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
+ ,19
+ ,18
+ ,18
+ ,8
+ ,8
+ ,12
+ ,12
+ ,9
+ ,9
+ ,22
+ ,22
+ ,0
+ ,29
+ ,16
+ ,0
+ ,10
+ ,0
+ ,12
+ ,0
+ ,7
+ ,0
+ ,22
+ ,0
+ ,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 19 1 18 18 8 8 12 12 9 9 22 22
6 29 0 16 0 10 0 12 0 7 0 22 0
7 25 1 20 20 10 10 11 11 4 4 25 25
8 21 1 16 16 11 11 11 11 11 11 23 23
9 22 1 18 18 16 16 12 12 7 7 17 17
10 25 1 17 17 11 11 13 13 7 7 21 21
11 24 1 23 23 13 13 14 14 12 12 19 19
12 18 1 30 30 12 12 16 16 10 10 19 19
13 22 1 23 23 8 8 11 11 10 10 15 15
14 15 1 18 18 12 12 10 10 8 8 16 16
15 22 1 15 15 11 11 11 11 8 8 23 23
16 28 1 12 12 4 4 15 15 4 4 27 27
17 20 1 21 21 9 9 9 9 9 9 22 22
18 12 1 15 15 8 8 11 11 8 8 14 14
19 24 1 20 20 8 8 17 17 7 7 22 22
20 20 1 31 31 14 14 17 17 11 11 23 23
21 21 1 27 27 15 15 11 11 9 9 23 23
22 20 1 34 34 16 16 18 18 11 11 21 21
23 21 1 21 21 9 9 14 14 13 13 19 19
24 23 1 31 31 14 14 10 10 8 8 18 18
25 28 1 19 19 11 11 11 11 8 8 20 20
26 24 1 16 16 8 8 15 15 9 9 23 23
27 24 1 20 20 9 9 15 15 6 6 25 25
28 24 1 21 21 9 9 13 13 9 9 19 19
29 23 1 22 22 9 9 16 16 9 9 24 24
30 23 1 17 17 9 9 13 13 6 6 22 22
31 29 1 24 24 10 10 9 9 6 6 25 25
32 24 1 25 25 16 16 18 18 16 16 26 26
33 18 1 26 26 11 11 18 18 5 5 29 29
34 25 1 25 25 8 8 12 12 7 7 32 32
35 21 1 17 17 9 9 17 17 9 9 25 25
36 26 1 32 32 16 16 9 9 6 6 29 29
37 22 1 33 33 11 11 9 9 6 6 28 28
38 22 1 13 13 16 16 12 12 5 5 17 17
39 22 0 32 0 12 0 18 0 12 0 28 0
40 23 1 25 25 12 12 12 12 7 7 29 29
41 30 1 29 29 14 14 18 18 10 10 26 26
42 23 1 22 22 9 9 14 14 9 9 25 25
43 17 1 18 18 10 10 15 15 8 8 14 14
44 23 1 17 17 9 9 16 16 5 5 25 25
45 23 1 20 20 10 10 10 10 8 8 26 26
46 25 1 15 15 12 12 11 11 8 8 20 20
47 24 1 20 20 14 14 14 14 10 10 18 18
48 24 1 33 33 14 14 9 9 6 6 32 32
49 23 1 29 29 10 10 12 12 8 8 25 25
50 21 1 23 23 14 14 17 17 7 7 25 25
51 24 1 26 26 16 16 5 5 4 4 23 23
52 24 1 18 18 9 9 12 12 8 8 21 21
53 28 1 20 20 10 10 12 12 8 8 20 20
54 16 1 11 11 6 6 6 6 4 4 15 15
55 20 1 28 28 8 8 24 24 20 20 30 30
56 29 1 26 26 13 13 12 12 8 8 24 24
57 27 1 22 22 10 10 12 12 8 8 26 26
58 22 1 17 17 8 8 14 14 6 6 24 24
59 28 1 12 12 7 7 7 7 4 4 22 22
60 16 1 14 14 15 15 13 13 8 8 14 14
61 25 1 17 17 9 9 12 12 9 9 24 24
62 24 1 21 21 10 10 13 13 6 6 24 24
63 28 0 19 0 12 0 14 0 7 0 24 0
64 24 1 18 18 13 13 8 8 9 9 24 24
65 23 1 10 10 10 10 11 11 5 5 19 19
66 30 1 29 29 11 11 9 9 5 5 31 31
67 24 1 31 31 8 8 11 11 8 8 22 22
68 21 1 19 19 9 9 13 13 8 8 27 27
69 25 1 9 9 13 13 10 10 6 6 19 19
70 25 0 20 0 11 0 11 0 8 0 25 0
71 22 1 28 28 8 8 12 12 7 7 20 20
72 23 1 19 19 9 9 9 9 7 7 21 21
73 26 1 30 30 9 9 15 15 9 9 27 27
74 23 1 29 29 15 15 18 18 11 11 23 23
75 25 1 26 26 9 9 15 15 6 6 25 25
76 21 1 23 23 10 10 12 12 8 8 20 20
77 25 1 13 13 14 14 13 13 6 6 21 21
78 24 1 21 21 12 12 14 14 9 9 22 22
79 29 1 19 19 12 12 10 10 8 8 23 23
80 22 1 28 28 11 11 13 13 6 6 25 25
81 27 1 23 23 14 14 13 13 10 10 25 25
82 26 0 18 0 6 0 11 0 8 0 17 0
83 22 1 21 21 12 12 13 13 8 8 19 19
84 24 1 20 20 8 8 16 16 10 10 25 25
85 27 0 23 0 14 0 8 0 5 0 19 0
86 24 1 21 21 11 11 16 16 7 7 20 20
87 24 1 21 21 10 10 11 11 5 5 26 26
88 29 1 15 15 14 14 9 9 8 8 23 23
89 22 1 28 28 12 12 16 16 14 14 27 27
90 21 0 19 0 10 0 12 0 7 0 17 0
91 24 1 26 26 14 14 14 14 8 8 17 17
92 24 1 10 10 5 5 8 8 6 6 19 19
93 23 0 16 0 11 0 9 0 5 0 17 0
94 20 1 22 22 10 10 15 15 6 6 22 22
95 27 1 19 19 9 9 11 11 10 10 21 21
96 26 1 31 31 10 10 21 21 12 12 32 32
97 25 1 31 31 16 16 14 14 9 9 21 21
98 21 1 29 29 13 13 18 18 12 12 21 21
99 21 1 19 19 9 9 12 12 7 7 18 18
100 19 1 22 22 10 10 13 13 8 8 18 18
101 21 1 23 23 10 10 15 15 10 10 23 23
102 21 1 15 15 7 7 12 12 6 6 19 19
103 16 1 20 20 9 9 19 19 10 10 20 20
104 22 1 18 18 8 8 15 15 10 10 21 21
105 29 1 23 23 14 14 11 11 10 10 20 20
106 15 0 25 0 14 0 11 0 5 0 17 0
107 17 1 21 21 8 8 10 10 7 7 18 18
108 15 1 24 24 9 9 13 13 10 10 19 19
109 21 1 25 25 14 14 15 15 11 11 22 22
110 21 0 17 0 14 0 12 0 6 0 15 0
111 19 1 13 13 8 8 12 12 7 7 14 14
112 24 1 28 28 8 8 16 16 12 12 18 18
113 20 1 21 21 8 8 9 9 11 11 24 24
114 17 0 25 0 7 0 18 0 11 0 35 0
115 23 1 9 9 6 6 8 8 11 11 29 29
116 24 1 16 16 8 8 13 13 5 5 21 21
117 14 1 19 19 6 6 17 17 8 8 25 25
118 19 1 17 17 11 11 9 9 6 6 20 20
119 24 1 25 25 14 14 15 15 9 9 22 22
120 13 1 20 20 11 11 8 8 4 4 13 13
121 22 1 29 29 11 11 7 7 4 4 26 26
122 16 1 14 14 11 11 12 12 7 7 17 17
123 19 0 22 0 14 0 14 0 11 0 25 0
124 25 1 15 15 8 8 6 6 6 6 20 20
125 25 1 19 19 20 20 8 8 7 7 19 19
126 23 1 20 20 11 11 17 17 8 8 21 21
127 24 0 15 0 8 0 10 0 4 0 22 0
128 26 1 20 20 11 11 11 11 8 8 24 24
129 26 1 18 18 10 10 14 14 9 9 21 21
130 25 1 33 33 14 14 11 11 8 8 26 26
131 18 1 22 22 11 11 13 13 11 11 24 24
132 21 1 16 16 9 9 12 12 8 8 16 16
133 26 1 17 17 9 9 11 11 5 5 23 23
134 23 1 16 16 8 8 9 9 4 4 18 18
135 23 1 21 21 10 10 12 12 8 8 16 16
136 22 1 26 26 13 13 20 20 10 10 26 26
137 20 1 18 18 13 13 12 12 6 6 19 19
138 13 1 18 18 12 12 13 13 9 9 21 21
139 24 1 17 17 8 8 12 12 9 9 21 21
140 15 1 22 22 13 13 12 12 13 13 22 22
141 14 1 30 30 14 14 9 9 9 9 23 23
142 22 0 30 0 12 0 15 0 10 0 29 0
143 10 1 24 24 14 14 24 24 20 20 21 21
144 24 1 21 21 15 15 7 7 5 5 21 21
145 22 1 21 21 13 13 17 17 11 11 23 23
146 24 1 29 29 16 16 11 11 6 6 27 27
147 19 1 31 31 9 9 17 17 9 9 25 25
148 20 0 20 0 9 0 11 0 7 0 21 0
149 13 1 16 16 9 9 12 12 9 9 10 10
150 20 1 22 22 8 8 14 14 10 10 20 20
151 22 1 20 20 7 7 11 11 9 9 26 26
152 24 1 28 28 16 16 16 16 8 8 24 24
153 29 1 38 38 11 11 21 21 7 7 29 29
154 12 1 22 22 9 9 14 14 6 6 19 19
155 20 1 20 20 11 11 20 20 13 13 24 24
156 21 1 17 17 9 9 13 13 6 6 19 19
157 24 1 28 28 14 14 11 11 8 8 24 24
158 22 1 22 22 13 13 15 15 10 10 22 22
159 20 1 31 31 16 16 19 19 16 16 17 17
> 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
29.54946 -15.21818 -0.38008 0.31736 0.02094 0.20754
PE PE_B PC PC_B PS PS_B
-0.54164 0.41074 0.27593 -0.50948 0.25113 0.22172
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.244 -1.776 0.242 2.390 7.231
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 29.54946 6.41144 4.609 8.72e-06 ***
B -15.21818 6.76030 -2.251 0.0259 *
CM -0.38008 0.28349 -1.341 0.1821
CM_B 0.31736 0.29079 1.091 0.2769
D 0.02094 0.42217 0.050 0.9605
D_B 0.20754 0.43807 0.474 0.6364
PE -0.54164 0.62507 -0.867 0.3876
PE_B 0.41074 0.63394 0.648 0.5181
PC 0.27593 0.74480 0.370 0.7116
PC_B -0.50948 0.75668 -0.673 0.5018
PS 0.25113 0.28976 0.867 0.3875
PS_B 0.22172 0.30117 0.736 0.4628
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.485 on 147 degrees of freedom
Multiple R-squared: 0.2589, Adjusted R-squared: 0.2035
F-statistic: 4.669 on 11 and 147 DF, p-value: 4.207e-06
> 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,] 0.910952404 0.178095192 0.0890476
[2,] 0.871980473 0.256039053 0.1280195
[3,] 0.804268671 0.391462658 0.1957313
[4,] 0.833764165 0.332471671 0.1662358
[5,] 0.757143770 0.485712460 0.2428562
[6,] 0.781648336 0.436703327 0.2183517
[7,] 0.723861243 0.552277513 0.2761388
[8,] 0.652161982 0.695676035 0.3478380
[9,] 0.572663309 0.854673382 0.4273367
[10,] 0.586142800 0.827714399 0.4138572
[11,] 0.741411063 0.517177874 0.2585889
[12,] 0.677637474 0.644725052 0.3223625
[13,] 0.608004512 0.783990975 0.3919955
[14,] 0.596970966 0.806058068 0.4030290
[15,] 0.525835847 0.948328305 0.4741642
[16,] 0.452699710 0.905399420 0.5473003
[17,] 0.477398229 0.954796459 0.5226018
[18,] 0.410270669 0.820541339 0.5897293
[19,] 0.662138434 0.675723131 0.3378616
[20,] 0.616638439 0.766723123 0.3833616
[21,] 0.574059994 0.851880012 0.4259400
[22,] 0.511436431 0.977127138 0.4885636
[23,] 0.488347417 0.976694833 0.5116526
[24,] 0.426191383 0.852382765 0.5738086
[25,] 0.385503168 0.771006336 0.6144968
[26,] 0.353624898 0.707249796 0.6463751
[27,] 0.516638596 0.966722808 0.4833614
[28,] 0.457579603 0.915159206 0.5424204
[29,] 0.413692045 0.827384089 0.5863080
[30,] 0.359976524 0.719953047 0.6400235
[31,] 0.315213168 0.630426337 0.6847868
[32,] 0.293093310 0.586186620 0.7069067
[33,] 0.278522132 0.557044264 0.7214779
[34,] 0.267062340 0.534124681 0.7329377
[35,] 0.223858909 0.447717819 0.7761411
[36,] 0.211085706 0.422171411 0.7889143
[37,] 0.179031618 0.358063237 0.8209684
[38,] 0.156219524 0.312439047 0.8437805
[39,] 0.248674090 0.497348180 0.7513259
[40,] 0.271914854 0.543829709 0.7280851
[41,] 0.264838102 0.529676204 0.7351619
[42,] 0.334204333 0.668408665 0.6657957
[43,] 0.320777331 0.641554661 0.6792227
[44,] 0.280632365 0.561264731 0.7193676
[45,] 0.303334480 0.606668960 0.6966655
[46,] 0.326888374 0.653776748 0.6731116
[47,] 0.292748479 0.585496957 0.7072515
[48,] 0.251390077 0.502780155 0.7486099
[49,] 0.288273738 0.576547476 0.7117263
[50,] 0.248001795 0.496003590 0.7519982
[51,] 0.210610363 0.421220726 0.7893896
[52,] 0.201194471 0.402388942 0.7988055
[53,] 0.184919449 0.369838898 0.8150806
[54,] 0.187077140 0.374154281 0.8129229
[55,] 0.165430478 0.330860955 0.8345695
[56,] 0.136373421 0.272746843 0.8636266
[57,] 0.114023617 0.228047234 0.8859764
[58,] 0.092595492 0.185190984 0.9074045
[59,] 0.084912735 0.169825470 0.9150873
[60,] 0.068709508 0.137419016 0.9312905
[61,] 0.057316653 0.114633305 0.9426833
[62,] 0.044906768 0.089813536 0.9550932
[63,] 0.036281207 0.072562414 0.9637188
[64,] 0.029364230 0.058728459 0.9706358
[65,] 0.043475610 0.086951221 0.9565244
[66,] 0.035738900 0.071477799 0.9642611
[67,] 0.033931928 0.067863856 0.9660681
[68,] 0.027703230 0.055406460 0.9722968
[69,] 0.021115741 0.042231482 0.9788843
[70,] 0.016843225 0.033686450 0.9831568
[71,] 0.013907534 0.027815067 0.9860925
[72,] 0.012352020 0.024704040 0.9876480
[73,] 0.009121198 0.018242396 0.9908788
[74,] 0.012316132 0.024632265 0.9876839
[75,] 0.009442541 0.018885082 0.9905575
[76,] 0.011188552 0.022377104 0.9888114
[77,] 0.011749920 0.023499840 0.9882501
[78,] 0.010555940 0.021111880 0.9894441
[79,] 0.009030362 0.018060724 0.9909696
[80,] 0.007431583 0.014863166 0.9925684
[81,] 0.013773918 0.027547836 0.9862261
[82,] 0.011011590 0.022023181 0.9889884
[83,] 0.010180474 0.020360948 0.9898195
[84,] 0.007571800 0.015143600 0.9924282
[85,] 0.005485987 0.010971974 0.9945140
[86,] 0.004059027 0.008118053 0.9959410
[87,] 0.002908874 0.005817749 0.9970911
[88,] 0.002003864 0.004007729 0.9979961
[89,] 0.002154030 0.004308061 0.9978460
[90,] 0.001622749 0.003245498 0.9983773
[91,] 0.007481118 0.014962235 0.9925189
[92,] 0.011732978 0.023465956 0.9882670
[93,] 0.011148229 0.022296457 0.9888518
[94,] 0.014124797 0.028249594 0.9858752
[95,] 0.010530756 0.021061513 0.9894692
[96,] 0.007509778 0.015019557 0.9924902
[97,] 0.005305573 0.010611146 0.9946944
[98,] 0.014817316 0.029634632 0.9851827
[99,] 0.012447941 0.024895883 0.9875521
[100,] 0.015713163 0.031426325 0.9842868
[101,] 0.012450179 0.024900358 0.9875498
[102,] 0.009549474 0.019098948 0.9904505
[103,] 0.039032795 0.078065590 0.9609672
[104,] 0.036310259 0.072620518 0.9636897
[105,] 0.030152752 0.060305505 0.9698472
[106,] 0.055531860 0.111063720 0.9444681
[107,] 0.052428408 0.104856817 0.9475716
[108,] 0.064426618 0.128853236 0.9355734
[109,] 0.058813923 0.117627847 0.9411861
[110,] 0.056200244 0.112400488 0.9437998
[111,] 0.048543557 0.097087113 0.9514564
[112,] 0.035681778 0.071363557 0.9643182
[113,] 0.025476865 0.050953731 0.9745231
[114,] 0.024979645 0.049959290 0.9750204
[115,] 0.038802894 0.077605788 0.9611971
[116,] 0.029962005 0.059924009 0.9700380
[117,] 0.024569263 0.049138525 0.9754307
[118,] 0.019292273 0.038584546 0.9807077
[119,] 0.016171755 0.032343510 0.9838282
[120,] 0.011676189 0.023352379 0.9883238
[121,] 0.017125436 0.034250872 0.9828746
[122,] 0.010684701 0.021369403 0.9893153
[123,] 0.006361123 0.012722245 0.9936389
[124,] 0.021476093 0.042952185 0.9785239
[125,] 0.032536665 0.065073329 0.9674633
[126,] 0.025646548 0.051293095 0.9743535
[127,] 0.089798709 0.179597418 0.9102013
[128,] 0.051863244 0.103726489 0.9481368
[129,] 0.157261793 0.314523586 0.8427382
[130,] 0.100360520 0.200721040 0.8996395
> postscript(file="/var/www/html/freestat/rcomp/tmp/1u3gf1290180125.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2u3gf1290180125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35cy01290180125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/45cy01290180125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/55cy01290180125.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
2.869399611 -1.313099793 1.252689949 1.249176430 -2.760101466 4.365675664
7 8 9 10 11 12
0.191157071 -1.707638988 0.309203534 2.628378037 3.792123458 -1.745640052
13 14 15 16 17 18
3.966134643 -5.332265356 -1.471021081 3.638159981 -2.193115880 -6.529901734
19 20 21 22 23 24
2.552740561 -1.666837709 -2.398719827 -0.859026269 1.814157061 2.080463355
25 26 27 28 29 30
6.198428618 2.034301514 0.410346733 3.749044280 0.840204920 0.378935361
31 32 33 34 35 36
4.647353730 1.379969570 -7.402541744 -1.516674897 -1.815361686 -1.113163662
37 38 39 40 41 42
-3.435180214 -0.471516969 3.768492152 -3.012042954 6.686505748 0.105551209
43 44 45 46 47 48
-1.275094311 -0.880474468 -1.478383858 2.719055566 3.381219868 -4.012035870
49 50 51 52 53 54
-0.179224309 -3.048538448 -1.643092553 2.250716631 6.620533543 -4.385397547
55 56 57 58 59 60
-0.775805012 5.420015113 2.908862857 -1.207388061 4.269775076 -3.930194608
61 62 63 64 65 66
2.002988619 0.455639801 5.045053469 -0.371816727 0.634597864 2.661817015
67 68 69 70 71 72
2.690842297 -3.392776440 1.989082876 0.294095315 1.345726573 0.687184969
73 74 75 76 77 78
2.792529187 1.110135468 1.786683958 -0.191297844 1.458488775 1.775941849
79 80 81 82 83 84
5.420488349 -1.806634408 3.128518828 2.647695272 0.830046503 1.703941082
85 86 87 88 89 90
4.081205344 2.744823718 -0.985419722 4.581733243 -0.719695403 -1.238405277
91 92 93 94 95 96
3.763303684 2.617857318 -1.472647355 -2.274130879 5.649645163 1.748568940
97 98 99 100 101 102
2.962097025 0.746357154 0.498444642 -1.177414752 -0.750048451 -0.001889518
103 104 105 106 107 108
-3.767575336 1.339005806 7.230981545 -5.031447094 -3.409429021 -4.829234033
109 110 111 112 113 114
-0.832123170 -1.304119167 0.241999937 5.982799364 -2.443233641 -5.269411507
115 116 117 118 119 120
-2.234109133 1.783993722 -8.238024314 -3.655924245 1.700770683 -6.755793099
121 122 123 124 125 126
-3.469273342 -4.799280110 -4.211418857 2.511373258 0.988692711 1.573701669
127 128 129 130 131 132
-1.228027217 2.369740536 4.517589110 0.553987679 -4.542353528 1.489534579
133 134 135 136 137 138
2.410728576 1.445396917 3.574667366 -1.011380324 -2.184610312 -9.070274513
139 140 141 142 143 144
2.650028402 -6.717405526 -9.243870619 1.684133709 -8.141911261 -0.287165817
145 146 147 148 149 150
-0.065584851 -2.093825826 -2.937241493 -3.383564403 -3.439795919 -0.068150459
151 152 153 154 155 156
-1.428485594 0.383618097 5.209940315 -8.757991584 -1.284389717 -0.202506425
157 158 159
0.186078800 -0.025363288 2.142881925
> postscript(file="/var/www/html/freestat/rcomp/tmp/6g3f31290180125.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 2.869399611 NA
1 -1.313099793 2.869399611
2 1.252689949 -1.313099793
3 1.249176430 1.252689949
4 -2.760101466 1.249176430
5 4.365675664 -2.760101466
6 0.191157071 4.365675664
7 -1.707638988 0.191157071
8 0.309203534 -1.707638988
9 2.628378037 0.309203534
10 3.792123458 2.628378037
11 -1.745640052 3.792123458
12 3.966134643 -1.745640052
13 -5.332265356 3.966134643
14 -1.471021081 -5.332265356
15 3.638159981 -1.471021081
16 -2.193115880 3.638159981
17 -6.529901734 -2.193115880
18 2.552740561 -6.529901734
19 -1.666837709 2.552740561
20 -2.398719827 -1.666837709
21 -0.859026269 -2.398719827
22 1.814157061 -0.859026269
23 2.080463355 1.814157061
24 6.198428618 2.080463355
25 2.034301514 6.198428618
26 0.410346733 2.034301514
27 3.749044280 0.410346733
28 0.840204920 3.749044280
29 0.378935361 0.840204920
30 4.647353730 0.378935361
31 1.379969570 4.647353730
32 -7.402541744 1.379969570
33 -1.516674897 -7.402541744
34 -1.815361686 -1.516674897
35 -1.113163662 -1.815361686
36 -3.435180214 -1.113163662
37 -0.471516969 -3.435180214
38 3.768492152 -0.471516969
39 -3.012042954 3.768492152
40 6.686505748 -3.012042954
41 0.105551209 6.686505748
42 -1.275094311 0.105551209
43 -0.880474468 -1.275094311
44 -1.478383858 -0.880474468
45 2.719055566 -1.478383858
46 3.381219868 2.719055566
47 -4.012035870 3.381219868
48 -0.179224309 -4.012035870
49 -3.048538448 -0.179224309
50 -1.643092553 -3.048538448
51 2.250716631 -1.643092553
52 6.620533543 2.250716631
53 -4.385397547 6.620533543
54 -0.775805012 -4.385397547
55 5.420015113 -0.775805012
56 2.908862857 5.420015113
57 -1.207388061 2.908862857
58 4.269775076 -1.207388061
59 -3.930194608 4.269775076
60 2.002988619 -3.930194608
61 0.455639801 2.002988619
62 5.045053469 0.455639801
63 -0.371816727 5.045053469
64 0.634597864 -0.371816727
65 2.661817015 0.634597864
66 2.690842297 2.661817015
67 -3.392776440 2.690842297
68 1.989082876 -3.392776440
69 0.294095315 1.989082876
70 1.345726573 0.294095315
71 0.687184969 1.345726573
72 2.792529187 0.687184969
73 1.110135468 2.792529187
74 1.786683958 1.110135468
75 -0.191297844 1.786683958
76 1.458488775 -0.191297844
77 1.775941849 1.458488775
78 5.420488349 1.775941849
79 -1.806634408 5.420488349
80 3.128518828 -1.806634408
81 2.647695272 3.128518828
82 0.830046503 2.647695272
83 1.703941082 0.830046503
84 4.081205344 1.703941082
85 2.744823718 4.081205344
86 -0.985419722 2.744823718
87 4.581733243 -0.985419722
88 -0.719695403 4.581733243
89 -1.238405277 -0.719695403
90 3.763303684 -1.238405277
91 2.617857318 3.763303684
92 -1.472647355 2.617857318
93 -2.274130879 -1.472647355
94 5.649645163 -2.274130879
95 1.748568940 5.649645163
96 2.962097025 1.748568940
97 0.746357154 2.962097025
98 0.498444642 0.746357154
99 -1.177414752 0.498444642
100 -0.750048451 -1.177414752
101 -0.001889518 -0.750048451
102 -3.767575336 -0.001889518
103 1.339005806 -3.767575336
104 7.230981545 1.339005806
105 -5.031447094 7.230981545
106 -3.409429021 -5.031447094
107 -4.829234033 -3.409429021
108 -0.832123170 -4.829234033
109 -1.304119167 -0.832123170
110 0.241999937 -1.304119167
111 5.982799364 0.241999937
112 -2.443233641 5.982799364
113 -5.269411507 -2.443233641
114 -2.234109133 -5.269411507
115 1.783993722 -2.234109133
116 -8.238024314 1.783993722
117 -3.655924245 -8.238024314
118 1.700770683 -3.655924245
119 -6.755793099 1.700770683
120 -3.469273342 -6.755793099
121 -4.799280110 -3.469273342
122 -4.211418857 -4.799280110
123 2.511373258 -4.211418857
124 0.988692711 2.511373258
125 1.573701669 0.988692711
126 -1.228027217 1.573701669
127 2.369740536 -1.228027217
128 4.517589110 2.369740536
129 0.553987679 4.517589110
130 -4.542353528 0.553987679
131 1.489534579 -4.542353528
132 2.410728576 1.489534579
133 1.445396917 2.410728576
134 3.574667366 1.445396917
135 -1.011380324 3.574667366
136 -2.184610312 -1.011380324
137 -9.070274513 -2.184610312
138 2.650028402 -9.070274513
139 -6.717405526 2.650028402
140 -9.243870619 -6.717405526
141 1.684133709 -9.243870619
142 -8.141911261 1.684133709
143 -0.287165817 -8.141911261
144 -0.065584851 -0.287165817
145 -2.093825826 -0.065584851
146 -2.937241493 -2.093825826
147 -3.383564403 -2.937241493
148 -3.439795919 -3.383564403
149 -0.068150459 -3.439795919
150 -1.428485594 -0.068150459
151 0.383618097 -1.428485594
152 5.209940315 0.383618097
153 -8.757991584 5.209940315
154 -1.284389717 -8.757991584
155 -0.202506425 -1.284389717
156 0.186078800 -0.202506425
157 -0.025363288 0.186078800
158 2.142881925 -0.025363288
159 NA 2.142881925
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.313099793 2.869399611
[2,] 1.252689949 -1.313099793
[3,] 1.249176430 1.252689949
[4,] -2.760101466 1.249176430
[5,] 4.365675664 -2.760101466
[6,] 0.191157071 4.365675664
[7,] -1.707638988 0.191157071
[8,] 0.309203534 -1.707638988
[9,] 2.628378037 0.309203534
[10,] 3.792123458 2.628378037
[11,] -1.745640052 3.792123458
[12,] 3.966134643 -1.745640052
[13,] -5.332265356 3.966134643
[14,] -1.471021081 -5.332265356
[15,] 3.638159981 -1.471021081
[16,] -2.193115880 3.638159981
[17,] -6.529901734 -2.193115880
[18,] 2.552740561 -6.529901734
[19,] -1.666837709 2.552740561
[20,] -2.398719827 -1.666837709
[21,] -0.859026269 -2.398719827
[22,] 1.814157061 -0.859026269
[23,] 2.080463355 1.814157061
[24,] 6.198428618 2.080463355
[25,] 2.034301514 6.198428618
[26,] 0.410346733 2.034301514
[27,] 3.749044280 0.410346733
[28,] 0.840204920 3.749044280
[29,] 0.378935361 0.840204920
[30,] 4.647353730 0.378935361
[31,] 1.379969570 4.647353730
[32,] -7.402541744 1.379969570
[33,] -1.516674897 -7.402541744
[34,] -1.815361686 -1.516674897
[35,] -1.113163662 -1.815361686
[36,] -3.435180214 -1.113163662
[37,] -0.471516969 -3.435180214
[38,] 3.768492152 -0.471516969
[39,] -3.012042954 3.768492152
[40,] 6.686505748 -3.012042954
[41,] 0.105551209 6.686505748
[42,] -1.275094311 0.105551209
[43,] -0.880474468 -1.275094311
[44,] -1.478383858 -0.880474468
[45,] 2.719055566 -1.478383858
[46,] 3.381219868 2.719055566
[47,] -4.012035870 3.381219868
[48,] -0.179224309 -4.012035870
[49,] -3.048538448 -0.179224309
[50,] -1.643092553 -3.048538448
[51,] 2.250716631 -1.643092553
[52,] 6.620533543 2.250716631
[53,] -4.385397547 6.620533543
[54,] -0.775805012 -4.385397547
[55,] 5.420015113 -0.775805012
[56,] 2.908862857 5.420015113
[57,] -1.207388061 2.908862857
[58,] 4.269775076 -1.207388061
[59,] -3.930194608 4.269775076
[60,] 2.002988619 -3.930194608
[61,] 0.455639801 2.002988619
[62,] 5.045053469 0.455639801
[63,] -0.371816727 5.045053469
[64,] 0.634597864 -0.371816727
[65,] 2.661817015 0.634597864
[66,] 2.690842297 2.661817015
[67,] -3.392776440 2.690842297
[68,] 1.989082876 -3.392776440
[69,] 0.294095315 1.989082876
[70,] 1.345726573 0.294095315
[71,] 0.687184969 1.345726573
[72,] 2.792529187 0.687184969
[73,] 1.110135468 2.792529187
[74,] 1.786683958 1.110135468
[75,] -0.191297844 1.786683958
[76,] 1.458488775 -0.191297844
[77,] 1.775941849 1.458488775
[78,] 5.420488349 1.775941849
[79,] -1.806634408 5.420488349
[80,] 3.128518828 -1.806634408
[81,] 2.647695272 3.128518828
[82,] 0.830046503 2.647695272
[83,] 1.703941082 0.830046503
[84,] 4.081205344 1.703941082
[85,] 2.744823718 4.081205344
[86,] -0.985419722 2.744823718
[87,] 4.581733243 -0.985419722
[88,] -0.719695403 4.581733243
[89,] -1.238405277 -0.719695403
[90,] 3.763303684 -1.238405277
[91,] 2.617857318 3.763303684
[92,] -1.472647355 2.617857318
[93,] -2.274130879 -1.472647355
[94,] 5.649645163 -2.274130879
[95,] 1.748568940 5.649645163
[96,] 2.962097025 1.748568940
[97,] 0.746357154 2.962097025
[98,] 0.498444642 0.746357154
[99,] -1.177414752 0.498444642
[100,] -0.750048451 -1.177414752
[101,] -0.001889518 -0.750048451
[102,] -3.767575336 -0.001889518
[103,] 1.339005806 -3.767575336
[104,] 7.230981545 1.339005806
[105,] -5.031447094 7.230981545
[106,] -3.409429021 -5.031447094
[107,] -4.829234033 -3.409429021
[108,] -0.832123170 -4.829234033
[109,] -1.304119167 -0.832123170
[110,] 0.241999937 -1.304119167
[111,] 5.982799364 0.241999937
[112,] -2.443233641 5.982799364
[113,] -5.269411507 -2.443233641
[114,] -2.234109133 -5.269411507
[115,] 1.783993722 -2.234109133
[116,] -8.238024314 1.783993722
[117,] -3.655924245 -8.238024314
[118,] 1.700770683 -3.655924245
[119,] -6.755793099 1.700770683
[120,] -3.469273342 -6.755793099
[121,] -4.799280110 -3.469273342
[122,] -4.211418857 -4.799280110
[123,] 2.511373258 -4.211418857
[124,] 0.988692711 2.511373258
[125,] 1.573701669 0.988692711
[126,] -1.228027217 1.573701669
[127,] 2.369740536 -1.228027217
[128,] 4.517589110 2.369740536
[129,] 0.553987679 4.517589110
[130,] -4.542353528 0.553987679
[131,] 1.489534579 -4.542353528
[132,] 2.410728576 1.489534579
[133,] 1.445396917 2.410728576
[134,] 3.574667366 1.445396917
[135,] -1.011380324 3.574667366
[136,] -2.184610312 -1.011380324
[137,] -9.070274513 -2.184610312
[138,] 2.650028402 -9.070274513
[139,] -6.717405526 2.650028402
[140,] -9.243870619 -6.717405526
[141,] 1.684133709 -9.243870619
[142,] -8.141911261 1.684133709
[143,] -0.287165817 -8.141911261
[144,] -0.065584851 -0.287165817
[145,] -2.093825826 -0.065584851
[146,] -2.937241493 -2.093825826
[147,] -3.383564403 -2.937241493
[148,] -3.439795919 -3.383564403
[149,] -0.068150459 -3.439795919
[150,] -1.428485594 -0.068150459
[151,] 0.383618097 -1.428485594
[152,] 5.209940315 0.383618097
[153,] -8.757991584 5.209940315
[154,] -1.284389717 -8.757991584
[155,] -0.202506425 -1.284389717
[156,] 0.186078800 -0.202506425
[157,] -0.025363288 0.186078800
[158,] 2.142881925 -0.025363288
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.313099793 2.869399611
2 1.252689949 -1.313099793
3 1.249176430 1.252689949
4 -2.760101466 1.249176430
5 4.365675664 -2.760101466
6 0.191157071 4.365675664
7 -1.707638988 0.191157071
8 0.309203534 -1.707638988
9 2.628378037 0.309203534
10 3.792123458 2.628378037
11 -1.745640052 3.792123458
12 3.966134643 -1.745640052
13 -5.332265356 3.966134643
14 -1.471021081 -5.332265356
15 3.638159981 -1.471021081
16 -2.193115880 3.638159981
17 -6.529901734 -2.193115880
18 2.552740561 -6.529901734
19 -1.666837709 2.552740561
20 -2.398719827 -1.666837709
21 -0.859026269 -2.398719827
22 1.814157061 -0.859026269
23 2.080463355 1.814157061
24 6.198428618 2.080463355
25 2.034301514 6.198428618
26 0.410346733 2.034301514
27 3.749044280 0.410346733
28 0.840204920 3.749044280
29 0.378935361 0.840204920
30 4.647353730 0.378935361
31 1.379969570 4.647353730
32 -7.402541744 1.379969570
33 -1.516674897 -7.402541744
34 -1.815361686 -1.516674897
35 -1.113163662 -1.815361686
36 -3.435180214 -1.113163662
37 -0.471516969 -3.435180214
38 3.768492152 -0.471516969
39 -3.012042954 3.768492152
40 6.686505748 -3.012042954
41 0.105551209 6.686505748
42 -1.275094311 0.105551209
43 -0.880474468 -1.275094311
44 -1.478383858 -0.880474468
45 2.719055566 -1.478383858
46 3.381219868 2.719055566
47 -4.012035870 3.381219868
48 -0.179224309 -4.012035870
49 -3.048538448 -0.179224309
50 -1.643092553 -3.048538448
51 2.250716631 -1.643092553
52 6.620533543 2.250716631
53 -4.385397547 6.620533543
54 -0.775805012 -4.385397547
55 5.420015113 -0.775805012
56 2.908862857 5.420015113
57 -1.207388061 2.908862857
58 4.269775076 -1.207388061
59 -3.930194608 4.269775076
60 2.002988619 -3.930194608
61 0.455639801 2.002988619
62 5.045053469 0.455639801
63 -0.371816727 5.045053469
64 0.634597864 -0.371816727
65 2.661817015 0.634597864
66 2.690842297 2.661817015
67 -3.392776440 2.690842297
68 1.989082876 -3.392776440
69 0.294095315 1.989082876
70 1.345726573 0.294095315
71 0.687184969 1.345726573
72 2.792529187 0.687184969
73 1.110135468 2.792529187
74 1.786683958 1.110135468
75 -0.191297844 1.786683958
76 1.458488775 -0.191297844
77 1.775941849 1.458488775
78 5.420488349 1.775941849
79 -1.806634408 5.420488349
80 3.128518828 -1.806634408
81 2.647695272 3.128518828
82 0.830046503 2.647695272
83 1.703941082 0.830046503
84 4.081205344 1.703941082
85 2.744823718 4.081205344
86 -0.985419722 2.744823718
87 4.581733243 -0.985419722
88 -0.719695403 4.581733243
89 -1.238405277 -0.719695403
90 3.763303684 -1.238405277
91 2.617857318 3.763303684
92 -1.472647355 2.617857318
93 -2.274130879 -1.472647355
94 5.649645163 -2.274130879
95 1.748568940 5.649645163
96 2.962097025 1.748568940
97 0.746357154 2.962097025
98 0.498444642 0.746357154
99 -1.177414752 0.498444642
100 -0.750048451 -1.177414752
101 -0.001889518 -0.750048451
102 -3.767575336 -0.001889518
103 1.339005806 -3.767575336
104 7.230981545 1.339005806
105 -5.031447094 7.230981545
106 -3.409429021 -5.031447094
107 -4.829234033 -3.409429021
108 -0.832123170 -4.829234033
109 -1.304119167 -0.832123170
110 0.241999937 -1.304119167
111 5.982799364 0.241999937
112 -2.443233641 5.982799364
113 -5.269411507 -2.443233641
114 -2.234109133 -5.269411507
115 1.783993722 -2.234109133
116 -8.238024314 1.783993722
117 -3.655924245 -8.238024314
118 1.700770683 -3.655924245
119 -6.755793099 1.700770683
120 -3.469273342 -6.755793099
121 -4.799280110 -3.469273342
122 -4.211418857 -4.799280110
123 2.511373258 -4.211418857
124 0.988692711 2.511373258
125 1.573701669 0.988692711
126 -1.228027217 1.573701669
127 2.369740536 -1.228027217
128 4.517589110 2.369740536
129 0.553987679 4.517589110
130 -4.542353528 0.553987679
131 1.489534579 -4.542353528
132 2.410728576 1.489534579
133 1.445396917 2.410728576
134 3.574667366 1.445396917
135 -1.011380324 3.574667366
136 -2.184610312 -1.011380324
137 -9.070274513 -2.184610312
138 2.650028402 -9.070274513
139 -6.717405526 2.650028402
140 -9.243870619 -6.717405526
141 1.684133709 -9.243870619
142 -8.141911261 1.684133709
143 -0.287165817 -8.141911261
144 -0.065584851 -0.287165817
145 -2.093825826 -0.065584851
146 -2.937241493 -2.093825826
147 -3.383564403 -2.937241493
148 -3.439795919 -3.383564403
149 -0.068150459 -3.439795919
150 -1.428485594 -0.068150459
151 0.383618097 -1.428485594
152 5.209940315 0.383618097
153 -8.757991584 5.209940315
154 -1.284389717 -8.757991584
155 -0.202506425 -1.284389717
156 0.186078800 -0.202506425
157 -0.025363288 0.186078800
158 2.142881925 -0.025363288
> 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/79dwo1290180125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/89dwo1290180125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/99dwo1290180125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/1014vq1290180125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1154ce1290180125.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/12qnak1290180125.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/13xo7w1290180125.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/14pfpz1290180125.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/15bgnn1290180125.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/16783w1290180125.tab")
+ }
> try(system("convert tmp/1u3gf1290180125.ps tmp/1u3gf1290180125.png",intern=TRUE))
character(0)
> try(system("convert tmp/2u3gf1290180125.ps tmp/2u3gf1290180125.png",intern=TRUE))
character(0)
> try(system("convert tmp/35cy01290180125.ps tmp/35cy01290180125.png",intern=TRUE))
character(0)
> try(system("convert tmp/45cy01290180125.ps tmp/45cy01290180125.png",intern=TRUE))
character(0)
> try(system("convert tmp/55cy01290180125.ps tmp/55cy01290180125.png",intern=TRUE))
character(0)
> try(system("convert tmp/6g3f31290180125.ps tmp/6g3f31290180125.png",intern=TRUE))
character(0)
> try(system("convert tmp/79dwo1290180125.ps tmp/79dwo1290180125.png",intern=TRUE))
character(0)
> try(system("convert tmp/89dwo1290180125.ps tmp/89dwo1290180125.png",intern=TRUE))
character(0)
> try(system("convert tmp/99dwo1290180125.ps tmp/99dwo1290180125.png",intern=TRUE))
character(0)
> try(system("convert tmp/1014vq1290180125.ps tmp/1014vq1290180125.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.876 2.687 36.868