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(1
+ ,26
+ ,24
+ ,24
+ ,14
+ ,14
+ ,11
+ ,11
+ ,12
+ ,12
+ ,24
+ ,24
+ ,1
+ ,23
+ ,25
+ ,25
+ ,11
+ ,11
+ ,7
+ ,7
+ ,8
+ ,8
+ ,25
+ ,25
+ ,0
+ ,25
+ ,17
+ ,0
+ ,6
+ ,0
+ ,17
+ ,0
+ ,8
+ ,0
+ ,30
+ ,0
+ ,1
+ ,23
+ ,18
+ ,18
+ ,12
+ ,12
+ ,10
+ ,10
+ ,8
+ ,8
+ ,19
+ ,19
+ ,1
+ ,20
+ ,18
+ ,18
+ ,8
+ ,8
+ ,12
+ ,12
+ ,9
+ ,9
+ ,22
+ ,22
+ ,0
+ ,29
+ ,16
+ ,10
+ ,0
+ ,12
+ ,0
+ ,7
+ ,0
+ ,22
+ ,1
+ ,25
+ ,20
+ ,20
+ ,10
+ ,10
+ ,11
+ ,11
+ ,4
+ ,4
+ ,25
+ ,25
+ ,1
+ ,21
+ ,16
+ ,16
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,23
+ ,23
+ ,1
+ ,22
+ ,18
+ ,18
+ ,16
+ ,16
+ ,12
+ ,12
+ ,7
+ ,7
+ ,17
+ ,17
+ ,1
+ ,25
+ ,17
+ ,17
+ ,11
+ ,11
+ ,13
+ ,13
+ ,7
+ ,7
+ ,21
+ ,21
+ ,1
+ ,24
+ ,23
+ ,23
+ ,13
+ ,13
+ ,14
+ ,14
+ ,12
+ ,12
+ ,19
+ ,19
+ ,1
+ ,18
+ ,30
+ ,30
+ ,12
+ ,12
+ ,16
+ ,16
+ ,10
+ ,10
+ ,19
+ ,19
+ ,1
+ ,22
+ ,23
+ ,23
+ ,8
+ ,8
+ ,11
+ ,11
+ ,10
+ ,10
+ ,15
+ ,15
+ ,1
+ ,15
+ ,18
+ ,18
+ ,12
+ ,12
+ ,10
+ ,10
+ ,8
+ ,8
+ ,16
+ ,16
+ ,1
+ ,22
+ ,15
+ ,15
+ ,11
+ ,11
+ ,11
+ ,11
+ ,8
+ ,8
+ ,23
+ ,23
+ ,1
+ ,28
+ ,12
+ ,12
+ ,4
+ ,4
+ ,15
+ ,15
+ ,4
+ ,4
+ ,27
+ ,27
+ ,1
+ ,20
+ ,21
+ ,21
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,22
+ ,22
+ ,1
+ ,12
+ ,15
+ ,15
+ ,8
+ ,8
+ ,11
+ ,11
+ ,8
+ ,8
+ ,14
+ ,14
+ ,1
+ ,24
+ ,20
+ ,20
+ ,8
+ ,8
+ ,17
+ ,17
+ ,7
+ ,7
+ ,22
+ ,22
+ ,1
+ ,20
+ ,31
+ ,31
+ ,14
+ ,14
+ ,17
+ ,17
+ ,11
+ ,11
+ ,23
+ ,23
+ ,1
+ ,21
+ ,27
+ ,27
+ ,15
+ ,15
+ ,11
+ ,11
+ ,9
+ ,9
+ ,23
+ ,23
+ ,1
+ ,20
+ ,34
+ ,34
+ ,16
+ ,16
+ ,18
+ ,18
+ ,11
+ ,11
+ ,21
+ ,21
+ ,1
+ ,21
+ ,21
+ ,21
+ ,9
+ ,9
+ ,14
+ ,14
+ ,13
+ ,13
+ ,19
+ ,19
+ ,1
+ ,23
+ ,31
+ ,31
+ ,14
+ ,14
+ ,10
+ ,10
+ ,8
+ ,8
+ ,18
+ ,18
+ ,1
+ ,28
+ ,19
+ ,19
+ ,11
+ ,11
+ ,11
+ ,11
+ ,8
+ ,8
+ ,20
+ ,20
+ ,1
+ ,24
+ ,16
+ ,16
+ ,8
+ ,8
+ ,15
+ ,15
+ ,9
+ ,9
+ ,23
+ ,23
+ ,1
+ ,24
+ ,20
+ ,20
+ ,9
+ ,9
+ ,15
+ ,15
+ ,6
+ ,6
+ ,25
+ ,25
+ ,1
+ ,24
+ ,21
+ ,21
+ ,9
+ ,9
+ ,13
+ ,13
+ ,9
+ ,9
+ ,19
+ ,19
+ ,1
+ ,23
+ ,22
+ ,22
+ ,9
+ ,9
+ ,16
+ ,16
+ ,9
+ ,9
+ ,24
+ ,24
+ ,1
+ ,23
+ ,17
+ ,17
+ ,9
+ ,9
+ ,13
+ ,13
+ ,6
+ ,6
+ ,22
+ ,22
+ ,1
+ ,29
+ ,24
+ ,24
+ ,10
+ ,10
+ ,9
+ ,9
+ ,6
+ ,6
+ ,25
+ ,25
+ ,1
+ ,24
+ ,25
+ ,25
+ ,16
+ ,16
+ ,18
+ ,18
+ ,16
+ ,16
+ ,26
+ ,26
+ ,1
+ ,18
+ ,26
+ ,26
+ ,11
+ ,11
+ ,18
+ ,18
+ ,5
+ ,5
+ ,29
+ ,29
+ ,1
+ ,25
+ ,25
+ ,25
+ ,8
+ ,8
+ ,12
+ ,12
+ ,7
+ ,7
+ ,32
+ ,32
+ ,1
+ ,21
+ ,17
+ ,17
+ ,9
+ ,9
+ ,17
+ ,17
+ ,9
+ ,9
+ ,25
+ ,25
+ ,1
+ ,26
+ ,32
+ ,32
+ ,16
+ ,16
+ ,9
+ ,9
+ ,6
+ ,6
+ ,29
+ ,29
+ ,1
+ ,22
+ ,33
+ ,33
+ ,11
+ ,11
+ ,9
+ ,9
+ ,6
+ ,6
+ ,28
+ ,28
+ ,1
+ ,22
+ ,13
+ ,13
+ ,16
+ ,16
+ ,12
+ ,12
+ ,5
+ ,5
+ ,17
+ ,17
+ ,0
+ ,22
+ ,32
+ ,0
+ ,12
+ ,0
+ ,18
+ ,0
+ ,12
+ ,0
+ ,28
+ ,0
+ ,1
+ ,23
+ ,25
+ ,25
+ ,12
+ ,12
+ ,12
+ ,12
+ ,7
+ ,7
+ ,29
+ ,29
+ ,1
+ ,30
+ ,29
+ ,29
+ ,14
+ ,14
+ ,18
+ ,18
+ ,10
+ ,10
+ ,26
+ ,26
+ ,1
+ ,23
+ ,22
+ ,22
+ ,9
+ ,9
+ ,14
+ ,14
+ ,9
+ ,9
+ ,25
+ ,25
+ ,1
+ ,17
+ ,18
+ ,18
+ ,10
+ ,10
+ ,15
+ ,15
+ ,8
+ ,8
+ ,14
+ ,14
+ ,1
+ ,23
+ ,17
+ ,17
+ ,9
+ ,9
+ ,16
+ ,16
+ ,5
+ ,5
+ ,25
+ ,25
+ ,1
+ ,23
+ ,20
+ ,20
+ ,10
+ ,10
+ ,10
+ ,10
+ ,8
+ ,8
+ ,26
+ ,26
+ ,1
+ ,25
+ ,15
+ ,15
+ ,12
+ ,12
+ ,11
+ ,11
+ ,8
+ ,8
+ ,20
+ ,20
+ ,1
+ ,24
+ ,20
+ ,20
+ ,14
+ ,14
+ ,14
+ ,14
+ ,10
+ ,10
+ ,18
+ ,18
+ ,1
+ ,24
+ ,33
+ ,33
+ ,14
+ ,14
+ ,9
+ ,9
+ ,6
+ ,6
+ ,32
+ ,32
+ ,1
+ ,23
+ ,29
+ ,29
+ ,10
+ ,10
+ ,12
+ ,12
+ ,8
+ ,8
+ ,25
+ ,25
+ ,1
+ ,21
+ ,23
+ ,23
+ ,14
+ ,14
+ ,17
+ ,17
+ ,7
+ ,7
+ ,25
+ ,25
+ ,1
+ ,24
+ ,26
+ ,26
+ ,16
+ ,16
+ ,5
+ ,5
+ ,4
+ ,4
+ ,23
+ ,23
+ ,1
+ ,24
+ ,18
+ ,18
+ ,9
+ ,9
+ ,12
+ ,12
+ ,8
+ ,8
+ ,21
+ ,21
+ ,1
+ ,28
+ ,20
+ ,20
+ ,10
+ ,10
+ ,12
+ ,12
+ ,8
+ ,8
+ ,20
+ ,20
+ ,1
+ ,16
+ ,11
+ ,11
+ ,6
+ ,6
+ ,6
+ ,6
+ ,4
+ ,4
+ ,15
+ ,15
+ ,1
+ ,20
+ ,28
+ ,28
+ ,8
+ ,8
+ ,24
+ ,24
+ ,20
+ ,20
+ ,30
+ ,30
+ ,1
+ ,29
+ ,26
+ ,26
+ ,13
+ ,13
+ ,12
+ ,12
+ ,8
+ ,8
+ ,24
+ ,24
+ ,1
+ ,27
+ ,22
+ ,22
+ ,10
+ ,10
+ ,12
+ ,12
+ ,8
+ ,8
+ ,26
+ ,26
+ ,1
+ ,22
+ ,17
+ ,17
+ ,8
+ ,8
+ ,14
+ ,14
+ ,6
+ ,6
+ ,24
+ ,24
+ ,1
+ ,28
+ ,12
+ ,12
+ ,7
+ ,7
+ ,7
+ ,7
+ ,4
+ ,4
+ ,22
+ ,22
+ ,1
+ ,16
+ ,14
+ ,14
+ ,15
+ ,15
+ ,13
+ ,13
+ ,8
+ ,8
+ ,14
+ ,14
+ ,1
+ ,25
+ ,17
+ ,17
+ ,9
+ ,9
+ ,12
+ ,12
+ ,9
+ ,9
+ ,24
+ ,24
+ ,1
+ ,24
+ ,21
+ ,21
+ ,10
+ ,10
+ ,13
+ ,13
+ ,6
+ ,6
+ ,24
+ ,24
+ ,0
+ ,28
+ ,19
+ ,0
+ ,12
+ ,0
+ ,14
+ ,0
+ ,7
+ ,0
+ ,24
+ ,0
+ ,1
+ ,24
+ ,18
+ ,18
+ ,13
+ ,13
+ ,8
+ ,8
+ ,9
+ ,9
+ ,24
+ ,24
+ ,1
+ ,23
+ ,10
+ ,10
+ ,10
+ ,10
+ ,11
+ ,11
+ ,5
+ ,5
+ ,19
+ ,19
+ ,1
+ ,30
+ ,29
+ ,29
+ ,11
+ ,11
+ ,9
+ ,9
+ ,5
+ ,5
+ ,31
+ ,31
+ ,1
+ ,24
+ ,31
+ ,31
+ ,8
+ ,8
+ ,11
+ ,11
+ ,8
+ ,8
+ ,22
+ ,22
+ ,1
+ ,21
+ ,19
+ ,19
+ ,9
+ ,9
+ ,13
+ ,13
+ ,8
+ ,8
+ ,27
+ ,27
+ ,1
+ ,25
+ ,9
+ ,9
+ ,13
+ ,13
+ ,10
+ ,10
+ ,6
+ ,6
+ ,19
+ ,19
+ ,0
+ ,25
+ ,20
+ ,0
+ ,11
+ ,0
+ ,11
+ ,0
+ ,8
+ ,0
+ ,25
+ ,0
+ ,1
+ ,22
+ ,28
+ ,28
+ ,8
+ ,8
+ ,12
+ ,12
+ ,7
+ ,7
+ ,20
+ ,20
+ ,1
+ ,23
+ ,19
+ ,19
+ ,9
+ ,9
+ ,9
+ ,9
+ ,7
+ ,7
+ ,21
+ ,21
+ ,1
+ ,26
+ ,30
+ ,30
+ ,9
+ ,9
+ ,15
+ ,15
+ ,9
+ ,9
+ ,27
+ ,27
+ ,1
+ ,23
+ ,29
+ ,29
+ ,15
+ ,15
+ ,18
+ ,18
+ ,11
+ ,11
+ ,23
+ ,23
+ ,1
+ ,25
+ ,26
+ ,26
+ ,9
+ ,9
+ ,15
+ ,15
+ ,6
+ ,6
+ ,25
+ ,25
+ ,1
+ ,21
+ ,23
+ ,23
+ ,10
+ ,10
+ ,12
+ ,12
+ ,8
+ ,8
+ ,20
+ ,20
+ ,1
+ ,25
+ ,13
+ ,13
+ ,14
+ ,14
+ ,13
+ ,13
+ ,6
+ ,6
+ ,21
+ ,21
+ ,1
+ ,24
+ ,21
+ ,21
+ ,12
+ ,12
+ ,14
+ ,14
+ ,9
+ ,9
+ ,22
+ ,22
+ ,1
+ ,29
+ ,19
+ ,19
+ ,12
+ ,12
+ ,10
+ ,10
+ ,8
+ ,8
+ ,23
+ ,23
+ ,1
+ ,22
+ ,28
+ ,28
+ ,11
+ ,11
+ ,13
+ ,13
+ ,6
+ ,6
+ ,25
+ ,25
+ ,1
+ ,27
+ ,23
+ ,23
+ ,14
+ ,14
+ ,13
+ ,13
+ ,10
+ ,10
+ ,25
+ ,25
+ ,0
+ ,26
+ ,18
+ ,0
+ ,6
+ ,0
+ ,11
+ ,0
+ ,8
+ ,0
+ ,17
+ ,0
+ ,1
+ ,22
+ ,21
+ ,21
+ ,12
+ ,12
+ ,13
+ ,13
+ ,8
+ ,8
+ ,19
+ ,19
+ ,1
+ ,24
+ ,20
+ ,20
+ ,8
+ ,8
+ ,16
+ ,16
+ ,10
+ ,10
+ ,25
+ ,25
+ ,0
+ ,27
+ ,23
+ ,0
+ ,14
+ ,0
+ ,8
+ ,0
+ ,5
+ ,0
+ ,19
+ ,0
+ ,1
+ ,24
+ ,21
+ ,21
+ ,11
+ ,11
+ ,16
+ ,16
+ ,7
+ ,7
+ ,20
+ ,20
+ ,1
+ ,24
+ ,21
+ ,21
+ ,10
+ ,10
+ ,11
+ ,11
+ ,5
+ ,5
+ ,26
+ ,26
+ ,1
+ ,29
+ ,15
+ ,15
+ ,14
+ ,14
+ ,9
+ ,9
+ ,8
+ ,8
+ ,23
+ ,23
+ ,1
+ ,22
+ ,28
+ ,28
+ ,12
+ ,12
+ ,16
+ ,16
+ ,14
+ ,14
+ ,27
+ ,27
+ ,0
+ ,21
+ ,19
+ ,0
+ ,10
+ ,0
+ ,12
+ ,0
+ ,7
+ ,0
+ ,17
+ ,0
+ ,1
+ ,24
+ ,26
+ ,26
+ ,14
+ ,14
+ ,14
+ ,14
+ ,8
+ ,8
+ ,17
+ ,17
+ ,1
+ ,24
+ ,10
+ ,10
+ ,5
+ ,5
+ ,8
+ ,8
+ ,6
+ ,6
+ ,19
+ ,19
+ ,0
+ ,23
+ ,16
+ ,0
+ ,11
+ ,0
+ ,9
+ ,0
+ ,5
+ ,0
+ ,17
+ ,0
+ ,1
+ ,20
+ ,22
+ ,22
+ ,10
+ ,10
+ ,15
+ ,15
+ ,6
+ ,6
+ ,22
+ ,22
+ ,1
+ ,27
+ ,19
+ ,19
+ ,9
+ ,9
+ ,11
+ ,11
+ ,10
+ ,10
+ ,21
+ ,21
+ ,1
+ ,26
+ ,31
+ ,31
+ ,10
+ ,10
+ ,21
+ ,21
+ ,12
+ ,12
+ ,32
+ ,32
+ ,1
+ ,25
+ ,31
+ ,31
+ ,16
+ ,16
+ ,14
+ ,14
+ ,9
+ ,9
+ ,21
+ ,21
+ ,1
+ ,21
+ ,29
+ ,29
+ ,13
+ ,13
+ ,18
+ ,18
+ ,12
+ ,12
+ ,21
+ ,21
+ ,1
+ ,21
+ ,19
+ ,19
+ ,9
+ ,9
+ ,12
+ ,12
+ ,7
+ ,7
+ ,18
+ ,18
+ ,1
+ ,19
+ ,22
+ ,22
+ ,10
+ ,10
+ ,13
+ ,13
+ ,8
+ ,8
+ ,18
+ ,18
+ ,1
+ ,21
+ ,23
+ ,23
+ ,10
+ ,10
+ ,15
+ ,15
+ ,10
+ ,10
+ ,23
+ ,23
+ ,1
+ ,21
+ ,15
+ ,15
+ ,7
+ ,7
+ ,12
+ ,12
+ ,6
+ ,6
+ ,19
+ ,19
+ ,1
+ ,16
+ ,20
+ ,20
+ ,9
+ ,9
+ ,19
+ ,19
+ ,10
+ ,10
+ ,20
+ ,20
+ ,1
+ ,22
+ ,18
+ ,18
+ ,8
+ ,8
+ ,15
+ ,15
+ ,10
+ ,10
+ ,21
+ ,21
+ ,1
+ ,29
+ ,23
+ ,23
+ ,14
+ ,14
+ ,11
+ ,11
+ ,10
+ ,10
+ ,20
+ ,20
+ ,0
+ ,15
+ ,25
+ ,0
+ ,14
+ ,0
+ ,11
+ ,0
+ ,5
+ ,0
+ ,17
+ ,0
+ ,1
+ ,17
+ ,21
+ ,21
+ ,8
+ ,8
+ ,10
+ ,10
+ ,7
+ ,7
+ ,18
+ ,18
+ ,1
+ ,15
+ ,24
+ ,24
+ ,9
+ ,9
+ ,13
+ ,13
+ ,10
+ ,10
+ ,19
+ ,19
+ ,1
+ ,21
+ ,25
+ ,25
+ ,14
+ ,14
+ ,15
+ ,15
+ ,11
+ ,11
+ ,22
+ ,22
+ ,0
+ ,21
+ ,17
+ ,0
+ ,14
+ ,0
+ ,12
+ ,0
+ ,6
+ ,0
+ ,15
+ ,0
+ ,1
+ ,19
+ ,13
+ ,13
+ ,8
+ ,8
+ ,12
+ ,12
+ ,7
+ ,7
+ ,14
+ ,14
+ ,1
+ ,24
+ ,28
+ ,28
+ ,8
+ ,8
+ ,16
+ ,16
+ ,12
+ ,12
+ ,18
+ ,18
+ ,1
+ ,20
+ ,21
+ ,21
+ ,8
+ ,8
+ ,9
+ ,9
+ ,11
+ ,11
+ ,24
+ ,24
+ ,0
+ ,17
+ ,25
+ ,0
+ ,7
+ ,0
+ ,18
+ ,0
+ ,11
+ ,0
+ ,35
+ ,0
+ ,1
+ ,23
+ ,9
+ ,9
+ ,6
+ ,6
+ ,8
+ ,8
+ ,11
+ ,11
+ ,29
+ ,29
+ ,1
+ ,24
+ ,16
+ ,16
+ ,8
+ ,8
+ ,13
+ ,13
+ ,5
+ ,5
+ ,21
+ ,21
+ ,1
+ ,14
+ ,19
+ ,19
+ ,6
+ ,6
+ ,17
+ ,17
+ ,8
+ ,8
+ ,25
+ ,25
+ ,1
+ ,19
+ ,17
+ ,17
+ ,11
+ ,11
+ ,9
+ ,9
+ ,6
+ ,6
+ ,20
+ ,20
+ ,1
+ ,24
+ ,25
+ ,25
+ ,14
+ ,14
+ ,15
+ ,15
+ ,9
+ ,9
+ ,22
+ ,22
+ ,1
+ ,13
+ ,20
+ ,20
+ ,11
+ ,11
+ ,8
+ ,8
+ ,4
+ ,4
+ ,13
+ ,13
+ ,1
+ ,22
+ ,29
+ ,29
+ ,11
+ ,11
+ ,7
+ ,7
+ ,4
+ ,4
+ ,26
+ ,26
+ ,1
+ ,16
+ ,14
+ ,14
+ ,11
+ ,11
+ ,12
+ ,12
+ ,7
+ ,7
+ ,17
+ ,17
+ ,0
+ ,19
+ ,22
+ ,0
+ ,14
+ ,0
+ ,14
+ ,0
+ ,11
+ ,0
+ ,25
+ ,0
+ ,1
+ ,25
+ ,15
+ ,15
+ ,8
+ ,8
+ ,6
+ ,6
+ ,6
+ ,6
+ ,20
+ ,20
+ ,1
+ ,25
+ ,19
+ ,19
+ ,20
+ ,20
+ ,8
+ ,8
+ ,7
+ ,7
+ ,19
+ ,19
+ ,1
+ ,23
+ ,20
+ ,20
+ ,11
+ ,11
+ ,17
+ ,17
+ ,8
+ ,8
+ ,21
+ ,21
+ ,0
+ ,24
+ ,15
+ ,0
+ ,8
+ ,0
+ ,10
+ ,0
+ ,4
+ ,0
+ ,22
+ ,0
+ ,1
+ ,26
+ ,20
+ ,20
+ ,11
+ ,11
+ ,11
+ ,11
+ ,8
+ ,8
+ ,24
+ ,24
+ ,1
+ ,26
+ ,18
+ ,18
+ ,10
+ ,10
+ ,14
+ ,14
+ ,9
+ ,9
+ ,21
+ ,21
+ ,1
+ ,25
+ ,33
+ ,33
+ ,14
+ ,14
+ ,11
+ ,11
+ ,8
+ ,8
+ ,26
+ ,26
+ ,1
+ ,18
+ ,22
+ ,22
+ ,11
+ ,11
+ ,13
+ ,13
+ ,11
+ ,11
+ ,24
+ ,24
+ ,1
+ ,21
+ ,16
+ ,16
+ ,9
+ ,9
+ ,12
+ ,12
+ ,8
+ ,8
+ ,16
+ ,16
+ ,1
+ ,26
+ ,17
+ ,17
+ ,9
+ ,9
+ ,11
+ ,11
+ ,5
+ ,5
+ ,23
+ ,23
+ ,1
+ ,23
+ ,16
+ ,16
+ ,8
+ ,8
+ ,9
+ ,9
+ ,4
+ ,4
+ ,18
+ ,18
+ ,1
+ ,23
+ ,21
+ ,21
+ ,10
+ ,10
+ ,12
+ ,12
+ ,8
+ ,8
+ ,16
+ ,16
+ ,1
+ ,22
+ ,26
+ ,26
+ ,13
+ ,13
+ ,20
+ ,20
+ ,10
+ ,10
+ ,26
+ ,26
+ ,1
+ ,20
+ ,18
+ ,18
+ ,13
+ ,13
+ ,12
+ ,12
+ ,6
+ ,6
+ ,19
+ ,19
+ ,1
+ ,13
+ ,18
+ ,18
+ ,12
+ ,12
+ ,13
+ ,13
+ ,9
+ ,9
+ ,21
+ ,21
+ ,1
+ ,24
+ ,17
+ ,17
+ ,8
+ ,8
+ ,12
+ ,12
+ ,9
+ ,9
+ ,21
+ ,21
+ ,1
+ ,15
+ ,22
+ ,22
+ ,13
+ ,13
+ ,12
+ ,12
+ ,13
+ ,13
+ ,22
+ ,22
+ ,1
+ ,14
+ ,30
+ ,30
+ ,14
+ ,14
+ ,9
+ ,9
+ ,9
+ ,9
+ ,23
+ ,23
+ ,0
+ ,22
+ ,30
+ ,0
+ ,12
+ ,0
+ ,15
+ ,0
+ ,10
+ ,0
+ ,29
+ ,0
+ ,1
+ ,10
+ ,24
+ ,24
+ ,14
+ ,14
+ ,24
+ ,24
+ ,20
+ ,20
+ ,21
+ ,21
+ ,1
+ ,24
+ ,21
+ ,21
+ ,15
+ ,15
+ ,7
+ ,7
+ ,5
+ ,5
+ ,21
+ ,21
+ ,1
+ ,22
+ ,21
+ ,21
+ ,13
+ ,13
+ ,17
+ ,17
+ ,11
+ ,11
+ ,23
+ ,23
+ ,1
+ ,24
+ ,29
+ ,29
+ ,16
+ ,16
+ ,11
+ ,11
+ ,6
+ ,6
+ ,27
+ ,27
+ ,1
+ ,19
+ ,31
+ ,31
+ ,9
+ ,9
+ ,17
+ ,17
+ ,9
+ ,9
+ ,25
+ ,25
+ ,0
+ ,20
+ ,20
+ ,0
+ ,9
+ ,0
+ ,11
+ ,0
+ ,7
+ ,0
+ ,21
+ ,0
+ ,1
+ ,13
+ ,16
+ ,16
+ ,9
+ ,9
+ ,12
+ ,12
+ ,9
+ ,9
+ ,10
+ ,10
+ ,1
+ ,20
+ ,22
+ ,22
+ ,8
+ ,8
+ ,14
+ ,14
+ ,10
+ ,10
+ ,20
+ ,20
+ ,1
+ ,22
+ ,20
+ ,20
+ ,7
+ ,7
+ ,11
+ ,11
+ ,9
+ ,9
+ ,26
+ ,26
+ ,1
+ ,24
+ ,28
+ ,28
+ ,16
+ ,16
+ ,16
+ ,16
+ ,8
+ ,8
+ ,24
+ ,24
+ ,1
+ ,29
+ ,38
+ ,38
+ ,11
+ ,11
+ ,21
+ ,21
+ ,7
+ ,7
+ ,29
+ ,29
+ ,1
+ ,12
+ ,22
+ ,22
+ ,9
+ ,9
+ ,14
+ ,14
+ ,6
+ ,6
+ ,19
+ ,19
+ ,1
+ ,20
+ ,20
+ ,20
+ ,11
+ ,11
+ ,20
+ ,20
+ ,13
+ ,13
+ ,24
+ ,24
+ ,1
+ ,21
+ ,17
+ ,17
+ ,9
+ ,9
+ ,13
+ ,13
+ ,6
+ ,6
+ ,19
+ ,19
+ ,1
+ ,24
+ ,28
+ ,28
+ ,14
+ ,14
+ ,11
+ ,11
+ ,8
+ ,8
+ ,24
+ ,24
+ ,1
+ ,22
+ ,22
+ ,22
+ ,13
+ ,13
+ ,15
+ ,15
+ ,10
+ ,10
+ ,22
+ ,22
+ ,1
+ ,20
+ ,31
+ ,31
+ ,16
+ ,16
+ ,19
+ ,19
+ ,16
+ ,16
+ ,17
+ ,17)
+ ,dim=c(12
+ ,159)
+ ,dimnames=list(c('B'
+ ,'O'
+ ,'CM'
+ ,'CM_B'
+ ,'D'
+ ,'D_B'
+ ,'PE'
+ ,'PE_B'
+ ,'PC'
+ ,'PC_B'
+ ,'PS'
+ ,'PS_B')
+ ,1:159))
> y <- array(NA,dim=c(12,159),dimnames=list(c('B','O','CM','CM_B','D','D_B','PE','PE_B','PC','PC_B','PS','PS_B'),1:159))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '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
O B CM CM_B D D_B PE PE_B PC PC_B PS PS_B
1 26 1 24 24 14 14 11 11 12 12 24 24
2 23 1 25 25 11 11 7 7 8 8 25 25
3 25 0 17 0 6 0 17 0 8 0 30 0
4 23 1 18 18 12 12 10 10 8 8 19 19
5 20 1 18 18 8 8 12 12 9 9 22 22
6 29 0 16 10 0 12 0 7 0 22 1 25
7 20 20 10 10 11 11 4 4 25 25 1 21
8 16 16 11 11 11 11 11 11 23 23 1 22
9 18 18 16 16 12 12 7 7 17 17 1 25
10 17 17 11 11 13 13 7 7 21 21 1 24
11 23 23 13 13 14 14 12 12 19 19 1 18
12 30 30 12 12 16 16 10 10 19 19 1 22
13 23 23 8 8 11 11 10 10 15 15 1 15
14 18 18 12 12 10 10 8 8 16 16 1 22
15 15 15 11 11 11 11 8 8 23 23 1 28
16 12 12 4 4 15 15 4 4 27 27 1 20
17 21 21 9 9 9 9 9 9 22 22 1 12
18 15 15 8 8 11 11 8 8 14 14 1 24
19 20 20 8 8 17 17 7 7 22 22 1 20
20 31 31 14 14 17 17 11 11 23 23 1 21
21 27 27 15 15 11 11 9 9 23 23 1 20
22 34 34 16 16 18 18 11 11 21 21 1 21
23 21 21 9 9 14 14 13 13 19 19 1 23
24 31 31 14 14 10 10 8 8 18 18 1 28
25 19 19 11 11 11 11 8 8 20 20 1 24
26 16 16 8 8 15 15 9 9 23 23 1 24
27 20 20 9 9 15 15 6 6 25 25 1 24
28 21 21 9 9 13 13 9 9 19 19 1 23
29 22 22 9 9 16 16 9 9 24 24 1 23
30 17 17 9 9 13 13 6 6 22 22 1 29
31 24 24 10 10 9 9 6 6 25 25 1 24
32 25 25 16 16 18 18 16 16 26 26 1 18
33 26 26 11 11 18 18 5 5 29 29 1 25
34 25 25 8 8 12 12 7 7 32 32 1 21
35 17 17 9 9 17 17 9 9 25 25 1 26
36 32 32 16 16 9 9 6 6 29 29 1 22
37 33 33 11 11 9 9 6 6 28 28 1 22
38 13 13 16 16 12 12 5 5 17 17 0 22
39 0 32 12 0 18 0 12 0 28 0 1 23
40 25 25 12 12 12 12 7 7 29 29 1 30
41 29 29 14 14 18 18 10 10 26 26 1 23
42 22 22 9 9 14 14 9 9 25 25 1 17
43 18 18 10 10 15 15 8 8 14 14 1 23
44 17 17 9 9 16 16 5 5 25 25 1 23
45 20 20 10 10 10 10 8 8 26 26 1 25
46 15 15 12 12 11 11 8 8 20 20 1 24
47 20 20 14 14 14 14 10 10 18 18 1 24
48 33 33 14 14 9 9 6 6 32 32 1 23
49 29 29 10 10 12 12 8 8 25 25 1 21
50 23 23 14 14 17 17 7 7 25 25 1 24
51 26 26 16 16 5 5 4 4 23 23 1 24
52 18 18 9 9 12 12 8 8 21 21 1 28
53 20 20 10 10 12 12 8 8 20 20 1 16
54 11 11 6 6 6 6 4 4 15 15 1 20
55 28 28 8 8 24 24 20 20 30 30 1 29
56 26 26 13 13 12 12 8 8 24 24 1 27
57 22 22 10 10 12 12 8 8 26 26 1 22
58 17 17 8 8 14 14 6 6 24 24 1 28
59 12 12 7 7 7 7 4 4 22 22 1 16
60 14 14 15 15 13 13 8 8 14 14 1 25
61 17 17 9 9 12 12 9 9 24 24 1 24
62 21 21 10 10 13 13 6 6 24 24 0 28
63 0 19 12 0 14 0 7 0 24 0 1 24
64 18 18 13 13 8 8 9 9 24 24 1 23
65 10 10 10 10 11 11 5 5 19 19 1 30
66 29 29 11 11 9 9 5 5 31 31 1 24
67 31 31 8 8 11 11 8 8 22 22 1 21
68 19 19 9 9 13 13 8 8 27 27 1 25
69 9 9 13 13 10 10 6 6 19 19 0 25
70 0 20 11 0 11 0 8 0 25 0 1 22
71 28 28 8 8 12 12 7 7 20 20 1 23
72 19 19 9 9 9 9 7 7 21 21 1 26
73 30 30 9 9 15 15 9 9 27 27 1 23
74 29 29 15 15 18 18 11 11 23 23 1 25
75 26 26 9 9 15 15 6 6 25 25 1 21
76 23 23 10 10 12 12 8 8 20 20 1 25
77 13 13 14 14 13 13 6 6 21 21 1 24
78 21 21 12 12 14 14 9 9 22 22 1 29
79 19 19 12 12 10 10 8 8 23 23 1 22
80 28 28 11 11 13 13 6 6 25 25 1 27
81 23 23 14 14 13 13 10 10 25 25 0 26
82 0 18 6 0 11 0 8 0 17 0 1 22
83 21 21 12 12 13 13 8 8 19 19 1 24
84 20 20 8 8 16 16 10 10 25 25 0 27
85 0 23 14 0 8 0 5 0 19 0 1 24
86 21 21 11 11 16 16 7 7 20 20 1 24
87 21 21 10 10 11 11 5 5 26 26 1 29
88 15 15 14 14 9 9 8 8 23 23 1 22
89 28 28 12 12 16 16 14 14 27 27 0 21
90 0 19 10 0 12 0 7 0 17 0 1 24
91 26 26 14 14 14 14 8 8 17 17 1 24
92 10 10 5 5 8 8 6 6 19 19 0 23
93 0 16 11 0 9 0 5 0 17 0 1 20
94 22 22 10 10 15 15 6 6 22 22 1 27
95 19 19 9 9 11 11 10 10 21 21 1 26
96 31 31 10 10 21 21 12 12 32 32 1 25
97 31 31 16 16 14 14 9 9 21 21 1 21
98 29 29 13 13 18 18 12 12 21 21 1 21
99 19 19 9 9 12 12 7 7 18 18 1 19
100 22 22 10 10 13 13 8 8 18 18 1 21
101 23 23 10 10 15 15 10 10 23 23 1 21
102 15 15 7 7 12 12 6 6 19 19 1 16
103 20 20 9 9 19 19 10 10 20 20 1 22
104 18 18 8 8 15 15 10 10 21 21 1 29
105 23 23 14 14 11 11 10 10 20 20 0 15
106 0 25 14 0 11 0 5 0 17 0 1 17
107 21 21 8 8 10 10 7 7 18 18 1 15
108 24 24 9 9 13 13 10 10 19 19 1 21
109 25 25 14 14 15 15 11 11 22 22 0 21
110 0 17 14 0 12 0 6 0 15 0 1 19
111 13 13 8 8 12 12 7 7 14 14 1 24
112 28 28 8 8 16 16 12 12 18 18 1 20
113 21 21 8 8 9 9 11 11 24 24 0 17
114 0 25 7 0 18 0 11 0 35 0 1 23
115 9 9 6 6 8 8 11 11 29 29 1 24
116 16 16 8 8 13 13 5 5 21 21 1 14
117 19 19 6 6 17 17 8 8 25 25 1 19
118 17 17 11 11 9 9 6 6 20 20 1 24
119 25 25 14 14 15 15 9 9 22 22 1 13
120 20 20 11 11 8 8 4 4 13 13 1 22
121 29 29 11 11 7 7 4 4 26 26 1 16
122 14 14 11 11 12 12 7 7 17 17 0 19
123 0 22 14 0 14 0 11 0 25 0 1 25
124 15 15 8 8 6 6 6 6 20 20 1 25
125 19 19 20 20 8 8 7 7 19 19 1 23
126 20 20 11 11 17 17 8 8 21 21 0 24
127 0 15 8 0 10 0 4 0 22 0 1 26
128 20 20 11 11 11 11 8 8 24 24 1 26
129 18 18 10 10 14 14 9 9 21 21 1 25
130 33 33 14 14 11 11 8 8 26 26 1 18
131 22 22 11 11 13 13 11 11 24 24 1 21
132 16 16 9 9 12 12 8 8 16 16 1 26
133 17 17 9 9 11 11 5 5 23 23 1 23
134 16 16 8 8 9 9 4 4 18 18 1 23
135 21 21 10 10 12 12 8 8 16 16 1 22
136 26 26 13 13 20 20 10 10 26 26 1 20
137 18 18 13 13 12 12 6 6 19 19 1 13
138 18 18 12 12 13 13 9 9 21 21 1 24
139 17 17 8 8 12 12 9 9 21 21 1 15
140 22 22 13 13 12 12 13 13 22 22 1 14
141 30 30 14 14 9 9 9 9 23 23 0 22
142 0 30 12 0 15 0 10 0 29 0 1 10
143 24 24 14 14 24 24 20 20 21 21 1 24
144 21 21 15 15 7 7 5 5 21 21 1 22
145 21 21 13 13 17 17 11 11 23 23 1 24
146 29 29 16 16 11 11 6 6 27 27 1 19
147 31 31 9 9 17 17 9 9 25 25 0 20
148 0 20 9 0 11 0 7 0 21 0 1 13
149 16 16 9 9 12 12 9 9 10 10 1 20
150 22 22 8 8 14 14 10 10 20 20 1 22
151 20 20 7 7 11 11 9 9 26 26 1 24
152 28 28 16 16 16 16 8 8 24 24 1 29
153 38 38 11 11 21 21 7 7 29 29 1 12
154 22 22 9 9 14 14 6 6 19 19 1 20
155 20 20 11 11 20 20 13 13 24 24 1 21
156 17 17 9 9 13 13 6 6 19 19 1 24
157 28 28 14 14 11 11 8 8 24 24 1 22
158 22 22 13 13 15 15 10 10 22 22 1 20
159 31 31 16 16 19 19 16 16 17 17 1 26
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) B CM CM_B D D_B
-0.57826 0.92554 0.01322 0.01600 -1.31049 1.32525
PE PE_B PC PC_B PS PS_B
0.40633 -0.39459 -0.34199 0.36346 0.94211 0.01465
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.2377 -0.4046 -0.1673 0.2627 7.1572
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.57826 1.01873 -0.568 0.57115
B 0.92554 0.02646 34.981 < 2e-16 ***
CM 0.01322 0.08117 0.163 0.87086
CM_B 0.01600 0.07904 0.202 0.83983
D -1.31049 0.21818 -6.006 1.42e-08 ***
D_B 1.32525 0.22104 5.995 1.50e-08 ***
PE 0.40633 0.14355 2.831 0.00530 **
PE_B -0.39459 0.15185 -2.599 0.01031 *
PC -0.34199 0.10802 -3.166 0.00188 **
PC_B 0.36346 0.10928 3.326 0.00111 **
PS 0.94211 0.04818 19.552 < 2e-16 ***
PS_B 0.01465 0.03057 0.479 0.63246
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.433 on 147 degrees of freedom
Multiple R-squared: 0.9723, Adjusted R-squared: 0.9702
F-statistic: 469.1 on 11 and 147 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,] 0.931890325 1.362194e-01 6.810968e-02
[2,] 0.996805082 6.389836e-03 3.194918e-03
[3,] 0.992876311 1.424738e-02 7.123689e-03
[4,] 0.985384368 2.923126e-02 1.461563e-02
[5,] 0.983095290 3.380942e-02 1.690471e-02
[6,] 0.970856332 5.828734e-02 2.914367e-02
[7,] 0.963207522 7.358496e-02 3.679248e-02
[8,] 0.942149602 1.157008e-01 5.785040e-02
[9,] 0.912732285 1.745354e-01 8.726771e-02
[10,] 0.965010013 6.997997e-02 3.498999e-02
[11,] 0.947600649 1.047987e-01 5.239935e-02
[12,] 0.924726269 1.505475e-01 7.527373e-02
[13,] 0.895200220 2.095996e-01 1.047998e-01
[14,] 0.858080980 2.838380e-01 1.419190e-01
[15,] 0.813311839 3.733763e-01 1.866882e-01
[16,] 0.769778375 4.604433e-01 2.302216e-01
[17,] 0.762113147 4.757737e-01 2.378869e-01
[18,] 0.722634650 5.547307e-01 2.773654e-01
[19,] 0.663850487 6.722990e-01 3.361495e-01
[20,] 0.620709085 7.585818e-01 3.792909e-01
[21,] 0.560556488 8.788870e-01 4.394435e-01
[22,] 0.513812576 9.723748e-01 4.861874e-01
[23,] 0.470394143 9.407883e-01 5.296059e-01
[24,] 0.413657578 8.273152e-01 5.863424e-01
[25,] 0.598974370 8.020513e-01 4.010256e-01
[26,] 0.554492920 8.910142e-01 4.455071e-01
[27,] 0.495662277 9.913246e-01 5.043377e-01
[28,] 0.439305589 8.786112e-01 5.606944e-01
[29,] 0.385478527 7.709571e-01 6.145215e-01
[30,] 0.336057625 6.721153e-01 6.639424e-01
[31,] 0.287567284 5.751346e-01 7.124327e-01
[32,] 0.242217504 4.844350e-01 7.577825e-01
[33,] 0.201131977 4.022640e-01 7.988680e-01
[34,] 0.167170787 3.343416e-01 8.328292e-01
[35,] 0.136007452 2.720149e-01 8.639925e-01
[36,] 0.110051711 2.201034e-01 8.899483e-01
[37,] 0.086335833 1.726717e-01 9.136642e-01
[38,] 0.067658307 1.353166e-01 9.323417e-01
[39,] 0.052917541 1.058351e-01 9.470825e-01
[40,] 0.039803085 7.960617e-02 9.601969e-01
[41,] 0.030474924 6.094985e-02 9.695251e-01
[42,] 0.022482019 4.496404e-02 9.775180e-01
[43,] 0.016168704 3.233741e-02 9.838313e-01
[44,] 0.011709907 2.341981e-02 9.882901e-01
[45,] 0.008294450 1.658890e-02 9.917055e-01
[46,] 0.005993238 1.198648e-02 9.940068e-01
[47,] 0.004111996 8.223992e-03 9.958880e-01
[48,] 0.004409943 8.819886e-03 9.955901e-01
[49,] 0.011382821 2.276564e-02 9.886172e-01
[50,] 0.008065125 1.613025e-02 9.919349e-01
[51,] 0.005984439 1.196888e-02 9.940156e-01
[52,] 0.004203603 8.407205e-03 9.957964e-01
[53,] 0.003076827 6.153655e-03 9.969232e-01
[54,] 0.002100396 4.200792e-03 9.978996e-01
[55,] 0.001843086 3.686173e-03 9.981569e-01
[56,] 0.085734192 1.714684e-01 9.142658e-01
[57,] 0.070459362 1.409187e-01 9.295406e-01
[58,] 0.055587752 1.111755e-01 9.444122e-01
[59,] 0.043478561 8.695712e-02 9.565214e-01
[60,] 0.033217970 6.643594e-02 9.667820e-01
[61,] 0.025167636 5.033527e-02 9.748324e-01
[62,] 0.018841494 3.768299e-02 9.811585e-01
[63,] 0.015380349 3.076070e-02 9.846197e-01
[64,] 0.011556599 2.311320e-02 9.884434e-01
[65,] 0.008360040 1.672008e-02 9.916400e-01
[66,] 0.006000731 1.200146e-02 9.939993e-01
[67,] 0.005696939 1.139388e-02 9.943031e-01
[68,] 0.019309507 3.861901e-02 9.806905e-01
[69,] 0.014320149 2.864030e-02 9.856799e-01
[70,] 0.013347280 2.669456e-02 9.866527e-01
[71,] 0.969588943 6.082211e-02 3.041106e-02
[72,] 0.960508884 7.898223e-02 3.949112e-02
[73,] 0.951200003 9.759999e-02 4.880000e-02
[74,] 0.939805351 1.203893e-01 6.019465e-02
[75,] 0.927099248 1.458015e-01 7.290075e-02
[76,] 0.935662044 1.286759e-01 6.433796e-02
[77,] 0.918652260 1.626955e-01 8.134774e-02
[78,] 0.903973951 1.920521e-01 9.602605e-02
[79,] 0.925192040 1.496159e-01 7.480796e-02
[80,] 0.909344025 1.813119e-01 9.065597e-02
[81,] 0.888459865 2.230803e-01 1.115401e-01
[82,] 0.862500289 2.749994e-01 1.374997e-01
[83,] 0.833935875 3.321283e-01 1.660641e-01
[84,] 0.801463296 3.970734e-01 1.985367e-01
[85,] 0.764136061 4.717279e-01 2.358639e-01
[86,] 0.722561571 5.548769e-01 2.774384e-01
[87,] 0.677870535 6.442589e-01 3.221295e-01
[88,] 0.633106557 7.337869e-01 3.668934e-01
[89,] 0.584624928 8.307501e-01 4.153751e-01
[90,] 0.549708909 9.005822e-01 4.502911e-01
[91,] 0.509341852 9.813163e-01 4.906581e-01
[92,] 0.999533615 9.327693e-04 4.663847e-04
[93,] 0.999336541 1.326919e-03 6.634594e-04
[94,] 0.998921075 2.157850e-03 1.078925e-03
[95,] 0.998317423 3.365153e-03 1.682577e-03
[96,] 0.999676731 6.465379e-04 3.232690e-04
[97,] 0.999455626 1.088748e-03 5.443739e-04
[98,] 0.999142714 1.714571e-03 8.572857e-04
[99,] 0.998775300 2.449400e-03 1.224700e-03
[100,] 1.000000000 0.000000e+00 0.000000e+00
[101,] 1.000000000 0.000000e+00 0.000000e+00
[102,] 1.000000000 0.000000e+00 0.000000e+00
[103,] 1.000000000 0.000000e+00 0.000000e+00
[104,] 1.000000000 0.000000e+00 0.000000e+00
[105,] 1.000000000 0.000000e+00 0.000000e+00
[106,] 1.000000000 0.000000e+00 0.000000e+00
[107,] 1.000000000 0.000000e+00 0.000000e+00
[108,] 1.000000000 0.000000e+00 0.000000e+00
[109,] 1.000000000 0.000000e+00 0.000000e+00
[110,] 1.000000000 0.000000e+00 0.000000e+00
[111,] 1.000000000 4.256109e-304 2.128055e-304
[112,] 1.000000000 4.843851e-294 2.421926e-294
[113,] 1.000000000 8.751293e-278 4.375646e-278
[114,] 1.000000000 5.654166e-263 2.827083e-263
[115,] 1.000000000 2.304846e-245 1.152423e-245
[116,] 1.000000000 3.059729e-230 1.529864e-230
[117,] 1.000000000 1.324108e-223 6.620542e-224
[118,] 1.000000000 3.792935e-209 1.896467e-209
[119,] 1.000000000 8.418930e-195 4.209465e-195
[120,] 1.000000000 1.016467e-182 5.082336e-183
[121,] 1.000000000 5.718625e-168 2.859312e-168
[122,] 1.000000000 2.353286e-154 1.176643e-154
[123,] 1.000000000 4.302268e-142 2.151134e-142
[124,] 1.000000000 9.927646e-126 4.963823e-126
[125,] 1.000000000 1.066143e-112 5.330713e-113
[126,] 1.000000000 1.954390e-97 9.771950e-98
[127,] 1.000000000 7.627920e-82 3.813960e-82
[128,] 1.000000000 2.038774e-73 1.019387e-73
[129,] 1.000000000 5.912955e-59 2.956477e-59
[130,] 1.000000000 2.512948e-45 1.256474e-45
> postscript(file="/var/www/html/freestat/rcomp/tmp/1lm4q1290504805.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2ve4b1290504805.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ve4b1290504805.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4ve4b1290504805.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5ve4b1290504805.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 = 159
Frequency = 1
1 2 3 4 5 6
1.395800449 -2.413032788 0.781681660 3.482063340 -2.374116531 6.761298370
7 8 9 10 11 12
-0.220585670 -0.601559460 -0.481638719 -0.496000138 -0.050280333 0.435558252
13 14 15 16 17 18
0.293437781 -0.281549585 -0.728696109 -0.728310616 0.050180467 -0.389167335
19 20 21 22 23 24
-0.206857914 0.353830947 0.153445761 0.546968292 -0.167311138 0.497201350
25 26 27 28 29 30
-0.307818030 -0.578750858 -0.317837141 -0.105584042 -0.182769976 -0.520536110
31 32 33 34 35 36
0.039361490 -0.245342281 -0.062577910 0.009878480 -0.635275720 0.403150219
37 38 39 40 41 42
0.645198248 0.155575440 -2.187949684 -0.174433175 0.108162397 -0.086826020
43 44 45 46 47 48
-0.268609338 -0.529600068 -0.332863995 -0.634898291 -0.345835867 0.456987455
49 50 51 52 53 54
0.387868522 -0.281815348 0.138431795 -0.418671119 -0.101695749 -0.470689116
55 56 57 58 59 60
-0.170742395 0.010385800 -0.169506995 -0.534372816 -0.531925928 -0.712355551
61 62 63 64 65 66
-0.510700971 0.661907253 5.251085666 -0.479433049 -0.979975583 0.265360888
67 68 69 70 71 72
0.674423540 -0.443861652 -0.123734455 0.372268939 0.461659829 -0.258884715
73 74 75 76 77 78
0.363285434 0.102321421 0.172898940 -0.010147652 -0.869782539 -0.360328907
79 80 81 82 83 84
-0.357402194 0.205007913 0.654807203 -0.446493780 -0.196157611 0.547814294
85 86 87 88 89 90
-7.237715680 -0.220948998 -0.296532798 -0.698944002 1.024625958 0.262612920
91 92 93 94 95 96
0.145908909 0.243325191 -0.034208564 -0.177656777 -0.323630495 0.148072407
97 98 99 100 101 102
0.406099356 0.250569405 -0.136197527 0.002173240 -0.083735695 -0.341395702
103 104 105 106 107 108
-0.287176871 -0.471863188 0.952842063 -5.738759161 0.130072364 0.135367075
109 110 111 112 113 114
0.900143213 1.856403753 -0.541115192 0.430806279 0.881828825 7.157152280
115 116 117 118 119 120
-1.090563268 -0.312820441 -0.284392499 -0.403743108 0.098717550 0.037510619
121 122 123 124 125 126
0.531188875 0.396614773 1.150074462 -0.435374972 -0.478669049 0.598715656
127 128 129 130 131 132
4.269853030 -0.348548020 -0.445206888 0.606074262 -0.191117032 -0.430931831
133 134 135 136 137 138
-0.412850471 -0.309461307 -0.029232731 -0.071581630 -0.249384851 -0.474240827
139 140 141 142 143 144
-0.285211738 -0.112789455 1.348388305 -2.923251842 -0.377419654 -0.173684838
145 146 147 148 149 150
-0.405541573 0.237132051 1.437230792 -0.431084022 -0.225933092 -0.035223958
151 152 153 154 155 156
-0.257050791 -0.016691257 0.953676418 0.033293784 -0.466852130 -0.382866862
157 158 159
0.218101021 -0.209742613 0.262753698
> postscript(file="/var/www/html/freestat/rcomp/tmp/66n3e1290504805.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 1.395800449 NA
1 -2.413032788 1.395800449
2 0.781681660 -2.413032788
3 3.482063340 0.781681660
4 -2.374116531 3.482063340
5 6.761298370 -2.374116531
6 -0.220585670 6.761298370
7 -0.601559460 -0.220585670
8 -0.481638719 -0.601559460
9 -0.496000138 -0.481638719
10 -0.050280333 -0.496000138
11 0.435558252 -0.050280333
12 0.293437781 0.435558252
13 -0.281549585 0.293437781
14 -0.728696109 -0.281549585
15 -0.728310616 -0.728696109
16 0.050180467 -0.728310616
17 -0.389167335 0.050180467
18 -0.206857914 -0.389167335
19 0.353830947 -0.206857914
20 0.153445761 0.353830947
21 0.546968292 0.153445761
22 -0.167311138 0.546968292
23 0.497201350 -0.167311138
24 -0.307818030 0.497201350
25 -0.578750858 -0.307818030
26 -0.317837141 -0.578750858
27 -0.105584042 -0.317837141
28 -0.182769976 -0.105584042
29 -0.520536110 -0.182769976
30 0.039361490 -0.520536110
31 -0.245342281 0.039361490
32 -0.062577910 -0.245342281
33 0.009878480 -0.062577910
34 -0.635275720 0.009878480
35 0.403150219 -0.635275720
36 0.645198248 0.403150219
37 0.155575440 0.645198248
38 -2.187949684 0.155575440
39 -0.174433175 -2.187949684
40 0.108162397 -0.174433175
41 -0.086826020 0.108162397
42 -0.268609338 -0.086826020
43 -0.529600068 -0.268609338
44 -0.332863995 -0.529600068
45 -0.634898291 -0.332863995
46 -0.345835867 -0.634898291
47 0.456987455 -0.345835867
48 0.387868522 0.456987455
49 -0.281815348 0.387868522
50 0.138431795 -0.281815348
51 -0.418671119 0.138431795
52 -0.101695749 -0.418671119
53 -0.470689116 -0.101695749
54 -0.170742395 -0.470689116
55 0.010385800 -0.170742395
56 -0.169506995 0.010385800
57 -0.534372816 -0.169506995
58 -0.531925928 -0.534372816
59 -0.712355551 -0.531925928
60 -0.510700971 -0.712355551
61 0.661907253 -0.510700971
62 5.251085666 0.661907253
63 -0.479433049 5.251085666
64 -0.979975583 -0.479433049
65 0.265360888 -0.979975583
66 0.674423540 0.265360888
67 -0.443861652 0.674423540
68 -0.123734455 -0.443861652
69 0.372268939 -0.123734455
70 0.461659829 0.372268939
71 -0.258884715 0.461659829
72 0.363285434 -0.258884715
73 0.102321421 0.363285434
74 0.172898940 0.102321421
75 -0.010147652 0.172898940
76 -0.869782539 -0.010147652
77 -0.360328907 -0.869782539
78 -0.357402194 -0.360328907
79 0.205007913 -0.357402194
80 0.654807203 0.205007913
81 -0.446493780 0.654807203
82 -0.196157611 -0.446493780
83 0.547814294 -0.196157611
84 -7.237715680 0.547814294
85 -0.220948998 -7.237715680
86 -0.296532798 -0.220948998
87 -0.698944002 -0.296532798
88 1.024625958 -0.698944002
89 0.262612920 1.024625958
90 0.145908909 0.262612920
91 0.243325191 0.145908909
92 -0.034208564 0.243325191
93 -0.177656777 -0.034208564
94 -0.323630495 -0.177656777
95 0.148072407 -0.323630495
96 0.406099356 0.148072407
97 0.250569405 0.406099356
98 -0.136197527 0.250569405
99 0.002173240 -0.136197527
100 -0.083735695 0.002173240
101 -0.341395702 -0.083735695
102 -0.287176871 -0.341395702
103 -0.471863188 -0.287176871
104 0.952842063 -0.471863188
105 -5.738759161 0.952842063
106 0.130072364 -5.738759161
107 0.135367075 0.130072364
108 0.900143213 0.135367075
109 1.856403753 0.900143213
110 -0.541115192 1.856403753
111 0.430806279 -0.541115192
112 0.881828825 0.430806279
113 7.157152280 0.881828825
114 -1.090563268 7.157152280
115 -0.312820441 -1.090563268
116 -0.284392499 -0.312820441
117 -0.403743108 -0.284392499
118 0.098717550 -0.403743108
119 0.037510619 0.098717550
120 0.531188875 0.037510619
121 0.396614773 0.531188875
122 1.150074462 0.396614773
123 -0.435374972 1.150074462
124 -0.478669049 -0.435374972
125 0.598715656 -0.478669049
126 4.269853030 0.598715656
127 -0.348548020 4.269853030
128 -0.445206888 -0.348548020
129 0.606074262 -0.445206888
130 -0.191117032 0.606074262
131 -0.430931831 -0.191117032
132 -0.412850471 -0.430931831
133 -0.309461307 -0.412850471
134 -0.029232731 -0.309461307
135 -0.071581630 -0.029232731
136 -0.249384851 -0.071581630
137 -0.474240827 -0.249384851
138 -0.285211738 -0.474240827
139 -0.112789455 -0.285211738
140 1.348388305 -0.112789455
141 -2.923251842 1.348388305
142 -0.377419654 -2.923251842
143 -0.173684838 -0.377419654
144 -0.405541573 -0.173684838
145 0.237132051 -0.405541573
146 1.437230792 0.237132051
147 -0.431084022 1.437230792
148 -0.225933092 -0.431084022
149 -0.035223958 -0.225933092
150 -0.257050791 -0.035223958
151 -0.016691257 -0.257050791
152 0.953676418 -0.016691257
153 0.033293784 0.953676418
154 -0.466852130 0.033293784
155 -0.382866862 -0.466852130
156 0.218101021 -0.382866862
157 -0.209742613 0.218101021
158 0.262753698 -0.209742613
159 NA 0.262753698
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.413032788 1.395800449
[2,] 0.781681660 -2.413032788
[3,] 3.482063340 0.781681660
[4,] -2.374116531 3.482063340
[5,] 6.761298370 -2.374116531
[6,] -0.220585670 6.761298370
[7,] -0.601559460 -0.220585670
[8,] -0.481638719 -0.601559460
[9,] -0.496000138 -0.481638719
[10,] -0.050280333 -0.496000138
[11,] 0.435558252 -0.050280333
[12,] 0.293437781 0.435558252
[13,] -0.281549585 0.293437781
[14,] -0.728696109 -0.281549585
[15,] -0.728310616 -0.728696109
[16,] 0.050180467 -0.728310616
[17,] -0.389167335 0.050180467
[18,] -0.206857914 -0.389167335
[19,] 0.353830947 -0.206857914
[20,] 0.153445761 0.353830947
[21,] 0.546968292 0.153445761
[22,] -0.167311138 0.546968292
[23,] 0.497201350 -0.167311138
[24,] -0.307818030 0.497201350
[25,] -0.578750858 -0.307818030
[26,] -0.317837141 -0.578750858
[27,] -0.105584042 -0.317837141
[28,] -0.182769976 -0.105584042
[29,] -0.520536110 -0.182769976
[30,] 0.039361490 -0.520536110
[31,] -0.245342281 0.039361490
[32,] -0.062577910 -0.245342281
[33,] 0.009878480 -0.062577910
[34,] -0.635275720 0.009878480
[35,] 0.403150219 -0.635275720
[36,] 0.645198248 0.403150219
[37,] 0.155575440 0.645198248
[38,] -2.187949684 0.155575440
[39,] -0.174433175 -2.187949684
[40,] 0.108162397 -0.174433175
[41,] -0.086826020 0.108162397
[42,] -0.268609338 -0.086826020
[43,] -0.529600068 -0.268609338
[44,] -0.332863995 -0.529600068
[45,] -0.634898291 -0.332863995
[46,] -0.345835867 -0.634898291
[47,] 0.456987455 -0.345835867
[48,] 0.387868522 0.456987455
[49,] -0.281815348 0.387868522
[50,] 0.138431795 -0.281815348
[51,] -0.418671119 0.138431795
[52,] -0.101695749 -0.418671119
[53,] -0.470689116 -0.101695749
[54,] -0.170742395 -0.470689116
[55,] 0.010385800 -0.170742395
[56,] -0.169506995 0.010385800
[57,] -0.534372816 -0.169506995
[58,] -0.531925928 -0.534372816
[59,] -0.712355551 -0.531925928
[60,] -0.510700971 -0.712355551
[61,] 0.661907253 -0.510700971
[62,] 5.251085666 0.661907253
[63,] -0.479433049 5.251085666
[64,] -0.979975583 -0.479433049
[65,] 0.265360888 -0.979975583
[66,] 0.674423540 0.265360888
[67,] -0.443861652 0.674423540
[68,] -0.123734455 -0.443861652
[69,] 0.372268939 -0.123734455
[70,] 0.461659829 0.372268939
[71,] -0.258884715 0.461659829
[72,] 0.363285434 -0.258884715
[73,] 0.102321421 0.363285434
[74,] 0.172898940 0.102321421
[75,] -0.010147652 0.172898940
[76,] -0.869782539 -0.010147652
[77,] -0.360328907 -0.869782539
[78,] -0.357402194 -0.360328907
[79,] 0.205007913 -0.357402194
[80,] 0.654807203 0.205007913
[81,] -0.446493780 0.654807203
[82,] -0.196157611 -0.446493780
[83,] 0.547814294 -0.196157611
[84,] -7.237715680 0.547814294
[85,] -0.220948998 -7.237715680
[86,] -0.296532798 -0.220948998
[87,] -0.698944002 -0.296532798
[88,] 1.024625958 -0.698944002
[89,] 0.262612920 1.024625958
[90,] 0.145908909 0.262612920
[91,] 0.243325191 0.145908909
[92,] -0.034208564 0.243325191
[93,] -0.177656777 -0.034208564
[94,] -0.323630495 -0.177656777
[95,] 0.148072407 -0.323630495
[96,] 0.406099356 0.148072407
[97,] 0.250569405 0.406099356
[98,] -0.136197527 0.250569405
[99,] 0.002173240 -0.136197527
[100,] -0.083735695 0.002173240
[101,] -0.341395702 -0.083735695
[102,] -0.287176871 -0.341395702
[103,] -0.471863188 -0.287176871
[104,] 0.952842063 -0.471863188
[105,] -5.738759161 0.952842063
[106,] 0.130072364 -5.738759161
[107,] 0.135367075 0.130072364
[108,] 0.900143213 0.135367075
[109,] 1.856403753 0.900143213
[110,] -0.541115192 1.856403753
[111,] 0.430806279 -0.541115192
[112,] 0.881828825 0.430806279
[113,] 7.157152280 0.881828825
[114,] -1.090563268 7.157152280
[115,] -0.312820441 -1.090563268
[116,] -0.284392499 -0.312820441
[117,] -0.403743108 -0.284392499
[118,] 0.098717550 -0.403743108
[119,] 0.037510619 0.098717550
[120,] 0.531188875 0.037510619
[121,] 0.396614773 0.531188875
[122,] 1.150074462 0.396614773
[123,] -0.435374972 1.150074462
[124,] -0.478669049 -0.435374972
[125,] 0.598715656 -0.478669049
[126,] 4.269853030 0.598715656
[127,] -0.348548020 4.269853030
[128,] -0.445206888 -0.348548020
[129,] 0.606074262 -0.445206888
[130,] -0.191117032 0.606074262
[131,] -0.430931831 -0.191117032
[132,] -0.412850471 -0.430931831
[133,] -0.309461307 -0.412850471
[134,] -0.029232731 -0.309461307
[135,] -0.071581630 -0.029232731
[136,] -0.249384851 -0.071581630
[137,] -0.474240827 -0.249384851
[138,] -0.285211738 -0.474240827
[139,] -0.112789455 -0.285211738
[140,] 1.348388305 -0.112789455
[141,] -2.923251842 1.348388305
[142,] -0.377419654 -2.923251842
[143,] -0.173684838 -0.377419654
[144,] -0.405541573 -0.173684838
[145,] 0.237132051 -0.405541573
[146,] 1.437230792 0.237132051
[147,] -0.431084022 1.437230792
[148,] -0.225933092 -0.431084022
[149,] -0.035223958 -0.225933092
[150,] -0.257050791 -0.035223958
[151,] -0.016691257 -0.257050791
[152,] 0.953676418 -0.016691257
[153,] 0.033293784 0.953676418
[154,] -0.466852130 0.033293784
[155,] -0.382866862 -0.466852130
[156,] 0.218101021 -0.382866862
[157,] -0.209742613 0.218101021
[158,] 0.262753698 -0.209742613
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.413032788 1.395800449
2 0.781681660 -2.413032788
3 3.482063340 0.781681660
4 -2.374116531 3.482063340
5 6.761298370 -2.374116531
6 -0.220585670 6.761298370
7 -0.601559460 -0.220585670
8 -0.481638719 -0.601559460
9 -0.496000138 -0.481638719
10 -0.050280333 -0.496000138
11 0.435558252 -0.050280333
12 0.293437781 0.435558252
13 -0.281549585 0.293437781
14 -0.728696109 -0.281549585
15 -0.728310616 -0.728696109
16 0.050180467 -0.728310616
17 -0.389167335 0.050180467
18 -0.206857914 -0.389167335
19 0.353830947 -0.206857914
20 0.153445761 0.353830947
21 0.546968292 0.153445761
22 -0.167311138 0.546968292
23 0.497201350 -0.167311138
24 -0.307818030 0.497201350
25 -0.578750858 -0.307818030
26 -0.317837141 -0.578750858
27 -0.105584042 -0.317837141
28 -0.182769976 -0.105584042
29 -0.520536110 -0.182769976
30 0.039361490 -0.520536110
31 -0.245342281 0.039361490
32 -0.062577910 -0.245342281
33 0.009878480 -0.062577910
34 -0.635275720 0.009878480
35 0.403150219 -0.635275720
36 0.645198248 0.403150219
37 0.155575440 0.645198248
38 -2.187949684 0.155575440
39 -0.174433175 -2.187949684
40 0.108162397 -0.174433175
41 -0.086826020 0.108162397
42 -0.268609338 -0.086826020
43 -0.529600068 -0.268609338
44 -0.332863995 -0.529600068
45 -0.634898291 -0.332863995
46 -0.345835867 -0.634898291
47 0.456987455 -0.345835867
48 0.387868522 0.456987455
49 -0.281815348 0.387868522
50 0.138431795 -0.281815348
51 -0.418671119 0.138431795
52 -0.101695749 -0.418671119
53 -0.470689116 -0.101695749
54 -0.170742395 -0.470689116
55 0.010385800 -0.170742395
56 -0.169506995 0.010385800
57 -0.534372816 -0.169506995
58 -0.531925928 -0.534372816
59 -0.712355551 -0.531925928
60 -0.510700971 -0.712355551
61 0.661907253 -0.510700971
62 5.251085666 0.661907253
63 -0.479433049 5.251085666
64 -0.979975583 -0.479433049
65 0.265360888 -0.979975583
66 0.674423540 0.265360888
67 -0.443861652 0.674423540
68 -0.123734455 -0.443861652
69 0.372268939 -0.123734455
70 0.461659829 0.372268939
71 -0.258884715 0.461659829
72 0.363285434 -0.258884715
73 0.102321421 0.363285434
74 0.172898940 0.102321421
75 -0.010147652 0.172898940
76 -0.869782539 -0.010147652
77 -0.360328907 -0.869782539
78 -0.357402194 -0.360328907
79 0.205007913 -0.357402194
80 0.654807203 0.205007913
81 -0.446493780 0.654807203
82 -0.196157611 -0.446493780
83 0.547814294 -0.196157611
84 -7.237715680 0.547814294
85 -0.220948998 -7.237715680
86 -0.296532798 -0.220948998
87 -0.698944002 -0.296532798
88 1.024625958 -0.698944002
89 0.262612920 1.024625958
90 0.145908909 0.262612920
91 0.243325191 0.145908909
92 -0.034208564 0.243325191
93 -0.177656777 -0.034208564
94 -0.323630495 -0.177656777
95 0.148072407 -0.323630495
96 0.406099356 0.148072407
97 0.250569405 0.406099356
98 -0.136197527 0.250569405
99 0.002173240 -0.136197527
100 -0.083735695 0.002173240
101 -0.341395702 -0.083735695
102 -0.287176871 -0.341395702
103 -0.471863188 -0.287176871
104 0.952842063 -0.471863188
105 -5.738759161 0.952842063
106 0.130072364 -5.738759161
107 0.135367075 0.130072364
108 0.900143213 0.135367075
109 1.856403753 0.900143213
110 -0.541115192 1.856403753
111 0.430806279 -0.541115192
112 0.881828825 0.430806279
113 7.157152280 0.881828825
114 -1.090563268 7.157152280
115 -0.312820441 -1.090563268
116 -0.284392499 -0.312820441
117 -0.403743108 -0.284392499
118 0.098717550 -0.403743108
119 0.037510619 0.098717550
120 0.531188875 0.037510619
121 0.396614773 0.531188875
122 1.150074462 0.396614773
123 -0.435374972 1.150074462
124 -0.478669049 -0.435374972
125 0.598715656 -0.478669049
126 4.269853030 0.598715656
127 -0.348548020 4.269853030
128 -0.445206888 -0.348548020
129 0.606074262 -0.445206888
130 -0.191117032 0.606074262
131 -0.430931831 -0.191117032
132 -0.412850471 -0.430931831
133 -0.309461307 -0.412850471
134 -0.029232731 -0.309461307
135 -0.071581630 -0.029232731
136 -0.249384851 -0.071581630
137 -0.474240827 -0.249384851
138 -0.285211738 -0.474240827
139 -0.112789455 -0.285211738
140 1.348388305 -0.112789455
141 -2.923251842 1.348388305
142 -0.377419654 -2.923251842
143 -0.173684838 -0.377419654
144 -0.405541573 -0.173684838
145 0.237132051 -0.405541573
146 1.437230792 0.237132051
147 -0.431084022 1.437230792
148 -0.225933092 -0.431084022
149 -0.035223958 -0.225933092
150 -0.257050791 -0.035223958
151 -0.016691257 -0.257050791
152 0.953676418 -0.016691257
153 0.033293784 0.953676418
154 -0.466852130 0.033293784
155 -0.382866862 -0.466852130
156 0.218101021 -0.382866862
157 -0.209742613 0.218101021
158 0.262753698 -0.209742613
> 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/7zw2h1290504805.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8zw2h1290504805.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9ankk1290504805.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10ankk1290504805.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11vo071290504805.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/12gohd1290504805.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/1358e71290504805.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/14gzds1290504805.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/151hcg1290504805.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/16xrr71290504805.tab")
+ }
> try(system("convert tmp/1lm4q1290504805.ps tmp/1lm4q1290504805.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ve4b1290504805.ps tmp/2ve4b1290504805.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ve4b1290504805.ps tmp/3ve4b1290504805.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ve4b1290504805.ps tmp/4ve4b1290504805.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ve4b1290504805.ps tmp/5ve4b1290504805.png",intern=TRUE))
character(0)
> try(system("convert tmp/66n3e1290504805.ps tmp/66n3e1290504805.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zw2h1290504805.ps tmp/7zw2h1290504805.png",intern=TRUE))
character(0)
> try(system("convert tmp/8zw2h1290504805.ps tmp/8zw2h1290504805.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ankk1290504805.ps tmp/9ankk1290504805.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ankk1290504805.ps tmp/10ankk1290504805.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.814 2.730 9.252