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(9
+ ,13
+ ,13
+ ,14
+ ,13
+ ,3
+ ,1
+ ,1
+ ,0
+ ,9
+ ,12
+ ,12
+ ,8
+ ,13
+ ,5
+ ,1
+ ,0
+ ,0
+ ,9
+ ,15
+ ,10
+ ,12
+ ,16
+ ,6
+ ,0
+ ,0
+ ,0
+ ,9
+ ,12
+ ,9
+ ,7
+ ,12
+ ,6
+ ,2
+ ,0
+ ,1
+ ,9
+ ,10
+ ,10
+ ,10
+ ,11
+ ,5
+ ,0
+ ,1
+ ,2
+ ,9
+ ,12
+ ,12
+ ,7
+ ,12
+ ,3
+ ,0
+ ,0
+ ,1
+ ,9
+ ,15
+ ,13
+ ,16
+ ,18
+ ,8
+ ,1
+ ,1
+ ,1
+ ,9
+ ,9
+ ,12
+ ,11
+ ,11
+ ,4
+ ,1
+ ,0
+ ,0
+ ,9
+ ,12
+ ,12
+ ,14
+ ,14
+ ,4
+ ,4
+ ,0
+ ,0
+ ,9
+ ,11
+ ,6
+ ,6
+ ,9
+ ,4
+ ,0
+ ,0
+ ,0
+ ,9
+ ,11
+ ,5
+ ,16
+ ,14
+ ,6
+ ,0
+ ,2
+ ,1
+ ,9
+ ,11
+ ,12
+ ,11
+ ,12
+ ,6
+ ,2
+ ,0
+ ,0
+ ,9
+ ,15
+ ,11
+ ,16
+ ,11
+ ,5
+ ,0
+ ,2
+ ,2
+ ,9
+ ,7
+ ,14
+ ,12
+ ,12
+ ,4
+ ,1
+ ,1
+ ,1
+ ,9
+ ,11
+ ,14
+ ,7
+ ,13
+ ,6
+ ,0
+ ,1
+ ,0
+ ,9
+ ,11
+ ,12
+ ,13
+ ,11
+ ,4
+ ,0
+ ,0
+ ,1
+ ,9
+ ,10
+ ,12
+ ,11
+ ,12
+ ,6
+ ,1
+ ,1
+ ,0
+ ,9
+ ,14
+ ,11
+ ,15
+ ,16
+ ,6
+ ,2
+ ,0
+ ,1
+ ,9
+ ,10
+ ,11
+ ,7
+ ,9
+ ,4
+ ,1
+ ,0
+ ,0
+ ,9
+ ,6
+ ,7
+ ,9
+ ,11
+ ,4
+ ,1
+ ,0
+ ,0
+ ,9
+ ,11
+ ,9
+ ,7
+ ,13
+ ,2
+ ,0
+ ,1
+ ,1
+ ,9
+ ,15
+ ,11
+ ,14
+ ,15
+ ,7
+ ,1
+ ,2
+ ,0
+ ,9
+ ,11
+ ,11
+ ,15
+ ,10
+ ,5
+ ,1
+ ,2
+ ,1
+ ,9
+ ,12
+ ,12
+ ,7
+ ,11
+ ,4
+ ,2
+ ,0
+ ,0
+ ,9
+ ,14
+ ,12
+ ,15
+ ,13
+ ,6
+ ,1
+ ,0
+ ,0
+ ,9
+ ,15
+ ,11
+ ,17
+ ,16
+ ,6
+ ,1
+ ,1
+ ,0
+ ,9
+ ,9
+ ,11
+ ,15
+ ,15
+ ,7
+ ,1
+ ,1
+ ,0
+ ,9
+ ,13
+ ,8
+ ,14
+ ,14
+ ,5
+ ,2
+ ,2
+ ,0
+ ,9
+ ,13
+ ,9
+ ,14
+ ,14
+ ,6
+ ,0
+ ,0
+ ,2
+ ,9
+ ,16
+ ,12
+ ,8
+ ,14
+ ,4
+ ,1
+ ,1
+ ,1
+ ,9
+ ,13
+ ,10
+ ,8
+ ,8
+ ,4
+ ,0
+ ,1
+ ,2
+ ,9
+ ,12
+ ,10
+ ,14
+ ,13
+ ,7
+ ,1
+ ,1
+ ,1
+ ,9
+ ,14
+ ,12
+ ,14
+ ,15
+ ,7
+ ,1
+ ,2
+ ,1
+ ,9
+ ,11
+ ,8
+ ,8
+ ,13
+ ,4
+ ,0
+ ,2
+ ,0
+ ,9
+ ,9
+ ,12
+ ,11
+ ,11
+ ,4
+ ,1
+ ,1
+ ,0
+ ,9
+ ,16
+ ,11
+ ,16
+ ,15
+ ,6
+ ,2
+ ,2
+ ,0
+ ,9
+ ,12
+ ,12
+ ,10
+ ,15
+ ,6
+ ,1
+ ,1
+ ,1
+ ,9
+ ,10
+ ,7
+ ,8
+ ,9
+ ,5
+ ,1
+ ,1
+ ,2
+ ,9
+ ,13
+ ,11
+ ,14
+ ,13
+ ,6
+ ,1
+ ,0
+ ,1
+ ,9
+ ,16
+ ,11
+ ,16
+ ,16
+ ,7
+ ,1
+ ,3
+ ,1
+ ,9
+ ,14
+ ,12
+ ,13
+ ,13
+ ,6
+ ,0
+ ,1
+ ,2
+ ,9
+ ,15
+ ,9
+ ,5
+ ,11
+ ,3
+ ,1
+ ,0
+ ,0
+ ,9
+ ,5
+ ,15
+ ,8
+ ,12
+ ,3
+ ,1
+ ,0
+ ,0
+ ,9
+ ,8
+ ,11
+ ,10
+ ,12
+ ,4
+ ,1
+ ,0
+ ,0
+ ,9
+ ,11
+ ,11
+ ,8
+ ,12
+ ,6
+ ,0
+ ,1
+ ,1
+ ,9
+ ,16
+ ,11
+ ,13
+ ,14
+ ,7
+ ,2
+ ,0
+ ,1
+ ,9
+ ,17
+ ,11
+ ,15
+ ,14
+ ,5
+ ,1
+ ,4
+ ,4
+ ,9
+ ,9
+ ,15
+ ,6
+ ,8
+ ,4
+ ,0
+ ,0
+ ,0
+ ,9
+ ,9
+ ,11
+ ,12
+ ,13
+ ,5
+ ,0
+ ,0
+ ,0
+ ,9
+ ,13
+ ,12
+ ,16
+ ,16
+ ,6
+ ,1
+ ,0
+ ,1
+ ,9
+ ,10
+ ,12
+ ,5
+ ,13
+ ,6
+ ,1
+ ,1
+ ,0
+ ,9
+ ,6
+ ,9
+ ,15
+ ,11
+ ,6
+ ,0
+ ,2
+ ,1
+ ,9
+ ,12
+ ,12
+ ,12
+ ,14
+ ,5
+ ,0
+ ,1
+ ,0
+ ,9
+ ,8
+ ,12
+ ,8
+ ,13
+ ,4
+ ,0
+ ,1
+ ,1
+ ,9
+ ,14
+ ,13
+ ,13
+ ,13
+ ,5
+ ,0
+ ,0
+ ,0
+ ,9
+ ,12
+ ,11
+ ,14
+ ,13
+ ,5
+ ,1
+ ,2
+ ,2
+ ,10
+ ,11
+ ,9
+ ,12
+ ,12
+ ,4
+ ,0
+ ,0
+ ,2
+ ,10
+ ,16
+ ,9
+ ,16
+ ,16
+ ,6
+ ,0
+ ,3
+ ,1
+ ,10
+ ,8
+ ,11
+ ,10
+ ,15
+ ,2
+ ,1
+ ,2
+ ,0
+ ,10
+ ,15
+ ,11
+ ,15
+ ,15
+ ,8
+ ,0
+ ,0
+ ,0
+ ,10
+ ,7
+ ,12
+ ,8
+ ,12
+ ,3
+ ,0
+ ,0
+ ,0
+ ,10
+ ,16
+ ,12
+ ,16
+ ,14
+ ,6
+ ,2
+ ,2
+ ,0
+ ,10
+ ,14
+ ,9
+ ,19
+ ,12
+ ,6
+ ,0
+ ,1
+ ,0
+ ,10
+ ,16
+ ,11
+ ,14
+ ,15
+ ,6
+ ,0
+ ,0
+ ,1
+ ,10
+ ,9
+ ,9
+ ,6
+ ,12
+ ,5
+ ,1
+ ,2
+ ,1
+ ,10
+ ,14
+ ,12
+ ,13
+ ,13
+ ,5
+ ,2
+ ,0
+ ,0
+ ,10
+ ,11
+ ,12
+ ,15
+ ,12
+ ,6
+ ,3
+ ,1
+ ,0
+ ,10
+ ,13
+ ,12
+ ,7
+ ,12
+ ,5
+ ,1
+ ,0
+ ,0
+ ,10
+ ,15
+ ,12
+ ,13
+ ,13
+ ,6
+ ,1
+ ,2
+ ,1
+ ,10
+ ,5
+ ,14
+ ,4
+ ,5
+ ,2
+ ,2
+ ,0
+ ,0
+ ,10
+ ,15
+ ,11
+ ,14
+ ,13
+ ,5
+ ,1
+ ,2
+ ,2
+ ,10
+ ,13
+ ,12
+ ,13
+ ,13
+ ,5
+ ,1
+ ,3
+ ,0
+ ,10
+ ,11
+ ,11
+ ,11
+ ,14
+ ,5
+ ,2
+ ,0
+ ,2
+ ,10
+ ,11
+ ,6
+ ,14
+ ,17
+ ,6
+ ,1
+ ,2
+ ,1
+ ,10
+ ,12
+ ,10
+ ,12
+ ,13
+ ,6
+ ,0
+ ,3
+ ,1
+ ,10
+ ,12
+ ,12
+ ,15
+ ,13
+ ,6
+ ,1
+ ,1
+ ,1
+ ,10
+ ,12
+ ,13
+ ,14
+ ,12
+ ,5
+ ,1
+ ,0
+ ,2
+ ,10
+ ,12
+ ,8
+ ,13
+ ,13
+ ,5
+ ,0
+ ,1
+ ,2
+ ,10
+ ,14
+ ,12
+ ,8
+ ,14
+ ,4
+ ,2
+ ,0
+ ,0
+ ,10
+ ,6
+ ,12
+ ,6
+ ,11
+ ,2
+ ,1
+ ,0
+ ,0
+ ,10
+ ,7
+ ,12
+ ,7
+ ,12
+ ,4
+ ,0
+ ,1
+ ,0
+ ,10
+ ,14
+ ,6
+ ,13
+ ,12
+ ,6
+ ,3
+ ,1
+ ,1
+ ,10
+ ,14
+ ,11
+ ,13
+ ,16
+ ,6
+ ,1
+ ,2
+ ,1
+ ,10
+ ,10
+ ,10
+ ,11
+ ,12
+ ,5
+ ,1
+ ,1
+ ,0
+ ,10
+ ,13
+ ,12
+ ,5
+ ,12
+ ,3
+ ,3
+ ,0
+ ,0
+ ,10
+ ,12
+ ,13
+ ,12
+ ,12
+ ,6
+ ,2
+ ,0
+ ,0
+ ,10
+ ,9
+ ,11
+ ,8
+ ,10
+ ,4
+ ,1
+ ,1
+ ,0
+ ,10
+ ,12
+ ,7
+ ,11
+ ,15
+ ,5
+ ,0
+ ,0
+ ,2
+ ,10
+ ,16
+ ,11
+ ,14
+ ,15
+ ,8
+ ,1
+ ,0
+ ,1
+ ,10
+ ,10
+ ,11
+ ,9
+ ,12
+ ,4
+ ,2
+ ,0
+ ,1
+ ,10
+ ,14
+ ,11
+ ,10
+ ,16
+ ,6
+ ,1
+ ,1
+ ,0
+ ,10
+ ,10
+ ,11
+ ,13
+ ,15
+ ,6
+ ,1
+ ,1
+ ,1
+ ,10
+ ,16
+ ,12
+ ,16
+ ,16
+ ,7
+ ,0
+ ,3
+ ,1
+ ,10
+ ,15
+ ,10
+ ,16
+ ,13
+ ,6
+ ,2
+ ,1
+ ,0
+ ,10
+ ,12
+ ,11
+ ,11
+ ,12
+ ,5
+ ,1
+ ,1
+ ,1
+ ,10
+ ,10
+ ,12
+ ,8
+ ,11
+ ,4
+ ,0
+ ,0
+ ,0
+ ,10
+ ,8
+ ,7
+ ,4
+ ,13
+ ,6
+ ,0
+ ,0
+ ,1
+ ,10
+ ,8
+ ,13
+ ,7
+ ,10
+ ,3
+ ,1
+ ,1
+ ,0
+ ,10
+ ,11
+ ,8
+ ,14
+ ,15
+ ,5
+ ,1
+ ,1
+ ,0
+ ,10
+ ,13
+ ,12
+ ,11
+ ,13
+ ,6
+ ,1
+ ,0
+ ,2
+ ,10
+ ,16
+ ,11
+ ,17
+ ,16
+ ,7
+ ,1
+ ,1
+ ,2
+ ,10
+ ,16
+ ,12
+ ,15
+ ,15
+ ,7
+ ,1
+ ,1
+ ,2
+ ,10
+ ,14
+ ,14
+ ,17
+ ,18
+ ,6
+ ,0
+ ,0
+ ,1
+ ,10
+ ,11
+ ,10
+ ,5
+ ,13
+ ,3
+ ,0
+ ,1
+ ,1
+ ,10
+ ,4
+ ,10
+ ,4
+ ,10
+ ,2
+ ,1
+ ,0
+ ,1
+ ,10
+ ,14
+ ,13
+ ,10
+ ,16
+ ,8
+ ,2
+ ,1
+ ,0
+ ,10
+ ,9
+ ,10
+ ,11
+ ,13
+ ,3
+ ,1
+ ,1
+ ,1
+ ,10
+ ,14
+ ,11
+ ,15
+ ,15
+ ,8
+ ,1
+ ,1
+ ,1
+ ,10
+ ,8
+ ,10
+ ,10
+ ,14
+ ,3
+ ,0
+ ,1
+ ,0
+ ,10
+ ,8
+ ,7
+ ,9
+ ,15
+ ,4
+ ,0
+ ,1
+ ,0
+ ,10
+ ,11
+ ,10
+ ,12
+ ,14
+ ,5
+ ,1
+ ,0
+ ,0
+ ,10
+ ,12
+ ,8
+ ,15
+ ,13
+ ,7
+ ,1
+ ,0
+ ,0
+ ,10
+ ,11
+ ,12
+ ,7
+ ,13
+ ,6
+ ,0
+ ,0
+ ,0
+ ,10
+ ,14
+ ,12
+ ,13
+ ,15
+ ,6
+ ,0
+ ,1
+ ,0
+ ,10
+ ,15
+ ,12
+ ,12
+ ,16
+ ,7
+ ,2
+ ,1
+ ,0
+ ,10
+ ,16
+ ,11
+ ,14
+ ,14
+ ,6
+ ,2
+ ,1
+ ,0
+ ,10
+ ,16
+ ,12
+ ,14
+ ,14
+ ,6
+ ,0
+ ,0
+ ,0
+ ,10
+ ,11
+ ,12
+ ,8
+ ,16
+ ,6
+ ,1
+ ,1
+ ,0
+ ,10
+ ,14
+ ,12
+ ,15
+ ,14
+ ,6
+ ,0
+ ,4
+ ,1
+ ,10
+ ,14
+ ,11
+ ,12
+ ,12
+ ,4
+ ,2
+ ,0
+ ,0
+ ,10
+ ,12
+ ,12
+ ,12
+ ,13
+ ,4
+ ,1
+ ,1
+ ,1
+ ,10
+ ,14
+ ,11
+ ,16
+ ,12
+ ,5
+ ,0
+ ,0
+ ,3
+ ,10
+ ,8
+ ,11
+ ,9
+ ,12
+ ,4
+ ,1
+ ,2
+ ,2
+ ,10
+ ,13
+ ,13
+ ,15
+ ,14
+ ,6
+ ,1
+ ,1
+ ,2
+ ,10
+ ,16
+ ,12
+ ,15
+ ,14
+ ,6
+ ,2
+ ,0
+ ,2
+ ,10
+ ,12
+ ,12
+ ,6
+ ,14
+ ,5
+ ,0
+ ,0
+ ,0
+ ,10
+ ,16
+ ,12
+ ,14
+ ,16
+ ,8
+ ,2
+ ,0
+ ,1
+ ,10
+ ,12
+ ,12
+ ,15
+ ,13
+ ,6
+ ,0
+ ,0
+ ,0
+ ,10
+ ,11
+ ,8
+ ,10
+ ,14
+ ,5
+ ,1
+ ,1
+ ,0
+ ,10
+ ,4
+ ,8
+ ,6
+ ,4
+ ,4
+ ,0
+ ,0
+ ,0
+ ,10
+ ,16
+ ,12
+ ,14
+ ,16
+ ,8
+ ,3
+ ,2
+ ,1
+ ,10
+ ,15
+ ,11
+ ,12
+ ,13
+ ,6
+ ,1
+ ,0
+ ,2
+ ,10
+ ,10
+ ,12
+ ,8
+ ,16
+ ,4
+ ,0
+ ,1
+ ,0
+ ,10
+ ,13
+ ,13
+ ,11
+ ,15
+ ,6
+ ,0
+ ,2
+ ,4
+ ,10
+ ,15
+ ,12
+ ,13
+ ,14
+ ,6
+ ,0
+ ,2
+ ,0
+ ,10
+ ,12
+ ,12
+ ,9
+ ,13
+ ,4
+ ,0
+ ,1
+ ,0
+ ,10
+ ,14
+ ,11
+ ,15
+ ,14
+ ,6
+ ,0
+ ,3
+ ,0
+ ,10
+ ,7
+ ,12
+ ,13
+ ,12
+ ,3
+ ,1
+ ,0
+ ,0
+ ,10
+ ,19
+ ,12
+ ,15
+ ,15
+ ,6
+ ,1
+ ,1
+ ,0
+ ,10
+ ,12
+ ,10
+ ,14
+ ,14
+ ,5
+ ,2
+ ,1
+ ,1
+ ,10
+ ,12
+ ,11
+ ,16
+ ,13
+ ,4
+ ,1
+ ,0
+ ,0
+ ,10
+ ,13
+ ,12
+ ,14
+ ,14
+ ,6
+ ,0
+ ,1
+ ,1
+ ,10
+ ,15
+ ,12
+ ,14
+ ,16
+ ,4
+ ,0
+ ,0
+ ,0
+ ,10
+ ,8
+ ,10
+ ,10
+ ,6
+ ,4
+ ,2
+ ,1
+ ,2
+ ,10
+ ,12
+ ,12
+ ,10
+ ,13
+ ,4
+ ,1
+ ,0
+ ,1
+ ,10
+ ,10
+ ,13
+ ,4
+ ,13
+ ,6
+ ,0
+ ,1
+ ,0
+ ,10
+ ,8
+ ,12
+ ,8
+ ,14
+ ,5
+ ,1
+ ,0
+ ,0
+ ,10
+ ,10
+ ,15
+ ,15
+ ,15
+ ,6
+ ,2
+ ,2
+ ,0
+ ,10
+ ,15
+ ,11
+ ,16
+ ,14
+ ,6
+ ,2
+ ,0
+ ,1
+ ,10
+ ,16
+ ,12
+ ,12
+ ,15
+ ,8
+ ,0
+ ,0
+ ,0
+ ,10
+ ,13
+ ,11
+ ,12
+ ,13
+ ,7
+ ,1
+ ,1
+ ,1
+ ,10
+ ,16
+ ,12
+ ,15
+ ,16
+ ,7
+ ,2
+ ,1
+ ,0
+ ,10
+ ,9
+ ,11
+ ,9
+ ,12
+ ,4
+ ,0
+ ,0
+ ,0
+ ,10
+ ,14
+ ,10
+ ,12
+ ,15
+ ,6
+ ,1
+ ,0
+ ,1
+ ,10
+ ,14
+ ,11
+ ,14
+ ,12
+ ,6
+ ,2
+ ,1
+ ,2
+ ,10
+ ,12
+ ,11
+ ,11
+ ,14
+ ,2
+ ,1
+ ,1
+ ,0)
+ ,dim=c(9
+ ,156)
+ ,dimnames=list(c('month'
+ ,'Popularity'
+ ,'FindingFriends'
+ ,'KnowingPeople'
+ ,'Liked'
+ ,'Celebrity'
+ ,'bestfriend'
+ ,'secondbestfriend'
+ ,'thirdbestfriend')
+ ,1:156))
> y <- array(NA,dim=c(9,156),dimnames=list(c('month','Popularity','FindingFriends','KnowingPeople','Liked','Celebrity','bestfriend','secondbestfriend','thirdbestfriend'),1:156))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> ylab = ''
> xlab = ''
> main = ''
> #'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
Popularity month FindingFriends KnowingPeople Liked Celebrity bestfriend
1 13 9 13 14 13 3 1
2 12 9 12 8 13 5 1
3 15 9 10 12 16 6 0
4 12 9 9 7 12 6 2
5 10 9 10 10 11 5 0
6 12 9 12 7 12 3 0
7 15 9 13 16 18 8 1
8 9 9 12 11 11 4 1
9 12 9 12 14 14 4 4
10 11 9 6 6 9 4 0
11 11 9 5 16 14 6 0
12 11 9 12 11 12 6 2
13 15 9 11 16 11 5 0
14 7 9 14 12 12 4 1
15 11 9 14 7 13 6 0
16 11 9 12 13 11 4 0
17 10 9 12 11 12 6 1
18 14 9 11 15 16 6 2
19 10 9 11 7 9 4 1
20 6 9 7 9 11 4 1
21 11 9 9 7 13 2 0
22 15 9 11 14 15 7 1
23 11 9 11 15 10 5 1
24 12 9 12 7 11 4 2
25 14 9 12 15 13 6 1
26 15 9 11 17 16 6 1
27 9 9 11 15 15 7 1
28 13 9 8 14 14 5 2
29 13 9 9 14 14 6 0
30 16 9 12 8 14 4 1
31 13 9 10 8 8 4 0
32 12 9 10 14 13 7 1
33 14 9 12 14 15 7 1
34 11 9 8 8 13 4 0
35 9 9 12 11 11 4 1
36 16 9 11 16 15 6 2
37 12 9 12 10 15 6 1
38 10 9 7 8 9 5 1
39 13 9 11 14 13 6 1
40 16 9 11 16 16 7 1
41 14 9 12 13 13 6 0
42 15 9 9 5 11 3 1
43 5 9 15 8 12 3 1
44 8 9 11 10 12 4 1
45 11 9 11 8 12 6 0
46 16 9 11 13 14 7 2
47 17 9 11 15 14 5 1
48 9 9 15 6 8 4 0
49 9 9 11 12 13 5 0
50 13 9 12 16 16 6 1
51 10 9 12 5 13 6 1
52 6 9 9 15 11 6 0
53 12 9 12 12 14 5 0
54 8 9 12 8 13 4 0
55 14 9 13 13 13 5 0
56 12 9 11 14 13 5 1
57 11 10 9 12 12 4 0
58 16 10 9 16 16 6 0
59 8 10 11 10 15 2 1
60 15 10 11 15 15 8 0
61 7 10 12 8 12 3 0
62 16 10 12 16 14 6 2
63 14 10 9 19 12 6 0
64 16 10 11 14 15 6 0
65 9 10 9 6 12 5 1
66 14 10 12 13 13 5 2
67 11 10 12 15 12 6 3
68 13 10 12 7 12 5 1
69 15 10 12 13 13 6 1
70 5 10 14 4 5 2 2
71 15 10 11 14 13 5 1
72 13 10 12 13 13 5 1
73 11 10 11 11 14 5 2
74 11 10 6 14 17 6 1
75 12 10 10 12 13 6 0
76 12 10 12 15 13 6 1
77 12 10 13 14 12 5 1
78 12 10 8 13 13 5 0
79 14 10 12 8 14 4 2
80 6 10 12 6 11 2 1
81 7 10 12 7 12 4 0
82 14 10 6 13 12 6 3
83 14 10 11 13 16 6 1
84 10 10 10 11 12 5 1
85 13 10 12 5 12 3 3
86 12 10 13 12 12 6 2
87 9 10 11 8 10 4 1
88 12 10 7 11 15 5 0
89 16 10 11 14 15 8 1
90 10 10 11 9 12 4 2
91 14 10 11 10 16 6 1
92 10 10 11 13 15 6 1
93 16 10 12 16 16 7 0
94 15 10 10 16 13 6 2
95 12 10 11 11 12 5 1
96 10 10 12 8 11 4 0
97 8 10 7 4 13 6 0
98 8 10 13 7 10 3 1
99 11 10 8 14 15 5 1
100 13 10 12 11 13 6 1
101 16 10 11 17 16 7 1
102 16 10 12 15 15 7 1
103 14 10 14 17 18 6 0
104 11 10 10 5 13 3 0
105 4 10 10 4 10 2 1
106 14 10 13 10 16 8 2
107 9 10 10 11 13 3 1
108 14 10 11 15 15 8 1
109 8 10 10 10 14 3 0
110 8 10 7 9 15 4 0
111 11 10 10 12 14 5 1
112 12 10 8 15 13 7 1
113 11 10 12 7 13 6 0
114 14 10 12 13 15 6 0
115 15 10 12 12 16 7 2
116 16 10 11 14 14 6 2
117 16 10 12 14 14 6 0
118 11 10 12 8 16 6 1
119 14 10 12 15 14 6 0
120 14 10 11 12 12 4 2
121 12 10 12 12 13 4 1
122 14 10 11 16 12 5 0
123 8 10 11 9 12 4 1
124 13 10 13 15 14 6 1
125 16 10 12 15 14 6 2
126 12 10 12 6 14 5 0
127 16 10 12 14 16 8 2
128 12 10 12 15 13 6 0
129 11 10 8 10 14 5 1
130 4 10 8 6 4 4 0
131 16 10 12 14 16 8 3
132 15 10 11 12 13 6 1
133 10 10 12 8 16 4 0
134 13 10 13 11 15 6 0
135 15 10 12 13 14 6 0
136 12 10 12 9 13 4 0
137 14 10 11 15 14 6 0
138 7 10 12 13 12 3 1
139 19 10 12 15 15 6 1
140 12 10 10 14 14 5 2
141 12 10 11 16 13 4 1
142 13 10 12 14 14 6 0
143 15 10 12 14 16 4 0
144 8 10 10 10 6 4 2
145 12 10 12 10 13 4 1
146 10 10 13 4 13 6 0
147 8 10 12 8 14 5 1
148 10 10 15 15 15 6 2
149 15 10 11 16 14 6 2
150 16 10 12 12 15 8 0
151 13 10 11 12 13 7 1
152 16 10 12 15 16 7 2
153 9 10 11 9 12 4 0
154 14 10 10 12 15 6 1
155 14 10 11 14 12 6 2
156 12 10 11 11 14 2 1
secondbestfriend thirdbestfriend t
1 1 0 1
2 0 0 2
3 0 0 3
4 0 1 4
5 1 2 5
6 0 1 6
7 1 1 7
8 0 0 8
9 0 0 9
10 0 0 10
11 2 1 11
12 0 0 12
13 2 2 13
14 1 1 14
15 1 0 15
16 0 1 16
17 1 0 17
18 0 1 18
19 0 0 19
20 0 0 20
21 1 1 21
22 2 0 22
23 2 1 23
24 0 0 24
25 0 0 25
26 1 0 26
27 1 0 27
28 2 0 28
29 0 2 29
30 1 1 30
31 1 2 31
32 1 1 32
33 2 1 33
34 2 0 34
35 1 0 35
36 2 0 36
37 1 1 37
38 1 2 38
39 0 1 39
40 3 1 40
41 1 2 41
42 0 0 42
43 0 0 43
44 0 0 44
45 1 1 45
46 0 1 46
47 4 4 47
48 0 0 48
49 0 0 49
50 0 1 50
51 1 0 51
52 2 1 52
53 1 0 53
54 1 1 54
55 0 0 55
56 2 2 56
57 0 2 57
58 3 1 58
59 2 0 59
60 0 0 60
61 0 0 61
62 2 0 62
63 1 0 63
64 0 1 64
65 2 1 65
66 0 0 66
67 1 0 67
68 0 0 68
69 2 1 69
70 0 0 70
71 2 2 71
72 3 0 72
73 0 2 73
74 2 1 74
75 3 1 75
76 1 1 76
77 0 2 77
78 1 2 78
79 0 0 79
80 0 0 80
81 1 0 81
82 1 1 82
83 2 1 83
84 1 0 84
85 0 0 85
86 0 0 86
87 1 0 87
88 0 2 88
89 0 1 89
90 0 1 90
91 1 0 91
92 1 1 92
93 3 1 93
94 1 0 94
95 1 1 95
96 0 0 96
97 0 1 97
98 1 0 98
99 1 0 99
100 0 2 100
101 1 2 101
102 1 2 102
103 0 1 103
104 1 1 104
105 0 1 105
106 1 0 106
107 1 1 107
108 1 1 108
109 1 0 109
110 1 0 110
111 0 0 111
112 0 0 112
113 0 0 113
114 1 0 114
115 1 0 115
116 1 0 116
117 0 0 117
118 1 0 118
119 4 1 119
120 0 0 120
121 1 1 121
122 0 3 122
123 2 2 123
124 1 2 124
125 0 2 125
126 0 0 126
127 0 1 127
128 0 0 128
129 1 0 129
130 0 0 130
131 2 1 131
132 0 2 132
133 1 0 133
134 2 4 134
135 2 0 135
136 1 0 136
137 3 0 137
138 0 0 138
139 1 0 139
140 1 1 140
141 0 0 141
142 1 1 142
143 0 0 143
144 1 2 144
145 0 1 145
146 1 0 146
147 0 0 147
148 2 0 148
149 0 1 149
150 0 0 150
151 1 1 151
152 1 0 152
153 0 0 153
154 0 1 154
155 1 2 155
156 1 0 156
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) month FindingFriends KnowingPeople
-0.98336 0.08938 0.10438 0.21162
Liked Celebrity bestfriend secondbestfriend
0.38519 0.59441 0.30765 -0.03240
thirdbestfriend t
0.41155 -0.00181
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.99104 -1.25241 -0.06006 1.37531 6.92942
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.983357 6.011657 -0.164 0.870292
month 0.089381 0.640842 0.139 0.889267
FindingFriends 0.104378 0.098559 1.059 0.291327
KnowingPeople 0.211623 0.064050 3.304 0.001199 **
Liked 0.385194 0.099038 3.889 0.000152 ***
Celebrity 0.594410 0.156961 3.787 0.000222 ***
bestfriend 0.307650 0.211991 1.451 0.148859
secondbestfriend -0.032400 0.202360 -0.160 0.873014
thirdbestfriend 0.411553 0.214612 1.918 0.057107 .
t -0.001810 0.006864 -0.264 0.792389
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.103 on 146 degrees of freedom
Multiple R-squared: 0.5171, Adjusted R-squared: 0.4873
F-statistic: 17.37 on 9 and 146 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.8360018 0.32799642 0.163998208
[2,] 0.9019584 0.19608321 0.098041606
[3,] 0.8792406 0.24151888 0.120759442
[4,] 0.8086168 0.38276641 0.191383206
[5,] 0.7265852 0.54682968 0.273414838
[6,] 0.6667970 0.66640592 0.333202962
[7,] 0.6079103 0.78417931 0.392089653
[8,] 0.7021284 0.59574329 0.297871643
[9,] 0.6941923 0.61161543 0.305807713
[10,] 0.7453161 0.50936776 0.254683880
[11,] 0.6834184 0.63316315 0.316581576
[12,] 0.7077771 0.58444574 0.292222868
[13,] 0.6845358 0.63092845 0.315464225
[14,] 0.6260798 0.74784047 0.373920236
[15,] 0.8061672 0.38766567 0.193832837
[16,] 0.7709757 0.45804869 0.229024347
[17,] 0.7170351 0.56592977 0.282964883
[18,] 0.8186239 0.36275218 0.181376090
[19,] 0.8601566 0.27968676 0.139843380
[20,] 0.8296662 0.34066758 0.170333789
[21,] 0.7873126 0.42537481 0.212687407
[22,] 0.7549054 0.49018918 0.245094589
[23,] 0.7364121 0.52717586 0.263587932
[24,] 0.7567195 0.48656107 0.243280537
[25,] 0.7400780 0.51984393 0.259921964
[26,] 0.6964622 0.60707562 0.303537811
[27,] 0.6463023 0.70739531 0.353697656
[28,] 0.6025817 0.79483663 0.397418313
[29,] 0.5526456 0.89470872 0.447354360
[30,] 0.8431763 0.31364740 0.156823699
[31,] 0.9708149 0.05837016 0.029185082
[32,] 0.9717545 0.05649105 0.028245524
[33,] 0.9631161 0.07376786 0.036883931
[34,] 0.9686958 0.06260831 0.031304154
[35,] 0.9750191 0.04996173 0.024980863
[36,] 0.9715922 0.05681560 0.028407799
[37,] 0.9680591 0.06388174 0.031940869
[38,] 0.9594328 0.08113448 0.040567241
[39,] 0.9527291 0.09454171 0.047270857
[40,] 0.9892727 0.02145455 0.010727273
[41,] 0.9858034 0.02839315 0.014196574
[42,] 0.9891549 0.02169017 0.010845085
[43,] 0.9927679 0.01446422 0.007232109
[44,] 0.9902117 0.01957656 0.009788281
[45,] 0.9865132 0.02697369 0.013486846
[46,] 0.9858478 0.02830434 0.014152169
[47,] 0.9890596 0.02188084 0.010940421
[48,] 0.9878590 0.02428193 0.012140966
[49,] 0.9877983 0.02440334 0.012201668
[50,] 0.9894472 0.02110556 0.010552781
[51,] 0.9878856 0.02422885 0.012114425
[52,] 0.9882874 0.02342512 0.011712559
[53,] 0.9876599 0.02468020 0.012340102
[54,] 0.9856703 0.02865938 0.014329692
[55,] 0.9873147 0.02537057 0.012685284
[56,] 0.9888699 0.02226019 0.011130094
[57,] 0.9882981 0.02340382 0.011701909
[58,] 0.9850824 0.02983529 0.014917645
[59,] 0.9855569 0.02888612 0.014443059
[60,] 0.9821643 0.03567137 0.017835683
[61,] 0.9824988 0.03500242 0.017501208
[62,] 0.9874030 0.02519398 0.012596990
[63,] 0.9833495 0.03330093 0.016650464
[64,] 0.9801562 0.03968752 0.019843758
[65,] 0.9742931 0.05141377 0.025706883
[66,] 0.9666617 0.06667654 0.033338268
[67,] 0.9756401 0.04871974 0.024359872
[68,] 0.9739943 0.05201140 0.026005701
[69,] 0.9749669 0.05006611 0.025033056
[70,] 0.9729835 0.05403303 0.027016517
[71,] 0.9644489 0.07110211 0.035551055
[72,] 0.9552106 0.08957884 0.044789419
[73,] 0.9819981 0.03600385 0.018001926
[74,] 0.9760141 0.04797185 0.023985925
[75,] 0.9687225 0.06255498 0.031277489
[76,] 0.9597490 0.08050210 0.040251050
[77,] 0.9510082 0.09798364 0.048991821
[78,] 0.9384758 0.12304839 0.061524195
[79,] 0.9306312 0.13873764 0.069368822
[80,] 0.9547721 0.09045584 0.045227922
[81,] 0.9451513 0.10969748 0.054848740
[82,] 0.9434308 0.11313849 0.056569244
[83,] 0.9320032 0.13599367 0.067996833
[84,] 0.9197028 0.16059440 0.080297199
[85,] 0.9127614 0.17447721 0.087238604
[86,] 0.8944600 0.21108008 0.105540038
[87,] 0.8789282 0.24214355 0.121071775
[88,] 0.8530227 0.29395459 0.146977293
[89,] 0.8222659 0.35546822 0.177734111
[90,] 0.7957130 0.40857395 0.204286977
[91,] 0.8081799 0.38364027 0.191820133
[92,] 0.8644359 0.27112825 0.135564125
[93,] 0.8618594 0.27628122 0.138140608
[94,] 0.8298873 0.34022549 0.170112744
[95,] 0.7974679 0.40506430 0.202532148
[96,] 0.7836564 0.43268725 0.216343627
[97,] 0.7648546 0.47029075 0.235145374
[98,] 0.7841433 0.43171338 0.215856691
[99,] 0.7690454 0.46190923 0.230954614
[100,] 0.8343243 0.33135149 0.165675744
[101,] 0.7972502 0.40549959 0.202749796
[102,] 0.7663322 0.46733567 0.233667837
[103,] 0.7229761 0.55404774 0.277023870
[104,] 0.7321701 0.53565974 0.267829868
[105,] 0.7443748 0.51125035 0.255625177
[106,] 0.7256230 0.54875406 0.274377029
[107,] 0.6753799 0.64924016 0.324620082
[108,] 0.7713185 0.45736291 0.228681453
[109,] 0.7409584 0.51808329 0.259041646
[110,] 0.6882524 0.62349516 0.311747581
[111,] 0.6743284 0.65134328 0.325671638
[112,] 0.6314835 0.73703293 0.368516463
[113,] 0.6121769 0.77564624 0.387823122
[114,] 0.6125528 0.77489436 0.387447181
[115,] 0.5460357 0.90792866 0.453964329
[116,] 0.5048787 0.99024254 0.495121271
[117,] 0.4829941 0.96598828 0.517005862
[118,] 0.4650093 0.93001850 0.534990750
[119,] 0.3929231 0.78584620 0.607076898
[120,] 0.3714623 0.74292451 0.628537747
[121,] 0.3256673 0.65133467 0.674332666
[122,] 0.2813238 0.56264765 0.718676176
[123,] 0.2375462 0.47509230 0.762453848
[124,] 0.2190692 0.43813830 0.780930849
[125,] 0.1843067 0.36861338 0.815693311
[126,] 0.1975809 0.39516182 0.802419092
[127,] 0.6160697 0.76786051 0.383930255
[128,] 0.5261505 0.94769896 0.473849482
[129,] 0.4055699 0.81113973 0.594430137
[130,] 0.4008517 0.80170336 0.599148318
[131,] 0.2780890 0.55617801 0.721910994
> postscript(file="/var/www/html/freestat/rcomp/tmp/1jjwd1291316906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2tswy1291316906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3tswy1291316906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4mkd11291316906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5mkd11291316906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 156
Frequency = 1
1 2 3 4 5 6
1.79509733 0.94980238 1.87153577 0.54976103 -0.97192460 2.63877668
7 8 9 10 11 12
-1.92686083 -1.30940794 -1.02099786 3.45663378 -2.01494858 -1.18383172
13 14 15 16 17 18
2.70084073 -4.48327347 -0.27816030 0.17792392 -1.83473119 -0.86741282
19 20 21 22 23 24
1.43176030 -3.34255000 2.22067907 0.92623737 -0.58033883 2.25839475
25 26 27 28 29 30
0.91566386 0.47542513 -5.30873535 0.51659678 -0.45298795 4.83054800
31 32 33 34 35 36
4.24837473 -1.62484908 -0.56978303 1.39209830 -1.22813575 1.81509285
37 38 39 40 41 42
-1.15404134 0.28692610 -0.15454691 0.77122638 0.88481507 6.92941668
43 44 45 46 47 48
-4.71510529 -2.31343802 -0.14870526 1.78249253 2.75246916 0.97120603
49 50 51 52 53 54
-2.39958743 -1.81784166 -0.88864535 -5.99104422 0.15048096 -2.43316644
55 56 57 58 59 60
2.19089361 -0.87611803 -0.10923356 1.82524294 -2.27762915 0.34245529
61 62 63 64 65 66
-2.15112060 2.05358897 1.08695261 2.33858579 -1.25072250 1.61050142
67 68 69 70 71 72
-2.29540026 2.57670256 1.98241895 -0.82162780 2.06165169 1.02621245
73 74 75 76 77 78
-2.05750378 -3.13465937 -0.24629106 -1.46055649 -0.81585098 -0.12567040
79 80 81 82 83 84
2.90136299 -2.02152943 -2.46530619 1.36971323 -0.04344332 -0.99967099
85 86 87 88 89 90
3.60423992 -0.45526880 -0.09895259 -0.38273359 0.88736740 -0.82713604
91 92 93 94 95 96
0.98505859 -3.67435902 0.98105057 1.67306147 0.50430862 0.70301568
97 98 99 100 101 102
-2.29755236 -0.48176532 -1.55421352 -0.01457641 0.10428325 0.81015464
103 104 105 106 107 108
-1.69440630 2.00599111 -3.37063442 -0.69301706 -1.56596582 -1.25746386
109 110 111 112 113 114
-2.01671378 -2.46975003 -0.96520980 -1.19313822 -0.01379818 0.98028720
115 116 117 118 119 120
0.59881626 2.64655670 3.12688819 -1.64720197 0.63693340 3.00385049
121 122 123 124 125 126
0.44458568 0.94721013 -2.80650618 -1.27479802 1.49134015 1.43057192
127 128 129 130 131 132
0.15892812 -0.67963002 -0.26822600 -1.69832608 -0.07668110 1.93610133
133 134 135 136 137 138
-1.12358065 -1.27845537 2.43589280 1.82580825 1.15304570 -3.37750924
139 140 141 142 143 144
5.29464335 -1.02276607 0.11782665 -0.20701270 2.59238266 -0.90462629
145 146 147 148 149 150
0.87887295 -0.39117521 -3.26231222 -4.27745052 0.83909024 2.03585222
151 152 153 154 155 156
-0.17996427 1.03092037 -0.68624860 0.72146643 0.66443140 2.03911872
> postscript(file="/var/www/html/freestat/rcomp/tmp/6mkd11291316906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 1.79509733 NA
1 0.94980238 1.79509733
2 1.87153577 0.94980238
3 0.54976103 1.87153577
4 -0.97192460 0.54976103
5 2.63877668 -0.97192460
6 -1.92686083 2.63877668
7 -1.30940794 -1.92686083
8 -1.02099786 -1.30940794
9 3.45663378 -1.02099786
10 -2.01494858 3.45663378
11 -1.18383172 -2.01494858
12 2.70084073 -1.18383172
13 -4.48327347 2.70084073
14 -0.27816030 -4.48327347
15 0.17792392 -0.27816030
16 -1.83473119 0.17792392
17 -0.86741282 -1.83473119
18 1.43176030 -0.86741282
19 -3.34255000 1.43176030
20 2.22067907 -3.34255000
21 0.92623737 2.22067907
22 -0.58033883 0.92623737
23 2.25839475 -0.58033883
24 0.91566386 2.25839475
25 0.47542513 0.91566386
26 -5.30873535 0.47542513
27 0.51659678 -5.30873535
28 -0.45298795 0.51659678
29 4.83054800 -0.45298795
30 4.24837473 4.83054800
31 -1.62484908 4.24837473
32 -0.56978303 -1.62484908
33 1.39209830 -0.56978303
34 -1.22813575 1.39209830
35 1.81509285 -1.22813575
36 -1.15404134 1.81509285
37 0.28692610 -1.15404134
38 -0.15454691 0.28692610
39 0.77122638 -0.15454691
40 0.88481507 0.77122638
41 6.92941668 0.88481507
42 -4.71510529 6.92941668
43 -2.31343802 -4.71510529
44 -0.14870526 -2.31343802
45 1.78249253 -0.14870526
46 2.75246916 1.78249253
47 0.97120603 2.75246916
48 -2.39958743 0.97120603
49 -1.81784166 -2.39958743
50 -0.88864535 -1.81784166
51 -5.99104422 -0.88864535
52 0.15048096 -5.99104422
53 -2.43316644 0.15048096
54 2.19089361 -2.43316644
55 -0.87611803 2.19089361
56 -0.10923356 -0.87611803
57 1.82524294 -0.10923356
58 -2.27762915 1.82524294
59 0.34245529 -2.27762915
60 -2.15112060 0.34245529
61 2.05358897 -2.15112060
62 1.08695261 2.05358897
63 2.33858579 1.08695261
64 -1.25072250 2.33858579
65 1.61050142 -1.25072250
66 -2.29540026 1.61050142
67 2.57670256 -2.29540026
68 1.98241895 2.57670256
69 -0.82162780 1.98241895
70 2.06165169 -0.82162780
71 1.02621245 2.06165169
72 -2.05750378 1.02621245
73 -3.13465937 -2.05750378
74 -0.24629106 -3.13465937
75 -1.46055649 -0.24629106
76 -0.81585098 -1.46055649
77 -0.12567040 -0.81585098
78 2.90136299 -0.12567040
79 -2.02152943 2.90136299
80 -2.46530619 -2.02152943
81 1.36971323 -2.46530619
82 -0.04344332 1.36971323
83 -0.99967099 -0.04344332
84 3.60423992 -0.99967099
85 -0.45526880 3.60423992
86 -0.09895259 -0.45526880
87 -0.38273359 -0.09895259
88 0.88736740 -0.38273359
89 -0.82713604 0.88736740
90 0.98505859 -0.82713604
91 -3.67435902 0.98505859
92 0.98105057 -3.67435902
93 1.67306147 0.98105057
94 0.50430862 1.67306147
95 0.70301568 0.50430862
96 -2.29755236 0.70301568
97 -0.48176532 -2.29755236
98 -1.55421352 -0.48176532
99 -0.01457641 -1.55421352
100 0.10428325 -0.01457641
101 0.81015464 0.10428325
102 -1.69440630 0.81015464
103 2.00599111 -1.69440630
104 -3.37063442 2.00599111
105 -0.69301706 -3.37063442
106 -1.56596582 -0.69301706
107 -1.25746386 -1.56596582
108 -2.01671378 -1.25746386
109 -2.46975003 -2.01671378
110 -0.96520980 -2.46975003
111 -1.19313822 -0.96520980
112 -0.01379818 -1.19313822
113 0.98028720 -0.01379818
114 0.59881626 0.98028720
115 2.64655670 0.59881626
116 3.12688819 2.64655670
117 -1.64720197 3.12688819
118 0.63693340 -1.64720197
119 3.00385049 0.63693340
120 0.44458568 3.00385049
121 0.94721013 0.44458568
122 -2.80650618 0.94721013
123 -1.27479802 -2.80650618
124 1.49134015 -1.27479802
125 1.43057192 1.49134015
126 0.15892812 1.43057192
127 -0.67963002 0.15892812
128 -0.26822600 -0.67963002
129 -1.69832608 -0.26822600
130 -0.07668110 -1.69832608
131 1.93610133 -0.07668110
132 -1.12358065 1.93610133
133 -1.27845537 -1.12358065
134 2.43589280 -1.27845537
135 1.82580825 2.43589280
136 1.15304570 1.82580825
137 -3.37750924 1.15304570
138 5.29464335 -3.37750924
139 -1.02276607 5.29464335
140 0.11782665 -1.02276607
141 -0.20701270 0.11782665
142 2.59238266 -0.20701270
143 -0.90462629 2.59238266
144 0.87887295 -0.90462629
145 -0.39117521 0.87887295
146 -3.26231222 -0.39117521
147 -4.27745052 -3.26231222
148 0.83909024 -4.27745052
149 2.03585222 0.83909024
150 -0.17996427 2.03585222
151 1.03092037 -0.17996427
152 -0.68624860 1.03092037
153 0.72146643 -0.68624860
154 0.66443140 0.72146643
155 2.03911872 0.66443140
156 NA 2.03911872
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.94980238 1.79509733
[2,] 1.87153577 0.94980238
[3,] 0.54976103 1.87153577
[4,] -0.97192460 0.54976103
[5,] 2.63877668 -0.97192460
[6,] -1.92686083 2.63877668
[7,] -1.30940794 -1.92686083
[8,] -1.02099786 -1.30940794
[9,] 3.45663378 -1.02099786
[10,] -2.01494858 3.45663378
[11,] -1.18383172 -2.01494858
[12,] 2.70084073 -1.18383172
[13,] -4.48327347 2.70084073
[14,] -0.27816030 -4.48327347
[15,] 0.17792392 -0.27816030
[16,] -1.83473119 0.17792392
[17,] -0.86741282 -1.83473119
[18,] 1.43176030 -0.86741282
[19,] -3.34255000 1.43176030
[20,] 2.22067907 -3.34255000
[21,] 0.92623737 2.22067907
[22,] -0.58033883 0.92623737
[23,] 2.25839475 -0.58033883
[24,] 0.91566386 2.25839475
[25,] 0.47542513 0.91566386
[26,] -5.30873535 0.47542513
[27,] 0.51659678 -5.30873535
[28,] -0.45298795 0.51659678
[29,] 4.83054800 -0.45298795
[30,] 4.24837473 4.83054800
[31,] -1.62484908 4.24837473
[32,] -0.56978303 -1.62484908
[33,] 1.39209830 -0.56978303
[34,] -1.22813575 1.39209830
[35,] 1.81509285 -1.22813575
[36,] -1.15404134 1.81509285
[37,] 0.28692610 -1.15404134
[38,] -0.15454691 0.28692610
[39,] 0.77122638 -0.15454691
[40,] 0.88481507 0.77122638
[41,] 6.92941668 0.88481507
[42,] -4.71510529 6.92941668
[43,] -2.31343802 -4.71510529
[44,] -0.14870526 -2.31343802
[45,] 1.78249253 -0.14870526
[46,] 2.75246916 1.78249253
[47,] 0.97120603 2.75246916
[48,] -2.39958743 0.97120603
[49,] -1.81784166 -2.39958743
[50,] -0.88864535 -1.81784166
[51,] -5.99104422 -0.88864535
[52,] 0.15048096 -5.99104422
[53,] -2.43316644 0.15048096
[54,] 2.19089361 -2.43316644
[55,] -0.87611803 2.19089361
[56,] -0.10923356 -0.87611803
[57,] 1.82524294 -0.10923356
[58,] -2.27762915 1.82524294
[59,] 0.34245529 -2.27762915
[60,] -2.15112060 0.34245529
[61,] 2.05358897 -2.15112060
[62,] 1.08695261 2.05358897
[63,] 2.33858579 1.08695261
[64,] -1.25072250 2.33858579
[65,] 1.61050142 -1.25072250
[66,] -2.29540026 1.61050142
[67,] 2.57670256 -2.29540026
[68,] 1.98241895 2.57670256
[69,] -0.82162780 1.98241895
[70,] 2.06165169 -0.82162780
[71,] 1.02621245 2.06165169
[72,] -2.05750378 1.02621245
[73,] -3.13465937 -2.05750378
[74,] -0.24629106 -3.13465937
[75,] -1.46055649 -0.24629106
[76,] -0.81585098 -1.46055649
[77,] -0.12567040 -0.81585098
[78,] 2.90136299 -0.12567040
[79,] -2.02152943 2.90136299
[80,] -2.46530619 -2.02152943
[81,] 1.36971323 -2.46530619
[82,] -0.04344332 1.36971323
[83,] -0.99967099 -0.04344332
[84,] 3.60423992 -0.99967099
[85,] -0.45526880 3.60423992
[86,] -0.09895259 -0.45526880
[87,] -0.38273359 -0.09895259
[88,] 0.88736740 -0.38273359
[89,] -0.82713604 0.88736740
[90,] 0.98505859 -0.82713604
[91,] -3.67435902 0.98505859
[92,] 0.98105057 -3.67435902
[93,] 1.67306147 0.98105057
[94,] 0.50430862 1.67306147
[95,] 0.70301568 0.50430862
[96,] -2.29755236 0.70301568
[97,] -0.48176532 -2.29755236
[98,] -1.55421352 -0.48176532
[99,] -0.01457641 -1.55421352
[100,] 0.10428325 -0.01457641
[101,] 0.81015464 0.10428325
[102,] -1.69440630 0.81015464
[103,] 2.00599111 -1.69440630
[104,] -3.37063442 2.00599111
[105,] -0.69301706 -3.37063442
[106,] -1.56596582 -0.69301706
[107,] -1.25746386 -1.56596582
[108,] -2.01671378 -1.25746386
[109,] -2.46975003 -2.01671378
[110,] -0.96520980 -2.46975003
[111,] -1.19313822 -0.96520980
[112,] -0.01379818 -1.19313822
[113,] 0.98028720 -0.01379818
[114,] 0.59881626 0.98028720
[115,] 2.64655670 0.59881626
[116,] 3.12688819 2.64655670
[117,] -1.64720197 3.12688819
[118,] 0.63693340 -1.64720197
[119,] 3.00385049 0.63693340
[120,] 0.44458568 3.00385049
[121,] 0.94721013 0.44458568
[122,] -2.80650618 0.94721013
[123,] -1.27479802 -2.80650618
[124,] 1.49134015 -1.27479802
[125,] 1.43057192 1.49134015
[126,] 0.15892812 1.43057192
[127,] -0.67963002 0.15892812
[128,] -0.26822600 -0.67963002
[129,] -1.69832608 -0.26822600
[130,] -0.07668110 -1.69832608
[131,] 1.93610133 -0.07668110
[132,] -1.12358065 1.93610133
[133,] -1.27845537 -1.12358065
[134,] 2.43589280 -1.27845537
[135,] 1.82580825 2.43589280
[136,] 1.15304570 1.82580825
[137,] -3.37750924 1.15304570
[138,] 5.29464335 -3.37750924
[139,] -1.02276607 5.29464335
[140,] 0.11782665 -1.02276607
[141,] -0.20701270 0.11782665
[142,] 2.59238266 -0.20701270
[143,] -0.90462629 2.59238266
[144,] 0.87887295 -0.90462629
[145,] -0.39117521 0.87887295
[146,] -3.26231222 -0.39117521
[147,] -4.27745052 -3.26231222
[148,] 0.83909024 -4.27745052
[149,] 2.03585222 0.83909024
[150,] -0.17996427 2.03585222
[151,] 1.03092037 -0.17996427
[152,] -0.68624860 1.03092037
[153,] 0.72146643 -0.68624860
[154,] 0.66443140 0.72146643
[155,] 2.03911872 0.66443140
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.94980238 1.79509733
2 1.87153577 0.94980238
3 0.54976103 1.87153577
4 -0.97192460 0.54976103
5 2.63877668 -0.97192460
6 -1.92686083 2.63877668
7 -1.30940794 -1.92686083
8 -1.02099786 -1.30940794
9 3.45663378 -1.02099786
10 -2.01494858 3.45663378
11 -1.18383172 -2.01494858
12 2.70084073 -1.18383172
13 -4.48327347 2.70084073
14 -0.27816030 -4.48327347
15 0.17792392 -0.27816030
16 -1.83473119 0.17792392
17 -0.86741282 -1.83473119
18 1.43176030 -0.86741282
19 -3.34255000 1.43176030
20 2.22067907 -3.34255000
21 0.92623737 2.22067907
22 -0.58033883 0.92623737
23 2.25839475 -0.58033883
24 0.91566386 2.25839475
25 0.47542513 0.91566386
26 -5.30873535 0.47542513
27 0.51659678 -5.30873535
28 -0.45298795 0.51659678
29 4.83054800 -0.45298795
30 4.24837473 4.83054800
31 -1.62484908 4.24837473
32 -0.56978303 -1.62484908
33 1.39209830 -0.56978303
34 -1.22813575 1.39209830
35 1.81509285 -1.22813575
36 -1.15404134 1.81509285
37 0.28692610 -1.15404134
38 -0.15454691 0.28692610
39 0.77122638 -0.15454691
40 0.88481507 0.77122638
41 6.92941668 0.88481507
42 -4.71510529 6.92941668
43 -2.31343802 -4.71510529
44 -0.14870526 -2.31343802
45 1.78249253 -0.14870526
46 2.75246916 1.78249253
47 0.97120603 2.75246916
48 -2.39958743 0.97120603
49 -1.81784166 -2.39958743
50 -0.88864535 -1.81784166
51 -5.99104422 -0.88864535
52 0.15048096 -5.99104422
53 -2.43316644 0.15048096
54 2.19089361 -2.43316644
55 -0.87611803 2.19089361
56 -0.10923356 -0.87611803
57 1.82524294 -0.10923356
58 -2.27762915 1.82524294
59 0.34245529 -2.27762915
60 -2.15112060 0.34245529
61 2.05358897 -2.15112060
62 1.08695261 2.05358897
63 2.33858579 1.08695261
64 -1.25072250 2.33858579
65 1.61050142 -1.25072250
66 -2.29540026 1.61050142
67 2.57670256 -2.29540026
68 1.98241895 2.57670256
69 -0.82162780 1.98241895
70 2.06165169 -0.82162780
71 1.02621245 2.06165169
72 -2.05750378 1.02621245
73 -3.13465937 -2.05750378
74 -0.24629106 -3.13465937
75 -1.46055649 -0.24629106
76 -0.81585098 -1.46055649
77 -0.12567040 -0.81585098
78 2.90136299 -0.12567040
79 -2.02152943 2.90136299
80 -2.46530619 -2.02152943
81 1.36971323 -2.46530619
82 -0.04344332 1.36971323
83 -0.99967099 -0.04344332
84 3.60423992 -0.99967099
85 -0.45526880 3.60423992
86 -0.09895259 -0.45526880
87 -0.38273359 -0.09895259
88 0.88736740 -0.38273359
89 -0.82713604 0.88736740
90 0.98505859 -0.82713604
91 -3.67435902 0.98505859
92 0.98105057 -3.67435902
93 1.67306147 0.98105057
94 0.50430862 1.67306147
95 0.70301568 0.50430862
96 -2.29755236 0.70301568
97 -0.48176532 -2.29755236
98 -1.55421352 -0.48176532
99 -0.01457641 -1.55421352
100 0.10428325 -0.01457641
101 0.81015464 0.10428325
102 -1.69440630 0.81015464
103 2.00599111 -1.69440630
104 -3.37063442 2.00599111
105 -0.69301706 -3.37063442
106 -1.56596582 -0.69301706
107 -1.25746386 -1.56596582
108 -2.01671378 -1.25746386
109 -2.46975003 -2.01671378
110 -0.96520980 -2.46975003
111 -1.19313822 -0.96520980
112 -0.01379818 -1.19313822
113 0.98028720 -0.01379818
114 0.59881626 0.98028720
115 2.64655670 0.59881626
116 3.12688819 2.64655670
117 -1.64720197 3.12688819
118 0.63693340 -1.64720197
119 3.00385049 0.63693340
120 0.44458568 3.00385049
121 0.94721013 0.44458568
122 -2.80650618 0.94721013
123 -1.27479802 -2.80650618
124 1.49134015 -1.27479802
125 1.43057192 1.49134015
126 0.15892812 1.43057192
127 -0.67963002 0.15892812
128 -0.26822600 -0.67963002
129 -1.69832608 -0.26822600
130 -0.07668110 -1.69832608
131 1.93610133 -0.07668110
132 -1.12358065 1.93610133
133 -1.27845537 -1.12358065
134 2.43589280 -1.27845537
135 1.82580825 2.43589280
136 1.15304570 1.82580825
137 -3.37750924 1.15304570
138 5.29464335 -3.37750924
139 -1.02276607 5.29464335
140 0.11782665 -1.02276607
141 -0.20701270 0.11782665
142 2.59238266 -0.20701270
143 -0.90462629 2.59238266
144 0.87887295 -0.90462629
145 -0.39117521 0.87887295
146 -3.26231222 -0.39117521
147 -4.27745052 -3.26231222
148 0.83909024 -4.27745052
149 2.03585222 0.83909024
150 -0.17996427 2.03585222
151 1.03092037 -0.17996427
152 -0.68624860 1.03092037
153 0.72146643 -0.68624860
154 0.66443140 0.72146643
155 2.03911872 0.66443140
> 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/7xbum1291316906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8xbum1291316906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/97kt71291316906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/107kt71291316906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11mu9y1291316906.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/12pc7m1291316906.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/1334nd1291316906.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/14on401291316906.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/15a5261291316906.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/166f0f1291316906.tab")
+ }
> try(system("convert tmp/1jjwd1291316906.ps tmp/1jjwd1291316906.png",intern=TRUE))
character(0)
> try(system("convert tmp/2tswy1291316906.ps tmp/2tswy1291316906.png",intern=TRUE))
character(0)
> try(system("convert tmp/3tswy1291316906.ps tmp/3tswy1291316906.png",intern=TRUE))
character(0)
> try(system("convert tmp/4mkd11291316906.ps tmp/4mkd11291316906.png",intern=TRUE))
character(0)
> try(system("convert tmp/5mkd11291316906.ps tmp/5mkd11291316906.png",intern=TRUE))
character(0)
> try(system("convert tmp/6mkd11291316906.ps tmp/6mkd11291316906.png",intern=TRUE))
character(0)
> try(system("convert tmp/7xbum1291316906.ps tmp/7xbum1291316906.png",intern=TRUE))
character(0)
> try(system("convert tmp/8xbum1291316906.ps tmp/8xbum1291316906.png",intern=TRUE))
character(0)
> try(system("convert tmp/97kt71291316906.ps tmp/97kt71291316906.png",intern=TRUE))
character(0)
> try(system("convert tmp/107kt71291316906.ps tmp/107kt71291316906.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.071 2.729 6.446