R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
> x <- array(list(8.2
+ ,3.7
+ ,8.5
+ ,5
+ ,5.7
+ ,4.9
+ ,8.2
+ ,3.9
+ ,8.9
+ ,4.5
+ ,9.2
+ ,5.4
+ ,4.8
+ ,3
+ ,6.4
+ ,4.3
+ ,7.1
+ ,3.5
+ ,9
+ ,4.5
+ ,4.7
+ ,3.3
+ ,6.5
+ ,3.6
+ ,5.7
+ ,2
+ ,6.9
+ ,2.1
+ ,6.3
+ ,3.7
+ ,6.2
+ ,4.3
+ ,7
+ ,4.6
+ ,5.8
+ ,4.4
+ ,5.5
+ ,4.4
+ ,6.4
+ ,4.1
+ ,7.4
+ ,4
+ ,8.7
+ ,3.8
+ ,6
+ ,3.2
+ ,6.1
+ ,3
+ ,8.4
+ ,4.4
+ ,9.5
+ ,5.1
+ ,7.6
+ ,4.2
+ ,9.2
+ ,4.5
+ ,8
+ ,5.2
+ ,6.3
+ ,4.8
+ ,6.6
+ ,4.5
+ ,8.7
+ ,4.3
+ ,6.4
+ ,4.5
+ ,5.7
+ ,4.2
+ ,7.4
+ ,4.8
+ ,5.9
+ ,5.7
+ ,6.8
+ ,4.5
+ ,5.6
+ ,5
+ ,7.6
+ ,4.4
+ ,9.1
+ ,4.5
+ ,5.4
+ ,3.3
+ ,5.2
+ ,3.3
+ ,9.9
+ ,4.3
+ ,9.6
+ ,4.3
+ ,7
+ ,4
+ ,8.6
+ ,4.8
+ ,8.6
+ ,4.5
+ ,9.3
+ ,6.7
+ ,4.8
+ ,4
+ ,6
+ ,4.7
+ ,6.6
+ ,3.9
+ ,6.4
+ ,5.6
+ ,6.3
+ ,4.4
+ ,8.5
+ ,5.3
+ ,5.4
+ ,3.7
+ ,7
+ ,4.3
+ ,6.3
+ ,4.4
+ ,8.5
+ ,5.7
+ ,5.4
+ ,3.5
+ ,7.6
+ ,4.7
+ ,6.1
+ ,3.3
+ ,6.9
+ ,3.7
+ ,6.4
+ ,3
+ ,8.1
+ ,3
+ ,5.4
+ ,3.4
+ ,6.7
+ ,3.5
+ ,7.3
+ ,4.2
+ ,8
+ ,4.7
+ ,6.3
+ ,3.5
+ ,6.7
+ ,2.5
+ ,5.4
+ ,2.5
+ ,8.7
+ ,3.1
+ ,7.1
+ ,3.5
+ ,9
+ ,3.9
+ ,8.7
+ ,4.9
+ ,9.6
+ ,5.2
+ ,7.6
+ ,4.5
+ ,8.2
+ ,4.7
+ ,6
+ ,3.2
+ ,6.1
+ ,4.5
+ ,7
+ ,3.9
+ ,8.3
+ ,4.6
+ ,7.6
+ ,4.1
+ ,9.4
+ ,4.1
+ ,8.9
+ ,4.3
+ ,9.3
+ ,4.6
+ ,7.6
+ ,4.5
+ ,5.1
+ ,4.9
+ ,5.5
+ ,4.7
+ ,8
+ ,4.3
+ ,7.4
+ ,4.8
+ ,5.9
+ ,5.2
+ ,7.1
+ ,3.5
+ ,10
+ ,5
+ ,7.6
+ ,5.2
+ ,5.7
+ ,6.5
+ ,8.7
+ ,3.9
+ ,9.9
+ ,4.5
+ ,8.6
+ ,4.3
+ ,7.9
+ ,4.1
+ ,5.4
+ ,2.8
+ ,6.7
+ ,4
+ ,5.7
+ ,4.9
+ ,8.2
+ ,4.5
+ ,8.7
+ ,4.6
+ ,9.4
+ ,4.7
+ ,6.1
+ ,3.3
+ ,6.9
+ ,3.2
+ ,7.3
+ ,4.2
+ ,8
+ ,4.9
+ ,7.7
+ ,3.4
+ ,9.3
+ ,4.1
+ ,9
+ ,5.5
+ ,7.4
+ ,5.7
+ ,8.2
+ ,4
+ ,7.6
+ ,4.6
+ ,7.1
+ ,3.5
+ ,10
+ ,3.7
+ ,7.9
+ ,4
+ ,9.9
+ ,5.6
+ ,6.6
+ ,4.5
+ ,8.7
+ ,5.4
+ ,8
+ ,3.6
+ ,8.4
+ ,2.7
+ ,6.3
+ ,2.9
+ ,8.8
+ ,4.4
+ ,6
+ ,2.6
+ ,7.7
+ ,3.3
+ ,5.4
+ ,2.8
+ ,6.6
+ ,3.5
+ ,7.6
+ ,5.2
+ ,5.7
+ ,4.7
+ ,6.4
+ ,4.5
+ ,5.7
+ ,5
+ ,6.1
+ ,4.3
+ ,5.5
+ ,4.5
+ ,5.2
+ ,3.4
+ ,7.5
+ ,4
+ ,6.6
+ ,3.9
+ ,6.4
+ ,4.7
+ ,7.6
+ ,4.4
+ ,9.1
+ ,5.4
+ ,5.8
+ ,3.1
+ ,6.7
+ ,2.9
+ ,7.9
+ ,4.6
+ ,6.5
+ ,4.6
+ ,8.6
+ ,3.9
+ ,9.9
+ ,4.1
+ ,8.2
+ ,3.7
+ ,8.5
+ ,4.4
+ ,7.1
+ ,3.8
+ ,9.9
+ ,3.1
+ ,6.4
+ ,3.9
+ ,7.6
+ ,4.5
+ ,7.6
+ ,4.1
+ ,9.4
+ ,4.3
+ ,8.9
+ ,4.6
+ ,9.3
+ ,5.2
+ ,5.7
+ ,2.7
+ ,7.1
+ ,2.6
+ ,7.1
+ ,3.8
+ ,9.9
+ ,3.2
+ ,7.4
+ ,4
+ ,8.7
+ ,4.3
+ ,6.6
+ ,3
+ ,8.6
+ ,2.7
+ ,5
+ ,1.6
+ ,6.4
+ ,2
+ ,8.2
+ ,4.3
+ ,7.7
+ ,4.7
+ ,5.2
+ ,3.4
+ ,7.5
+ ,3.4
+ ,5.2
+ ,3.1
+ ,5
+ ,2.4
+ ,8.2
+ ,4.3
+ ,7.7
+ ,5.1
+ ,7.3
+ ,3.9
+ ,9.1
+ ,4.6
+ ,8.2
+ ,4.9
+ ,5.5
+ ,5.5
+ ,7.4
+ ,3.3
+ ,9.1
+ ,4.4
+ ,4.8
+ ,2.4
+ ,7.1
+ ,2
+ ,7.6
+ ,4.2
+ ,9.2
+ ,4.4
+ ,8.9
+ ,4.6
+ ,9.3
+ ,4.8
+ ,7.7
+ ,3.4
+ ,9.3
+ ,3.6
+ ,7.3
+ ,3.6
+ ,8.6
+ ,4.9
+ ,6.3
+ ,3.7
+ ,7.4
+ ,4.2
+ ,5.4
+ ,2.5
+ ,8.7
+ ,3.1
+ ,6.4
+ ,3.9
+ ,7.8
+ ,4.3
+ ,6.4
+ ,3.5
+ ,7.9
+ ,3.4
+ ,5.4
+ ,3.5
+ ,7.6
+ ,3.1
+ ,8.7
+ ,4.2
+ ,9.2
+ ,5.1
+ ,6.1
+ ,3.7
+ ,7.7
+ ,4
+ ,8.4
+ ,4.4
+ ,9.5
+ ,5.6
+ ,7.9
+ ,4.6
+ ,6.5
+ ,5
+ ,7
+ ,3.9
+ ,8.3
+ ,4.2
+ ,8.7
+ ,4.9
+ ,9.6
+ ,4.4
+ ,7.9
+ ,5.4
+ ,5.9
+ ,5.8
+ ,7.1
+ ,4.2
+ ,8.7
+ ,4.6
+ ,5.8
+ ,3.1
+ ,6.7
+ ,3.8
+ ,8.4
+ ,4.1
+ ,9.7
+ ,3.7
+ ,7.1
+ ,3.9
+ ,8.8
+ ,4
+ ,7.6
+ ,4.5
+ ,8.2
+ ,4.5
+ ,7.3
+ ,4.2
+ ,8.9
+ ,4.2
+ ,8
+ ,3.6
+ ,8.4
+ ,4
+ ,6.1
+ ,3.7
+ ,7.7
+ ,5.1
+ ,8.7
+ ,4.2
+ ,9.2
+ ,4.2
+ ,5.8
+ ,2.9
+ ,7.3
+ ,2.8
+ ,6.4
+ ,3.1
+ ,9
+ ,3.3
+ ,6.4
+ ,3
+ ,8.1
+ ,2.6
+ ,9
+ ,5.5
+ ,7.4
+ ,5.7
+ ,6.4
+ ,3.5
+ ,7.9
+ ,4.8
+ ,6
+ ,2.6
+ ,7.7
+ ,3.2
+ ,8.7
+ ,4.6
+ ,9.4
+ ,5.8
+ ,5
+ ,2.5
+ ,7.2
+ ,3.2
+ ,7.4
+ ,3.1
+ ,8.3
+ ,4.1
+ ,8.6
+ ,4.3
+ ,7.9
+ ,4.6
+ ,5.8
+ ,2.9
+ ,7.3
+ ,3.3
+ ,9.8
+ ,4.3
+ ,9.6
+ ,4.4
+ ,4.8
+ ,2.1
+ ,8.3
+ ,1.2
+ ,7
+ ,4
+ ,8.6
+ ,5
+ ,5.5
+ ,4.7
+ ,8
+ ,4.6
+ ,5
+ ,1.6
+ ,6.4
+ ,2.4
+ ,6
+ ,3.3
+ ,6.6
+ ,4.3
+ ,8
+ ,4.2
+ ,7.6
+ ,3.6
+ ,7.9
+ ,4.4
+ ,9.4
+ ,5.1
+ ,4.8
+ ,2.1
+ ,8.3
+ ,1.8
+ ,6.4
+ ,3.9
+ ,7.8
+ ,4.1
+ ,4.8
+ ,2.4
+ ,7.1
+ ,2.8
+ ,6.4
+ ,3.9
+ ,7.6
+ ,4.4
+ ,6.8
+ ,4.5
+ ,5.6
+ ,4.5
+ ,7.9
+ ,4
+ ,9.9
+ ,4
+ ,8.9
+ ,4.5
+ ,9.2
+ ,4.2
+ ,7.4
+ ,4.2
+ ,9.1
+ ,4.5
+ ,7
+ ,3.5
+ ,9.9
+ ,3.8
+ ,7
+ ,3.5
+ ,9.9
+ ,4.1
+ ,6
+ ,3.3
+ ,6.6
+ ,4.6
+ ,7.4
+ ,3.3
+ ,9.1
+ ,3.7
+ ,7.6
+ ,4.5
+ ,5.1
+ ,5.1
+ ,4.8
+ ,4
+ ,6
+ ,4.3
+ ,7.3
+ ,4.2
+ ,8.9
+ ,5
+ ,6.3
+ ,3.7
+ ,6.2
+ ,4
+ ,5
+ ,2.5
+ ,7.2
+ ,3
+ ,7.1
+ ,3.9
+ ,8.8
+ ,4.1
+ ,6.3
+ ,3.4
+ ,6.3
+ ,4.4
+ ,6.8
+ ,3.6
+ ,9.7
+ ,4
+ ,5.2
+ ,3.1
+ ,5
+ ,3.7
+ ,6.3
+ ,3.7
+ ,7.4
+ ,4
+ ,6.1
+ ,4.3
+ ,5.5
+ ,4.3
+ ,7.3
+ ,3.9
+ ,9.1
+ ,4.6
+ ,5.4
+ ,3.4
+ ,6.7
+ ,3.7
+ ,8
+ ,5.2
+ ,6.3
+ ,6.4
+ ,7.4
+ ,3.1
+ ,8.3
+ ,3.6
+ ,7.3
+ ,3
+ ,8.2
+ ,4.7
+ ,7.3
+ ,3
+ ,8.2
+ ,4
+ ,6.4
+ ,3.1
+ ,9
+ ,4.3
+ ,5.7
+ ,2.7
+ ,7.1
+ ,3.6
+ ,5.7
+ ,2
+ ,6.9
+ ,2.7
+ ,6.6
+ ,3
+ ,8.6
+ ,4
+ ,6.3
+ ,3.5
+ ,6.7
+ ,3.8
+ ,5.4
+ ,3.7
+ ,7
+ ,3.3
+ ,7.4
+ ,3.8
+ ,9.7
+ ,4.5
+ ,8.6
+ ,3.9
+ ,9.9
+ ,5
+ ,7.3
+ ,3.6
+ ,8.6
+ ,4.8
+ ,6.3
+ ,3.4
+ ,6.3
+ ,2.8
+ ,8.7
+ ,3.9
+ ,9.9
+ ,4.3
+ ,8.6
+ ,4.5
+ ,9.3
+ ,4
+ ,8.4
+ ,4.1
+ ,9.7
+ ,4.9
+ ,7.4
+ ,3.8
+ ,9.7
+ ,4.6
+ ,9.9
+ ,4.3
+ ,9.6
+ ,4
+ ,8
+ ,4.2
+ ,7.6
+ ,4.4
+ ,7.9
+ ,4.4
+ ,9.4
+ ,4.7
+ ,9.8
+ ,4.3
+ ,9.6
+ ,4.6
+ ,8.9
+ ,4.3
+ ,9.3
+ ,4.4
+ ,6.8
+ ,3.6
+ ,9.7
+ ,4.7
+ ,7.4
+ ,4.2
+ ,9.1
+ ,6
+ ,4.7
+ ,3.3
+ ,6.5
+ ,4.3
+ ,5.4
+ ,2.8
+ ,6.6
+ ,3.2
+ ,7
+ ,4.6
+ ,5.8
+ ,5.9
+ ,7.1
+ ,4.2
+ ,8.7
+ ,5.5
+ ,6.3
+ ,2.9
+ ,8.8
+ ,3.8
+ ,5.5
+ ,4.4
+ ,6.4
+ ,4
+ ,5.4
+ ,2.8
+ ,6.7
+ ,2.9
+ ,5.4
+ ,3.3
+ ,5.2
+ ,4.3
+ ,4.8
+ ,3
+ ,6.4
+ ,3.6
+ ,8.2
+ ,4
+ ,7.6
+ ,4.4
+ ,7.9
+ ,5.4
+ ,5.9
+ ,6
+ ,8.6
+ ,4.2
+ ,9.7
+ ,4.4
+ ,8.2
+ ,4.9
+ ,5.5
+ ,5.9
+ ,8.6
+ ,4.2
+ ,9.7
+ ,4.3)
+ ,dim=c(4
+ ,200)
+ ,dimnames=list(c('Klantentevredenheid'
+ ,'Leveringssnelheid'
+ ,'Productkwaliteit'
+ ,'Facturatie')
+ ,1:200))
> y <- array(NA,dim=c(4,200),dimnames=list(c('Klantentevredenheid','Leveringssnelheid','Productkwaliteit','Facturatie'),1:200))
> 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 = 'Do not include Seasonal 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
> 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
Klantentevredenheid Leveringssnelheid Productkwaliteit Facturatie t
1 8.2 3.7 8.5 5.0 1
2 5.7 4.9 8.2 3.9 2
3 8.9 4.5 9.2 5.4 3
4 4.8 3.0 6.4 4.3 4
5 7.1 3.5 9.0 4.5 5
6 4.7 3.3 6.5 3.6 6
7 5.7 2.0 6.9 2.1 7
8 6.3 3.7 6.2 4.3 8
9 7.0 4.6 5.8 4.4 9
10 5.5 4.4 6.4 4.1 10
11 7.4 4.0 8.7 3.8 11
12 6.0 3.2 6.1 3.0 12
13 8.4 4.4 9.5 5.1 13
14 7.6 4.2 9.2 4.5 14
15 8.0 5.2 6.3 4.8 15
16 6.6 4.5 8.7 4.3 16
17 6.4 4.5 5.7 4.2 17
18 7.4 4.8 5.9 5.7 18
19 6.8 4.5 5.6 5.0 19
20 7.6 4.4 9.1 4.5 20
21 5.4 3.3 5.2 3.3 21
22 9.9 4.3 9.6 4.3 22
23 7.0 4.0 8.6 4.8 23
24 8.6 4.5 9.3 6.7 24
25 4.8 4.0 6.0 4.7 25
26 6.6 3.9 6.4 5.6 26
27 6.3 4.4 8.5 5.3 27
28 5.4 3.7 7.0 4.3 28
29 6.3 4.4 8.5 5.7 29
30 5.4 3.5 7.6 4.7 30
31 6.1 3.3 6.9 3.7 31
32 6.4 3.0 8.1 3.0 32
33 5.4 3.4 6.7 3.5 33
34 7.3 4.2 8.0 4.7 34
35 6.3 3.5 6.7 2.5 35
36 5.4 2.5 8.7 3.1 36
37 7.1 3.5 9.0 3.9 37
38 8.7 4.9 9.6 5.2 38
39 7.6 4.5 8.2 4.7 39
40 6.0 3.2 6.1 4.5 40
41 7.0 3.9 8.3 4.6 41
42 7.6 4.1 9.4 4.1 42
43 8.9 4.3 9.3 4.6 43
44 7.6 4.5 5.1 4.9 44
45 5.5 4.7 8.0 4.3 45
46 7.4 4.8 5.9 5.2 46
47 7.1 3.5 10.0 5.0 47
48 7.6 5.2 5.7 6.5 48
49 8.7 3.9 9.9 4.5 49
50 8.6 4.3 7.9 4.1 50
51 5.4 2.8 6.7 4.0 51
52 5.7 4.9 8.2 4.5 52
53 8.7 4.6 9.4 4.7 53
54 6.1 3.3 6.9 3.2 54
55 7.3 4.2 8.0 4.9 55
56 7.7 3.4 9.3 4.1 56
57 9.0 5.5 7.4 5.7 57
58 8.2 4.0 7.6 4.6 58
59 7.1 3.5 10.0 3.7 59
60 7.9 4.0 9.9 5.6 60
61 6.6 4.5 8.7 5.4 61
62 8.0 3.6 8.4 2.7 62
63 6.3 2.9 8.8 4.4 63
64 6.0 2.6 7.7 3.3 64
65 5.4 2.8 6.6 3.5 65
66 7.6 5.2 5.7 4.7 66
67 6.4 4.5 5.7 5.0 67
68 6.1 4.3 5.5 4.5 68
69 5.2 3.4 7.5 4.0 69
70 6.6 3.9 6.4 4.7 70
71 7.6 4.4 9.1 5.4 71
72 5.8 3.1 6.7 2.9 72
73 7.9 4.6 6.5 4.6 73
74 8.6 3.9 9.9 4.1 74
75 8.2 3.7 8.5 4.4 75
76 7.1 3.8 9.9 3.1 76
77 6.4 3.9 7.6 4.5 77
78 7.6 4.1 9.4 4.3 78
79 8.9 4.6 9.3 5.2 79
80 5.7 2.7 7.1 2.6 80
81 7.1 3.8 9.9 3.2 81
82 7.4 4.0 8.7 4.3 82
83 6.6 3.0 8.6 2.7 83
84 5.0 1.6 6.4 2.0 84
85 8.2 4.3 7.7 4.7 85
86 5.2 3.4 7.5 3.4 86
87 5.2 3.1 5.0 2.4 87
88 8.2 4.3 7.7 5.1 88
89 7.3 3.9 9.1 4.6 89
90 8.2 4.9 5.5 5.5 90
91 7.4 3.3 9.1 4.4 91
92 4.8 2.4 7.1 2.0 92
93 7.6 4.2 9.2 4.4 93
94 8.9 4.6 9.3 4.8 94
95 7.7 3.4 9.3 3.6 95
96 7.3 3.6 8.6 4.9 96
97 6.3 3.7 7.4 4.2 97
98 5.4 2.5 8.7 3.1 98
99 6.4 3.9 7.8 4.3 99
100 6.4 3.5 7.9 3.4 100
101 5.4 3.5 7.6 3.1 101
102 8.7 4.2 9.2 5.1 102
103 6.1 3.7 7.7 4.0 103
104 8.4 4.4 9.5 5.6 104
105 7.9 4.6 6.5 5.0 105
106 7.0 3.9 8.3 4.2 106
107 8.7 4.9 9.6 4.4 107
108 7.9 5.4 5.9 5.8 108
109 7.1 4.2 8.7 4.6 109
110 5.8 3.1 6.7 3.8 110
111 8.4 4.1 9.7 3.7 111
112 7.1 3.9 8.8 4.0 112
113 7.6 4.5 8.2 4.5 113
114 7.3 4.2 8.9 4.2 114
115 8.0 3.6 8.4 4.0 115
116 6.1 3.7 7.7 5.1 116
117 8.7 4.2 9.2 4.2 117
118 5.8 2.9 7.3 2.8 118
119 6.4 3.1 9.0 3.3 119
120 6.4 3.0 8.1 2.6 120
121 9.0 5.5 7.4 5.7 121
122 6.4 3.5 7.9 4.8 122
123 6.0 2.6 7.7 3.2 123
124 8.7 4.6 9.4 5.8 124
125 5.0 2.5 7.2 3.2 125
126 7.4 3.1 8.3 4.1 126
127 8.6 4.3 7.9 4.6 127
128 5.8 2.9 7.3 3.3 128
129 9.8 4.3 9.6 4.4 129
130 4.8 2.1 8.3 1.2 130
131 7.0 4.0 8.6 5.0 131
132 5.5 4.7 8.0 4.6 132
133 5.0 1.6 6.4 2.4 133
134 6.0 3.3 6.6 4.3 134
135 8.0 4.2 7.6 3.6 135
136 7.9 4.4 9.4 5.1 136
137 4.8 2.1 8.3 1.8 137
138 6.4 3.9 7.8 4.1 138
139 4.8 2.4 7.1 2.8 139
140 6.4 3.9 7.6 4.4 140
141 6.8 4.5 5.6 4.5 141
142 7.9 4.0 9.9 4.0 142
143 8.9 4.5 9.2 4.2 143
144 7.4 4.2 9.1 4.5 144
145 7.0 3.5 9.9 3.8 145
146 7.0 3.5 9.9 4.1 146
147 6.0 3.3 6.6 4.6 147
148 7.4 3.3 9.1 3.7 148
149 7.6 4.5 5.1 5.1 149
150 4.8 4.0 6.0 4.3 150
151 7.3 4.2 8.9 5.0 151
152 6.3 3.7 6.2 4.0 152
153 5.0 2.5 7.2 3.0 153
154 7.1 3.9 8.8 4.1 154
155 6.3 3.4 6.3 4.4 155
156 6.8 3.6 9.7 4.0 156
157 5.2 3.1 5.0 3.7 157
158 6.3 3.7 7.4 4.0 158
159 6.1 4.3 5.5 4.3 159
160 7.3 3.9 9.1 4.6 160
161 5.4 3.4 6.7 3.7 161
162 8.0 5.2 6.3 6.4 162
163 7.4 3.1 8.3 3.6 163
164 7.3 3.0 8.2 4.7 164
165 7.3 3.0 8.2 4.0 165
166 6.4 3.1 9.0 4.3 166
167 5.7 2.7 7.1 3.6 167
168 5.7 2.0 6.9 2.7 168
169 6.6 3.0 8.6 4.0 169
170 6.3 3.5 6.7 3.8 170
171 5.4 3.7 7.0 3.3 171
172 7.4 3.8 9.7 4.5 172
173 8.6 3.9 9.9 5.0 173
174 7.3 3.6 8.6 4.8 174
175 6.3 3.4 6.3 2.8 175
176 8.7 3.9 9.9 4.3 176
177 8.6 4.5 9.3 4.0 177
178 8.4 4.1 9.7 4.9 178
179 7.4 3.8 9.7 4.6 179
180 9.9 4.3 9.6 4.0 180
181 8.0 4.2 7.6 4.4 181
182 7.9 4.4 9.4 4.7 182
183 9.8 4.3 9.6 4.6 183
184 8.9 4.3 9.3 4.4 184
185 6.8 3.6 9.7 4.7 185
186 7.4 4.2 9.1 6.0 186
187 4.7 3.3 6.5 4.3 187
188 5.4 2.8 6.6 3.2 188
189 7.0 4.6 5.8 5.9 189
190 7.1 4.2 8.7 5.5 190
191 6.3 2.9 8.8 3.8 191
192 5.5 4.4 6.4 4.0 192
193 5.4 2.8 6.7 2.9 193
194 5.4 3.3 5.2 4.3 194
195 4.8 3.0 6.4 3.6 195
196 8.2 4.0 7.6 4.4 196
197 7.9 5.4 5.9 6.0 197
198 8.6 4.2 9.7 4.4 198
199 8.2 4.9 5.5 5.9 199
200 8.6 4.2 9.7 4.3 200
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Leveringssnelheid Productkwaliteit Facturatie
-0.49027 0.89730 0.42375 0.11793
t
0.00172
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.3865 -0.4568 0.0274 0.4429 1.9189
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.4902742 0.4155472 -1.180 0.240
Leveringssnelheid 0.8972982 0.1135894 7.899 2e-13 ***
Productkwaliteit 0.4237541 0.0389349 10.884 <2e-16 ***
Facturatie 0.1179277 0.0928156 1.271 0.205
t 0.0017196 0.0009369 1.836 0.068 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7548 on 195 degrees of freedom
Multiple R-squared: 0.6375, Adjusted R-squared: 0.6301
F-statistic: 85.75 on 4 and 195 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.9178577 0.164284564 0.082142282
[2,] 0.9355638 0.128872301 0.064436151
[3,] 0.9448651 0.110269821 0.055134910
[4,] 0.9114292 0.177141670 0.088570835
[5,] 0.8794411 0.241117846 0.120558923
[6,] 0.8649063 0.270187359 0.135093680
[7,] 0.8369377 0.326124532 0.163062266
[8,] 0.8475599 0.304880257 0.152440128
[9,] 0.8973585 0.205283044 0.102641522
[10,] 0.8559135 0.288172939 0.144086469
[11,] 0.8130403 0.373919333 0.186959667
[12,] 0.7594571 0.481085827 0.240542913
[13,] 0.7106063 0.578787361 0.289393681
[14,] 0.6460422 0.707915675 0.353957837
[15,] 0.8289870 0.342025911 0.171012955
[16,] 0.8791629 0.241674163 0.120837081
[17,] 0.8724710 0.255058057 0.127529028
[18,] 0.9532821 0.093435833 0.046717916
[19,] 0.9370724 0.125855275 0.062927638
[20,] 0.9701096 0.059780891 0.029890446
[21,] 0.9717192 0.056561652 0.028280826
[22,] 0.9834093 0.033181380 0.016590690
[23,] 0.9842597 0.031480657 0.015740328
[24,] 0.9807967 0.038406538 0.019203269
[25,] 0.9764378 0.047124346 0.023562173
[26,] 0.9689016 0.062196783 0.031098392
[27,] 0.9609827 0.078034597 0.039017299
[28,] 0.9572500 0.085499966 0.042749983
[29,] 0.9502057 0.099588691 0.049794345
[30,] 0.9368165 0.126367013 0.063183507
[31,] 0.9243200 0.151359951 0.075679976
[32,] 0.9068400 0.186320023 0.093160011
[33,] 0.8948725 0.210255014 0.105127507
[34,] 0.8701771 0.259645824 0.129822912
[35,] 0.8419541 0.316091782 0.158045891
[36,] 0.8672110 0.265578073 0.132789037
[37,] 0.9123804 0.175239236 0.087619618
[38,] 0.9804735 0.039052977 0.019526489
[39,] 0.9772127 0.045574590 0.022787295
[40,] 0.9726258 0.054748387 0.027374193
[41,] 0.9651704 0.069659115 0.034829558
[42,] 0.9683289 0.063342218 0.031671109
[43,] 0.9803065 0.039386925 0.019693463
[44,] 0.9746473 0.050705484 0.025352742
[45,] 0.9968646 0.006270839 0.003135420
[46,] 0.9962641 0.007471752 0.003735876
[47,] 0.9948704 0.010259226 0.005129613
[48,] 0.9929741 0.014051734 0.007025867
[49,] 0.9916655 0.016668927 0.008334464
[50,] 0.9913666 0.017266873 0.008633437
[51,] 0.9937664 0.012467148 0.006233574
[52,] 0.9922476 0.015504891 0.007752446
[53,] 0.9899206 0.020158814 0.010079407
[54,] 0.9949696 0.010060783 0.005030391
[55,] 0.9966669 0.006666267 0.003333133
[56,] 0.9956879 0.008624119 0.004312060
[57,] 0.9943090 0.011381945 0.005690973
[58,] 0.9924636 0.015072752 0.007536376
[59,] 0.9903084 0.019383212 0.009691606
[60,] 0.9879107 0.024178575 0.012089288
[61,] 0.9849592 0.030081613 0.015040807
[62,] 0.9902163 0.019567476 0.009783738
[63,] 0.9871514 0.025697242 0.012848621
[64,] 0.9851807 0.029638664 0.014819332
[65,] 0.9807987 0.038402654 0.019201327
[66,] 0.9808182 0.038363511 0.019181755
[67,] 0.9800112 0.039977649 0.019988824
[68,] 0.9833572 0.033285628 0.016642814
[69,] 0.9817282 0.036543671 0.018271836
[70,] 0.9797743 0.040451385 0.020225692
[71,] 0.9749738 0.050052405 0.025026202
[72,] 0.9714082 0.057183516 0.028591758
[73,] 0.9646174 0.070765250 0.035382625
[74,] 0.9615549 0.076890191 0.038445095
[75,] 0.9523002 0.095399617 0.047699808
[76,] 0.9418722 0.116255544 0.058127772
[77,] 0.9451148 0.109770335 0.054885167
[78,] 0.9456391 0.108721766 0.054360883
[79,] 0.9604224 0.079155202 0.039577601
[80,] 0.9525740 0.094852024 0.047426012
[81,] 0.9517938 0.096412366 0.048206183
[82,] 0.9429949 0.114010226 0.057005113
[83,] 0.9540818 0.091836453 0.045918227
[84,] 0.9456055 0.108789067 0.054394533
[85,] 0.9372057 0.125588677 0.062794338
[86,] 0.9260378 0.147924406 0.073962203
[87,] 0.9183805 0.163239053 0.081619526
[88,] 0.9111119 0.177776174 0.088888087
[89,] 0.8948215 0.210356941 0.105178471
[90,] 0.8815286 0.236942784 0.118471392
[91,] 0.8760964 0.247807279 0.123903639
[92,] 0.8705138 0.258972448 0.129486224
[93,] 0.8497829 0.300434168 0.150217084
[94,] 0.8711877 0.257624646 0.128812323
[95,] 0.8679578 0.264084447 0.132042224
[96,] 0.8641818 0.271636362 0.135818181
[97,] 0.8404638 0.319072372 0.159536186
[98,] 0.8364837 0.327032615 0.163516308
[99,] 0.8126601 0.374679835 0.187339918
[100,] 0.7841295 0.431740937 0.215870468
[101,] 0.7541049 0.491790234 0.245895117
[102,] 0.7445704 0.510859206 0.255429603
[103,] 0.7116818 0.576636448 0.288318224
[104,] 0.6866693 0.626661331 0.313330665
[105,] 0.6570894 0.685821273 0.342910637
[106,] 0.6209017 0.758196681 0.379098340
[107,] 0.5988163 0.802367364 0.401183682
[108,] 0.6258833 0.748233338 0.374116669
[109,] 0.6333134 0.733373164 0.366686582
[110,] 0.6342658 0.731468308 0.365734154
[111,] 0.5950344 0.809931280 0.404965640
[112,] 0.5617992 0.876401547 0.438200773
[113,] 0.5239312 0.952137548 0.476068774
[114,] 0.5005531 0.998893717 0.499446859
[115,] 0.4701308 0.940261627 0.529869186
[116,] 0.4359813 0.871962639 0.564018681
[117,] 0.3966157 0.793231459 0.603384271
[118,] 0.3677094 0.735418732 0.632290634
[119,] 0.3856431 0.771286102 0.614356949
[120,] 0.4418479 0.883695876 0.558152062
[121,] 0.4028489 0.805697826 0.597151087
[122,] 0.5792142 0.841571625 0.420785813
[123,] 0.5516645 0.896671061 0.448335530
[124,] 0.5254455 0.949109003 0.474554502
[125,] 0.8399525 0.320094904 0.160047452
[126,] 0.8644790 0.271042047 0.135521023
[127,] 0.8427422 0.314515534 0.157257767
[128,] 0.8530216 0.293956880 0.146978440
[129,] 0.8305813 0.338837369 0.169418685
[130,] 0.8114811 0.377037764 0.188518882
[131,] 0.7981548 0.403690410 0.201845205
[132,] 0.7713639 0.457272292 0.228636146
[133,] 0.7531355 0.493729004 0.246864502
[134,] 0.7174111 0.565177889 0.282588944
[135,] 0.6781797 0.643640660 0.321820330
[136,] 0.6723635 0.655273021 0.327636510
[137,] 0.6464143 0.707171496 0.353585748
[138,] 0.6242097 0.751580626 0.375790313
[139,] 0.6073556 0.785288888 0.392644444
[140,] 0.5627080 0.874583945 0.437291973
[141,] 0.5270441 0.945911745 0.472955872
[142,] 0.6052008 0.789598357 0.394799178
[143,] 0.7290672 0.541865666 0.270932833
[144,] 0.7164046 0.567190832 0.283595416
[145,] 0.6751967 0.649606597 0.324803298
[146,] 0.6397328 0.720534397 0.360267198
[147,] 0.6135272 0.772945523 0.386472761
[148,] 0.5778934 0.844213257 0.422106628
[149,] 0.6123792 0.775241658 0.387620829
[150,] 0.5686774 0.862645195 0.431322598
[151,] 0.5390806 0.921838731 0.460919366
[152,] 0.5054743 0.989051310 0.494525655
[153,] 0.4913248 0.982649514 0.508675243
[154,] 0.5149417 0.970116695 0.485058348
[155,] 0.4640966 0.928193179 0.535903410
[156,] 0.4510542 0.902108456 0.548945772
[157,] 0.4677375 0.935475078 0.532262461
[158,] 0.4945612 0.989122317 0.505438842
[159,] 0.4551740 0.910347963 0.544826019
[160,] 0.4039601 0.807920289 0.596039856
[161,] 0.5219145 0.956171009 0.478085505
[162,] 0.4780576 0.956115239 0.521942380
[163,] 0.4307072 0.861414409 0.569292796
[164,] 0.5330346 0.933930735 0.466965368
[165,] 0.5095266 0.980946845 0.490473422
[166,] 0.4728431 0.945686220 0.527156890
[167,] 0.4216119 0.843223808 0.578388096
[168,] 0.3640343 0.728068679 0.635965661
[169,] 0.3329613 0.665922525 0.667038737
[170,] 0.2935517 0.587103313 0.706448344
[171,] 0.2391044 0.478208708 0.760895646
[172,] 0.2023399 0.404679879 0.797660060
[173,] 0.2702886 0.540577105 0.729711448
[174,] 0.2639627 0.527925413 0.736037294
[175,] 0.2250210 0.450041942 0.774979029
[176,] 0.4692278 0.938455660 0.530772170
[177,] 0.8187079 0.362584145 0.181292073
[178,] 0.7573552 0.485289529 0.242644764
[179,] 0.6755086 0.648982751 0.324491376
[180,] 0.6445863 0.710827431 0.355413715
[181,] 0.6997232 0.600553648 0.300276824
[182,] 0.6789807 0.642038583 0.321019292
[183,] 0.5912220 0.817555987 0.408777994
[184,] 0.4674657 0.934931493 0.532534254
[185,] 0.4012529 0.802505779 0.598747111
> postscript(file="/var/wessaorg/rcomp/tmp/1soow1322147370.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/20tg31322147370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3z34i1322147370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4i5cz1322147370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5gqjq1322147370.ps",horizontal=F,onefile=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 = 200
Frequency = 1
1 2 3 4 5 6
1.177003040 -2.144627738 0.811926315 -0.627614071 0.096671092 -0.960068756
7 8 9 10 11 12
1.212089281 0.322149564 0.370570408 -1.170563677 0.147379970 0.659601696
13 14 15 16 17 18
0.292712189 -0.131664921 0.562825726 -1.168831022 -0.087495653 0.379952892
19 20 21 22 23 24
0.257098376 -0.279066786 0.300395782 1.918932338 -0.348807586 0.280133194
25 26 27 28 29 30
-1.438693447 0.173680204 -1.431193744 -0.951245773 -1.481804038 -1.076648861
31 32 33 34 35 36
0.215646741 0.357161124 -0.469185935 0.018862334 0.455572746 -0.467113389
37 38 39 40 41 42
0.112400385 0.046904791 -0.043675970 0.434561210 -0.139318875 -0.127663744
43 44 45 46 47 48
0.974568557 1.237778076 -2.201531343 0.390767831 -0.458270208 0.159854117
49 50 51 52 53 54
0.880710555 1.314750878 -0.020723727 -2.301364574 0.434014868 0.235059701
55 56 57 58 59 60
-0.040834896 0.618745966 0.649148466 1.238345878 -0.325599432 -0.157655398
61 62 63 64 65 66
-1.375933694 1.275446153 -0.168143427 0.395176396 0.056541077 0.341171124
67 68 69 70 71 72
-0.267818032 -0.246363320 -1.129058795 0.204152561 -0.472901540 0.203695599
73 74 75 76 77 78
0.840302351 0.784891535 1.120508958 -0.510890138 -0.492804007 -0.213155037
79 80 81 82 83 84
0.572716709 0.314734745 -0.531280930 -0.033675783 0.292962590 0.962268857
85 86 87 88 89 90
0.868558918 -1.087535439 0.357247307 0.816229020 -0.260863130 1.159498737
91 92 93 94 95 96
0.397662143 -0.265954409 -0.255720880 0.594093732 0.610645259 0.172787830
97 98 99 100 101 102
-0.327607317 -0.573728849 -0.591800570 -0.170841348 -1.010056418 0.746253284
103 104 105 106 107 108
-0.641465620 0.077264353 0.738103932 -0.203922062 0.022594273 0.175016806
109 110 111 112 113 114
-0.594943056 0.032215699 0.473728433 -0.302531178 -0.147341137 -0.441120805
115 116 117 118 119 120
1.031001106 -0.793540958 0.826594162 0.061593784 -0.298931238 0.253007041
121 122 123 124 125 126
0.539093798 -0.373771437 0.305512520 0.182202487 -0.396319831 0.891317211
127 128 129 130 131 132
1.123377500 -0.014566114 1.623141919 -0.476272609 -0.558110380 -2.386515220
133 134 135 136 137 138
0.830837167 -0.005102897 0.844404422 -0.376423718 -0.559066466 -0.635279591
139 140 141 142 143 144
-0.441117975 -0.589346299 0.106270522 -0.109978601 0.712694984 -0.512838058
145 146 147 148 149 150
-0.542902756 -0.580000673 -0.062836065 0.382194103 1.033634095 -1.606472885
151 152 153 154 155 156
-0.599088330 0.109904876 -0.420883206 -0.386547325 0.284389041 -0.790382954
157 158 159 160 161 162
0.083568989 -0.408917630 -0.379261758 -0.382955027 -0.712880812 0.121359572
163 164 165 166 167 168
0.886655712 0.787320854 0.868150649 -0.497680346 0.047201469 0.864476381
169 170 171 172 173 174
-0.008229395 0.070120156 -1.079221459 -0.456320123 0.508515780 0.050451476
175 176 177 178 179 180
0.438681297 0.685906366 0.335438578 0.217001698 -0.480150123 1.682613191
181 182 183 184 185 186
0.670960460 -0.408354425 1.506697751 0.755689910 -0.922800873 -0.761953002
187 188 189 190 191 192
-1.353866512 -0.119591923 -0.115849913 -0.840365935 -0.317496135 -1.471738866
193 194 195 196 197 198
-0.135187037 -0.115023453 -0.873509070 1.024626044 -0.001613509 0.351843647
199 200
0.924890797 0.360197210
> postscript(file="/var/wessaorg/rcomp/tmp/6anzy1322147370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 200
Frequency = 1
lag(myerror, k = 1) myerror
0 1.177003040 NA
1 -2.144627738 1.177003040
2 0.811926315 -2.144627738
3 -0.627614071 0.811926315
4 0.096671092 -0.627614071
5 -0.960068756 0.096671092
6 1.212089281 -0.960068756
7 0.322149564 1.212089281
8 0.370570408 0.322149564
9 -1.170563677 0.370570408
10 0.147379970 -1.170563677
11 0.659601696 0.147379970
12 0.292712189 0.659601696
13 -0.131664921 0.292712189
14 0.562825726 -0.131664921
15 -1.168831022 0.562825726
16 -0.087495653 -1.168831022
17 0.379952892 -0.087495653
18 0.257098376 0.379952892
19 -0.279066786 0.257098376
20 0.300395782 -0.279066786
21 1.918932338 0.300395782
22 -0.348807586 1.918932338
23 0.280133194 -0.348807586
24 -1.438693447 0.280133194
25 0.173680204 -1.438693447
26 -1.431193744 0.173680204
27 -0.951245773 -1.431193744
28 -1.481804038 -0.951245773
29 -1.076648861 -1.481804038
30 0.215646741 -1.076648861
31 0.357161124 0.215646741
32 -0.469185935 0.357161124
33 0.018862334 -0.469185935
34 0.455572746 0.018862334
35 -0.467113389 0.455572746
36 0.112400385 -0.467113389
37 0.046904791 0.112400385
38 -0.043675970 0.046904791
39 0.434561210 -0.043675970
40 -0.139318875 0.434561210
41 -0.127663744 -0.139318875
42 0.974568557 -0.127663744
43 1.237778076 0.974568557
44 -2.201531343 1.237778076
45 0.390767831 -2.201531343
46 -0.458270208 0.390767831
47 0.159854117 -0.458270208
48 0.880710555 0.159854117
49 1.314750878 0.880710555
50 -0.020723727 1.314750878
51 -2.301364574 -0.020723727
52 0.434014868 -2.301364574
53 0.235059701 0.434014868
54 -0.040834896 0.235059701
55 0.618745966 -0.040834896
56 0.649148466 0.618745966
57 1.238345878 0.649148466
58 -0.325599432 1.238345878
59 -0.157655398 -0.325599432
60 -1.375933694 -0.157655398
61 1.275446153 -1.375933694
62 -0.168143427 1.275446153
63 0.395176396 -0.168143427
64 0.056541077 0.395176396
65 0.341171124 0.056541077
66 -0.267818032 0.341171124
67 -0.246363320 -0.267818032
68 -1.129058795 -0.246363320
69 0.204152561 -1.129058795
70 -0.472901540 0.204152561
71 0.203695599 -0.472901540
72 0.840302351 0.203695599
73 0.784891535 0.840302351
74 1.120508958 0.784891535
75 -0.510890138 1.120508958
76 -0.492804007 -0.510890138
77 -0.213155037 -0.492804007
78 0.572716709 -0.213155037
79 0.314734745 0.572716709
80 -0.531280930 0.314734745
81 -0.033675783 -0.531280930
82 0.292962590 -0.033675783
83 0.962268857 0.292962590
84 0.868558918 0.962268857
85 -1.087535439 0.868558918
86 0.357247307 -1.087535439
87 0.816229020 0.357247307
88 -0.260863130 0.816229020
89 1.159498737 -0.260863130
90 0.397662143 1.159498737
91 -0.265954409 0.397662143
92 -0.255720880 -0.265954409
93 0.594093732 -0.255720880
94 0.610645259 0.594093732
95 0.172787830 0.610645259
96 -0.327607317 0.172787830
97 -0.573728849 -0.327607317
98 -0.591800570 -0.573728849
99 -0.170841348 -0.591800570
100 -1.010056418 -0.170841348
101 0.746253284 -1.010056418
102 -0.641465620 0.746253284
103 0.077264353 -0.641465620
104 0.738103932 0.077264353
105 -0.203922062 0.738103932
106 0.022594273 -0.203922062
107 0.175016806 0.022594273
108 -0.594943056 0.175016806
109 0.032215699 -0.594943056
110 0.473728433 0.032215699
111 -0.302531178 0.473728433
112 -0.147341137 -0.302531178
113 -0.441120805 -0.147341137
114 1.031001106 -0.441120805
115 -0.793540958 1.031001106
116 0.826594162 -0.793540958
117 0.061593784 0.826594162
118 -0.298931238 0.061593784
119 0.253007041 -0.298931238
120 0.539093798 0.253007041
121 -0.373771437 0.539093798
122 0.305512520 -0.373771437
123 0.182202487 0.305512520
124 -0.396319831 0.182202487
125 0.891317211 -0.396319831
126 1.123377500 0.891317211
127 -0.014566114 1.123377500
128 1.623141919 -0.014566114
129 -0.476272609 1.623141919
130 -0.558110380 -0.476272609
131 -2.386515220 -0.558110380
132 0.830837167 -2.386515220
133 -0.005102897 0.830837167
134 0.844404422 -0.005102897
135 -0.376423718 0.844404422
136 -0.559066466 -0.376423718
137 -0.635279591 -0.559066466
138 -0.441117975 -0.635279591
139 -0.589346299 -0.441117975
140 0.106270522 -0.589346299
141 -0.109978601 0.106270522
142 0.712694984 -0.109978601
143 -0.512838058 0.712694984
144 -0.542902756 -0.512838058
145 -0.580000673 -0.542902756
146 -0.062836065 -0.580000673
147 0.382194103 -0.062836065
148 1.033634095 0.382194103
149 -1.606472885 1.033634095
150 -0.599088330 -1.606472885
151 0.109904876 -0.599088330
152 -0.420883206 0.109904876
153 -0.386547325 -0.420883206
154 0.284389041 -0.386547325
155 -0.790382954 0.284389041
156 0.083568989 -0.790382954
157 -0.408917630 0.083568989
158 -0.379261758 -0.408917630
159 -0.382955027 -0.379261758
160 -0.712880812 -0.382955027
161 0.121359572 -0.712880812
162 0.886655712 0.121359572
163 0.787320854 0.886655712
164 0.868150649 0.787320854
165 -0.497680346 0.868150649
166 0.047201469 -0.497680346
167 0.864476381 0.047201469
168 -0.008229395 0.864476381
169 0.070120156 -0.008229395
170 -1.079221459 0.070120156
171 -0.456320123 -1.079221459
172 0.508515780 -0.456320123
173 0.050451476 0.508515780
174 0.438681297 0.050451476
175 0.685906366 0.438681297
176 0.335438578 0.685906366
177 0.217001698 0.335438578
178 -0.480150123 0.217001698
179 1.682613191 -0.480150123
180 0.670960460 1.682613191
181 -0.408354425 0.670960460
182 1.506697751 -0.408354425
183 0.755689910 1.506697751
184 -0.922800873 0.755689910
185 -0.761953002 -0.922800873
186 -1.353866512 -0.761953002
187 -0.119591923 -1.353866512
188 -0.115849913 -0.119591923
189 -0.840365935 -0.115849913
190 -0.317496135 -0.840365935
191 -1.471738866 -0.317496135
192 -0.135187037 -1.471738866
193 -0.115023453 -0.135187037
194 -0.873509070 -0.115023453
195 1.024626044 -0.873509070
196 -0.001613509 1.024626044
197 0.351843647 -0.001613509
198 0.924890797 0.351843647
199 0.360197210 0.924890797
200 NA 0.360197210
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.144627738 1.177003040
[2,] 0.811926315 -2.144627738
[3,] -0.627614071 0.811926315
[4,] 0.096671092 -0.627614071
[5,] -0.960068756 0.096671092
[6,] 1.212089281 -0.960068756
[7,] 0.322149564 1.212089281
[8,] 0.370570408 0.322149564
[9,] -1.170563677 0.370570408
[10,] 0.147379970 -1.170563677
[11,] 0.659601696 0.147379970
[12,] 0.292712189 0.659601696
[13,] -0.131664921 0.292712189
[14,] 0.562825726 -0.131664921
[15,] -1.168831022 0.562825726
[16,] -0.087495653 -1.168831022
[17,] 0.379952892 -0.087495653
[18,] 0.257098376 0.379952892
[19,] -0.279066786 0.257098376
[20,] 0.300395782 -0.279066786
[21,] 1.918932338 0.300395782
[22,] -0.348807586 1.918932338
[23,] 0.280133194 -0.348807586
[24,] -1.438693447 0.280133194
[25,] 0.173680204 -1.438693447
[26,] -1.431193744 0.173680204
[27,] -0.951245773 -1.431193744
[28,] -1.481804038 -0.951245773
[29,] -1.076648861 -1.481804038
[30,] 0.215646741 -1.076648861
[31,] 0.357161124 0.215646741
[32,] -0.469185935 0.357161124
[33,] 0.018862334 -0.469185935
[34,] 0.455572746 0.018862334
[35,] -0.467113389 0.455572746
[36,] 0.112400385 -0.467113389
[37,] 0.046904791 0.112400385
[38,] -0.043675970 0.046904791
[39,] 0.434561210 -0.043675970
[40,] -0.139318875 0.434561210
[41,] -0.127663744 -0.139318875
[42,] 0.974568557 -0.127663744
[43,] 1.237778076 0.974568557
[44,] -2.201531343 1.237778076
[45,] 0.390767831 -2.201531343
[46,] -0.458270208 0.390767831
[47,] 0.159854117 -0.458270208
[48,] 0.880710555 0.159854117
[49,] 1.314750878 0.880710555
[50,] -0.020723727 1.314750878
[51,] -2.301364574 -0.020723727
[52,] 0.434014868 -2.301364574
[53,] 0.235059701 0.434014868
[54,] -0.040834896 0.235059701
[55,] 0.618745966 -0.040834896
[56,] 0.649148466 0.618745966
[57,] 1.238345878 0.649148466
[58,] -0.325599432 1.238345878
[59,] -0.157655398 -0.325599432
[60,] -1.375933694 -0.157655398
[61,] 1.275446153 -1.375933694
[62,] -0.168143427 1.275446153
[63,] 0.395176396 -0.168143427
[64,] 0.056541077 0.395176396
[65,] 0.341171124 0.056541077
[66,] -0.267818032 0.341171124
[67,] -0.246363320 -0.267818032
[68,] -1.129058795 -0.246363320
[69,] 0.204152561 -1.129058795
[70,] -0.472901540 0.204152561
[71,] 0.203695599 -0.472901540
[72,] 0.840302351 0.203695599
[73,] 0.784891535 0.840302351
[74,] 1.120508958 0.784891535
[75,] -0.510890138 1.120508958
[76,] -0.492804007 -0.510890138
[77,] -0.213155037 -0.492804007
[78,] 0.572716709 -0.213155037
[79,] 0.314734745 0.572716709
[80,] -0.531280930 0.314734745
[81,] -0.033675783 -0.531280930
[82,] 0.292962590 -0.033675783
[83,] 0.962268857 0.292962590
[84,] 0.868558918 0.962268857
[85,] -1.087535439 0.868558918
[86,] 0.357247307 -1.087535439
[87,] 0.816229020 0.357247307
[88,] -0.260863130 0.816229020
[89,] 1.159498737 -0.260863130
[90,] 0.397662143 1.159498737
[91,] -0.265954409 0.397662143
[92,] -0.255720880 -0.265954409
[93,] 0.594093732 -0.255720880
[94,] 0.610645259 0.594093732
[95,] 0.172787830 0.610645259
[96,] -0.327607317 0.172787830
[97,] -0.573728849 -0.327607317
[98,] -0.591800570 -0.573728849
[99,] -0.170841348 -0.591800570
[100,] -1.010056418 -0.170841348
[101,] 0.746253284 -1.010056418
[102,] -0.641465620 0.746253284
[103,] 0.077264353 -0.641465620
[104,] 0.738103932 0.077264353
[105,] -0.203922062 0.738103932
[106,] 0.022594273 -0.203922062
[107,] 0.175016806 0.022594273
[108,] -0.594943056 0.175016806
[109,] 0.032215699 -0.594943056
[110,] 0.473728433 0.032215699
[111,] -0.302531178 0.473728433
[112,] -0.147341137 -0.302531178
[113,] -0.441120805 -0.147341137
[114,] 1.031001106 -0.441120805
[115,] -0.793540958 1.031001106
[116,] 0.826594162 -0.793540958
[117,] 0.061593784 0.826594162
[118,] -0.298931238 0.061593784
[119,] 0.253007041 -0.298931238
[120,] 0.539093798 0.253007041
[121,] -0.373771437 0.539093798
[122,] 0.305512520 -0.373771437
[123,] 0.182202487 0.305512520
[124,] -0.396319831 0.182202487
[125,] 0.891317211 -0.396319831
[126,] 1.123377500 0.891317211
[127,] -0.014566114 1.123377500
[128,] 1.623141919 -0.014566114
[129,] -0.476272609 1.623141919
[130,] -0.558110380 -0.476272609
[131,] -2.386515220 -0.558110380
[132,] 0.830837167 -2.386515220
[133,] -0.005102897 0.830837167
[134,] 0.844404422 -0.005102897
[135,] -0.376423718 0.844404422
[136,] -0.559066466 -0.376423718
[137,] -0.635279591 -0.559066466
[138,] -0.441117975 -0.635279591
[139,] -0.589346299 -0.441117975
[140,] 0.106270522 -0.589346299
[141,] -0.109978601 0.106270522
[142,] 0.712694984 -0.109978601
[143,] -0.512838058 0.712694984
[144,] -0.542902756 -0.512838058
[145,] -0.580000673 -0.542902756
[146,] -0.062836065 -0.580000673
[147,] 0.382194103 -0.062836065
[148,] 1.033634095 0.382194103
[149,] -1.606472885 1.033634095
[150,] -0.599088330 -1.606472885
[151,] 0.109904876 -0.599088330
[152,] -0.420883206 0.109904876
[153,] -0.386547325 -0.420883206
[154,] 0.284389041 -0.386547325
[155,] -0.790382954 0.284389041
[156,] 0.083568989 -0.790382954
[157,] -0.408917630 0.083568989
[158,] -0.379261758 -0.408917630
[159,] -0.382955027 -0.379261758
[160,] -0.712880812 -0.382955027
[161,] 0.121359572 -0.712880812
[162,] 0.886655712 0.121359572
[163,] 0.787320854 0.886655712
[164,] 0.868150649 0.787320854
[165,] -0.497680346 0.868150649
[166,] 0.047201469 -0.497680346
[167,] 0.864476381 0.047201469
[168,] -0.008229395 0.864476381
[169,] 0.070120156 -0.008229395
[170,] -1.079221459 0.070120156
[171,] -0.456320123 -1.079221459
[172,] 0.508515780 -0.456320123
[173,] 0.050451476 0.508515780
[174,] 0.438681297 0.050451476
[175,] 0.685906366 0.438681297
[176,] 0.335438578 0.685906366
[177,] 0.217001698 0.335438578
[178,] -0.480150123 0.217001698
[179,] 1.682613191 -0.480150123
[180,] 0.670960460 1.682613191
[181,] -0.408354425 0.670960460
[182,] 1.506697751 -0.408354425
[183,] 0.755689910 1.506697751
[184,] -0.922800873 0.755689910
[185,] -0.761953002 -0.922800873
[186,] -1.353866512 -0.761953002
[187,] -0.119591923 -1.353866512
[188,] -0.115849913 -0.119591923
[189,] -0.840365935 -0.115849913
[190,] -0.317496135 -0.840365935
[191,] -1.471738866 -0.317496135
[192,] -0.135187037 -1.471738866
[193,] -0.115023453 -0.135187037
[194,] -0.873509070 -0.115023453
[195,] 1.024626044 -0.873509070
[196,] -0.001613509 1.024626044
[197,] 0.351843647 -0.001613509
[198,] 0.924890797 0.351843647
[199,] 0.360197210 0.924890797
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.144627738 1.177003040
2 0.811926315 -2.144627738
3 -0.627614071 0.811926315
4 0.096671092 -0.627614071
5 -0.960068756 0.096671092
6 1.212089281 -0.960068756
7 0.322149564 1.212089281
8 0.370570408 0.322149564
9 -1.170563677 0.370570408
10 0.147379970 -1.170563677
11 0.659601696 0.147379970
12 0.292712189 0.659601696
13 -0.131664921 0.292712189
14 0.562825726 -0.131664921
15 -1.168831022 0.562825726
16 -0.087495653 -1.168831022
17 0.379952892 -0.087495653
18 0.257098376 0.379952892
19 -0.279066786 0.257098376
20 0.300395782 -0.279066786
21 1.918932338 0.300395782
22 -0.348807586 1.918932338
23 0.280133194 -0.348807586
24 -1.438693447 0.280133194
25 0.173680204 -1.438693447
26 -1.431193744 0.173680204
27 -0.951245773 -1.431193744
28 -1.481804038 -0.951245773
29 -1.076648861 -1.481804038
30 0.215646741 -1.076648861
31 0.357161124 0.215646741
32 -0.469185935 0.357161124
33 0.018862334 -0.469185935
34 0.455572746 0.018862334
35 -0.467113389 0.455572746
36 0.112400385 -0.467113389
37 0.046904791 0.112400385
38 -0.043675970 0.046904791
39 0.434561210 -0.043675970
40 -0.139318875 0.434561210
41 -0.127663744 -0.139318875
42 0.974568557 -0.127663744
43 1.237778076 0.974568557
44 -2.201531343 1.237778076
45 0.390767831 -2.201531343
46 -0.458270208 0.390767831
47 0.159854117 -0.458270208
48 0.880710555 0.159854117
49 1.314750878 0.880710555
50 -0.020723727 1.314750878
51 -2.301364574 -0.020723727
52 0.434014868 -2.301364574
53 0.235059701 0.434014868
54 -0.040834896 0.235059701
55 0.618745966 -0.040834896
56 0.649148466 0.618745966
57 1.238345878 0.649148466
58 -0.325599432 1.238345878
59 -0.157655398 -0.325599432
60 -1.375933694 -0.157655398
61 1.275446153 -1.375933694
62 -0.168143427 1.275446153
63 0.395176396 -0.168143427
64 0.056541077 0.395176396
65 0.341171124 0.056541077
66 -0.267818032 0.341171124
67 -0.246363320 -0.267818032
68 -1.129058795 -0.246363320
69 0.204152561 -1.129058795
70 -0.472901540 0.204152561
71 0.203695599 -0.472901540
72 0.840302351 0.203695599
73 0.784891535 0.840302351
74 1.120508958 0.784891535
75 -0.510890138 1.120508958
76 -0.492804007 -0.510890138
77 -0.213155037 -0.492804007
78 0.572716709 -0.213155037
79 0.314734745 0.572716709
80 -0.531280930 0.314734745
81 -0.033675783 -0.531280930
82 0.292962590 -0.033675783
83 0.962268857 0.292962590
84 0.868558918 0.962268857
85 -1.087535439 0.868558918
86 0.357247307 -1.087535439
87 0.816229020 0.357247307
88 -0.260863130 0.816229020
89 1.159498737 -0.260863130
90 0.397662143 1.159498737
91 -0.265954409 0.397662143
92 -0.255720880 -0.265954409
93 0.594093732 -0.255720880
94 0.610645259 0.594093732
95 0.172787830 0.610645259
96 -0.327607317 0.172787830
97 -0.573728849 -0.327607317
98 -0.591800570 -0.573728849
99 -0.170841348 -0.591800570
100 -1.010056418 -0.170841348
101 0.746253284 -1.010056418
102 -0.641465620 0.746253284
103 0.077264353 -0.641465620
104 0.738103932 0.077264353
105 -0.203922062 0.738103932
106 0.022594273 -0.203922062
107 0.175016806 0.022594273
108 -0.594943056 0.175016806
109 0.032215699 -0.594943056
110 0.473728433 0.032215699
111 -0.302531178 0.473728433
112 -0.147341137 -0.302531178
113 -0.441120805 -0.147341137
114 1.031001106 -0.441120805
115 -0.793540958 1.031001106
116 0.826594162 -0.793540958
117 0.061593784 0.826594162
118 -0.298931238 0.061593784
119 0.253007041 -0.298931238
120 0.539093798 0.253007041
121 -0.373771437 0.539093798
122 0.305512520 -0.373771437
123 0.182202487 0.305512520
124 -0.396319831 0.182202487
125 0.891317211 -0.396319831
126 1.123377500 0.891317211
127 -0.014566114 1.123377500
128 1.623141919 -0.014566114
129 -0.476272609 1.623141919
130 -0.558110380 -0.476272609
131 -2.386515220 -0.558110380
132 0.830837167 -2.386515220
133 -0.005102897 0.830837167
134 0.844404422 -0.005102897
135 -0.376423718 0.844404422
136 -0.559066466 -0.376423718
137 -0.635279591 -0.559066466
138 -0.441117975 -0.635279591
139 -0.589346299 -0.441117975
140 0.106270522 -0.589346299
141 -0.109978601 0.106270522
142 0.712694984 -0.109978601
143 -0.512838058 0.712694984
144 -0.542902756 -0.512838058
145 -0.580000673 -0.542902756
146 -0.062836065 -0.580000673
147 0.382194103 -0.062836065
148 1.033634095 0.382194103
149 -1.606472885 1.033634095
150 -0.599088330 -1.606472885
151 0.109904876 -0.599088330
152 -0.420883206 0.109904876
153 -0.386547325 -0.420883206
154 0.284389041 -0.386547325
155 -0.790382954 0.284389041
156 0.083568989 -0.790382954
157 -0.408917630 0.083568989
158 -0.379261758 -0.408917630
159 -0.382955027 -0.379261758
160 -0.712880812 -0.382955027
161 0.121359572 -0.712880812
162 0.886655712 0.121359572
163 0.787320854 0.886655712
164 0.868150649 0.787320854
165 -0.497680346 0.868150649
166 0.047201469 -0.497680346
167 0.864476381 0.047201469
168 -0.008229395 0.864476381
169 0.070120156 -0.008229395
170 -1.079221459 0.070120156
171 -0.456320123 -1.079221459
172 0.508515780 -0.456320123
173 0.050451476 0.508515780
174 0.438681297 0.050451476
175 0.685906366 0.438681297
176 0.335438578 0.685906366
177 0.217001698 0.335438578
178 -0.480150123 0.217001698
179 1.682613191 -0.480150123
180 0.670960460 1.682613191
181 -0.408354425 0.670960460
182 1.506697751 -0.408354425
183 0.755689910 1.506697751
184 -0.922800873 0.755689910
185 -0.761953002 -0.922800873
186 -1.353866512 -0.761953002
187 -0.119591923 -1.353866512
188 -0.115849913 -0.119591923
189 -0.840365935 -0.115849913
190 -0.317496135 -0.840365935
191 -1.471738866 -0.317496135
192 -0.135187037 -1.471738866
193 -0.115023453 -0.135187037
194 -0.873509070 -0.115023453
195 1.024626044 -0.873509070
196 -0.001613509 1.024626044
197 0.351843647 -0.001613509
198 0.924890797 0.351843647
199 0.360197210 0.924890797
> 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/wessaorg/rcomp/tmp/76po11322147370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/818vu1322147370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9xkqy1322147370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10d71c1322147370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11a5us1322147370.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/wessaorg/rcomp/tmp/12b7sr1322147370.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/wessaorg/rcomp/tmp/13xch81322147370.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/wessaorg/rcomp/tmp/14zh751322147370.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/wessaorg/rcomp/tmp/156v781322147370.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/wessaorg/rcomp/tmp/1643211322147370.tab")
+ }
>
> try(system("convert tmp/1soow1322147370.ps tmp/1soow1322147370.png",intern=TRUE))
character(0)
> try(system("convert tmp/20tg31322147370.ps tmp/20tg31322147370.png",intern=TRUE))
character(0)
> try(system("convert tmp/3z34i1322147370.ps tmp/3z34i1322147370.png",intern=TRUE))
character(0)
> try(system("convert tmp/4i5cz1322147370.ps tmp/4i5cz1322147370.png",intern=TRUE))
character(0)
> try(system("convert tmp/5gqjq1322147370.ps tmp/5gqjq1322147370.png",intern=TRUE))
character(0)
> try(system("convert tmp/6anzy1322147370.ps tmp/6anzy1322147370.png",intern=TRUE))
character(0)
> try(system("convert tmp/76po11322147370.ps tmp/76po11322147370.png",intern=TRUE))
character(0)
> try(system("convert tmp/818vu1322147370.ps tmp/818vu1322147370.png",intern=TRUE))
character(0)
> try(system("convert tmp/9xkqy1322147370.ps tmp/9xkqy1322147370.png",intern=TRUE))
character(0)
> try(system("convert tmp/10d71c1322147370.ps tmp/10d71c1322147370.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.665 0.487 6.211