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
+ ,1
+ ,14
+ ,41
+ ,38
+ ,13
+ ,12
+ ,1
+ ,1
+ ,18
+ ,39
+ ,32
+ ,16
+ ,11
+ ,1
+ ,1
+ ,11
+ ,30
+ ,35
+ ,19
+ ,15
+ ,1
+ ,0
+ ,12
+ ,31
+ ,33
+ ,15
+ ,6
+ ,1
+ ,1
+ ,16
+ ,34
+ ,37
+ ,14
+ ,13
+ ,1
+ ,1
+ ,18
+ ,35
+ ,29
+ ,13
+ ,10
+ ,1
+ ,1
+ ,14
+ ,39
+ ,31
+ ,19
+ ,12
+ ,1
+ ,1
+ ,14
+ ,34
+ ,36
+ ,15
+ ,14
+ ,1
+ ,1
+ ,15
+ ,36
+ ,35
+ ,14
+ ,12
+ ,1
+ ,1
+ ,15
+ ,37
+ ,38
+ ,15
+ ,9
+ ,1
+ ,0
+ ,17
+ ,38
+ ,31
+ ,16
+ ,10
+ ,1
+ ,1
+ ,19
+ ,36
+ ,34
+ ,16
+ ,12
+ ,1
+ ,0
+ ,10
+ ,38
+ ,35
+ ,16
+ ,12
+ ,1
+ ,1
+ ,16
+ ,39
+ ,38
+ ,16
+ ,11
+ ,1
+ ,1
+ ,18
+ ,33
+ ,37
+ ,17
+ ,15
+ ,1
+ ,0
+ ,14
+ ,32
+ ,33
+ ,15
+ ,12
+ ,1
+ ,0
+ ,14
+ ,36
+ ,32
+ ,15
+ ,10
+ ,1
+ ,1
+ ,17
+ ,38
+ ,38
+ ,20
+ ,12
+ ,1
+ ,0
+ ,14
+ ,39
+ ,38
+ ,18
+ ,11
+ ,1
+ ,1
+ ,16
+ ,32
+ ,32
+ ,16
+ ,12
+ ,1
+ ,0
+ ,18
+ ,32
+ ,33
+ ,16
+ ,11
+ ,1
+ ,1
+ ,11
+ ,31
+ ,31
+ ,16
+ ,12
+ ,1
+ ,1
+ ,14
+ ,39
+ ,38
+ ,19
+ ,13
+ ,1
+ ,1
+ ,12
+ ,37
+ ,39
+ ,16
+ ,11
+ ,1
+ ,0
+ ,17
+ ,39
+ ,32
+ ,17
+ ,12
+ ,1
+ ,1
+ ,9
+ ,41
+ ,32
+ ,17
+ ,13
+ ,1
+ ,0
+ ,16
+ ,36
+ ,35
+ ,16
+ ,10
+ ,1
+ ,1
+ ,14
+ ,33
+ ,37
+ ,15
+ ,14
+ ,1
+ ,1
+ ,15
+ ,33
+ ,33
+ ,16
+ ,12
+ ,1
+ ,0
+ ,11
+ ,34
+ ,33
+ ,14
+ ,10
+ ,1
+ ,1
+ ,16
+ ,31
+ ,31
+ ,15
+ ,12
+ ,1
+ ,0
+ ,13
+ ,27
+ ,32
+ ,12
+ ,8
+ ,1
+ ,1
+ ,17
+ ,37
+ ,31
+ ,14
+ ,10
+ ,1
+ ,1
+ ,15
+ ,34
+ ,37
+ ,16
+ ,12
+ ,1
+ ,0
+ ,14
+ ,34
+ ,30
+ ,14
+ ,12
+ ,1
+ ,0
+ ,16
+ ,32
+ ,33
+ ,10
+ ,7
+ ,1
+ ,0
+ ,9
+ ,29
+ ,31
+ ,10
+ ,9
+ ,1
+ ,0
+ ,15
+ ,36
+ ,33
+ ,14
+ ,12
+ ,1
+ ,1
+ ,17
+ ,29
+ ,31
+ ,16
+ ,10
+ ,1
+ ,0
+ ,13
+ ,35
+ ,33
+ ,16
+ ,10
+ ,1
+ ,0
+ ,15
+ ,37
+ ,32
+ ,16
+ ,10
+ ,1
+ ,1
+ ,16
+ ,34
+ ,33
+ ,14
+ ,12
+ ,1
+ ,0
+ ,16
+ ,38
+ ,32
+ ,20
+ ,15
+ ,1
+ ,0
+ ,12
+ ,35
+ ,33
+ ,14
+ ,10
+ ,1
+ ,1
+ ,15
+ ,38
+ ,28
+ ,14
+ ,10
+ ,1
+ ,1
+ ,11
+ ,37
+ ,35
+ ,11
+ ,12
+ ,1
+ ,1
+ ,15
+ ,38
+ ,39
+ ,14
+ ,13
+ ,1
+ ,1
+ ,15
+ ,33
+ ,34
+ ,15
+ ,11
+ ,1
+ ,1
+ ,17
+ ,36
+ ,38
+ ,16
+ ,11
+ ,1
+ ,0
+ ,13
+ ,38
+ ,32
+ ,14
+ ,12
+ ,1
+ ,1
+ ,16
+ ,32
+ ,38
+ ,16
+ ,14
+ ,1
+ ,0
+ ,14
+ ,32
+ ,30
+ ,14
+ ,10
+ ,1
+ ,0
+ ,11
+ ,32
+ ,33
+ ,12
+ ,12
+ ,1
+ ,1
+ ,12
+ ,34
+ ,38
+ ,16
+ ,13
+ ,1
+ ,0
+ ,12
+ ,32
+ ,32
+ ,9
+ ,5
+ ,1
+ ,1
+ ,15
+ ,37
+ ,35
+ ,14
+ ,6
+ ,1
+ ,1
+ ,16
+ ,39
+ ,34
+ ,16
+ ,12
+ ,1
+ ,1
+ ,15
+ ,29
+ ,34
+ ,16
+ ,12
+ ,1
+ ,0
+ ,12
+ ,37
+ ,36
+ ,15
+ ,11
+ ,1
+ ,1
+ ,12
+ ,35
+ ,34
+ ,16
+ ,10
+ ,1
+ ,0
+ ,8
+ ,30
+ ,28
+ ,12
+ ,7
+ ,1
+ ,0
+ ,13
+ ,38
+ ,34
+ ,16
+ ,12
+ ,1
+ ,1
+ ,11
+ ,34
+ ,35
+ ,16
+ ,14
+ ,1
+ ,1
+ ,14
+ ,31
+ ,35
+ ,14
+ ,11
+ ,1
+ ,1
+ ,15
+ ,34
+ ,31
+ ,16
+ ,12
+ ,1
+ ,0
+ ,10
+ ,35
+ ,37
+ ,17
+ ,13
+ ,1
+ ,1
+ ,11
+ ,36
+ ,35
+ ,18
+ ,14
+ ,1
+ ,0
+ ,12
+ ,30
+ ,27
+ ,18
+ ,11
+ ,1
+ ,1
+ ,15
+ ,39
+ ,40
+ ,12
+ ,12
+ ,1
+ ,0
+ ,15
+ ,35
+ ,37
+ ,16
+ ,12
+ ,1
+ ,0
+ ,14
+ ,38
+ ,36
+ ,10
+ ,8
+ ,1
+ ,1
+ ,16
+ ,31
+ ,38
+ ,14
+ ,11
+ ,1
+ ,1
+ ,15
+ ,34
+ ,39
+ ,18
+ ,14
+ ,1
+ ,0
+ ,15
+ ,38
+ ,41
+ ,18
+ ,14
+ ,1
+ ,0
+ ,13
+ ,34
+ ,27
+ ,16
+ ,12
+ ,1
+ ,1
+ ,12
+ ,39
+ ,30
+ ,17
+ ,9
+ ,1
+ ,1
+ ,17
+ ,37
+ ,37
+ ,16
+ ,13
+ ,1
+ ,1
+ ,13
+ ,34
+ ,31
+ ,16
+ ,11
+ ,1
+ ,0
+ ,15
+ ,28
+ ,31
+ ,13
+ ,12
+ ,1
+ ,0
+ ,13
+ ,37
+ ,27
+ ,16
+ ,12
+ ,1
+ ,0
+ ,15
+ ,33
+ ,36
+ ,16
+ ,12
+ ,1
+ ,1
+ ,15
+ ,35
+ ,37
+ ,16
+ ,12
+ ,1
+ ,0
+ ,16
+ ,37
+ ,33
+ ,15
+ ,12
+ ,1
+ ,1
+ ,15
+ ,32
+ ,34
+ ,15
+ ,11
+ ,1
+ ,1
+ ,14
+ ,33
+ ,31
+ ,16
+ ,10
+ ,1
+ ,0
+ ,15
+ ,38
+ ,39
+ ,14
+ ,9
+ ,1
+ ,1
+ ,14
+ ,33
+ ,34
+ ,16
+ ,12
+ ,1
+ ,1
+ ,13
+ ,29
+ ,32
+ ,16
+ ,12
+ ,1
+ ,1
+ ,7
+ ,33
+ ,33
+ ,15
+ ,12
+ ,1
+ ,1
+ ,17
+ ,31
+ ,36
+ ,12
+ ,9
+ ,1
+ ,1
+ ,13
+ ,36
+ ,32
+ ,17
+ ,15
+ ,1
+ ,1
+ ,15
+ ,35
+ ,41
+ ,16
+ ,12
+ ,1
+ ,1
+ ,14
+ ,32
+ ,28
+ ,15
+ ,12
+ ,1
+ ,1
+ ,13
+ ,29
+ ,30
+ ,13
+ ,12
+ ,1
+ ,1
+ ,16
+ ,39
+ ,36
+ ,16
+ ,10
+ ,1
+ ,1
+ ,12
+ ,37
+ ,35
+ ,16
+ ,13
+ ,1
+ ,1
+ ,14
+ ,35
+ ,31
+ ,16
+ ,9
+ ,1
+ ,0
+ ,17
+ ,37
+ ,34
+ ,16
+ ,12
+ ,1
+ ,0
+ ,15
+ ,32
+ ,36
+ ,14
+ ,10
+ ,1
+ ,1
+ ,17
+ ,38
+ ,36
+ ,16
+ ,14
+ ,1
+ ,0
+ ,12
+ ,37
+ ,35
+ ,16
+ ,11
+ ,1
+ ,1
+ ,16
+ ,36
+ ,37
+ ,20
+ ,15
+ ,1
+ ,0
+ ,11
+ ,32
+ ,28
+ ,15
+ ,11
+ ,1
+ ,1
+ ,15
+ ,33
+ ,39
+ ,16
+ ,11
+ ,1
+ ,0
+ ,9
+ ,40
+ ,32
+ ,13
+ ,12
+ ,1
+ ,1
+ ,16
+ ,38
+ ,35
+ ,17
+ ,12
+ ,1
+ ,0
+ ,15
+ ,41
+ ,39
+ ,16
+ ,12
+ ,1
+ ,0
+ ,10
+ ,36
+ ,35
+ ,16
+ ,11
+ ,1
+ ,1
+ ,10
+ ,43
+ ,42
+ ,12
+ ,7
+ ,1
+ ,1
+ ,15
+ ,30
+ ,34
+ ,16
+ ,12
+ ,1
+ ,1
+ ,11
+ ,31
+ ,33
+ ,16
+ ,14
+ ,1
+ ,1
+ ,13
+ ,32
+ ,41
+ ,17
+ ,11
+ ,1
+ ,1
+ ,18
+ ,37
+ ,34
+ ,12
+ ,10
+ ,1
+ ,0
+ ,16
+ ,37
+ ,32
+ ,18
+ ,13
+ ,1
+ ,1
+ ,14
+ ,33
+ ,40
+ ,14
+ ,13
+ ,1
+ ,1
+ ,14
+ ,34
+ ,40
+ ,14
+ ,8
+ ,1
+ ,1
+ ,14
+ ,33
+ ,35
+ ,13
+ ,11
+ ,1
+ ,1
+ ,14
+ ,38
+ ,36
+ ,16
+ ,12
+ ,1
+ ,0
+ ,12
+ ,33
+ ,37
+ ,13
+ ,11
+ ,1
+ ,1
+ ,14
+ ,31
+ ,27
+ ,16
+ ,13
+ ,1
+ ,1
+ ,15
+ ,38
+ ,39
+ ,13
+ ,12
+ ,1
+ ,1
+ ,15
+ ,37
+ ,38
+ ,16
+ ,14
+ ,1
+ ,1
+ ,15
+ ,36
+ ,31
+ ,15
+ ,13
+ ,1
+ ,1
+ ,13
+ ,31
+ ,33
+ ,16
+ ,15
+ ,1
+ ,0
+ ,17
+ ,39
+ ,32
+ ,15
+ ,10
+ ,1
+ ,1
+ ,17
+ ,44
+ ,39
+ ,17
+ ,11
+ ,1
+ ,1
+ ,19
+ ,33
+ ,36
+ ,15
+ ,9
+ ,1
+ ,1
+ ,15
+ ,35
+ ,33
+ ,12
+ ,11
+ ,1
+ ,0
+ ,13
+ ,32
+ ,33
+ ,16
+ ,10
+ ,1
+ ,0
+ ,9
+ ,28
+ ,32
+ ,10
+ ,11
+ ,1
+ ,1
+ ,15
+ ,40
+ ,37
+ ,16
+ ,8
+ ,1
+ ,0
+ ,15
+ ,27
+ ,30
+ ,12
+ ,11
+ ,1
+ ,0
+ ,15
+ ,37
+ ,38
+ ,14
+ ,12
+ ,1
+ ,1
+ ,16
+ ,32
+ ,29
+ ,15
+ ,12
+ ,1
+ ,0
+ ,11
+ ,28
+ ,22
+ ,13
+ ,9
+ ,1
+ ,0
+ ,14
+ ,34
+ ,35
+ ,15
+ ,11
+ ,1
+ ,1
+ ,11
+ ,30
+ ,35
+ ,11
+ ,10
+ ,1
+ ,1
+ ,15
+ ,35
+ ,34
+ ,12
+ ,8
+ ,1
+ ,0
+ ,13
+ ,31
+ ,35
+ ,11
+ ,9
+ ,1
+ ,1
+ ,15
+ ,32
+ ,34
+ ,16
+ ,8
+ ,1
+ ,0
+ ,16
+ ,30
+ ,37
+ ,15
+ ,9
+ ,1
+ ,1
+ ,14
+ ,30
+ ,35
+ ,17
+ ,15
+ ,1
+ ,0
+ ,15
+ ,31
+ ,23
+ ,16
+ ,11
+ ,1
+ ,1
+ ,16
+ ,40
+ ,31
+ ,10
+ ,8
+ ,1
+ ,1
+ ,16
+ ,32
+ ,27
+ ,18
+ ,13
+ ,1
+ ,0
+ ,11
+ ,36
+ ,36
+ ,13
+ ,12
+ ,1
+ ,0
+ ,12
+ ,32
+ ,31
+ ,16
+ ,12
+ ,1
+ ,0
+ ,9
+ ,35
+ ,32
+ ,13
+ ,9
+ ,1
+ ,1
+ ,16
+ ,38
+ ,39
+ ,10
+ ,7
+ ,1
+ ,1
+ ,13
+ ,42
+ ,37
+ ,15
+ ,13
+ ,1
+ ,0
+ ,16
+ ,34
+ ,38
+ ,16
+ ,9
+ ,1
+ ,1
+ ,12
+ ,35
+ ,39
+ ,16
+ ,6
+ ,1
+ ,1
+ ,9
+ ,38
+ ,34
+ ,14
+ ,8
+ ,1
+ ,1
+ ,13
+ ,33
+ ,31
+ ,10
+ ,8
+ ,1
+ ,1
+ ,14
+ ,32
+ ,37
+ ,13
+ ,6
+ ,1
+ ,1
+ ,19
+ ,33
+ ,36
+ ,15
+ ,9
+ ,1
+ ,1
+ ,13
+ ,34
+ ,32
+ ,16
+ ,11
+ ,1
+ ,1
+ ,12
+ ,32
+ ,38
+ ,12
+ ,8
+ ,0
+ ,0
+ ,10
+ ,27
+ ,26
+ ,13
+ ,10
+ ,0
+ ,0
+ ,14
+ ,31
+ ,26
+ ,12
+ ,8
+ ,0
+ ,0
+ ,16
+ ,38
+ ,33
+ ,17
+ ,14
+ ,0
+ ,1
+ ,10
+ ,34
+ ,39
+ ,15
+ ,10
+ ,0
+ ,0
+ ,11
+ ,24
+ ,30
+ ,10
+ ,8
+ ,0
+ ,0
+ ,14
+ ,30
+ ,33
+ ,14
+ ,11
+ ,0
+ ,1
+ ,12
+ ,26
+ ,25
+ ,11
+ ,12
+ ,0
+ ,1
+ ,9
+ ,34
+ ,38
+ ,13
+ ,12
+ ,0
+ ,0
+ ,9
+ ,27
+ ,37
+ ,16
+ ,12
+ ,0
+ ,0
+ ,11
+ ,37
+ ,31
+ ,12
+ ,5
+ ,0
+ ,1
+ ,16
+ ,36
+ ,37
+ ,16
+ ,12
+ ,0
+ ,0
+ ,9
+ ,41
+ ,35
+ ,12
+ ,10
+ ,0
+ ,1
+ ,13
+ ,29
+ ,25
+ ,9
+ ,7
+ ,0
+ ,1
+ ,16
+ ,36
+ ,28
+ ,12
+ ,12
+ ,0
+ ,0
+ ,13
+ ,32
+ ,35
+ ,15
+ ,11
+ ,0
+ ,1
+ ,9
+ ,37
+ ,33
+ ,12
+ ,8
+ ,0
+ ,0
+ ,12
+ ,30
+ ,30
+ ,12
+ ,9
+ ,0
+ ,1
+ ,16
+ ,31
+ ,31
+ ,14
+ ,10
+ ,0
+ ,1
+ ,11
+ ,38
+ ,37
+ ,12
+ ,9
+ ,0
+ ,1
+ ,14
+ ,36
+ ,36
+ ,16
+ ,12
+ ,0
+ ,0
+ ,13
+ ,35
+ ,30
+ ,11
+ ,6
+ ,0
+ ,0
+ ,15
+ ,31
+ ,36
+ ,19
+ ,15
+ ,0
+ ,0
+ ,14
+ ,38
+ ,32
+ ,15
+ ,12
+ ,0
+ ,1
+ ,16
+ ,22
+ ,28
+ ,8
+ ,12
+ ,0
+ ,1
+ ,13
+ ,32
+ ,36
+ ,16
+ ,12
+ ,0
+ ,0
+ ,14
+ ,36
+ ,34
+ ,17
+ ,11
+ ,0
+ ,1
+ ,15
+ ,39
+ ,31
+ ,12
+ ,7
+ ,0
+ ,0
+ ,13
+ ,28
+ ,28
+ ,11
+ ,7
+ ,0
+ ,0
+ ,11
+ ,32
+ ,36
+ ,11
+ ,5
+ ,0
+ ,1
+ ,11
+ ,32
+ ,36
+ ,14
+ ,12
+ ,0
+ ,1
+ ,14
+ ,38
+ ,40
+ ,16
+ ,12
+ ,0
+ ,1
+ ,15
+ ,32
+ ,33
+ ,12
+ ,3
+ ,0
+ ,1
+ ,11
+ ,35
+ ,37
+ ,16
+ ,11
+ ,0
+ ,1
+ ,15
+ ,32
+ ,32
+ ,13
+ ,10
+ ,0
+ ,0
+ ,12
+ ,37
+ ,38
+ ,15
+ ,12
+ ,0
+ ,1
+ ,14
+ ,34
+ ,31
+ ,16
+ ,9
+ ,0
+ ,1
+ ,14
+ ,33
+ ,37
+ ,16
+ ,12
+ ,0
+ ,0
+ ,8
+ ,33
+ ,33
+ ,14
+ ,9
+ ,0
+ ,0
+ ,9
+ ,30
+ ,30
+ ,16
+ ,12
+ ,0
+ ,0
+ ,15
+ ,24
+ ,30
+ ,14
+ ,10
+ ,0
+ ,0
+ ,17
+ ,34
+ ,31
+ ,11
+ ,9
+ ,0
+ ,0
+ ,13
+ ,34
+ ,32
+ ,12
+ ,12
+ ,0
+ ,1
+ ,15
+ ,33
+ ,34
+ ,15
+ ,8
+ ,0
+ ,1
+ ,15
+ ,34
+ ,36
+ ,15
+ ,11
+ ,0
+ ,1
+ ,14
+ ,35
+ ,37
+ ,16
+ ,11
+ ,0
+ ,0
+ ,16
+ ,35
+ ,36
+ ,16
+ ,12
+ ,0
+ ,0
+ ,13
+ ,36
+ ,33
+ ,11
+ ,10
+ ,0
+ ,0
+ ,16
+ ,34
+ ,33
+ ,15
+ ,10
+ ,0
+ ,1
+ ,9
+ ,34
+ ,33
+ ,12
+ ,12
+ ,0
+ ,0
+ ,16
+ ,41
+ ,44
+ ,12
+ ,12
+ ,0
+ ,0
+ ,11
+ ,32
+ ,39
+ ,15
+ ,11
+ ,0
+ ,0
+ ,10
+ ,30
+ ,32
+ ,15
+ ,8
+ ,0
+ ,1
+ ,11
+ ,35
+ ,35
+ ,16
+ ,12
+ ,0
+ ,0
+ ,15
+ ,28
+ ,25
+ ,14
+ ,10
+ ,0
+ ,1
+ ,17
+ ,33
+ ,35
+ ,17
+ ,11
+ ,0
+ ,1
+ ,14
+ ,39
+ ,34
+ ,14
+ ,10
+ ,0
+ ,0
+ ,8
+ ,36
+ ,35
+ ,13
+ ,8
+ ,0
+ ,1
+ ,15
+ ,36
+ ,39
+ ,15
+ ,12
+ ,0
+ ,0
+ ,11
+ ,35
+ ,33
+ ,13
+ ,12
+ ,0
+ ,0
+ ,16
+ ,38
+ ,36
+ ,14
+ ,10
+ ,0
+ ,1
+ ,10
+ ,33
+ ,32
+ ,15
+ ,12
+ ,0
+ ,0
+ ,15
+ ,31
+ ,32
+ ,12
+ ,9
+ ,0
+ ,1
+ ,16
+ ,32
+ ,36
+ ,8
+ ,6
+ ,0
+ ,0
+ ,19
+ ,31
+ ,32
+ ,14
+ ,10
+ ,0
+ ,0
+ ,12
+ ,33
+ ,34
+ ,14
+ ,9
+ ,0
+ ,0
+ ,8
+ ,34
+ ,33
+ ,11
+ ,9
+ ,0
+ ,0
+ ,11
+ ,34
+ ,35
+ ,12
+ ,9
+ ,0
+ ,1
+ ,14
+ ,34
+ ,30
+ ,13
+ ,6
+ ,0
+ ,0
+ ,9
+ ,33
+ ,38
+ ,10
+ ,10
+ ,0
+ ,0
+ ,15
+ ,32
+ ,34
+ ,16
+ ,6
+ ,0
+ ,1
+ ,13
+ ,41
+ ,33
+ ,18
+ ,14
+ ,0
+ ,1
+ ,16
+ ,34
+ ,32
+ ,13
+ ,10
+ ,0
+ ,0
+ ,11
+ ,36
+ ,31
+ ,11
+ ,10
+ ,0
+ ,0
+ ,12
+ ,37
+ ,30
+ ,4
+ ,6
+ ,0
+ ,0
+ ,13
+ ,36
+ ,27
+ ,13
+ ,12
+ ,0
+ ,1
+ ,10
+ ,29
+ ,31
+ ,16
+ ,12
+ ,0
+ ,0
+ ,11
+ ,37
+ ,30
+ ,10
+ ,7
+ ,0
+ ,0
+ ,12
+ ,27
+ ,32
+ ,12
+ ,8
+ ,0
+ ,0
+ ,8
+ ,35
+ ,35
+ ,12
+ ,11
+ ,0
+ ,0
+ ,12
+ ,28
+ ,28
+ ,10
+ ,3
+ ,0
+ ,0
+ ,12
+ ,35
+ ,33
+ ,13
+ ,6
+ ,0
+ ,0
+ ,11
+ ,29
+ ,35
+ ,12
+ ,8
+ ,0
+ ,0
+ ,13
+ ,32
+ ,35
+ ,14
+ ,9
+ ,0
+ ,1
+ ,14
+ ,36
+ ,32
+ ,10
+ ,9
+ ,0
+ ,1
+ ,10
+ ,19
+ ,21
+ ,12
+ ,8
+ ,0
+ ,1
+ ,12
+ ,21
+ ,20
+ ,12
+ ,9
+ ,0
+ ,0
+ ,15
+ ,31
+ ,34
+ ,11
+ ,7
+ ,0
+ ,0
+ ,13
+ ,33
+ ,32
+ ,10
+ ,7
+ ,0
+ ,1
+ ,13
+ ,36
+ ,34
+ ,12
+ ,6
+ ,0
+ ,1
+ ,13
+ ,33
+ ,32
+ ,16
+ ,9
+ ,0
+ ,0
+ ,12
+ ,37
+ ,33
+ ,12
+ ,10
+ ,0
+ ,0
+ ,12
+ ,34
+ ,33
+ ,14
+ ,11
+ ,0
+ ,0
+ ,9
+ ,35
+ ,37
+ ,16
+ ,12
+ ,0
+ ,1
+ ,9
+ ,31
+ ,32
+ ,14
+ ,8
+ ,0
+ ,1
+ ,15
+ ,37
+ ,34
+ ,13
+ ,11
+ ,0
+ ,1
+ ,10
+ ,35
+ ,30
+ ,4
+ ,3
+ ,0
+ ,1
+ ,14
+ ,27
+ ,30
+ ,15
+ ,11
+ ,0
+ ,0
+ ,15
+ ,34
+ ,38
+ ,11
+ ,12
+ ,0
+ ,0
+ ,7
+ ,40
+ ,36
+ ,11
+ ,7
+ ,0
+ ,0
+ ,14
+ ,29
+ ,32
+ ,14
+ ,9
+ ,0
+ ,0
+ ,8
+ ,38
+ ,34
+ ,15
+ ,12
+ ,0
+ ,1
+ ,10
+ ,34
+ ,33
+ ,14
+ ,8
+ ,0
+ ,0
+ ,13
+ ,21
+ ,27
+ ,13
+ ,11
+ ,0
+ ,0
+ ,13
+ ,36
+ ,32
+ ,11
+ ,8
+ ,0
+ ,1
+ ,13
+ ,38
+ ,34
+ ,15
+ ,10
+ ,0
+ ,0
+ ,8
+ ,30
+ ,29
+ ,11
+ ,8
+ ,0
+ ,0
+ ,12
+ ,35
+ ,35
+ ,13
+ ,7
+ ,0
+ ,1
+ ,13
+ ,30
+ ,27
+ ,13
+ ,8
+ ,0
+ ,1
+ ,12
+ ,36
+ ,33
+ ,16
+ ,10
+ ,0
+ ,0
+ ,10
+ ,34
+ ,38
+ ,13
+ ,8
+ ,0
+ ,1
+ ,13
+ ,35
+ ,36
+ ,16
+ ,12
+ ,0
+ ,0
+ ,12
+ ,34
+ ,33
+ ,16
+ ,14
+ ,0
+ ,0
+ ,9
+ ,32
+ ,39
+ ,12
+ ,7
+ ,0
+ ,1
+ ,15
+ ,33
+ ,29
+ ,7
+ ,6
+ ,0
+ ,0
+ ,13
+ ,33
+ ,32
+ ,16
+ ,11
+ ,0
+ ,1
+ ,13
+ ,26
+ ,34
+ ,5
+ ,4
+ ,0
+ ,0
+ ,13
+ ,35
+ ,38
+ ,16
+ ,9
+ ,0
+ ,0
+ ,15
+ ,21
+ ,17
+ ,4
+ ,5
+ ,0
+ ,0
+ ,15
+ ,38
+ ,35
+ ,12
+ ,9
+ ,0
+ ,0
+ ,14
+ ,35
+ ,32
+ ,15
+ ,11
+ ,0
+ ,1
+ ,15
+ ,33
+ ,34
+ ,14
+ ,12
+ ,0
+ ,0
+ ,11
+ ,37
+ ,36
+ ,11
+ ,9
+ ,0
+ ,0
+ ,15
+ ,38
+ ,31
+ ,16
+ ,12
+ ,0
+ ,1
+ ,14
+ ,34
+ ,35
+ ,15
+ ,10
+ ,0
+ ,0
+ ,13
+ ,27
+ ,29
+ ,12
+ ,9
+ ,0
+ ,1
+ ,12
+ ,16
+ ,22
+ ,6
+ ,6
+ ,0
+ ,0
+ ,16
+ ,40
+ ,41
+ ,16
+ ,10
+ ,0
+ ,0
+ ,16
+ ,36
+ ,36
+ ,10
+ ,9
+ ,0
+ ,1
+ ,9
+ ,42
+ ,42
+ ,15
+ ,13
+ ,0
+ ,1
+ ,14
+ ,30
+ ,33
+ ,14
+ ,12)
+ ,dim=c(7
+ ,288)
+ ,dimnames=list(c('Populatie'
+ ,'Geslacht'
+ ,'Happiness'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software')
+ ,1:288))
> y <- array(NA,dim=c(7,288),dimnames=list(c('Populatie','Geslacht','Happiness','Connected','Separate','Learning','Software'),1:288))
> 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 = '5'
> #'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
Separate Populatie Geslacht Happiness Connected Learning Software
1 38 1 1 14 41 13 12
2 32 1 1 18 39 16 11
3 35 1 1 11 30 19 15
4 33 1 0 12 31 15 6
5 37 1 1 16 34 14 13
6 29 1 1 18 35 13 10
7 31 1 1 14 39 19 12
8 36 1 1 14 34 15 14
9 35 1 1 15 36 14 12
10 38 1 1 15 37 15 9
11 31 1 0 17 38 16 10
12 34 1 1 19 36 16 12
13 35 1 0 10 38 16 12
14 38 1 1 16 39 16 11
15 37 1 1 18 33 17 15
16 33 1 0 14 32 15 12
17 32 1 0 14 36 15 10
18 38 1 1 17 38 20 12
19 38 1 0 14 39 18 11
20 32 1 1 16 32 16 12
21 33 1 0 18 32 16 11
22 31 1 1 11 31 16 12
23 38 1 1 14 39 19 13
24 39 1 1 12 37 16 11
25 32 1 0 17 39 17 12
26 32 1 1 9 41 17 13
27 35 1 0 16 36 16 10
28 37 1 1 14 33 15 14
29 33 1 1 15 33 16 12
30 33 1 0 11 34 14 10
31 31 1 1 16 31 15 12
32 32 1 0 13 27 12 8
33 31 1 1 17 37 14 10
34 37 1 1 15 34 16 12
35 30 1 0 14 34 14 12
36 33 1 0 16 32 10 7
37 31 1 0 9 29 10 9
38 33 1 0 15 36 14 12
39 31 1 1 17 29 16 10
40 33 1 0 13 35 16 10
41 32 1 0 15 37 16 10
42 33 1 1 16 34 14 12
43 32 1 0 16 38 20 15
44 33 1 0 12 35 14 10
45 28 1 1 15 38 14 10
46 35 1 1 11 37 11 12
47 39 1 1 15 38 14 13
48 34 1 1 15 33 15 11
49 38 1 1 17 36 16 11
50 32 1 0 13 38 14 12
51 38 1 1 16 32 16 14
52 30 1 0 14 32 14 10
53 33 1 0 11 32 12 12
54 38 1 1 12 34 16 13
55 32 1 0 12 32 9 5
56 35 1 1 15 37 14 6
57 34 1 1 16 39 16 12
58 34 1 1 15 29 16 12
59 36 1 0 12 37 15 11
60 34 1 1 12 35 16 10
61 28 1 0 8 30 12 7
62 34 1 0 13 38 16 12
63 35 1 1 11 34 16 14
64 35 1 1 14 31 14 11
65 31 1 1 15 34 16 12
66 37 1 0 10 35 17 13
67 35 1 1 11 36 18 14
68 27 1 0 12 30 18 11
69 40 1 1 15 39 12 12
70 37 1 0 15 35 16 12
71 36 1 0 14 38 10 8
72 38 1 1 16 31 14 11
73 39 1 1 15 34 18 14
74 41 1 0 15 38 18 14
75 27 1 0 13 34 16 12
76 30 1 1 12 39 17 9
77 37 1 1 17 37 16 13
78 31 1 1 13 34 16 11
79 31 1 0 15 28 13 12
80 27 1 0 13 37 16 12
81 36 1 0 15 33 16 12
82 37 1 1 15 35 16 12
83 33 1 0 16 37 15 12
84 34 1 1 15 32 15 11
85 31 1 1 14 33 16 10
86 39 1 0 15 38 14 9
87 34 1 1 14 33 16 12
88 32 1 1 13 29 16 12
89 33 1 1 7 33 15 12
90 36 1 1 17 31 12 9
91 32 1 1 13 36 17 15
92 41 1 1 15 35 16 12
93 28 1 1 14 32 15 12
94 30 1 1 13 29 13 12
95 36 1 1 16 39 16 10
96 35 1 1 12 37 16 13
97 31 1 1 14 35 16 9
98 34 1 0 17 37 16 12
99 36 1 0 15 32 14 10
100 36 1 1 17 38 16 14
101 35 1 0 12 37 16 11
102 37 1 1 16 36 20 15
103 28 1 0 11 32 15 11
104 39 1 1 15 33 16 11
105 32 1 0 9 40 13 12
106 35 1 1 16 38 17 12
107 39 1 0 15 41 16 12
108 35 1 0 10 36 16 11
109 42 1 1 10 43 12 7
110 34 1 1 15 30 16 12
111 33 1 1 11 31 16 14
112 41 1 1 13 32 17 11
113 34 1 1 18 37 12 10
114 32 1 0 16 37 18 13
115 40 1 1 14 33 14 13
116 40 1 1 14 34 14 8
117 35 1 1 14 33 13 11
118 36 1 1 14 38 16 12
119 37 1 0 12 33 13 11
120 27 1 1 14 31 16 13
121 39 1 1 15 38 13 12
122 38 1 1 15 37 16 14
123 31 1 1 15 36 15 13
124 33 1 1 13 31 16 15
125 32 1 0 17 39 15 10
126 39 1 1 17 44 17 11
127 36 1 1 19 33 15 9
128 33 1 1 15 35 12 11
129 33 1 0 13 32 16 10
130 32 1 0 9 28 10 11
131 37 1 1 15 40 16 8
132 30 1 0 15 27 12 11
133 38 1 0 15 37 14 12
134 29 1 1 16 32 15 12
135 22 1 0 11 28 13 9
136 35 1 0 14 34 15 11
137 35 1 1 11 30 11 10
138 34 1 1 15 35 12 8
139 35 1 0 13 31 11 9
140 34 1 1 15 32 16 8
141 37 1 0 16 30 15 9
142 35 1 1 14 30 17 15
143 23 1 0 15 31 16 11
144 31 1 1 16 40 10 8
145 27 1 1 16 32 18 13
146 36 1 0 11 36 13 12
147 31 1 0 12 32 16 12
148 32 1 0 9 35 13 9
149 39 1 1 16 38 10 7
150 37 1 1 13 42 15 13
151 38 1 0 16 34 16 9
152 39 1 1 12 35 16 6
153 34 1 1 9 38 14 8
154 31 1 1 13 33 10 8
155 37 1 1 14 32 13 6
156 36 1 1 19 33 15 9
157 32 1 1 13 34 16 11
158 38 1 1 12 32 12 8
159 26 0 0 10 27 13 10
160 26 0 0 14 31 12 8
161 33 0 0 16 38 17 14
162 39 0 1 10 34 15 10
163 30 0 0 11 24 10 8
164 33 0 0 14 30 14 11
165 25 0 1 12 26 11 12
166 38 0 1 9 34 13 12
167 37 0 0 9 27 16 12
168 31 0 0 11 37 12 5
169 37 0 1 16 36 16 12
170 35 0 0 9 41 12 10
171 25 0 1 13 29 9 7
172 28 0 1 16 36 12 12
173 35 0 0 13 32 15 11
174 33 0 1 9 37 12 8
175 30 0 0 12 30 12 9
176 31 0 1 16 31 14 10
177 37 0 1 11 38 12 9
178 36 0 1 14 36 16 12
179 30 0 0 13 35 11 6
180 36 0 0 15 31 19 15
181 32 0 0 14 38 15 12
182 28 0 1 16 22 8 12
183 36 0 1 13 32 16 12
184 34 0 0 14 36 17 11
185 31 0 1 15 39 12 7
186 28 0 0 13 28 11 7
187 36 0 0 11 32 11 5
188 36 0 1 11 32 14 12
189 40 0 1 14 38 16 12
190 33 0 1 15 32 12 3
191 37 0 1 11 35 16 11
192 32 0 1 15 32 13 10
193 38 0 0 12 37 15 12
194 31 0 1 14 34 16 9
195 37 0 1 14 33 16 12
196 33 0 0 8 33 14 9
197 30 0 0 9 30 16 12
198 30 0 0 15 24 14 10
199 31 0 0 17 34 11 9
200 32 0 0 13 34 12 12
201 34 0 1 15 33 15 8
202 36 0 1 15 34 15 11
203 37 0 1 14 35 16 11
204 36 0 0 16 35 16 12
205 33 0 0 13 36 11 10
206 33 0 0 16 34 15 10
207 33 0 1 9 34 12 12
208 44 0 0 16 41 12 12
209 39 0 0 11 32 15 11
210 32 0 0 10 30 15 8
211 35 0 1 11 35 16 12
212 25 0 0 15 28 14 10
213 35 0 1 17 33 17 11
214 34 0 1 14 39 14 10
215 35 0 0 8 36 13 8
216 39 0 1 15 36 15 12
217 33 0 0 11 35 13 12
218 36 0 0 16 38 14 10
219 32 0 1 10 33 15 12
220 32 0 0 15 31 12 9
221 36 0 1 16 32 8 6
222 32 0 0 19 31 14 10
223 34 0 0 12 33 14 9
224 33 0 0 8 34 11 9
225 35 0 0 11 34 12 9
226 30 0 1 14 34 13 6
227 38 0 0 9 33 10 10
228 34 0 0 15 32 16 6
229 33 0 1 13 41 18 14
230 32 0 1 16 34 13 10
231 31 0 0 11 36 11 10
232 30 0 0 12 37 4 6
233 27 0 0 13 36 13 12
234 31 0 1 10 29 16 12
235 30 0 0 11 37 10 7
236 32 0 0 12 27 12 8
237 35 0 0 8 35 12 11
238 28 0 0 12 28 10 3
239 33 0 0 12 35 13 6
240 35 0 0 11 29 12 8
241 35 0 0 13 32 14 9
242 32 0 1 14 36 10 9
243 21 0 1 10 19 12 8
244 20 0 1 12 21 12 9
245 34 0 0 15 31 11 7
246 32 0 0 13 33 10 7
247 34 0 1 13 36 12 6
248 32 0 1 13 33 16 9
249 33 0 0 12 37 12 10
250 33 0 0 12 34 14 11
251 37 0 0 9 35 16 12
252 32 0 1 9 31 14 8
253 34 0 1 15 37 13 11
254 30 0 1 10 35 4 3
255 30 0 1 14 27 15 11
256 38 0 0 15 34 11 12
257 36 0 0 7 40 11 7
258 32 0 0 14 29 14 9
259 34 0 0 8 38 15 12
260 33 0 1 10 34 14 8
261 27 0 0 13 21 13 11
262 32 0 0 13 36 11 8
263 34 0 1 13 38 15 10
264 29 0 0 8 30 11 8
265 35 0 0 12 35 13 7
266 27 0 1 13 30 13 8
267 33 0 1 12 36 16 10
268 38 0 0 10 34 13 8
269 36 0 1 13 35 16 12
270 33 0 0 12 34 16 14
271 39 0 0 9 32 12 7
272 29 0 1 15 33 7 6
273 32 0 0 13 33 16 11
274 34 0 1 13 26 5 4
275 38 0 0 13 35 16 9
276 17 0 0 15 21 4 5
277 35 0 0 15 38 12 9
278 32 0 0 14 35 15 11
279 34 0 1 15 33 14 12
280 36 0 0 11 37 11 9
281 31 0 0 15 38 16 12
282 35 0 1 14 34 15 10
283 29 0 0 13 27 12 9
284 22 0 1 12 16 6 6
285 41 0 0 16 40 16 10
286 36 0 0 16 36 10 9
287 42 0 1 9 42 15 13
288 33 0 1 14 30 14 12
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Populatie Geslacht Happiness Connected Learning
14.77986 -0.04862 0.70053 -0.03084 0.46064 0.16910
Software
0.08317
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.1692 -1.9114 0.1335 2.1617 7.7998
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.77986 1.98005 7.464 1.05e-12 ***
Populatie -0.04862 0.43554 -0.112 0.911
Geslacht 0.70053 0.40401 1.734 0.084 .
Happiness -0.03084 0.08281 -0.372 0.710
Connected 0.46064 0.05039 9.142 < 2e-16 ***
Learning 0.16910 0.10281 1.645 0.101
Software 0.08317 0.11013 0.755 0.451
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.289 on 281 degrees of freedom
Multiple R-squared: 0.306, Adjusted R-squared: 0.2912
F-statistic: 20.65 on 6 and 281 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.671718758 0.656562485 0.32828124
[2,] 0.563349762 0.873300475 0.43665024
[3,] 0.547351250 0.905297500 0.45264875
[4,] 0.425183590 0.850367180 0.57481641
[5,] 0.473818924 0.947637847 0.52618108
[6,] 0.504815941 0.990368119 0.49518406
[7,] 0.404479091 0.808958183 0.59552091
[8,] 0.319736708 0.639473416 0.68026329
[9,] 0.387007917 0.774015835 0.61299208
[10,] 0.432235833 0.864471666 0.56776417
[11,] 0.380673117 0.761346234 0.61932688
[12,] 0.315209025 0.630418051 0.68479097
[13,] 0.325407007 0.650814015 0.67459299
[14,] 0.269055764 0.538111528 0.73094424
[15,] 0.275123267 0.550246534 0.72487673
[16,] 0.257133801 0.514267602 0.74286620
[17,] 0.391916916 0.783833832 0.60808308
[18,] 0.342979523 0.685959047 0.65702048
[19,] 0.311731858 0.623463717 0.68826814
[20,] 0.266465970 0.532931940 0.73353403
[21,] 0.216010016 0.432020032 0.78398998
[22,] 0.202634050 0.405268100 0.79736595
[23,] 0.166879054 0.333758109 0.83312095
[24,] 0.167201168 0.334402335 0.83279883
[25,] 0.156848465 0.313696930 0.84315153
[26,] 0.161593682 0.323187363 0.83840632
[27,] 0.140262999 0.280525998 0.85973700
[28,] 0.111984545 0.223969091 0.88801545
[29,] 0.087490215 0.174980430 0.91250979
[30,] 0.074231637 0.148463273 0.92576836
[31,] 0.056767019 0.113534038 0.94323298
[32,] 0.046581371 0.093162741 0.95341863
[33,] 0.035715030 0.071430059 0.96428497
[34,] 0.034469823 0.068939646 0.96553018
[35,] 0.025424894 0.050849789 0.97457511
[36,] 0.071990311 0.143980621 0.92800969
[37,] 0.056299304 0.112598608 0.94370070
[38,] 0.068076938 0.136153875 0.93192306
[39,] 0.052993087 0.105986174 0.94700691
[40,] 0.059976829 0.119953657 0.94002317
[41,] 0.052108941 0.104217882 0.94789106
[42,] 0.057251092 0.114502183 0.94274891
[43,] 0.050836431 0.101672861 0.94916357
[44,] 0.039547258 0.079094516 0.96045274
[45,] 0.038881749 0.077763497 0.96111825
[46,] 0.031403867 0.062807735 0.96859613
[47,] 0.025254230 0.050508461 0.97474577
[48,] 0.020578838 0.041157676 0.97942116
[49,] 0.015685320 0.031370640 0.98431468
[50,] 0.014048969 0.028097939 0.98595103
[51,] 0.010631875 0.021263751 0.98936812
[52,] 0.012033826 0.024067652 0.98796617
[53,] 0.009080413 0.018160826 0.99091959
[54,] 0.006691321 0.013382643 0.99330868
[55,] 0.005250491 0.010500982 0.99474951
[56,] 0.006038453 0.012076906 0.99396155
[57,] 0.005916213 0.011832426 0.99408379
[58,] 0.004451446 0.008902891 0.99554855
[59,] 0.008590532 0.017181063 0.99140947
[60,] 0.011001355 0.022002711 0.98899864
[61,] 0.012427048 0.024854095 0.98757295
[62,] 0.011264703 0.022529405 0.98873530
[63,] 0.015909241 0.031818483 0.98409076
[64,] 0.019002754 0.038005508 0.98099725
[65,] 0.035594886 0.071189771 0.96440511
[66,] 0.071978426 0.143956852 0.92802157
[67,] 0.095405547 0.190811095 0.90459445
[68,] 0.081276863 0.162553726 0.91872314
[69,] 0.080651325 0.161302650 0.91934867
[70,] 0.069452589 0.138905178 0.93054741
[71,] 0.149023297 0.298046594 0.85097670
[72,] 0.145103111 0.290206222 0.85489689
[73,] 0.133457718 0.266915435 0.86654228
[74,] 0.118134441 0.236268882 0.88186556
[75,] 0.100212202 0.200424404 0.89978780
[76,] 0.094227878 0.188455757 0.90577212
[77,] 0.127732038 0.255464076 0.87226796
[78,] 0.108773881 0.217547761 0.89122612
[79,] 0.093187425 0.186374849 0.90681258
[80,] 0.079177808 0.158355615 0.92082219
[81,] 0.078383220 0.156766439 0.92161678
[82,] 0.085676889 0.171353779 0.91432311
[83,] 0.134857888 0.269715776 0.86514211
[84,] 0.189727951 0.379455902 0.81027205
[85,] 0.185718436 0.371436873 0.81428156
[86,] 0.163634526 0.327269052 0.83636547
[87,] 0.142915442 0.285830884 0.85708456
[88,] 0.141177112 0.282354224 0.85882289
[89,] 0.123247910 0.246495821 0.87675209
[90,] 0.127482421 0.254964842 0.87251758
[91,] 0.109895929 0.219791859 0.89010407
[92,] 0.097044344 0.194088687 0.90295566
[93,] 0.084296048 0.168592097 0.91570395
[94,] 0.098902541 0.197805082 0.90109746
[95,] 0.125800586 0.251601172 0.87419941
[96,] 0.129536902 0.259073803 0.87046310
[97,] 0.114001830 0.228003661 0.88599817
[98,] 0.113203728 0.226407455 0.88679627
[99,] 0.103421448 0.206842896 0.89657855
[100,] 0.142950804 0.285901607 0.85704920
[101,] 0.125642333 0.251284667 0.87435767
[102,] 0.108630927 0.217261854 0.89136907
[103,] 0.199474676 0.398949352 0.80052532
[104,] 0.180819903 0.361639805 0.81918010
[105,] 0.180610023 0.361220047 0.81938998
[106,] 0.235242161 0.470484322 0.76475784
[107,] 0.309072720 0.618145439 0.69092728
[108,] 0.283703959 0.567407919 0.71629604
[109,] 0.255009695 0.510019391 0.74499030
[110,] 0.273141545 0.546283090 0.72685846
[111,] 0.367069827 0.734139654 0.63293017
[112,] 0.362386028 0.724772057 0.63761397
[113,] 0.342977245 0.685954491 0.65702275
[114,] 0.370494454 0.740988908 0.62950555
[115,] 0.340160363 0.680320726 0.65983964
[116,] 0.345279805 0.690559610 0.65472019
[117,] 0.317750175 0.635500349 0.68224983
[118,] 0.301401053 0.602802106 0.69859895
[119,] 0.281411531 0.562823063 0.71858847
[120,] 0.255185308 0.510370616 0.74481469
[121,] 0.234055542 0.468111084 0.76594446
[122,] 0.210514352 0.421028704 0.78948565
[123,] 0.188623107 0.377246213 0.81137689
[124,] 0.190112808 0.380225617 0.80988719
[125,] 0.215620402 0.431240804 0.78437960
[126,] 0.393153924 0.786307848 0.60684608
[127,] 0.368450429 0.736900858 0.63154957
[128,] 0.361931724 0.723863449 0.63806828
[129,] 0.331144042 0.662288083 0.66885596
[130,] 0.332603470 0.665206940 0.66739653
[131,] 0.303830599 0.607661199 0.69616940
[132,] 0.359726672 0.719453344 0.64027333
[133,] 0.342659419 0.685318838 0.65734058
[134,] 0.582489781 0.835020439 0.41751022
[135,] 0.635999457 0.728001086 0.36400054
[136,] 0.759301245 0.481397511 0.24069876
[137,] 0.737428047 0.525143907 0.26257195
[138,] 0.733830907 0.532338185 0.26616909
[139,] 0.730702114 0.538595771 0.26929789
[140,] 0.729903119 0.540193762 0.27009688
[141,] 0.715182992 0.569634017 0.28481701
[142,] 0.727738862 0.544522276 0.27226114
[143,] 0.740530163 0.518939674 0.25946984
[144,] 0.737166278 0.525667445 0.26283372
[145,] 0.736623746 0.526752508 0.26337625
[146,] 0.732027539 0.535944922 0.26797246
[147,] 0.708452143 0.583095714 0.29154786
[148,] 0.742894830 0.514210341 0.25710517
[149,] 0.736399122 0.527201755 0.26360088
[150,] 0.737822928 0.524354144 0.26217707
[151,] 0.757228753 0.485542493 0.24277125
[152,] 0.764389245 0.471221510 0.23561076
[153,] 0.826763071 0.346473859 0.17323693
[154,] 0.816235266 0.367529468 0.18376473
[155,] 0.799700761 0.400598477 0.20029924
[156,] 0.824400397 0.351199206 0.17559960
[157,] 0.844648573 0.310702855 0.15535143
[158,] 0.896436437 0.207127125 0.10356356
[159,] 0.896287244 0.207425512 0.10371276
[160,] 0.885492804 0.229014391 0.11450720
[161,] 0.873156796 0.253686408 0.12684320
[162,] 0.902241174 0.195517653 0.09775883
[163,] 0.936723152 0.126553696 0.06327685
[164,] 0.933030955 0.133938090 0.06696905
[165,] 0.923981608 0.152036784 0.07601839
[166,] 0.911711873 0.176576254 0.08828813
[167,] 0.898287468 0.203425063 0.10171253
[168,] 0.887983847 0.224032307 0.11201615
[169,] 0.872087826 0.255824348 0.12791217
[170,] 0.869023643 0.261952715 0.13097636
[171,] 0.866924787 0.266150426 0.13307521
[172,] 0.874706822 0.250586356 0.12529318
[173,] 0.865486789 0.269026421 0.13451321
[174,] 0.861677703 0.276644594 0.13832230
[175,] 0.846917090 0.306165819 0.15308291
[176,] 0.868265120 0.263469760 0.13173488
[177,] 0.853154371 0.293691258 0.14684563
[178,] 0.870490888 0.259018225 0.12950911
[179,] 0.872110143 0.255779715 0.12788986
[180,] 0.880530545 0.238938909 0.11946945
[181,] 0.863412993 0.273174014 0.13658701
[182,] 0.854062519 0.291874963 0.14593748
[183,] 0.832595623 0.334808753 0.16740438
[184,] 0.826758326 0.346483347 0.17324167
[185,] 0.824806484 0.350387032 0.17519352
[186,] 0.828585859 0.342828282 0.17141414
[187,] 0.804280074 0.391439852 0.19571993
[188,] 0.789203054 0.421593891 0.21079695
[189,] 0.769053481 0.461893039 0.23094652
[190,] 0.748991347 0.502017307 0.25100865
[191,] 0.722354749 0.555290502 0.27764525
[192,] 0.691875399 0.616249201 0.30812460
[193,] 0.675155268 0.649689464 0.32484473
[194,] 0.662472627 0.675054745 0.33752737
[195,] 0.635988040 0.728023920 0.36401196
[196,] 0.604262588 0.791474825 0.39573741
[197,] 0.570202840 0.859594319 0.42979716
[198,] 0.533237321 0.933525357 0.46676268
[199,] 0.687781755 0.624436490 0.31221825
[200,] 0.772160338 0.455679325 0.22783966
[201,] 0.741400930 0.517198140 0.25859907
[202,] 0.709979355 0.580041290 0.29002065
[203,] 0.768782083 0.462435835 0.23121792
[204,] 0.745322693 0.509354615 0.25467731
[205,] 0.723631337 0.552737325 0.27636866
[206,] 0.690376526 0.619246947 0.30962347
[207,] 0.728627543 0.542744914 0.27137246
[208,] 0.696174678 0.607650643 0.30382532
[209,] 0.661040943 0.677918114 0.33895906
[210,] 0.627920278 0.744159445 0.37207972
[211,] 0.589124250 0.821751500 0.41087575
[212,] 0.670223274 0.659553452 0.32977673
[213,] 0.632815643 0.734368714 0.36718436
[214,] 0.594727240 0.810545521 0.40527276
[215,] 0.554446760 0.891106480 0.44555324
[216,] 0.523128653 0.953742693 0.47687135
[217,] 0.516556674 0.966886652 0.48344333
[218,] 0.614447013 0.771105974 0.38555299
[219,] 0.575146750 0.849706499 0.42485325
[220,] 0.626414119 0.747171763 0.37358588
[221,] 0.588382956 0.823234089 0.41161704
[222,] 0.575796388 0.848407223 0.42420361
[223,] 0.555814558 0.888370884 0.44418544
[224,] 0.742182072 0.515635856 0.25781793
[225,] 0.705402534 0.589194933 0.29459747
[226,] 0.755666289 0.488667421 0.24433371
[227,] 0.742384691 0.515230618 0.25761531
[228,] 0.704920417 0.590159167 0.29507958
[229,] 0.673784794 0.652430412 0.32621521
[230,] 0.643372248 0.713255504 0.35662775
[231,] 0.678713146 0.642573709 0.32128685
[232,] 0.657047532 0.685904936 0.34295247
[233,] 0.623786729 0.752426541 0.37621327
[234,] 0.631806140 0.736387720 0.36819386
[235,] 0.733249304 0.533501392 0.26675070
[236,] 0.724789175 0.550421649 0.27521082
[237,] 0.679639623 0.640720754 0.32036038
[238,] 0.631270624 0.737458753 0.36872938
[239,] 0.593736568 0.812526864 0.40626343
[240,] 0.561858607 0.876282785 0.43814139
[241,] 0.513129183 0.973741634 0.48687082
[242,] 0.472733605 0.945467210 0.52726639
[243,] 0.419503569 0.839007137 0.58049643
[244,] 0.370542878 0.741085755 0.62945712
[245,] 0.350450215 0.700900430 0.64954979
[246,] 0.299726196 0.599452392 0.70027380
[247,] 0.409256787 0.818513574 0.59074321
[248,] 0.378175377 0.756350755 0.62182462
[249,] 0.331803575 0.663607150 0.66819642
[250,] 0.335398463 0.670796926 0.66460154
[251,] 0.319577633 0.639155266 0.68042237
[252,] 0.304496482 0.608992964 0.69550352
[253,] 0.285677036 0.571354072 0.71432296
[254,] 0.297596489 0.595192978 0.70240351
[255,] 0.345705453 0.691410906 0.65429455
[256,] 0.296437824 0.592875647 0.70356218
[257,] 0.438765984 0.877531968 0.56123402
[258,] 0.679157351 0.641685299 0.32084265
[259,] 0.611103371 0.777793257 0.38889663
[260,] 0.534277393 0.931445215 0.46572261
[261,] 0.466167053 0.932334106 0.53383295
[262,] 0.427434531 0.854869061 0.57256547
[263,] 0.703457459 0.593085081 0.29654254
[264,] 0.606001740 0.787996520 0.39399826
[265,] 0.530337366 0.939325267 0.46966263
[266,] 0.445618159 0.891236318 0.55438184
[267,] 0.629150952 0.741698096 0.37084905
[268,] 0.547323093 0.905353814 0.45267691
[269,] 0.392519143 0.785038285 0.60748086
> postscript(file="/var/www/html/freestat/rcomp/tmp/1r6z51292940095.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2cpjl1292940096.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3cpjl1292940096.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4cpjl1292940096.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5nyin1292940096.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 = 288
Frequency = 1
1 2 3 4 5 6
0.91711635 -4.46238299 1.62758263 1.32325652 2.95102693 -5.02933078
7 8 9 10 11 12
-6.17619759 1.63708391 0.08207433 2.70184503 -4.24886820 -1.13278574
13 14 15 16 17 18
-0.63105890 1.47594650 2.79969653 0.42525058 -2.25098411 1.20785228
19 20 21 22 23 24
1.77660834 -1.38271288 0.46266301 -2.07624449 0.74063044 3.27389480
25 26 27 28 29 30
-4.04495733 -5.99663405 0.64158586 3.09772857 -0.87419279 -0.25310002
31 32 33 34 35 36
-1.75296769 2.53762808 -4.15055588 2.66516256 -3.32693819 1.74828362
37 38 39 40 41 42
0.74802688 -1.21739225 -0.80359973 -0.99027525 -2.84989405 -0.96580111
43 44 45 46 47 48
-4.37196545 -0.68290942 -7.67287104 0.00539029 3.07761306 0.37807972
49 50 51 52 53 54
2.88871572 -3.20035206 4.45094318 -2.23930495 0.84004644 3.48948483
55 56 57 58 59 60
0.96038709 0.12046148 -2.60722546 1.96838583 1.14352877 -0.72164393
61 62 63 64 65 66
-2.91531018 -1.53855314 0.37547761 2.43763431 -3.33483744 2.49860255
67 68 69 70 71 72
-0.88401278 -5.13926028 4.03834145 2.90505133 1.83957323 5.49930482
73 74 75 76 77 78
4.16061754 5.01857235 -6.69597453 -6.65015112 1.26172713 -3.31333598
79 80 81 82 83 84
0.63686553 -8.07790849 2.82634063 2.20451790 -1.81630219 0.83872437
85 86 87 88 89 90
-2.73868411 4.11083435 0.09497196 -0.09328468 -0.95177427 4.03468509
91 92 93 94 95 96
-3.73641370 6.20451790 -5.27528285 -1.58598305 -0.44088153 -0.89244913
97 98 99 100 101 102
-3.57680145 -0.95456748 3.79153030 -0.28208949 -0.02557178 0.84879043
103 104 105 106 107 108
-4.58408322 5.20897918 -4.07588184 -1.31568135 2.14118340 0.37340237
109 110 111 112 113 114
4.45744640 1.50774117 -0.24258843 7.43885278 -0.78151955 -3.40677578
115 116 117 118 119 120
6.35000107 6.30521625 1.68544555 -0.20825131 4.32430847 -6.06691070
121 122 123 124 125 126
3.32988556 2.11688466 -4.17019818 -0.26408989 -3.54041231 0.03445795
127 128 129 130 131 132
2.66776466 -1.03590796 0.39165871 2.04232760 0.23398250 0.34978269
133 134 135 136 137 138
3.32196310 -4.21361234 -8.23695958 1.58713323 3.39624680 0.21360793
139 140 141 142 143 144
3.78097804 0.91913973 5.65772629 2.05828948 -9.16919809 -4.72057900
145 146 147 148 149 150
-6.80408593 1.82836728 -1.80552047 -1.52314267 4.28388228 -0.99573661
151 152 153 154 155 156
4.64604713 4.61104394 -1.69153863 -1.58857218 4.56195003 2.66776466
157 158 159 160 161 162
-2.31333598 5.50303614 -3.93893834 -5.32273147 -2.83010805 4.79781457
163 164 165 166 167 168
2.14747643 1.55019620 -4.94529946 3.93883647 6.35658084 -2.92958926
169 170 171 172 173 174
1.72609231 -1.24969821 -5.54233725 -6.59750553 2.42897109 -1.94130909
175 176 177 178 179 180
-1.00692929 -1.46613941 1.57654480 0.66442180 -2.86070087 2.94219622
181 182 183 184 185 186
-3.38723354 0.52792180 2.47616516 -0.72097335 -4.59441491 -1.71936026
187 188 189 190 191 192
4.54273455 2.75269574 3.74313249 0.96278553 2.11573266 -0.78851877
193 194 195 196 197 198
3.01174060 -3.16477299 3.04635576 0.14959465 -2.02535312 1.42807134
199 200 201 202 203 204
-1.52623110 -1.06818855 0.57897942 1.86881887 2.20823842 1.88727038
205 206 207 208 209 210
-0.65403339 -0.31664049 -0.89206299 7.79980463 6.36730059 0.50727054
211 212 213 214 215 216
0.03256069 -5.41450727 1.05293295 -2.21296715 1.01993320 3.86435759
217 218 219 220 221 222
-0.75960426 1.00988144 -1.90788471 0.62493181 4.42050706 0.32689978
223 224 225 226 227 228
1.27293566 0.19625162 2.11965684 -3.40795547 5.77366010 1.73740089
229 230 231 232 233 234
-5.17418174 -1.67897283 -2.71570389 -2.62912164 -7.15857840 -1.23440663
235 236 237 238 239 240
-3.75773211 2.45817664 1.40016249 -1.24840710 -0.22973721 4.50605208
241 242 243 244 245 246
2.76441557 -2.07145905 -5.61887006 -7.56166083 2.96037629 0.14651702
247 248 249 250 251 252
-0.19097949 -1.73496359 -1.31461384 -0.35405293 2.67142361 -0.51564225
253 254 255 256 257 258
-1.17491401 -2.22052036 -0.93750381 5.16258249 0.56789238 1.17718478
259 260 261 262 263 264
-1.57224506 -0.86674096 -0.16573663 -1.48768946 -1.95225829 -1.87799780
265 266 267 268 269 270
1.68709083 -4.76255604 -2.23090477 5.00289301 1.09423120 -0.94176991
271 272 273 274 275 276
7.14561957 -2.90187231 -1.20077410 5.76551478 4.04428052 -8.08312944
277 278 279 280 281 282
0.40041923 -1.92212761 0.41539210 1.90682342 -4.52549883 0.92115558
283 284 285 286 287 288
-0.59416008 -1.99431841 4.75039105 2.69074488 3.83230618 0.76649081
> postscript(file="/var/www/html/freestat/rcomp/tmp/6nyin1292940096.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 = 288
Frequency = 1
lag(myerror, k = 1) myerror
0 0.91711635 NA
1 -4.46238299 0.91711635
2 1.62758263 -4.46238299
3 1.32325652 1.62758263
4 2.95102693 1.32325652
5 -5.02933078 2.95102693
6 -6.17619759 -5.02933078
7 1.63708391 -6.17619759
8 0.08207433 1.63708391
9 2.70184503 0.08207433
10 -4.24886820 2.70184503
11 -1.13278574 -4.24886820
12 -0.63105890 -1.13278574
13 1.47594650 -0.63105890
14 2.79969653 1.47594650
15 0.42525058 2.79969653
16 -2.25098411 0.42525058
17 1.20785228 -2.25098411
18 1.77660834 1.20785228
19 -1.38271288 1.77660834
20 0.46266301 -1.38271288
21 -2.07624449 0.46266301
22 0.74063044 -2.07624449
23 3.27389480 0.74063044
24 -4.04495733 3.27389480
25 -5.99663405 -4.04495733
26 0.64158586 -5.99663405
27 3.09772857 0.64158586
28 -0.87419279 3.09772857
29 -0.25310002 -0.87419279
30 -1.75296769 -0.25310002
31 2.53762808 -1.75296769
32 -4.15055588 2.53762808
33 2.66516256 -4.15055588
34 -3.32693819 2.66516256
35 1.74828362 -3.32693819
36 0.74802688 1.74828362
37 -1.21739225 0.74802688
38 -0.80359973 -1.21739225
39 -0.99027525 -0.80359973
40 -2.84989405 -0.99027525
41 -0.96580111 -2.84989405
42 -4.37196545 -0.96580111
43 -0.68290942 -4.37196545
44 -7.67287104 -0.68290942
45 0.00539029 -7.67287104
46 3.07761306 0.00539029
47 0.37807972 3.07761306
48 2.88871572 0.37807972
49 -3.20035206 2.88871572
50 4.45094318 -3.20035206
51 -2.23930495 4.45094318
52 0.84004644 -2.23930495
53 3.48948483 0.84004644
54 0.96038709 3.48948483
55 0.12046148 0.96038709
56 -2.60722546 0.12046148
57 1.96838583 -2.60722546
58 1.14352877 1.96838583
59 -0.72164393 1.14352877
60 -2.91531018 -0.72164393
61 -1.53855314 -2.91531018
62 0.37547761 -1.53855314
63 2.43763431 0.37547761
64 -3.33483744 2.43763431
65 2.49860255 -3.33483744
66 -0.88401278 2.49860255
67 -5.13926028 -0.88401278
68 4.03834145 -5.13926028
69 2.90505133 4.03834145
70 1.83957323 2.90505133
71 5.49930482 1.83957323
72 4.16061754 5.49930482
73 5.01857235 4.16061754
74 -6.69597453 5.01857235
75 -6.65015112 -6.69597453
76 1.26172713 -6.65015112
77 -3.31333598 1.26172713
78 0.63686553 -3.31333598
79 -8.07790849 0.63686553
80 2.82634063 -8.07790849
81 2.20451790 2.82634063
82 -1.81630219 2.20451790
83 0.83872437 -1.81630219
84 -2.73868411 0.83872437
85 4.11083435 -2.73868411
86 0.09497196 4.11083435
87 -0.09328468 0.09497196
88 -0.95177427 -0.09328468
89 4.03468509 -0.95177427
90 -3.73641370 4.03468509
91 6.20451790 -3.73641370
92 -5.27528285 6.20451790
93 -1.58598305 -5.27528285
94 -0.44088153 -1.58598305
95 -0.89244913 -0.44088153
96 -3.57680145 -0.89244913
97 -0.95456748 -3.57680145
98 3.79153030 -0.95456748
99 -0.28208949 3.79153030
100 -0.02557178 -0.28208949
101 0.84879043 -0.02557178
102 -4.58408322 0.84879043
103 5.20897918 -4.58408322
104 -4.07588184 5.20897918
105 -1.31568135 -4.07588184
106 2.14118340 -1.31568135
107 0.37340237 2.14118340
108 4.45744640 0.37340237
109 1.50774117 4.45744640
110 -0.24258843 1.50774117
111 7.43885278 -0.24258843
112 -0.78151955 7.43885278
113 -3.40677578 -0.78151955
114 6.35000107 -3.40677578
115 6.30521625 6.35000107
116 1.68544555 6.30521625
117 -0.20825131 1.68544555
118 4.32430847 -0.20825131
119 -6.06691070 4.32430847
120 3.32988556 -6.06691070
121 2.11688466 3.32988556
122 -4.17019818 2.11688466
123 -0.26408989 -4.17019818
124 -3.54041231 -0.26408989
125 0.03445795 -3.54041231
126 2.66776466 0.03445795
127 -1.03590796 2.66776466
128 0.39165871 -1.03590796
129 2.04232760 0.39165871
130 0.23398250 2.04232760
131 0.34978269 0.23398250
132 3.32196310 0.34978269
133 -4.21361234 3.32196310
134 -8.23695958 -4.21361234
135 1.58713323 -8.23695958
136 3.39624680 1.58713323
137 0.21360793 3.39624680
138 3.78097804 0.21360793
139 0.91913973 3.78097804
140 5.65772629 0.91913973
141 2.05828948 5.65772629
142 -9.16919809 2.05828948
143 -4.72057900 -9.16919809
144 -6.80408593 -4.72057900
145 1.82836728 -6.80408593
146 -1.80552047 1.82836728
147 -1.52314267 -1.80552047
148 4.28388228 -1.52314267
149 -0.99573661 4.28388228
150 4.64604713 -0.99573661
151 4.61104394 4.64604713
152 -1.69153863 4.61104394
153 -1.58857218 -1.69153863
154 4.56195003 -1.58857218
155 2.66776466 4.56195003
156 -2.31333598 2.66776466
157 5.50303614 -2.31333598
158 -3.93893834 5.50303614
159 -5.32273147 -3.93893834
160 -2.83010805 -5.32273147
161 4.79781457 -2.83010805
162 2.14747643 4.79781457
163 1.55019620 2.14747643
164 -4.94529946 1.55019620
165 3.93883647 -4.94529946
166 6.35658084 3.93883647
167 -2.92958926 6.35658084
168 1.72609231 -2.92958926
169 -1.24969821 1.72609231
170 -5.54233725 -1.24969821
171 -6.59750553 -5.54233725
172 2.42897109 -6.59750553
173 -1.94130909 2.42897109
174 -1.00692929 -1.94130909
175 -1.46613941 -1.00692929
176 1.57654480 -1.46613941
177 0.66442180 1.57654480
178 -2.86070087 0.66442180
179 2.94219622 -2.86070087
180 -3.38723354 2.94219622
181 0.52792180 -3.38723354
182 2.47616516 0.52792180
183 -0.72097335 2.47616516
184 -4.59441491 -0.72097335
185 -1.71936026 -4.59441491
186 4.54273455 -1.71936026
187 2.75269574 4.54273455
188 3.74313249 2.75269574
189 0.96278553 3.74313249
190 2.11573266 0.96278553
191 -0.78851877 2.11573266
192 3.01174060 -0.78851877
193 -3.16477299 3.01174060
194 3.04635576 -3.16477299
195 0.14959465 3.04635576
196 -2.02535312 0.14959465
197 1.42807134 -2.02535312
198 -1.52623110 1.42807134
199 -1.06818855 -1.52623110
200 0.57897942 -1.06818855
201 1.86881887 0.57897942
202 2.20823842 1.86881887
203 1.88727038 2.20823842
204 -0.65403339 1.88727038
205 -0.31664049 -0.65403339
206 -0.89206299 -0.31664049
207 7.79980463 -0.89206299
208 6.36730059 7.79980463
209 0.50727054 6.36730059
210 0.03256069 0.50727054
211 -5.41450727 0.03256069
212 1.05293295 -5.41450727
213 -2.21296715 1.05293295
214 1.01993320 -2.21296715
215 3.86435759 1.01993320
216 -0.75960426 3.86435759
217 1.00988144 -0.75960426
218 -1.90788471 1.00988144
219 0.62493181 -1.90788471
220 4.42050706 0.62493181
221 0.32689978 4.42050706
222 1.27293566 0.32689978
223 0.19625162 1.27293566
224 2.11965684 0.19625162
225 -3.40795547 2.11965684
226 5.77366010 -3.40795547
227 1.73740089 5.77366010
228 -5.17418174 1.73740089
229 -1.67897283 -5.17418174
230 -2.71570389 -1.67897283
231 -2.62912164 -2.71570389
232 -7.15857840 -2.62912164
233 -1.23440663 -7.15857840
234 -3.75773211 -1.23440663
235 2.45817664 -3.75773211
236 1.40016249 2.45817664
237 -1.24840710 1.40016249
238 -0.22973721 -1.24840710
239 4.50605208 -0.22973721
240 2.76441557 4.50605208
241 -2.07145905 2.76441557
242 -5.61887006 -2.07145905
243 -7.56166083 -5.61887006
244 2.96037629 -7.56166083
245 0.14651702 2.96037629
246 -0.19097949 0.14651702
247 -1.73496359 -0.19097949
248 -1.31461384 -1.73496359
249 -0.35405293 -1.31461384
250 2.67142361 -0.35405293
251 -0.51564225 2.67142361
252 -1.17491401 -0.51564225
253 -2.22052036 -1.17491401
254 -0.93750381 -2.22052036
255 5.16258249 -0.93750381
256 0.56789238 5.16258249
257 1.17718478 0.56789238
258 -1.57224506 1.17718478
259 -0.86674096 -1.57224506
260 -0.16573663 -0.86674096
261 -1.48768946 -0.16573663
262 -1.95225829 -1.48768946
263 -1.87799780 -1.95225829
264 1.68709083 -1.87799780
265 -4.76255604 1.68709083
266 -2.23090477 -4.76255604
267 5.00289301 -2.23090477
268 1.09423120 5.00289301
269 -0.94176991 1.09423120
270 7.14561957 -0.94176991
271 -2.90187231 7.14561957
272 -1.20077410 -2.90187231
273 5.76551478 -1.20077410
274 4.04428052 5.76551478
275 -8.08312944 4.04428052
276 0.40041923 -8.08312944
277 -1.92212761 0.40041923
278 0.41539210 -1.92212761
279 1.90682342 0.41539210
280 -4.52549883 1.90682342
281 0.92115558 -4.52549883
282 -0.59416008 0.92115558
283 -1.99431841 -0.59416008
284 4.75039105 -1.99431841
285 2.69074488 4.75039105
286 3.83230618 2.69074488
287 0.76649081 3.83230618
288 NA 0.76649081
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -4.46238299 0.91711635
[2,] 1.62758263 -4.46238299
[3,] 1.32325652 1.62758263
[4,] 2.95102693 1.32325652
[5,] -5.02933078 2.95102693
[6,] -6.17619759 -5.02933078
[7,] 1.63708391 -6.17619759
[8,] 0.08207433 1.63708391
[9,] 2.70184503 0.08207433
[10,] -4.24886820 2.70184503
[11,] -1.13278574 -4.24886820
[12,] -0.63105890 -1.13278574
[13,] 1.47594650 -0.63105890
[14,] 2.79969653 1.47594650
[15,] 0.42525058 2.79969653
[16,] -2.25098411 0.42525058
[17,] 1.20785228 -2.25098411
[18,] 1.77660834 1.20785228
[19,] -1.38271288 1.77660834
[20,] 0.46266301 -1.38271288
[21,] -2.07624449 0.46266301
[22,] 0.74063044 -2.07624449
[23,] 3.27389480 0.74063044
[24,] -4.04495733 3.27389480
[25,] -5.99663405 -4.04495733
[26,] 0.64158586 -5.99663405
[27,] 3.09772857 0.64158586
[28,] -0.87419279 3.09772857
[29,] -0.25310002 -0.87419279
[30,] -1.75296769 -0.25310002
[31,] 2.53762808 -1.75296769
[32,] -4.15055588 2.53762808
[33,] 2.66516256 -4.15055588
[34,] -3.32693819 2.66516256
[35,] 1.74828362 -3.32693819
[36,] 0.74802688 1.74828362
[37,] -1.21739225 0.74802688
[38,] -0.80359973 -1.21739225
[39,] -0.99027525 -0.80359973
[40,] -2.84989405 -0.99027525
[41,] -0.96580111 -2.84989405
[42,] -4.37196545 -0.96580111
[43,] -0.68290942 -4.37196545
[44,] -7.67287104 -0.68290942
[45,] 0.00539029 -7.67287104
[46,] 3.07761306 0.00539029
[47,] 0.37807972 3.07761306
[48,] 2.88871572 0.37807972
[49,] -3.20035206 2.88871572
[50,] 4.45094318 -3.20035206
[51,] -2.23930495 4.45094318
[52,] 0.84004644 -2.23930495
[53,] 3.48948483 0.84004644
[54,] 0.96038709 3.48948483
[55,] 0.12046148 0.96038709
[56,] -2.60722546 0.12046148
[57,] 1.96838583 -2.60722546
[58,] 1.14352877 1.96838583
[59,] -0.72164393 1.14352877
[60,] -2.91531018 -0.72164393
[61,] -1.53855314 -2.91531018
[62,] 0.37547761 -1.53855314
[63,] 2.43763431 0.37547761
[64,] -3.33483744 2.43763431
[65,] 2.49860255 -3.33483744
[66,] -0.88401278 2.49860255
[67,] -5.13926028 -0.88401278
[68,] 4.03834145 -5.13926028
[69,] 2.90505133 4.03834145
[70,] 1.83957323 2.90505133
[71,] 5.49930482 1.83957323
[72,] 4.16061754 5.49930482
[73,] 5.01857235 4.16061754
[74,] -6.69597453 5.01857235
[75,] -6.65015112 -6.69597453
[76,] 1.26172713 -6.65015112
[77,] -3.31333598 1.26172713
[78,] 0.63686553 -3.31333598
[79,] -8.07790849 0.63686553
[80,] 2.82634063 -8.07790849
[81,] 2.20451790 2.82634063
[82,] -1.81630219 2.20451790
[83,] 0.83872437 -1.81630219
[84,] -2.73868411 0.83872437
[85,] 4.11083435 -2.73868411
[86,] 0.09497196 4.11083435
[87,] -0.09328468 0.09497196
[88,] -0.95177427 -0.09328468
[89,] 4.03468509 -0.95177427
[90,] -3.73641370 4.03468509
[91,] 6.20451790 -3.73641370
[92,] -5.27528285 6.20451790
[93,] -1.58598305 -5.27528285
[94,] -0.44088153 -1.58598305
[95,] -0.89244913 -0.44088153
[96,] -3.57680145 -0.89244913
[97,] -0.95456748 -3.57680145
[98,] 3.79153030 -0.95456748
[99,] -0.28208949 3.79153030
[100,] -0.02557178 -0.28208949
[101,] 0.84879043 -0.02557178
[102,] -4.58408322 0.84879043
[103,] 5.20897918 -4.58408322
[104,] -4.07588184 5.20897918
[105,] -1.31568135 -4.07588184
[106,] 2.14118340 -1.31568135
[107,] 0.37340237 2.14118340
[108,] 4.45744640 0.37340237
[109,] 1.50774117 4.45744640
[110,] -0.24258843 1.50774117
[111,] 7.43885278 -0.24258843
[112,] -0.78151955 7.43885278
[113,] -3.40677578 -0.78151955
[114,] 6.35000107 -3.40677578
[115,] 6.30521625 6.35000107
[116,] 1.68544555 6.30521625
[117,] -0.20825131 1.68544555
[118,] 4.32430847 -0.20825131
[119,] -6.06691070 4.32430847
[120,] 3.32988556 -6.06691070
[121,] 2.11688466 3.32988556
[122,] -4.17019818 2.11688466
[123,] -0.26408989 -4.17019818
[124,] -3.54041231 -0.26408989
[125,] 0.03445795 -3.54041231
[126,] 2.66776466 0.03445795
[127,] -1.03590796 2.66776466
[128,] 0.39165871 -1.03590796
[129,] 2.04232760 0.39165871
[130,] 0.23398250 2.04232760
[131,] 0.34978269 0.23398250
[132,] 3.32196310 0.34978269
[133,] -4.21361234 3.32196310
[134,] -8.23695958 -4.21361234
[135,] 1.58713323 -8.23695958
[136,] 3.39624680 1.58713323
[137,] 0.21360793 3.39624680
[138,] 3.78097804 0.21360793
[139,] 0.91913973 3.78097804
[140,] 5.65772629 0.91913973
[141,] 2.05828948 5.65772629
[142,] -9.16919809 2.05828948
[143,] -4.72057900 -9.16919809
[144,] -6.80408593 -4.72057900
[145,] 1.82836728 -6.80408593
[146,] -1.80552047 1.82836728
[147,] -1.52314267 -1.80552047
[148,] 4.28388228 -1.52314267
[149,] -0.99573661 4.28388228
[150,] 4.64604713 -0.99573661
[151,] 4.61104394 4.64604713
[152,] -1.69153863 4.61104394
[153,] -1.58857218 -1.69153863
[154,] 4.56195003 -1.58857218
[155,] 2.66776466 4.56195003
[156,] -2.31333598 2.66776466
[157,] 5.50303614 -2.31333598
[158,] -3.93893834 5.50303614
[159,] -5.32273147 -3.93893834
[160,] -2.83010805 -5.32273147
[161,] 4.79781457 -2.83010805
[162,] 2.14747643 4.79781457
[163,] 1.55019620 2.14747643
[164,] -4.94529946 1.55019620
[165,] 3.93883647 -4.94529946
[166,] 6.35658084 3.93883647
[167,] -2.92958926 6.35658084
[168,] 1.72609231 -2.92958926
[169,] -1.24969821 1.72609231
[170,] -5.54233725 -1.24969821
[171,] -6.59750553 -5.54233725
[172,] 2.42897109 -6.59750553
[173,] -1.94130909 2.42897109
[174,] -1.00692929 -1.94130909
[175,] -1.46613941 -1.00692929
[176,] 1.57654480 -1.46613941
[177,] 0.66442180 1.57654480
[178,] -2.86070087 0.66442180
[179,] 2.94219622 -2.86070087
[180,] -3.38723354 2.94219622
[181,] 0.52792180 -3.38723354
[182,] 2.47616516 0.52792180
[183,] -0.72097335 2.47616516
[184,] -4.59441491 -0.72097335
[185,] -1.71936026 -4.59441491
[186,] 4.54273455 -1.71936026
[187,] 2.75269574 4.54273455
[188,] 3.74313249 2.75269574
[189,] 0.96278553 3.74313249
[190,] 2.11573266 0.96278553
[191,] -0.78851877 2.11573266
[192,] 3.01174060 -0.78851877
[193,] -3.16477299 3.01174060
[194,] 3.04635576 -3.16477299
[195,] 0.14959465 3.04635576
[196,] -2.02535312 0.14959465
[197,] 1.42807134 -2.02535312
[198,] -1.52623110 1.42807134
[199,] -1.06818855 -1.52623110
[200,] 0.57897942 -1.06818855
[201,] 1.86881887 0.57897942
[202,] 2.20823842 1.86881887
[203,] 1.88727038 2.20823842
[204,] -0.65403339 1.88727038
[205,] -0.31664049 -0.65403339
[206,] -0.89206299 -0.31664049
[207,] 7.79980463 -0.89206299
[208,] 6.36730059 7.79980463
[209,] 0.50727054 6.36730059
[210,] 0.03256069 0.50727054
[211,] -5.41450727 0.03256069
[212,] 1.05293295 -5.41450727
[213,] -2.21296715 1.05293295
[214,] 1.01993320 -2.21296715
[215,] 3.86435759 1.01993320
[216,] -0.75960426 3.86435759
[217,] 1.00988144 -0.75960426
[218,] -1.90788471 1.00988144
[219,] 0.62493181 -1.90788471
[220,] 4.42050706 0.62493181
[221,] 0.32689978 4.42050706
[222,] 1.27293566 0.32689978
[223,] 0.19625162 1.27293566
[224,] 2.11965684 0.19625162
[225,] -3.40795547 2.11965684
[226,] 5.77366010 -3.40795547
[227,] 1.73740089 5.77366010
[228,] -5.17418174 1.73740089
[229,] -1.67897283 -5.17418174
[230,] -2.71570389 -1.67897283
[231,] -2.62912164 -2.71570389
[232,] -7.15857840 -2.62912164
[233,] -1.23440663 -7.15857840
[234,] -3.75773211 -1.23440663
[235,] 2.45817664 -3.75773211
[236,] 1.40016249 2.45817664
[237,] -1.24840710 1.40016249
[238,] -0.22973721 -1.24840710
[239,] 4.50605208 -0.22973721
[240,] 2.76441557 4.50605208
[241,] -2.07145905 2.76441557
[242,] -5.61887006 -2.07145905
[243,] -7.56166083 -5.61887006
[244,] 2.96037629 -7.56166083
[245,] 0.14651702 2.96037629
[246,] -0.19097949 0.14651702
[247,] -1.73496359 -0.19097949
[248,] -1.31461384 -1.73496359
[249,] -0.35405293 -1.31461384
[250,] 2.67142361 -0.35405293
[251,] -0.51564225 2.67142361
[252,] -1.17491401 -0.51564225
[253,] -2.22052036 -1.17491401
[254,] -0.93750381 -2.22052036
[255,] 5.16258249 -0.93750381
[256,] 0.56789238 5.16258249
[257,] 1.17718478 0.56789238
[258,] -1.57224506 1.17718478
[259,] -0.86674096 -1.57224506
[260,] -0.16573663 -0.86674096
[261,] -1.48768946 -0.16573663
[262,] -1.95225829 -1.48768946
[263,] -1.87799780 -1.95225829
[264,] 1.68709083 -1.87799780
[265,] -4.76255604 1.68709083
[266,] -2.23090477 -4.76255604
[267,] 5.00289301 -2.23090477
[268,] 1.09423120 5.00289301
[269,] -0.94176991 1.09423120
[270,] 7.14561957 -0.94176991
[271,] -2.90187231 7.14561957
[272,] -1.20077410 -2.90187231
[273,] 5.76551478 -1.20077410
[274,] 4.04428052 5.76551478
[275,] -8.08312944 4.04428052
[276,] 0.40041923 -8.08312944
[277,] -1.92212761 0.40041923
[278,] 0.41539210 -1.92212761
[279,] 1.90682342 0.41539210
[280,] -4.52549883 1.90682342
[281,] 0.92115558 -4.52549883
[282,] -0.59416008 0.92115558
[283,] -1.99431841 -0.59416008
[284,] 4.75039105 -1.99431841
[285,] 2.69074488 4.75039105
[286,] 3.83230618 2.69074488
[287,] 0.76649081 3.83230618
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -4.46238299 0.91711635
2 1.62758263 -4.46238299
3 1.32325652 1.62758263
4 2.95102693 1.32325652
5 -5.02933078 2.95102693
6 -6.17619759 -5.02933078
7 1.63708391 -6.17619759
8 0.08207433 1.63708391
9 2.70184503 0.08207433
10 -4.24886820 2.70184503
11 -1.13278574 -4.24886820
12 -0.63105890 -1.13278574
13 1.47594650 -0.63105890
14 2.79969653 1.47594650
15 0.42525058 2.79969653
16 -2.25098411 0.42525058
17 1.20785228 -2.25098411
18 1.77660834 1.20785228
19 -1.38271288 1.77660834
20 0.46266301 -1.38271288
21 -2.07624449 0.46266301
22 0.74063044 -2.07624449
23 3.27389480 0.74063044
24 -4.04495733 3.27389480
25 -5.99663405 -4.04495733
26 0.64158586 -5.99663405
27 3.09772857 0.64158586
28 -0.87419279 3.09772857
29 -0.25310002 -0.87419279
30 -1.75296769 -0.25310002
31 2.53762808 -1.75296769
32 -4.15055588 2.53762808
33 2.66516256 -4.15055588
34 -3.32693819 2.66516256
35 1.74828362 -3.32693819
36 0.74802688 1.74828362
37 -1.21739225 0.74802688
38 -0.80359973 -1.21739225
39 -0.99027525 -0.80359973
40 -2.84989405 -0.99027525
41 -0.96580111 -2.84989405
42 -4.37196545 -0.96580111
43 -0.68290942 -4.37196545
44 -7.67287104 -0.68290942
45 0.00539029 -7.67287104
46 3.07761306 0.00539029
47 0.37807972 3.07761306
48 2.88871572 0.37807972
49 -3.20035206 2.88871572
50 4.45094318 -3.20035206
51 -2.23930495 4.45094318
52 0.84004644 -2.23930495
53 3.48948483 0.84004644
54 0.96038709 3.48948483
55 0.12046148 0.96038709
56 -2.60722546 0.12046148
57 1.96838583 -2.60722546
58 1.14352877 1.96838583
59 -0.72164393 1.14352877
60 -2.91531018 -0.72164393
61 -1.53855314 -2.91531018
62 0.37547761 -1.53855314
63 2.43763431 0.37547761
64 -3.33483744 2.43763431
65 2.49860255 -3.33483744
66 -0.88401278 2.49860255
67 -5.13926028 -0.88401278
68 4.03834145 -5.13926028
69 2.90505133 4.03834145
70 1.83957323 2.90505133
71 5.49930482 1.83957323
72 4.16061754 5.49930482
73 5.01857235 4.16061754
74 -6.69597453 5.01857235
75 -6.65015112 -6.69597453
76 1.26172713 -6.65015112
77 -3.31333598 1.26172713
78 0.63686553 -3.31333598
79 -8.07790849 0.63686553
80 2.82634063 -8.07790849
81 2.20451790 2.82634063
82 -1.81630219 2.20451790
83 0.83872437 -1.81630219
84 -2.73868411 0.83872437
85 4.11083435 -2.73868411
86 0.09497196 4.11083435
87 -0.09328468 0.09497196
88 -0.95177427 -0.09328468
89 4.03468509 -0.95177427
90 -3.73641370 4.03468509
91 6.20451790 -3.73641370
92 -5.27528285 6.20451790
93 -1.58598305 -5.27528285
94 -0.44088153 -1.58598305
95 -0.89244913 -0.44088153
96 -3.57680145 -0.89244913
97 -0.95456748 -3.57680145
98 3.79153030 -0.95456748
99 -0.28208949 3.79153030
100 -0.02557178 -0.28208949
101 0.84879043 -0.02557178
102 -4.58408322 0.84879043
103 5.20897918 -4.58408322
104 -4.07588184 5.20897918
105 -1.31568135 -4.07588184
106 2.14118340 -1.31568135
107 0.37340237 2.14118340
108 4.45744640 0.37340237
109 1.50774117 4.45744640
110 -0.24258843 1.50774117
111 7.43885278 -0.24258843
112 -0.78151955 7.43885278
113 -3.40677578 -0.78151955
114 6.35000107 -3.40677578
115 6.30521625 6.35000107
116 1.68544555 6.30521625
117 -0.20825131 1.68544555
118 4.32430847 -0.20825131
119 -6.06691070 4.32430847
120 3.32988556 -6.06691070
121 2.11688466 3.32988556
122 -4.17019818 2.11688466
123 -0.26408989 -4.17019818
124 -3.54041231 -0.26408989
125 0.03445795 -3.54041231
126 2.66776466 0.03445795
127 -1.03590796 2.66776466
128 0.39165871 -1.03590796
129 2.04232760 0.39165871
130 0.23398250 2.04232760
131 0.34978269 0.23398250
132 3.32196310 0.34978269
133 -4.21361234 3.32196310
134 -8.23695958 -4.21361234
135 1.58713323 -8.23695958
136 3.39624680 1.58713323
137 0.21360793 3.39624680
138 3.78097804 0.21360793
139 0.91913973 3.78097804
140 5.65772629 0.91913973
141 2.05828948 5.65772629
142 -9.16919809 2.05828948
143 -4.72057900 -9.16919809
144 -6.80408593 -4.72057900
145 1.82836728 -6.80408593
146 -1.80552047 1.82836728
147 -1.52314267 -1.80552047
148 4.28388228 -1.52314267
149 -0.99573661 4.28388228
150 4.64604713 -0.99573661
151 4.61104394 4.64604713
152 -1.69153863 4.61104394
153 -1.58857218 -1.69153863
154 4.56195003 -1.58857218
155 2.66776466 4.56195003
156 -2.31333598 2.66776466
157 5.50303614 -2.31333598
158 -3.93893834 5.50303614
159 -5.32273147 -3.93893834
160 -2.83010805 -5.32273147
161 4.79781457 -2.83010805
162 2.14747643 4.79781457
163 1.55019620 2.14747643
164 -4.94529946 1.55019620
165 3.93883647 -4.94529946
166 6.35658084 3.93883647
167 -2.92958926 6.35658084
168 1.72609231 -2.92958926
169 -1.24969821 1.72609231
170 -5.54233725 -1.24969821
171 -6.59750553 -5.54233725
172 2.42897109 -6.59750553
173 -1.94130909 2.42897109
174 -1.00692929 -1.94130909
175 -1.46613941 -1.00692929
176 1.57654480 -1.46613941
177 0.66442180 1.57654480
178 -2.86070087 0.66442180
179 2.94219622 -2.86070087
180 -3.38723354 2.94219622
181 0.52792180 -3.38723354
182 2.47616516 0.52792180
183 -0.72097335 2.47616516
184 -4.59441491 -0.72097335
185 -1.71936026 -4.59441491
186 4.54273455 -1.71936026
187 2.75269574 4.54273455
188 3.74313249 2.75269574
189 0.96278553 3.74313249
190 2.11573266 0.96278553
191 -0.78851877 2.11573266
192 3.01174060 -0.78851877
193 -3.16477299 3.01174060
194 3.04635576 -3.16477299
195 0.14959465 3.04635576
196 -2.02535312 0.14959465
197 1.42807134 -2.02535312
198 -1.52623110 1.42807134
199 -1.06818855 -1.52623110
200 0.57897942 -1.06818855
201 1.86881887 0.57897942
202 2.20823842 1.86881887
203 1.88727038 2.20823842
204 -0.65403339 1.88727038
205 -0.31664049 -0.65403339
206 -0.89206299 -0.31664049
207 7.79980463 -0.89206299
208 6.36730059 7.79980463
209 0.50727054 6.36730059
210 0.03256069 0.50727054
211 -5.41450727 0.03256069
212 1.05293295 -5.41450727
213 -2.21296715 1.05293295
214 1.01993320 -2.21296715
215 3.86435759 1.01993320
216 -0.75960426 3.86435759
217 1.00988144 -0.75960426
218 -1.90788471 1.00988144
219 0.62493181 -1.90788471
220 4.42050706 0.62493181
221 0.32689978 4.42050706
222 1.27293566 0.32689978
223 0.19625162 1.27293566
224 2.11965684 0.19625162
225 -3.40795547 2.11965684
226 5.77366010 -3.40795547
227 1.73740089 5.77366010
228 -5.17418174 1.73740089
229 -1.67897283 -5.17418174
230 -2.71570389 -1.67897283
231 -2.62912164 -2.71570389
232 -7.15857840 -2.62912164
233 -1.23440663 -7.15857840
234 -3.75773211 -1.23440663
235 2.45817664 -3.75773211
236 1.40016249 2.45817664
237 -1.24840710 1.40016249
238 -0.22973721 -1.24840710
239 4.50605208 -0.22973721
240 2.76441557 4.50605208
241 -2.07145905 2.76441557
242 -5.61887006 -2.07145905
243 -7.56166083 -5.61887006
244 2.96037629 -7.56166083
245 0.14651702 2.96037629
246 -0.19097949 0.14651702
247 -1.73496359 -0.19097949
248 -1.31461384 -1.73496359
249 -0.35405293 -1.31461384
250 2.67142361 -0.35405293
251 -0.51564225 2.67142361
252 -1.17491401 -0.51564225
253 -2.22052036 -1.17491401
254 -0.93750381 -2.22052036
255 5.16258249 -0.93750381
256 0.56789238 5.16258249
257 1.17718478 0.56789238
258 -1.57224506 1.17718478
259 -0.86674096 -1.57224506
260 -0.16573663 -0.86674096
261 -1.48768946 -0.16573663
262 -1.95225829 -1.48768946
263 -1.87799780 -1.95225829
264 1.68709083 -1.87799780
265 -4.76255604 1.68709083
266 -2.23090477 -4.76255604
267 5.00289301 -2.23090477
268 1.09423120 5.00289301
269 -0.94176991 1.09423120
270 7.14561957 -0.94176991
271 -2.90187231 7.14561957
272 -1.20077410 -2.90187231
273 5.76551478 -1.20077410
274 4.04428052 5.76551478
275 -8.08312944 4.04428052
276 0.40041923 -8.08312944
277 -1.92212761 0.40041923
278 0.41539210 -1.92212761
279 1.90682342 0.41539210
280 -4.52549883 1.90682342
281 0.92115558 -4.52549883
282 -0.59416008 0.92115558
283 -1.99431841 -0.59416008
284 4.75039105 -1.99431841
285 2.69074488 4.75039105
286 3.83230618 2.69074488
287 0.76649081 3.83230618
> 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/7yqz91292940096.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8yqz91292940096.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9qhgb1292940096.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10qhgb1292940096.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/11cixh1292940096.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/12fiw51292940096.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/134jth1292940096.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/14fask1292940096.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/15it9q1292940096.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/16wl6y1292940096.tab")
+ }
>
> try(system("convert tmp/1r6z51292940095.ps tmp/1r6z51292940095.png",intern=TRUE))
character(0)
> try(system("convert tmp/2cpjl1292940096.ps tmp/2cpjl1292940096.png",intern=TRUE))
character(0)
> try(system("convert tmp/3cpjl1292940096.ps tmp/3cpjl1292940096.png",intern=TRUE))
character(0)
> try(system("convert tmp/4cpjl1292940096.ps tmp/4cpjl1292940096.png",intern=TRUE))
character(0)
> try(system("convert tmp/5nyin1292940096.ps tmp/5nyin1292940096.png",intern=TRUE))
character(0)
> try(system("convert tmp/6nyin1292940096.ps tmp/6nyin1292940096.png",intern=TRUE))
character(0)
> try(system("convert tmp/7yqz91292940096.ps tmp/7yqz91292940096.png",intern=TRUE))
character(0)
> try(system("convert tmp/8yqz91292940096.ps tmp/8yqz91292940096.png",intern=TRUE))
character(0)
> try(system("convert tmp/9qhgb1292940096.ps tmp/9qhgb1292940096.png",intern=TRUE))
character(0)
> try(system("convert tmp/10qhgb1292940096.ps tmp/10qhgb1292940096.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.288 3.041 11.852