R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(24
+ ,24
+ ,14
+ ,14
+ ,11
+ ,11
+ ,12
+ ,12
+ ,24
+ ,24
+ ,26
+ ,26
+ ,10
+ ,10
+ ,25
+ ,0
+ ,11
+ ,0
+ ,7
+ ,0
+ ,8
+ ,0
+ ,25
+ ,0
+ ,23
+ ,0
+ ,14
+ ,0
+ ,17
+ ,0
+ ,6
+ ,0
+ ,17
+ ,0
+ ,8
+ ,0
+ ,30
+ ,0
+ ,25
+ ,0
+ ,18
+ ,0
+ ,18
+ ,18
+ ,12
+ ,12
+ ,10
+ ,10
+ ,8
+ ,8
+ ,19
+ ,19
+ ,23
+ ,23
+ ,15
+ ,15
+ ,18
+ ,0
+ ,8
+ ,0
+ ,12
+ ,0
+ ,9
+ ,0
+ ,22
+ ,0
+ ,19
+ ,0
+ ,18
+ ,0
+ ,16
+ ,0
+ ,10
+ ,0
+ ,12
+ ,0
+ ,7
+ ,0
+ ,22
+ ,0
+ ,29
+ ,0
+ ,11
+ ,0
+ ,20
+ ,0
+ ,10
+ ,0
+ ,11
+ ,0
+ ,4
+ ,0
+ ,25
+ ,0
+ ,25
+ ,0
+ ,17
+ ,0
+ ,16
+ ,0
+ ,11
+ ,0
+ ,11
+ ,0
+ ,11
+ ,0
+ ,23
+ ,0
+ ,21
+ ,0
+ ,19
+ ,0
+ ,18
+ ,0
+ ,16
+ ,0
+ ,12
+ ,0
+ ,7
+ ,0
+ ,17
+ ,0
+ ,22
+ ,0
+ ,7
+ ,0
+ ,17
+ ,0
+ ,11
+ ,0
+ ,13
+ ,0
+ ,7
+ ,0
+ ,21
+ ,0
+ ,25
+ ,0
+ ,12
+ ,0
+ ,23
+ ,23
+ ,13
+ ,13
+ ,14
+ ,14
+ ,12
+ ,12
+ ,19
+ ,19
+ ,24
+ ,24
+ ,13
+ ,13
+ ,30
+ ,0
+ ,12
+ ,0
+ ,16
+ ,0
+ ,10
+ ,0
+ ,19
+ ,0
+ ,18
+ ,0
+ ,15
+ ,0
+ ,23
+ ,0
+ ,8
+ ,0
+ ,11
+ ,0
+ ,10
+ ,0
+ ,15
+ ,0
+ ,22
+ ,0
+ ,14
+ ,0
+ ,18
+ ,0
+ ,12
+ ,0
+ ,10
+ ,0
+ ,8
+ ,0
+ ,16
+ ,0
+ ,15
+ ,0
+ ,14
+ ,0
+ ,15
+ ,15
+ ,11
+ ,11
+ ,11
+ ,11
+ ,8
+ ,8
+ ,23
+ ,23
+ ,22
+ ,22
+ ,16
+ ,16
+ ,12
+ ,12
+ ,4
+ ,4
+ ,15
+ ,15
+ ,4
+ ,4
+ ,27
+ ,27
+ ,28
+ ,28
+ ,16
+ ,16
+ ,21
+ ,0
+ ,9
+ ,0
+ ,9
+ ,0
+ ,9
+ ,0
+ ,22
+ ,0
+ ,20
+ ,0
+ ,12
+ ,0
+ ,15
+ ,15
+ ,8
+ ,8
+ ,11
+ ,11
+ ,8
+ ,8
+ ,14
+ ,14
+ ,12
+ ,12
+ ,12
+ ,12
+ ,20
+ ,20
+ ,8
+ ,8
+ ,17
+ ,17
+ ,7
+ ,7
+ ,22
+ ,22
+ ,24
+ ,24
+ ,13
+ ,13
+ ,31
+ ,0
+ ,14
+ ,0
+ ,17
+ ,0
+ ,11
+ ,0
+ ,23
+ ,0
+ ,20
+ ,0
+ ,16
+ ,0
+ ,27
+ ,0
+ ,15
+ ,0
+ ,11
+ ,0
+ ,9
+ ,0
+ ,23
+ ,0
+ ,21
+ ,0
+ ,9
+ ,0
+ ,21
+ ,0
+ ,9
+ ,0
+ ,14
+ ,0
+ ,13
+ ,0
+ ,19
+ ,0
+ ,21
+ ,0
+ ,11
+ ,0
+ ,31
+ ,31
+ ,14
+ ,14
+ ,10
+ ,10
+ ,8
+ ,8
+ ,18
+ ,18
+ ,23
+ ,23
+ ,12
+ ,12
+ ,19
+ ,19
+ ,11
+ ,11
+ ,11
+ ,11
+ ,8
+ ,8
+ ,20
+ ,20
+ ,28
+ ,28
+ ,11
+ ,11
+ ,16
+ ,0
+ ,8
+ ,0
+ ,15
+ ,0
+ ,9
+ ,0
+ ,23
+ ,0
+ ,24
+ ,0
+ ,14
+ ,0
+ ,20
+ ,0
+ ,9
+ ,0
+ ,15
+ ,0
+ ,6
+ ,0
+ ,25
+ ,0
+ ,24
+ ,0
+ ,18
+ ,0
+ ,21
+ ,21
+ ,9
+ ,9
+ ,13
+ ,13
+ ,9
+ ,9
+ ,19
+ ,19
+ ,24
+ ,24
+ ,11
+ ,11
+ ,22
+ ,22
+ ,9
+ ,9
+ ,16
+ ,16
+ ,9
+ ,9
+ ,24
+ ,24
+ ,23
+ ,23
+ ,14
+ ,14
+ ,17
+ ,0
+ ,9
+ ,0
+ ,13
+ ,0
+ ,6
+ ,0
+ ,22
+ ,0
+ ,23
+ ,0
+ ,17
+ ,0
+ ,25
+ ,0
+ ,16
+ ,0
+ ,18
+ ,0
+ ,16
+ ,0
+ ,26
+ ,0
+ ,24
+ ,0
+ ,12
+ ,0
+ ,26
+ ,0
+ ,11
+ ,0
+ ,18
+ ,0
+ ,5
+ ,0
+ ,29
+ ,0
+ ,18
+ ,0
+ ,14
+ ,0
+ ,25
+ ,0
+ ,8
+ ,0
+ ,12
+ ,0
+ ,7
+ ,0
+ ,32
+ ,0
+ ,25
+ ,0
+ ,14
+ ,0
+ ,17
+ ,0
+ ,9
+ ,0
+ ,17
+ ,0
+ ,9
+ ,0
+ ,25
+ ,0
+ ,21
+ ,0
+ ,15
+ ,0
+ ,32
+ ,32
+ ,16
+ ,16
+ ,9
+ ,9
+ ,6
+ ,6
+ ,29
+ ,29
+ ,26
+ ,26
+ ,11
+ ,11
+ ,33
+ ,33
+ ,11
+ ,11
+ ,9
+ ,9
+ ,6
+ ,6
+ ,28
+ ,28
+ ,22
+ ,22
+ ,15
+ ,15
+ ,13
+ ,13
+ ,16
+ ,16
+ ,12
+ ,12
+ ,5
+ ,5
+ ,17
+ ,17
+ ,22
+ ,22
+ ,14
+ ,14
+ ,32
+ ,0
+ ,12
+ ,0
+ ,18
+ ,0
+ ,12
+ ,0
+ ,28
+ ,0
+ ,22
+ ,0
+ ,11
+ ,0
+ ,25
+ ,25
+ ,12
+ ,12
+ ,12
+ ,12
+ ,7
+ ,7
+ ,29
+ ,29
+ ,23
+ ,23
+ ,12
+ ,12
+ ,29
+ ,29
+ ,14
+ ,14
+ ,18
+ ,18
+ ,10
+ ,10
+ ,26
+ ,26
+ ,30
+ ,30
+ ,17
+ ,17
+ ,22
+ ,0
+ ,9
+ ,0
+ ,14
+ ,0
+ ,9
+ ,0
+ ,25
+ ,0
+ ,23
+ ,0
+ ,15
+ ,0
+ ,18
+ ,18
+ ,10
+ ,10
+ ,15
+ ,15
+ ,8
+ ,8
+ ,14
+ ,14
+ ,17
+ ,17
+ ,9
+ ,9
+ ,17
+ ,0
+ ,9
+ ,0
+ ,16
+ ,0
+ ,5
+ ,0
+ ,25
+ ,0
+ ,23
+ ,0
+ ,16
+ ,0
+ ,20
+ ,20
+ ,10
+ ,10
+ ,10
+ ,10
+ ,8
+ ,8
+ ,26
+ ,26
+ ,23
+ ,23
+ ,13
+ ,13
+ ,15
+ ,15
+ ,12
+ ,12
+ ,11
+ ,11
+ ,8
+ ,8
+ ,20
+ ,20
+ ,25
+ ,25
+ ,15
+ ,15
+ ,20
+ ,0
+ ,14
+ ,0
+ ,14
+ ,0
+ ,10
+ ,0
+ ,18
+ ,0
+ ,24
+ ,0
+ ,11
+ ,0
+ ,33
+ ,33
+ ,14
+ ,14
+ ,9
+ ,9
+ ,6
+ ,6
+ ,32
+ ,32
+ ,24
+ ,24
+ ,10
+ ,10
+ ,29
+ ,0
+ ,10
+ ,0
+ ,12
+ ,0
+ ,8
+ ,0
+ ,25
+ ,0
+ ,23
+ ,0
+ ,16
+ ,0
+ ,23
+ ,0
+ ,14
+ ,0
+ ,17
+ ,0
+ ,7
+ ,0
+ ,25
+ ,0
+ ,21
+ ,0
+ ,13
+ ,0
+ ,26
+ ,26
+ ,16
+ ,16
+ ,5
+ ,5
+ ,4
+ ,4
+ ,23
+ ,23
+ ,24
+ ,24
+ ,9
+ ,9
+ ,18
+ ,18
+ ,9
+ ,9
+ ,12
+ ,12
+ ,8
+ ,8
+ ,21
+ ,21
+ ,24
+ ,24
+ ,14
+ ,14
+ ,20
+ ,0
+ ,10
+ ,0
+ ,12
+ ,0
+ ,8
+ ,0
+ ,20
+ ,0
+ ,28
+ ,0
+ ,16
+ ,0
+ ,11
+ ,0
+ ,6
+ ,0
+ ,6
+ ,0
+ ,4
+ ,0
+ ,15
+ ,0
+ ,16
+ ,0
+ ,15
+ ,0
+ ,28
+ ,28
+ ,8
+ ,8
+ ,24
+ ,24
+ ,20
+ ,20
+ ,30
+ ,30
+ ,20
+ ,20
+ ,14
+ ,14
+ ,26
+ ,0
+ ,13
+ ,0
+ ,12
+ ,0
+ ,8
+ ,0
+ ,24
+ ,0
+ ,29
+ ,0
+ ,13
+ ,0
+ ,22
+ ,0
+ ,10
+ ,0
+ ,12
+ ,0
+ ,8
+ ,0
+ ,26
+ ,0
+ ,27
+ ,0
+ ,14
+ ,0
+ ,17
+ ,17
+ ,8
+ ,8
+ ,14
+ ,14
+ ,6
+ ,6
+ ,24
+ ,24
+ ,22
+ ,22
+ ,16
+ ,16
+ ,12
+ ,12
+ ,7
+ ,7
+ ,7
+ ,7
+ ,4
+ ,4
+ ,22
+ ,22
+ ,28
+ ,28
+ ,15
+ ,15
+ ,14
+ ,0
+ ,15
+ ,0
+ ,13
+ ,0
+ ,8
+ ,0
+ ,14
+ ,0
+ ,16
+ ,0
+ ,16
+ ,0
+ ,17
+ ,17
+ ,9
+ ,9
+ ,12
+ ,12
+ ,9
+ ,9
+ ,24
+ ,24
+ ,25
+ ,25
+ ,15
+ ,15
+ ,21
+ ,21
+ ,10
+ ,10
+ ,13
+ ,13
+ ,6
+ ,6
+ ,24
+ ,24
+ ,24
+ ,24
+ ,13
+ ,13
+ ,19
+ ,0
+ ,12
+ ,0
+ ,14
+ ,0
+ ,7
+ ,0
+ ,24
+ ,0
+ ,28
+ ,0
+ ,11
+ ,0
+ ,18
+ ,18
+ ,13
+ ,13
+ ,8
+ ,8
+ ,9
+ ,9
+ ,24
+ ,24
+ ,24
+ ,24
+ ,16
+ ,16
+ ,10
+ ,10
+ ,10
+ ,10
+ ,11
+ ,11
+ ,5
+ ,5
+ ,19
+ ,19
+ ,23
+ ,23
+ ,17
+ ,17
+ ,29
+ ,29
+ ,11
+ ,11
+ ,9
+ ,9
+ ,5
+ ,5
+ ,31
+ ,31
+ ,30
+ ,30
+ ,10
+ ,10
+ ,31
+ ,31
+ ,8
+ ,8
+ ,11
+ ,11
+ ,8
+ ,8
+ ,22
+ ,22
+ ,24
+ ,24
+ ,17
+ ,17
+ ,19
+ ,19
+ ,9
+ ,9
+ ,13
+ ,13
+ ,8
+ ,8
+ ,27
+ ,27
+ ,21
+ ,21
+ ,11
+ ,11
+ ,9
+ ,9
+ ,13
+ ,13
+ ,10
+ ,10
+ ,6
+ ,6
+ ,19
+ ,19
+ ,25
+ ,25
+ ,14
+ ,14
+ ,20
+ ,0
+ ,11
+ ,0
+ ,11
+ ,0
+ ,8
+ ,0
+ ,25
+ ,0
+ ,25
+ ,0
+ ,15
+ ,0
+ ,28
+ ,0
+ ,8
+ ,0
+ ,12
+ ,0
+ ,7
+ ,0
+ ,20
+ ,0
+ ,22
+ ,0
+ ,16
+ ,0
+ ,19
+ ,0
+ ,9
+ ,0
+ ,9
+ ,0
+ ,7
+ ,0
+ ,21
+ ,0
+ ,23
+ ,0
+ ,15
+ ,0
+ ,30
+ ,0
+ ,9
+ ,0
+ ,15
+ ,0
+ ,9
+ ,0
+ ,27
+ ,0
+ ,26
+ ,0
+ ,16
+ ,0
+ ,29
+ ,0
+ ,15
+ ,0
+ ,18
+ ,0
+ ,11
+ ,0
+ ,23
+ ,0
+ ,23
+ ,0
+ ,15
+ ,0
+ ,26
+ ,0
+ ,9
+ ,0
+ ,15
+ ,0
+ ,6
+ ,0
+ ,25
+ ,0
+ ,25
+ ,0
+ ,14
+ ,0
+ ,23
+ ,0
+ ,10
+ ,0
+ ,12
+ ,0
+ ,8
+ ,0
+ ,20
+ ,0
+ ,21
+ ,0
+ ,17
+ ,0
+ ,21
+ ,0
+ ,12
+ ,0
+ ,14
+ ,0
+ ,9
+ ,0
+ ,22
+ ,0
+ ,24
+ ,0
+ ,12
+ ,0
+ ,19
+ ,19
+ ,12
+ ,12
+ ,10
+ ,10
+ ,8
+ ,8
+ ,23
+ ,23
+ ,29
+ ,29
+ ,12
+ ,12
+ ,28
+ ,0
+ ,11
+ ,0
+ ,13
+ ,0
+ ,6
+ ,0
+ ,25
+ ,0
+ ,22
+ ,0
+ ,9
+ ,0
+ ,23
+ ,0
+ ,14
+ ,0
+ ,13
+ ,0
+ ,10
+ ,0
+ ,25
+ ,0
+ ,27
+ ,0
+ ,12
+ ,0
+ ,18
+ ,0
+ ,6
+ ,0
+ ,11
+ ,0
+ ,8
+ ,0
+ ,17
+ ,0
+ ,26
+ ,0
+ ,17
+ ,0
+ ,21
+ ,21
+ ,12
+ ,12
+ ,13
+ ,13
+ ,8
+ ,8
+ ,19
+ ,19
+ ,22
+ ,22
+ ,11
+ ,11
+ ,20
+ ,0
+ ,8
+ ,0
+ ,16
+ ,0
+ ,10
+ ,0
+ ,25
+ ,0
+ ,24
+ ,0
+ ,16
+ ,0
+ ,23
+ ,23
+ ,14
+ ,14
+ ,8
+ ,8
+ ,5
+ ,5
+ ,19
+ ,19
+ ,27
+ ,27
+ ,9
+ ,9
+ ,21
+ ,21
+ ,11
+ ,11
+ ,16
+ ,16
+ ,7
+ ,7
+ ,20
+ ,20
+ ,24
+ ,24
+ ,15
+ ,15
+ ,21
+ ,0
+ ,10
+ ,0
+ ,11
+ ,0
+ ,5
+ ,0
+ ,26
+ ,0
+ ,24
+ ,0
+ ,17
+ ,0
+ ,15
+ ,15
+ ,14
+ ,14
+ ,9
+ ,9
+ ,8
+ ,8
+ ,23
+ ,23
+ ,29
+ ,29
+ ,17
+ ,17
+ ,28
+ ,0
+ ,12
+ ,0
+ ,16
+ ,0
+ ,14
+ ,0
+ ,27
+ ,0
+ ,22
+ ,0
+ ,12
+ ,0
+ ,19
+ ,19
+ ,10
+ ,10
+ ,12
+ ,12
+ ,7
+ ,7
+ ,17
+ ,17
+ ,21
+ ,21
+ ,15
+ ,15
+ ,26
+ ,26
+ ,14
+ ,14
+ ,14
+ ,14
+ ,8
+ ,8
+ ,17
+ ,17
+ ,24
+ ,24
+ ,18
+ ,18
+ ,16
+ ,16
+ ,11
+ ,11
+ ,9
+ ,9
+ ,5
+ ,5
+ ,17
+ ,17
+ ,23
+ ,23
+ ,13
+ ,13
+ ,22
+ ,0
+ ,10
+ ,0
+ ,15
+ ,0
+ ,6
+ ,0
+ ,22
+ ,0
+ ,20
+ ,0
+ ,15
+ ,0
+ ,19
+ ,19
+ ,9
+ ,9
+ ,11
+ ,11
+ ,10
+ ,10
+ ,21
+ ,21
+ ,27
+ ,27
+ ,16
+ ,16
+ ,31
+ ,0
+ ,10
+ ,0
+ ,21
+ ,0
+ ,12
+ ,0
+ ,32
+ ,0
+ ,26
+ ,0
+ ,17
+ ,0
+ ,31
+ ,31
+ ,16
+ ,16
+ ,14
+ ,14
+ ,9
+ ,9
+ ,21
+ ,21
+ ,25
+ ,25
+ ,15
+ ,15
+ ,29
+ ,0
+ ,13
+ ,0
+ ,18
+ ,0
+ ,12
+ ,0
+ ,21
+ ,0
+ ,21
+ ,0
+ ,13
+ ,0
+ ,19
+ ,19
+ ,9
+ ,9
+ ,12
+ ,12
+ ,7
+ ,7
+ ,18
+ ,18
+ ,21
+ ,21
+ ,12
+ ,12
+ ,22
+ ,0
+ ,10
+ ,0
+ ,13
+ ,0
+ ,8
+ ,0
+ ,18
+ ,0
+ ,19
+ ,0
+ ,11
+ ,0
+ ,23
+ ,0
+ ,10
+ ,0
+ ,15
+ ,0
+ ,10
+ ,0
+ ,23
+ ,0
+ ,21
+ ,0
+ ,15
+ ,0
+ ,15
+ ,15
+ ,7
+ ,7
+ ,12
+ ,12
+ ,6
+ ,6
+ ,19
+ ,19
+ ,21
+ ,21
+ ,15
+ ,15
+ ,20
+ ,0
+ ,9
+ ,0
+ ,19
+ ,0
+ ,10
+ ,0
+ ,20
+ ,0
+ ,16
+ ,0
+ ,15
+ ,0
+ ,18
+ ,0
+ ,8
+ ,0
+ ,15
+ ,0
+ ,10
+ ,0
+ ,21
+ ,0
+ ,22
+ ,0
+ ,18
+ ,0
+ ,23
+ ,23
+ ,14
+ ,14
+ ,11
+ ,11
+ ,10
+ ,10
+ ,20
+ ,20
+ ,29
+ ,29
+ ,16
+ ,16
+ ,25
+ ,0
+ ,14
+ ,0
+ ,11
+ ,0
+ ,5
+ ,0
+ ,17
+ ,0
+ ,15
+ ,0
+ ,12
+ ,0
+ ,21
+ ,0
+ ,8
+ ,0
+ ,10
+ ,0
+ ,7
+ ,0
+ ,18
+ ,0
+ ,17
+ ,0
+ ,16
+ ,0
+ ,24
+ ,0
+ ,9
+ ,0
+ ,13
+ ,0
+ ,10
+ ,0
+ ,19
+ ,0
+ ,15
+ ,0
+ ,15
+ ,0
+ ,25
+ ,0
+ ,14
+ ,0
+ ,15
+ ,0
+ ,11
+ ,0
+ ,22
+ ,0
+ ,21
+ ,0
+ ,15
+ ,0
+ ,17
+ ,17
+ ,14
+ ,14
+ ,12
+ ,12
+ ,6
+ ,6
+ ,15
+ ,15
+ ,21
+ ,21
+ ,15
+ ,15
+ ,13
+ ,0
+ ,8
+ ,0
+ ,12
+ ,0
+ ,7
+ ,0
+ ,14
+ ,0
+ ,19
+ ,0
+ ,17
+ ,0
+ ,28
+ ,0
+ ,8
+ ,0
+ ,16
+ ,0
+ ,12
+ ,0
+ ,18
+ ,0
+ ,24
+ ,0
+ ,15
+ ,0
+ ,21
+ ,21
+ ,8
+ ,8
+ ,9
+ ,9
+ ,11
+ ,11
+ ,24
+ ,24
+ ,20
+ ,20
+ ,13
+ ,13
+ ,25
+ ,0
+ ,7
+ ,0
+ ,18
+ ,0
+ ,11
+ ,0
+ ,35
+ ,0
+ ,17
+ ,0
+ ,16
+ ,0
+ ,9
+ ,0
+ ,6
+ ,0
+ ,8
+ ,0
+ ,11
+ ,0
+ ,29
+ ,0
+ ,23
+ ,0
+ ,13
+ ,0
+ ,16
+ ,0
+ ,8
+ ,0
+ ,13
+ ,0
+ ,5
+ ,0
+ ,21
+ ,0
+ ,24
+ ,0
+ ,13
+ ,0
+ ,17
+ ,17
+ ,11
+ ,11
+ ,9
+ ,9
+ ,6
+ ,6
+ ,20
+ ,20
+ ,19
+ ,19
+ ,15
+ ,15
+ ,25
+ ,25
+ ,14
+ ,14
+ ,15
+ ,15
+ ,9
+ ,9
+ ,22
+ ,22
+ ,24
+ ,24
+ ,13
+ ,13
+ ,20
+ ,20
+ ,11
+ ,11
+ ,8
+ ,8
+ ,4
+ ,4
+ ,13
+ ,13
+ ,13
+ ,13
+ ,16
+ ,16
+ ,29
+ ,0
+ ,11
+ ,0
+ ,7
+ ,0
+ ,4
+ ,0
+ ,26
+ ,0
+ ,22
+ ,0
+ ,14
+ ,0
+ ,14
+ ,0
+ ,11
+ ,0
+ ,12
+ ,0
+ ,7
+ ,0
+ ,17
+ ,0
+ ,16
+ ,0
+ ,15
+ ,0
+ ,22
+ ,0
+ ,14
+ ,0
+ ,14
+ ,0
+ ,11
+ ,0
+ ,25
+ ,0
+ ,19
+ ,0
+ ,11
+ ,0
+ ,15
+ ,0
+ ,8
+ ,0
+ ,6
+ ,0
+ ,6
+ ,0
+ ,20
+ ,0
+ ,25
+ ,0
+ ,15
+ ,0
+ ,19
+ ,19
+ ,20
+ ,20
+ ,8
+ ,8
+ ,7
+ ,7
+ ,19
+ ,19
+ ,25
+ ,25
+ ,14
+ ,14
+ ,20
+ ,20
+ ,11
+ ,11
+ ,17
+ ,17
+ ,8
+ ,8
+ ,21
+ ,21
+ ,23
+ ,23
+ ,14
+ ,14
+ ,15
+ ,0
+ ,8
+ ,0
+ ,10
+ ,0
+ ,4
+ ,0
+ ,22
+ ,0
+ ,24
+ ,0
+ ,17
+ ,0
+ ,20
+ ,0
+ ,11
+ ,0
+ ,11
+ ,0
+ ,8
+ ,0
+ ,24
+ ,0
+ ,26
+ ,0
+ ,15
+ ,0
+ ,18
+ ,0
+ ,10
+ ,0
+ ,14
+ ,0
+ ,9
+ ,0
+ ,21
+ ,0
+ ,26
+ ,0
+ ,14
+ ,0
+ ,33
+ ,0
+ ,14
+ ,0
+ ,11
+ ,0
+ ,8
+ ,0
+ ,26
+ ,0
+ ,25
+ ,0
+ ,15
+ ,0
+ ,22
+ ,0
+ ,11
+ ,0
+ ,13
+ ,0
+ ,11
+ ,0
+ ,24
+ ,0
+ ,18
+ ,0
+ ,13
+ ,0
+ ,16
+ ,0
+ ,9
+ ,0
+ ,12
+ ,0
+ ,8
+ ,0
+ ,16
+ ,0
+ ,21
+ ,0
+ ,15
+ ,0
+ ,17
+ ,17
+ ,9
+ ,9
+ ,11
+ ,11
+ ,5
+ ,5
+ ,23
+ ,23
+ ,26
+ ,26
+ ,16
+ ,16
+ ,16
+ ,0
+ ,8
+ ,0
+ ,9
+ ,0
+ ,4
+ ,0
+ ,18
+ ,0
+ ,23
+ ,0
+ ,12
+ ,0
+ ,21
+ ,21
+ ,10
+ ,10
+ ,12
+ ,12
+ ,8
+ ,8
+ ,16
+ ,16
+ ,23
+ ,23
+ ,14
+ ,14
+ ,26
+ ,26
+ ,13
+ ,13
+ ,20
+ ,20
+ ,10
+ ,10
+ ,26
+ ,26
+ ,22
+ ,22
+ ,12
+ ,12
+ ,18
+ ,0
+ ,13
+ ,0
+ ,12
+ ,0
+ ,6
+ ,0
+ ,19
+ ,0
+ ,20
+ ,0
+ ,14
+ ,0
+ ,18
+ ,0
+ ,12
+ ,0
+ ,13
+ ,0
+ ,9
+ ,0
+ ,21
+ ,0
+ ,13
+ ,0
+ ,14
+ ,0
+ ,17
+ ,0
+ ,8
+ ,0
+ ,12
+ ,0
+ ,9
+ ,0
+ ,21
+ ,0
+ ,24
+ ,0
+ ,15
+ ,0
+ ,22
+ ,0
+ ,13
+ ,0
+ ,12
+ ,0
+ ,13
+ ,0
+ ,22
+ ,0
+ ,15
+ ,0
+ ,13
+ ,0
+ ,30
+ ,0
+ ,14
+ ,0
+ ,9
+ ,0
+ ,9
+ ,0
+ ,23
+ ,0
+ ,14
+ ,0
+ ,15
+ ,0
+ ,30
+ ,0
+ ,12
+ ,0
+ ,15
+ ,0
+ ,10
+ ,0
+ ,29
+ ,0
+ ,22
+ ,0
+ ,16
+ ,0
+ ,24
+ ,0
+ ,14
+ ,0
+ ,24
+ ,0
+ ,20
+ ,0
+ ,21
+ ,0
+ ,10
+ ,0
+ ,10
+ ,0
+ ,21
+ ,21
+ ,15
+ ,15
+ ,7
+ ,7
+ ,5
+ ,5
+ ,21
+ ,21
+ ,24
+ ,24
+ ,8
+ ,8
+ ,21
+ ,0
+ ,13
+ ,0
+ ,17
+ ,0
+ ,11
+ ,0
+ ,23
+ ,0
+ ,22
+ ,0
+ ,15
+ ,0
+ ,29
+ ,0
+ ,16
+ ,0
+ ,11
+ ,0
+ ,6
+ ,0
+ ,27
+ ,0
+ ,24
+ ,0
+ ,14
+ ,0
+ ,31
+ ,0
+ ,9
+ ,0
+ ,17
+ ,0
+ ,9
+ ,0
+ ,25
+ ,0
+ ,19
+ ,0
+ ,13
+ ,0
+ ,20
+ ,0
+ ,9
+ ,0
+ ,11
+ ,0
+ ,7
+ ,0
+ ,21
+ ,0
+ ,20
+ ,0
+ ,15
+ ,0
+ ,16
+ ,0
+ ,9
+ ,0
+ ,12
+ ,0
+ ,9
+ ,0
+ ,10
+ ,0
+ ,13
+ ,0
+ ,13
+ ,0
+ ,22
+ ,0
+ ,8
+ ,0
+ ,14
+ ,0
+ ,10
+ ,0
+ ,20
+ ,0
+ ,20
+ ,0
+ ,14
+ ,0
+ ,20
+ ,0
+ ,7
+ ,0
+ ,11
+ ,0
+ ,9
+ ,0
+ ,26
+ ,0
+ ,22
+ ,0
+ ,19
+ ,0
+ ,28
+ ,0
+ ,16
+ ,0
+ ,16
+ ,0
+ ,8
+ ,0
+ ,24
+ ,0
+ ,24
+ ,0
+ ,17
+ ,0
+ ,38
+ ,0
+ ,11
+ ,0
+ ,21
+ ,0
+ ,7
+ ,0
+ ,29
+ ,0
+ ,29
+ ,0
+ ,16
+ ,0
+ ,22
+ ,0
+ ,9
+ ,0
+ ,14
+ ,0
+ ,6
+ ,0
+ ,19
+ ,0
+ ,12
+ ,0
+ ,16
+ ,0
+ ,20
+ ,0
+ ,11
+ ,0
+ ,20
+ ,0
+ ,13
+ ,0
+ ,24
+ ,0
+ ,20
+ ,0
+ ,14
+ ,0
+ ,17
+ ,0
+ ,9
+ ,0
+ ,13
+ ,0
+ ,6
+ ,0
+ ,19
+ ,0
+ ,21
+ ,0
+ ,12
+ ,0
+ ,28
+ ,28
+ ,14
+ ,14
+ ,11
+ ,11
+ ,8
+ ,8
+ ,24
+ ,24
+ ,24
+ ,24
+ ,13
+ ,13
+ ,22
+ ,0
+ ,13
+ ,0
+ ,15
+ ,0
+ ,10
+ ,0
+ ,22
+ ,0
+ ,22
+ ,0
+ ,14
+ ,0
+ ,31
+ ,0
+ ,16
+ ,0
+ ,19
+ ,0
+ ,16
+ ,0
+ ,17
+ ,0
+ ,20
+ ,0
+ ,15
+ ,0)
+ ,dim=c(14
+ ,154)
+ ,dimnames=list(c('CM'
+ ,'CM_G'
+ ,'D'
+ ,'D_G'
+ ,'PE'
+ ,'PE_G'
+ ,'PC'
+ ,'PC_G'
+ ,'PS'
+ ,'PS_G'
+ ,'O'
+ ,'O_G'
+ ,'H'
+ ,'H_G')
+ ,1:154))
> y <- array(NA,dim=c(14,154),dimnames=list(c('CM','CM_G','D','D_G','PE','PE_G','PC','PC_G','PS','PS_G','O','O_G','H','H_G'),1:154))
> 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 = '9'
> #'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
PS CM CM_G D D_G PE PE_G PC PC_G PS_G O O_G H H_G
1 24 24 24 14 14 11 11 12 12 24 26 26 10 10
2 25 25 0 11 0 7 0 8 0 0 23 0 14 0
3 30 17 0 6 0 17 0 8 0 0 25 0 18 0
4 19 18 18 12 12 10 10 8 8 19 23 23 15 15
5 22 18 0 8 0 12 0 9 0 0 19 0 18 0
6 22 16 0 10 0 12 0 7 0 0 29 0 11 0
7 25 20 0 10 0 11 0 4 0 0 25 0 17 0
8 23 16 0 11 0 11 0 11 0 0 21 0 19 0
9 17 18 0 16 0 12 0 7 0 0 22 0 7 0
10 21 17 0 11 0 13 0 7 0 0 25 0 12 0
11 19 23 23 13 13 14 14 12 12 19 24 24 13 13
12 19 30 0 12 0 16 0 10 0 0 18 0 15 0
13 15 23 0 8 0 11 0 10 0 0 22 0 14 0
14 16 18 0 12 0 10 0 8 0 0 15 0 14 0
15 23 15 15 11 11 11 11 8 8 23 22 22 16 16
16 27 12 12 4 4 15 15 4 4 27 28 28 16 16
17 22 21 0 9 0 9 0 9 0 0 20 0 12 0
18 14 15 15 8 8 11 11 8 8 14 12 12 12 12
19 22 20 20 8 8 17 17 7 7 22 24 24 13 13
20 23 31 0 14 0 17 0 11 0 0 20 0 16 0
21 23 27 0 15 0 11 0 9 0 0 21 0 9 0
22 19 21 0 9 0 14 0 13 0 0 21 0 11 0
23 18 31 31 14 14 10 10 8 8 18 23 23 12 12
24 20 19 19 11 11 11 11 8 8 20 28 28 11 11
25 23 16 0 8 0 15 0 9 0 0 24 0 14 0
26 25 20 0 9 0 15 0 6 0 0 24 0 18 0
27 19 21 21 9 9 13 13 9 9 19 24 24 11 11
28 24 22 22 9 9 16 16 9 9 24 23 23 14 14
29 22 17 0 9 0 13 0 6 0 0 23 0 17 0
30 26 25 0 16 0 18 0 16 0 0 24 0 12 0
31 29 26 0 11 0 18 0 5 0 0 18 0 14 0
32 32 25 0 8 0 12 0 7 0 0 25 0 14 0
33 25 17 0 9 0 17 0 9 0 0 21 0 15 0
34 29 32 32 16 16 9 9 6 6 29 26 26 11 11
35 28 33 33 11 11 9 9 6 6 28 22 22 15 15
36 17 13 13 16 16 12 12 5 5 17 22 22 14 14
37 28 32 0 12 0 18 0 12 0 0 22 0 11 0
38 29 25 25 12 12 12 12 7 7 29 23 23 12 12
39 26 29 29 14 14 18 18 10 10 26 30 30 17 17
40 25 22 0 9 0 14 0 9 0 0 23 0 15 0
41 14 18 18 10 10 15 15 8 8 14 17 17 9 9
42 25 17 0 9 0 16 0 5 0 0 23 0 16 0
43 26 20 20 10 10 10 10 8 8 26 23 23 13 13
44 20 15 15 12 12 11 11 8 8 20 25 25 15 15
45 18 20 0 14 0 14 0 10 0 0 24 0 11 0
46 32 33 33 14 14 9 9 6 6 32 24 24 10 10
47 25 29 0 10 0 12 0 8 0 0 23 0 16 0
48 25 23 0 14 0 17 0 7 0 0 21 0 13 0
49 23 26 26 16 16 5 5 4 4 23 24 24 9 9
50 21 18 18 9 9 12 12 8 8 21 24 24 14 14
51 20 20 0 10 0 12 0 8 0 0 28 0 16 0
52 15 11 0 6 0 6 0 4 0 0 16 0 15 0
53 30 28 28 8 8 24 24 20 20 30 20 20 14 14
54 24 26 0 13 0 12 0 8 0 0 29 0 13 0
55 26 22 0 10 0 12 0 8 0 0 27 0 14 0
56 24 17 17 8 8 14 14 6 6 24 22 22 16 16
57 22 12 12 7 7 7 7 4 4 22 28 28 15 15
58 14 14 0 15 0 13 0 8 0 0 16 0 16 0
59 24 17 17 9 9 12 12 9 9 24 25 25 15 15
60 24 21 21 10 10 13 13 6 6 24 24 24 13 13
61 24 19 0 12 0 14 0 7 0 0 28 0 11 0
62 24 18 18 13 13 8 8 9 9 24 24 24 16 16
63 19 10 10 10 10 11 11 5 5 19 23 23 17 17
64 31 29 29 11 11 9 9 5 5 31 30 30 10 10
65 22 31 31 8 8 11 11 8 8 22 24 24 17 17
66 27 19 19 9 9 13 13 8 8 27 21 21 11 11
67 19 9 9 13 13 10 10 6 6 19 25 25 14 14
68 25 20 0 11 0 11 0 8 0 0 25 0 15 0
69 20 28 0 8 0 12 0 7 0 0 22 0 16 0
70 21 19 0 9 0 9 0 7 0 0 23 0 15 0
71 27 30 0 9 0 15 0 9 0 0 26 0 16 0
72 23 29 0 15 0 18 0 11 0 0 23 0 15 0
73 25 26 0 9 0 15 0 6 0 0 25 0 14 0
74 20 23 0 10 0 12 0 8 0 0 21 0 17 0
75 22 21 0 12 0 14 0 9 0 0 24 0 12 0
76 23 19 19 12 12 10 10 8 8 23 29 29 12 12
77 25 28 0 11 0 13 0 6 0 0 22 0 9 0
78 25 23 0 14 0 13 0 10 0 0 27 0 12 0
79 17 18 0 6 0 11 0 8 0 0 26 0 17 0
80 19 21 21 12 12 13 13 8 8 19 22 22 11 11
81 25 20 0 8 0 16 0 10 0 0 24 0 16 0
82 19 23 23 14 14 8 8 5 5 19 27 27 9 9
83 20 21 21 11 11 16 16 7 7 20 24 24 15 15
84 26 21 0 10 0 11 0 5 0 0 24 0 17 0
85 23 15 15 14 14 9 9 8 8 23 29 29 17 17
86 27 28 0 12 0 16 0 14 0 0 22 0 12 0
87 17 19 19 10 10 12 12 7 7 17 21 21 15 15
88 17 26 26 14 14 14 14 8 8 17 24 24 18 18
89 17 16 16 11 11 9 9 5 5 17 23 23 13 13
90 22 22 0 10 0 15 0 6 0 0 20 0 15 0
91 21 19 19 9 9 11 11 10 10 21 27 27 16 16
92 32 31 0 10 0 21 0 12 0 0 26 0 17 0
93 21 31 31 16 16 14 14 9 9 21 25 25 15 15
94 21 29 0 13 0 18 0 12 0 0 21 0 13 0
95 18 19 19 9 9 12 12 7 7 18 21 21 12 12
96 18 22 0 10 0 13 0 8 0 0 19 0 11 0
97 23 23 0 10 0 15 0 10 0 0 21 0 15 0
98 19 15 15 7 7 12 12 6 6 19 21 21 15 15
99 20 20 0 9 0 19 0 10 0 0 16 0 15 0
100 21 18 0 8 0 15 0 10 0 0 22 0 18 0
101 20 23 23 14 14 11 11 10 10 20 29 29 16 16
102 17 25 0 14 0 11 0 5 0 0 15 0 12 0
103 18 21 0 8 0 10 0 7 0 0 17 0 16 0
104 19 24 0 9 0 13 0 10 0 0 15 0 15 0
105 22 25 0 14 0 15 0 11 0 0 21 0 15 0
106 15 17 17 14 14 12 12 6 6 15 21 21 15 15
107 14 13 0 8 0 12 0 7 0 0 19 0 17 0
108 18 28 0 8 0 16 0 12 0 0 24 0 15 0
109 24 21 21 8 8 9 9 11 11 24 20 20 13 13
110 35 25 0 7 0 18 0 11 0 0 17 0 16 0
111 29 9 0 6 0 8 0 11 0 0 23 0 13 0
112 21 16 0 8 0 13 0 5 0 0 24 0 13 0
113 20 17 17 11 11 9 9 6 6 20 19 19 15 15
114 22 25 25 14 14 15 15 9 9 22 24 24 13 13
115 13 20 20 11 11 8 8 4 4 13 13 13 16 16
116 26 29 0 11 0 7 0 4 0 0 22 0 14 0
117 17 14 0 11 0 12 0 7 0 0 16 0 15 0
118 25 22 0 14 0 14 0 11 0 0 19 0 11 0
119 20 15 0 8 0 6 0 6 0 0 25 0 15 0
120 19 19 19 20 20 8 8 7 7 19 25 25 14 14
121 21 20 20 11 11 17 17 8 8 21 23 23 14 14
122 22 15 0 8 0 10 0 4 0 0 24 0 17 0
123 24 20 0 11 0 11 0 8 0 0 26 0 15 0
124 21 18 0 10 0 14 0 9 0 0 26 0 14 0
125 26 33 0 14 0 11 0 8 0 0 25 0 15 0
126 24 22 0 11 0 13 0 11 0 0 18 0 13 0
127 16 16 0 9 0 12 0 8 0 0 21 0 15 0
128 23 17 17 9 9 11 11 5 5 23 26 26 16 16
129 18 16 0 8 0 9 0 4 0 0 23 0 12 0
130 16 21 21 10 10 12 12 8 8 16 23 23 14 14
131 26 26 26 13 13 20 20 10 10 26 22 22 12 12
132 19 18 0 13 0 12 0 6 0 0 20 0 14 0
133 21 18 0 12 0 13 0 9 0 0 13 0 14 0
134 21 17 0 8 0 12 0 9 0 0 24 0 15 0
135 22 22 0 13 0 12 0 13 0 0 15 0 13 0
136 23 30 0 14 0 9 0 9 0 0 14 0 15 0
137 29 30 0 12 0 15 0 10 0 0 22 0 16 0
138 21 24 0 14 0 24 0 20 0 0 10 0 10 0
139 21 21 21 15 15 7 7 5 5 21 24 24 8 8
140 23 21 0 13 0 17 0 11 0 0 22 0 15 0
141 27 29 0 16 0 11 0 6 0 0 24 0 14 0
142 25 31 0 9 0 17 0 9 0 0 19 0 13 0
143 21 20 0 9 0 11 0 7 0 0 20 0 15 0
144 10 16 0 9 0 12 0 9 0 0 13 0 13 0
145 20 22 0 8 0 14 0 10 0 0 20 0 14 0
146 26 20 0 7 0 11 0 9 0 0 22 0 19 0
147 24 28 0 16 0 16 0 8 0 0 24 0 17 0
148 29 38 0 11 0 21 0 7 0 0 29 0 16 0
149 19 22 0 9 0 14 0 6 0 0 12 0 16 0
150 24 20 0 11 0 20 0 13 0 0 20 0 14 0
151 19 17 0 9 0 13 0 6 0 0 21 0 12 0
152 24 28 28 14 14 11 11 8 8 24 24 24 13 13
153 22 22 0 13 0 15 0 10 0 0 22 0 14 0
154 17 31 0 16 0 19 0 16 0 0 20 0 15 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) CM CM_G D D_G PE
3.02312 0.28981 -0.29363 -0.19925 0.15354 0.25596
PE_G PC PC_G PS_G O O_G
-0.28323 0.01327 -0.01175 0.97857 0.41592 -0.44454
H H_G
0.17814 -0.24798
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.45277 -0.70711 -0.02976 0.68570 11.45210
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.02312 2.61223 1.157 0.24912
CM 0.28981 0.06346 4.567 1.08e-05 ***
CM_G -0.29363 0.10615 -2.766 0.00644 **
D -0.19925 0.13003 -1.532 0.12769
D_G 0.15354 0.18875 0.813 0.41734
PE 0.25596 0.11122 2.301 0.02285 *
PE_G -0.28323 0.18093 -1.565 0.11974
PC 0.01327 0.13106 0.101 0.91947
PC_G -0.01175 0.22985 -0.051 0.95931
PS_G 0.97857 0.11379 8.600 1.45e-14 ***
O 0.41592 0.07216 5.764 5.02e-08 ***
O_G -0.44454 0.13941 -3.189 0.00176 **
H 0.17814 0.12584 1.416 0.15912
H_G -0.24798 0.17240 -1.438 0.15256
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.806 on 140 degrees of freedom
Multiple R-squared: 0.6037, Adjusted R-squared: 0.5669
F-statistic: 16.4 on 13 and 140 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,] 9.781439e-01 4.371216e-02 0.02185608
[2,] 9.526787e-01 9.464257e-02 0.04732129
[3,] 9.119939e-01 1.760122e-01 0.08800609
[4,] 8.714543e-01 2.570913e-01 0.12854567
[5,] 9.180650e-01 1.638700e-01 0.08193498
[6,] 8.800292e-01 2.399417e-01 0.11997083
[7,] 8.248027e-01 3.503946e-01 0.17519728
[8,] 7.588942e-01 4.822116e-01 0.24110580
[9,] 6.871602e-01 6.256796e-01 0.31283981
[10,] 6.115839e-01 7.768323e-01 0.38841613
[11,] 5.277421e-01 9.445158e-01 0.47225789
[12,] 4.445735e-01 8.891471e-01 0.55542647
[13,] 3.927320e-01 7.854641e-01 0.60726796
[14,] 3.768796e-01 7.537592e-01 0.62312039
[15,] 6.751201e-01 6.497598e-01 0.32487988
[16,] 8.752954e-01 2.494092e-01 0.12470461
[17,] 8.647530e-01 2.704941e-01 0.13524703
[18,] 8.243113e-01 3.513773e-01 0.17568865
[19,] 7.775676e-01 4.448648e-01 0.22243242
[20,] 7.250672e-01 5.498656e-01 0.27493280
[21,] 6.917246e-01 6.165507e-01 0.30827537
[22,] 6.327188e-01 7.345625e-01 0.36728123
[23,] 5.745086e-01 8.509828e-01 0.42549142
[24,] 5.176399e-01 9.647201e-01 0.48236007
[25,] 4.577608e-01 9.155217e-01 0.54223915
[26,] 4.236864e-01 8.473728e-01 0.57631358
[27,] 3.646931e-01 7.293861e-01 0.63530693
[28,] 3.091777e-01 6.183554e-01 0.69082229
[29,] 3.468634e-01 6.937268e-01 0.65313659
[30,] 2.939793e-01 5.879587e-01 0.70602065
[31,] 2.578106e-01 5.156212e-01 0.74218939
[32,] 2.375128e-01 4.750257e-01 0.76248716
[33,] 1.951017e-01 3.902034e-01 0.80489828
[34,] 1.578545e-01 3.157089e-01 0.84214553
[35,] 2.568005e-01 5.136010e-01 0.74319952
[36,] 2.168651e-01 4.337302e-01 0.78313491
[37,] 1.780679e-01 3.561358e-01 0.82193209
[38,] 1.573029e-01 3.146058e-01 0.84269709
[39,] 1.346164e-01 2.692329e-01 0.86538356
[40,] 1.070486e-01 2.140971e-01 0.89295143
[41,] 8.386752e-02 1.677350e-01 0.91613248
[42,] 7.732036e-02 1.546407e-01 0.92267964
[43,] 5.947941e-02 1.189588e-01 0.94052059
[44,] 4.509179e-02 9.018359e-02 0.95490821
[45,] 3.404079e-02 6.808157e-02 0.96595921
[46,] 2.510087e-02 5.020173e-02 0.97489913
[47,] 1.821985e-02 3.643970e-02 0.98178015
[48,] 1.308016e-02 2.616032e-02 0.98691984
[49,] 9.251983e-03 1.850397e-02 0.99074802
[50,] 6.428341e-03 1.285668e-02 0.99357166
[51,] 4.404637e-03 8.809275e-03 0.99559536
[52,] 4.159153e-03 8.318306e-03 0.99584085
[53,] 1.316626e-02 2.633253e-02 0.98683374
[54,] 9.422168e-03 1.884434e-02 0.99057783
[55,] 7.197111e-03 1.439422e-02 0.99280289
[56,] 6.804866e-03 1.360973e-02 0.99319513
[57,] 5.471681e-03 1.094336e-02 0.99452832
[58,] 5.128019e-03 1.025604e-02 0.99487198
[59,] 3.563131e-03 7.126262e-03 0.99643687
[60,] 2.405889e-03 4.811777e-03 0.99759411
[61,] 1.881719e-03 3.763438e-03 0.99811828
[62,] 1.421058e-03 2.842115e-03 0.99857894
[63,] 9.688886e-03 1.937777e-02 0.99031111
[64,] 6.862045e-03 1.372409e-02 0.99313795
[65,] 4.899102e-03 9.798204e-03 0.99510090
[66,] 3.368559e-03 6.737119e-03 0.99663144
[67,] 2.280207e-03 4.560414e-03 0.99771979
[68,] 2.386758e-03 4.773517e-03 0.99761324
[69,] 1.606193e-03 3.212386e-03 0.99839381
[70,] 1.711732e-03 3.423465e-03 0.99828827
[71,] 1.129384e-03 2.258768e-03 0.99887062
[72,] 7.368061e-04 1.473612e-03 0.99926319
[73,] 4.725281e-04 9.450561e-04 0.99952747
[74,] 3.302821e-04 6.605642e-04 0.99966972
[75,] 2.053046e-04 4.106092e-04 0.99979470
[76,] 2.014602e-04 4.029204e-04 0.99979854
[77,] 1.249634e-04 2.499268e-04 0.99987504
[78,] 1.731660e-04 3.463320e-04 0.99982683
[79,] 1.066372e-04 2.132745e-04 0.99989336
[80,] 1.014902e-04 2.029804e-04 0.99989851
[81,] 6.043144e-05 1.208629e-04 0.99993957
[82,] 3.563988e-05 7.127976e-05 0.99996436
[83,] 2.314708e-05 4.629415e-05 0.99997685
[84,] 1.537497e-05 3.074995e-05 0.99998463
[85,] 8.752282e-06 1.750456e-05 0.99999125
[86,] 5.905854e-06 1.181171e-05 0.99999409
[87,] 4.490527e-06 8.981055e-06 0.99999551
[88,] 3.286879e-06 6.573758e-06 0.99999671
[89,] 1.795887e-06 3.591774e-06 0.99999820
[90,] 9.399380e-07 1.879876e-06 0.99999906
[91,] 3.379000e-06 6.757999e-06 0.99999662
[92,] 3.600745e-04 7.201491e-04 0.99963993
[93,] 2.191390e-04 4.382779e-04 0.99978086
[94,] 6.324852e-02 1.264970e-01 0.93675148
[95,] 4.649230e-01 9.298459e-01 0.53507704
[96,] 4.159092e-01 8.318184e-01 0.58409081
[97,] 3.615881e-01 7.231762e-01 0.63841191
[98,] 3.058755e-01 6.117509e-01 0.69412454
[99,] 2.540774e-01 5.081549e-01 0.74592256
[100,] 2.394513e-01 4.789026e-01 0.76054871
[101,] 1.937323e-01 3.874646e-01 0.80626769
[102,] 2.607950e-01 5.215900e-01 0.73920500
[103,] 2.119475e-01 4.238950e-01 0.78805248
[104,] 1.666870e-01 3.333740e-01 0.83331302
[105,] 1.276527e-01 2.553053e-01 0.87234735
[106,] 9.982137e-02 1.996427e-01 0.90017863
[107,] 7.773665e-02 1.554733e-01 0.92226335
[108,] 5.835869e-02 1.167174e-01 0.94164131
[109,] 4.141779e-02 8.283558e-02 0.95858221
[110,] 4.357352e-02 8.714704e-02 0.95642648
[111,] 5.253589e-02 1.050718e-01 0.94746411
[112,] 3.446458e-02 6.892916e-02 0.96553542
[113,] 2.398513e-02 4.797025e-02 0.97601487
[114,] 1.438357e-02 2.876714e-02 0.98561643
[115,] 8.152560e-03 1.630512e-02 0.99184744
[116,] 4.506496e-03 9.012992e-03 0.99549350
[117,] 4.213910e-03 8.427820e-03 0.99578609
[118,] 2.133098e-03 4.266197e-03 0.99786690
[119,] 1.398346e-03 2.796693e-03 0.99860165
[120,] 8.085160e-04 1.617032e-03 0.99919148
[121,] 9.569618e-04 1.913924e-03 0.99904304
> postscript(file="/var/www/html/rcomp/tmp/1vcjr1292167472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/263ic1292167472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/363ic1292167472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4zdif1292167472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5zdif1292167472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 154
Frequency = 1
1 2 3 4 5 6
-0.05307656 2.96522024 5.18343267 -0.03241199 1.05419598 -0.85339235
7 8 9 10 11 12
1.87800225 2.45098493 -1.61348265 -0.71435776 0.02432376 -4.71329412
13 14 15 16 17 18
-7.68737453 -1.24735010 0.06460106 0.10571754 1.80482075 -0.83090205
19 20 21 22 23 24
-0.06200069 -1.88383059 1.86803443 -2.76586309 -0.12221143 -0.16187320
25 26 27 28 29 30
0.49887672 0.86614488 -0.32853822 0.04511885 -0.15843895 1.98006140
31 32 33 34 35 36
5.97928724 7.26909531 2.96602631 0.20054371 0.11925871 0.04911941
37 38 39 40 41 42
2.21748394 0.05518298 0.80614105 1.45301101 -0.68536885 2.26509447
43 44 45 46 47 48
-0.10586837 0.06204236 -3.68775802 0.05015580 -0.02935374 2.60624405
49 50 51 52 53 54
-0.25388825 -0.11338333 -4.50068740 -0.93130155 0.26641078 -1.52329949
55 56 57 58 59 60
1.69189978 0.04138915 -0.15224918 -3.03045270 0.04400379 -0.03143318
61 62 63 64 65 66
0.57967939 0.16283585 0.01708758 0.04951744 0.09429163 -0.24908661
67 68 69 70 71 72
-0.03066889 2.38043644 -4.70884347 -0.37120759 -0.54734186 -2.43055441
73 74 75 76 77 78
-0.57607317 -2.63678779 -0.54093277 0.01931097 1.89320571 1.27285930
79 80 81 82 83 84
-6.80839600 -0.24710651 0.71412026 -0.27637664 0.14851456 2.99084248
85 86 87 88 89 90
0.41735989 2.68395454 -0.16404402 0.39394368 -0.29099124 -0.31610479
91 92 93 94 95 96
0.08564639 2.60836910 0.40778069 -3.65419583 -0.39784057 -2.70224293
97 98 99 100 101 102
-0.07493611 -0.27209740 -1.34897719 -1.97473332 0.36531238 -1.73737566
103 104 105 106 107 108
-2.08863227 -1.55652774 -0.87083374 -0.03017072 -5.29206539 -8.45276524
109 110 111 112 113 114
-0.35401940 11.45209679 10.48828868 -0.75796343 -0.19920084 0.17381302
115 116 117 118 119 120
-0.46379910 3.27500276 -0.38007270 4.79896964 -0.46190980 0.27150466
121 122 123 124 125 126
0.09340461 0.60043794 0.96451215 -2.25813260 0.21065673 3.51682508
127 128 129 130 131 132
-4.45108613 0.09986781 -2.12678082 -0.19195393 0.22537011 -0.61309530
133 134 135 136 137 138
3.80334209 -1.20119215 3.39250814 3.15390038 3.70082818 1.46173419
139 140 141 142 143 144
-0.37840898 0.16131055 3.38900801 0.09682068 0.07483415 -6.78068365
145 146 147 148 149 150
-2.33359826 3.10537401 -1.16195796 -3.22431028 -0.11013996 1.26818128
151 152 153 154
-1.43588435 0.12060319 -0.42516229 -7.88548426
> postscript(file="/var/www/html/rcomp/tmp/6zdif1292167472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 154
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.05307656 NA
1 2.96522024 -0.05307656
2 5.18343267 2.96522024
3 -0.03241199 5.18343267
4 1.05419598 -0.03241199
5 -0.85339235 1.05419598
6 1.87800225 -0.85339235
7 2.45098493 1.87800225
8 -1.61348265 2.45098493
9 -0.71435776 -1.61348265
10 0.02432376 -0.71435776
11 -4.71329412 0.02432376
12 -7.68737453 -4.71329412
13 -1.24735010 -7.68737453
14 0.06460106 -1.24735010
15 0.10571754 0.06460106
16 1.80482075 0.10571754
17 -0.83090205 1.80482075
18 -0.06200069 -0.83090205
19 -1.88383059 -0.06200069
20 1.86803443 -1.88383059
21 -2.76586309 1.86803443
22 -0.12221143 -2.76586309
23 -0.16187320 -0.12221143
24 0.49887672 -0.16187320
25 0.86614488 0.49887672
26 -0.32853822 0.86614488
27 0.04511885 -0.32853822
28 -0.15843895 0.04511885
29 1.98006140 -0.15843895
30 5.97928724 1.98006140
31 7.26909531 5.97928724
32 2.96602631 7.26909531
33 0.20054371 2.96602631
34 0.11925871 0.20054371
35 0.04911941 0.11925871
36 2.21748394 0.04911941
37 0.05518298 2.21748394
38 0.80614105 0.05518298
39 1.45301101 0.80614105
40 -0.68536885 1.45301101
41 2.26509447 -0.68536885
42 -0.10586837 2.26509447
43 0.06204236 -0.10586837
44 -3.68775802 0.06204236
45 0.05015580 -3.68775802
46 -0.02935374 0.05015580
47 2.60624405 -0.02935374
48 -0.25388825 2.60624405
49 -0.11338333 -0.25388825
50 -4.50068740 -0.11338333
51 -0.93130155 -4.50068740
52 0.26641078 -0.93130155
53 -1.52329949 0.26641078
54 1.69189978 -1.52329949
55 0.04138915 1.69189978
56 -0.15224918 0.04138915
57 -3.03045270 -0.15224918
58 0.04400379 -3.03045270
59 -0.03143318 0.04400379
60 0.57967939 -0.03143318
61 0.16283585 0.57967939
62 0.01708758 0.16283585
63 0.04951744 0.01708758
64 0.09429163 0.04951744
65 -0.24908661 0.09429163
66 -0.03066889 -0.24908661
67 2.38043644 -0.03066889
68 -4.70884347 2.38043644
69 -0.37120759 -4.70884347
70 -0.54734186 -0.37120759
71 -2.43055441 -0.54734186
72 -0.57607317 -2.43055441
73 -2.63678779 -0.57607317
74 -0.54093277 -2.63678779
75 0.01931097 -0.54093277
76 1.89320571 0.01931097
77 1.27285930 1.89320571
78 -6.80839600 1.27285930
79 -0.24710651 -6.80839600
80 0.71412026 -0.24710651
81 -0.27637664 0.71412026
82 0.14851456 -0.27637664
83 2.99084248 0.14851456
84 0.41735989 2.99084248
85 2.68395454 0.41735989
86 -0.16404402 2.68395454
87 0.39394368 -0.16404402
88 -0.29099124 0.39394368
89 -0.31610479 -0.29099124
90 0.08564639 -0.31610479
91 2.60836910 0.08564639
92 0.40778069 2.60836910
93 -3.65419583 0.40778069
94 -0.39784057 -3.65419583
95 -2.70224293 -0.39784057
96 -0.07493611 -2.70224293
97 -0.27209740 -0.07493611
98 -1.34897719 -0.27209740
99 -1.97473332 -1.34897719
100 0.36531238 -1.97473332
101 -1.73737566 0.36531238
102 -2.08863227 -1.73737566
103 -1.55652774 -2.08863227
104 -0.87083374 -1.55652774
105 -0.03017072 -0.87083374
106 -5.29206539 -0.03017072
107 -8.45276524 -5.29206539
108 -0.35401940 -8.45276524
109 11.45209679 -0.35401940
110 10.48828868 11.45209679
111 -0.75796343 10.48828868
112 -0.19920084 -0.75796343
113 0.17381302 -0.19920084
114 -0.46379910 0.17381302
115 3.27500276 -0.46379910
116 -0.38007270 3.27500276
117 4.79896964 -0.38007270
118 -0.46190980 4.79896964
119 0.27150466 -0.46190980
120 0.09340461 0.27150466
121 0.60043794 0.09340461
122 0.96451215 0.60043794
123 -2.25813260 0.96451215
124 0.21065673 -2.25813260
125 3.51682508 0.21065673
126 -4.45108613 3.51682508
127 0.09986781 -4.45108613
128 -2.12678082 0.09986781
129 -0.19195393 -2.12678082
130 0.22537011 -0.19195393
131 -0.61309530 0.22537011
132 3.80334209 -0.61309530
133 -1.20119215 3.80334209
134 3.39250814 -1.20119215
135 3.15390038 3.39250814
136 3.70082818 3.15390038
137 1.46173419 3.70082818
138 -0.37840898 1.46173419
139 0.16131055 -0.37840898
140 3.38900801 0.16131055
141 0.09682068 3.38900801
142 0.07483415 0.09682068
143 -6.78068365 0.07483415
144 -2.33359826 -6.78068365
145 3.10537401 -2.33359826
146 -1.16195796 3.10537401
147 -3.22431028 -1.16195796
148 -0.11013996 -3.22431028
149 1.26818128 -0.11013996
150 -1.43588435 1.26818128
151 0.12060319 -1.43588435
152 -0.42516229 0.12060319
153 -7.88548426 -0.42516229
154 NA -7.88548426
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.96522024 -0.05307656
[2,] 5.18343267 2.96522024
[3,] -0.03241199 5.18343267
[4,] 1.05419598 -0.03241199
[5,] -0.85339235 1.05419598
[6,] 1.87800225 -0.85339235
[7,] 2.45098493 1.87800225
[8,] -1.61348265 2.45098493
[9,] -0.71435776 -1.61348265
[10,] 0.02432376 -0.71435776
[11,] -4.71329412 0.02432376
[12,] -7.68737453 -4.71329412
[13,] -1.24735010 -7.68737453
[14,] 0.06460106 -1.24735010
[15,] 0.10571754 0.06460106
[16,] 1.80482075 0.10571754
[17,] -0.83090205 1.80482075
[18,] -0.06200069 -0.83090205
[19,] -1.88383059 -0.06200069
[20,] 1.86803443 -1.88383059
[21,] -2.76586309 1.86803443
[22,] -0.12221143 -2.76586309
[23,] -0.16187320 -0.12221143
[24,] 0.49887672 -0.16187320
[25,] 0.86614488 0.49887672
[26,] -0.32853822 0.86614488
[27,] 0.04511885 -0.32853822
[28,] -0.15843895 0.04511885
[29,] 1.98006140 -0.15843895
[30,] 5.97928724 1.98006140
[31,] 7.26909531 5.97928724
[32,] 2.96602631 7.26909531
[33,] 0.20054371 2.96602631
[34,] 0.11925871 0.20054371
[35,] 0.04911941 0.11925871
[36,] 2.21748394 0.04911941
[37,] 0.05518298 2.21748394
[38,] 0.80614105 0.05518298
[39,] 1.45301101 0.80614105
[40,] -0.68536885 1.45301101
[41,] 2.26509447 -0.68536885
[42,] -0.10586837 2.26509447
[43,] 0.06204236 -0.10586837
[44,] -3.68775802 0.06204236
[45,] 0.05015580 -3.68775802
[46,] -0.02935374 0.05015580
[47,] 2.60624405 -0.02935374
[48,] -0.25388825 2.60624405
[49,] -0.11338333 -0.25388825
[50,] -4.50068740 -0.11338333
[51,] -0.93130155 -4.50068740
[52,] 0.26641078 -0.93130155
[53,] -1.52329949 0.26641078
[54,] 1.69189978 -1.52329949
[55,] 0.04138915 1.69189978
[56,] -0.15224918 0.04138915
[57,] -3.03045270 -0.15224918
[58,] 0.04400379 -3.03045270
[59,] -0.03143318 0.04400379
[60,] 0.57967939 -0.03143318
[61,] 0.16283585 0.57967939
[62,] 0.01708758 0.16283585
[63,] 0.04951744 0.01708758
[64,] 0.09429163 0.04951744
[65,] -0.24908661 0.09429163
[66,] -0.03066889 -0.24908661
[67,] 2.38043644 -0.03066889
[68,] -4.70884347 2.38043644
[69,] -0.37120759 -4.70884347
[70,] -0.54734186 -0.37120759
[71,] -2.43055441 -0.54734186
[72,] -0.57607317 -2.43055441
[73,] -2.63678779 -0.57607317
[74,] -0.54093277 -2.63678779
[75,] 0.01931097 -0.54093277
[76,] 1.89320571 0.01931097
[77,] 1.27285930 1.89320571
[78,] -6.80839600 1.27285930
[79,] -0.24710651 -6.80839600
[80,] 0.71412026 -0.24710651
[81,] -0.27637664 0.71412026
[82,] 0.14851456 -0.27637664
[83,] 2.99084248 0.14851456
[84,] 0.41735989 2.99084248
[85,] 2.68395454 0.41735989
[86,] -0.16404402 2.68395454
[87,] 0.39394368 -0.16404402
[88,] -0.29099124 0.39394368
[89,] -0.31610479 -0.29099124
[90,] 0.08564639 -0.31610479
[91,] 2.60836910 0.08564639
[92,] 0.40778069 2.60836910
[93,] -3.65419583 0.40778069
[94,] -0.39784057 -3.65419583
[95,] -2.70224293 -0.39784057
[96,] -0.07493611 -2.70224293
[97,] -0.27209740 -0.07493611
[98,] -1.34897719 -0.27209740
[99,] -1.97473332 -1.34897719
[100,] 0.36531238 -1.97473332
[101,] -1.73737566 0.36531238
[102,] -2.08863227 -1.73737566
[103,] -1.55652774 -2.08863227
[104,] -0.87083374 -1.55652774
[105,] -0.03017072 -0.87083374
[106,] -5.29206539 -0.03017072
[107,] -8.45276524 -5.29206539
[108,] -0.35401940 -8.45276524
[109,] 11.45209679 -0.35401940
[110,] 10.48828868 11.45209679
[111,] -0.75796343 10.48828868
[112,] -0.19920084 -0.75796343
[113,] 0.17381302 -0.19920084
[114,] -0.46379910 0.17381302
[115,] 3.27500276 -0.46379910
[116,] -0.38007270 3.27500276
[117,] 4.79896964 -0.38007270
[118,] -0.46190980 4.79896964
[119,] 0.27150466 -0.46190980
[120,] 0.09340461 0.27150466
[121,] 0.60043794 0.09340461
[122,] 0.96451215 0.60043794
[123,] -2.25813260 0.96451215
[124,] 0.21065673 -2.25813260
[125,] 3.51682508 0.21065673
[126,] -4.45108613 3.51682508
[127,] 0.09986781 -4.45108613
[128,] -2.12678082 0.09986781
[129,] -0.19195393 -2.12678082
[130,] 0.22537011 -0.19195393
[131,] -0.61309530 0.22537011
[132,] 3.80334209 -0.61309530
[133,] -1.20119215 3.80334209
[134,] 3.39250814 -1.20119215
[135,] 3.15390038 3.39250814
[136,] 3.70082818 3.15390038
[137,] 1.46173419 3.70082818
[138,] -0.37840898 1.46173419
[139,] 0.16131055 -0.37840898
[140,] 3.38900801 0.16131055
[141,] 0.09682068 3.38900801
[142,] 0.07483415 0.09682068
[143,] -6.78068365 0.07483415
[144,] -2.33359826 -6.78068365
[145,] 3.10537401 -2.33359826
[146,] -1.16195796 3.10537401
[147,] -3.22431028 -1.16195796
[148,] -0.11013996 -3.22431028
[149,] 1.26818128 -0.11013996
[150,] -1.43588435 1.26818128
[151,] 0.12060319 -1.43588435
[152,] -0.42516229 0.12060319
[153,] -7.88548426 -0.42516229
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.96522024 -0.05307656
2 5.18343267 2.96522024
3 -0.03241199 5.18343267
4 1.05419598 -0.03241199
5 -0.85339235 1.05419598
6 1.87800225 -0.85339235
7 2.45098493 1.87800225
8 -1.61348265 2.45098493
9 -0.71435776 -1.61348265
10 0.02432376 -0.71435776
11 -4.71329412 0.02432376
12 -7.68737453 -4.71329412
13 -1.24735010 -7.68737453
14 0.06460106 -1.24735010
15 0.10571754 0.06460106
16 1.80482075 0.10571754
17 -0.83090205 1.80482075
18 -0.06200069 -0.83090205
19 -1.88383059 -0.06200069
20 1.86803443 -1.88383059
21 -2.76586309 1.86803443
22 -0.12221143 -2.76586309
23 -0.16187320 -0.12221143
24 0.49887672 -0.16187320
25 0.86614488 0.49887672
26 -0.32853822 0.86614488
27 0.04511885 -0.32853822
28 -0.15843895 0.04511885
29 1.98006140 -0.15843895
30 5.97928724 1.98006140
31 7.26909531 5.97928724
32 2.96602631 7.26909531
33 0.20054371 2.96602631
34 0.11925871 0.20054371
35 0.04911941 0.11925871
36 2.21748394 0.04911941
37 0.05518298 2.21748394
38 0.80614105 0.05518298
39 1.45301101 0.80614105
40 -0.68536885 1.45301101
41 2.26509447 -0.68536885
42 -0.10586837 2.26509447
43 0.06204236 -0.10586837
44 -3.68775802 0.06204236
45 0.05015580 -3.68775802
46 -0.02935374 0.05015580
47 2.60624405 -0.02935374
48 -0.25388825 2.60624405
49 -0.11338333 -0.25388825
50 -4.50068740 -0.11338333
51 -0.93130155 -4.50068740
52 0.26641078 -0.93130155
53 -1.52329949 0.26641078
54 1.69189978 -1.52329949
55 0.04138915 1.69189978
56 -0.15224918 0.04138915
57 -3.03045270 -0.15224918
58 0.04400379 -3.03045270
59 -0.03143318 0.04400379
60 0.57967939 -0.03143318
61 0.16283585 0.57967939
62 0.01708758 0.16283585
63 0.04951744 0.01708758
64 0.09429163 0.04951744
65 -0.24908661 0.09429163
66 -0.03066889 -0.24908661
67 2.38043644 -0.03066889
68 -4.70884347 2.38043644
69 -0.37120759 -4.70884347
70 -0.54734186 -0.37120759
71 -2.43055441 -0.54734186
72 -0.57607317 -2.43055441
73 -2.63678779 -0.57607317
74 -0.54093277 -2.63678779
75 0.01931097 -0.54093277
76 1.89320571 0.01931097
77 1.27285930 1.89320571
78 -6.80839600 1.27285930
79 -0.24710651 -6.80839600
80 0.71412026 -0.24710651
81 -0.27637664 0.71412026
82 0.14851456 -0.27637664
83 2.99084248 0.14851456
84 0.41735989 2.99084248
85 2.68395454 0.41735989
86 -0.16404402 2.68395454
87 0.39394368 -0.16404402
88 -0.29099124 0.39394368
89 -0.31610479 -0.29099124
90 0.08564639 -0.31610479
91 2.60836910 0.08564639
92 0.40778069 2.60836910
93 -3.65419583 0.40778069
94 -0.39784057 -3.65419583
95 -2.70224293 -0.39784057
96 -0.07493611 -2.70224293
97 -0.27209740 -0.07493611
98 -1.34897719 -0.27209740
99 -1.97473332 -1.34897719
100 0.36531238 -1.97473332
101 -1.73737566 0.36531238
102 -2.08863227 -1.73737566
103 -1.55652774 -2.08863227
104 -0.87083374 -1.55652774
105 -0.03017072 -0.87083374
106 -5.29206539 -0.03017072
107 -8.45276524 -5.29206539
108 -0.35401940 -8.45276524
109 11.45209679 -0.35401940
110 10.48828868 11.45209679
111 -0.75796343 10.48828868
112 -0.19920084 -0.75796343
113 0.17381302 -0.19920084
114 -0.46379910 0.17381302
115 3.27500276 -0.46379910
116 -0.38007270 3.27500276
117 4.79896964 -0.38007270
118 -0.46190980 4.79896964
119 0.27150466 -0.46190980
120 0.09340461 0.27150466
121 0.60043794 0.09340461
122 0.96451215 0.60043794
123 -2.25813260 0.96451215
124 0.21065673 -2.25813260
125 3.51682508 0.21065673
126 -4.45108613 3.51682508
127 0.09986781 -4.45108613
128 -2.12678082 0.09986781
129 -0.19195393 -2.12678082
130 0.22537011 -0.19195393
131 -0.61309530 0.22537011
132 3.80334209 -0.61309530
133 -1.20119215 3.80334209
134 3.39250814 -1.20119215
135 3.15390038 3.39250814
136 3.70082818 3.15390038
137 1.46173419 3.70082818
138 -0.37840898 1.46173419
139 0.16131055 -0.37840898
140 3.38900801 0.16131055
141 0.09682068 3.38900801
142 0.07483415 0.09682068
143 -6.78068365 0.07483415
144 -2.33359826 -6.78068365
145 3.10537401 -2.33359826
146 -1.16195796 3.10537401
147 -3.22431028 -1.16195796
148 -0.11013996 -3.22431028
149 1.26818128 -0.11013996
150 -1.43588435 1.26818128
151 0.12060319 -1.43588435
152 -0.42516229 0.12060319
153 -7.88548426 -0.42516229
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/794h01292167472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8kdy31292167472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9kdy31292167472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10kdy31292167472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11ynwb1292167472.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12j5dh1292167472.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13q7d31292167473.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14b8t91292167473.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15mhtu1292167473.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/1609831292167473.tab")
+ }
>
> try(system("convert tmp/1vcjr1292167472.ps tmp/1vcjr1292167472.png",intern=TRUE))
character(0)
> try(system("convert tmp/263ic1292167472.ps tmp/263ic1292167472.png",intern=TRUE))
character(0)
> try(system("convert tmp/363ic1292167472.ps tmp/363ic1292167472.png",intern=TRUE))
character(0)
> try(system("convert tmp/4zdif1292167472.ps tmp/4zdif1292167472.png",intern=TRUE))
character(0)
> try(system("convert tmp/5zdif1292167472.ps tmp/5zdif1292167472.png",intern=TRUE))
character(0)
> try(system("convert tmp/6zdif1292167472.ps tmp/6zdif1292167472.png",intern=TRUE))
character(0)
> try(system("convert tmp/794h01292167472.ps tmp/794h01292167472.png",intern=TRUE))
character(0)
> try(system("convert tmp/8kdy31292167472.ps tmp/8kdy31292167472.png",intern=TRUE))
character(0)
> try(system("convert tmp/9kdy31292167472.ps tmp/9kdy31292167472.png",intern=TRUE))
character(0)
> try(system("convert tmp/10kdy31292167472.ps tmp/10kdy31292167472.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.832 1.784 12.167