R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
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(45
+ ,64
+ ,64
+ ,62
+ ,64
+ ,62
+ ,45
+ ,69
+ ,64
+ ,64
+ ,62
+ ,64
+ ,49
+ ,69
+ ,69
+ ,64
+ ,64
+ ,62
+ ,50
+ ,65
+ ,69
+ ,69
+ ,64
+ ,64
+ ,54
+ ,56
+ ,65
+ ,69
+ ,69
+ ,64
+ ,59
+ ,58
+ ,56
+ ,65
+ ,69
+ ,69
+ ,58
+ ,53
+ ,58
+ ,56
+ ,65
+ ,69
+ ,56
+ ,62
+ ,53
+ ,58
+ ,56
+ ,65
+ ,48
+ ,55
+ ,62
+ ,53
+ ,58
+ ,56
+ ,50
+ ,60
+ ,55
+ ,62
+ ,53
+ ,58
+ ,52
+ ,59
+ ,60
+ ,55
+ ,62
+ ,53
+ ,53
+ ,58
+ ,59
+ ,60
+ ,55
+ ,62
+ ,55
+ ,53
+ ,58
+ ,59
+ ,60
+ ,55
+ ,43
+ ,57
+ ,53
+ ,58
+ ,59
+ ,60
+ ,42
+ ,57
+ ,57
+ ,53
+ ,58
+ ,59
+ ,38
+ ,53
+ ,57
+ ,57
+ ,53
+ ,58
+ ,41
+ ,54
+ ,53
+ ,57
+ ,57
+ ,53
+ ,41
+ ,53
+ ,54
+ ,53
+ ,57
+ ,57
+ ,39
+ ,57
+ ,53
+ ,54
+ ,53
+ ,57
+ ,34
+ ,57
+ ,57
+ ,53
+ ,54
+ ,53
+ ,27
+ ,55
+ ,57
+ ,57
+ ,53
+ ,54
+ ,15
+ ,49
+ ,55
+ ,57
+ ,57
+ ,53
+ ,14
+ ,50
+ ,49
+ ,55
+ ,57
+ ,57
+ ,31
+ ,49
+ ,50
+ ,49
+ ,55
+ ,57
+ ,41
+ ,54
+ ,49
+ ,50
+ ,49
+ ,55
+ ,43
+ ,58
+ ,54
+ ,49
+ ,50
+ ,49
+ ,46
+ ,58
+ ,58
+ ,54
+ ,49
+ ,50
+ ,42
+ ,52
+ ,58
+ ,58
+ ,54
+ ,49
+ ,45
+ ,56
+ ,52
+ ,58
+ ,58
+ ,54
+ ,45
+ ,52
+ ,56
+ ,52
+ ,58
+ ,58
+ ,40
+ ,59
+ ,52
+ ,56
+ ,52
+ ,58
+ ,35
+ ,53
+ ,59
+ ,52
+ ,56
+ ,52
+ ,36
+ ,52
+ ,53
+ ,59
+ ,52
+ ,56
+ ,38
+ ,53
+ ,52
+ ,53
+ ,59
+ ,52
+ ,39
+ ,51
+ ,53
+ ,52
+ ,53
+ ,59
+ ,32
+ ,50
+ ,51
+ ,53
+ ,52
+ ,53
+ ,24
+ ,56
+ ,50
+ ,51
+ ,53
+ ,52
+ ,21
+ ,52
+ ,56
+ ,50
+ ,51
+ ,53
+ ,12
+ ,46
+ ,52
+ ,56
+ ,50
+ ,51
+ ,29
+ ,48
+ ,46
+ ,52
+ ,56
+ ,50
+ ,36
+ ,46
+ ,48
+ ,46
+ ,52
+ ,56
+ ,31
+ ,48
+ ,46
+ ,48
+ ,46
+ ,52
+ ,28
+ ,48
+ ,48
+ ,46
+ ,48
+ ,46
+ ,30
+ ,49
+ ,48
+ ,48
+ ,46
+ ,48
+ ,38
+ ,53
+ ,49
+ ,48
+ ,48
+ ,46
+ ,27
+ ,48
+ ,53
+ ,49
+ ,48
+ ,48
+ ,40
+ ,51
+ ,48
+ ,53
+ ,49
+ ,48
+ ,40
+ ,48
+ ,51
+ ,48
+ ,53
+ ,49
+ ,44
+ ,50
+ ,48
+ ,51
+ ,48
+ ,53
+ ,47
+ ,55
+ ,50
+ ,48
+ ,51
+ ,48
+ ,45
+ ,52
+ ,55
+ ,50
+ ,48
+ ,51
+ ,42
+ ,53
+ ,52
+ ,55
+ ,50
+ ,48
+ ,38
+ ,52
+ ,53
+ ,52
+ ,55
+ ,50
+ ,46
+ ,55
+ ,52
+ ,53
+ ,52
+ ,55
+ ,37
+ ,53
+ ,55
+ ,52
+ ,53
+ ,52
+ ,41
+ ,53
+ ,53
+ ,55
+ ,52
+ ,53
+ ,40
+ ,56
+ ,53
+ ,53
+ ,55
+ ,52
+ ,33
+ ,54
+ ,56
+ ,53
+ ,53
+ ,55
+ ,34
+ ,52
+ ,54
+ ,56
+ ,53
+ ,53
+ ,36
+ ,55
+ ,52
+ ,54
+ ,56
+ ,53
+ ,36
+ ,54
+ ,55
+ ,52
+ ,54
+ ,56
+ ,38
+ ,59
+ ,54
+ ,55
+ ,52
+ ,54
+ ,42
+ ,56
+ ,59
+ ,54
+ ,55
+ ,52
+ ,35
+ ,56
+ ,56
+ ,59
+ ,54
+ ,55
+ ,25
+ ,51
+ ,56
+ ,56
+ ,59
+ ,54
+ ,24
+ ,53
+ ,51
+ ,56
+ ,56
+ ,59
+ ,22
+ ,52
+ ,53
+ ,51
+ ,56
+ ,56
+ ,27
+ ,51
+ ,52
+ ,53
+ ,51
+ ,56
+ ,17
+ ,46
+ ,51
+ ,52
+ ,53
+ ,51
+ ,30
+ ,49
+ ,46
+ ,51
+ ,52
+ ,53
+ ,30
+ ,46
+ ,49
+ ,46
+ ,51
+ ,52
+ ,34
+ ,55
+ ,46
+ ,49
+ ,46
+ ,51
+ ,37
+ ,57
+ ,55
+ ,46
+ ,49
+ ,46
+ ,36
+ ,53
+ ,57
+ ,55
+ ,46
+ ,49
+ ,33
+ ,52
+ ,53
+ ,57
+ ,55
+ ,46
+ ,33
+ ,53
+ ,52
+ ,53
+ ,57
+ ,55
+ ,33
+ ,50
+ ,53
+ ,52
+ ,53
+ ,57
+ ,37
+ ,54
+ ,50
+ ,53
+ ,52
+ ,53
+ ,40
+ ,53
+ ,54
+ ,50
+ ,53
+ ,52
+ ,35
+ ,50
+ ,53
+ ,54
+ ,50
+ ,53
+ ,37
+ ,51
+ ,50
+ ,53
+ ,54
+ ,50
+ ,43
+ ,52
+ ,51
+ ,50
+ ,53
+ ,54
+ ,42
+ ,47
+ ,52
+ ,51
+ ,50
+ ,53
+ ,33
+ ,51
+ ,47
+ ,52
+ ,51
+ ,50
+ ,39
+ ,49
+ ,51
+ ,47
+ ,52
+ ,51
+ ,40
+ ,53
+ ,49
+ ,51
+ ,47
+ ,52
+ ,37
+ ,52
+ ,53
+ ,49
+ ,51
+ ,47
+ ,44
+ ,45
+ ,52
+ ,53
+ ,49
+ ,51
+ ,42
+ ,53
+ ,45
+ ,52
+ ,53
+ ,49
+ ,43
+ ,51
+ ,53
+ ,45
+ ,52
+ ,53
+ ,40
+ ,48
+ ,51
+ ,53
+ ,45
+ ,52
+ ,30
+ ,48
+ ,48
+ ,51
+ ,53
+ ,45
+ ,30
+ ,48
+ ,48
+ ,48
+ ,51
+ ,53
+ ,31
+ ,48
+ ,48
+ ,48
+ ,48
+ ,51
+ ,18
+ ,40
+ ,48
+ ,48
+ ,48
+ ,48
+ ,24
+ ,43
+ ,40
+ ,48
+ ,48
+ ,48
+ ,22
+ ,40
+ ,43
+ ,40
+ ,48
+ ,48
+ ,26
+ ,39
+ ,40
+ ,43
+ ,40
+ ,48
+ ,28
+ ,39
+ ,39
+ ,40
+ ,43
+ ,40
+ ,23
+ ,36
+ ,39
+ ,39
+ ,40
+ ,43
+ ,17
+ ,41
+ ,36
+ ,39
+ ,39
+ ,40
+ ,12
+ ,39
+ ,41
+ ,36
+ ,39
+ ,39
+ ,9
+ ,40
+ ,39
+ ,41
+ ,36
+ ,39
+ ,19
+ ,39
+ ,40
+ ,39
+ ,41
+ ,36
+ ,21
+ ,46
+ ,39
+ ,40
+ ,39
+ ,41
+ ,18
+ ,40
+ ,46
+ ,39
+ ,40
+ ,39
+ ,18
+ ,37
+ ,40
+ ,46
+ ,39
+ ,40
+ ,15
+ ,37
+ ,37
+ ,40
+ ,46
+ ,39
+ ,24
+ ,44
+ ,37
+ ,37
+ ,40
+ ,46
+ ,18
+ ,41
+ ,44
+ ,37
+ ,37
+ ,40
+ ,19
+ ,40
+ ,41
+ ,44
+ ,37
+ ,37
+ ,30
+ ,36
+ ,40
+ ,41
+ ,44
+ ,37
+ ,33
+ ,38
+ ,36
+ ,40
+ ,41
+ ,44
+ ,35
+ ,43
+ ,38
+ ,36
+ ,40
+ ,41
+ ,36
+ ,42
+ ,43
+ ,38
+ ,36
+ ,40
+ ,47
+ ,45
+ ,42
+ ,43
+ ,38
+ ,36
+ ,46
+ ,46
+ ,45
+ ,42
+ ,43
+ ,38)
+ ,dim=c(6
+ ,117)
+ ,dimnames=list(c('X'
+ ,'Y'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:117))
> y <- array(NA,dim=c(6,117),dimnames=list(c('X','Y','Y1','Y2','Y3','Y4'),1:117))
> 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 = '2'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 64 45 64 62 64 62 1 0 0 0 0 0 0 0 0 0 0 1
2 69 45 64 64 62 64 0 1 0 0 0 0 0 0 0 0 0 2
3 69 49 69 64 64 62 0 0 1 0 0 0 0 0 0 0 0 3
4 65 50 69 69 64 64 0 0 0 1 0 0 0 0 0 0 0 4
5 56 54 65 69 69 64 0 0 0 0 1 0 0 0 0 0 0 5
6 58 59 56 65 69 69 0 0 0 0 0 1 0 0 0 0 0 6
7 53 58 58 56 65 69 0 0 0 0 0 0 1 0 0 0 0 7
8 62 56 53 58 56 65 0 0 0 0 0 0 0 1 0 0 0 8
9 55 48 62 53 58 56 0 0 0 0 0 0 0 0 1 0 0 9
10 60 50 55 62 53 58 0 0 0 0 0 0 0 0 0 1 0 10
11 59 52 60 55 62 53 0 0 0 0 0 0 0 0 0 0 1 11
12 58 53 59 60 55 62 0 0 0 0 0 0 0 0 0 0 0 12
13 53 55 58 59 60 55 1 0 0 0 0 0 0 0 0 0 0 13
14 57 43 53 58 59 60 0 1 0 0 0 0 0 0 0 0 0 14
15 57 42 57 53 58 59 0 0 1 0 0 0 0 0 0 0 0 15
16 53 38 57 57 53 58 0 0 0 1 0 0 0 0 0 0 0 16
17 54 41 53 57 57 53 0 0 0 0 1 0 0 0 0 0 0 17
18 53 41 54 53 57 57 0 0 0 0 0 1 0 0 0 0 0 18
19 57 39 53 54 53 57 0 0 0 0 0 0 1 0 0 0 0 19
20 57 34 57 53 54 53 0 0 0 0 0 0 0 1 0 0 0 20
21 55 27 57 57 53 54 0 0 0 0 0 0 0 0 1 0 0 21
22 49 15 55 57 57 53 0 0 0 0 0 0 0 0 0 1 0 22
23 50 14 49 55 57 57 0 0 0 0 0 0 0 0 0 0 1 23
24 49 31 50 49 55 57 0 0 0 0 0 0 0 0 0 0 0 24
25 54 41 49 50 49 55 1 0 0 0 0 0 0 0 0 0 0 25
26 58 43 54 49 50 49 0 1 0 0 0 0 0 0 0 0 0 26
27 58 46 58 54 49 50 0 0 1 0 0 0 0 0 0 0 0 27
28 52 42 58 58 54 49 0 0 0 1 0 0 0 0 0 0 0 28
29 56 45 52 58 58 54 0 0 0 0 1 0 0 0 0 0 0 29
30 52 45 56 52 58 58 0 0 0 0 0 1 0 0 0 0 0 30
31 59 40 52 56 52 58 0 0 0 0 0 0 1 0 0 0 0 31
32 53 35 59 52 56 52 0 0 0 0 0 0 0 1 0 0 0 32
33 52 36 53 59 52 56 0 0 0 0 0 0 0 0 1 0 0 33
34 53 38 52 53 59 52 0 0 0 0 0 0 0 0 0 1 0 34
35 51 39 53 52 53 59 0 0 0 0 0 0 0 0 0 0 1 35
36 50 32 51 53 52 53 0 0 0 0 0 0 0 0 0 0 0 36
37 56 24 50 51 53 52 1 0 0 0 0 0 0 0 0 0 0 37
38 52 21 56 50 51 53 0 1 0 0 0 0 0 0 0 0 0 38
39 46 12 52 56 50 51 0 0 1 0 0 0 0 0 0 0 0 39
40 48 29 46 52 56 50 0 0 0 1 0 0 0 0 0 0 0 40
41 46 36 48 46 52 56 0 0 0 0 1 0 0 0 0 0 0 41
42 48 31 46 48 46 52 0 0 0 0 0 1 0 0 0 0 0 42
43 48 28 48 46 48 46 0 0 0 0 0 0 1 0 0 0 0 43
44 49 30 48 48 46 48 0 0 0 0 0 0 0 1 0 0 0 44
45 53 38 49 48 48 46 0 0 0 0 0 0 0 0 1 0 0 45
46 48 27 53 49 48 48 0 0 0 0 0 0 0 0 0 1 0 46
47 51 40 48 53 49 48 0 0 0 0 0 0 0 0 0 0 1 47
48 48 40 51 48 53 49 0 0 0 0 0 0 0 0 0 0 0 48
49 50 44 48 51 48 53 1 0 0 0 0 0 0 0 0 0 0 49
50 55 47 50 48 51 48 0 1 0 0 0 0 0 0 0 0 0 50
51 52 45 55 50 48 51 0 0 1 0 0 0 0 0 0 0 0 51
52 53 42 52 55 50 48 0 0 0 1 0 0 0 0 0 0 0 52
53 52 38 53 52 55 50 0 0 0 0 1 0 0 0 0 0 0 53
54 55 46 52 53 52 55 0 0 0 0 0 1 0 0 0 0 0 54
55 53 37 55 52 53 52 0 0 0 0 0 0 1 0 0 0 0 55
56 53 41 53 55 52 53 0 0 0 0 0 0 0 1 0 0 0 56
57 56 40 53 53 55 52 0 0 0 0 0 0 0 0 1 0 0 57
58 54 33 56 53 53 55 0 0 0 0 0 0 0 0 0 1 0 58
59 52 34 54 56 53 53 0 0 0 0 0 0 0 0 0 0 1 59
60 55 36 52 54 56 53 0 0 0 0 0 0 0 0 0 0 0 60
61 54 36 55 52 54 56 1 0 0 0 0 0 0 0 0 0 0 61
62 59 38 54 55 52 54 0 1 0 0 0 0 0 0 0 0 0 62
63 56 42 59 54 55 52 0 0 1 0 0 0 0 0 0 0 0 63
64 56 35 56 59 54 55 0 0 0 1 0 0 0 0 0 0 0 64
65 51 25 56 56 59 54 0 0 0 0 1 0 0 0 0 0 0 65
66 53 24 51 56 56 59 0 0 0 0 0 1 0 0 0 0 0 66
67 52 22 53 51 56 56 0 0 0 0 0 0 1 0 0 0 0 67
68 51 27 52 53 51 56 0 0 0 0 0 0 0 1 0 0 0 68
69 46 17 51 52 53 51 0 0 0 0 0 0 0 0 1 0 0 69
70 49 30 46 51 52 53 0 0 0 0 0 0 0 0 0 1 0 70
71 46 30 49 46 51 52 0 0 0 0 0 0 0 0 0 0 1 71
72 55 34 46 49 46 51 0 0 0 0 0 0 0 0 0 0 0 72
73 57 37 55 46 49 46 1 0 0 0 0 0 0 0 0 0 0 73
74 53 36 57 55 46 49 0 1 0 0 0 0 0 0 0 0 0 74
75 52 33 53 57 55 46 0 0 1 0 0 0 0 0 0 0 0 75
76 53 33 52 53 57 55 0 0 0 1 0 0 0 0 0 0 0 76
77 50 33 53 52 53 57 0 0 0 0 1 0 0 0 0 0 0 77
78 54 37 50 53 52 53 0 0 0 0 0 1 0 0 0 0 0 78
79 53 40 54 50 53 52 0 0 0 0 0 0 1 0 0 0 0 79
80 50 35 53 54 50 53 0 0 0 0 0 0 0 1 0 0 0 80
81 51 37 50 53 54 50 0 0 0 0 0 0 0 0 1 0 0 81
82 52 43 51 50 53 54 0 0 0 0 0 0 0 0 0 1 0 82
83 47 42 52 51 50 53 0 0 0 0 0 0 0 0 0 0 1 83
84 51 33 47 52 51 50 0 0 0 0 0 0 0 0 0 0 0 84
85 49 39 51 47 52 51 1 0 0 0 0 0 0 0 0 0 0 85
86 53 40 49 51 47 52 0 1 0 0 0 0 0 0 0 0 0 86
87 52 37 53 49 51 47 0 0 1 0 0 0 0 0 0 0 0 87
88 45 44 52 53 49 51 0 0 0 1 0 0 0 0 0 0 0 88
89 53 42 45 52 53 49 0 0 0 0 1 0 0 0 0 0 0 89
90 51 43 53 45 52 53 0 0 0 0 0 1 0 0 0 0 0 90
91 48 40 51 53 45 52 0 0 0 0 0 0 1 0 0 0 0 91
92 48 30 48 51 53 45 0 0 0 0 0 0 0 1 0 0 0 92
93 48 30 48 48 51 53 0 0 0 0 0 0 0 0 1 0 0 93
94 48 31 48 48 48 51 0 0 0 0 0 0 0 0 0 1 0 94
95 40 18 48 48 48 48 0 0 0 0 0 0 0 0 0 0 1 95
96 43 24 40 48 48 48 0 0 0 0 0 0 0 0 0 0 0 96
97 40 22 43 40 48 48 1 0 0 0 0 0 0 0 0 0 0 97
98 39 26 40 43 40 48 0 1 0 0 0 0 0 0 0 0 0 98
99 39 28 39 40 43 40 0 0 1 0 0 0 0 0 0 0 0 99
100 36 23 39 39 40 43 0 0 0 1 0 0 0 0 0 0 0 100
101 41 17 36 39 39 40 0 0 0 0 1 0 0 0 0 0 0 101
102 39 12 41 36 39 39 0 0 0 0 0 1 0 0 0 0 0 102
103 40 9 39 41 36 39 0 0 0 0 0 0 1 0 0 0 0 103
104 39 19 40 39 41 36 0 0 0 0 0 0 0 1 0 0 0 104
105 46 21 39 40 39 41 0 0 0 0 0 0 0 0 1 0 0 105
106 40 18 46 39 40 39 0 0 0 0 0 0 0 0 0 1 0 106
107 37 18 40 46 39 40 0 0 0 0 0 0 0 0 0 0 1 107
108 37 15 37 40 46 39 0 0 0 0 0 0 0 0 0 0 0 108
109 44 24 37 37 40 46 1 0 0 0 0 0 0 0 0 0 0 109
110 41 18 44 37 37 40 0 1 0 0 0 0 0 0 0 0 0 110
111 40 19 41 44 37 37 0 0 1 0 0 0 0 0 0 0 0 111
112 36 30 40 41 44 37 0 0 0 1 0 0 0 0 0 0 0 112
113 38 33 36 40 41 44 0 0 0 0 1 0 0 0 0 0 0 113
114 43 35 38 36 40 41 0 0 0 0 0 1 0 0 0 0 0 114
115 42 36 43 38 36 40 0 0 0 0 0 0 1 0 0 0 0 115
116 45 47 42 43 38 36 0 0 0 0 0 0 0 1 0 0 0 116
117 46 46 45 42 43 38 0 0 0 0 0 0 0 0 1 0 0 117
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
14.98401 0.12878 0.32218 0.30678 -0.01857 0.03875
M1 M2 M3 M4 M5 M6
1.23353 2.06185 -0.06644 -2.62061 -1.34841 0.11381
M7 M8 M9 M10 M11 t
0.07430 0.08686 0.37792 -0.43901 -2.34191 -0.02783
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.75819 -1.75889 0.00524 1.79134 6.66700
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.98401 4.95620 3.023 0.003184 **
X 0.12878 0.03335 3.861 0.000201 ***
Y1 0.32218 0.09571 3.366 0.001086 **
Y2 0.30678 0.10083 3.043 0.003003 **
Y3 -0.01857 0.10080 -0.184 0.854233
Y4 0.03875 0.09356 0.414 0.679681
M1 1.23353 1.40890 0.876 0.383406
M2 2.06185 1.44647 1.425 0.157176
M3 -0.06644 1.47187 -0.045 0.964089
M4 -2.62061 1.42050 -1.845 0.068049 .
M5 -1.34841 1.38314 -0.975 0.331989
M6 0.11381 1.40176 0.081 0.935453
M7 0.07430 1.42028 0.052 0.958382
M8 0.08686 1.41125 0.062 0.951049
M9 0.37792 1.40560 0.269 0.788590
M10 -0.43901 1.43765 -0.305 0.760726
M11 -2.34191 1.42316 -1.646 0.103023
t -0.02783 0.01506 -1.848 0.067559 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.986 on 99 degrees of freedom
Multiple R-squared: 0.828, Adjusted R-squared: 0.7984
F-statistic: 28.03 on 17 and 99 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,] 0.2859947 0.57198932 0.71400534
[2,] 0.1970326 0.39406517 0.80296741
[3,] 0.1872654 0.37453079 0.81273461
[4,] 0.1970757 0.39415131 0.80292434
[5,] 0.7656101 0.46877974 0.23438987
[6,] 0.7269260 0.54614808 0.27307404
[7,] 0.6657402 0.66851967 0.33425983
[8,] 0.6095381 0.78092381 0.39046190
[9,] 0.9104318 0.17913637 0.08956818
[10,] 0.8955143 0.20897142 0.10448571
[11,] 0.9152029 0.16959430 0.08479715
[12,] 0.8894066 0.22118685 0.11059343
[13,] 0.8758089 0.24838230 0.12419115
[14,] 0.9461398 0.10772041 0.05386020
[15,] 0.9364404 0.12711923 0.06355962
[16,] 0.9179778 0.16404435 0.08202218
[17,] 0.9603786 0.07924287 0.03962144
[18,] 0.9581209 0.08375815 0.04187907
[19,] 0.9874076 0.02518471 0.01259235
[20,] 0.9848226 0.03035473 0.01517736
[21,] 0.9810847 0.03783057 0.01891529
[22,] 0.9751518 0.04969645 0.02484822
[23,] 0.9646249 0.07075026 0.03537513
[24,] 0.9525642 0.09487153 0.04743576
[25,] 0.9552641 0.08947179 0.04473590
[26,] 0.9463945 0.10721100 0.05360550
[27,] 0.9295903 0.14081941 0.07040971
[28,] 0.9447021 0.11059579 0.05529790
[29,] 0.9558956 0.08820876 0.04410438
[30,] 0.9402704 0.11945914 0.05972957
[31,] 0.9345670 0.13086601 0.06543301
[32,] 0.9211239 0.15775222 0.07887611
[33,] 0.9286200 0.14275999 0.07137999
[34,] 0.9393377 0.12132457 0.06066228
[35,] 0.9314449 0.13711017 0.06855509
[36,] 0.9283115 0.14337691 0.07168845
[37,] 0.9349657 0.13006852 0.06503426
[38,] 0.9228216 0.15435683 0.07717841
[39,] 0.8978821 0.20423572 0.10211786
[40,] 0.9014153 0.19716933 0.09858467
[41,] 0.8908181 0.21836379 0.10918190
[42,] 0.8794451 0.24110983 0.12055492
[43,] 0.8460415 0.30791696 0.15395848
[44,] 0.8535320 0.29293606 0.14646803
[45,] 0.8263691 0.34726176 0.17363088
[46,] 0.7909940 0.41801203 0.20900602
[47,] 0.7583869 0.48322613 0.24161307
[48,] 0.7115614 0.57687716 0.28843858
[49,] 0.7967176 0.40656470 0.20328235
[50,] 0.7696726 0.46065471 0.23032736
[51,] 0.7236662 0.55266752 0.27633376
[52,] 0.7770348 0.44593041 0.22296520
[53,] 0.8069250 0.38615008 0.19307504
[54,] 0.8174856 0.36502887 0.18251444
[55,] 0.7771102 0.44577969 0.22288984
[56,] 0.9165222 0.16695563 0.08347782
[57,] 0.8904000 0.21919990 0.10959995
[58,] 0.8590433 0.28191337 0.14095668
[59,] 0.8281723 0.34365532 0.17182766
[60,] 0.8034634 0.39307315 0.19653658
[61,] 0.7987136 0.40257277 0.20128639
[62,] 0.7392214 0.52155719 0.26077860
[63,] 0.6980499 0.60390017 0.30195009
[64,] 0.6469497 0.70610056 0.35305028
[65,] 0.6374093 0.72518148 0.36259074
[66,] 0.6146483 0.77070342 0.38535171
[67,] 0.6605084 0.67898317 0.33949159
[68,] 0.6407451 0.71850977 0.35925488
[69,] 0.7595618 0.48087642 0.24043821
[70,] 0.7294226 0.54115472 0.27057736
[71,] 0.6614909 0.67701826 0.33850913
[72,] 0.6597579 0.68048413 0.34024207
[73,] 0.5435391 0.91292190 0.45646095
[74,] 0.5899911 0.82001788 0.41000894
[75,] 0.6949684 0.61006319 0.30503160
[76,] 0.9026597 0.19468063 0.09734032
> postscript(file="/var/www/html/rcomp/tmp/1j2vf1258733442.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2pz3p1258733442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/320oz1258733442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4zzi61258733442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/586151258733442.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 = 117
Frequency = 1
1 2 3 4 5
1.1618157387 4.6331421267 4.7778964497 1.6197474355 -7.7581882320
6 7 8 9 10
-3.9034978224 -6.6650144392 3.5930333719 -3.6198488371 1.2912843635
11 12 13 14 15
2.8618467840 -2.2714111292 -7.7416482351 -1.2914684197 1.2587800698
16 17 18 19 20
-0.9253043734 0.0007049783 -1.6837484319 2.5822732051 2.4330534122
21 22 23 24 25
-0.2131748849 -3.0657288208 2.3853999186 -1.6365240080 0.8515070175
26 27 28 29 30
2.7404041588 1.6302853236 -2.3681315603 1.8147604242 -3.2226704670
31 32 33 34 35
4.4387462243 -1.6234795672 -3.4591257031 0.5758824737 -0.0201993539
36 37 38 39 40
-1.8813567185 4.9361930800 -1.1801372527 -4.3580628064 1.3450689456
41 42 43 44 45
-1.9111796045 -0.6273064542 0.0651740715 0.0947167772 2.5937207909
46 47 48 49 50
-1.8179571086 1.8410245527 -2.8701851557 -2.7926139477 1.5459786462
51 52 53 54 55
-1.4367344194 2.1176211284 1.0018443482 1.3032052228 0.0045795027
56 57 58 59 60
-0.8285385837 2.7449998958 1.3712913429 0.9747581285 2.7167315971
61 62 63 64 65
0.0046761539 3.3888393660 0.8589373477 3.6402136768 -0.2644885641
66 67 68 69 70
1.7913415599 2.1220018189 0.1091876759 -3.0064678202 0.9858112855
71 72 73 74 75
0.5040699007 6.6669970667 5.3451389912 -2.9038650479 -0.4029242510
76 77 78 79 80
4.4167753736 0.0052400001 2.8519169643 1.2218616904 -2.1183599420
81 82 83 84 85
-0.1753339992 1.3213739995 -2.2650321274 2.0187858312 -1.7345773956
86 87 88 89 90
0.6218202698 1.7571124457 -4.6593663697 5.0675896843 0.9008845618
91 92 93 94 95
-3.5465321418 -0.2436466236 0.0663345806 0.8041183694 -3.4748272271
96 97 98 99 100
-0.9841492428 -3.4446162972 -5.8625457333 -2.3557964452 -1.9950754894
101 102 103 104 105
3.5974126396 0.1550897036 0.6635235765 -1.1085080993 5.1552320643
106 107 108 109 110
-1.4660759051 -2.8070405762 -1.7588882407 3.4141248943 -1.6921681139
111 112 113 114 115
-1.7294937146 -3.1915487670 -1.5536956740 2.4347851631 -0.8866135085
116 117
-0.3074584215 -0.0863360872
> postscript(file="/var/www/html/rcomp/tmp/6v9pc1258733442.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 = 117
Frequency = 1
lag(myerror, k = 1) myerror
0 1.1618157387 NA
1 4.6331421267 1.1618157387
2 4.7778964497 4.6331421267
3 1.6197474355 4.7778964497
4 -7.7581882320 1.6197474355
5 -3.9034978224 -7.7581882320
6 -6.6650144392 -3.9034978224
7 3.5930333719 -6.6650144392
8 -3.6198488371 3.5930333719
9 1.2912843635 -3.6198488371
10 2.8618467840 1.2912843635
11 -2.2714111292 2.8618467840
12 -7.7416482351 -2.2714111292
13 -1.2914684197 -7.7416482351
14 1.2587800698 -1.2914684197
15 -0.9253043734 1.2587800698
16 0.0007049783 -0.9253043734
17 -1.6837484319 0.0007049783
18 2.5822732051 -1.6837484319
19 2.4330534122 2.5822732051
20 -0.2131748849 2.4330534122
21 -3.0657288208 -0.2131748849
22 2.3853999186 -3.0657288208
23 -1.6365240080 2.3853999186
24 0.8515070175 -1.6365240080
25 2.7404041588 0.8515070175
26 1.6302853236 2.7404041588
27 -2.3681315603 1.6302853236
28 1.8147604242 -2.3681315603
29 -3.2226704670 1.8147604242
30 4.4387462243 -3.2226704670
31 -1.6234795672 4.4387462243
32 -3.4591257031 -1.6234795672
33 0.5758824737 -3.4591257031
34 -0.0201993539 0.5758824737
35 -1.8813567185 -0.0201993539
36 4.9361930800 -1.8813567185
37 -1.1801372527 4.9361930800
38 -4.3580628064 -1.1801372527
39 1.3450689456 -4.3580628064
40 -1.9111796045 1.3450689456
41 -0.6273064542 -1.9111796045
42 0.0651740715 -0.6273064542
43 0.0947167772 0.0651740715
44 2.5937207909 0.0947167772
45 -1.8179571086 2.5937207909
46 1.8410245527 -1.8179571086
47 -2.8701851557 1.8410245527
48 -2.7926139477 -2.8701851557
49 1.5459786462 -2.7926139477
50 -1.4367344194 1.5459786462
51 2.1176211284 -1.4367344194
52 1.0018443482 2.1176211284
53 1.3032052228 1.0018443482
54 0.0045795027 1.3032052228
55 -0.8285385837 0.0045795027
56 2.7449998958 -0.8285385837
57 1.3712913429 2.7449998958
58 0.9747581285 1.3712913429
59 2.7167315971 0.9747581285
60 0.0046761539 2.7167315971
61 3.3888393660 0.0046761539
62 0.8589373477 3.3888393660
63 3.6402136768 0.8589373477
64 -0.2644885641 3.6402136768
65 1.7913415599 -0.2644885641
66 2.1220018189 1.7913415599
67 0.1091876759 2.1220018189
68 -3.0064678202 0.1091876759
69 0.9858112855 -3.0064678202
70 0.5040699007 0.9858112855
71 6.6669970667 0.5040699007
72 5.3451389912 6.6669970667
73 -2.9038650479 5.3451389912
74 -0.4029242510 -2.9038650479
75 4.4167753736 -0.4029242510
76 0.0052400001 4.4167753736
77 2.8519169643 0.0052400001
78 1.2218616904 2.8519169643
79 -2.1183599420 1.2218616904
80 -0.1753339992 -2.1183599420
81 1.3213739995 -0.1753339992
82 -2.2650321274 1.3213739995
83 2.0187858312 -2.2650321274
84 -1.7345773956 2.0187858312
85 0.6218202698 -1.7345773956
86 1.7571124457 0.6218202698
87 -4.6593663697 1.7571124457
88 5.0675896843 -4.6593663697
89 0.9008845618 5.0675896843
90 -3.5465321418 0.9008845618
91 -0.2436466236 -3.5465321418
92 0.0663345806 -0.2436466236
93 0.8041183694 0.0663345806
94 -3.4748272271 0.8041183694
95 -0.9841492428 -3.4748272271
96 -3.4446162972 -0.9841492428
97 -5.8625457333 -3.4446162972
98 -2.3557964452 -5.8625457333
99 -1.9950754894 -2.3557964452
100 3.5974126396 -1.9950754894
101 0.1550897036 3.5974126396
102 0.6635235765 0.1550897036
103 -1.1085080993 0.6635235765
104 5.1552320643 -1.1085080993
105 -1.4660759051 5.1552320643
106 -2.8070405762 -1.4660759051
107 -1.7588882407 -2.8070405762
108 3.4141248943 -1.7588882407
109 -1.6921681139 3.4141248943
110 -1.7294937146 -1.6921681139
111 -3.1915487670 -1.7294937146
112 -1.5536956740 -3.1915487670
113 2.4347851631 -1.5536956740
114 -0.8866135085 2.4347851631
115 -0.3074584215 -0.8866135085
116 -0.0863360872 -0.3074584215
117 NA -0.0863360872
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.6331421267 1.1618157387
[2,] 4.7778964497 4.6331421267
[3,] 1.6197474355 4.7778964497
[4,] -7.7581882320 1.6197474355
[5,] -3.9034978224 -7.7581882320
[6,] -6.6650144392 -3.9034978224
[7,] 3.5930333719 -6.6650144392
[8,] -3.6198488371 3.5930333719
[9,] 1.2912843635 -3.6198488371
[10,] 2.8618467840 1.2912843635
[11,] -2.2714111292 2.8618467840
[12,] -7.7416482351 -2.2714111292
[13,] -1.2914684197 -7.7416482351
[14,] 1.2587800698 -1.2914684197
[15,] -0.9253043734 1.2587800698
[16,] 0.0007049783 -0.9253043734
[17,] -1.6837484319 0.0007049783
[18,] 2.5822732051 -1.6837484319
[19,] 2.4330534122 2.5822732051
[20,] -0.2131748849 2.4330534122
[21,] -3.0657288208 -0.2131748849
[22,] 2.3853999186 -3.0657288208
[23,] -1.6365240080 2.3853999186
[24,] 0.8515070175 -1.6365240080
[25,] 2.7404041588 0.8515070175
[26,] 1.6302853236 2.7404041588
[27,] -2.3681315603 1.6302853236
[28,] 1.8147604242 -2.3681315603
[29,] -3.2226704670 1.8147604242
[30,] 4.4387462243 -3.2226704670
[31,] -1.6234795672 4.4387462243
[32,] -3.4591257031 -1.6234795672
[33,] 0.5758824737 -3.4591257031
[34,] -0.0201993539 0.5758824737
[35,] -1.8813567185 -0.0201993539
[36,] 4.9361930800 -1.8813567185
[37,] -1.1801372527 4.9361930800
[38,] -4.3580628064 -1.1801372527
[39,] 1.3450689456 -4.3580628064
[40,] -1.9111796045 1.3450689456
[41,] -0.6273064542 -1.9111796045
[42,] 0.0651740715 -0.6273064542
[43,] 0.0947167772 0.0651740715
[44,] 2.5937207909 0.0947167772
[45,] -1.8179571086 2.5937207909
[46,] 1.8410245527 -1.8179571086
[47,] -2.8701851557 1.8410245527
[48,] -2.7926139477 -2.8701851557
[49,] 1.5459786462 -2.7926139477
[50,] -1.4367344194 1.5459786462
[51,] 2.1176211284 -1.4367344194
[52,] 1.0018443482 2.1176211284
[53,] 1.3032052228 1.0018443482
[54,] 0.0045795027 1.3032052228
[55,] -0.8285385837 0.0045795027
[56,] 2.7449998958 -0.8285385837
[57,] 1.3712913429 2.7449998958
[58,] 0.9747581285 1.3712913429
[59,] 2.7167315971 0.9747581285
[60,] 0.0046761539 2.7167315971
[61,] 3.3888393660 0.0046761539
[62,] 0.8589373477 3.3888393660
[63,] 3.6402136768 0.8589373477
[64,] -0.2644885641 3.6402136768
[65,] 1.7913415599 -0.2644885641
[66,] 2.1220018189 1.7913415599
[67,] 0.1091876759 2.1220018189
[68,] -3.0064678202 0.1091876759
[69,] 0.9858112855 -3.0064678202
[70,] 0.5040699007 0.9858112855
[71,] 6.6669970667 0.5040699007
[72,] 5.3451389912 6.6669970667
[73,] -2.9038650479 5.3451389912
[74,] -0.4029242510 -2.9038650479
[75,] 4.4167753736 -0.4029242510
[76,] 0.0052400001 4.4167753736
[77,] 2.8519169643 0.0052400001
[78,] 1.2218616904 2.8519169643
[79,] -2.1183599420 1.2218616904
[80,] -0.1753339992 -2.1183599420
[81,] 1.3213739995 -0.1753339992
[82,] -2.2650321274 1.3213739995
[83,] 2.0187858312 -2.2650321274
[84,] -1.7345773956 2.0187858312
[85,] 0.6218202698 -1.7345773956
[86,] 1.7571124457 0.6218202698
[87,] -4.6593663697 1.7571124457
[88,] 5.0675896843 -4.6593663697
[89,] 0.9008845618 5.0675896843
[90,] -3.5465321418 0.9008845618
[91,] -0.2436466236 -3.5465321418
[92,] 0.0663345806 -0.2436466236
[93,] 0.8041183694 0.0663345806
[94,] -3.4748272271 0.8041183694
[95,] -0.9841492428 -3.4748272271
[96,] -3.4446162972 -0.9841492428
[97,] -5.8625457333 -3.4446162972
[98,] -2.3557964452 -5.8625457333
[99,] -1.9950754894 -2.3557964452
[100,] 3.5974126396 -1.9950754894
[101,] 0.1550897036 3.5974126396
[102,] 0.6635235765 0.1550897036
[103,] -1.1085080993 0.6635235765
[104,] 5.1552320643 -1.1085080993
[105,] -1.4660759051 5.1552320643
[106,] -2.8070405762 -1.4660759051
[107,] -1.7588882407 -2.8070405762
[108,] 3.4141248943 -1.7588882407
[109,] -1.6921681139 3.4141248943
[110,] -1.7294937146 -1.6921681139
[111,] -3.1915487670 -1.7294937146
[112,] -1.5536956740 -3.1915487670
[113,] 2.4347851631 -1.5536956740
[114,] -0.8866135085 2.4347851631
[115,] -0.3074584215 -0.8866135085
[116,] -0.0863360872 -0.3074584215
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.6331421267 1.1618157387
2 4.7778964497 4.6331421267
3 1.6197474355 4.7778964497
4 -7.7581882320 1.6197474355
5 -3.9034978224 -7.7581882320
6 -6.6650144392 -3.9034978224
7 3.5930333719 -6.6650144392
8 -3.6198488371 3.5930333719
9 1.2912843635 -3.6198488371
10 2.8618467840 1.2912843635
11 -2.2714111292 2.8618467840
12 -7.7416482351 -2.2714111292
13 -1.2914684197 -7.7416482351
14 1.2587800698 -1.2914684197
15 -0.9253043734 1.2587800698
16 0.0007049783 -0.9253043734
17 -1.6837484319 0.0007049783
18 2.5822732051 -1.6837484319
19 2.4330534122 2.5822732051
20 -0.2131748849 2.4330534122
21 -3.0657288208 -0.2131748849
22 2.3853999186 -3.0657288208
23 -1.6365240080 2.3853999186
24 0.8515070175 -1.6365240080
25 2.7404041588 0.8515070175
26 1.6302853236 2.7404041588
27 -2.3681315603 1.6302853236
28 1.8147604242 -2.3681315603
29 -3.2226704670 1.8147604242
30 4.4387462243 -3.2226704670
31 -1.6234795672 4.4387462243
32 -3.4591257031 -1.6234795672
33 0.5758824737 -3.4591257031
34 -0.0201993539 0.5758824737
35 -1.8813567185 -0.0201993539
36 4.9361930800 -1.8813567185
37 -1.1801372527 4.9361930800
38 -4.3580628064 -1.1801372527
39 1.3450689456 -4.3580628064
40 -1.9111796045 1.3450689456
41 -0.6273064542 -1.9111796045
42 0.0651740715 -0.6273064542
43 0.0947167772 0.0651740715
44 2.5937207909 0.0947167772
45 -1.8179571086 2.5937207909
46 1.8410245527 -1.8179571086
47 -2.8701851557 1.8410245527
48 -2.7926139477 -2.8701851557
49 1.5459786462 -2.7926139477
50 -1.4367344194 1.5459786462
51 2.1176211284 -1.4367344194
52 1.0018443482 2.1176211284
53 1.3032052228 1.0018443482
54 0.0045795027 1.3032052228
55 -0.8285385837 0.0045795027
56 2.7449998958 -0.8285385837
57 1.3712913429 2.7449998958
58 0.9747581285 1.3712913429
59 2.7167315971 0.9747581285
60 0.0046761539 2.7167315971
61 3.3888393660 0.0046761539
62 0.8589373477 3.3888393660
63 3.6402136768 0.8589373477
64 -0.2644885641 3.6402136768
65 1.7913415599 -0.2644885641
66 2.1220018189 1.7913415599
67 0.1091876759 2.1220018189
68 -3.0064678202 0.1091876759
69 0.9858112855 -3.0064678202
70 0.5040699007 0.9858112855
71 6.6669970667 0.5040699007
72 5.3451389912 6.6669970667
73 -2.9038650479 5.3451389912
74 -0.4029242510 -2.9038650479
75 4.4167753736 -0.4029242510
76 0.0052400001 4.4167753736
77 2.8519169643 0.0052400001
78 1.2218616904 2.8519169643
79 -2.1183599420 1.2218616904
80 -0.1753339992 -2.1183599420
81 1.3213739995 -0.1753339992
82 -2.2650321274 1.3213739995
83 2.0187858312 -2.2650321274
84 -1.7345773956 2.0187858312
85 0.6218202698 -1.7345773956
86 1.7571124457 0.6218202698
87 -4.6593663697 1.7571124457
88 5.0675896843 -4.6593663697
89 0.9008845618 5.0675896843
90 -3.5465321418 0.9008845618
91 -0.2436466236 -3.5465321418
92 0.0663345806 -0.2436466236
93 0.8041183694 0.0663345806
94 -3.4748272271 0.8041183694
95 -0.9841492428 -3.4748272271
96 -3.4446162972 -0.9841492428
97 -5.8625457333 -3.4446162972
98 -2.3557964452 -5.8625457333
99 -1.9950754894 -2.3557964452
100 3.5974126396 -1.9950754894
101 0.1550897036 3.5974126396
102 0.6635235765 0.1550897036
103 -1.1085080993 0.6635235765
104 5.1552320643 -1.1085080993
105 -1.4660759051 5.1552320643
106 -2.8070405762 -1.4660759051
107 -1.7588882407 -2.8070405762
108 3.4141248943 -1.7588882407
109 -1.6921681139 3.4141248943
110 -1.7294937146 -1.6921681139
111 -3.1915487670 -1.7294937146
112 -1.5536956740 -3.1915487670
113 2.4347851631 -1.5536956740
114 -0.8866135085 2.4347851631
115 -0.3074584215 -0.8866135085
116 -0.0863360872 -0.3074584215
> 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/7vnxz1258733442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8voqe1258733442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9w4791258733442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10m3zc1258733442.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/115zn71258733442.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/12u8jr1258733442.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/13l3j01258733442.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/14pr5v1258733442.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/15tgta1258733442.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/16capg1258733442.tab")
+ }
>
> system("convert tmp/1j2vf1258733442.ps tmp/1j2vf1258733442.png")
> system("convert tmp/2pz3p1258733442.ps tmp/2pz3p1258733442.png")
> system("convert tmp/320oz1258733442.ps tmp/320oz1258733442.png")
> system("convert tmp/4zzi61258733442.ps tmp/4zzi61258733442.png")
> system("convert tmp/586151258733442.ps tmp/586151258733442.png")
> system("convert tmp/6v9pc1258733442.ps tmp/6v9pc1258733442.png")
> system("convert tmp/7vnxz1258733442.ps tmp/7vnxz1258733442.png")
> system("convert tmp/8voqe1258733442.ps tmp/8voqe1258733442.png")
> system("convert tmp/9w4791258733442.ps tmp/9w4791258733442.png")
> system("convert tmp/10m3zc1258733442.ps tmp/10m3zc1258733442.png")
>
>
> proc.time()
user system elapsed
3.359 1.664 8.325