R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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.
Natural language support but running in an English locale
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(0
+ ,24
+ ,18
+ ,17
+ ,25
+ ,24
+ ,0
+ ,25
+ ,18
+ ,18
+ ,17
+ ,25
+ ,0
+ ,17
+ ,16
+ ,18
+ ,18
+ ,17
+ ,0
+ ,18
+ ,20
+ ,16
+ ,18
+ ,18
+ ,0
+ ,18
+ ,16
+ ,20
+ ,16
+ ,18
+ ,0
+ ,16
+ ,18
+ ,16
+ ,20
+ ,16
+ ,1
+ ,20
+ ,17
+ ,18
+ ,16
+ ,20
+ ,1
+ ,16
+ ,23
+ ,17
+ ,18
+ ,16
+ ,1
+ ,18
+ ,30
+ ,23
+ ,17
+ ,18
+ ,1
+ ,17
+ ,23
+ ,30
+ ,23
+ ,17
+ ,1
+ ,23
+ ,18
+ ,23
+ ,30
+ ,23
+ ,1
+ ,30
+ ,15
+ ,18
+ ,23
+ ,30
+ ,1
+ ,23
+ ,12
+ ,15
+ ,18
+ ,23
+ ,1
+ ,18
+ ,21
+ ,12
+ ,15
+ ,18
+ ,1
+ ,15
+ ,15
+ ,21
+ ,12
+ ,15
+ ,1
+ ,12
+ ,20
+ ,15
+ ,21
+ ,12
+ ,1
+ ,21
+ ,31
+ ,20
+ ,15
+ ,21
+ ,1
+ ,15
+ ,27
+ ,31
+ ,20
+ ,15
+ ,1
+ ,20
+ ,34
+ ,27
+ ,31
+ ,20
+ ,1
+ ,31
+ ,21
+ ,34
+ ,27
+ ,31
+ ,1
+ ,27
+ ,31
+ ,21
+ ,34
+ ,27
+ ,1
+ ,34
+ ,19
+ ,31
+ ,21
+ ,34
+ ,1
+ ,21
+ ,16
+ ,19
+ ,31
+ ,21
+ ,1
+ ,31
+ ,20
+ ,16
+ ,19
+ ,31
+ ,1
+ ,19
+ ,21
+ ,20
+ ,16
+ ,19
+ ,1
+ ,16
+ ,22
+ ,21
+ ,20
+ ,16
+ ,1
+ ,20
+ ,17
+ ,22
+ ,21
+ ,20
+ ,1
+ ,21
+ ,24
+ ,17
+ ,22
+ ,21
+ ,1
+ ,22
+ ,25
+ ,24
+ ,17
+ ,22
+ ,1
+ ,17
+ ,26
+ ,25
+ ,24
+ ,17
+ ,1
+ ,24
+ ,25
+ ,26
+ ,25
+ ,24
+ ,1
+ ,25
+ ,17
+ ,25
+ ,26
+ ,25
+ ,1
+ ,26
+ ,32
+ ,17
+ ,25
+ ,26
+ ,1
+ ,25
+ ,33
+ ,32
+ ,17
+ ,25
+ ,1
+ ,17
+ ,13
+ ,33
+ ,32
+ ,17
+ ,1
+ ,32
+ ,32
+ ,13
+ ,33
+ ,32
+ ,1
+ ,33
+ ,25
+ ,32
+ ,13
+ ,33
+ ,1
+ ,13
+ ,29
+ ,25
+ ,32
+ ,13
+ ,1
+ ,32
+ ,22
+ ,29
+ ,25
+ ,32
+ ,1
+ ,25
+ ,18
+ ,22
+ ,29
+ ,25
+ ,1
+ ,29
+ ,17
+ ,18
+ ,22
+ ,29
+ ,1
+ ,22
+ ,20
+ ,17
+ ,18
+ ,22
+ ,1
+ ,18
+ ,15
+ ,20
+ ,17
+ ,18
+ ,1
+ ,17
+ ,20
+ ,15
+ ,20
+ ,17
+ ,1
+ ,20
+ ,33
+ ,20
+ ,15
+ ,20
+ ,1
+ ,15
+ ,29
+ ,33
+ ,20
+ ,15
+ ,1
+ ,20
+ ,23
+ ,29
+ ,33
+ ,20
+ ,1
+ ,33
+ ,26
+ ,23
+ ,29
+ ,33
+ ,1
+ ,29
+ ,18
+ ,26
+ ,23
+ ,29
+ ,1
+ ,23
+ ,20
+ ,18
+ ,26
+ ,23
+ ,1
+ ,26
+ ,11
+ ,20
+ ,18
+ ,26
+ ,1
+ ,18
+ ,28
+ ,11
+ ,20
+ ,18
+ ,1
+ ,20
+ ,26
+ ,28
+ ,11
+ ,20
+ ,1
+ ,11
+ ,22
+ ,26
+ ,28
+ ,11
+ ,1
+ ,28
+ ,17
+ ,22
+ ,26
+ ,28
+ ,1
+ ,26
+ ,12
+ ,17
+ ,22
+ ,26
+ ,1
+ ,22
+ ,14
+ ,12
+ ,17
+ ,22
+ ,1
+ ,17
+ ,17
+ ,14
+ ,12
+ ,17
+ ,1
+ ,12
+ ,21
+ ,17
+ ,14
+ ,12
+ ,1
+ ,14
+ ,19
+ ,21
+ ,17
+ ,14
+ ,1
+ ,17
+ ,18
+ ,19
+ ,21
+ ,17
+ ,1
+ ,21
+ ,10
+ ,18
+ ,19
+ ,21
+ ,1
+ ,19
+ ,29
+ ,10
+ ,18
+ ,19
+ ,1
+ ,18
+ ,31
+ ,29
+ ,10
+ ,18
+ ,1
+ ,10
+ ,19
+ ,31
+ ,29
+ ,10
+ ,1
+ ,29
+ ,9
+ ,19
+ ,31
+ ,29
+ ,1
+ ,31
+ ,20
+ ,9
+ ,19
+ ,31
+ ,1
+ ,19
+ ,28
+ ,20
+ ,9
+ ,19
+ ,1
+ ,9
+ ,19
+ ,28
+ ,20
+ ,9
+ ,1
+ ,20
+ ,30
+ ,19
+ ,28
+ ,20
+ ,1
+ ,28
+ ,29
+ ,30
+ ,19
+ ,28
+ ,1
+ ,19
+ ,26
+ ,29
+ ,30
+ ,19
+ ,1
+ ,30
+ ,23
+ ,26
+ ,29
+ ,30
+ ,1
+ ,29
+ ,13
+ ,23
+ ,26
+ ,29
+ ,1
+ ,26
+ ,21
+ ,13
+ ,23
+ ,26
+ ,1
+ ,23
+ ,19
+ ,21
+ ,13
+ ,23
+ ,1
+ ,13
+ ,28
+ ,19
+ ,21
+ ,13
+ ,1
+ ,21
+ ,23
+ ,28
+ ,19
+ ,21
+ ,1
+ ,19
+ ,18
+ ,23
+ ,28
+ ,19
+ ,1
+ ,28
+ ,21
+ ,18
+ ,23
+ ,28
+ ,1
+ ,23
+ ,20
+ ,21
+ ,18
+ ,23
+ ,1
+ ,18
+ ,23
+ ,20
+ ,21
+ ,18
+ ,1
+ ,21
+ ,21
+ ,23
+ ,20
+ ,21
+ ,1
+ ,20
+ ,21
+ ,21
+ ,23
+ ,20
+ ,1
+ ,23
+ ,15
+ ,21
+ ,21
+ ,23
+ ,1
+ ,21
+ ,28
+ ,15
+ ,21
+ ,21
+ ,1
+ ,21
+ ,19
+ ,28
+ ,15
+ ,21
+ ,1
+ ,15
+ ,26
+ ,19
+ ,28
+ ,15
+ ,1
+ ,28
+ ,10
+ ,26
+ ,19
+ ,28
+ ,1
+ ,19
+ ,16
+ ,10
+ ,26
+ ,19
+ ,1
+ ,26
+ ,22
+ ,16
+ ,10
+ ,26
+ ,1
+ ,10
+ ,19
+ ,22
+ ,16
+ ,10
+ ,1
+ ,16
+ ,31
+ ,19
+ ,22
+ ,16
+ ,1
+ ,22
+ ,31
+ ,31
+ ,19
+ ,22
+ ,1
+ ,19
+ ,29
+ ,31
+ ,31
+ ,19
+ ,1
+ ,31
+ ,19
+ ,29
+ ,31
+ ,31
+ ,1
+ ,31
+ ,22
+ ,19
+ ,29
+ ,31
+ ,1
+ ,29
+ ,23
+ ,22
+ ,19
+ ,29
+ ,1
+ ,19
+ ,15
+ ,23
+ ,22
+ ,19
+ ,1
+ ,22
+ ,20
+ ,15
+ ,23
+ ,22
+ ,1
+ ,23
+ ,18
+ ,20
+ ,15
+ ,23
+ ,1
+ ,15
+ ,23
+ ,18
+ ,20
+ ,15
+ ,1
+ ,20
+ ,25
+ ,23
+ ,18
+ ,20
+ ,1
+ ,18
+ ,21
+ ,25
+ ,23
+ ,18
+ ,1
+ ,23
+ ,24
+ ,21
+ ,25
+ ,23
+ ,1
+ ,25
+ ,25
+ ,24
+ ,21
+ ,25
+ ,1
+ ,21
+ ,17
+ ,25
+ ,24
+ ,21
+ ,1
+ ,24
+ ,13
+ ,17
+ ,25
+ ,24
+ ,1
+ ,25
+ ,28
+ ,13
+ ,17
+ ,25
+ ,1
+ ,17
+ ,21
+ ,28
+ ,13
+ ,17
+ ,1
+ ,13
+ ,25
+ ,21
+ ,28
+ ,13
+ ,1
+ ,28
+ ,9
+ ,25
+ ,21
+ ,28
+ ,1
+ ,21
+ ,16
+ ,9
+ ,25
+ ,21
+ ,1
+ ,25
+ ,19
+ ,16
+ ,9
+ ,25
+ ,1
+ ,9
+ ,17
+ ,19
+ ,16
+ ,9
+ ,1
+ ,16
+ ,25
+ ,17
+ ,19
+ ,16
+ ,1
+ ,19
+ ,20
+ ,25
+ ,17
+ ,19
+ ,1
+ ,17
+ ,29
+ ,20
+ ,25
+ ,17
+ ,1
+ ,25
+ ,14
+ ,29
+ ,20
+ ,25
+ ,1
+ ,20
+ ,22
+ ,14
+ ,29
+ ,20
+ ,1
+ ,29
+ ,15
+ ,22
+ ,14
+ ,29
+ ,1
+ ,14
+ ,19
+ ,15
+ ,22
+ ,14
+ ,1
+ ,22
+ ,20
+ ,19
+ ,15
+ ,22
+ ,1
+ ,15
+ ,15
+ ,20
+ ,19
+ ,15
+ ,1
+ ,19
+ ,20
+ ,15
+ ,20
+ ,19
+ ,1
+ ,20
+ ,18
+ ,20
+ ,15
+ ,20
+ ,1
+ ,15
+ ,33
+ ,18
+ ,20
+ ,15
+ ,1
+ ,20
+ ,22
+ ,33
+ ,18
+ ,20
+ ,1
+ ,18
+ ,16
+ ,22
+ ,33
+ ,18
+ ,1
+ ,33
+ ,17
+ ,16
+ ,22
+ ,33
+ ,1
+ ,22
+ ,16
+ ,17
+ ,16
+ ,22
+ ,1
+ ,16
+ ,21
+ ,16
+ ,17
+ ,16
+ ,1
+ ,17
+ ,26
+ ,21
+ ,16
+ ,17
+ ,1
+ ,16
+ ,18
+ ,26
+ ,21
+ ,16
+ ,1
+ ,21
+ ,18
+ ,18
+ ,26
+ ,21
+ ,1
+ ,26
+ ,17
+ ,18
+ ,18
+ ,26
+ ,1
+ ,18
+ ,22
+ ,17
+ ,18
+ ,18
+ ,1
+ ,18
+ ,30
+ ,22
+ ,17
+ ,18
+ ,1
+ ,17
+ ,30
+ ,30
+ ,22
+ ,17
+ ,1
+ ,22
+ ,24
+ ,30
+ ,30
+ ,22
+ ,1
+ ,30
+ ,21
+ ,24
+ ,30
+ ,30
+ ,1
+ ,30
+ ,21
+ ,21
+ ,24
+ ,30
+ ,1
+ ,24
+ ,29
+ ,21
+ ,21
+ ,24
+ ,1
+ ,21
+ ,31
+ ,29
+ ,21
+ ,21
+ ,1
+ ,21
+ ,20
+ ,31
+ ,29
+ ,21
+ ,1
+ ,29
+ ,16
+ ,20
+ ,31
+ ,29
+ ,1
+ ,31
+ ,22
+ ,16
+ ,20
+ ,31
+ ,1
+ ,20
+ ,20
+ ,22
+ ,16
+ ,20
+ ,1
+ ,16
+ ,28
+ ,20
+ ,22
+ ,16
+ ,1
+ ,22
+ ,38
+ ,28
+ ,20
+ ,22
+ ,1
+ ,20
+ ,22
+ ,38
+ ,28
+ ,20
+ ,1
+ ,28
+ ,20
+ ,22
+ ,38
+ ,28
+ ,1
+ ,38
+ ,17
+ ,20
+ ,22
+ ,38
+ ,1
+ ,22
+ ,28
+ ,17
+ ,20
+ ,22
+ ,1
+ ,20
+ ,22
+ ,28
+ ,17
+ ,20
+ ,1
+ ,17
+ ,31
+ ,22
+ ,28
+ ,17)
+ ,dim=c(6
+ ,156)
+ ,dimnames=list(c('Month'
+ ,'Concernovermistakes'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:156))
> y <- array(NA,dim=c(6,156),dimnames=list(c('Month','Concernovermistakes','Y1','Y2','Y3','Y4'),1:156))
> 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
Concernovermistakes Month Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 24 0 18 17 25 24 1 0 0 0 0 0 0 0 0 0 0
2 25 0 18 18 17 25 0 1 0 0 0 0 0 0 0 0 0
3 17 0 16 18 18 17 0 0 1 0 0 0 0 0 0 0 0
4 18 0 20 16 18 18 0 0 0 1 0 0 0 0 0 0 0
5 18 0 16 20 16 18 0 0 0 0 1 0 0 0 0 0 0
6 16 0 18 16 20 16 0 0 0 0 0 1 0 0 0 0 0
7 20 1 17 18 16 20 0 0 0 0 0 0 1 0 0 0 0
8 16 1 23 17 18 16 0 0 0 0 0 0 0 1 0 0 0
9 18 1 30 23 17 18 0 0 0 0 0 0 0 0 1 0 0
10 17 1 23 30 23 17 0 0 0 0 0 0 0 0 0 1 0
11 23 1 18 23 30 23 0 0 0 0 0 0 0 0 0 0 1
12 30 1 15 18 23 30 0 0 0 0 0 0 0 0 0 0 0
13 23 1 12 15 18 23 1 0 0 0 0 0 0 0 0 0 0
14 18 1 21 12 15 18 0 1 0 0 0 0 0 0 0 0 0
15 15 1 15 21 12 15 0 0 1 0 0 0 0 0 0 0 0
16 12 1 20 15 21 12 0 0 0 1 0 0 0 0 0 0 0
17 21 1 31 20 15 21 0 0 0 0 1 0 0 0 0 0 0
18 15 1 27 31 20 15 0 0 0 0 0 1 0 0 0 0 0
19 20 1 34 27 31 20 0 0 0 0 0 0 1 0 0 0 0
20 31 1 21 34 27 31 0 0 0 0 0 0 0 1 0 0 0
21 27 1 31 21 34 27 0 0 0 0 0 0 0 0 1 0 0
22 34 1 19 31 21 34 0 0 0 0 0 0 0 0 0 1 0
23 21 1 16 19 31 21 0 0 0 0 0 0 0 0 0 0 1
24 31 1 20 16 19 31 0 0 0 0 0 0 0 0 0 0 0
25 19 1 21 20 16 19 1 0 0 0 0 0 0 0 0 0 0
26 16 1 22 21 20 16 0 1 0 0 0 0 0 0 0 0 0
27 20 1 17 22 21 20 0 0 1 0 0 0 0 0 0 0 0
28 21 1 24 17 22 21 0 0 0 1 0 0 0 0 0 0 0
29 22 1 25 24 17 22 0 0 0 0 1 0 0 0 0 0 0
30 17 1 26 25 24 17 0 0 0 0 0 1 0 0 0 0 0
31 24 1 25 26 25 24 0 0 0 0 0 0 1 0 0 0 0
32 25 1 17 25 26 25 0 0 0 0 0 0 0 1 0 0 0
33 26 1 32 17 25 26 0 0 0 0 0 0 0 0 1 0 0
34 25 1 33 32 17 25 0 0 0 0 0 0 0 0 0 1 0
35 17 1 13 33 32 17 0 0 0 0 0 0 0 0 0 0 1
36 32 1 32 13 33 32 0 0 0 0 0 0 0 0 0 0 0
37 33 1 25 32 13 33 1 0 0 0 0 0 0 0 0 0 0
38 13 1 29 25 32 13 0 1 0 0 0 0 0 0 0 0 0
39 32 1 22 29 25 32 0 0 1 0 0 0 0 0 0 0 0
40 25 1 18 22 29 25 0 0 0 1 0 0 0 0 0 0 0
41 29 1 17 18 22 29 0 0 0 0 1 0 0 0 0 0 0
42 22 1 20 17 18 22 0 0 0 0 0 1 0 0 0 0 0
43 18 1 15 20 17 18 0 0 0 0 0 0 1 0 0 0 0
44 17 1 20 15 20 17 0 0 0 0 0 0 0 1 0 0 0
45 20 1 33 20 15 20 0 0 0 0 0 0 0 0 1 0 0
46 15 1 29 33 20 15 0 0 0 0 0 0 0 0 0 1 0
47 20 1 23 29 33 20 0 0 0 0 0 0 0 0 0 0 1
48 33 1 26 23 29 33 0 0 0 0 0 0 0 0 0 0 0
49 29 1 18 26 23 29 1 0 0 0 0 0 0 0 0 0 0
50 23 1 20 18 26 23 0 1 0 0 0 0 0 0 0 0 0
51 26 1 11 20 18 26 0 0 1 0 0 0 0 0 0 0 0
52 18 1 28 11 20 18 0 0 0 1 0 0 0 0 0 0 0
53 20 1 26 28 11 20 0 0 0 0 1 0 0 0 0 0 0
54 11 1 22 26 28 11 0 0 0 0 0 1 0 0 0 0 0
55 28 1 17 22 26 28 0 0 0 0 0 0 1 0 0 0 0
56 26 1 12 17 22 26 0 0 0 0 0 0 0 1 0 0 0
57 22 1 14 12 17 22 0 0 0 0 0 0 0 0 1 0 0
58 17 1 17 14 12 17 0 0 0 0 0 0 0 0 0 1 0
59 12 1 21 17 14 12 0 0 0 0 0 0 0 0 0 0 1
60 14 1 19 21 17 14 0 0 0 0 0 0 0 0 0 0 0
61 17 1 18 19 21 17 1 0 0 0 0 0 0 0 0 0 0
62 21 1 10 18 19 21 0 1 0 0 0 0 0 0 0 0 0
63 19 1 29 10 18 19 0 0 1 0 0 0 0 0 0 0 0
64 18 1 31 29 10 18 0 0 0 1 0 0 0 0 0 0 0
65 10 1 19 31 29 10 0 0 0 0 1 0 0 0 0 0 0
66 29 1 9 19 31 29 0 0 0 0 0 1 0 0 0 0 0
67 31 1 20 9 19 31 0 0 0 0 0 0 1 0 0 0 0
68 19 1 28 20 9 19 0 0 0 0 0 0 0 1 0 0 0
69 9 1 19 28 20 9 0 0 0 0 0 0 0 0 1 0 0
70 20 1 30 19 28 20 0 0 0 0 0 0 0 0 0 1 0
71 28 1 29 30 19 28 0 0 0 0 0 0 0 0 0 0 1
72 19 1 26 29 30 19 0 0 0 0 0 0 0 0 0 0 0
73 30 1 23 26 29 30 1 0 0 0 0 0 0 0 0 0 0
74 29 1 13 23 26 29 0 1 0 0 0 0 0 0 0 0 0
75 26 1 21 13 23 26 0 0 1 0 0 0 0 0 0 0 0
76 23 1 19 21 13 23 0 0 0 1 0 0 0 0 0 0 0
77 13 1 28 19 21 13 0 0 0 0 1 0 0 0 0 0 0
78 21 1 23 28 19 21 0 0 0 0 0 1 0 0 0 0 0
79 19 1 18 23 28 19 0 0 0 0 0 0 1 0 0 0 0
80 28 1 21 18 23 28 0 0 0 0 0 0 0 1 0 0 0
81 23 1 20 21 18 23 0 0 0 0 0 0 0 0 1 0 0
82 18 1 23 20 21 18 0 0 0 0 0 0 0 0 0 1 0
83 21 1 21 23 20 21 0 0 0 0 0 0 0 0 0 0 1
84 20 1 21 21 23 20 0 0 0 0 0 0 0 0 0 0 0
85 23 1 15 21 21 23 1 0 0 0 0 0 0 0 0 0 0
86 21 1 28 15 21 21 0 1 0 0 0 0 0 0 0 0 0
87 21 1 19 28 15 21 0 0 1 0 0 0 0 0 0 0 0
88 15 1 26 19 28 15 0 0 0 1 0 0 0 0 0 0 0
89 28 1 10 26 19 28 0 0 0 0 1 0 0 0 0 0 0
90 19 1 16 10 26 19 0 0 0 0 0 1 0 0 0 0 0
91 26 1 22 16 10 26 0 0 0 0 0 0 1 0 0 0 0
92 10 1 19 22 16 10 0 0 0 0 0 0 0 1 0 0 0
93 16 1 31 19 22 16 0 0 0 0 0 0 0 0 1 0 0
94 22 1 31 31 19 22 0 0 0 0 0 0 0 0 0 1 0
95 19 1 29 31 31 19 0 0 0 0 0 0 0 0 0 0 1
96 31 1 19 29 31 31 0 0 0 0 0 0 0 0 0 0 0
97 31 1 22 19 29 31 1 0 0 0 0 0 0 0 0 0 0
98 29 1 23 22 19 29 0 1 0 0 0 0 0 0 0 0 0
99 19 1 15 23 22 19 0 0 1 0 0 0 0 0 0 0 0
100 22 1 20 15 23 22 0 0 0 1 0 0 0 0 0 0 0
101 23 1 18 20 15 23 0 0 0 0 1 0 0 0 0 0 0
102 15 1 23 18 20 15 0 0 0 0 0 1 0 0 0 0 0
103 20 1 25 23 18 20 0 0 0 0 0 0 1 0 0 0 0
104 18 1 21 25 23 18 0 0 0 0 0 0 0 1 0 0 0
105 23 1 24 21 25 23 0 0 0 0 0 0 0 0 1 0 0
106 25 1 25 24 21 25 0 0 0 0 0 0 0 0 0 1 0
107 21 1 17 25 24 21 0 0 0 0 0 0 0 0 0 0 1
108 24 1 13 17 25 24 0 0 0 0 0 0 0 0 0 0 0
109 25 1 28 13 17 25 1 0 0 0 0 0 0 0 0 0 0
110 17 1 21 28 13 17 0 1 0 0 0 0 0 0 0 0 0
111 13 1 25 21 28 13 0 0 1 0 0 0 0 0 0 0 0
112 28 1 9 25 21 28 0 0 0 1 0 0 0 0 0 0 0
113 21 1 16 9 25 21 0 0 0 0 1 0 0 0 0 0 0
114 25 1 19 16 9 25 0 0 0 0 0 1 0 0 0 0 0
115 9 1 17 19 16 9 0 0 0 0 0 0 1 0 0 0 0
116 16 1 25 17 19 16 0 0 0 0 0 0 0 1 0 0 0
117 19 1 20 25 17 19 0 0 0 0 0 0 0 0 1 0 0
118 17 1 29 20 25 17 0 0 0 0 0 0 0 0 0 1 0
119 25 1 14 29 20 25 0 0 0 0 0 0 0 0 0 0 1
120 20 1 22 14 29 20 0 0 0 0 0 0 0 0 0 0 0
121 29 1 15 22 14 29 1 0 0 0 0 0 0 0 0 0 0
122 14 1 19 15 22 14 0 1 0 0 0 0 0 0 0 0 0
123 22 1 20 19 15 22 0 0 1 0 0 0 0 0 0 0 0
124 15 1 15 20 19 15 0 0 0 1 0 0 0 0 0 0 0
125 19 1 20 15 20 19 0 0 0 0 1 0 0 0 0 0 0
126 20 1 18 20 15 20 0 0 0 0 0 1 0 0 0 0 0
127 15 1 33 18 20 15 0 0 0 0 0 0 1 0 0 0 0
128 20 1 22 33 18 20 0 0 0 0 0 0 0 1 0 0 0
129 18 1 16 22 33 18 0 0 0 0 0 0 0 0 1 0 0
130 33 1 17 16 22 33 0 0 0 0 0 0 0 0 0 1 0
131 22 1 16 17 16 22 0 0 0 0 0 0 0 0 0 0 1
132 16 1 21 16 17 16 0 0 0 0 0 0 0 0 0 0 0
133 17 1 26 21 16 17 1 0 0 0 0 0 0 0 0 0 0
134 16 1 18 26 21 16 0 1 0 0 0 0 0 0 0 0 0
135 21 1 18 18 26 21 0 0 1 0 0 0 0 0 0 0 0
136 26 1 17 18 18 26 0 0 0 1 0 0 0 0 0 0 0
137 18 1 22 17 18 18 0 0 0 0 1 0 0 0 0 0 0
138 18 1 30 22 17 18 0 0 0 0 0 1 0 0 0 0 0
139 17 1 30 30 22 17 0 0 0 0 0 0 1 0 0 0 0
140 22 1 24 30 30 22 0 0 0 0 0 0 0 1 0 0 0
141 30 1 21 24 30 30 0 0 0 0 0 0 0 0 1 0 0
142 30 1 21 21 24 30 0 0 0 0 0 0 0 0 0 1 0
143 24 1 29 21 21 24 0 0 0 0 0 0 0 0 0 0 1
144 21 1 31 29 21 21 0 0 0 0 0 0 0 0 0 0 0
145 21 1 20 31 29 21 1 0 0 0 0 0 0 0 0 0 0
146 29 1 16 20 31 29 0 1 0 0 0 0 0 0 0 0 0
147 31 1 22 16 20 31 0 0 1 0 0 0 0 0 0 0 0
148 20 1 20 22 16 20 0 0 0 1 0 0 0 0 0 0 0
149 16 1 28 20 22 16 0 0 0 0 1 0 0 0 0 0 0
150 22 1 38 28 20 22 0 0 0 0 0 1 0 0 0 0 0
151 20 1 22 38 28 20 0 0 0 0 0 0 1 0 0 0 0
152 28 1 20 22 38 28 0 0 0 0 0 0 0 1 0 0 0
153 38 1 17 20 22 38 0 0 0 0 0 0 0 0 1 0 0
154 22 1 28 17 20 22 0 0 0 0 0 0 0 0 0 1 0
155 20 1 22 28 17 20 0 0 0 0 0 0 0 0 0 0 1
156 17 1 31 22 28 17 0 0 0 0 0 0 0 0 0 0 0
t
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 53
54 54
55 55
56 56
57 57
58 58
59 59
60 60
61 61
62 62
63 63
64 64
65 65
66 66
67 67
68 68
69 69
70 70
71 71
72 72
73 73
74 74
75 75
76 76
77 77
78 78
79 79
80 80
81 81
82 82
83 83
84 84
85 85
86 86
87 87
88 88
89 89
90 90
91 91
92 92
93 93
94 94
95 95
96 96
97 97
98 98
99 99
100 100
101 101
102 102
103 103
104 104
105 105
106 106
107 107
108 108
109 109
110 110
111 111
112 112
113 113
114 114
115 115
116 116
117 117
118 118
119 119
120 120
121 121
122 122
123 123
124 124
125 125
126 126
127 127
128 128
129 129
130 130
131 131
132 132
133 133
134 134
135 135
136 136
137 137
138 138
139 139
140 140
141 141
142 142
143 143
144 144
145 145
146 146
147 147
148 148
149 149
150 150
151 151
152 152
153 153
154 154
155 155
156 156
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Month Y1 Y2 Y3 Y4
4.337e-15 4.811e-16 -3.928e-17 -9.275e-19 3.889e-17 1.000e+00
M1 M2 M3 M4 M5 M6
-1.704e-16 -5.545e-16 -9.080e-18 -2.605e-16 -1.294e-16 1.126e-15
M7 M8 M9 M10 M11 t
-8.791e-17 -1.415e-17 -1.306e-17 -6.232e-18 -6.084e-17 5.402e-19
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.447e-15 -1.480e-16 2.775e-17 2.260e-16 1.362e-14
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.337e-15 1.076e-15 4.032e+00 9.1e-05 ***
Month 4.811e-16 6.302e-16 7.630e-01 0.4465
Y1 -3.928e-17 2.097e-17 -1.873e+00 0.0631 .
Y2 -9.275e-19 2.093e-17 -4.400e-02 0.9647
Y3 3.889e-17 2.079e-17 1.871e+00 0.0635 .
Y4 1.000e+00 2.097e-17 4.768e+16 < 2e-16 ***
M1 -1.704e-16 5.567e-16 -3.060e-01 0.7600
M2 -5.545e-16 5.552e-16 -9.990e-01 0.3197
M3 -9.080e-18 5.596e-16 -1.600e-02 0.9871
M4 -2.605e-16 5.602e-16 -4.650e-01 0.6426
M5 -1.294e-16 5.623e-16 -2.300e-01 0.8184
M6 1.126e-15 5.590e-16 2.014e+00 0.0460 *
M7 -8.791e-17 5.535e-16 -1.590e-01 0.8741
M8 -1.415e-17 5.530e-16 -2.600e-02 0.9796
M9 -1.306e-17 5.463e-16 -2.400e-02 0.9810
M10 -6.232e-18 5.558e-16 -1.100e-02 0.9911
M11 -6.084e-17 5.588e-16 -1.090e-01 0.9135
t 5.402e-19 2.615e-18 2.070e-01 0.8366
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.386e-15 on 138 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 1.545e+32 on 17 and 138 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,] 2.068420e-01 4.136839e-01 7.931580e-01
[2,] 2.550921e-02 5.101843e-02 9.744908e-01
[3,] 9.141399e-04 1.828280e-03 9.990859e-01
[4,] 5.511909e-01 8.976182e-01 4.488091e-01
[5,] 2.251218e-01 4.502435e-01 7.748782e-01
[6,] 5.786811e-01 8.426377e-01 4.213189e-01
[7,] 2.979094e-01 5.958188e-01 7.020906e-01
[8,] 9.994141e-01 1.171701e-03 5.858504e-04
[9,] 1.539569e-03 3.079137e-03 9.984604e-01
[10,] 9.965910e-01 6.817953e-03 3.408976e-03
[11,] 7.385640e-01 5.228720e-01 2.614360e-01
[12,] 1.031803e-01 2.063607e-01 8.968197e-01
[13,] 1.242895e-03 2.485790e-03 9.987571e-01
[14,] 1.000000e+00 1.215375e-11 6.076874e-12
[15,] 5.280709e-09 1.056142e-08 1.000000e+00
[16,] 9.999974e-01 5.253709e-06 2.626855e-06
[17,] 9.997670e-01 4.660252e-04 2.330126e-04
[18,] 9.999998e-01 3.611717e-07 1.805859e-07
[19,] 7.959789e-01 4.080422e-01 2.040211e-01
[20,] 2.226553e-13 4.453106e-13 1.000000e+00
[21,] 1.000000e+00 4.142902e-23 2.071451e-23
[22,] 1.000000e+00 2.884030e-09 1.442015e-09
[23,] 9.999921e-01 1.583833e-05 7.919163e-06
[24,] 1.024839e-17 2.049677e-17 1.000000e+00
[25,] 9.917568e-01 1.648635e-02 8.243173e-03
[26,] 6.438618e-01 7.122764e-01 3.561382e-01
[27,] 2.289224e-11 4.578449e-11 1.000000e+00
[28,] 9.819762e-01 3.604770e-02 1.802385e-02
[29,] 1.000000e+00 2.448954e-18 1.224477e-18
[30,] 9.999998e-01 3.670294e-07 1.835147e-07
[31,] 1.884029e-13 3.768058e-13 1.000000e+00
[32,] 6.139792e-01 7.720416e-01 3.860208e-01
[33,] 1.000000e+00 1.618131e-22 8.090657e-23
[34,] 2.270566e-01 4.541132e-01 7.729434e-01
[35,] 4.230396e-04 8.460793e-04 9.995770e-01
[36,] 7.501387e-01 4.997226e-01 2.498613e-01
[37,] 1.000000e+00 8.493660e-19 4.246830e-19
[38,] 1.563908e-04 3.127815e-04 9.998436e-01
[39,] 9.999979e-01 4.162240e-06 2.081120e-06
[40,] 2.925530e-14 5.851060e-14 1.000000e+00
[41,] 9.999423e-01 1.153927e-04 5.769634e-05
[42,] 9.493633e-01 1.012733e-01 5.063667e-02
[43,] 1.000000e+00 2.101392e-17 1.050696e-17
[44,] 4.685053e-13 9.370107e-13 1.000000e+00
[45,] 9.712804e-01 5.743926e-02 2.871963e-02
[46,] 4.696485e-07 9.392969e-07 9.999995e-01
[47,] 1.000000e+00 1.213347e-15 6.066736e-16
[48,] 1.000000e+00 1.657169e-26 8.285846e-27
[49,] 1.000000e+00 1.020825e-13 5.104124e-14
[50,] 1.000000e+00 1.225561e-15 6.127806e-16
[51,] 9.474513e-01 1.050975e-01 5.254874e-02
[52,] 9.828347e-05 1.965669e-04 9.999017e-01
[53,] 9.928960e-01 1.420795e-02 7.103975e-03
[54,] 9.802644e-01 3.947119e-02 1.973559e-02
[55,] 4.848980e-01 9.697961e-01 5.151020e-01
[56,] 1.000000e+00 3.045430e-18 1.522715e-18
[57,] 1.000000e+00 2.846874e-23 1.423437e-23
[58,] 9.505428e-01 9.891440e-02 4.945720e-02
[59,] 9.999984e-01 3.200733e-06 1.600367e-06
[60,] 6.916889e-05 1.383378e-04 9.999308e-01
[61,] 4.735814e-17 9.471629e-17 1.000000e+00
[62,] 3.245750e-06 6.491501e-06 9.999968e-01
[63,] 1.000000e+00 4.065154e-09 2.032577e-09
[64,] 7.069446e-08 1.413889e-07 9.999999e-01
[65,] 5.859856e-02 1.171971e-01 9.414014e-01
[66,] 1.000000e+00 2.099216e-08 1.049608e-08
[67,] 6.857251e-23 1.371450e-22 1.000000e+00
[68,] 9.340629e-19 1.868126e-18 1.000000e+00
[69,] 5.539300e-31 1.107860e-30 1.000000e+00
[70,] 6.659520e-01 6.680959e-01 3.340480e-01
[71,] 9.688105e-01 6.237891e-02 3.118946e-02
[72,] 1.000000e+00 3.349423e-22 1.674712e-22
[73,] 1.000000e+00 1.576632e-16 7.883162e-17
[74,] 6.311646e-08 1.262329e-07 9.999999e-01
[75,] 7.257120e-08 1.451424e-07 9.999999e-01
[76,] 3.822781e-01 7.645561e-01 6.177219e-01
[77,] 3.239233e-06 6.478466e-06 9.999968e-01
[78,] 9.349344e-01 1.301311e-01 6.506557e-02
[79,] 1.000000e+00 7.107004e-10 3.553502e-10
[80,] 3.709829e-10 7.419658e-10 1.000000e+00
[81,] 1.900557e-17 3.801115e-17 1.000000e+00
[82,] 1.000000e+00 2.739863e-14 1.369931e-14
[83,] 9.999687e-01 6.260483e-05 3.130241e-05
[84,] 9.999997e-01 6.739975e-07 3.369988e-07
[85,] 6.932452e-07 1.386490e-06 9.999993e-01
[86,] 2.069456e-02 4.138913e-02 9.793054e-01
[87,] 9.981991e-01 3.601740e-03 1.800870e-03
[88,] 2.057331e-02 4.114663e-02 9.794267e-01
[89,] 2.604342e-12 5.208683e-12 1.000000e+00
[90,] 9.999998e-01 4.335189e-07 2.167595e-07
[91,] 1.000000e+00 5.676286e-10 2.838143e-10
[92,] 6.291002e-01 7.417997e-01 3.708998e-01
[93,] 3.482378e-17 6.964757e-17 1.000000e+00
[94,] 3.995372e-01 7.990745e-01 6.004628e-01
[95,] 2.461189e-15 4.922378e-15 1.000000e+00
[96,] 9.999996e-01 7.933988e-07 3.966994e-07
[97,] 5.796512e-09 1.159302e-08 1.000000e+00
[98,] 9.996077e-01 7.846752e-04 3.923376e-04
[99,] 2.598042e-01 5.196084e-01 7.401958e-01
[100,] 9.999997e-01 6.852755e-07 3.426378e-07
[101,] 1.323094e-08 2.646189e-08 1.000000e+00
[102,] 2.803237e-14 5.606473e-14 1.000000e+00
[103,] 1.439354e-04 2.878708e-04 9.998561e-01
[104,] 8.159814e-01 3.680371e-01 1.840186e-01
[105,] 9.999871e-01 2.575493e-05 1.287747e-05
[106,] 1.261490e-01 2.522980e-01 8.738510e-01
[107,] 3.685228e-01 7.370456e-01 6.314772e-01
[108,] 6.250345e-01 7.499310e-01 3.749655e-01
[109,] 3.436781e-12 6.873563e-12 1.000000e+00
[110,] 9.999836e-01 3.278256e-05 1.639128e-05
[111,] 6.736337e-02 1.347267e-01 9.326366e-01
[112,] 3.719326e-01 7.438653e-01 6.280674e-01
[113,] 4.421568e-01 8.843135e-01 5.578432e-01
[114,] 4.003249e-01 8.006498e-01 5.996751e-01
[115,] 9.945424e-01 1.091521e-02 5.457606e-03
> postscript(file="/var/www/html/freestat/rcomp/tmp/1qj911290854553.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2is841290854553.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3is841290854553.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4is841290854553.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5is841290854553.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 = 156
Frequency = 1
1 2 3 4 5
-2.780941e-15 -5.447144e-15 -2.014480e-15 -1.794321e-15 -1.578966e-15
6 7 8 9 10
1.361585e-14 -2.447897e-17 -8.464471e-17 -2.683076e-17 3.519691e-16
11 12 13 14 15
4.992254e-17 -5.618270e-18 1.403332e-16 5.331436e-16 1.826385e-16
16 17 18 19 20
-8.518582e-16 1.811183e-16 -1.040899e-15 8.689054e-18 3.474960e-16
21 22 23 24 25
-1.134529e-16 -5.508806e-16 -9.717824e-18 9.176333e-16 8.438403e-17
26 27 28 29 30
5.263047e-16 -3.629125e-17 2.260006e-16 1.460143e-16 -8.857552e-16
31 32 33 34 35
2.063476e-16 -1.066991e-17 1.375699e-16 5.156680e-16 -3.886717e-16
36 37 38 39 40
1.281194e-16 1.027959e-15 -3.422184e-16 1.097379e-15 1.049604e-16
41 42 43 44 45
3.434337e-16 -1.214145e-15 1.508907e-17 -3.130777e-16 1.080500e-16
46 47 48 49 50
-1.040815e-16 5.090538e-18 -6.770376e-16 3.680672e-16 4.982673e-16
51 52 53 54 55
1.133187e-16 9.318280e-17 1.690845e-16 -2.128786e-15 4.267599e-16
56 57 58 59 60
1.593113e-16 -1.572200e-16 -1.616477e-16 -1.846208e-16 6.400322e-17
61 62 63 64 65
-6.150118e-17 4.555324e-16 -8.360285e-17 3.881136e-16 5.915772e-16
66 67 68 69 70
-1.069062e-15 -7.506368e-16 6.042933e-17 4.888848e-16 1.274720e-17
71 72 73 74 75
5.460162e-16 -2.557295e-16 3.369581e-16 9.064049e-16 -1.730455e-16
76 77 78 79 80
2.957887e-16 -1.700103e-16 -1.110359e-15 -4.718651e-16 4.586911e-16
81 82 83 84 85
-7.728711e-17 -2.349770e-16 -7.925881e-18 -4.850714e-17 6.831230e-17
86 87 88 89 90
5.453824e-16 -2.183476e-17 2.699555e-16 3.429235e-16 -1.241085e-15
91 92 93 94 95
-1.449175e-16 1.035299e-16 -5.421925e-18 1.314923e-16 6.401132e-19
96 97 98 99 100
-3.553254e-16 6.238127e-16 4.485111e-16 9.844475e-17 1.483695e-16
101 102 103 104 105
1.047312e-16 -8.598339e-16 6.337910e-17 -2.715664e-16 2.681502e-17
106 107 108 109 110
-6.140686e-17 -1.434268e-16 -1.421956e-16 3.204633e-16 4.711254e-16
111 112 113 114 115
5.929140e-16 -6.111743e-17 7.497758e-18 -8.742332e-16 3.395197e-16
116 117 118 119 120
-4.098286e-16 -9.163290e-17 3.827426e-17 -4.307163e-17 7.159921e-17
121 122 123 124 125
-5.186899e-17 5.669213e-16 -1.220094e-16 3.800332e-16 -2.474437e-16
126 127 128 129 130
-1.219502e-15 7.618799e-17 -5.137709e-17 -4.049986e-16 1.132003e-16
131 132 133 134 135
-1.833291e-17 -1.955155e-17 3.668292e-17 2.260048e-16 -2.264240e-16
136 137 138 139 140
6.558263e-16 1.979027e-16 -1.133690e-15 1.981670e-16 -1.409857e-16
141 142 143 144 145
4.119086e-16 7.145217e-17 1.529800e-16 2.869259e-17 -1.126617e-16
146 147 148 149 150
6.117647e-16 5.929932e-16 1.450658e-16 -8.786288e-17 -8.384997e-16
151 152 153 154 155
5.775903e-17 1.526924e-16 -2.963841e-16 -1.218097e-16 4.111824e-17
156
2.939173e-16
> postscript(file="/var/www/html/freestat/rcomp/tmp/6bj771290854553.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.780941e-15 NA
1 -5.447144e-15 -2.780941e-15
2 -2.014480e-15 -5.447144e-15
3 -1.794321e-15 -2.014480e-15
4 -1.578966e-15 -1.794321e-15
5 1.361585e-14 -1.578966e-15
6 -2.447897e-17 1.361585e-14
7 -8.464471e-17 -2.447897e-17
8 -2.683076e-17 -8.464471e-17
9 3.519691e-16 -2.683076e-17
10 4.992254e-17 3.519691e-16
11 -5.618270e-18 4.992254e-17
12 1.403332e-16 -5.618270e-18
13 5.331436e-16 1.403332e-16
14 1.826385e-16 5.331436e-16
15 -8.518582e-16 1.826385e-16
16 1.811183e-16 -8.518582e-16
17 -1.040899e-15 1.811183e-16
18 8.689054e-18 -1.040899e-15
19 3.474960e-16 8.689054e-18
20 -1.134529e-16 3.474960e-16
21 -5.508806e-16 -1.134529e-16
22 -9.717824e-18 -5.508806e-16
23 9.176333e-16 -9.717824e-18
24 8.438403e-17 9.176333e-16
25 5.263047e-16 8.438403e-17
26 -3.629125e-17 5.263047e-16
27 2.260006e-16 -3.629125e-17
28 1.460143e-16 2.260006e-16
29 -8.857552e-16 1.460143e-16
30 2.063476e-16 -8.857552e-16
31 -1.066991e-17 2.063476e-16
32 1.375699e-16 -1.066991e-17
33 5.156680e-16 1.375699e-16
34 -3.886717e-16 5.156680e-16
35 1.281194e-16 -3.886717e-16
36 1.027959e-15 1.281194e-16
37 -3.422184e-16 1.027959e-15
38 1.097379e-15 -3.422184e-16
39 1.049604e-16 1.097379e-15
40 3.434337e-16 1.049604e-16
41 -1.214145e-15 3.434337e-16
42 1.508907e-17 -1.214145e-15
43 -3.130777e-16 1.508907e-17
44 1.080500e-16 -3.130777e-16
45 -1.040815e-16 1.080500e-16
46 5.090538e-18 -1.040815e-16
47 -6.770376e-16 5.090538e-18
48 3.680672e-16 -6.770376e-16
49 4.982673e-16 3.680672e-16
50 1.133187e-16 4.982673e-16
51 9.318280e-17 1.133187e-16
52 1.690845e-16 9.318280e-17
53 -2.128786e-15 1.690845e-16
54 4.267599e-16 -2.128786e-15
55 1.593113e-16 4.267599e-16
56 -1.572200e-16 1.593113e-16
57 -1.616477e-16 -1.572200e-16
58 -1.846208e-16 -1.616477e-16
59 6.400322e-17 -1.846208e-16
60 -6.150118e-17 6.400322e-17
61 4.555324e-16 -6.150118e-17
62 -8.360285e-17 4.555324e-16
63 3.881136e-16 -8.360285e-17
64 5.915772e-16 3.881136e-16
65 -1.069062e-15 5.915772e-16
66 -7.506368e-16 -1.069062e-15
67 6.042933e-17 -7.506368e-16
68 4.888848e-16 6.042933e-17
69 1.274720e-17 4.888848e-16
70 5.460162e-16 1.274720e-17
71 -2.557295e-16 5.460162e-16
72 3.369581e-16 -2.557295e-16
73 9.064049e-16 3.369581e-16
74 -1.730455e-16 9.064049e-16
75 2.957887e-16 -1.730455e-16
76 -1.700103e-16 2.957887e-16
77 -1.110359e-15 -1.700103e-16
78 -4.718651e-16 -1.110359e-15
79 4.586911e-16 -4.718651e-16
80 -7.728711e-17 4.586911e-16
81 -2.349770e-16 -7.728711e-17
82 -7.925881e-18 -2.349770e-16
83 -4.850714e-17 -7.925881e-18
84 6.831230e-17 -4.850714e-17
85 5.453824e-16 6.831230e-17
86 -2.183476e-17 5.453824e-16
87 2.699555e-16 -2.183476e-17
88 3.429235e-16 2.699555e-16
89 -1.241085e-15 3.429235e-16
90 -1.449175e-16 -1.241085e-15
91 1.035299e-16 -1.449175e-16
92 -5.421925e-18 1.035299e-16
93 1.314923e-16 -5.421925e-18
94 6.401132e-19 1.314923e-16
95 -3.553254e-16 6.401132e-19
96 6.238127e-16 -3.553254e-16
97 4.485111e-16 6.238127e-16
98 9.844475e-17 4.485111e-16
99 1.483695e-16 9.844475e-17
100 1.047312e-16 1.483695e-16
101 -8.598339e-16 1.047312e-16
102 6.337910e-17 -8.598339e-16
103 -2.715664e-16 6.337910e-17
104 2.681502e-17 -2.715664e-16
105 -6.140686e-17 2.681502e-17
106 -1.434268e-16 -6.140686e-17
107 -1.421956e-16 -1.434268e-16
108 3.204633e-16 -1.421956e-16
109 4.711254e-16 3.204633e-16
110 5.929140e-16 4.711254e-16
111 -6.111743e-17 5.929140e-16
112 7.497758e-18 -6.111743e-17
113 -8.742332e-16 7.497758e-18
114 3.395197e-16 -8.742332e-16
115 -4.098286e-16 3.395197e-16
116 -9.163290e-17 -4.098286e-16
117 3.827426e-17 -9.163290e-17
118 -4.307163e-17 3.827426e-17
119 7.159921e-17 -4.307163e-17
120 -5.186899e-17 7.159921e-17
121 5.669213e-16 -5.186899e-17
122 -1.220094e-16 5.669213e-16
123 3.800332e-16 -1.220094e-16
124 -2.474437e-16 3.800332e-16
125 -1.219502e-15 -2.474437e-16
126 7.618799e-17 -1.219502e-15
127 -5.137709e-17 7.618799e-17
128 -4.049986e-16 -5.137709e-17
129 1.132003e-16 -4.049986e-16
130 -1.833291e-17 1.132003e-16
131 -1.955155e-17 -1.833291e-17
132 3.668292e-17 -1.955155e-17
133 2.260048e-16 3.668292e-17
134 -2.264240e-16 2.260048e-16
135 6.558263e-16 -2.264240e-16
136 1.979027e-16 6.558263e-16
137 -1.133690e-15 1.979027e-16
138 1.981670e-16 -1.133690e-15
139 -1.409857e-16 1.981670e-16
140 4.119086e-16 -1.409857e-16
141 7.145217e-17 4.119086e-16
142 1.529800e-16 7.145217e-17
143 2.869259e-17 1.529800e-16
144 -1.126617e-16 2.869259e-17
145 6.117647e-16 -1.126617e-16
146 5.929932e-16 6.117647e-16
147 1.450658e-16 5.929932e-16
148 -8.786288e-17 1.450658e-16
149 -8.384997e-16 -8.786288e-17
150 5.775903e-17 -8.384997e-16
151 1.526924e-16 5.775903e-17
152 -2.963841e-16 1.526924e-16
153 -1.218097e-16 -2.963841e-16
154 4.111824e-17 -1.218097e-16
155 2.939173e-16 4.111824e-17
156 NA 2.939173e-16
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5.447144e-15 -2.780941e-15
[2,] -2.014480e-15 -5.447144e-15
[3,] -1.794321e-15 -2.014480e-15
[4,] -1.578966e-15 -1.794321e-15
[5,] 1.361585e-14 -1.578966e-15
[6,] -2.447897e-17 1.361585e-14
[7,] -8.464471e-17 -2.447897e-17
[8,] -2.683076e-17 -8.464471e-17
[9,] 3.519691e-16 -2.683076e-17
[10,] 4.992254e-17 3.519691e-16
[11,] -5.618270e-18 4.992254e-17
[12,] 1.403332e-16 -5.618270e-18
[13,] 5.331436e-16 1.403332e-16
[14,] 1.826385e-16 5.331436e-16
[15,] -8.518582e-16 1.826385e-16
[16,] 1.811183e-16 -8.518582e-16
[17,] -1.040899e-15 1.811183e-16
[18,] 8.689054e-18 -1.040899e-15
[19,] 3.474960e-16 8.689054e-18
[20,] -1.134529e-16 3.474960e-16
[21,] -5.508806e-16 -1.134529e-16
[22,] -9.717824e-18 -5.508806e-16
[23,] 9.176333e-16 -9.717824e-18
[24,] 8.438403e-17 9.176333e-16
[25,] 5.263047e-16 8.438403e-17
[26,] -3.629125e-17 5.263047e-16
[27,] 2.260006e-16 -3.629125e-17
[28,] 1.460143e-16 2.260006e-16
[29,] -8.857552e-16 1.460143e-16
[30,] 2.063476e-16 -8.857552e-16
[31,] -1.066991e-17 2.063476e-16
[32,] 1.375699e-16 -1.066991e-17
[33,] 5.156680e-16 1.375699e-16
[34,] -3.886717e-16 5.156680e-16
[35,] 1.281194e-16 -3.886717e-16
[36,] 1.027959e-15 1.281194e-16
[37,] -3.422184e-16 1.027959e-15
[38,] 1.097379e-15 -3.422184e-16
[39,] 1.049604e-16 1.097379e-15
[40,] 3.434337e-16 1.049604e-16
[41,] -1.214145e-15 3.434337e-16
[42,] 1.508907e-17 -1.214145e-15
[43,] -3.130777e-16 1.508907e-17
[44,] 1.080500e-16 -3.130777e-16
[45,] -1.040815e-16 1.080500e-16
[46,] 5.090538e-18 -1.040815e-16
[47,] -6.770376e-16 5.090538e-18
[48,] 3.680672e-16 -6.770376e-16
[49,] 4.982673e-16 3.680672e-16
[50,] 1.133187e-16 4.982673e-16
[51,] 9.318280e-17 1.133187e-16
[52,] 1.690845e-16 9.318280e-17
[53,] -2.128786e-15 1.690845e-16
[54,] 4.267599e-16 -2.128786e-15
[55,] 1.593113e-16 4.267599e-16
[56,] -1.572200e-16 1.593113e-16
[57,] -1.616477e-16 -1.572200e-16
[58,] -1.846208e-16 -1.616477e-16
[59,] 6.400322e-17 -1.846208e-16
[60,] -6.150118e-17 6.400322e-17
[61,] 4.555324e-16 -6.150118e-17
[62,] -8.360285e-17 4.555324e-16
[63,] 3.881136e-16 -8.360285e-17
[64,] 5.915772e-16 3.881136e-16
[65,] -1.069062e-15 5.915772e-16
[66,] -7.506368e-16 -1.069062e-15
[67,] 6.042933e-17 -7.506368e-16
[68,] 4.888848e-16 6.042933e-17
[69,] 1.274720e-17 4.888848e-16
[70,] 5.460162e-16 1.274720e-17
[71,] -2.557295e-16 5.460162e-16
[72,] 3.369581e-16 -2.557295e-16
[73,] 9.064049e-16 3.369581e-16
[74,] -1.730455e-16 9.064049e-16
[75,] 2.957887e-16 -1.730455e-16
[76,] -1.700103e-16 2.957887e-16
[77,] -1.110359e-15 -1.700103e-16
[78,] -4.718651e-16 -1.110359e-15
[79,] 4.586911e-16 -4.718651e-16
[80,] -7.728711e-17 4.586911e-16
[81,] -2.349770e-16 -7.728711e-17
[82,] -7.925881e-18 -2.349770e-16
[83,] -4.850714e-17 -7.925881e-18
[84,] 6.831230e-17 -4.850714e-17
[85,] 5.453824e-16 6.831230e-17
[86,] -2.183476e-17 5.453824e-16
[87,] 2.699555e-16 -2.183476e-17
[88,] 3.429235e-16 2.699555e-16
[89,] -1.241085e-15 3.429235e-16
[90,] -1.449175e-16 -1.241085e-15
[91,] 1.035299e-16 -1.449175e-16
[92,] -5.421925e-18 1.035299e-16
[93,] 1.314923e-16 -5.421925e-18
[94,] 6.401132e-19 1.314923e-16
[95,] -3.553254e-16 6.401132e-19
[96,] 6.238127e-16 -3.553254e-16
[97,] 4.485111e-16 6.238127e-16
[98,] 9.844475e-17 4.485111e-16
[99,] 1.483695e-16 9.844475e-17
[100,] 1.047312e-16 1.483695e-16
[101,] -8.598339e-16 1.047312e-16
[102,] 6.337910e-17 -8.598339e-16
[103,] -2.715664e-16 6.337910e-17
[104,] 2.681502e-17 -2.715664e-16
[105,] -6.140686e-17 2.681502e-17
[106,] -1.434268e-16 -6.140686e-17
[107,] -1.421956e-16 -1.434268e-16
[108,] 3.204633e-16 -1.421956e-16
[109,] 4.711254e-16 3.204633e-16
[110,] 5.929140e-16 4.711254e-16
[111,] -6.111743e-17 5.929140e-16
[112,] 7.497758e-18 -6.111743e-17
[113,] -8.742332e-16 7.497758e-18
[114,] 3.395197e-16 -8.742332e-16
[115,] -4.098286e-16 3.395197e-16
[116,] -9.163290e-17 -4.098286e-16
[117,] 3.827426e-17 -9.163290e-17
[118,] -4.307163e-17 3.827426e-17
[119,] 7.159921e-17 -4.307163e-17
[120,] -5.186899e-17 7.159921e-17
[121,] 5.669213e-16 -5.186899e-17
[122,] -1.220094e-16 5.669213e-16
[123,] 3.800332e-16 -1.220094e-16
[124,] -2.474437e-16 3.800332e-16
[125,] -1.219502e-15 -2.474437e-16
[126,] 7.618799e-17 -1.219502e-15
[127,] -5.137709e-17 7.618799e-17
[128,] -4.049986e-16 -5.137709e-17
[129,] 1.132003e-16 -4.049986e-16
[130,] -1.833291e-17 1.132003e-16
[131,] -1.955155e-17 -1.833291e-17
[132,] 3.668292e-17 -1.955155e-17
[133,] 2.260048e-16 3.668292e-17
[134,] -2.264240e-16 2.260048e-16
[135,] 6.558263e-16 -2.264240e-16
[136,] 1.979027e-16 6.558263e-16
[137,] -1.133690e-15 1.979027e-16
[138,] 1.981670e-16 -1.133690e-15
[139,] -1.409857e-16 1.981670e-16
[140,] 4.119086e-16 -1.409857e-16
[141,] 7.145217e-17 4.119086e-16
[142,] 1.529800e-16 7.145217e-17
[143,] 2.869259e-17 1.529800e-16
[144,] -1.126617e-16 2.869259e-17
[145,] 6.117647e-16 -1.126617e-16
[146,] 5.929932e-16 6.117647e-16
[147,] 1.450658e-16 5.929932e-16
[148,] -8.786288e-17 1.450658e-16
[149,] -8.384997e-16 -8.786288e-17
[150,] 5.775903e-17 -8.384997e-16
[151,] 1.526924e-16 5.775903e-17
[152,] -2.963841e-16 1.526924e-16
[153,] -1.218097e-16 -2.963841e-16
[154,] 4.111824e-17 -1.218097e-16
[155,] 2.939173e-16 4.111824e-17
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5.447144e-15 -2.780941e-15
2 -2.014480e-15 -5.447144e-15
3 -1.794321e-15 -2.014480e-15
4 -1.578966e-15 -1.794321e-15
5 1.361585e-14 -1.578966e-15
6 -2.447897e-17 1.361585e-14
7 -8.464471e-17 -2.447897e-17
8 -2.683076e-17 -8.464471e-17
9 3.519691e-16 -2.683076e-17
10 4.992254e-17 3.519691e-16
11 -5.618270e-18 4.992254e-17
12 1.403332e-16 -5.618270e-18
13 5.331436e-16 1.403332e-16
14 1.826385e-16 5.331436e-16
15 -8.518582e-16 1.826385e-16
16 1.811183e-16 -8.518582e-16
17 -1.040899e-15 1.811183e-16
18 8.689054e-18 -1.040899e-15
19 3.474960e-16 8.689054e-18
20 -1.134529e-16 3.474960e-16
21 -5.508806e-16 -1.134529e-16
22 -9.717824e-18 -5.508806e-16
23 9.176333e-16 -9.717824e-18
24 8.438403e-17 9.176333e-16
25 5.263047e-16 8.438403e-17
26 -3.629125e-17 5.263047e-16
27 2.260006e-16 -3.629125e-17
28 1.460143e-16 2.260006e-16
29 -8.857552e-16 1.460143e-16
30 2.063476e-16 -8.857552e-16
31 -1.066991e-17 2.063476e-16
32 1.375699e-16 -1.066991e-17
33 5.156680e-16 1.375699e-16
34 -3.886717e-16 5.156680e-16
35 1.281194e-16 -3.886717e-16
36 1.027959e-15 1.281194e-16
37 -3.422184e-16 1.027959e-15
38 1.097379e-15 -3.422184e-16
39 1.049604e-16 1.097379e-15
40 3.434337e-16 1.049604e-16
41 -1.214145e-15 3.434337e-16
42 1.508907e-17 -1.214145e-15
43 -3.130777e-16 1.508907e-17
44 1.080500e-16 -3.130777e-16
45 -1.040815e-16 1.080500e-16
46 5.090538e-18 -1.040815e-16
47 -6.770376e-16 5.090538e-18
48 3.680672e-16 -6.770376e-16
49 4.982673e-16 3.680672e-16
50 1.133187e-16 4.982673e-16
51 9.318280e-17 1.133187e-16
52 1.690845e-16 9.318280e-17
53 -2.128786e-15 1.690845e-16
54 4.267599e-16 -2.128786e-15
55 1.593113e-16 4.267599e-16
56 -1.572200e-16 1.593113e-16
57 -1.616477e-16 -1.572200e-16
58 -1.846208e-16 -1.616477e-16
59 6.400322e-17 -1.846208e-16
60 -6.150118e-17 6.400322e-17
61 4.555324e-16 -6.150118e-17
62 -8.360285e-17 4.555324e-16
63 3.881136e-16 -8.360285e-17
64 5.915772e-16 3.881136e-16
65 -1.069062e-15 5.915772e-16
66 -7.506368e-16 -1.069062e-15
67 6.042933e-17 -7.506368e-16
68 4.888848e-16 6.042933e-17
69 1.274720e-17 4.888848e-16
70 5.460162e-16 1.274720e-17
71 -2.557295e-16 5.460162e-16
72 3.369581e-16 -2.557295e-16
73 9.064049e-16 3.369581e-16
74 -1.730455e-16 9.064049e-16
75 2.957887e-16 -1.730455e-16
76 -1.700103e-16 2.957887e-16
77 -1.110359e-15 -1.700103e-16
78 -4.718651e-16 -1.110359e-15
79 4.586911e-16 -4.718651e-16
80 -7.728711e-17 4.586911e-16
81 -2.349770e-16 -7.728711e-17
82 -7.925881e-18 -2.349770e-16
83 -4.850714e-17 -7.925881e-18
84 6.831230e-17 -4.850714e-17
85 5.453824e-16 6.831230e-17
86 -2.183476e-17 5.453824e-16
87 2.699555e-16 -2.183476e-17
88 3.429235e-16 2.699555e-16
89 -1.241085e-15 3.429235e-16
90 -1.449175e-16 -1.241085e-15
91 1.035299e-16 -1.449175e-16
92 -5.421925e-18 1.035299e-16
93 1.314923e-16 -5.421925e-18
94 6.401132e-19 1.314923e-16
95 -3.553254e-16 6.401132e-19
96 6.238127e-16 -3.553254e-16
97 4.485111e-16 6.238127e-16
98 9.844475e-17 4.485111e-16
99 1.483695e-16 9.844475e-17
100 1.047312e-16 1.483695e-16
101 -8.598339e-16 1.047312e-16
102 6.337910e-17 -8.598339e-16
103 -2.715664e-16 6.337910e-17
104 2.681502e-17 -2.715664e-16
105 -6.140686e-17 2.681502e-17
106 -1.434268e-16 -6.140686e-17
107 -1.421956e-16 -1.434268e-16
108 3.204633e-16 -1.421956e-16
109 4.711254e-16 3.204633e-16
110 5.929140e-16 4.711254e-16
111 -6.111743e-17 5.929140e-16
112 7.497758e-18 -6.111743e-17
113 -8.742332e-16 7.497758e-18
114 3.395197e-16 -8.742332e-16
115 -4.098286e-16 3.395197e-16
116 -9.163290e-17 -4.098286e-16
117 3.827426e-17 -9.163290e-17
118 -4.307163e-17 3.827426e-17
119 7.159921e-17 -4.307163e-17
120 -5.186899e-17 7.159921e-17
121 5.669213e-16 -5.186899e-17
122 -1.220094e-16 5.669213e-16
123 3.800332e-16 -1.220094e-16
124 -2.474437e-16 3.800332e-16
125 -1.219502e-15 -2.474437e-16
126 7.618799e-17 -1.219502e-15
127 -5.137709e-17 7.618799e-17
128 -4.049986e-16 -5.137709e-17
129 1.132003e-16 -4.049986e-16
130 -1.833291e-17 1.132003e-16
131 -1.955155e-17 -1.833291e-17
132 3.668292e-17 -1.955155e-17
133 2.260048e-16 3.668292e-17
134 -2.264240e-16 2.260048e-16
135 6.558263e-16 -2.264240e-16
136 1.979027e-16 6.558263e-16
137 -1.133690e-15 1.979027e-16
138 1.981670e-16 -1.133690e-15
139 -1.409857e-16 1.981670e-16
140 4.119086e-16 -1.409857e-16
141 7.145217e-17 4.119086e-16
142 1.529800e-16 7.145217e-17
143 2.869259e-17 1.529800e-16
144 -1.126617e-16 2.869259e-17
145 6.117647e-16 -1.126617e-16
146 5.929932e-16 6.117647e-16
147 1.450658e-16 5.929932e-16
148 -8.786288e-17 1.450658e-16
149 -8.384997e-16 -8.786288e-17
150 5.775903e-17 -8.384997e-16
151 1.526924e-16 5.775903e-17
152 -2.963841e-16 1.526924e-16
153 -1.218097e-16 -2.963841e-16
154 4.111824e-17 -1.218097e-16
155 2.939173e-16 4.111824e-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/freestat/rcomp/tmp/7mt791290854553.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8mt791290854553.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9f26c1290854553.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10f26c1290854553.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11i2mi1290854553.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/freestat/rcomp/tmp/12m33o1290854553.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/freestat/rcomp/tmp/13am001290854553.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/freestat/rcomp/tmp/14lvh31290854553.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/freestat/rcomp/tmp/156eyr1290854553.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/freestat/rcomp/tmp/16awwe1290854553.tab")
+ }
>
> try(system("convert tmp/1qj911290854553.ps tmp/1qj911290854553.png",intern=TRUE))
character(0)
> try(system("convert tmp/2is841290854553.ps tmp/2is841290854553.png",intern=TRUE))
character(0)
> try(system("convert tmp/3is841290854553.ps tmp/3is841290854553.png",intern=TRUE))
character(0)
> try(system("convert tmp/4is841290854553.ps tmp/4is841290854553.png",intern=TRUE))
character(0)
> try(system("convert tmp/5is841290854553.ps tmp/5is841290854553.png",intern=TRUE))
character(0)
> try(system("convert tmp/6bj771290854553.ps tmp/6bj771290854553.png",intern=TRUE))
character(0)
> try(system("convert tmp/7mt791290854553.ps tmp/7mt791290854553.png",intern=TRUE))
character(0)
> try(system("convert tmp/8mt791290854553.ps tmp/8mt791290854553.png",intern=TRUE))
character(0)
> try(system("convert tmp/9f26c1290854553.ps tmp/9f26c1290854553.png",intern=TRUE))
character(0)
> try(system("convert tmp/10f26c1290854553.ps tmp/10f26c1290854553.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.786 2.616 6.164