4 2.1 0 0 0 0 0 0 0 0 0 0 1 35
36 21.3 1.9 0 0 0 0 0 0 0 0 0 0 0 36
37 20.3 1.9 1 0 0 0 0 0 0 0 0 0 0 37
38 19.3 1.7 0 1 0 0 0 0 0 0 0 0 0 38
39 18.7 1.7 0 0 1 0 0 0 0 0 0 0 0 39
40 21.0 1.6 0 0 0 1 0 0 0 0 0 0 0 40
41 24.0 1.4 0 0 0 0 1 0 0 0 0 0 0 41
42 24.8 1.1 0 0 0 0 0 1 0 0 0 0 0 42
43 24.2 0.8 0 0 0 0 0 0 1 0 0 0 0 43
44 23.3 0.9 0 0 0 0 0 0 0 1 0 0 0 44
45 22.7 1.0 0 0 0 0 0 0 0 0 1 0 0 45
46 22.3 1.0 0 0 0 0 0 0 0 0 0 1 0 46
47 21.8 1.1 0 0 0 0 0 0 0 0 0 0 1 47
48 21.2 1.3 0 0 0 0 0 0 0 0 0 0 0 48
49 20.5 1.4 1 0 0 0 0 0 0 0 0 0 0 49
50 19.7 1.4 0 1 0 0 0 0 0 0 0 0 0 50
51 19.2 1.6 0 0 1 0 0 0 0 0 0 0 0 51
52 21.2 2.0 0 0 0 1 0 0 0 0 0 0 0 52
53 23.9 2.1 0 0 0 0 1 0 0 0 0 0 0 53
54 24.8 1.9 0 0 0 0 0 1 0 0 0 0 0 54
55 24.2 1.5 0 0 0 0 0 0 1 0 0 0 0 55
56 23.0 1.2 0 0 0 0 0 0 0 1 0 0 0 56
57 22.2 1.5 0 0 0 0 0 0 0 0 1 0 0 57
58 21.8 2.2 0 0 0 0 0 0 0 0 0 1 0 58
59 21.2 2.1 0 0 0 0 0 0 0 0 0 0 1 59
60 20.5 2.1 0 0 0 0 0 0 0 0 0 0 0 60
61 19.7 2.1 1 0 0 0 0 0 0 0 0 0 0 61
62 19.0 1.9 0 1 0 0 0 0 0 0 0 0 0 62
63 18.4 1.3 0 0 1 0 0 0 0 0 0 0 0 63
64 20.7 1.1 0 0 0 1 0 0 0 0 0 0 0 64
65 24.5 1.4 0 0 0 0 1 0 0 0 0 0 0 65
66 26.0 1.6 0 0 0 0 0 1 0 0 0 0 0 66
67 25.2 1.9 0 0 0 0 0 0 1 0 0 0 0 67
68 24.1 1.7 0 0 0 0 0 0 0 1 0 0 0 68
69 23.7 1.6 0 0 0 0 0 0 0 0 1 0 0 69
70 23.5 1.2 0 0 0 0 0 0 0 0 0 1 0 70
71 23.1 1.3 0 0 0 0 0 0 0 0 0 0 1 71
72 22.7 0.9 0 0 0 0 0 0 0 0 0 0 0 72
73 22.5 0.5 1 0 0 0 0 0 0 0 0 0 0 73
74 21.7 0.8 0 1 0 0 0 0 0 0 0 0 0 74
75 20.5 1.0 0 0 1 0 0 0 0 0 0 0 0 75
76 21.9 1.3 0 0 0 1 0 0 0 0 0 0 0 76
77 22.9 1.3 0 0 0 0 1 0 0 0 0 0 0 77
78 21.5 1.2 0 0 0 0 0 1 0 0 0 0 0 78
79 19.0 1.2 0 0 0 0 0 0 1 0 0 0 0 79
80 17.0 1.0 0 0 0 0 0 0 0 1 0 0 0 80
81 16.1 0.8 0 0 0 0 0 0 0 0 1 0 0 81
82 15.9 0.7 0 0 0 0 0 0 0 0 0 1 0 82
83 15.7 0.6 0 0 0 0 0 0 0 0 0 0 1 83
84 15.1 0.7 0 0 0 0 0 0 0 0 0 0 0 84
85 14.8 1.0 1 0 0 0 0 0 0 0 0 0 0 85
86 14.3 1.0 0 1 0 0 0 0 0 0 0 0 0 86
87 14.5 1.3 0 0 1 0 0 0 0 0 0 0 0 87
88 18.9 1.1 0 0 0 1 0 0 0 0 0 0 0 88
89 21.6 0.8 0 0 0 0 1 0 0 0 0 0 0 89
90 20.4 0.7 0 0 0 0 0 1 0 0 0 0 0 90
91 17.9 0.7 0 0 0 0 0 0 1 0 0 0 0 91
92 15.7 0.9 0 0 0 0 0 0 0 1 0 0 0 92
93 14.5 1.3 0 0 0 0 0 0 0 0 1 0 0 93
94 14.0 1.4 0 0 0 0 0 0 0 0 0 1 0 94
95 13.9 1.6 0 0 0 0 0 0 0 0 0 0 1 95
96 14.4 2.1 0 0 0 0 0 0 0 0 0 0 0 96
97 15.8 0.3 1 0 0 0 0 0 0 0 0 0 0 97
98 15.6 2.1 0 1 0 0 0 0 0 0 0 0 0 98
99 14.7 2.5 0 0 1 0 0 0 0 0 0 0 0 99
100 16.7 2.3 0 0 0 1 0 0 0 0 0 0 0 100
101 17.9 2.4 0 0 0 0 1 0 0 0 0 0 0 101
102 18.7 3.0 0 0 0 0 0 1 0 0 0 0 0 102
103 20.1 1.7 0 0 0 0 0 0 1 0 0 0 0 103
104 19.5 3.5 0 0 0 0 0 0 0 1 0 0 0 104
105 19.4 4.0 0 0 0 0 0 0 0 0 1 0 0 105
106 18.6 3.7 0 0 0 0 0 0 0 0 0 1 0 106
107 17.8 3.7 0 0 0 0 0 0 0 0 0 0 1 107
108 17.1 3.0 0 0 0 0 0 0 0 0 0 0 0 108
109 16.5 2.7 1 0 0 0 0 0 0 0 0 0 0 109
110 15.5 2.5 0 1 0 0 0 0 0 0 0 0 0 110
111 14.9 2.2 0 0 1 0 0 0 0 0 0 0 0 111
112 18.6 2.9 0 0 0 1 0 0 0 0 0 0 0 112
113 19.1 3.1 0 0 0 0 1 0 0 0 0 0 0 113
114 18.8 3.0 0 0 0 0 0 1 0 0 0 0 0 114
115 18.2 2.8 0 0 0 0 0 0 1 0 0 0 0 115
116 18.0 2.5 0 0 0 0 0 0 0 1 0 0 0 116
117 19.0 1.9 0 0 0 0 0 0 0 0 1 0 0 117
118 20.7 1.9 0 0 0 0 0 0 0 0 0 1 0 118
119 21.2 1.8 0 0 0 0 0 0 0 0 0 0 1 119
120 20.7 2.0 0 0 0 0 0 0 0 0 0 0 0 120
121 19.6 2.6 1 0 0 0 0 0 0 0 0 0 0 121
122 18.6 2.5 0 1 0 0 0 0 0 0 0 0 0 122
123 18.7 2.5 0 0 1 0 0 0 0 0 0 0 0 123
124 23.8 1.6 0 0 0 1 0 0 0 0 0 0 0 124
125 24.9 1.4 0 0 0 0 1 0 0 0 0 0 0 125
126 24.8 0.8 0 0 0 0 0 1 0 0 0 0 0 126
127 23.8 1.1 0 0 0 0 0 0 1 0 0 0 0 127
128 22.3 1.3 0 0 0 0 0 0 0 1 0 0 0 128
129 21.7 1.2 0 0 0 0 0 0 0 0 1 0 0 129
130 20.7 1.3 0 0 0 0 0 0 0 0 0 1 0 130
131 19.7 1.1 0 0 0 0 0 0 0 0 0 0 1 131
132 18.4 1.3 0 0 0 0 0 0 0 0 0 0 0 132
133 17.4 1.2 1 0 0 0 0 0 0 0 0 0 0 133
134 17.0 1.6 0 1 0 0 0 0 0 0 0 0 0 134
135 18.0 1.7 0 0 1 0 0 0 0 0 0 0 0 135
136 23.8 1.5 0 0 0 1 0 0 0 0 0 0 0 136
137 25.5 0.9 0 0 0 0 1 0 0 0 0 0 0 137
138 25.6 1.5 0 0 0 0 0 1 0 0 0 0 0 138
139 23.7 1.4 0 0 0 0 0 0 1 0 0 0 0 139
140 22.0 1.6 0 0 0 0 0 0 0 1 0 0 0 140
141 21.3 1.7 0 0 0 0 0 0 0 0 1 0 0 141
142 20.7 1.4 0 0 0 0 0 0 0 0 0 1 0 142
143 20.4 1.8 0 0 0 0 0 0 0 0 0 0 1 143
144 20.3 1.7 0 0 0 0 0 0 0 0 0 0 0 144
145 20.4 1.4 1 0 0 0 0 0 0 0 0 0 0 145
146 19.8 1.2 0 1 0 0 0 0 0 0 0 0 0 146
147 19.5 1.0 0 0 1 0 0 0 0 0 0 0 0 147
148 23.1 1.7 0 0 0 1 0 0 0 0 0 0 0 148
149 23.5 2.4 0 0 0 0 1 0 0 0 0 0 0 149
150 23.5 2.0 0 0 0 0 0 1 0 0 0 0 0 150
151 22.9 2.1 0 0 0 0 0 0 1 0 0 0 0 151
152 21.9 2.0 0 0 0 0 0 0 0 1 0 0 0 152
153 21.5 1.8 0 0 0 0 0 0 0 0 1 0 0 153
154 20.5 2.7 0 0 0 0 0 0 0 0 0 1 0 154
155 20.2 2.3 0 0 0 0 0 0 0 0 0 0 1 155
156 19.4 1.9 0 0 0 0 0 0 0 0 0 0 0 156
157 19.2 2.0 1 0 0 0 0 0 0 0 0 0 0 157
158 18.8 2.3 0 1 0 0 0 0 0 0 0 0 0 158
159 18.8 2.8 0 0 1 0 0 0 0 0 0 0 0 159
160 22.6 2.4 0 0 0 1 0 0 0 0 0 0 0 160
161 23.3 2.3 0 0 0 0 1 0 0 0 0 0 0 161
162 23.0 2.7 0 0 0 0 0 1 0 0 0 0 0 162
163 21.4 2.7 0 0 0 0 0 0 1 0 0 0 0 163
164 19.9 2.9 0 0 0 0 0 0 0 1 0 0 0 164
165 18.8 3.0 0 0 0 0 0 0 0 0 1 0 0 165
166 18.6 2.2 0 0 0 0 0 0 0 0 0 1 0 166
167 18.4 2.3 0 0 0 0 0 0 0 0 0 0 1 167
168 18.6 2.8 0 0 0 0 0 0 0 0 0 0 0 168
169 19.9 2.8 1 0 0 0 0 0 0 0 0 0 0 169
170 19.2 2.8 0 1 0 0 0 0 0 0 0 0 0 170
171 18.4 2.2 0 0 1 0 0 0 0 0 0 0 0 171
172 21.1 2.6 0 0 0 1 0 0 0 0 0 0 0 172
173 20.5 2.8 0 0 0 0 1 0 0 0 0 0 0 173
174 19.1 2.5 0 0 0 0 0 1 0 0 0 0 0 174
175 18.1 2.4 0 0 0 0 0 0 1 0 0 0 0 175
176 17.0 2.3 0 0 0 0 0 0 0 1 0 0 0 176
177 17.1 1.9 0 0 0 0 0 0 0 0 1 0 0 177
178 17.4 1.7 0 0 0 0 0 0 0 0 0 1 0 178
179 16.8 2.0 0 0 0 0 0 0 0 0 0 0 1 179
180 15.3 2.1 0 0 0 0 0 0 0 0 0 0 0 180
181 14.3 1.7 1 0 0 0 0 0 0 0 0 0 0 181
182 13.4 1.8 0 1 0 0 0 0 0 0 0 0 0 182
183 15.3 1.8 0 0 1 0 0 0 0 0 0 0 0 183
184 22.1 1.8 0 0 0 1 0 0 0 0 0 0 0 184
185 23.7 1.3 0 0 0 0 1 0 0 0 0 0 0 185
186 22.2 1.3 0 0 0 0 0 1 0 0 0 0 0 186
187 19.5 1.3 0 0 0 0 0 0 1 0 0 0 0 187
188 16.6 1.2 0 0 0 0 0 0 0 1 0 0 0 188
189 17.3 1.4 0 0 0 0 0 0 0 0 1 0 0 189
190 19.8 2.2 0 0 0 0 0 0 0 0 0 1 0 190
191 21.2 2.9 0 0 0 0 0 0 0 0 0 0 1 191
192 21.5 3.1 0 0 0 0 0 0 0 0 0 0 0 192
193 20.6 3.5 1 0 0 0 0 0 0 0 0 0 0 193
194 19.1 3.6 0 1 0 0 0 0 0 0 0 0 0 194
195 19.6 4.4 0 0 1 0 0 0 0 0 0 0 0 195
196 23.5 4.1 0 0 0 1 0 0 0 0 0 0 0 196
197 24.0 5.1 0 0 0 0 1 0 0 0 0 0 0 197
198 23.2 5.8 0 0 0 0 0 1 0 0 0 0 0 198
199 21.2 5.9 0 0 0 0 0 0 1 0 0 0 0 199
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `X(inflatie)` M1 M2 M3
20.45069 0.33438 -0.53568 -1.33319 -1.53065
M4 M5 M6 M7 M8
1.82490 3.55101 3.53598 2.53280 1.31178
M9 M10 M11 t
0.92813 0.85907 0.55869 -0.01635
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.2413 -1.6178 0.7586 1.6983 3.6111
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 20.450692 0.743980 27.488 < 2e-16 ***
`X(inflatie)` 0.334381 0.200794 1.665 0.09755 .
M1 -0.535680 0.828463 -0.647 0.51870
M2 -1.333194 0.828331 -1.609 0.10921
M3 -1.530653 0.828601 -1.847 0.06630 .
M4 1.824903 0.828592 2.202 0.02887 *
M5 3.551010 0.828843 4.284 2.94e-05 ***
M6 3.535978 0.828860 4.266 3.17e-05 ***
M7 2.532803 0.828352 3.058 0.00256 **
M8 1.311782 0.840838 1.560 0.12045
M9 0.928129 0.840813 1.104 0.27109
M10 0.859065 0.840822 1.022 0.30826
M11 0.558693 0.840677 0.665 0.50715
t -0.016347 0.003002 -5.445 1.63e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.378 on 185 degrees of freedom
Multiple R-squared: 0.4014, Adjusted R-squared: 0.3593
F-statistic: 9.541 on 13 and 185 DF, p-value: 4.827e-15
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 1.726382e-03 3.452765e-03 0.998273618
[2,] 1.772023e-04 3.544045e-04 0.999822798
[3,] 2.630010e-04 5.260021e-04 0.999736999
[4,] 4.094159e-03 8.188319e-03 0.995905841
[5,] 4.044960e-03 8.089921e-03 0.995955040
[6,] 3.348688e-03 6.697376e-03 0.996651312
[7,] 2.461066e-03 4.922133e-03 0.997538934
[8,] 1.492376e-03 2.984751e-03 0.998507624
[9,] 5.750822e-04 1.150164e-03 0.999424918
[10,] 2.264393e-04 4.528786e-04 0.999773561
[11,] 1.235867e-04 2.471735e-04 0.999876413
[12,] 6.066502e-05 1.213300e-04 0.999939335
[13,] 2.660042e-05 5.320083e-05 0.999973400
[14,] 1.259339e-05 2.518678e-05 0.999987407
[15,] 6.415872e-06 1.283174e-05 0.999993584
[16,] 4.104303e-06 8.208605e-06 0.999995896
[17,] 3.222289e-06 6.444579e-06 0.999996778
[18,] 2.726615e-06 5.453230e-06 0.999997273
[19,] 2.432325e-06 4.864651e-06 0.999997568
[20,] 2.263024e-06 4.526047e-06 0.999997737
[21,] 9.856160e-07 1.971232e-06 0.999999014
[22,] 4.061598e-07 8.123197e-07 0.999999594
[23,] 1.589655e-07 3.179310e-07 0.999999841
[24,] 5.911354e-08 1.182271e-07 0.999999941
[25,] 2.066229e-08 4.132458e-08 0.999999979
[26,] 7.258059e-09 1.451612e-08 0.999999993
[27,] 2.610334e-09 5.220667e-09 0.999999997
[28,] 1.026316e-09 2.052631e-09 0.999999999
[29,] 4.221786e-10 8.443572e-10 1.000000000
[30,] 2.017811e-10 4.035623e-10 1.000000000
[31,] 9.522817e-11 1.904563e-10 1.000000000
[32,] 3.758748e-11 7.517496e-11 1.000000000
[33,] 1.277362e-11 2.554725e-11 1.000000000
[34,] 4.355455e-12 8.710911e-12 1.000000000
[35,] 1.402038e-12 2.804076e-12 1.000000000
[36,] 4.773278e-13 9.546556e-13 1.000000000
[37,] 2.175062e-13 4.350125e-13 1.000000000
[38,] 9.061109e-14 1.812222e-13 1.000000000
[39,] 3.877295e-14 7.754591e-14 1.000000000
[40,] 2.081088e-14 4.162176e-14 1.000000000
[41,] 1.916516e-14 3.833032e-14 1.000000000
[42,] 5.370543e-14 1.074109e-13 1.000000000
[43,] 8.819145e-14 1.763829e-13 1.000000000
[44,] 8.055018e-14 1.611004e-13 1.000000000
[45,] 3.354451e-14 6.708903e-14 1.000000000
[46,] 1.214213e-14 2.428425e-14 1.000000000
[47,] 3.986048e-15 7.972096e-15 1.000000000
[48,] 1.246040e-15 2.492080e-15 1.000000000
[49,] 6.092061e-16 1.218412e-15 1.000000000
[50,] 7.532973e-16 1.506595e-15 1.000000000
[51,] 6.250425e-16 1.250085e-15 1.000000000
[52,] 5.532684e-16 1.106537e-15 1.000000000
[53,] 5.936809e-16 1.187362e-15 1.000000000
[54,] 7.777169e-16 1.555434e-15 1.000000000
[55,] 1.115119e-15 2.230238e-15 1.000000000
[56,] 3.635194e-15 7.270388e-15 1.000000000
[57,] 1.100324e-13 2.200648e-13 1.000000000
[58,] 1.637673e-12 3.275345e-12 1.000000000
[59,] 3.978103e-12 7.956205e-12 1.000000000
[60,] 2.186430e-12 4.372861e-12 1.000000000
[61,] 2.668541e-12 5.337081e-12 1.000000000
[62,] 1.442547e-10 2.885093e-10 1.000000000
[63,] 1.219764e-07 2.439528e-07 0.999999878
[64,] 3.318989e-05 6.637978e-05 0.999966810
[65,] 1.115858e-03 2.231716e-03 0.998884142
[66,] 8.666010e-03 1.733202e-02 0.991333990
[67,] 2.732152e-02 5.464303e-02 0.972678483
[68,] 5.915226e-02 1.183045e-01 0.940847735
[69,] 8.697786e-02 1.739557e-01 0.913022140
[70,] 1.085863e-01 2.171725e-01 0.891413729
[71,] 1.164876e-01 2.329752e-01 0.883512411
[72,] 1.005629e-01 2.011258e-01 0.899437075
[73,] 8.286438e-02 1.657288e-01 0.917135617
[74,] 7.653115e-02 1.530623e-01 0.923468846
[75,] 9.512658e-02 1.902532e-01 0.904873423
[76,] 1.537432e-01 3.074864e-01 0.846256820
[77,] 2.730254e-01 5.460508e-01 0.726974583
[78,] 4.412965e-01 8.825930e-01 0.558703509
[79,] 5.899147e-01 8.201705e-01 0.410085271
[80,] 6.463596e-01 7.072807e-01 0.353640354
[81,] 6.349488e-01 7.301024e-01 0.365051212
[82,] 6.032399e-01 7.935202e-01 0.396760112
[83,] 5.893300e-01 8.213400e-01 0.410670020
[84,] 6.611911e-01 6.776179e-01 0.338808934
[85,] 7.344397e-01 5.311205e-01 0.265560273
[86,] 7.543585e-01 4.912830e-01 0.245641491
[87,] 7.266236e-01 5.467528e-01 0.273376411
[88,] 7.023197e-01 5.953606e-01 0.297680300
[89,] 6.806614e-01 6.386771e-01 0.319338562
[90,] 6.501003e-01 6.997995e-01 0.349899745
[91,] 6.250383e-01 7.499234e-01 0.374961723
[92,] 6.064041e-01 7.871917e-01 0.393595867
[93,] 5.976954e-01 8.046093e-01 0.402304627
[94,] 5.976570e-01 8.046859e-01 0.402342961
[95,] 6.361329e-01 7.277342e-01 0.363867109
[96,] 7.330975e-01 5.338050e-01 0.266902516
[97,] 8.442945e-01 3.114110e-01 0.155705510
[98,] 9.326261e-01 1.347477e-01 0.067373851
[99,] 9.721481e-01 5.570378e-02 0.027851892
[100,] 9.838333e-01 3.233348e-02 0.016166742
[101,] 9.859919e-01 2.801620e-02 0.014008101
[102,] 9.865192e-01 2.696152e-02 0.013480759
[103,] 9.880804e-01 2.383926e-02 0.011919630
[104,] 9.892766e-01 2.144673e-02 0.010723367
[105,] 9.910012e-01 1.799765e-02 0.008998824
[106,] 9.922669e-01 1.546629e-02 0.007733143
[107,] 9.943711e-01 1.125785e-02 0.005628926
[108,] 9.969457e-01 6.108684e-03 0.003054342
[109,] 9.974704e-01 5.059179e-03 0.002529590
[110,] 9.977374e-01 4.525289e-03 0.002262644
[111,] 9.979335e-01 4.132933e-03 0.002066467
[112,] 9.978665e-01 4.267077e-03 0.002133538
[113,] 9.976297e-01 4.740519e-03 0.002370259
[114,] 9.968996e-01 6.200809e-03 0.003100405
[115,] 9.959227e-01 8.154563e-03 0.004077282
[116,] 9.953779e-01 9.244169e-03 0.004622085
[117,] 9.959652e-01 8.069621e-03 0.004034811
[118,] 9.965761e-01 6.847752e-03 0.003423876
[119,] 9.967797e-01 6.440599e-03 0.003220300
[120,] 9.971179e-01 5.764235e-03 0.002882118
[121,] 9.974626e-01 5.074839e-03 0.002537420
[122,] 9.979843e-01 4.031434e-03 0.002015717
[123,] 9.980985e-01 3.802927e-03 0.001901464
[124,] 9.978814e-01 4.237297e-03 0.002118648
[125,] 9.972785e-01 5.442968e-03 0.002721484
[126,] 9.962034e-01 7.593191e-03 0.003796596
[127,] 9.946842e-01 1.063163e-02 0.005315815
[128,] 9.929403e-01 1.411946e-02 0.007059729
[129,] 9.917294e-01 1.654112e-02 0.008270558
[130,] 9.913851e-01 1.722976e-02 0.008614882
[131,] 9.905002e-01 1.899951e-02 0.009499757
[132,] 9.883375e-01 2.332503e-02 0.011662513
[133,] 9.842438e-01 3.151232e-02 0.015756159
[134,] 9.798893e-01 4.022134e-02 0.020110669
[135,] 9.801874e-01 3.962512e-02 0.019812561
[136,] 9.846766e-01 3.064673e-02 0.015323364
[137,] 9.887564e-01 2.248711e-02 0.011243557
[138,] 9.841680e-01 3.166402e-02 0.015832012
[139,] 9.785147e-01 4.297050e-02 0.021485252
[140,] 9.716353e-01 5.672946e-02 0.028364728
[141,] 9.631974e-01 7.360514e-02 0.036802571
[142,] 9.562944e-01 8.741127e-02 0.043705635
[143,] 9.433123e-01 1.133753e-01 0.056687674
[144,] 9.275839e-01 1.448322e-01 0.072416114
[145,] 9.089933e-01 1.820135e-01 0.091006749
[146,] 8.982958e-01 2.034084e-01 0.101704181
[147,] 8.983499e-01 2.033002e-01 0.101650122
[148,] 8.979101e-01 2.041797e-01 0.102089860
[149,] 8.726916e-01 2.546167e-01 0.127308351
[150,] 8.341707e-01 3.316586e-01 0.165829279
[151,] 7.854972e-01 4.290056e-01 0.214502823
[152,] 7.362312e-01 5.275376e-01 0.263768822
[153,] 7.844674e-01 4.310652e-01 0.215532577
[154,] 9.023283e-01 1.953434e-01 0.097671699
[155,] 9.473270e-01 1.053461e-01 0.052673035
[156,] 9.336268e-01 1.327463e-01 0.066373175
[157,] 8.989003e-01 2.021993e-01 0.101099657
[158,] 8.533406e-01 2.933187e-01 0.146659350
[159,] 8.220517e-01 3.558967e-01 0.177948327
[160,] 8.728993e-01 2.542013e-01 0.127100673
[161,] 9.638105e-01 7.237902e-02 0.036189510
[162,] 9.827793e-01 3.444147e-02 0.017220736
[163,] 9.740431e-01 5.191374e-02 0.025956871
[164,] 9.399378e-01 1.201244e-01 0.060062198
[165,] 9.116026e-01 1.767949e-01 0.088397438
[166,] 8.897874e-01 2.204252e-01 0.110212585
> postscript(file="/var/www/html/rcomp/tmp/1lvx01262211753.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2zty41262211753.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/346po1262211753.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ha2v1262211753.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/59knt1262211753.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 = 199
Frequency = 1
1 2 3 4 5 6
-2.70086465 -2.38700363 -2.34038833 -2.97959786 -1.62279671 -0.52454190
7 8 9 10 11 12
0.52841776 1.63297496 1.83266080 2.01838508 1.80166605 1.40982921
13 14 15 16 17 18
0.96154116 0.77540217 0.32233164 -0.88343984 0.40711352 1.33880639
19 20 21 22 23 24
1.85801383 1.99475244 1.92850466 1.71391478 1.59750991 1.30567308
25 26 27 28 29 30
1.12457529 0.80499825 0.28567993 -0.92040571 0.30295738 0.86745999
31 32 33 34 35 36
1.48698159 2.02466268 1.72466268 1.61038696 1.26054404 0.80245942
37 38 39 40 41 42
0.35448553 0.23522265 -0.15097178 -1.15674326 0.20037205 1.13206492
43 44 45 46 47 48
1.65190068 1.95582956 1.72239151 1.40780163 1.19108260 1.09924576
49 50 51 52 53 54
0.91783382 0.93169483 0.57862429 -0.89433745 0.06246370 1.06071851
55 56 57 58 59 60
1.61399233 1.75167342 1.25135926 0.70270301 0.45286009 0.32789936
61 62 63 64 65 66
0.07992546 0.26066258 0.07509647 -0.89723694 1.09268809 2.55719070
67 68 69 70 71 72
2.67639814 2.88064117 2.91407923 2.93324156 2.81652254 3.12531402
73 74 75 76 77 78
3.61109234 3.52463919 2.47156866 0.43204497 -0.27771583 -1.61289907
79 80 81 82 83 84
-3.09337747 -3.78913443 -4.22225832 -4.30341015 -4.15325307 -4.21165185
85 86 87 88 89 90
-4.05993990 -3.74607889 -3.43258748 -2.30492090 -1.21436754 -2.34955078
91 92 93 94 95 96
-3.83002918 -4.85953835 -5.79329057 -6.24131850 -6.09147558 -5.18362658
97 98 99 100 101 102
-2.62971550 -2.61773946 -3.43768610 -4.71001952 -5.25321838 -4.62246799
103 104 105 106 107 108
-1.76825169 -1.73276973 -1.59995999 -2.21423571 -2.69751668 -2.58841104
109 110 111 112 113 114
-2.53607077 -2.65533365 -2.94121392 -2.81448982 -4.09112673 -4.32630997
115 116 117 118 119 120
-3.83991226 -2.70223117 -1.10160285 0.68380728 1.53396436 1.54212752
121 122 123 124 125 126
0.79352531 0.64082437 0.95462994 3.01636290 2.47347820 2.60548523
127 128 129 130 131 132
2.52469268 2.19518350 2.02862155 1.08059362 0.46418875 -0.32764808
133 134 135 136 137 138
-0.74218392 -0.46207513 0.71829239 3.24595897 3.43682649 3.36757688
139 140 141 142 143 144
2.52053654 1.99102736 1.65758931 1.24331359 1.12628040 1.63475772
145 146 147 148 149 150
2.38709799 2.66783511 2.64851679 2.67524089 1.13141371 1.29654463
151 152 153 154 155 156
1.68262818 1.95343317 2.02030927 0.80477691 0.95524815 0.86403964
157 158 159 160 161 162
1.18262769 1.49617454 1.54278985 2.13733253 1.16100979 0.75863628
163 164 165 166 167 168
0.17815788 -0.15135129 -0.88478935 -0.73187480 -0.64859382 -0.04074482
169 170 171 172 173 174
1.81128129 1.92514230 1.53957619 0.76661445 -1.61002246 -2.87832959
175 176 177 178 179 180
-2.82536993 -2.65456495 -2.02081274 -1.56852651 -1.95212164 -2.91052042
181 182 183 184 185 186
-3.22474210 -3.34431914 -1.23051357 2.23027690 2.28770637 0.81908508
187 188 189 190 191 192
-0.86139332 -2.49058834 -1.45746445 0.86044125 2.34309390 3.15125706
193 194 195 196 197 198
2.66953095 1.94995391 2.39625505 3.05735969 1.51321835 0.51053068
199
-0.50338577
> postscript(file="/var/www/html/rcomp/tmp/6oe2s1262211753.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 = 199
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.70086465 NA
1 -2.38700363 -2.70086465
2 -2.34038833 -2.38700363
3 -2.97959786 -2.34038833
4 -1.62279671 -2.97959786
5 -0.52454190 -1.62279671
6 0.52841776 -0.52454190
7 1.63297496 0.52841776
8 1.83266080 1.63297496
9 2.01838508 1.83266080
10 1.80166605 2.01838508
11 1.40982921 1.80166605
12 0.96154116 1.40982921
13 0.77540217 0.96154116
14 0.32233164 0.77540217
15 -0.88343984 0.32233164
16 0.40711352 -0.88343984
17 1.33880639 0.40711352
18 1.85801383 1.33880639
19 1.99475244 1.85801383
20 1.92850466 1.99475244
21 1.71391478 1.92850466
22 1.59750991 1.71391478
23 1.30567308 1.59750991
24 1.12457529 1.30567308
25 0.80499825 1.12457529
26 0.28567993 0.80499825
27 -0.92040571 0.28567993
28 0.30295738 -0.92040571
29 0.86745999 0.30295738
30 1.48698159 0.86745999
31 2.02466268 1.48698159
32 1.72466268 2.02466268
33 1.61038696 1.72466268
34 1.26054404 1.61038696
35 0.80245942 1.26054404
36 0.35448553 0.80245942
37 0.23522265 0.35448553
38 -0.15097178 0.23522265
39 -1.15674326 -0.15097178
40 0.20037205 -1.15674326
41 1.13206492 0.20037205
42 1.65190068 1.13206492
43 1.95582956 1.65190068
44 1.72239151 1.95582956
45 1.40780163 1.72239151
46 1.19108260 1.40780163
47 1.09924576 1.19108260
48 0.91783382 1.09924576
49 0.93169483 0.91783382
50 0.57862429 0.93169483
51 -0.89433745 0.57862429
52 0.06246370 -0.89433745
53 1.06071851 0.06246370
54 1.61399233 1.06071851
55 1.75167342 1.61399233
56 1.25135926 1.75167342
57 0.70270301 1.25135926
58 0.45286009 0.70270301
59 0.32789936 0.45286009
60 0.07992546 0.32789936
61 0.26066258 0.07992546
62 0.07509647 0.26066258
63 -0.89723694 0.07509647
64 1.09268809 -0.89723694
65 2.55719070 1.09268809
66 2.67639814 2.55719070
67 2.88064117 2.67639814
68 2.91407923 2.88064117
69 2.93324156 2.91407923
70 2.81652254 2.93324156
71 3.12531402 2.81652254
72 3.61109234 3.12531402
73 3.52463919 3.61109234
74 2.47156866 3.52463919
75 0.43204497 2.47156866
76 -0.27771583 0.43204497
77 -1.61289907 -0.27771583
78 -3.09337747 -1.61289907
79 -3.78913443 -3.09337747
80 -4.22225832 -3.78913443
81 -4.30341015 -4.22225832
82 -4.15325307 -4.30341015
83 -4.21165185 -4.15325307
84 -4.05993990 -4.21165185
85 -3.74607889 -4.05993990
86 -3.43258748 -3.74607889
87 -2.30492090 -3.43258748
88 -1.21436754 -2.30492090
89 -2.34955078 -1.21436754
90 -3.83002918 -2.34955078
91 -4.85953835 -3.83002918
92 -5.79329057 -4.85953835
93 -6.24131850 -5.79329057
94 -6.09147558 -6.24131850
95 -5.18362658 -6.09147558
96 -2.62971550 -5.18362658
97 -2.61773946 -2.62971550
98 -3.43768610 -2.61773946
99 -4.71001952 -3.43768610
100 -5.25321838 -4.71001952
101 -4.62246799 -5.25321838
102 -1.76825169 -4.62246799
103 -1.73276973 -1.76825169
104 -1.59995999 -1.73276973
105 -2.21423571 -1.59995999
106 -2.69751668 -2.21423571
107 -2.58841104 -2.69751668
108 -2.53607077 -2.58841104
109 -2.65533365 -2.53607077
110 -2.94121392 -2.65533365
111 -2.81448982 -2.94121392
112 -4.09112673 -2.81448982
113 -4.32630997 -4.09112673
114 -3.83991226 -4.32630997
115 -2.70223117 -3.83991226
116 -1.10160285 -2.70223117
117 0.68380728 -1.10160285
118 1.53396436 0.68380728
119 1.54212752 1.53396436
120 0.79352531 1.54212752
121 0.64082437 0.79352531
122 0.95462994 0.64082437
123 3.01636290 0.95462994
124 2.47347820 3.01636290
125 2.60548523 2.47347820
126 2.52469268 2.60548523
127 2.19518350 2.52469268
128 2.02862155 2.19518350
129 1.08059362 2.02862155
130 0.46418875 1.08059362
131 -0.32764808 0.46418875
132 -0.74218392 -0.32764808
133 -0.46207513 -0.74218392
134 0.71829239 -0.46207513
135 3.24595897 0.71829239
136 3.43682649 3.24595897
137 3.36757688 3.43682649
138 2.52053654 3.36757688
139 1.99102736 2.52053654
140 1.65758931 1.99102736
141 1.24331359 1.65758931
142 1.12628040 1.24331359
143 1.63475772 1.12628040
144 2.38709799 1.63475772
145 2.66783511 2.38709799
146 2.64851679 2.66783511
147 2.67524089 2.64851679
148 1.13141371 2.67524089
149 1.29654463 1.13141371
150 1.68262818 1.29654463
151 1.95343317 1.68262818
152 2.02030927 1.95343317
153 0.80477691 2.02030927
154 0.95524815 0.80477691
155 0.86403964 0.95524815
156 1.18262769 0.86403964
157 1.49617454 1.18262769
158 1.54278985 1.49617454
159 2.13733253 1.54278985
160 1.16100979 2.13733253
161 0.75863628 1.16100979
162 0.17815788 0.75863628
163 -0.15135129 0.17815788
164 -0.88478935 -0.15135129
165 -0.73187480 -0.88478935
166 -0.64859382 -0.73187480
167 -0.04074482 -0.64859382
168 1.81128129 -0.04074482
169 1.92514230 1.81128129
170 1.53957619 1.92514230
171 0.76661445 1.53957619
172 -1.61002246 0.76661445
173 -2.87832959 -1.61002246
174 -2.82536993 -2.87832959
175 -2.65456495 -2.82536993
176 -2.02081274 -2.65456495
177 -1.56852651 -2.02081274
178 -1.95212164 -1.56852651
179 -2.91052042 -1.95212164
180 -3.22474210 -2.91052042
181 -3.34431914 -3.22474210
182 -1.23051357 -3.34431914
183 2.23027690 -1.23051357
184 2.28770637 2.23027690
185 0.81908508 2.28770637
186 -0.86139332 0.81908508
187 -2.49058834 -0.86139332
188 -1.45746445 -2.49058834
189 0.86044125 -1.45746445
190 2.34309390 0.86044125
191 3.15125706 2.34309390
192 2.66953095 3.15125706
193 1.94995391 2.66953095
194 2.39625505 1.94995391
195 3.05735969 2.39625505
196 1.51321835 3.05735969
197 0.51053068 1.51321835
198 -0.50338577 0.51053068
199 NA -0.50338577
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.38700363 -2.70086465
[2,] -2.34038833 -2.38700363
[3,] -2.97959786 -2.34038833
[4,] -1.62279671 -2.97959786
[5,] -0.52454190 -1.62279671
[6,] 0.52841776 -0.52454190
[7,] 1.63297496 0.52841776
[8,] 1.83266080 1.63297496
[9,] 2.01838508 1.83266080
[10,] 1.80166605 2.01838508
[11,] 1.40982921 1.80166605
[12,] 0.96154116 1.40982921
[13,] 0.77540217 0.96154116
[14,] 0.32233164 0.77540217
[15,] -0.88343984 0.32233164
[16,] 0.40711352 -0.88343984
[17,] 1.33880639 0.40711352
[18,] 1.85801383 1.33880639
[19,] 1.99475244 1.85801383
[20,] 1.92850466 1.99475244
[21,] 1.71391478 1.92850466
[22,] 1.59750991 1.71391478
[23,] 1.30567308 1.59750991
[24,] 1.12457529 1.30567308
[25,] 0.80499825 1.12457529
[26,] 0.28567993 0.80499825
[27,] -0.92040571 0.28567993
[28,] 0.30295738 -0.92040571
[29,] 0.86745999 0.30295738
[30,] 1.48698159 0.86745999
[31,] 2.02466268 1.48698159
[32,] 1.72466268 2.02466268
[33,] 1.61038696 1.72466268
[34,] 1.26054404 1.61038696
[35,] 0.80245942 1.26054404
[36,] 0.35448553 0.80245942
[37,] 0.23522265 0.35448553
[38,] -0.15097178 0.23522265
[39,] -1.15674326 -0.15097178
[40,] 0.20037205 -1.15674326
[41,] 1.13206492 0.20037205
[42,] 1.65190068 1.13206492
[43,] 1.95582956 1.65190068
[44,] 1.72239151 1.95582956
[45,] 1.40780163 1.72239151
[46,] 1.19108260 1.40780163
[47,] 1.09924576 1.19108260
[48,] 0.91783382 1.09924576
[49,] 0.93169483 0.91783382
[50,] 0.57862429 0.93169483
[51,] -0.89433745 0.57862429
[52,] 0.06246370 -0.89433745
[53,] 1.06071851 0.06246370
[54,] 1.61399233 1.06071851
[55,] 1.75167342 1.61399233
[56,] 1.25135926 1.75167342
[57,] 0.70270301 1.25135926
[58,] 0.45286009 0.70270301
[59,] 0.32789936 0.45286009
[60,] 0.07992546 0.32789936
[61,] 0.26066258 0.07992546
[62,] 0.07509647 0.26066258
[63,] -0.89723694 0.07509647
[64,] 1.09268809 -0.89723694
[65,] 2.55719070 1.09268809
[66,] 2.67639814 2.55719070
[67,] 2.88064117 2.67639814
[68,] 2.91407923 2.88064117
[69,] 2.93324156 2.91407923
[70,] 2.81652254 2.93324156
[71,] 3.12531402 2.81652254
[72,] 3.61109234 3.12531402
[73,] 3.52463919 3.61109234
[74,] 2.47156866 3.52463919
[75,] 0.43204497 2.47156866
[76,] -0.27771583 0.43204497
[77,] -1.61289907 -0.27771583
[78,] -3.09337747 -1.61289907
[79,] -3.78913443 -3.09337747
[80,] -4.22225832 -3.78913443
[81,] -4.30341015 -4.22225832
[82,] -4.15325307 -4.30341015
[83,] -4.21165185 -4.15325307
[84,] -4.05993990 -4.21165185
[85,] -3.74607889 -4.05993990
[86,] -3.43258748 -3.74607889
[87,] -2.30492090 -3.43258748
[88,] -1.21436754 -2.30492090
[89,] -2.34955078 -1.21436754
[90,] -3.83002918 -2.34955078
[91,] -4.85953835 -3.83002918
[92,] -5.79329057 -4.85953835
[93,] -6.24131850 -5.79329057
[94,] -6.09147558 -6.24131850
[95,] -5.18362658 -6.09147558
[96,] -2.62971550 -5.18362658
[97,] -2.61773946 -2.62971550
[98,] -3.43768610 -2.61773946
[99,] -4.71001952 -3.43768610
[100,] -5.25321838 -4.71001952
[101,] -4.62246799 -5.25321838
[102,] -1.76825169 -4.62246799
[103,] -1.73276973 -1.76825169
[104,] -1.59995999 -1.73276973
[105,] -2.21423571 -1.59995999
[106,] -2.69751668 -2.21423571
[107,] -2.58841104 -2.69751668
[108,] -2.53607077 -2.58841104
[109,] -2.65533365 -2.53607077
[110,] -2.94121392 -2.65533365
[111,] -2.81448982 -2.94121392
[112,] -4.09112673 -2.81448982
[113,] -4.32630997 -4.09112673
[114,] -3.83991226 -4.32630997
[115,] -2.70223117 -3.83991226
[116,] -1.10160285 -2.70223117
[117,] 0.68380728 -1.10160285
[118,] 1.53396436 0.68380728
[119,] 1.54212752 1.53396436
[120,] 0.79352531 1.54212752
[121,] 0.64082437 0.79352531
[122,] 0.95462994 0.64082437
[123,] 3.01636290 0.95462994
[124,] 2.47347820 3.01636290
[125,] 2.60548523 2.47347820
[126,] 2.52469268 2.60548523
[127,] 2.19518350 2.52469268
[128,] 2.02862155 2.19518350
[129,] 1.08059362 2.02862155
[130,] 0.46418875 1.08059362
[131,] -0.32764808 0.46418875
[132,] -0.74218392 -0.32764808
[133,] -0.46207513 -0.74218392
[134,] 0.71829239 -0.46207513
[135,] 3.24595897 0.71829239
[136,] 3.43682649 3.24595897
[137,] 3.36757688 3.43682649
[138,] 2.52053654 3.36757688
[139,] 1.99102736 2.52053654
[140,] 1.65758931 1.99102736
[141,] 1.24331359 1.65758931
[142,] 1.12628040 1.24331359
[143,] 1.63475772 1.12628040
[144,] 2.38709799 1.63475772
[145,] 2.66783511 2.38709799
[146,] 2.64851679 2.66783511
[147,] 2.67524089 2.64851679
[148,] 1.13141371 2.67524089
[149,] 1.29654463 1.13141371
[150,] 1.68262818 1.29654463
[151,] 1.95343317 1.68262818
[152,] 2.02030927 1.95343317
[153,] 0.80477691 2.02030927
[154,] 0.95524815 0.80477691
[155,] 0.86403964 0.95524815
[156,] 1.18262769 0.86403964
[157,] 1.49617454 1.18262769
[158,] 1.54278985 1.49617454
[159,] 2.13733253 1.54278985
[160,] 1.16100979 2.13733253
[161,] 0.75863628 1.16100979
[162,] 0.17815788 0.75863628
[163,] -0.15135129 0.17815788
[164,] -0.88478935 -0.15135129
[165,] -0.73187480 -0.88478935
[166,] -0.64859382 -0.73187480
[167,] -0.04074482 -0.64859382
[168,] 1.81128129 -0.04074482
[169,] 1.92514230 1.81128129
[170,] 1.53957619 1.92514230
[171,] 0.76661445 1.53957619
[172,] -1.61002246 0.76661445
[173,] -2.87832959 -1.61002246
[174,] -2.82536993 -2.87832959
[175,] -2.65456495 -2.82536993
[176,] -2.02081274 -2.65456495
[177,] -1.56852651 -2.02081274
[178,] -1.95212164 -1.56852651
[179,] -2.91052042 -1.95212164
[180,] -3.22474210 -2.91052042
[181,] -3.34431914 -3.22474210
[182,] -1.23051357 -3.34431914
[183,] 2.23027690 -1.23051357
[184,] 2.28770637 2.23027690
[185,] 0.81908508 2.28770637
[186,] -0.86139332 0.81908508
[187,] -2.49058834 -0.86139332
[188,] -1.45746445 -2.49058834
[189,] 0.86044125 -1.45746445
[190,] 2.34309390 0.86044125
[191,] 3.15125706 2.34309390
[192,] 2.66953095 3.15125706
[193,] 1.94995391 2.66953095
[194,] 2.39625505 1.94995391
[195,] 3.05735969 2.39625505
[196,] 1.51321835 3.05735969
[197,] 0.51053068 1.51321835
[198,] -0.50338577 0.51053068
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.38700363 -2.70086465
2 -2.34038833 -2.38700363
3 -2.97959786 -2.34038833
4 -1.62279671 -2.97959786
5 -0.52454190 -1.62279671
6 0.52841776 -0.52454190
7 1.63297496 0.52841776
8 1.83266080 1.63297496
9 2.01838508 1.83266080
10 1.80166605 2.01838508
11 1.40982921 1.80166605
12 0.96154116 1.40982921
13 0.77540217 0.96154116
14 0.32233164 0.77540217
15 -0.88343984 0.32233164
16 0.40711352 -0.88343984
17 1.33880639 0.40711352
18 1.85801383 1.33880639
19 1.99475244 1.85801383
20 1.92850466 1.99475244
21 1.71391478 1.92850466
22 1.59750991 1.71391478
23 1.30567308 1.59750991
24 1.12457529 1.30567308
25 0.80499825 1.12457529
26 0.28567993 0.80499825
27 -0.92040571 0.28567993
28 0.30295738 -0.92040571
29 0.86745999 0.30295738
30 1.48698159 0.86745999
31 2.02466268 1.48698159
32 1.72466268 2.02466268
33 1.61038696 1.72466268
34 1.26054404 1.61038696
35 0.80245942 1.26054404
36 0.35448553 0.80245942
37 0.23522265 0.35448553
38 -0.15097178 0.23522265
39 -1.15674326 -0.15097178
40 0.20037205 -1.15674326
41 1.13206492 0.20037205
42 1.65190068 1.13206492
43 1.95582956 1.65190068
44 1.72239151 1.95582956
45 1.40780163 1.72239151
46 1.19108260 1.40780163
47 1.09924576 1.19108260
48 0.91783382 1.09924576
49 0.93169483 0.91783382
50 0.57862429 0.93169483
51 -0.89433745 0.57862429
52 0.06246370 -0.89433745
53 1.06071851 0.06246370
54 1.61399233 1.06071851
55 1.75167342 1.61399233
56 1.25135926 1.75167342
57 0.70270301 1.25135926
58 0.45286009 0.70270301
59 0.32789936 0.45286009
60 0.07992546 0.32789936
61 0.26066258 0.07992546
62 0.07509647 0.26066258
63 -0.89723694 0.07509647
64 1.09268809 -0.89723694
65 2.55719070 1.09268809
66 2.67639814 2.55719070
67 2.88064117 2.67639814
68 2.91407923 2.88064117
69 2.93324156 2.91407923
70 2.81652254 2.93324156
71 3.12531402 2.81652254
72 3.61109234 3.12531402
73 3.52463919 3.61109234
74 2.47156866 3.52463919
75 0.43204497 2.47156866
76 -0.27771583 0.43204497
77 -1.61289907 -0.27771583
78 -3.09337747 -1.61289907
79 -3.78913443 -3.09337747
80 -4.22225832 -3.78913443
81 -4.30341015 -4.22225832
82 -4.15325307 -4.30341015
83 -4.21165185 -4.15325307
84 -4.05993990 -4.21165185
85 -3.74607889 -4.05993990
86 -3.43258748 -3.74607889
87 -2.30492090 -3.43258748
88 -1.21436754 -2.30492090
89 -2.34955078 -1.21436754
90 -3.83002918 -2.34955078
91 -4.85953835 -3.83002918
92 -5.79329057 -4.85953835
93 -6.24131850 -5.79329057
94 -6.09147558 -6.24131850
95 -5.18362658 -6.09147558
96 -2.62971550 -5.18362658
97 -2.61773946 -2.62971550
98 -3.43768610 -2.61773946
99 -4.71001952 -3.43768610
100 -5.25321838 -4.71001952
101 -4.62246799 -5.25321838
102 -1.76825169 -4.62246799
103 -1.73276973 -1.76825169
104 -1.59995999 -1.73276973
105 -2.21423571 -1.59995999
106 -2.69751668 -2.21423571
107 -2.58841104 -2.69751668
108 -2.53607077 -2.58841104
109 -2.65533365 -2.53607077
110 -2.94121392 -2.65533365
111 -2.81448982 -2.94121392
112 -4.09112673 -2.81448982
113 -4.32630997 -4.09112673
114 -3.83991226 -4.32630997
115 -2.70223117 -3.83991226
116 -1.10160285 -2.70223117
117 0.68380728 -1.10160285
118 1.53396436 0.68380728
119 1.54212752 1.53396436
120 0.79352531 1.54212752
121 0.64082437 0.79352531
122 0.95462994 0.64082437
123 3.01636290 0.95462994
124 2.47347820 3.01636290
125 2.60548523 2.47347820
126 2.52469268 2.60548523
127 2.19518350 2.52469268
128 2.02862155 2.19518350
129 1.08059362 2.02862155
130 0.46418875 1.08059362
131 -0.32764808 0.46418875
132 -0.74218392 -0.32764808
133 -0.46207513 -0.74218392
134 0.71829239 -0.46207513
135 3.24595897 0.71829239
136 3.43682649 3.24595897
137 3.36757688 3.43682649
138 2.52053654 3.36757688
139 1.99102736 2.52053654
140 1.65758931 1.99102736
141 1.24331359 1.65758931
142 1.12628040 1.24331359
143 1.63475772 1.12628040
144 2.38709799 1.63475772
145 2.66783511 2.38709799
146 2.64851679 2.66783511
147 2.67524089 2.64851679
148 1.13141371 2.67524089
149 1.29654463 1.13141371
150 1.68262818 1.29654463
151 1.95343317 1.68262818
152 2.02030927 1.95343317
153 0.80477691 2.02030927
154 0.95524815 0.80477691
155 0.86403964 0.95524815
156 1.18262769 0.86403964
157 1.49617454 1.18262769
158 1.54278985 1.49617454
159 2.13733253 1.54278985
160 1.16100979 2.13733253
161 0.75863628 1.16100979
162 0.17815788 0.75863628
163 -0.15135129 0.17815788
164 -0.88478935 -0.15135129
165 -0.73187480 -0.88478935
166 -0.64859382 -0.73187480
167 -0.04074482 -0.64859382
168 1.81128129 -0.04074482
169 1.92514230 1.81128129
170 1.53957619 1.92514230
171 0.76661445 1.53957619
172 -1.61002246 0.76661445
173 -2.87832959 -1.61002246
174 -2.82536993 -2.87832959
175 -2.65456495 -2.82536993
176 -2.02081274 -2.65456495
177 -1.56852651 -2.02081274
178 -1.95212164 -1.56852651
179 -2.91052042 -1.95212164
180 -3.22474210 -2.91052042
181 -3.34431914 -3.22474210
182 -1.23051357 -3.34431914
183 2.23027690 -1.23051357
184 2.28770637 2.23027690
185 0.81908508 2.28770637
186 -0.86139332 0.81908508
187 -2.49058834 -0.86139332
188 -1.45746445 -2.49058834
189 0.86044125 -1.45746445
190 2.34309390 0.86044125
191 3.15125706 2.34309390
192 2.66953095 3.15125706
193 1.94995391 2.66953095
194 2.39625505 1.94995391
195 3.05735969 2.39625505
196 1.51321835 3.05735969
197 0.51053068 1.51321835
198 -0.50338577 0.51053068
> 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/79j7j1262211753.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/85eez1262211753.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9vmik1262211753.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10e84n1262211753.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11nscr1262211753.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/12ht4f1262211753.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/13ph5e1262211753.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/14zf051262211753.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/15tkre1262211753.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/16pjin1262211753.tab")
+ }
> try(system("convert tmp/1lvx01262211753.ps tmp/1lvx01262211753.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zty41262211753.ps tmp/2zty41262211753.png",intern=TRUE))
character(0)
> try(system("convert tmp/346po1262211753.ps tmp/346po1262211753.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ha2v1262211753.ps tmp/4ha2v1262211753.png",intern=TRUE))
character(0)
> try(system("convert tmp/59knt1262211753.ps tmp/59knt1262211753.png",intern=TRUE))
character(0)
> try(system("convert tmp/6oe2s1262211753.ps tmp/6oe2s1262211753.png",intern=TRUE))
character(0)
> try(system("convert tmp/79j7j1262211753.ps tmp/79j7j1262211753.png",intern=TRUE))
character(0)
> try(system("convert tmp/85eez1262211753.ps tmp/85eez1262211753.png",intern=TRUE))
character(0)
> try(system("convert tmp/9vmik1262211753.ps tmp/9vmik1262211753.png",intern=TRUE))
character(0)
> try(system("convert tmp/10e84n1262211753.ps tmp/10e84n1262211753.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.936 1.874 6.413