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(24
+ ,3
+ ,4
+ ,4
+ ,4
+ ,3
+ ,2
+ ,4
+ ,25
+ ,4
+ ,4
+ ,4
+ ,3
+ ,2
+ ,4
+ ,4
+ ,30
+ ,4
+ ,5
+ ,5
+ ,4
+ ,4
+ ,4
+ ,4
+ ,19
+ ,2
+ ,2
+ ,3
+ ,4
+ ,2
+ ,3
+ ,3
+ ,22
+ ,2
+ ,2
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,22
+ ,3
+ ,4
+ ,3
+ ,4
+ ,2
+ ,2
+ ,4
+ ,25
+ ,2
+ ,4
+ ,4
+ ,4
+ ,3
+ ,4
+ ,4
+ ,23
+ ,3
+ ,4
+ ,3
+ ,4
+ ,4
+ ,2
+ ,3
+ ,17
+ ,3
+ ,4
+ ,2
+ ,2
+ ,1
+ ,3
+ ,2
+ ,21
+ ,3
+ ,2
+ ,4
+ ,4
+ ,2
+ ,3
+ ,3
+ ,19
+ ,3
+ ,3
+ ,2
+ ,3
+ ,2
+ ,3
+ ,3
+ ,19
+ ,4
+ ,4
+ ,2
+ ,2
+ ,2
+ ,3
+ ,2
+ ,15
+ ,2
+ ,3
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,16
+ ,2
+ ,4
+ ,1
+ ,3
+ ,1
+ ,3
+ ,2
+ ,23
+ ,1
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,27
+ ,1
+ ,4
+ ,4
+ ,5
+ ,4
+ ,5
+ ,4
+ ,22
+ ,3
+ ,4
+ ,2
+ ,4
+ ,2
+ ,4
+ ,3
+ ,14
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,22
+ ,2
+ ,4
+ ,3
+ ,4
+ ,3
+ ,3
+ ,3
+ ,23
+ ,3
+ ,4
+ ,3
+ ,3
+ ,2
+ ,4
+ ,4
+ ,23
+ ,4
+ ,4
+ ,3
+ ,4
+ ,3
+ ,2
+ ,3
+ ,21
+ ,4
+ ,3
+ ,4
+ ,2
+ ,2
+ ,3
+ ,3
+ ,19
+ ,3
+ ,2
+ ,3
+ ,2
+ ,3
+ ,2
+ ,4
+ ,18
+ ,2
+ ,4
+ ,2
+ ,4
+ ,2
+ ,2
+ ,2
+ ,20
+ ,3
+ ,2
+ ,2
+ ,4
+ ,3
+ ,2
+ ,4
+ ,23
+ ,4
+ ,3
+ ,4
+ ,4
+ ,3
+ ,3
+ ,2
+ ,25
+ ,3
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,3
+ ,19
+ ,2
+ ,3
+ ,3
+ ,3
+ ,2
+ ,3
+ ,3
+ ,24
+ ,1
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,4
+ ,22
+ ,3
+ ,4
+ ,2
+ ,5
+ ,3
+ ,3
+ ,2
+ ,25
+ ,2
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,4
+ ,26
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,3
+ ,4
+ ,29
+ ,4
+ ,4
+ ,5
+ ,4
+ ,4
+ ,4
+ ,4
+ ,32
+ ,5
+ ,4
+ ,5
+ ,4
+ ,4
+ ,5
+ ,5
+ ,25
+ ,2
+ ,4
+ ,4
+ ,4
+ ,3
+ ,4
+ ,4
+ ,29
+ ,4
+ ,5
+ ,4
+ ,4
+ ,5
+ ,4
+ ,3
+ ,28
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,17
+ ,2
+ ,2
+ ,2
+ ,4
+ ,2
+ ,3
+ ,2
+ ,28
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,29
+ ,4
+ ,4
+ ,4
+ ,5
+ ,4
+ ,4
+ ,4
+ ,26
+ ,2
+ ,5
+ ,4
+ ,4
+ ,5
+ ,2
+ ,4
+ ,25
+ ,3
+ ,4
+ ,3
+ ,4
+ ,4
+ ,4
+ ,3
+ ,14
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,25
+ ,4
+ ,4
+ ,3
+ ,4
+ ,4
+ ,3
+ ,3
+ ,26
+ ,3
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,4
+ ,20
+ ,1
+ ,4
+ ,3
+ ,4
+ ,1
+ ,4
+ ,3
+ ,18
+ ,2
+ ,2
+ ,2
+ ,4
+ ,2
+ ,2
+ ,4
+ ,32
+ ,5
+ ,5
+ ,4
+ ,4
+ ,5
+ ,4
+ ,5
+ ,25
+ ,2
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,4
+ ,25
+ ,4
+ ,4
+ ,3
+ ,4
+ ,2
+ ,4
+ ,4
+ ,23
+ ,4
+ ,4
+ ,4
+ ,3
+ ,3
+ ,2
+ ,3
+ ,21
+ ,2
+ ,4
+ ,3
+ ,4
+ ,4
+ ,2
+ ,2
+ ,20
+ ,2
+ ,4
+ ,4
+ ,4
+ ,2
+ ,2
+ ,2
+ ,15
+ ,2
+ ,3
+ ,2
+ ,4
+ ,1
+ ,1
+ ,2
+ ,30
+ ,4
+ ,5
+ ,4
+ ,3
+ ,4
+ ,5
+ ,5
+ ,24
+ ,3
+ ,4
+ ,4
+ ,3
+ ,2
+ ,4
+ ,4
+ ,26
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,24
+ ,2
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,22
+ ,3
+ ,4
+ ,3
+ ,5
+ ,3
+ ,2
+ ,2
+ ,14
+ ,2
+ ,2
+ ,1
+ ,3
+ ,2
+ ,2
+ ,2
+ ,24
+ ,2
+ ,4
+ ,3
+ ,4
+ ,3
+ ,4
+ ,4
+ ,24
+ ,3
+ ,4
+ ,3
+ ,4
+ ,2
+ ,4
+ ,4
+ ,24
+ ,2
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,24
+ ,3
+ ,4
+ ,2
+ ,4
+ ,3
+ ,4
+ ,4
+ ,19
+ ,2
+ ,3
+ ,3
+ ,4
+ ,1
+ ,3
+ ,3
+ ,31
+ ,4
+ ,4
+ ,5
+ ,4
+ ,5
+ ,4
+ ,5
+ ,22
+ ,2
+ ,2
+ ,4
+ ,4
+ ,4
+ ,3
+ ,3
+ ,27
+ ,5
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,19
+ ,1
+ ,4
+ ,3
+ ,4
+ ,1
+ ,3
+ ,3
+ ,25
+ ,3
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,20
+ ,2
+ ,4
+ ,2
+ ,4
+ ,2
+ ,4
+ ,2
+ ,21
+ ,2
+ ,4
+ ,3
+ ,4
+ ,2
+ ,3
+ ,3
+ ,27
+ ,3
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,23
+ ,4
+ ,3
+ ,3
+ ,3
+ ,2
+ ,4
+ ,4
+ ,25
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,20
+ ,2
+ ,4
+ ,4
+ ,4
+ ,2
+ ,2
+ ,2
+ ,21
+ ,3
+ ,4
+ ,3
+ ,3
+ ,2
+ ,3
+ ,3
+ ,22
+ ,4
+ ,4
+ ,3
+ ,4
+ ,2
+ ,3
+ ,2
+ ,23
+ ,2
+ ,4
+ ,2
+ ,4
+ ,4
+ ,3
+ ,4
+ ,25
+ ,2
+ ,4
+ ,4
+ ,4
+ ,3
+ ,4
+ ,4
+ ,25
+ ,4
+ ,4
+ ,3
+ ,4
+ ,3
+ ,3
+ ,4
+ ,17
+ ,2
+ ,2
+ ,2
+ ,4
+ ,2
+ ,3
+ ,2
+ ,19
+ ,4
+ ,3
+ ,2
+ ,2
+ ,2
+ ,4
+ ,2
+ ,25
+ ,2
+ ,5
+ ,3
+ ,4
+ ,3
+ ,4
+ ,4
+ ,19
+ ,3
+ ,3
+ ,2
+ ,3
+ ,2
+ ,3
+ ,3
+ ,20
+ ,4
+ ,3
+ ,2
+ ,3
+ ,2
+ ,3
+ ,3
+ ,26
+ ,3
+ ,4
+ ,4
+ ,4
+ ,3
+ ,4
+ ,4
+ ,23
+ ,1
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,3
+ ,27
+ ,3
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,17
+ ,2
+ ,4
+ ,2
+ ,3
+ ,2
+ ,2
+ ,2
+ ,17
+ ,2
+ ,4
+ ,2
+ ,2
+ ,2
+ ,3
+ ,2
+ ,19
+ ,1
+ ,3
+ ,4
+ ,5
+ ,2
+ ,2
+ ,2
+ ,17
+ ,3
+ ,4
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,22
+ ,2
+ ,4
+ ,3
+ ,4
+ ,3
+ ,3
+ ,3
+ ,21
+ ,3
+ ,4
+ ,3
+ ,4
+ ,2
+ ,2
+ ,3
+ ,32
+ ,5
+ ,5
+ ,5
+ ,4
+ ,5
+ ,4
+ ,4
+ ,21
+ ,3
+ ,4
+ ,3
+ ,3
+ ,2
+ ,3
+ ,3
+ ,21
+ ,NA
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,3
+ ,18
+ ,2
+ ,2
+ ,3
+ ,3
+ ,3
+ ,3
+ ,2
+ ,18
+ ,2
+ ,3
+ ,2
+ ,4
+ ,3
+ ,2
+ ,2
+ ,23
+ ,3
+ ,3
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,19
+ ,2
+ ,3
+ ,3
+ ,3
+ ,2
+ ,3
+ ,3
+ ,20
+ ,2
+ ,4
+ ,3
+ ,2
+ ,3
+ ,3
+ ,3
+ ,21
+ ,2
+ ,4
+ ,3
+ ,4
+ ,2
+ ,3
+ ,3
+ ,20
+ ,3
+ ,2
+ ,4
+ ,3
+ ,2
+ ,2
+ ,4
+ ,17
+ ,5
+ ,3
+ ,1
+ ,2
+ ,1
+ ,3
+ ,2
+ ,18
+ ,2
+ ,3
+ ,3
+ ,3
+ ,2
+ ,3
+ ,2
+ ,19
+ ,3
+ ,4
+ ,2
+ ,4
+ ,2
+ ,2
+ ,2
+ ,22
+ ,3
+ ,4
+ ,3
+ ,4
+ ,3
+ ,2
+ ,3
+ ,15
+ ,1
+ ,2
+ ,2
+ ,4
+ ,2
+ ,2
+ ,2
+ ,14
+ ,1
+ ,3
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,18
+ ,3
+ ,4
+ ,2
+ ,2
+ ,2
+ ,2
+ ,3
+ ,24
+ ,4
+ ,4
+ ,4
+ ,3
+ ,2
+ ,3
+ ,4
+ ,35
+ ,5
+ ,5
+ ,5
+ ,5
+ ,5
+ ,5
+ ,5
+ ,29
+ ,3
+ ,3
+ ,5
+ ,4
+ ,4
+ ,5
+ ,5
+ ,21
+ ,3
+ ,3
+ ,3
+ ,4
+ ,3
+ ,2
+ ,3
+ ,25
+ ,3
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,20
+ ,2
+ ,3
+ ,4
+ ,4
+ ,2
+ ,2
+ ,3
+ ,22
+ ,3
+ ,3
+ ,3
+ ,4
+ ,2
+ ,4
+ ,3
+ ,13
+ ,2
+ ,2
+ ,2
+ ,3
+ ,1
+ ,2
+ ,1
+ ,26
+ ,3
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,17
+ ,2
+ ,3
+ ,2
+ ,4
+ ,2
+ ,2
+ ,2
+ ,25
+ ,4
+ ,4
+ ,3
+ ,3
+ ,4
+ ,4
+ ,3
+ ,20
+ ,2
+ ,5
+ ,2
+ ,4
+ ,1
+ ,4
+ ,2
+ ,19
+ ,2
+ ,4
+ ,2
+ ,3
+ ,2
+ ,4
+ ,2
+ ,21
+ ,4
+ ,3
+ ,2
+ ,4
+ ,3
+ ,3
+ ,2
+ ,22
+ ,4
+ ,3
+ ,4
+ ,4
+ ,2
+ ,2
+ ,3
+ ,24
+ ,3
+ ,3
+ ,4
+ ,4
+ ,3
+ ,3
+ ,4
+ ,21
+ ,3
+ ,3
+ ,4
+ ,4
+ ,2
+ ,2
+ ,3
+ ,26
+ ,3
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,4
+ ,24
+ ,4
+ ,4
+ ,3
+ ,4
+ ,3
+ ,3
+ ,3
+ ,16
+ ,2
+ ,3
+ ,2
+ ,4
+ ,2
+ ,1
+ ,2
+ ,23
+ ,1
+ ,2
+ ,4
+ ,5
+ ,4
+ ,3
+ ,4
+ ,18
+ ,3
+ ,3
+ ,3
+ ,2
+ ,3
+ ,1
+ ,3
+ ,16
+ ,2
+ ,2
+ ,2
+ ,4
+ ,2
+ ,2
+ ,2
+ ,26
+ ,3
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,4
+ ,19
+ ,2
+ ,4
+ ,3
+ ,3
+ ,1
+ ,4
+ ,2
+ ,21
+ ,2
+ ,4
+ ,3
+ ,3
+ ,2
+ ,4
+ ,3
+ ,21
+ ,3
+ ,4
+ ,3
+ ,4
+ ,1
+ ,3
+ ,3
+ ,22
+ ,2
+ ,4
+ ,3
+ ,4
+ ,2
+ ,4
+ ,3
+ ,23
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,4
+ ,2
+ ,29
+ ,4
+ ,5
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,21
+ ,2
+ ,3
+ ,5
+ ,1
+ ,4
+ ,3
+ ,3
+ ,21
+ ,2
+ ,4
+ ,2
+ ,3
+ ,4
+ ,2
+ ,4
+ ,23
+ ,3
+ ,4
+ ,2
+ ,4
+ ,3
+ ,3
+ ,4
+ ,27
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,25
+ ,4
+ ,5
+ ,3
+ ,4
+ ,4
+ ,3
+ ,2
+ ,21
+ ,4
+ ,3
+ ,3
+ ,4
+ ,3
+ ,2
+ ,2
+ ,10
+ ,1
+ ,2
+ ,1
+ ,3
+ ,1
+ ,1
+ ,1
+ ,20
+ ,4
+ ,4
+ ,2
+ ,4
+ ,2
+ ,2
+ ,2
+ ,26
+ ,4
+ ,3
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,24
+ ,3
+ ,4
+ ,4
+ ,4
+ ,3
+ ,2
+ ,4
+ ,29
+ ,1
+ ,5
+ ,5
+ ,4
+ ,5
+ ,4
+ ,5
+ ,19
+ ,2
+ ,3
+ ,2
+ ,4
+ ,3
+ ,3
+ ,2
+ ,24
+ ,2
+ ,3
+ ,4
+ ,3
+ ,4
+ ,5
+ ,3
+ ,19
+ ,2
+ ,3
+ ,3
+ ,4
+ ,2
+ ,2
+ ,3
+ ,24
+ ,3
+ ,5
+ ,2
+ ,4
+ ,4
+ ,3
+ ,3
+ ,22
+ ,3
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,2
+ ,17
+ ,3
+ ,2
+ ,2
+ ,3
+ ,3
+ ,2
+ ,2)
+ ,dim=c(8
+ ,159)
+ ,dimnames=list(c('Yt'
+ ,'X1t'
+ ,'X2t'
+ ,'X3t'
+ ,'X4t'
+ ,'X5t'
+ ,'X6t'
+ ,'X7t')
+ ,1:159))
> y <- array(NA,dim=c(8,159),dimnames=list(c('Yt','X1t','X2t','X3t','X4t','X5t','X6t','X7t'),1:159))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = '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
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
Yt X1t X2t X3t X4t X5t X6t X7t
1 24 3 4 4 4 3 2 4
2 25 4 4 4 3 2 4 4
3 30 4 5 5 4 4 4 4
4 19 2 2 3 4 2 3 3
5 22 2 2 4 4 4 4 2
6 22 3 4 3 4 2 2 4
7 25 2 4 4 4 3 4 4
8 23 3 4 3 4 4 2 3
9 17 3 4 2 2 1 3 2
10 21 3 2 4 4 2 3 3
11 19 3 3 2 3 2 3 3
12 19 4 4 2 2 2 3 2
13 15 2 3 2 2 2 2 2
14 16 2 4 1 3 1 3 2
15 23 1 4 4 4 2 4 4
16 27 1 4 4 5 4 5 4
17 22 3 4 2 4 2 4 3
18 14 2 2 2 2 2 2 2
19 22 2 4 3 4 3 3 3
20 23 3 4 3 3 2 4 4
21 23 4 4 3 4 3 2 3
22 21 4 3 4 2 2 3 3
23 19 3 2 3 2 3 2 4
24 18 2 4 2 4 2 2 2
25 20 3 2 2 4 3 2 4
26 23 4 3 4 4 3 3 2
27 25 3 4 4 4 4 3 3
28 19 2 3 3 3 2 3 3
29 24 1 4 4 4 4 3 4
30 22 3 4 2 5 3 3 2
31 25 2 4 4 4 4 3 4
32 26 4 4 4 4 3 3 4
33 29 4 4 5 4 4 4 4
34 32 5 4 5 4 4 5 5
35 25 2 4 4 4 3 4 4
36 29 4 5 4 4 5 4 3
37 28 4 4 4 4 4 4 4
38 17 2 2 2 4 2 3 2
39 28 4 4 4 4 4 4 4
40 29 4 4 4 5 4 4 4
41 26 2 5 4 4 5 2 4
42 25 3 4 3 4 4 4 3
43 14 2 2 2 2 2 2 2
44 25 4 4 3 4 4 3 3
45 26 3 4 4 4 4 3 4
46 20 1 4 3 4 1 4 3
47 18 2 2 2 4 2 2 4
48 32 5 5 4 4 5 4 5
49 25 2 4 4 4 4 3 4
50 25 4 4 3 4 2 4 4
51 23 4 4 4 3 3 2 3
52 21 2 4 3 4 4 2 2
53 20 2 4 4 4 2 2 2
54 15 2 3 2 4 1 1 2
55 30 4 5 4 3 4 5 5
56 24 3 4 4 3 2 4 4
57 26 4 4 4 4 4 2 4
58 24 2 4 4 4 2 4 4
59 22 3 4 3 5 3 2 2
60 14 2 2 1 3 2 2 2
61 24 2 4 3 4 3 4 4
62 24 3 4 3 4 2 4 4
63 24 2 4 4 4 2 4 4
64 24 3 4 2 4 3 4 4
65 19 2 3 3 4 1 3 3
66 31 4 4 5 4 5 4 5
67 22 2 2 4 4 4 3 3
68 27 5 4 4 4 4 4 2
69 19 1 4 3 4 1 3 3
70 25 3 4 4 4 2 4 4
71 20 2 4 2 4 2 4 2
72 21 2 4 3 4 2 3 3
73 27 3 4 4 4 4 4 4
74 23 4 3 3 3 2 4 4
75 25 4 4 4 4 3 3 3
76 20 2 4 4 4 2 2 2
77 21 3 4 3 3 2 3 3
78 22 4 4 3 4 2 3 2
79 23 2 4 2 4 4 3 4
80 25 2 4 4 4 3 4 4
81 25 4 4 3 4 3 3 4
82 17 2 2 2 4 2 3 2
83 19 4 3 2 2 2 4 2
84 25 2 5 3 4 3 4 4
85 19 3 3 2 3 2 3 3
86 20 4 3 2 3 2 3 3
87 26 3 4 4 4 3 4 4
88 23 1 4 4 4 4 3 3
89 27 3 4 4 4 4 4 4
90 17 2 4 2 3 2 2 2
91 17 2 4 2 2 2 3 2
92 19 1 3 4 5 2 2 2
93 17 3 4 2 2 2 2 2
94 22 2 4 3 4 3 3 3
95 21 3 4 3 4 2 2 3
96 32 5 5 5 4 5 4 4
97 21 3 4 3 3 2 3 3
98 21 NA 4 4 4 4 2 3
99 18 2 2 3 3 3 3 2
100 18 2 3 2 4 3 2 2
101 23 3 3 4 4 3 3 3
102 19 2 3 3 3 2 3 3
103 20 2 4 3 2 3 3 3
104 21 2 4 3 4 2 3 3
105 20 3 2 4 3 2 2 4
106 17 5 3 1 2 1 3 2
107 18 2 3 3 3 2 3 2
108 19 3 4 2 4 2 2 2
109 22 3 4 3 4 3 2 3
110 15 1 2 2 4 2 2 2
111 14 1 3 2 2 2 2 2
112 18 3 4 2 2 2 2 3
113 24 4 4 4 3 2 3 4
114 35 5 5 5 5 5 5 5
115 29 3 3 5 4 4 5 5
116 21 3 3 3 4 3 2 3
117 25 3 4 4 4 4 2 4
118 20 2 3 4 4 2 2 3
119 22 3 3 3 4 2 4 3
120 13 2 2 2 3 1 2 1
121 26 3 4 4 4 4 4 3
122 17 2 3 2 4 2 2 2
123 25 4 4 3 3 4 4 3
124 20 2 5 2 4 1 4 2
125 19 2 4 2 3 2 4 2
126 21 4 3 2 4 3 3 2
127 22 4 3 4 4 2 2 3
128 24 3 3 4 4 3 3 4
129 21 3 3 4 4 2 2 3
130 26 3 4 4 4 4 3 4
131 24 4 4 3 4 3 3 3
132 16 2 3 2 4 2 1 2
133 23 1 2 4 5 4 3 4
134 18 3 3 3 2 3 1 3
135 16 2 2 2 4 2 2 2
136 26 3 4 4 4 4 3 4
137 19 2 4 3 3 1 4 2
138 21 2 4 3 3 2 4 3
139 21 3 4 3 4 1 3 3
140 22 2 4 3 4 2 4 3
141 23 4 4 3 3 3 4 2
142 29 4 5 4 4 4 4 4
143 21 2 3 5 1 4 3 3
144 21 2 4 2 3 4 2 4
145 23 3 4 2 4 3 3 4
146 27 4 4 4 4 4 4 3
147 25 4 5 3 4 4 3 2
148 21 4 3 3 4 3 2 2
149 10 1 2 1 3 1 1 1
150 20 4 4 2 4 2 2 2
151 26 4 3 4 4 4 4 3
152 24 3 4 4 4 3 2 4
153 29 1 5 5 4 5 4 5
154 19 2 3 2 4 3 3 2
155 24 2 3 4 3 4 5 3
156 19 2 3 3 4 2 2 3
157 24 3 5 2 4 4 3 3
158 22 3 4 4 3 3 3 2
159 17 3 2 2 3 3 2 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X1t X2t X3t X4t X5t
5.653e-15 1.000e+00 1.000e+00 1.000e+00 1.000e+00 1.000e+00
X6t X7t
1.000e+00 1.000e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.345e-14 -8.791e-16 -1.987e-16 4.388e-16 5.468e-14
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.653e-15 2.603e-15 2.171e+00 0.0315 *
X1t 1.000e+00 4.357e-16 2.295e+15 <2e-16 ***
X2t 1.000e+00 5.757e-16 1.737e+15 <2e-16 ***
X3t 1.000e+00 5.949e-16 1.681e+15 <2e-16 ***
X4t 1.000e+00 5.915e-16 1.691e+15 <2e-16 ***
X5t 1.000e+00 5.051e-16 1.980e+15 <2e-16 ***
X6t 1.000e+00 5.243e-16 1.907e+15 <2e-16 ***
X7t 1.000e+00 6.025e-16 1.660e+15 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.023e-15 on 150 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 1.59e+31 on 7 and 150 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,] 1.987273e-01 3.974546e-01 8.012727e-01
[2,] 4.904915e-04 9.809830e-04 9.995095e-01
[3,] 1.946889e-06 3.893778e-06 9.999981e-01
[4,] 5.768266e-02 1.153653e-01 9.423173e-01
[5,] 1.329720e-04 2.659441e-04 9.998670e-01
[6,] 2.709063e-08 5.418125e-08 1.000000e+00
[7,] 7.822915e-13 1.564583e-12 1.000000e+00
[8,] 5.435888e-11 1.087178e-10 1.000000e+00
[9,] 4.865498e-08 9.730995e-08 1.000000e+00
[10,] 2.268730e-11 4.537459e-11 1.000000e+00
[11,] 1.216590e-07 2.433180e-07 9.999999e-01
[12,] 4.711272e-09 9.422545e-09 1.000000e+00
[13,] 9.214189e-01 1.571622e-01 7.858111e-02
[14,] 8.159317e-06 1.631863e-05 9.999918e-01
[15,] 2.339365e-07 4.678731e-07 9.999998e-01
[16,] 1.306950e-04 2.613900e-04 9.998693e-01
[17,] 6.877790e-01 6.244419e-01 3.122210e-01
[18,] 4.755555e-17 9.511110e-17 1.000000e+00
[19,] 9.969439e-01 6.112180e-03 3.056090e-03
[20,] 1.186408e-06 2.372817e-06 9.999988e-01
[21,] 2.379794e-03 4.759587e-03 9.976202e-01
[22,] 1.000000e+00 2.170322e-36 1.085161e-36
[23,] 1.213468e-05 2.426937e-05 9.999879e-01
[24,] 7.225372e-03 1.445074e-02 9.927746e-01
[25,] 1.025438e-03 2.050875e-03 9.989746e-01
[26,] 3.477981e-24 6.955963e-24 1.000000e+00
[27,] 3.565287e-15 7.130574e-15 1.000000e+00
[28,] 3.488542e-21 6.977084e-21 1.000000e+00
[29,] 7.641857e-20 1.528371e-19 1.000000e+00
[30,] 9.958180e-01 8.364073e-03 4.182036e-03
[31,] 1.267715e-01 2.535430e-01 8.732285e-01
[32,] 8.615209e-01 2.769581e-01 1.384791e-01
[33,] 1.522601e-06 3.045201e-06 9.999985e-01
[34,] 4.882631e-01 9.765261e-01 5.117369e-01
[35,] 6.051057e-15 1.210211e-14 1.000000e+00
[36,] 1.442731e-03 2.885463e-03 9.985573e-01
[37,] 5.910425e-17 1.182085e-16 1.000000e+00
[38,] 4.264117e-23 8.528233e-23 1.000000e+00
[39,] 1.951436e-21 3.902872e-21 1.000000e+00
[40,] 9.397784e-01 1.204431e-01 6.022157e-02
[41,] 9.994308e-01 1.138411e-03 5.692057e-04
[42,] 1.121496e-02 2.242992e-02 9.887850e-01
[43,] 2.030102e-06 4.060203e-06 9.999980e-01
[44,] 2.053097e-46 4.106194e-46 1.000000e+00
[45,] 1.000000e+00 7.145051e-17 3.572526e-17
[46,] 3.094158e-02 6.188317e-02 9.690584e-01
[47,] 1.000000e+00 1.003279e-32 5.016396e-33
[48,] 5.456169e-21 1.091234e-20 1.000000e+00
[49,] 9.999263e-01 1.473856e-04 7.369280e-05
[50,] 7.782531e-01 4.434937e-01 2.217469e-01
[51,] 1.000000e+00 6.576389e-33 3.288194e-33
[52,] 1.730561e-06 3.461122e-06 9.999983e-01
[53,] 2.483100e-36 4.966201e-36 1.000000e+00
[54,] 2.153168e-17 4.306337e-17 1.000000e+00
[55,] 9.137406e-01 1.725188e-01 8.625939e-02
[56,] 1.000000e+00 1.193019e-36 5.965097e-37
[57,] 9.978002e-01 4.399585e-03 2.199792e-03
[58,] 1.000000e+00 2.655264e-38 1.327632e-38
[59,] 1.598880e-19 3.197759e-19 1.000000e+00
[60,] 2.965203e-01 5.930405e-01 7.034797e-01
[61,] 1.466240e-01 2.932481e-01 8.533760e-01
[62,] 1.000000e+00 1.179933e-69 5.899667e-70
[63,] 4.723417e-45 9.446835e-45 1.000000e+00
[64,] 4.011248e-13 8.022496e-13 1.000000e+00
[65,] 2.734219e-23 5.468438e-23 1.000000e+00
[66,] 3.062980e-15 6.125961e-15 1.000000e+00
[67,] 1.058032e-44 2.116063e-44 1.000000e+00
[68,] 1.000000e+00 1.527309e-12 7.636547e-13
[69,] 1.491914e-37 2.983829e-37 1.000000e+00
[70,] 3.907895e-07 7.815790e-07 9.999996e-01
[71,] 1.000000e+00 1.994306e-95 9.971532e-96
[72,] 1.000000e+00 2.016489e-08 1.008244e-08
[73,] 1.403099e-33 2.806198e-33 1.000000e+00
[74,] 1.000000e+00 2.966745e-16 1.483373e-16
[75,] 1.000000e+00 2.806269e-09 1.403134e-09
[76,] 9.998946e-01 2.107132e-04 1.053566e-04
[77,] 1.000000e+00 5.467410e-29 2.733705e-29
[78,] 3.156334e-05 6.312669e-05 9.999684e-01
[79,] 1.569737e-02 3.139474e-02 9.843026e-01
[80,] 3.372008e-08 6.744015e-08 1.000000e+00
[81,] 1.000000e+00 6.017068e-25 3.008534e-25
[82,] 9.994481e-01 1.103789e-03 5.518946e-04
[83,] 1.000000e+00 4.589008e-11 2.294504e-11
[84,] 1.000000e+00 2.570984e-10 1.285492e-10
[85,] 9.999975e-01 5.036832e-06 2.518416e-06
[86,] 1.000000e+00 2.068458e-11 1.034229e-11
[87,] 9.720180e-04 1.944036e-03 9.990280e-01
[88,] 1.000000e+00 7.490595e-38 3.745298e-38
[89,] 2.536533e-07 5.073067e-07 9.999997e-01
[90,] 1.000000e+00 8.495983e-56 4.247991e-56
[91,] 1.000000e+00 2.941952e-08 1.470976e-08
[92,] 1.000000e+00 9.765393e-18 4.882696e-18
[93,] 1.370440e-17 2.740881e-17 1.000000e+00
[94,] 1.000000e+00 7.177167e-45 3.588584e-45
[95,] 1.000000e+00 1.316311e-15 6.581554e-16
[96,] 1.083180e-16 2.166361e-16 1.000000e+00
[97,] 9.974583e-01 5.083456e-03 2.541728e-03
[98,] 7.935177e-04 1.587035e-03 9.992065e-01
[99,] 1.000000e+00 8.953108e-18 4.476554e-18
[100,] 5.310618e-30 1.062124e-29 1.000000e+00
[101,] 1.000000e+00 1.283903e-18 6.419516e-19
[102,] 1.000000e+00 1.104006e-18 5.520028e-19
[103,] 1.000000e+00 6.214930e-17 3.107465e-17
[104,] 1.727472e-18 3.454944e-18 1.000000e+00
[105,] 5.420401e-41 1.084080e-40 1.000000e+00
[106,] 9.489738e-01 1.020524e-01 5.102619e-02
[107,] 2.823153e-03 5.646306e-03 9.971768e-01
[108,] 1.283675e-01 2.567350e-01 8.716325e-01
[109,] 1.000000e+00 1.813874e-10 9.069371e-11
[110,] 6.160049e-01 7.679902e-01 3.839951e-01
[111,] 1.000000e+00 3.813187e-29 1.906594e-29
[112,] 1.000000e+00 3.277133e-27 1.638567e-27
[113,] 9.999102e-01 1.795128e-04 8.975640e-05
[114,] 9.998559e-01 2.881685e-04 1.440842e-04
[115,] 9.999983e-01 3.332614e-06 1.666307e-06
[116,] 1.000000e+00 4.922317e-29 2.461159e-29
[117,] 1.000000e+00 1.774143e-11 8.870714e-12
[118,] 1.000000e+00 1.064175e-23 5.320874e-24
[119,] 9.933193e-01 1.336132e-02 6.680662e-03
[120,] 9.999998e-01 3.053487e-07 1.526744e-07
[121,] 1.781945e-05 3.563891e-05 9.999822e-01
[122,] 7.654053e-01 4.691894e-01 2.345947e-01
[123,] 1.000000e+00 2.686581e-10 1.343291e-10
[124,] 1.000000e+00 7.309045e-16 3.654522e-16
[125,] 9.999999e-01 1.367663e-07 6.838315e-08
[126,] 9.999999e-01 2.575518e-07 1.287759e-07
[127,] 9.999998e-01 4.750836e-07 2.375418e-07
[128,] 1.000000e+00 1.689548e-18 8.447742e-19
[129,] 1.000000e+00 3.641956e-14 1.820978e-14
[130,] 1.000000e+00 7.749172e-08 3.874586e-08
[131,] 1.000000e+00 5.485089e-09 2.742545e-09
[132,] 1.000000e+00 5.681398e-09 2.840699e-09
[133,] 1.000000e+00 2.254561e-10 1.127280e-10
[134,] 8.666271e-01 2.667459e-01 1.333729e-01
[135,] 9.996205e-01 7.589097e-04 3.794549e-04
[136,] 9.999999e-01 1.266066e-07 6.330331e-08
[137,] 9.967429e-01 6.514136e-03 3.257068e-03
[138,] 3.827862e-01 7.655724e-01 6.172138e-01
> postscript(file="/var/www/html/rcomp/tmp/1u6q81290506270.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$resid)
Warning message:
In x[, 1] - mysum$resid :
longer object length is not a multiple of shorter object length
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ngqb1290506270.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3ngqb1290506270.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ngqb1290506270.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ngqb1290506270.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 158
Frequency = 1
1 2 3 4 5
5.467923e-14 -2.344667e-14 4.400606e-15 -6.162446e-15 -6.950011e-16
6 7 8 9 10
-5.186692e-15 2.070999e-15 2.120195e-15 9.694495e-16 7.774362e-16
11 12 13 14 15
4.381414e-16 7.123155e-16 -3.736534e-16 1.340927e-15 -2.953421e-16
16 17 18 19 20
4.632170e-16 6.564393e-16 5.400822e-16 -4.674533e-16 2.165393e-16
21 22 23 24 25
-1.166677e-15 3.669429e-16 -1.385017e-16 -3.622269e-16 -3.462854e-16
26 27 28 29 30
1.022143e-15 -1.201465e-15 8.717172e-17 -1.754603e-15 -4.310169e-16
31 32 33 34 35
-1.047843e-15 -7.243436e-16 -1.356389e-16 -2.582699e-16 -5.287565e-17
36 37 38 39 40
-1.763253e-16 1.191941e-16 1.329785e-15 1.191941e-16 1.654415e-16
41 42 43 44 45
-3.123080e-15 -2.737924e-17 5.400822e-16 -2.259947e-16 -1.284773e-15
46 47 48 49 50
6.458702e-16 -3.111961e-16 -1.134984e-15 -1.047843e-15 8.585238e-16
51 52 53 54 55
-1.606535e-15 -7.129786e-16 -1.815582e-15 -1.228780e-15 1.160889e-15
56 57 58 59 60
7.111068e-16 -2.163400e-15 2.283926e-17 -2.049192e-15 3.970734e-16
61 62 63 64 65
6.460465e-16 3.182978e-16 2.283926e-17 2.198601e-16 4.866898e-16
66 67 68 69 70
-6.277292e-16 -3.027976e-16 3.819478e-16 -6.064493e-16 3.965318e-16
71 72 73 74 75
1.531789e-15 -3.431662e-16 3.561242e-16 1.281923e-15 -5.300128e-16
76 77 78 79 80
-1.815582e-15 -3.696047e-16 4.389551e-16 1.834677e-16 -5.287565e-17
81 82 83 84 85
-6.360441e-16 1.329785e-15 2.378770e-15 -2.121779e-16 4.381414e-16
86 87 88 89 90
8.673451e-16 2.375501e-16 -1.615783e-15 3.561242e-16 -2.974520e-16
91 92 93 94 95
1.075154e-15 -1.062758e-15 -6.361407e-16 -4.674533e-16 -1.880988e-15
96 97 99 100 101
-1.250997e-15 -3.696047e-16 9.529898e-16 -6.899404e-16 -1.565032e-16
102 103 104 105 106
8.717172e-17 -6.034779e-17 -3.431662e-16 -2.713723e-16 3.874012e-16
107 108 109 110 111
7.811029e-16 -1.265291e-15 -2.012214e-15 -1.852047e-16 -8.583682e-16
112 113 114 115 116
-8.859827e-16 -4.173204e-16 -9.239173e-16 7.461417e-17 -1.098479e-15
117 118 119 120 121
-2.370559e-15 -1.484756e-15 1.398609e-15 1.355976e-15 1.618770e-16
122 123 124 125 126
-5.587143e-16 1.077222e-15 2.496796e-16 1.874120e-15 8.101638e-16
127 128 129 130 131
-9.316597e-16 -4.618563e-16 -1.194330e-15 -1.284773e-15 -1.364020e-16
132 133 134 135 136
-1.700012e-15 -7.135512e-16 -1.277559e-15 2.856324e-16 -1.284773e-15
137 138 139 140 141
1.972558e-15 8.143337e-16 -7.160177e-16 8.883367e-16 1.680335e-15
142 143 144 145 146
-2.671856e-16 -3.333519e-16 -1.725722e-15 -1.143482e-15 2.580137e-16
147 148 149 150 151
-6.817215e-16 -5.304553e-16 -7.368483e-16 -6.140427e-16 1.227261e-15
152 153 154 155 156
-2.128311e-15 -1.330275e-15 3.403345e-16 2.185548e-15 -1.174412e-15
157 158 159
-1.050423e-15 7.704534e-17 4.124624e-16
> postscript(file="/var/www/html/rcomp/tmp/6g77d1290506270.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 = 158
Frequency = 1
lag(myerror, k = 1) myerror
0 5.467923e-14 NA
1 -2.344667e-14 5.467923e-14
2 4.400606e-15 -2.344667e-14
3 -6.162446e-15 4.400606e-15
4 -6.950011e-16 -6.162446e-15
5 -5.186692e-15 -6.950011e-16
6 2.070999e-15 -5.186692e-15
7 2.120195e-15 2.070999e-15
8 9.694495e-16 2.120195e-15
9 7.774362e-16 9.694495e-16
10 4.381414e-16 7.774362e-16
11 7.123155e-16 4.381414e-16
12 -3.736534e-16 7.123155e-16
13 1.340927e-15 -3.736534e-16
14 -2.953421e-16 1.340927e-15
15 4.632170e-16 -2.953421e-16
16 6.564393e-16 4.632170e-16
17 5.400822e-16 6.564393e-16
18 -4.674533e-16 5.400822e-16
19 2.165393e-16 -4.674533e-16
20 -1.166677e-15 2.165393e-16
21 3.669429e-16 -1.166677e-15
22 -1.385017e-16 3.669429e-16
23 -3.622269e-16 -1.385017e-16
24 -3.462854e-16 -3.622269e-16
25 1.022143e-15 -3.462854e-16
26 -1.201465e-15 1.022143e-15
27 8.717172e-17 -1.201465e-15
28 -1.754603e-15 8.717172e-17
29 -4.310169e-16 -1.754603e-15
30 -1.047843e-15 -4.310169e-16
31 -7.243436e-16 -1.047843e-15
32 -1.356389e-16 -7.243436e-16
33 -2.582699e-16 -1.356389e-16
34 -5.287565e-17 -2.582699e-16
35 -1.763253e-16 -5.287565e-17
36 1.191941e-16 -1.763253e-16
37 1.329785e-15 1.191941e-16
38 1.191941e-16 1.329785e-15
39 1.654415e-16 1.191941e-16
40 -3.123080e-15 1.654415e-16
41 -2.737924e-17 -3.123080e-15
42 5.400822e-16 -2.737924e-17
43 -2.259947e-16 5.400822e-16
44 -1.284773e-15 -2.259947e-16
45 6.458702e-16 -1.284773e-15
46 -3.111961e-16 6.458702e-16
47 -1.134984e-15 -3.111961e-16
48 -1.047843e-15 -1.134984e-15
49 8.585238e-16 -1.047843e-15
50 -1.606535e-15 8.585238e-16
51 -7.129786e-16 -1.606535e-15
52 -1.815582e-15 -7.129786e-16
53 -1.228780e-15 -1.815582e-15
54 1.160889e-15 -1.228780e-15
55 7.111068e-16 1.160889e-15
56 -2.163400e-15 7.111068e-16
57 2.283926e-17 -2.163400e-15
58 -2.049192e-15 2.283926e-17
59 3.970734e-16 -2.049192e-15
60 6.460465e-16 3.970734e-16
61 3.182978e-16 6.460465e-16
62 2.283926e-17 3.182978e-16
63 2.198601e-16 2.283926e-17
64 4.866898e-16 2.198601e-16
65 -6.277292e-16 4.866898e-16
66 -3.027976e-16 -6.277292e-16
67 3.819478e-16 -3.027976e-16
68 -6.064493e-16 3.819478e-16
69 3.965318e-16 -6.064493e-16
70 1.531789e-15 3.965318e-16
71 -3.431662e-16 1.531789e-15
72 3.561242e-16 -3.431662e-16
73 1.281923e-15 3.561242e-16
74 -5.300128e-16 1.281923e-15
75 -1.815582e-15 -5.300128e-16
76 -3.696047e-16 -1.815582e-15
77 4.389551e-16 -3.696047e-16
78 1.834677e-16 4.389551e-16
79 -5.287565e-17 1.834677e-16
80 -6.360441e-16 -5.287565e-17
81 1.329785e-15 -6.360441e-16
82 2.378770e-15 1.329785e-15
83 -2.121779e-16 2.378770e-15
84 4.381414e-16 -2.121779e-16
85 8.673451e-16 4.381414e-16
86 2.375501e-16 8.673451e-16
87 -1.615783e-15 2.375501e-16
88 3.561242e-16 -1.615783e-15
89 -2.974520e-16 3.561242e-16
90 1.075154e-15 -2.974520e-16
91 -1.062758e-15 1.075154e-15
92 -6.361407e-16 -1.062758e-15
93 -4.674533e-16 -6.361407e-16
94 -1.880988e-15 -4.674533e-16
95 -1.250997e-15 -1.880988e-15
96 -3.696047e-16 -1.250997e-15
97 9.529898e-16 -3.696047e-16
98 -6.899404e-16 9.529898e-16
99 -1.565032e-16 -6.899404e-16
100 8.717172e-17 -1.565032e-16
101 -6.034779e-17 8.717172e-17
102 -3.431662e-16 -6.034779e-17
103 -2.713723e-16 -3.431662e-16
104 3.874012e-16 -2.713723e-16
105 7.811029e-16 3.874012e-16
106 -1.265291e-15 7.811029e-16
107 -2.012214e-15 -1.265291e-15
108 -1.852047e-16 -2.012214e-15
109 -8.583682e-16 -1.852047e-16
110 -8.859827e-16 -8.583682e-16
111 -4.173204e-16 -8.859827e-16
112 -9.239173e-16 -4.173204e-16
113 7.461417e-17 -9.239173e-16
114 -1.098479e-15 7.461417e-17
115 -2.370559e-15 -1.098479e-15
116 -1.484756e-15 -2.370559e-15
117 1.398609e-15 -1.484756e-15
118 1.355976e-15 1.398609e-15
119 1.618770e-16 1.355976e-15
120 -5.587143e-16 1.618770e-16
121 1.077222e-15 -5.587143e-16
122 2.496796e-16 1.077222e-15
123 1.874120e-15 2.496796e-16
124 8.101638e-16 1.874120e-15
125 -9.316597e-16 8.101638e-16
126 -4.618563e-16 -9.316597e-16
127 -1.194330e-15 -4.618563e-16
128 -1.284773e-15 -1.194330e-15
129 -1.364020e-16 -1.284773e-15
130 -1.700012e-15 -1.364020e-16
131 -7.135512e-16 -1.700012e-15
132 -1.277559e-15 -7.135512e-16
133 2.856324e-16 -1.277559e-15
134 -1.284773e-15 2.856324e-16
135 1.972558e-15 -1.284773e-15
136 8.143337e-16 1.972558e-15
137 -7.160177e-16 8.143337e-16
138 8.883367e-16 -7.160177e-16
139 1.680335e-15 8.883367e-16
140 -2.671856e-16 1.680335e-15
141 -3.333519e-16 -2.671856e-16
142 -1.725722e-15 -3.333519e-16
143 -1.143482e-15 -1.725722e-15
144 2.580137e-16 -1.143482e-15
145 -6.817215e-16 2.580137e-16
146 -5.304553e-16 -6.817215e-16
147 -7.368483e-16 -5.304553e-16
148 -6.140427e-16 -7.368483e-16
149 1.227261e-15 -6.140427e-16
150 -2.128311e-15 1.227261e-15
151 -1.330275e-15 -2.128311e-15
152 3.403345e-16 -1.330275e-15
153 2.185548e-15 3.403345e-16
154 -1.174412e-15 2.185548e-15
155 -1.050423e-15 -1.174412e-15
156 7.704534e-17 -1.050423e-15
157 4.124624e-16 7.704534e-17
158 NA 4.124624e-16
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.344667e-14 5.467923e-14
[2,] 4.400606e-15 -2.344667e-14
[3,] -6.162446e-15 4.400606e-15
[4,] -6.950011e-16 -6.162446e-15
[5,] -5.186692e-15 -6.950011e-16
[6,] 2.070999e-15 -5.186692e-15
[7,] 2.120195e-15 2.070999e-15
[8,] 9.694495e-16 2.120195e-15
[9,] 7.774362e-16 9.694495e-16
[10,] 4.381414e-16 7.774362e-16
[11,] 7.123155e-16 4.381414e-16
[12,] -3.736534e-16 7.123155e-16
[13,] 1.340927e-15 -3.736534e-16
[14,] -2.953421e-16 1.340927e-15
[15,] 4.632170e-16 -2.953421e-16
[16,] 6.564393e-16 4.632170e-16
[17,] 5.400822e-16 6.564393e-16
[18,] -4.674533e-16 5.400822e-16
[19,] 2.165393e-16 -4.674533e-16
[20,] -1.166677e-15 2.165393e-16
[21,] 3.669429e-16 -1.166677e-15
[22,] -1.385017e-16 3.669429e-16
[23,] -3.622269e-16 -1.385017e-16
[24,] -3.462854e-16 -3.622269e-16
[25,] 1.022143e-15 -3.462854e-16
[26,] -1.201465e-15 1.022143e-15
[27,] 8.717172e-17 -1.201465e-15
[28,] -1.754603e-15 8.717172e-17
[29,] -4.310169e-16 -1.754603e-15
[30,] -1.047843e-15 -4.310169e-16
[31,] -7.243436e-16 -1.047843e-15
[32,] -1.356389e-16 -7.243436e-16
[33,] -2.582699e-16 -1.356389e-16
[34,] -5.287565e-17 -2.582699e-16
[35,] -1.763253e-16 -5.287565e-17
[36,] 1.191941e-16 -1.763253e-16
[37,] 1.329785e-15 1.191941e-16
[38,] 1.191941e-16 1.329785e-15
[39,] 1.654415e-16 1.191941e-16
[40,] -3.123080e-15 1.654415e-16
[41,] -2.737924e-17 -3.123080e-15
[42,] 5.400822e-16 -2.737924e-17
[43,] -2.259947e-16 5.400822e-16
[44,] -1.284773e-15 -2.259947e-16
[45,] 6.458702e-16 -1.284773e-15
[46,] -3.111961e-16 6.458702e-16
[47,] -1.134984e-15 -3.111961e-16
[48,] -1.047843e-15 -1.134984e-15
[49,] 8.585238e-16 -1.047843e-15
[50,] -1.606535e-15 8.585238e-16
[51,] -7.129786e-16 -1.606535e-15
[52,] -1.815582e-15 -7.129786e-16
[53,] -1.228780e-15 -1.815582e-15
[54,] 1.160889e-15 -1.228780e-15
[55,] 7.111068e-16 1.160889e-15
[56,] -2.163400e-15 7.111068e-16
[57,] 2.283926e-17 -2.163400e-15
[58,] -2.049192e-15 2.283926e-17
[59,] 3.970734e-16 -2.049192e-15
[60,] 6.460465e-16 3.970734e-16
[61,] 3.182978e-16 6.460465e-16
[62,] 2.283926e-17 3.182978e-16
[63,] 2.198601e-16 2.283926e-17
[64,] 4.866898e-16 2.198601e-16
[65,] -6.277292e-16 4.866898e-16
[66,] -3.027976e-16 -6.277292e-16
[67,] 3.819478e-16 -3.027976e-16
[68,] -6.064493e-16 3.819478e-16
[69,] 3.965318e-16 -6.064493e-16
[70,] 1.531789e-15 3.965318e-16
[71,] -3.431662e-16 1.531789e-15
[72,] 3.561242e-16 -3.431662e-16
[73,] 1.281923e-15 3.561242e-16
[74,] -5.300128e-16 1.281923e-15
[75,] -1.815582e-15 -5.300128e-16
[76,] -3.696047e-16 -1.815582e-15
[77,] 4.389551e-16 -3.696047e-16
[78,] 1.834677e-16 4.389551e-16
[79,] -5.287565e-17 1.834677e-16
[80,] -6.360441e-16 -5.287565e-17
[81,] 1.329785e-15 -6.360441e-16
[82,] 2.378770e-15 1.329785e-15
[83,] -2.121779e-16 2.378770e-15
[84,] 4.381414e-16 -2.121779e-16
[85,] 8.673451e-16 4.381414e-16
[86,] 2.375501e-16 8.673451e-16
[87,] -1.615783e-15 2.375501e-16
[88,] 3.561242e-16 -1.615783e-15
[89,] -2.974520e-16 3.561242e-16
[90,] 1.075154e-15 -2.974520e-16
[91,] -1.062758e-15 1.075154e-15
[92,] -6.361407e-16 -1.062758e-15
[93,] -4.674533e-16 -6.361407e-16
[94,] -1.880988e-15 -4.674533e-16
[95,] -1.250997e-15 -1.880988e-15
[96,] -3.696047e-16 -1.250997e-15
[97,] 9.529898e-16 -3.696047e-16
[98,] -6.899404e-16 9.529898e-16
[99,] -1.565032e-16 -6.899404e-16
[100,] 8.717172e-17 -1.565032e-16
[101,] -6.034779e-17 8.717172e-17
[102,] -3.431662e-16 -6.034779e-17
[103,] -2.713723e-16 -3.431662e-16
[104,] 3.874012e-16 -2.713723e-16
[105,] 7.811029e-16 3.874012e-16
[106,] -1.265291e-15 7.811029e-16
[107,] -2.012214e-15 -1.265291e-15
[108,] -1.852047e-16 -2.012214e-15
[109,] -8.583682e-16 -1.852047e-16
[110,] -8.859827e-16 -8.583682e-16
[111,] -4.173204e-16 -8.859827e-16
[112,] -9.239173e-16 -4.173204e-16
[113,] 7.461417e-17 -9.239173e-16
[114,] -1.098479e-15 7.461417e-17
[115,] -2.370559e-15 -1.098479e-15
[116,] -1.484756e-15 -2.370559e-15
[117,] 1.398609e-15 -1.484756e-15
[118,] 1.355976e-15 1.398609e-15
[119,] 1.618770e-16 1.355976e-15
[120,] -5.587143e-16 1.618770e-16
[121,] 1.077222e-15 -5.587143e-16
[122,] 2.496796e-16 1.077222e-15
[123,] 1.874120e-15 2.496796e-16
[124,] 8.101638e-16 1.874120e-15
[125,] -9.316597e-16 8.101638e-16
[126,] -4.618563e-16 -9.316597e-16
[127,] -1.194330e-15 -4.618563e-16
[128,] -1.284773e-15 -1.194330e-15
[129,] -1.364020e-16 -1.284773e-15
[130,] -1.700012e-15 -1.364020e-16
[131,] -7.135512e-16 -1.700012e-15
[132,] -1.277559e-15 -7.135512e-16
[133,] 2.856324e-16 -1.277559e-15
[134,] -1.284773e-15 2.856324e-16
[135,] 1.972558e-15 -1.284773e-15
[136,] 8.143337e-16 1.972558e-15
[137,] -7.160177e-16 8.143337e-16
[138,] 8.883367e-16 -7.160177e-16
[139,] 1.680335e-15 8.883367e-16
[140,] -2.671856e-16 1.680335e-15
[141,] -3.333519e-16 -2.671856e-16
[142,] -1.725722e-15 -3.333519e-16
[143,] -1.143482e-15 -1.725722e-15
[144,] 2.580137e-16 -1.143482e-15
[145,] -6.817215e-16 2.580137e-16
[146,] -5.304553e-16 -6.817215e-16
[147,] -7.368483e-16 -5.304553e-16
[148,] -6.140427e-16 -7.368483e-16
[149,] 1.227261e-15 -6.140427e-16
[150,] -2.128311e-15 1.227261e-15
[151,] -1.330275e-15 -2.128311e-15
[152,] 3.403345e-16 -1.330275e-15
[153,] 2.185548e-15 3.403345e-16
[154,] -1.174412e-15 2.185548e-15
[155,] -1.050423e-15 -1.174412e-15
[156,] 7.704534e-17 -1.050423e-15
[157,] 4.124624e-16 7.704534e-17
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.344667e-14 5.467923e-14
2 4.400606e-15 -2.344667e-14
3 -6.162446e-15 4.400606e-15
4 -6.950011e-16 -6.162446e-15
5 -5.186692e-15 -6.950011e-16
6 2.070999e-15 -5.186692e-15
7 2.120195e-15 2.070999e-15
8 9.694495e-16 2.120195e-15
9 7.774362e-16 9.694495e-16
10 4.381414e-16 7.774362e-16
11 7.123155e-16 4.381414e-16
12 -3.736534e-16 7.123155e-16
13 1.340927e-15 -3.736534e-16
14 -2.953421e-16 1.340927e-15
15 4.632170e-16 -2.953421e-16
16 6.564393e-16 4.632170e-16
17 5.400822e-16 6.564393e-16
18 -4.674533e-16 5.400822e-16
19 2.165393e-16 -4.674533e-16
20 -1.166677e-15 2.165393e-16
21 3.669429e-16 -1.166677e-15
22 -1.385017e-16 3.669429e-16
23 -3.622269e-16 -1.385017e-16
24 -3.462854e-16 -3.622269e-16
25 1.022143e-15 -3.462854e-16
26 -1.201465e-15 1.022143e-15
27 8.717172e-17 -1.201465e-15
28 -1.754603e-15 8.717172e-17
29 -4.310169e-16 -1.754603e-15
30 -1.047843e-15 -4.310169e-16
31 -7.243436e-16 -1.047843e-15
32 -1.356389e-16 -7.243436e-16
33 -2.582699e-16 -1.356389e-16
34 -5.287565e-17 -2.582699e-16
35 -1.763253e-16 -5.287565e-17
36 1.191941e-16 -1.763253e-16
37 1.329785e-15 1.191941e-16
38 1.191941e-16 1.329785e-15
39 1.654415e-16 1.191941e-16
40 -3.123080e-15 1.654415e-16
41 -2.737924e-17 -3.123080e-15
42 5.400822e-16 -2.737924e-17
43 -2.259947e-16 5.400822e-16
44 -1.284773e-15 -2.259947e-16
45 6.458702e-16 -1.284773e-15
46 -3.111961e-16 6.458702e-16
47 -1.134984e-15 -3.111961e-16
48 -1.047843e-15 -1.134984e-15
49 8.585238e-16 -1.047843e-15
50 -1.606535e-15 8.585238e-16
51 -7.129786e-16 -1.606535e-15
52 -1.815582e-15 -7.129786e-16
53 -1.228780e-15 -1.815582e-15
54 1.160889e-15 -1.228780e-15
55 7.111068e-16 1.160889e-15
56 -2.163400e-15 7.111068e-16
57 2.283926e-17 -2.163400e-15
58 -2.049192e-15 2.283926e-17
59 3.970734e-16 -2.049192e-15
60 6.460465e-16 3.970734e-16
61 3.182978e-16 6.460465e-16
62 2.283926e-17 3.182978e-16
63 2.198601e-16 2.283926e-17
64 4.866898e-16 2.198601e-16
65 -6.277292e-16 4.866898e-16
66 -3.027976e-16 -6.277292e-16
67 3.819478e-16 -3.027976e-16
68 -6.064493e-16 3.819478e-16
69 3.965318e-16 -6.064493e-16
70 1.531789e-15 3.965318e-16
71 -3.431662e-16 1.531789e-15
72 3.561242e-16 -3.431662e-16
73 1.281923e-15 3.561242e-16
74 -5.300128e-16 1.281923e-15
75 -1.815582e-15 -5.300128e-16
76 -3.696047e-16 -1.815582e-15
77 4.389551e-16 -3.696047e-16
78 1.834677e-16 4.389551e-16
79 -5.287565e-17 1.834677e-16
80 -6.360441e-16 -5.287565e-17
81 1.329785e-15 -6.360441e-16
82 2.378770e-15 1.329785e-15
83 -2.121779e-16 2.378770e-15
84 4.381414e-16 -2.121779e-16
85 8.673451e-16 4.381414e-16
86 2.375501e-16 8.673451e-16
87 -1.615783e-15 2.375501e-16
88 3.561242e-16 -1.615783e-15
89 -2.974520e-16 3.561242e-16
90 1.075154e-15 -2.974520e-16
91 -1.062758e-15 1.075154e-15
92 -6.361407e-16 -1.062758e-15
93 -4.674533e-16 -6.361407e-16
94 -1.880988e-15 -4.674533e-16
95 -1.250997e-15 -1.880988e-15
96 -3.696047e-16 -1.250997e-15
97 9.529898e-16 -3.696047e-16
98 -6.899404e-16 9.529898e-16
99 -1.565032e-16 -6.899404e-16
100 8.717172e-17 -1.565032e-16
101 -6.034779e-17 8.717172e-17
102 -3.431662e-16 -6.034779e-17
103 -2.713723e-16 -3.431662e-16
104 3.874012e-16 -2.713723e-16
105 7.811029e-16 3.874012e-16
106 -1.265291e-15 7.811029e-16
107 -2.012214e-15 -1.265291e-15
108 -1.852047e-16 -2.012214e-15
109 -8.583682e-16 -1.852047e-16
110 -8.859827e-16 -8.583682e-16
111 -4.173204e-16 -8.859827e-16
112 -9.239173e-16 -4.173204e-16
113 7.461417e-17 -9.239173e-16
114 -1.098479e-15 7.461417e-17
115 -2.370559e-15 -1.098479e-15
116 -1.484756e-15 -2.370559e-15
117 1.398609e-15 -1.484756e-15
118 1.355976e-15 1.398609e-15
119 1.618770e-16 1.355976e-15
120 -5.587143e-16 1.618770e-16
121 1.077222e-15 -5.587143e-16
122 2.496796e-16 1.077222e-15
123 1.874120e-15 2.496796e-16
124 8.101638e-16 1.874120e-15
125 -9.316597e-16 8.101638e-16
126 -4.618563e-16 -9.316597e-16
127 -1.194330e-15 -4.618563e-16
128 -1.284773e-15 -1.194330e-15
129 -1.364020e-16 -1.284773e-15
130 -1.700012e-15 -1.364020e-16
131 -7.135512e-16 -1.700012e-15
132 -1.277559e-15 -7.135512e-16
133 2.856324e-16 -1.277559e-15
134 -1.284773e-15 2.856324e-16
135 1.972558e-15 -1.284773e-15
136 8.143337e-16 1.972558e-15
137 -7.160177e-16 8.143337e-16
138 8.883367e-16 -7.160177e-16
139 1.680335e-15 8.883367e-16
140 -2.671856e-16 1.680335e-15
141 -3.333519e-16 -2.671856e-16
142 -1.725722e-15 -3.333519e-16
143 -1.143482e-15 -1.725722e-15
144 2.580137e-16 -1.143482e-15
145 -6.817215e-16 2.580137e-16
146 -5.304553e-16 -6.817215e-16
147 -7.368483e-16 -5.304553e-16
148 -6.140427e-16 -7.368483e-16
149 1.227261e-15 -6.140427e-16
150 -2.128311e-15 1.227261e-15
151 -1.330275e-15 -2.128311e-15
152 3.403345e-16 -1.330275e-15
153 2.185548e-15 3.403345e-16
154 -1.174412e-15 2.185548e-15
155 -1.050423e-15 -1.174412e-15
156 7.704534e-17 -1.050423e-15
157 4.124624e-16 7.704534e-17
> 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/7ry6h1290506270.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/8ry6h1290506270.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/9ry6h1290506270.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/10j7521290506270.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/1158mp1290506270.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12q8kd1290506270.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13m00m1290506270.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14qjza1290506270.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/15b1xg1290506270.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/16fkw41290506270.tab")
+ }
>
> try(system("convert tmp/1u6q81290506270.ps tmp/1u6q81290506270.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ngqb1290506270.ps tmp/2ngqb1290506270.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ngqb1290506270.ps tmp/3ngqb1290506270.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ngqb1290506270.ps tmp/4ngqb1290506270.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ngqb1290506270.ps tmp/5ngqb1290506270.png",intern=TRUE))
character(0)
> try(system("convert tmp/6g77d1290506270.ps tmp/6g77d1290506270.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ry6h1290506270.ps tmp/7ry6h1290506270.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ry6h1290506270.ps tmp/8ry6h1290506270.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ry6h1290506270.ps tmp/9ry6h1290506270.png",intern=TRUE))
character(0)
> try(system("convert tmp/10j7521290506270.ps tmp/10j7521290506270.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.665 1.997 29.352