R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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(38
+ ,12
+ ,14
+ ,12
+ ,53
+ ,32
+ ,9
+ ,41
+ ,32
+ ,11
+ ,18
+ ,11
+ ,83
+ ,51
+ ,9
+ ,39
+ ,35
+ ,15
+ ,11
+ ,14
+ ,66
+ ,42
+ ,9
+ ,30
+ ,33
+ ,6
+ ,12
+ ,12
+ ,67
+ ,41
+ ,9
+ ,31
+ ,37
+ ,13
+ ,16
+ ,21
+ ,76
+ ,46
+ ,9
+ ,34
+ ,29
+ ,10
+ ,18
+ ,12
+ ,78
+ ,47
+ ,9
+ ,35
+ ,31
+ ,12
+ ,14
+ ,22
+ ,53
+ ,37
+ ,9
+ ,39
+ ,36
+ ,14
+ ,14
+ ,11
+ ,80
+ ,49
+ ,9
+ ,34
+ ,35
+ ,12
+ ,15
+ ,10
+ ,74
+ ,45
+ ,9
+ ,36
+ ,38
+ ,9
+ ,15
+ ,13
+ ,76
+ ,47
+ ,9
+ ,37
+ ,31
+ ,10
+ ,17
+ ,10
+ ,79
+ ,49
+ ,9
+ ,38
+ ,34
+ ,12
+ ,19
+ ,8
+ ,54
+ ,33
+ ,9
+ ,36
+ ,35
+ ,12
+ ,10
+ ,15
+ ,67
+ ,42
+ ,9
+ ,38
+ ,38
+ ,11
+ ,16
+ ,14
+ ,54
+ ,33
+ ,9
+ ,39
+ ,37
+ ,15
+ ,18
+ ,10
+ ,87
+ ,53
+ ,9
+ ,33
+ ,33
+ ,12
+ ,14
+ ,14
+ ,58
+ ,36
+ ,9
+ ,32
+ ,32
+ ,10
+ ,14
+ ,14
+ ,75
+ ,45
+ ,9
+ ,36
+ ,38
+ ,12
+ ,17
+ ,11
+ ,88
+ ,54
+ ,9
+ ,38
+ ,38
+ ,11
+ ,14
+ ,10
+ ,64
+ ,41
+ ,9
+ ,39
+ ,32
+ ,12
+ ,16
+ ,13
+ ,57
+ ,36
+ ,9
+ ,32
+ ,33
+ ,11
+ ,18
+ ,9.5
+ ,66
+ ,41
+ ,9
+ ,32
+ ,31
+ ,12
+ ,11
+ ,14
+ ,68
+ ,44
+ ,9
+ ,31
+ ,38
+ ,13
+ ,14
+ ,12
+ ,54
+ ,33
+ ,9
+ ,39
+ ,39
+ ,11
+ ,12
+ ,14
+ ,56
+ ,37
+ ,9
+ ,37
+ ,32
+ ,12
+ ,17
+ ,11
+ ,86
+ ,52
+ ,9
+ ,39
+ ,32
+ ,13
+ ,9
+ ,9
+ ,80
+ ,47
+ ,9
+ ,41
+ ,35
+ ,10
+ ,16
+ ,11
+ ,76
+ ,43
+ ,9
+ ,36
+ ,37
+ ,14
+ ,14
+ ,15
+ ,69
+ ,44
+ ,9
+ ,33
+ ,33
+ ,12
+ ,15
+ ,14
+ ,78
+ ,45
+ ,9
+ ,33
+ ,33
+ ,10
+ ,11
+ ,13
+ ,67
+ ,44
+ ,9
+ ,34
+ ,31
+ ,12
+ ,16
+ ,9
+ ,80
+ ,49
+ ,9
+ ,31
+ ,32
+ ,8
+ ,13
+ ,15
+ ,54
+ ,33
+ ,9
+ ,27
+ ,31
+ ,10
+ ,17
+ ,10
+ ,71
+ ,43
+ ,9
+ ,37
+ ,37
+ ,12
+ ,15
+ ,11
+ ,84
+ ,54
+ ,9
+ ,34
+ ,30
+ ,12
+ ,14
+ ,13
+ ,74
+ ,42
+ ,9
+ ,34
+ ,33
+ ,7
+ ,16
+ ,8
+ ,71
+ ,44
+ ,9
+ ,32
+ ,31
+ ,9
+ ,9
+ ,20
+ ,63
+ ,37
+ ,9
+ ,29
+ ,33
+ ,12
+ ,15
+ ,12
+ ,71
+ ,43
+ ,9
+ ,36
+ ,31
+ ,10
+ ,17
+ ,10
+ ,76
+ ,46
+ ,9
+ ,29
+ ,33
+ ,10
+ ,13
+ ,10
+ ,69
+ ,42
+ ,9
+ ,35
+ ,32
+ ,10
+ ,15
+ ,9
+ ,74
+ ,45
+ ,9
+ ,37
+ ,33
+ ,12
+ ,16
+ ,14
+ ,75
+ ,44
+ ,9
+ ,34
+ ,32
+ ,15
+ ,16
+ ,8
+ ,54
+ ,33
+ ,9
+ ,38
+ ,33
+ ,10
+ ,12
+ ,14
+ ,52
+ ,31
+ ,9
+ ,35
+ ,28
+ ,10
+ ,15
+ ,11
+ ,69
+ ,42
+ ,9
+ ,38
+ ,35
+ ,12
+ ,11
+ ,13
+ ,68
+ ,40
+ ,9
+ ,37
+ ,39
+ ,13
+ ,15
+ ,9
+ ,65
+ ,43
+ ,9
+ ,38
+ ,34
+ ,11
+ ,15
+ ,11
+ ,75
+ ,46
+ ,9
+ ,33
+ ,38
+ ,11
+ ,17
+ ,15
+ ,74
+ ,42
+ ,9
+ ,36
+ ,32
+ ,12
+ ,13
+ ,11
+ ,75
+ ,45
+ ,9
+ ,38
+ ,38
+ ,14
+ ,16
+ ,10
+ ,72
+ ,44
+ ,9
+ ,32
+ ,30
+ ,10
+ ,14
+ ,14
+ ,67
+ ,40
+ ,9
+ ,32
+ ,33
+ ,12
+ ,11
+ ,18
+ ,63
+ ,37
+ ,9
+ ,32
+ ,38
+ ,13
+ ,12
+ ,14
+ ,62
+ ,46
+ ,9
+ ,34
+ ,32
+ ,5
+ ,12
+ ,11
+ ,63
+ ,36
+ ,9
+ ,32
+ ,35
+ ,6
+ ,15
+ ,14.5
+ ,76
+ ,47
+ ,9
+ ,37
+ ,34
+ ,12
+ ,16
+ ,13
+ ,74
+ ,45
+ ,9
+ ,39
+ ,34
+ ,12
+ ,15
+ ,9
+ ,67
+ ,42
+ ,9
+ ,29
+ ,36
+ ,11
+ ,12
+ ,10
+ ,73
+ ,43
+ ,9
+ ,37
+ ,34
+ ,10
+ ,12
+ ,15
+ ,70
+ ,43
+ ,9
+ ,35
+ ,28
+ ,7
+ ,8
+ ,20
+ ,53
+ ,32
+ ,9
+ ,30
+ ,34
+ ,12
+ ,13
+ ,12
+ ,77
+ ,45
+ ,9
+ ,38
+ ,35
+ ,14
+ ,11
+ ,12
+ ,80
+ ,48
+ ,9
+ ,34
+ ,35
+ ,11
+ ,14
+ ,14
+ ,52
+ ,31
+ ,9
+ ,31
+ ,31
+ ,12
+ ,15
+ ,13
+ ,54
+ ,33
+ ,9
+ ,34
+ ,37
+ ,13
+ ,10
+ ,11
+ ,80
+ ,49
+ ,10
+ ,35
+ ,35
+ ,14
+ ,11
+ ,17
+ ,66
+ ,42
+ ,10
+ ,36
+ ,27
+ ,11
+ ,12
+ ,12
+ ,73
+ ,41
+ ,10
+ ,30
+ ,40
+ ,12
+ ,15
+ ,13
+ ,63
+ ,38
+ ,10
+ ,39
+ ,37
+ ,12
+ ,15
+ ,14
+ ,69
+ ,42
+ ,10
+ ,35
+ ,36
+ ,8
+ ,14
+ ,13
+ ,67
+ ,44
+ ,10
+ ,38
+ ,38
+ ,11
+ ,16
+ ,15
+ ,54
+ ,33
+ ,10
+ ,31
+ ,39
+ ,14
+ ,15
+ ,13
+ ,81
+ ,48
+ ,10
+ ,34
+ ,41
+ ,14
+ ,15
+ ,10
+ ,69
+ ,40
+ ,10
+ ,38
+ ,27
+ ,12
+ ,13
+ ,11
+ ,84
+ ,50
+ ,10
+ ,34
+ ,30
+ ,9
+ ,12
+ ,19
+ ,80
+ ,49
+ ,10
+ ,39
+ ,37
+ ,13
+ ,17
+ ,13
+ ,70
+ ,43
+ ,10
+ ,37
+ ,31
+ ,11
+ ,13
+ ,17
+ ,69
+ ,44
+ ,10
+ ,34
+ ,31
+ ,12
+ ,15
+ ,13
+ ,77
+ ,47
+ ,10
+ ,28
+ ,27
+ ,12
+ ,13
+ ,9
+ ,54
+ ,33
+ ,10
+ ,37
+ ,36
+ ,12
+ ,15
+ ,11
+ ,79
+ ,46
+ ,10
+ ,33
+ ,37
+ ,12
+ ,15
+ ,9
+ ,71
+ ,45
+ ,10
+ ,35
+ ,33
+ ,12
+ ,16
+ ,12
+ ,73
+ ,43
+ ,10
+ ,37
+ ,34
+ ,11
+ ,15
+ ,12
+ ,72
+ ,44
+ ,10
+ ,32
+ ,31
+ ,10
+ ,14
+ ,13
+ ,77
+ ,47
+ ,10
+ ,33
+ ,39
+ ,9
+ ,15
+ ,13
+ ,75
+ ,45
+ ,10
+ ,38
+ ,34
+ ,12
+ ,14
+ ,12
+ ,69
+ ,42
+ ,10
+ ,33
+ ,32
+ ,12
+ ,13
+ ,15
+ ,54
+ ,33
+ ,10
+ ,29
+ ,33
+ ,12
+ ,7
+ ,22
+ ,70
+ ,43
+ ,10
+ ,33
+ ,36
+ ,9
+ ,17
+ ,13
+ ,73
+ ,46
+ ,10
+ ,31
+ ,32
+ ,15
+ ,13
+ ,15
+ ,54
+ ,33
+ ,10
+ ,36
+ ,41
+ ,12
+ ,15
+ ,13
+ ,77
+ ,46
+ ,10
+ ,35
+ ,28
+ ,12
+ ,14
+ ,15
+ ,82
+ ,48
+ ,10
+ ,32
+ ,30
+ ,12
+ ,13
+ ,12.5
+ ,80
+ ,47
+ ,10
+ ,29
+ ,36
+ ,10
+ ,16
+ ,11
+ ,80
+ ,47
+ ,10
+ ,39
+ ,35
+ ,13
+ ,12
+ ,16
+ ,69
+ ,43
+ ,10
+ ,37
+ ,31
+ ,9
+ ,14
+ ,11
+ ,78
+ ,46
+ ,10
+ ,35
+ ,34
+ ,12
+ ,17
+ ,11
+ ,81
+ ,48
+ ,10
+ ,37
+ ,36
+ ,10
+ ,15
+ ,10
+ ,76
+ ,46
+ ,10
+ ,32
+ ,36
+ ,14
+ ,17
+ ,10
+ ,76
+ ,45
+ ,10
+ ,38
+ ,35
+ ,11
+ ,12
+ ,16
+ ,73
+ ,45
+ ,10
+ ,37
+ ,37
+ ,15
+ ,16
+ ,12
+ ,85
+ ,52
+ ,10
+ ,36
+ ,28
+ ,11
+ ,11
+ ,11
+ ,66
+ ,42
+ ,10
+ ,32
+ ,39
+ ,11
+ ,15
+ ,16
+ ,79
+ ,47
+ ,10
+ ,33
+ ,32
+ ,12
+ ,9
+ ,19
+ ,68
+ ,41
+ ,10
+ ,40
+ ,35
+ ,12
+ ,16
+ ,11
+ ,76
+ ,47
+ ,10
+ ,38
+ ,39
+ ,12
+ ,15
+ ,16
+ ,71
+ ,43
+ ,10
+ ,41
+ ,35
+ ,11
+ ,10
+ ,15
+ ,54
+ ,33
+ ,10
+ ,36
+ ,42
+ ,7
+ ,10
+ ,24
+ ,46
+ ,30
+ ,10
+ ,43
+ ,34
+ ,12
+ ,15
+ ,14
+ ,85
+ ,52
+ ,10
+ ,30
+ ,33
+ ,14
+ ,11
+ ,15
+ ,74
+ ,44
+ ,10
+ ,31
+ ,41
+ ,11
+ ,13
+ ,11
+ ,88
+ ,55
+ ,10
+ ,32
+ ,33
+ ,11
+ ,14
+ ,15
+ ,38
+ ,11
+ ,10
+ ,32
+ ,34
+ ,10
+ ,18
+ ,12
+ ,76
+ ,47
+ ,10
+ ,37
+ ,32
+ ,13
+ ,16
+ ,10
+ ,86
+ ,53
+ ,10
+ ,37
+ ,40
+ ,13
+ ,14
+ ,14
+ ,54
+ ,33
+ ,10
+ ,33
+ ,40
+ ,8
+ ,14
+ ,13
+ ,67
+ ,44
+ ,10
+ ,34
+ ,35
+ ,11
+ ,14
+ ,9
+ ,69
+ ,42
+ ,10
+ ,33
+ ,36
+ ,12
+ ,14
+ ,15
+ ,90
+ ,55
+ ,10
+ ,38
+ ,37
+ ,11
+ ,12
+ ,15
+ ,54
+ ,33
+ ,10
+ ,33
+ ,27
+ ,13
+ ,14
+ ,14
+ ,76
+ ,46
+ ,10
+ ,31
+ ,39
+ ,12
+ ,15
+ ,11
+ ,89
+ ,54
+ ,10
+ ,38
+ ,38
+ ,14
+ ,15
+ ,8
+ ,76
+ ,47
+ ,10
+ ,37
+ ,31
+ ,13
+ ,15
+ ,11
+ ,73
+ ,45
+ ,10
+ ,36
+ ,33
+ ,15
+ ,13
+ ,11
+ ,79
+ ,47
+ ,10
+ ,31
+ ,32
+ ,10
+ ,17
+ ,8
+ ,90
+ ,55
+ ,10
+ ,39
+ ,39
+ ,11
+ ,17
+ ,10
+ ,74
+ ,44
+ ,10
+ ,44
+ ,36
+ ,9
+ ,19
+ ,11
+ ,81
+ ,53
+ ,10
+ ,33
+ ,33
+ ,11
+ ,15
+ ,13
+ ,72
+ ,44
+ ,10
+ ,35
+ ,33
+ ,10
+ ,13
+ ,11
+ ,71
+ ,42
+ ,10
+ ,32
+ ,32
+ ,11
+ ,9
+ ,20
+ ,66
+ ,40
+ ,10
+ ,28
+ ,37
+ ,8
+ ,15
+ ,10
+ ,77
+ ,46
+ ,10
+ ,40
+ ,30
+ ,11
+ ,15
+ ,15
+ ,65
+ ,40
+ ,10
+ ,27
+ ,38
+ ,12
+ ,15
+ ,12
+ ,74
+ ,46
+ ,10
+ ,37
+ ,29
+ ,12
+ ,16
+ ,14
+ ,85
+ ,53
+ ,10
+ ,32
+ ,22
+ ,9
+ ,11
+ ,23
+ ,54
+ ,33
+ ,10
+ ,28
+ ,35
+ ,11
+ ,14
+ ,14
+ ,63
+ ,42
+ ,10
+ ,34
+ ,35
+ ,10
+ ,11
+ ,16
+ ,54
+ ,35
+ ,10
+ ,30
+ ,34
+ ,8
+ ,15
+ ,11
+ ,64
+ ,40
+ ,10
+ ,35
+ ,35
+ ,9
+ ,13
+ ,12
+ ,69
+ ,41
+ ,10
+ ,31
+ ,34
+ ,8
+ ,15
+ ,10
+ ,54
+ ,33
+ ,10
+ ,32
+ ,37
+ ,9
+ ,16
+ ,14
+ ,84
+ ,51
+ ,10
+ ,30
+ ,35
+ ,15
+ ,14
+ ,12
+ ,86
+ ,53
+ ,10
+ ,30
+ ,23
+ ,11
+ ,15
+ ,12
+ ,77
+ ,46
+ ,10
+ ,31
+ ,31
+ ,8
+ ,16
+ ,11
+ ,89
+ ,55
+ ,10
+ ,40
+ ,27
+ ,13
+ ,16
+ ,12
+ ,76
+ ,47
+ ,10
+ ,32
+ ,36
+ ,12
+ ,11
+ ,13
+ ,60
+ ,38
+ ,10
+ ,36
+ ,31
+ ,12
+ ,12
+ ,11
+ ,75
+ ,46
+ ,10
+ ,32
+ ,32
+ ,9
+ ,9
+ ,19
+ ,73
+ ,46
+ ,10
+ ,35
+ ,39
+ ,7
+ ,16
+ ,12
+ ,85
+ ,53
+ ,10
+ ,38
+ ,37
+ ,13
+ ,13
+ ,17
+ ,79
+ ,47
+ ,10
+ ,42
+ ,38
+ ,9
+ ,16
+ ,9
+ ,71
+ ,41
+ ,10
+ ,34
+ ,39
+ ,6
+ ,12
+ ,12
+ ,72
+ ,44
+ ,10
+ ,35
+ ,34
+ ,8
+ ,9
+ ,19
+ ,69
+ ,43
+ ,9
+ ,38
+ ,31
+ ,8
+ ,13
+ ,18
+ ,78
+ ,51
+ ,10
+ ,33
+ ,32
+ ,15
+ ,13
+ ,15
+ ,54
+ ,33
+ ,10
+ ,36
+ ,37
+ ,6
+ ,14
+ ,14
+ ,69
+ ,43
+ ,10
+ ,32
+ ,36
+ ,9
+ ,19
+ ,11
+ ,81
+ ,53
+ ,10
+ ,33
+ ,32
+ ,11
+ ,13
+ ,9
+ ,84
+ ,51
+ ,10
+ ,34
+ ,38
+ ,8
+ ,12
+ ,18
+ ,84
+ ,50
+ ,10
+ ,32
+ ,36
+ ,8
+ ,13
+ ,16
+ ,69
+ ,46
+ ,10
+ ,34
+ ,26
+ ,10
+ ,10
+ ,24
+ ,66
+ ,43
+ ,11
+ ,27
+ ,26
+ ,8
+ ,14
+ ,14
+ ,81
+ ,47
+ ,11
+ ,31
+ ,33
+ ,14
+ ,16
+ ,20
+ ,82
+ ,50
+ ,11
+ ,38
+ ,39
+ ,10
+ ,10
+ ,18
+ ,72
+ ,43
+ ,11
+ ,34
+ ,30
+ ,8
+ ,11
+ ,23
+ ,54
+ ,33
+ ,11
+ ,24
+ ,33
+ ,11
+ ,14
+ ,12
+ ,78
+ ,48
+ ,11
+ ,30
+ ,25
+ ,12
+ ,12
+ ,14
+ ,74
+ ,44
+ ,11
+ ,26
+ ,38
+ ,12
+ ,9
+ ,16
+ ,82
+ ,50
+ ,11
+ ,34
+ ,37
+ ,12
+ ,9
+ ,18
+ ,73
+ ,41
+ ,11
+ ,27
+ ,31
+ ,5
+ ,11
+ ,20
+ ,55
+ ,34
+ ,11
+ ,37
+ ,37
+ ,12
+ ,16
+ ,12
+ ,72
+ ,44
+ ,11
+ ,36
+ ,35
+ ,10
+ ,9
+ ,12
+ ,78
+ ,47
+ ,11
+ ,41
+ ,25
+ ,7
+ ,13
+ ,17
+ ,59
+ ,35
+ ,11
+ ,29
+ ,28
+ ,12
+ ,16
+ ,13
+ ,72
+ ,44
+ ,11
+ ,36
+ ,35
+ ,11
+ ,13
+ ,9
+ ,78
+ ,44
+ ,11
+ ,32
+ ,33
+ ,8
+ ,9
+ ,16
+ ,68
+ ,43
+ ,11
+ ,37
+ ,30
+ ,9
+ ,12
+ ,18
+ ,69
+ ,41
+ ,11
+ ,30
+ ,31
+ ,10
+ ,16
+ ,10
+ ,67
+ ,41
+ ,11
+ ,31
+ ,37
+ ,9
+ ,11
+ ,14
+ ,74
+ ,42
+ ,11
+ ,38
+ ,36
+ ,12
+ ,14
+ ,11
+ ,54
+ ,33
+ ,11
+ ,36
+ ,30
+ ,6
+ ,13
+ ,9
+ ,67
+ ,41
+ ,11
+ ,35
+ ,36
+ ,15
+ ,15
+ ,11
+ ,70
+ ,44
+ ,11
+ ,31
+ ,32
+ ,12
+ ,14
+ ,10
+ ,80
+ ,48
+ ,11
+ ,38
+ ,28
+ ,12
+ ,16
+ ,11
+ ,89
+ ,55
+ ,11
+ ,22
+ ,36
+ ,12
+ ,13
+ ,19
+ ,76
+ ,44
+ ,11
+ ,32
+ ,34
+ ,11
+ ,14
+ ,14
+ ,74
+ ,43
+ ,11
+ ,36
+ ,31
+ ,7
+ ,15
+ ,12
+ ,87
+ ,52
+ ,11
+ ,39
+ ,28
+ ,7
+ ,13
+ ,14
+ ,54
+ ,30
+ ,11
+ ,28
+ ,36
+ ,5
+ ,11
+ ,21
+ ,61
+ ,39
+ ,11
+ ,32
+ ,36
+ ,12
+ ,11
+ ,13
+ ,38
+ ,11
+ ,11
+ ,32
+ ,40
+ ,12
+ ,14
+ ,10
+ ,75
+ ,44
+ ,11
+ ,38
+ ,33
+ ,3
+ ,15
+ ,15
+ ,69
+ ,42
+ ,11
+ ,32
+ ,37
+ ,11
+ ,11
+ ,16
+ ,62
+ ,41
+ ,11
+ ,35
+ ,32
+ ,10
+ ,15
+ ,14
+ ,72
+ ,44
+ ,11
+ ,32
+ ,38
+ ,12
+ ,12
+ ,12
+ ,70
+ ,44
+ ,11
+ ,37
+ ,31
+ ,9
+ ,14
+ ,19
+ ,79
+ ,48
+ ,11
+ ,34
+ ,37
+ ,12
+ ,14
+ ,15
+ ,87
+ ,53
+ ,11
+ ,33
+ ,33
+ ,9
+ ,8
+ ,19
+ ,62
+ ,37
+ ,11
+ ,33
+ ,32
+ ,12
+ ,13
+ ,13
+ ,77
+ ,44
+ ,11
+ ,26
+ ,30
+ ,12
+ ,9
+ ,17
+ ,69
+ ,44
+ ,11
+ ,30
+ ,30
+ ,10
+ ,15
+ ,12
+ ,69
+ ,40
+ ,11
+ ,24
+ ,31
+ ,9
+ ,17
+ ,11
+ ,75
+ ,42
+ ,11
+ ,34
+ ,32
+ ,12
+ ,13
+ ,14
+ ,54
+ ,35
+ ,11
+ ,34
+ ,34
+ ,8
+ ,15
+ ,11
+ ,72
+ ,43
+ ,11
+ ,33
+ ,36
+ ,11
+ ,15
+ ,13
+ ,74
+ ,45
+ ,11
+ ,34
+ ,37
+ ,11
+ ,14
+ ,12
+ ,85
+ ,55
+ ,11
+ ,35
+ ,36
+ ,12
+ ,16
+ ,15
+ ,52
+ ,31
+ ,11
+ ,35
+ ,33
+ ,10
+ ,13
+ ,14
+ ,70
+ ,44
+ ,11
+ ,36
+ ,33
+ ,10
+ ,16
+ ,12
+ ,84
+ ,50
+ ,11
+ ,34
+ ,33
+ ,12
+ ,9
+ ,17
+ ,64
+ ,40
+ ,11
+ ,34
+ ,44
+ ,12
+ ,16
+ ,11
+ ,84
+ ,53
+ ,11
+ ,41
+ ,39
+ ,11
+ ,11
+ ,18
+ ,87
+ ,54
+ ,11
+ ,32
+ ,32
+ ,8
+ ,10
+ ,13
+ ,79
+ ,49
+ ,11
+ ,30
+ ,35
+ ,12
+ ,11
+ ,17
+ ,67
+ ,40
+ ,11
+ ,35
+ ,25
+ ,10
+ ,15
+ ,13
+ ,65
+ ,41
+ ,11
+ ,28
+ ,35
+ ,11
+ ,17
+ ,11
+ ,85
+ ,52
+ ,11
+ ,33
+ ,34
+ ,10
+ ,14
+ ,12
+ ,83
+ ,52
+ ,11
+ ,39
+ ,35
+ ,8
+ ,8
+ ,22
+ ,61
+ ,36
+ ,11
+ ,36
+ ,39
+ ,12
+ ,15
+ ,14
+ ,82
+ ,52
+ ,11
+ ,36
+ ,33
+ ,12
+ ,11
+ ,12
+ ,76
+ ,46
+ ,11
+ ,35
+ ,36
+ ,10
+ ,16
+ ,12
+ ,58
+ ,31
+ ,11
+ ,38
+ ,32
+ ,12
+ ,10
+ ,17
+ ,72
+ ,44
+ ,11
+ ,33
+ ,32
+ ,9
+ ,15
+ ,9
+ ,72
+ ,44
+ ,11
+ ,31
+ ,36
+ ,9
+ ,9
+ ,21
+ ,38
+ ,11
+ ,11
+ ,34
+ ,36
+ ,6
+ ,16
+ ,10
+ ,78
+ ,46
+ ,11
+ ,32
+ ,32
+ ,10
+ ,19
+ ,11
+ ,54
+ ,33
+ ,11
+ ,31
+ ,34
+ ,9
+ ,12
+ ,12
+ ,63
+ ,34
+ ,11
+ ,33
+ ,33
+ ,9
+ ,8
+ ,23
+ ,66
+ ,42
+ ,11
+ ,34
+ ,35
+ ,9
+ ,11
+ ,13
+ ,70
+ ,43
+ ,11
+ ,34
+ ,30
+ ,6
+ ,14
+ ,12
+ ,71
+ ,43
+ ,11
+ ,34
+ ,38
+ ,10
+ ,9
+ ,16
+ ,67
+ ,44
+ ,11
+ ,33
+ ,34
+ ,6
+ ,15
+ ,9
+ ,58
+ ,36
+ ,11
+ ,32
+ ,33
+ ,14
+ ,13
+ ,17
+ ,72
+ ,46
+ ,11
+ ,41
+ ,32
+ ,10
+ ,16
+ ,9
+ ,72
+ ,44
+ ,11
+ ,34
+ ,31
+ ,10
+ ,11
+ ,14
+ ,70
+ ,43
+ ,11
+ ,36
+ ,30
+ ,6
+ ,12
+ ,17
+ ,76
+ ,50
+ ,11
+ ,37
+ ,27
+ ,12
+ ,13
+ ,13
+ ,50
+ ,33
+ ,11
+ ,36
+ ,31
+ ,12
+ ,10
+ ,11
+ ,72
+ ,43
+ ,11
+ ,29
+ ,30
+ ,7
+ ,11
+ ,12
+ ,72
+ ,44
+ ,11
+ ,37
+ ,32
+ ,8
+ ,12
+ ,10
+ ,88
+ ,53
+ ,11
+ ,27
+ ,35
+ ,11
+ ,8
+ ,19
+ ,53
+ ,34
+ ,11
+ ,35
+ ,28
+ ,3
+ ,12
+ ,16
+ ,58
+ ,35
+ ,11
+ ,28
+ ,33
+ ,6
+ ,12
+ ,16
+ ,66
+ ,40
+ ,11
+ ,35
+ ,31
+ ,10
+ ,15
+ ,14
+ ,82
+ ,53
+ ,11
+ ,37
+ ,35
+ ,8
+ ,11
+ ,20
+ ,69
+ ,42
+ ,11
+ ,29
+ ,35
+ ,9
+ ,13
+ ,15
+ ,68
+ ,43
+ ,11
+ ,32
+ ,32
+ ,9
+ ,14
+ ,23
+ ,44
+ ,29
+ ,11
+ ,36
+ ,21
+ ,8
+ ,10
+ ,20
+ ,56
+ ,36
+ ,11
+ ,19
+ ,20
+ ,9
+ ,12
+ ,16
+ ,53
+ ,30
+ ,11
+ ,21
+ ,34
+ ,7
+ ,15
+ ,14
+ ,70
+ ,42
+ ,11
+ ,31
+ ,32
+ ,7
+ ,13
+ ,17
+ ,78
+ ,47
+ ,11
+ ,33
+ ,34
+ ,6
+ ,13
+ ,11
+ ,71
+ ,44
+ ,11
+ ,36
+ ,32
+ ,9
+ ,13
+ ,13
+ ,72
+ ,45
+ ,11
+ ,33
+ ,33
+ ,10
+ ,12
+ ,17
+ ,68
+ ,44
+ ,11
+ ,37
+ ,33
+ ,11
+ ,12
+ ,15
+ ,67
+ ,43
+ ,11
+ ,34
+ ,37
+ ,12
+ ,9
+ ,21
+ ,75
+ ,43
+ ,11
+ ,35
+ ,32
+ ,8
+ ,9
+ ,18
+ ,62
+ ,40
+ ,11
+ ,31
+ ,34
+ ,11
+ ,15
+ ,15
+ ,67
+ ,41
+ ,11
+ ,37
+ ,30
+ ,3
+ ,10
+ ,8
+ ,83
+ ,52
+ ,11
+ ,35
+ ,30
+ ,11
+ ,14
+ ,12
+ ,64
+ ,38
+ ,11
+ ,27
+ ,38
+ ,12
+ ,15
+ ,12
+ ,68
+ ,41
+ ,11
+ ,34
+ ,36
+ ,7
+ ,7
+ ,22
+ ,62
+ ,39
+ ,11
+ ,40
+ ,32
+ ,9
+ ,14
+ ,12
+ ,72
+ ,43
+ ,11
+ ,29)
+ ,dim=c(8
+ ,264)
+ ,dimnames=list(c('Separate'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Sport1'
+ ,'Sport2'
+ ,'Month'
+ ,'Connected')
+ ,1:264))
> y <- array(NA,dim=c(8,264),dimnames=list(c('Separate','Software','Happiness','Depression','Sport1','Sport2','Month','Connected'),1:264))
> 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 = '8'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '8'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, 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
Connected Separate Software Happiness Depression Sport1 Sport2 Month
1 41 38 12 14 12.0 53 32 9
2 39 32 11 18 11.0 83 51 9
3 30 35 15 11 14.0 66 42 9
4 31 33 6 12 12.0 67 41 9
5 34 37 13 16 21.0 76 46 9
6 35 29 10 18 12.0 78 47 9
7 39 31 12 14 22.0 53 37 9
8 34 36 14 14 11.0 80 49 9
9 36 35 12 15 10.0 74 45 9
10 37 38 9 15 13.0 76 47 9
11 38 31 10 17 10.0 79 49 9
12 36 34 12 19 8.0 54 33 9
13 38 35 12 10 15.0 67 42 9
14 39 38 11 16 14.0 54 33 9
15 33 37 15 18 10.0 87 53 9
16 32 33 12 14 14.0 58 36 9
17 36 32 10 14 14.0 75 45 9
18 38 38 12 17 11.0 88 54 9
19 39 38 11 14 10.0 64 41 9
20 32 32 12 16 13.0 57 36 9
21 32 33 11 18 9.5 66 41 9
22 31 31 12 11 14.0 68 44 9
23 39 38 13 14 12.0 54 33 9
24 37 39 11 12 14.0 56 37 9
25 39 32 12 17 11.0 86 52 9
26 41 32 13 9 9.0 80 47 9
27 36 35 10 16 11.0 76 43 9
28 33 37 14 14 15.0 69 44 9
29 33 33 12 15 14.0 78 45 9
30 34 33 10 11 13.0 67 44 9
31 31 31 12 16 9.0 80 49 9
32 27 32 8 13 15.0 54 33 9
33 37 31 10 17 10.0 71 43 9
34 34 37 12 15 11.0 84 54 9
35 34 30 12 14 13.0 74 42 9
36 32 33 7 16 8.0 71 44 9
37 29 31 9 9 20.0 63 37 9
38 36 33 12 15 12.0 71 43 9
39 29 31 10 17 10.0 76 46 9
40 35 33 10 13 10.0 69 42 9
41 37 32 10 15 9.0 74 45 9
42 34 33 12 16 14.0 75 44 9
43 38 32 15 16 8.0 54 33 9
44 35 33 10 12 14.0 52 31 9
45 38 28 10 15 11.0 69 42 9
46 37 35 12 11 13.0 68 40 9
47 38 39 13 15 9.0 65 43 9
48 33 34 11 15 11.0 75 46 9
49 36 38 11 17 15.0 74 42 9
50 38 32 12 13 11.0 75 45 9
51 32 38 14 16 10.0 72 44 9
52 32 30 10 14 14.0 67 40 9
53 32 33 12 11 18.0 63 37 9
54 34 38 13 12 14.0 62 46 9
55 32 32 5 12 11.0 63 36 9
56 37 35 6 15 14.5 76 47 9
57 39 34 12 16 13.0 74 45 9
58 29 34 12 15 9.0 67 42 9
59 37 36 11 12 10.0 73 43 9
60 35 34 10 12 15.0 70 43 9
61 30 28 7 8 20.0 53 32 9
62 38 34 12 13 12.0 77 45 9
63 34 35 14 11 12.0 80 48 9
64 31 35 11 14 14.0 52 31 9
65 34 31 12 15 13.0 54 33 9
66 35 37 13 10 11.0 80 49 10
67 36 35 14 11 17.0 66 42 10
68 30 27 11 12 12.0 73 41 10
69 39 40 12 15 13.0 63 38 10
70 35 37 12 15 14.0 69 42 10
71 38 36 8 14 13.0 67 44 10
72 31 38 11 16 15.0 54 33 10
73 34 39 14 15 13.0 81 48 10
74 38 41 14 15 10.0 69 40 10
75 34 27 12 13 11.0 84 50 10
76 39 30 9 12 19.0 80 49 10
77 37 37 13 17 13.0 70 43 10
78 34 31 11 13 17.0 69 44 10
79 28 31 12 15 13.0 77 47 10
80 37 27 12 13 9.0 54 33 10
81 33 36 12 15 11.0 79 46 10
82 35 37 12 15 9.0 71 45 10
83 37 33 12 16 12.0 73 43 10
84 32 34 11 15 12.0 72 44 10
85 33 31 10 14 13.0 77 47 10
86 38 39 9 15 13.0 75 45 10
87 33 34 12 14 12.0 69 42 10
88 29 32 12 13 15.0 54 33 10
89 33 33 12 7 22.0 70 43 10
90 31 36 9 17 13.0 73 46 10
91 36 32 15 13 15.0 54 33 10
92 35 41 12 15 13.0 77 46 10
93 32 28 12 14 15.0 82 48 10
94 29 30 12 13 12.5 80 47 10
95 39 36 10 16 11.0 80 47 10
96 37 35 13 12 16.0 69 43 10
97 35 31 9 14 11.0 78 46 10
98 37 34 12 17 11.0 81 48 10
99 32 36 10 15 10.0 76 46 10
100 38 36 14 17 10.0 76 45 10
101 37 35 11 12 16.0 73 45 10
102 36 37 15 16 12.0 85 52 10
103 32 28 11 11 11.0 66 42 10
104 33 39 11 15 16.0 79 47 10
105 40 32 12 9 19.0 68 41 10
106 38 35 12 16 11.0 76 47 10
107 41 39 12 15 16.0 71 43 10
108 36 35 11 10 15.0 54 33 10
109 43 42 7 10 24.0 46 30 10
110 30 34 12 15 14.0 85 52 10
111 31 33 14 11 15.0 74 44 10
112 32 41 11 13 11.0 88 55 10
113 32 33 11 14 15.0 38 11 10
114 37 34 10 18 12.0 76 47 10
115 37 32 13 16 10.0 86 53 10
116 33 40 13 14 14.0 54 33 10
117 34 40 8 14 13.0 67 44 10
118 33 35 11 14 9.0 69 42 10
119 38 36 12 14 15.0 90 55 10
120 33 37 11 12 15.0 54 33 10
121 31 27 13 14 14.0 76 46 10
122 38 39 12 15 11.0 89 54 10
123 37 38 14 15 8.0 76 47 10
124 36 31 13 15 11.0 73 45 10
125 31 33 15 13 11.0 79 47 10
126 39 32 10 17 8.0 90 55 10
127 44 39 11 17 10.0 74 44 10
128 33 36 9 19 11.0 81 53 10
129 35 33 11 15 13.0 72 44 10
130 32 33 10 13 11.0 71 42 10
131 28 32 11 9 20.0 66 40 10
132 40 37 8 15 10.0 77 46 10
133 27 30 11 15 15.0 65 40 10
134 37 38 12 15 12.0 74 46 10
135 32 29 12 16 14.0 85 53 10
136 28 22 9 11 23.0 54 33 10
137 34 35 11 14 14.0 63 42 10
138 30 35 10 11 16.0 54 35 10
139 35 34 8 15 11.0 64 40 10
140 31 35 9 13 12.0 69 41 10
141 32 34 8 15 10.0 54 33 10
142 30 37 9 16 14.0 84 51 10
143 30 35 15 14 12.0 86 53 10
144 31 23 11 15 12.0 77 46 10
145 40 31 8 16 11.0 89 55 10
146 32 27 13 16 12.0 76 47 10
147 36 36 12 11 13.0 60 38 10
148 32 31 12 12 11.0 75 46 10
149 35 32 9 9 19.0 73 46 10
150 38 39 7 16 12.0 85 53 10
151 42 37 13 13 17.0 79 47 10
152 34 38 9 16 9.0 71 41 10
153 35 39 6 12 12.0 72 44 10
154 38 34 8 9 19.0 69 43 9
155 33 31 8 13 18.0 78 51 10
156 36 32 15 13 15.0 54 33 10
157 32 37 6 14 14.0 69 43 10
158 33 36 9 19 11.0 81 53 10
159 34 32 11 13 9.0 84 51 10
160 32 38 8 12 18.0 84 50 10
161 34 36 8 13 16.0 69 46 10
162 27 26 10 10 24.0 66 43 11
163 31 26 8 14 14.0 81 47 11
164 38 33 14 16 20.0 82 50 11
165 34 39 10 10 18.0 72 43 11
166 24 30 8 11 23.0 54 33 11
167 30 33 11 14 12.0 78 48 11
168 26 25 12 12 14.0 74 44 11
169 34 38 12 9 16.0 82 50 11
170 27 37 12 9 18.0 73 41 11
171 37 31 5 11 20.0 55 34 11
172 36 37 12 16 12.0 72 44 11
173 41 35 10 9 12.0 78 47 11
174 29 25 7 13 17.0 59 35 11
175 36 28 12 16 13.0 72 44 11
176 32 35 11 13 9.0 78 44 11
177 37 33 8 9 16.0 68 43 11
178 30 30 9 12 18.0 69 41 11
179 31 31 10 16 10.0 67 41 11
180 38 37 9 11 14.0 74 42 11
181 36 36 12 14 11.0 54 33 11
182 35 30 6 13 9.0 67 41 11
183 31 36 15 15 11.0 70 44 11
184 38 32 12 14 10.0 80 48 11
185 22 28 12 16 11.0 89 55 11
186 32 36 12 13 19.0 76 44 11
187 36 34 11 14 14.0 74 43 11
188 39 31 7 15 12.0 87 52 11
189 28 28 7 13 14.0 54 30 11
190 32 36 5 11 21.0 61 39 11
191 32 36 12 11 13.0 38 11 11
192 38 40 12 14 10.0 75 44 11
193 32 33 3 15 15.0 69 42 11
194 35 37 11 11 16.0 62 41 11
195 32 32 10 15 14.0 72 44 11
196 37 38 12 12 12.0 70 44 11
197 34 31 9 14 19.0 79 48 11
198 33 37 12 14 15.0 87 53 11
199 33 33 9 8 19.0 62 37 11
200 26 32 12 13 13.0 77 44 11
201 30 30 12 9 17.0 69 44 11
202 24 30 10 15 12.0 69 40 11
203 34 31 9 17 11.0 75 42 11
204 34 32 12 13 14.0 54 35 11
205 33 34 8 15 11.0 72 43 11
206 34 36 11 15 13.0 74 45 11
207 35 37 11 14 12.0 85 55 11
208 35 36 12 16 15.0 52 31 11
209 36 33 10 13 14.0 70 44 11
210 34 33 10 16 12.0 84 50 11
211 34 33 12 9 17.0 64 40 11
212 41 44 12 16 11.0 84 53 11
213 32 39 11 11 18.0 87 54 11
214 30 32 8 10 13.0 79 49 11
215 35 35 12 11 17.0 67 40 11
216 28 25 10 15 13.0 65 41 11
217 33 35 11 17 11.0 85 52 11
218 39 34 10 14 12.0 83 52 11
219 36 35 8 8 22.0 61 36 11
220 36 39 12 15 14.0 82 52 11
221 35 33 12 11 12.0 76 46 11
222 38 36 10 16 12.0 58 31 11
223 33 32 12 10 17.0 72 44 11
224 31 32 9 15 9.0 72 44 11
225 34 36 9 9 21.0 38 11 11
226 32 36 6 16 10.0 78 46 11
227 31 32 10 19 11.0 54 33 11
228 33 34 9 12 12.0 63 34 11
229 34 33 9 8 23.0 66 42 11
230 34 35 9 11 13.0 70 43 11
231 34 30 6 14 12.0 71 43 11
232 33 38 10 9 16.0 67 44 11
233 32 34 6 15 9.0 58 36 11
234 41 33 14 13 17.0 72 46 11
235 34 32 10 16 9.0 72 44 11
236 36 31 10 11 14.0 70 43 11
237 37 30 6 12 17.0 76 50 11
238 36 27 12 13 13.0 50 33 11
239 29 31 12 10 11.0 72 43 11
240 37 30 7 11 12.0 72 44 11
241 27 32 8 12 10.0 88 53 11
242 35 35 11 8 19.0 53 34 11
243 28 28 3 12 16.0 58 35 11
244 35 33 6 12 16.0 66 40 11
245 37 31 10 15 14.0 82 53 11
246 29 35 8 11 20.0 69 42 11
247 32 35 9 13 15.0 68 43 11
248 36 32 9 14 23.0 44 29 11
249 19 21 8 10 20.0 56 36 11
250 21 20 9 12 16.0 53 30 11
251 31 34 7 15 14.0 70 42 11
252 33 32 7 13 17.0 78 47 11
253 36 34 6 13 11.0 71 44 11
254 33 32 9 13 13.0 72 45 11
255 37 33 10 12 17.0 68 44 11
256 34 33 11 12 15.0 67 43 11
257 35 37 12 9 21.0 75 43 11
258 31 32 8 9 18.0 62 40 11
259 37 34 11 15 15.0 67 41 11
260 35 30 3 10 8.0 83 52 11
261 27 30 11 14 12.0 64 38 11
262 34 38 12 15 12.0 68 41 11
263 40 36 7 7 22.0 62 39 11
264 29 32 9 14 12.0 72 43 11
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Separate Software Happiness Depression Sport1
24.590010 0.435581 0.005609 0.020152 -0.044446 -0.050075
Sport2 Month
0.116447 -0.633094
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.6710 -2.2506 0.1298 2.3405 7.6638
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.590010 4.447938 5.528 7.96e-08 ***
Separate 0.435581 0.057304 7.601 5.55e-13 ***
Software 0.005609 0.094894 0.059 0.9529
Happiness 0.020152 0.104012 0.194 0.8465
Depression -0.044446 0.076261 -0.583 0.5605
Sport1 -0.050075 0.067841 -0.738 0.4611
Sport2 0.116447 0.100615 1.157 0.2482
Month -0.633094 0.284108 -2.228 0.0267 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.353 on 256 degrees of freedom
Multiple R-squared: 0.2406, Adjusted R-squared: 0.2198
F-statistic: 11.59 on 7 and 256 DF, p-value: 8.741e-13
> 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,] 0.04710089 0.09420177 0.9528991
[2,] 0.75772356 0.48455288 0.2422764
[3,] 0.84876233 0.30247534 0.1512377
[4,] 0.77177434 0.45645132 0.2282257
[5,] 0.74214102 0.51571797 0.2578590
[6,] 0.73287920 0.53424160 0.2671208
[7,] 0.68489455 0.63021090 0.3151054
[8,] 0.60755218 0.78489564 0.3924478
[9,] 0.52331984 0.95336033 0.4766802
[10,] 0.55545528 0.88908943 0.4445447
[11,] 0.60921325 0.78157349 0.3907867
[12,] 0.56324897 0.87350207 0.4367510
[13,] 0.53634254 0.92731491 0.4636575
[14,] 0.47268470 0.94536941 0.5273153
[15,] 0.54077668 0.91844663 0.4592233
[16,] 0.75864800 0.48270400 0.2413520
[17,] 0.71241899 0.57516203 0.2875810
[18,] 0.69051248 0.61897504 0.3094875
[19,] 0.66549602 0.66900796 0.3345040
[20,] 0.61353700 0.77292600 0.3864630
[21,] 0.62044642 0.75910715 0.3795536
[22,] 0.80556073 0.38887854 0.1944393
[23,] 0.78464607 0.43070785 0.2153539
[24,] 0.76274266 0.47451468 0.2372573
[25,] 0.71714442 0.56571116 0.2828556
[26,] 0.70393194 0.59213612 0.2960681
[27,] 0.70563047 0.58873906 0.2943695
[28,] 0.66137874 0.67724253 0.3386213
[29,] 0.72098613 0.55802773 0.2790139
[30,] 0.67538537 0.64922926 0.3246146
[31,] 0.65281949 0.69436103 0.3471805
[32,] 0.60510502 0.78978997 0.3948950
[33,] 0.57760444 0.84479112 0.4223956
[34,] 0.52988434 0.94023133 0.4701157
[35,] 0.58270176 0.83459648 0.4172982
[36,] 0.54601909 0.90796182 0.4539809
[37,] 0.49741957 0.99483914 0.5025804
[38,] 0.46893487 0.93786974 0.5310651
[39,] 0.42045107 0.84090215 0.5795489
[40,] 0.41684480 0.83368959 0.5831552
[41,] 0.47993263 0.95986527 0.5200674
[42,] 0.44292304 0.88584609 0.5570770
[43,] 0.41084936 0.82169873 0.5891506
[44,] 0.38084008 0.76168016 0.6191599
[45,] 0.34635873 0.69271746 0.6536413
[46,] 0.34126210 0.68252419 0.6587379
[47,] 0.35609654 0.71219309 0.6439035
[48,] 0.47806817 0.95613633 0.5219318
[49,] 0.44005110 0.88010221 0.5599489
[50,] 0.39800915 0.79601830 0.6019909
[51,] 0.36177528 0.72355056 0.6382247
[52,] 0.34927627 0.69855255 0.6507237
[53,] 0.31847575 0.63695149 0.6815243
[54,] 0.33271440 0.66542881 0.6672856
[55,] 0.29441549 0.58883099 0.7055845
[56,] 0.25891670 0.51783341 0.7410833
[57,] 0.23272679 0.46545358 0.7672732
[58,] 0.21758860 0.43517720 0.7824114
[59,] 0.20410065 0.40820130 0.7958994
[60,] 0.17672561 0.35345123 0.8232744
[61,] 0.16878111 0.33756222 0.8312189
[62,] 0.19800949 0.39601898 0.8019905
[63,] 0.18480710 0.36961420 0.8151929
[64,] 0.16040218 0.32080436 0.8395978
[65,] 0.14248602 0.28497204 0.8575140
[66,] 0.20695154 0.41390308 0.7930485
[67,] 0.18085853 0.36171706 0.8191415
[68,] 0.15587294 0.31174588 0.8441271
[69,] 0.22059908 0.44119815 0.7794009
[70,] 0.25095637 0.50191274 0.7490436
[71,] 0.23822432 0.47644865 0.7617757
[72,] 0.21186109 0.42372218 0.7881389
[73,] 0.20048651 0.40097301 0.7995135
[74,] 0.19248110 0.38496220 0.8075189
[75,] 0.16821258 0.33642516 0.8317874
[76,] 0.15287684 0.30575369 0.8471232
[77,] 0.13631915 0.27263830 0.8636808
[78,] 0.15185001 0.30370002 0.8481500
[79,] 0.13021032 0.26042064 0.8697897
[80,] 0.14309250 0.28618501 0.8569075
[81,] 0.13467210 0.26934420 0.8653279
[82,] 0.12079963 0.24159927 0.8792004
[83,] 0.10389673 0.20779347 0.8961033
[84,] 0.11357628 0.22715255 0.8864237
[85,] 0.12050212 0.24100423 0.8794979
[86,] 0.11281233 0.22562466 0.8871877
[87,] 0.09984005 0.19968009 0.9001600
[88,] 0.09105778 0.18211557 0.9089422
[89,] 0.09120508 0.18241016 0.9087949
[90,] 0.08419015 0.16838030 0.9158098
[91,] 0.07846569 0.15693138 0.9215343
[92,] 0.06559067 0.13118135 0.9344093
[93,] 0.05496741 0.10993482 0.9450326
[94,] 0.05308049 0.10616099 0.9469195
[95,] 0.09676463 0.19352927 0.9032354
[96,] 0.09282619 0.18565238 0.9071738
[97,] 0.11246347 0.22492693 0.8875365
[98,] 0.09994129 0.19988257 0.9000587
[99,] 0.15039961 0.30079922 0.8496004
[100,] 0.17211435 0.34422871 0.8278856
[101,] 0.16758605 0.33517209 0.8324140
[102,] 0.20597464 0.41194928 0.7940254
[103,] 0.19084973 0.38169945 0.8091503
[104,] 0.17769224 0.35538448 0.8223078
[105,] 0.17204540 0.34409079 0.8279546
[106,] 0.17374811 0.34749622 0.8262519
[107,] 0.16935372 0.33870745 0.8306463
[108,] 0.15371904 0.30743809 0.8462810
[109,] 0.14535982 0.29071963 0.8546402
[110,] 0.13371129 0.26742258 0.8662887
[111,] 0.11868838 0.23737676 0.8813116
[112,] 0.10461209 0.20922417 0.8953879
[113,] 0.08995174 0.17990349 0.9100483
[114,] 0.08447327 0.16894654 0.9155267
[115,] 0.08205872 0.16411744 0.9179413
[116,] 0.09839510 0.19679021 0.9016049
[117,] 0.17803128 0.35606256 0.8219687
[118,] 0.17442489 0.34884978 0.8255751
[119,] 0.15499800 0.30999599 0.8450020
[120,] 0.14049200 0.28098400 0.8595080
[121,] 0.16418140 0.32836280 0.8358186
[122,] 0.17959226 0.35918453 0.8204077
[123,] 0.22288855 0.44577711 0.7771114
[124,] 0.19846005 0.39692010 0.8015400
[125,] 0.17604540 0.35209079 0.8239546
[126,] 0.15543691 0.31087383 0.8445631
[127,] 0.13647748 0.27295496 0.8635225
[128,] 0.15229380 0.30458760 0.8477062
[129,] 0.13249779 0.26499558 0.8675022
[130,] 0.13522382 0.27044763 0.8647762
[131,] 0.12542854 0.25085708 0.8745715
[132,] 0.16502218 0.33004436 0.8349778
[133,] 0.19916148 0.39832296 0.8008385
[134,] 0.18095811 0.36191621 0.8190419
[135,] 0.25435332 0.50870664 0.7456467
[136,] 0.22928458 0.45856916 0.7707154
[137,] 0.20536459 0.41072917 0.7946354
[138,] 0.18324257 0.36648515 0.8167574
[139,] 0.16689612 0.33379224 0.8331039
[140,] 0.14712849 0.29425698 0.8528715
[141,] 0.22325077 0.44650153 0.7767492
[142,] 0.20473038 0.40946075 0.7952696
[143,] 0.18567677 0.37135354 0.8143232
[144,] 0.20301050 0.40602100 0.7969895
[145,] 0.18140611 0.36281221 0.8185939
[146,] 0.19895593 0.39791186 0.8010441
[147,] 0.18982371 0.37964741 0.8101763
[148,] 0.17525233 0.35050467 0.8247477
[149,] 0.16716227 0.33432454 0.8328377
[150,] 0.15702556 0.31405111 0.8429744
[151,] 0.13618669 0.27237339 0.8638133
[152,] 0.12712877 0.25425755 0.8728712
[153,] 0.11699401 0.23398802 0.8830060
[154,] 0.15166145 0.30332291 0.8483385
[155,] 0.13703634 0.27407268 0.8629637
[156,] 0.21769635 0.43539269 0.7823037
[157,] 0.21386982 0.42773964 0.7861302
[158,] 0.20353936 0.40707871 0.7964606
[159,] 0.18244876 0.36489753 0.8175512
[160,] 0.27716357 0.55432714 0.7228364
[161,] 0.32390097 0.64780193 0.6760990
[162,] 0.29333583 0.58667167 0.7066642
[163,] 0.40499925 0.80999851 0.5950007
[164,] 0.37048689 0.74097378 0.6295131
[165,] 0.44678640 0.89357281 0.5532136
[166,] 0.41808340 0.83616681 0.5819166
[167,] 0.42293415 0.84586830 0.5770658
[168,] 0.38918787 0.77837574 0.6108121
[169,] 0.35756276 0.71512552 0.6424372
[170,] 0.35601459 0.71202917 0.6439854
[171,] 0.32477923 0.64955847 0.6752208
[172,] 0.31889469 0.63778938 0.6811053
[173,] 0.33588886 0.67177772 0.6641111
[174,] 0.40678805 0.81357609 0.5932120
[175,] 0.61512611 0.76974778 0.3848739
[176,] 0.59349960 0.81300080 0.4065004
[177,] 0.58066735 0.83866531 0.4193327
[178,] 0.72851606 0.54296787 0.2714839
[179,] 0.70412230 0.59175539 0.2958777
[180,] 0.71287835 0.57424331 0.2871217
[181,] 0.67907181 0.64185638 0.3209282
[182,] 0.64847695 0.70304609 0.3515230
[183,] 0.61800956 0.76398087 0.3819904
[184,] 0.59468857 0.81062286 0.4053114
[185,] 0.55478121 0.89043758 0.4452188
[186,] 0.51622463 0.96755074 0.4837754
[187,] 0.49627721 0.99255441 0.5037228
[188,] 0.46890234 0.93780467 0.5310977
[189,] 0.42710418 0.85420836 0.5728958
[190,] 0.49338870 0.98677741 0.5066113
[191,] 0.46311652 0.92623303 0.5368835
[192,] 0.60392408 0.79215183 0.3960759
[193,] 0.59954120 0.80091761 0.4004588
[194,] 0.55911269 0.88177462 0.4408873
[195,] 0.51577893 0.96844213 0.4842211
[196,] 0.47561648 0.95123295 0.5243835
[197,] 0.44186492 0.88372985 0.5581351
[198,] 0.40198770 0.80397540 0.5980123
[199,] 0.37827841 0.75655682 0.6217216
[200,] 0.34364146 0.68728293 0.6563585
[201,] 0.30378084 0.60756168 0.6962192
[202,] 0.27026093 0.54052187 0.7297391
[203,] 0.32598645 0.65197291 0.6740135
[204,] 0.31307007 0.62614014 0.6869299
[205,] 0.27401896 0.54803793 0.7259810
[206,] 0.23799716 0.47599432 0.7620028
[207,] 0.21205065 0.42410131 0.7879493
[208,] 0.23796145 0.47592290 0.7620385
[209,] 0.21106491 0.42212982 0.7889351
[210,] 0.19298350 0.38596701 0.8070165
[211,] 0.16776892 0.33553784 0.8322311
[212,] 0.18371424 0.36742847 0.8162858
[213,] 0.15164772 0.30329544 0.8483523
[214,] 0.12822404 0.25644807 0.8717760
[215,] 0.20458557 0.40917113 0.7954144
[216,] 0.18188389 0.36376778 0.8181161
[217,] 0.15732548 0.31465096 0.8426745
[218,] 0.16951153 0.33902305 0.8304885
[219,] 0.13792447 0.27584894 0.8620755
[220,] 0.10930813 0.21861626 0.8906919
[221,] 0.10392238 0.20784476 0.8960776
[222,] 0.20066899 0.40133798 0.7993310
[223,] 0.17725568 0.35451135 0.8227443
[224,] 0.24884940 0.49769880 0.7511506
[225,] 0.20771506 0.41543012 0.7922849
[226,] 0.23437391 0.46874783 0.7656261
[227,] 0.21148188 0.42296376 0.7885181
[228,] 0.30486168 0.60972336 0.6951383
[229,] 0.25199120 0.50398240 0.7480088
[230,] 0.43588193 0.87176387 0.5641181
[231,] 0.49885246 0.99770493 0.5011475
[232,] 0.42342288 0.84684576 0.5765771
[233,] 0.35330802 0.70661604 0.6466920
[234,] 0.30578743 0.61157487 0.6942126
[235,] 0.30604511 0.61209021 0.6939549
[236,] 0.51176250 0.97647499 0.4882375
[237,] 0.55236930 0.89526140 0.4476307
[238,] 0.52112007 0.95775986 0.4788799
[239,] 0.70268705 0.59462589 0.2973129
[240,] 0.70271872 0.59456255 0.2972813
[241,] 0.67918579 0.64162843 0.3208142
[242,] 0.69783679 0.60432642 0.3021632
[243,] 0.53116569 0.93766862 0.4688343
> postscript(file="/var/wessaorg/rcomp/tmp/1834c1384707620.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2d2751384707620.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/36yu01384707620.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4nfxb1384707620.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ebmy1384707620.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 = 264
Frequency = 1
1 2 3 4 5
4.6673482414 4.4511606950 -5.4068761460 -3.4277531544 -2.0214947671
6 7 8 9 10
2.0233702703 5.5786409849 -2.1447154359 0.4028214660 0.1134991892
11 12 13 14 15
3.9006498062 1.0647578413 2.7246245031 2.6551730583 -3.8262203833
16 17 18 19 20
-2.2812643400 1.9687943555 0.7532520633 2.0868727285 -1.9805085596
21 22 23 24 25
-2.7379016129 -2.7804666562 2.5953676150 -0.0654363646 4.4994829804
26 27 28 29 30
6.8479819903 0.7713778020 -3.3711080954 -1.3479312401 -0.7349322458
31 32 33 34 35
-3.0847866501 -6.6096094316 3.1987282341 -2.9711637253 1.0835582843
36 37 38 39 40
-2.8407934037 -3.8919087617 1.4455433805 -4.9002356145 0.4244704981
41 42 43 44 45
2.6763377953 -0.4018626254 3.9795497061 1.0520404890 5.6065184922
46 47 48 49 50
1.8985498655 0.3926568158 -2.1779138470 -0.3670480390 3.8443909284
51 52 53 54 55
-4.9189966860 -0.9784114869 -1.9090946507 -3.3886430880 -1.6490755489
56 57 58 59 60
1.5037391253 3.9515880039 -6.2072299082 1.2161242167 0.1648988558
61 62 63 64 65
-1.4723136432 3.1178248405 -1.4877848874 -3.8650355945 0.6743395116
66 67 68 69 70
-0.8609851222 1.3651641624 -0.9087647977 2.2556453446 -0.5585002956
71 72 73 74 75
2.5421792789 -4.6672873387 -2.5831038861 0.7430668343 2.5238351112
76 77 78 79 80
6.5257825126 1.2847689131 1.0013446554 -5.1710897703 5.9122797056
81 82 83 84 85
-2.2212903068 -1.0299192541 3.1586357303 -2.4177065566 -0.1397193893
86 87 88 89 90
1.4938300969 -1.3204957879 -3.9989520313 -0.3657627085 -4.4563279557
91 92 93 94 95
2.9842206688 -2.4104559855 0.3786278834 -3.5672009027 3.7034041856
96 97 98 99 100
2.3399544828 1.9435203623 2.4768248996 -3.4047440057 2.6489621241
101 102 103 104 105
2.3185803606 -0.0476344543 0.1643858206 -3.4166430169 7.0289198847
106 107 108 109 110
2.9274658350 4.6429325275 1.7603696798 6.0824868822 -4.6150194358
111 112 113 114 115
-2.6848556824 -5.9506271362 0.3115493509 2.3784067862 2.9862271671
116 117 118 119 120
-3.5538096007 -3.2001459639 -1.8838054070 2.4794520781 -2.1510973038
121 122 123 124 125
-0.3034041172 1.0411442803 0.4963184757 2.7670018464 -3.0075162042
126 127 128 129 130
4.8614183582 7.3753416530 -3.0000492431 1.0623205569 -1.7978391683
131 132 133 134 135
-4.9047290515 4.2209682489 -5.4267838958 0.7016160478 -0.5737119551
136 137 138 139 140
-0.2304408401 -0.9620285853 -4.4426213822 0.6198595849 -3.6026507259
141 142 143 144 145
-2.1102116288 -5.8587167226 -5.2025389973 1.3911709043 6.4116320928
146 147 148 149 150
0.4509530233 0.9283532156 -1.1832286715 1.7138895523 1.0096288322
151 152 153 154 155
6.5280515695 -2.0030378787 -1.5071110670 3.3642814447 -0.3018321963
156 157 158 159 160
2.9842206688 -3.6211404065 -3.0000492431 0.1461991604 -3.9138501061
161 162 163 164 165
-1.4370741846 -2.8442479327 0.9272464285 4.7716277982 -1.4730275962
166 167 168 169 170
-7.0763884256 -3.4942146397 -3.6204911806 -1.4317784989 -7.3099618446
171 172 173 174 175
5.3051486288 0.8828820305 6.8574396360 -0.1823689077 4.8475596295
176 177 178 179 180
-2.0127749211 3.8826375820 -1.5048233745 -1.4823396623 3.4224063556
181 182 183 184 185
1.6938811339 2.9916887886 -3.8228083112 4.9470166962 -9.6709665748
186 187 188 189 190
-2.1096580348 2.5410286527 6.3641229215 -2.2905926159 -2.1100943888
191 192 193 194 195
-0.3961449433 1.6777769511 -1.0881535502 0.0156222686 -0.8189494295
196 197 198 199 200
1.4277587133 1.7493621664 -2.2403677449 0.4287472301 -6.5839322432
201 202 203 204 205
-1.8549806094 -7.7211169425 1.8317190904 1.3568021824 -0.6957843675
206 207 208 209 210
-0.6276257361 -0.7011405676 0.9641030357 2.6856228912 0.5386485067
211 212 213 214 215
1.0536861989 2.3422494109 -4.0285742067 -2.9831230285 1.2924453115
216 217 218 219 220
-1.8155129395 -1.5855396168 4.8604024091 2.7629026435 -0.3100582852
221 222 223 224 225
1.6933758564 4.1424360195 0.4039306842 -2.0355693434 2.0165531409
226 227 228 229 230
-2.6692152560 -1.6533363287 0.0008526166 1.2245974436 -0.0676251791
231 232 233 234 235
2.0722816937 -2.4730094286 -1.6593846455 7.6637808468 0.9386693756
236 237 238 239 240
3.7135367665 4.7700636538 5.5228552553 -3.3107159299 5.0607576105
241 242 243 244 245
-6.1718740779 1.4450287960 -2.5410454270 2.0825891835 4.0693635085
246 247 248 249 250
-4.6845239333 -2.1191886664 3.9514172234 -8.5185318013 -5.7581919090
251 252 253 254 255
-2.5405416981 0.3226311576 2.1892159374 0.0660713380 3.7389617498
256 257 258 259 260
0.7108325708 0.6906325108 -1.5440010185 3.4476885005 2.5448168649
261 262 263 264
-4.7240569046 -1.3835078852 6.0538173037 -3.7656328622
> postscript(file="/var/wessaorg/rcomp/tmp/61oiq1384707620.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 = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 4.6673482414 NA
1 4.4511606950 4.6673482414
2 -5.4068761460 4.4511606950
3 -3.4277531544 -5.4068761460
4 -2.0214947671 -3.4277531544
5 2.0233702703 -2.0214947671
6 5.5786409849 2.0233702703
7 -2.1447154359 5.5786409849
8 0.4028214660 -2.1447154359
9 0.1134991892 0.4028214660
10 3.9006498062 0.1134991892
11 1.0647578413 3.9006498062
12 2.7246245031 1.0647578413
13 2.6551730583 2.7246245031
14 -3.8262203833 2.6551730583
15 -2.2812643400 -3.8262203833
16 1.9687943555 -2.2812643400
17 0.7532520633 1.9687943555
18 2.0868727285 0.7532520633
19 -1.9805085596 2.0868727285
20 -2.7379016129 -1.9805085596
21 -2.7804666562 -2.7379016129
22 2.5953676150 -2.7804666562
23 -0.0654363646 2.5953676150
24 4.4994829804 -0.0654363646
25 6.8479819903 4.4994829804
26 0.7713778020 6.8479819903
27 -3.3711080954 0.7713778020
28 -1.3479312401 -3.3711080954
29 -0.7349322458 -1.3479312401
30 -3.0847866501 -0.7349322458
31 -6.6096094316 -3.0847866501
32 3.1987282341 -6.6096094316
33 -2.9711637253 3.1987282341
34 1.0835582843 -2.9711637253
35 -2.8407934037 1.0835582843
36 -3.8919087617 -2.8407934037
37 1.4455433805 -3.8919087617
38 -4.9002356145 1.4455433805
39 0.4244704981 -4.9002356145
40 2.6763377953 0.4244704981
41 -0.4018626254 2.6763377953
42 3.9795497061 -0.4018626254
43 1.0520404890 3.9795497061
44 5.6065184922 1.0520404890
45 1.8985498655 5.6065184922
46 0.3926568158 1.8985498655
47 -2.1779138470 0.3926568158
48 -0.3670480390 -2.1779138470
49 3.8443909284 -0.3670480390
50 -4.9189966860 3.8443909284
51 -0.9784114869 -4.9189966860
52 -1.9090946507 -0.9784114869
53 -3.3886430880 -1.9090946507
54 -1.6490755489 -3.3886430880
55 1.5037391253 -1.6490755489
56 3.9515880039 1.5037391253
57 -6.2072299082 3.9515880039
58 1.2161242167 -6.2072299082
59 0.1648988558 1.2161242167
60 -1.4723136432 0.1648988558
61 3.1178248405 -1.4723136432
62 -1.4877848874 3.1178248405
63 -3.8650355945 -1.4877848874
64 0.6743395116 -3.8650355945
65 -0.8609851222 0.6743395116
66 1.3651641624 -0.8609851222
67 -0.9087647977 1.3651641624
68 2.2556453446 -0.9087647977
69 -0.5585002956 2.2556453446
70 2.5421792789 -0.5585002956
71 -4.6672873387 2.5421792789
72 -2.5831038861 -4.6672873387
73 0.7430668343 -2.5831038861
74 2.5238351112 0.7430668343
75 6.5257825126 2.5238351112
76 1.2847689131 6.5257825126
77 1.0013446554 1.2847689131
78 -5.1710897703 1.0013446554
79 5.9122797056 -5.1710897703
80 -2.2212903068 5.9122797056
81 -1.0299192541 -2.2212903068
82 3.1586357303 -1.0299192541
83 -2.4177065566 3.1586357303
84 -0.1397193893 -2.4177065566
85 1.4938300969 -0.1397193893
86 -1.3204957879 1.4938300969
87 -3.9989520313 -1.3204957879
88 -0.3657627085 -3.9989520313
89 -4.4563279557 -0.3657627085
90 2.9842206688 -4.4563279557
91 -2.4104559855 2.9842206688
92 0.3786278834 -2.4104559855
93 -3.5672009027 0.3786278834
94 3.7034041856 -3.5672009027
95 2.3399544828 3.7034041856
96 1.9435203623 2.3399544828
97 2.4768248996 1.9435203623
98 -3.4047440057 2.4768248996
99 2.6489621241 -3.4047440057
100 2.3185803606 2.6489621241
101 -0.0476344543 2.3185803606
102 0.1643858206 -0.0476344543
103 -3.4166430169 0.1643858206
104 7.0289198847 -3.4166430169
105 2.9274658350 7.0289198847
106 4.6429325275 2.9274658350
107 1.7603696798 4.6429325275
108 6.0824868822 1.7603696798
109 -4.6150194358 6.0824868822
110 -2.6848556824 -4.6150194358
111 -5.9506271362 -2.6848556824
112 0.3115493509 -5.9506271362
113 2.3784067862 0.3115493509
114 2.9862271671 2.3784067862
115 -3.5538096007 2.9862271671
116 -3.2001459639 -3.5538096007
117 -1.8838054070 -3.2001459639
118 2.4794520781 -1.8838054070
119 -2.1510973038 2.4794520781
120 -0.3034041172 -2.1510973038
121 1.0411442803 -0.3034041172
122 0.4963184757 1.0411442803
123 2.7670018464 0.4963184757
124 -3.0075162042 2.7670018464
125 4.8614183582 -3.0075162042
126 7.3753416530 4.8614183582
127 -3.0000492431 7.3753416530
128 1.0623205569 -3.0000492431
129 -1.7978391683 1.0623205569
130 -4.9047290515 -1.7978391683
131 4.2209682489 -4.9047290515
132 -5.4267838958 4.2209682489
133 0.7016160478 -5.4267838958
134 -0.5737119551 0.7016160478
135 -0.2304408401 -0.5737119551
136 -0.9620285853 -0.2304408401
137 -4.4426213822 -0.9620285853
138 0.6198595849 -4.4426213822
139 -3.6026507259 0.6198595849
140 -2.1102116288 -3.6026507259
141 -5.8587167226 -2.1102116288
142 -5.2025389973 -5.8587167226
143 1.3911709043 -5.2025389973
144 6.4116320928 1.3911709043
145 0.4509530233 6.4116320928
146 0.9283532156 0.4509530233
147 -1.1832286715 0.9283532156
148 1.7138895523 -1.1832286715
149 1.0096288322 1.7138895523
150 6.5280515695 1.0096288322
151 -2.0030378787 6.5280515695
152 -1.5071110670 -2.0030378787
153 3.3642814447 -1.5071110670
154 -0.3018321963 3.3642814447
155 2.9842206688 -0.3018321963
156 -3.6211404065 2.9842206688
157 -3.0000492431 -3.6211404065
158 0.1461991604 -3.0000492431
159 -3.9138501061 0.1461991604
160 -1.4370741846 -3.9138501061
161 -2.8442479327 -1.4370741846
162 0.9272464285 -2.8442479327
163 4.7716277982 0.9272464285
164 -1.4730275962 4.7716277982
165 -7.0763884256 -1.4730275962
166 -3.4942146397 -7.0763884256
167 -3.6204911806 -3.4942146397
168 -1.4317784989 -3.6204911806
169 -7.3099618446 -1.4317784989
170 5.3051486288 -7.3099618446
171 0.8828820305 5.3051486288
172 6.8574396360 0.8828820305
173 -0.1823689077 6.8574396360
174 4.8475596295 -0.1823689077
175 -2.0127749211 4.8475596295
176 3.8826375820 -2.0127749211
177 -1.5048233745 3.8826375820
178 -1.4823396623 -1.5048233745
179 3.4224063556 -1.4823396623
180 1.6938811339 3.4224063556
181 2.9916887886 1.6938811339
182 -3.8228083112 2.9916887886
183 4.9470166962 -3.8228083112
184 -9.6709665748 4.9470166962
185 -2.1096580348 -9.6709665748
186 2.5410286527 -2.1096580348
187 6.3641229215 2.5410286527
188 -2.2905926159 6.3641229215
189 -2.1100943888 -2.2905926159
190 -0.3961449433 -2.1100943888
191 1.6777769511 -0.3961449433
192 -1.0881535502 1.6777769511
193 0.0156222686 -1.0881535502
194 -0.8189494295 0.0156222686
195 1.4277587133 -0.8189494295
196 1.7493621664 1.4277587133
197 -2.2403677449 1.7493621664
198 0.4287472301 -2.2403677449
199 -6.5839322432 0.4287472301
200 -1.8549806094 -6.5839322432
201 -7.7211169425 -1.8549806094
202 1.8317190904 -7.7211169425
203 1.3568021824 1.8317190904
204 -0.6957843675 1.3568021824
205 -0.6276257361 -0.6957843675
206 -0.7011405676 -0.6276257361
207 0.9641030357 -0.7011405676
208 2.6856228912 0.9641030357
209 0.5386485067 2.6856228912
210 1.0536861989 0.5386485067
211 2.3422494109 1.0536861989
212 -4.0285742067 2.3422494109
213 -2.9831230285 -4.0285742067
214 1.2924453115 -2.9831230285
215 -1.8155129395 1.2924453115
216 -1.5855396168 -1.8155129395
217 4.8604024091 -1.5855396168
218 2.7629026435 4.8604024091
219 -0.3100582852 2.7629026435
220 1.6933758564 -0.3100582852
221 4.1424360195 1.6933758564
222 0.4039306842 4.1424360195
223 -2.0355693434 0.4039306842
224 2.0165531409 -2.0355693434
225 -2.6692152560 2.0165531409
226 -1.6533363287 -2.6692152560
227 0.0008526166 -1.6533363287
228 1.2245974436 0.0008526166
229 -0.0676251791 1.2245974436
230 2.0722816937 -0.0676251791
231 -2.4730094286 2.0722816937
232 -1.6593846455 -2.4730094286
233 7.6637808468 -1.6593846455
234 0.9386693756 7.6637808468
235 3.7135367665 0.9386693756
236 4.7700636538 3.7135367665
237 5.5228552553 4.7700636538
238 -3.3107159299 5.5228552553
239 5.0607576105 -3.3107159299
240 -6.1718740779 5.0607576105
241 1.4450287960 -6.1718740779
242 -2.5410454270 1.4450287960
243 2.0825891835 -2.5410454270
244 4.0693635085 2.0825891835
245 -4.6845239333 4.0693635085
246 -2.1191886664 -4.6845239333
247 3.9514172234 -2.1191886664
248 -8.5185318013 3.9514172234
249 -5.7581919090 -8.5185318013
250 -2.5405416981 -5.7581919090
251 0.3226311576 -2.5405416981
252 2.1892159374 0.3226311576
253 0.0660713380 2.1892159374
254 3.7389617498 0.0660713380
255 0.7108325708 3.7389617498
256 0.6906325108 0.7108325708
257 -1.5440010185 0.6906325108
258 3.4476885005 -1.5440010185
259 2.5448168649 3.4476885005
260 -4.7240569046 2.5448168649
261 -1.3835078852 -4.7240569046
262 6.0538173037 -1.3835078852
263 -3.7656328622 6.0538173037
264 NA -3.7656328622
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.4511606950 4.6673482414
[2,] -5.4068761460 4.4511606950
[3,] -3.4277531544 -5.4068761460
[4,] -2.0214947671 -3.4277531544
[5,] 2.0233702703 -2.0214947671
[6,] 5.5786409849 2.0233702703
[7,] -2.1447154359 5.5786409849
[8,] 0.4028214660 -2.1447154359
[9,] 0.1134991892 0.4028214660
[10,] 3.9006498062 0.1134991892
[11,] 1.0647578413 3.9006498062
[12,] 2.7246245031 1.0647578413
[13,] 2.6551730583 2.7246245031
[14,] -3.8262203833 2.6551730583
[15,] -2.2812643400 -3.8262203833
[16,] 1.9687943555 -2.2812643400
[17,] 0.7532520633 1.9687943555
[18,] 2.0868727285 0.7532520633
[19,] -1.9805085596 2.0868727285
[20,] -2.7379016129 -1.9805085596
[21,] -2.7804666562 -2.7379016129
[22,] 2.5953676150 -2.7804666562
[23,] -0.0654363646 2.5953676150
[24,] 4.4994829804 -0.0654363646
[25,] 6.8479819903 4.4994829804
[26,] 0.7713778020 6.8479819903
[27,] -3.3711080954 0.7713778020
[28,] -1.3479312401 -3.3711080954
[29,] -0.7349322458 -1.3479312401
[30,] -3.0847866501 -0.7349322458
[31,] -6.6096094316 -3.0847866501
[32,] 3.1987282341 -6.6096094316
[33,] -2.9711637253 3.1987282341
[34,] 1.0835582843 -2.9711637253
[35,] -2.8407934037 1.0835582843
[36,] -3.8919087617 -2.8407934037
[37,] 1.4455433805 -3.8919087617
[38,] -4.9002356145 1.4455433805
[39,] 0.4244704981 -4.9002356145
[40,] 2.6763377953 0.4244704981
[41,] -0.4018626254 2.6763377953
[42,] 3.9795497061 -0.4018626254
[43,] 1.0520404890 3.9795497061
[44,] 5.6065184922 1.0520404890
[45,] 1.8985498655 5.6065184922
[46,] 0.3926568158 1.8985498655
[47,] -2.1779138470 0.3926568158
[48,] -0.3670480390 -2.1779138470
[49,] 3.8443909284 -0.3670480390
[50,] -4.9189966860 3.8443909284
[51,] -0.9784114869 -4.9189966860
[52,] -1.9090946507 -0.9784114869
[53,] -3.3886430880 -1.9090946507
[54,] -1.6490755489 -3.3886430880
[55,] 1.5037391253 -1.6490755489
[56,] 3.9515880039 1.5037391253
[57,] -6.2072299082 3.9515880039
[58,] 1.2161242167 -6.2072299082
[59,] 0.1648988558 1.2161242167
[60,] -1.4723136432 0.1648988558
[61,] 3.1178248405 -1.4723136432
[62,] -1.4877848874 3.1178248405
[63,] -3.8650355945 -1.4877848874
[64,] 0.6743395116 -3.8650355945
[65,] -0.8609851222 0.6743395116
[66,] 1.3651641624 -0.8609851222
[67,] -0.9087647977 1.3651641624
[68,] 2.2556453446 -0.9087647977
[69,] -0.5585002956 2.2556453446
[70,] 2.5421792789 -0.5585002956
[71,] -4.6672873387 2.5421792789
[72,] -2.5831038861 -4.6672873387
[73,] 0.7430668343 -2.5831038861
[74,] 2.5238351112 0.7430668343
[75,] 6.5257825126 2.5238351112
[76,] 1.2847689131 6.5257825126
[77,] 1.0013446554 1.2847689131
[78,] -5.1710897703 1.0013446554
[79,] 5.9122797056 -5.1710897703
[80,] -2.2212903068 5.9122797056
[81,] -1.0299192541 -2.2212903068
[82,] 3.1586357303 -1.0299192541
[83,] -2.4177065566 3.1586357303
[84,] -0.1397193893 -2.4177065566
[85,] 1.4938300969 -0.1397193893
[86,] -1.3204957879 1.4938300969
[87,] -3.9989520313 -1.3204957879
[88,] -0.3657627085 -3.9989520313
[89,] -4.4563279557 -0.3657627085
[90,] 2.9842206688 -4.4563279557
[91,] -2.4104559855 2.9842206688
[92,] 0.3786278834 -2.4104559855
[93,] -3.5672009027 0.3786278834
[94,] 3.7034041856 -3.5672009027
[95,] 2.3399544828 3.7034041856
[96,] 1.9435203623 2.3399544828
[97,] 2.4768248996 1.9435203623
[98,] -3.4047440057 2.4768248996
[99,] 2.6489621241 -3.4047440057
[100,] 2.3185803606 2.6489621241
[101,] -0.0476344543 2.3185803606
[102,] 0.1643858206 -0.0476344543
[103,] -3.4166430169 0.1643858206
[104,] 7.0289198847 -3.4166430169
[105,] 2.9274658350 7.0289198847
[106,] 4.6429325275 2.9274658350
[107,] 1.7603696798 4.6429325275
[108,] 6.0824868822 1.7603696798
[109,] -4.6150194358 6.0824868822
[110,] -2.6848556824 -4.6150194358
[111,] -5.9506271362 -2.6848556824
[112,] 0.3115493509 -5.9506271362
[113,] 2.3784067862 0.3115493509
[114,] 2.9862271671 2.3784067862
[115,] -3.5538096007 2.9862271671
[116,] -3.2001459639 -3.5538096007
[117,] -1.8838054070 -3.2001459639
[118,] 2.4794520781 -1.8838054070
[119,] -2.1510973038 2.4794520781
[120,] -0.3034041172 -2.1510973038
[121,] 1.0411442803 -0.3034041172
[122,] 0.4963184757 1.0411442803
[123,] 2.7670018464 0.4963184757
[124,] -3.0075162042 2.7670018464
[125,] 4.8614183582 -3.0075162042
[126,] 7.3753416530 4.8614183582
[127,] -3.0000492431 7.3753416530
[128,] 1.0623205569 -3.0000492431
[129,] -1.7978391683 1.0623205569
[130,] -4.9047290515 -1.7978391683
[131,] 4.2209682489 -4.9047290515
[132,] -5.4267838958 4.2209682489
[133,] 0.7016160478 -5.4267838958
[134,] -0.5737119551 0.7016160478
[135,] -0.2304408401 -0.5737119551
[136,] -0.9620285853 -0.2304408401
[137,] -4.4426213822 -0.9620285853
[138,] 0.6198595849 -4.4426213822
[139,] -3.6026507259 0.6198595849
[140,] -2.1102116288 -3.6026507259
[141,] -5.8587167226 -2.1102116288
[142,] -5.2025389973 -5.8587167226
[143,] 1.3911709043 -5.2025389973
[144,] 6.4116320928 1.3911709043
[145,] 0.4509530233 6.4116320928
[146,] 0.9283532156 0.4509530233
[147,] -1.1832286715 0.9283532156
[148,] 1.7138895523 -1.1832286715
[149,] 1.0096288322 1.7138895523
[150,] 6.5280515695 1.0096288322
[151,] -2.0030378787 6.5280515695
[152,] -1.5071110670 -2.0030378787
[153,] 3.3642814447 -1.5071110670
[154,] -0.3018321963 3.3642814447
[155,] 2.9842206688 -0.3018321963
[156,] -3.6211404065 2.9842206688
[157,] -3.0000492431 -3.6211404065
[158,] 0.1461991604 -3.0000492431
[159,] -3.9138501061 0.1461991604
[160,] -1.4370741846 -3.9138501061
[161,] -2.8442479327 -1.4370741846
[162,] 0.9272464285 -2.8442479327
[163,] 4.7716277982 0.9272464285
[164,] -1.4730275962 4.7716277982
[165,] -7.0763884256 -1.4730275962
[166,] -3.4942146397 -7.0763884256
[167,] -3.6204911806 -3.4942146397
[168,] -1.4317784989 -3.6204911806
[169,] -7.3099618446 -1.4317784989
[170,] 5.3051486288 -7.3099618446
[171,] 0.8828820305 5.3051486288
[172,] 6.8574396360 0.8828820305
[173,] -0.1823689077 6.8574396360
[174,] 4.8475596295 -0.1823689077
[175,] -2.0127749211 4.8475596295
[176,] 3.8826375820 -2.0127749211
[177,] -1.5048233745 3.8826375820
[178,] -1.4823396623 -1.5048233745
[179,] 3.4224063556 -1.4823396623
[180,] 1.6938811339 3.4224063556
[181,] 2.9916887886 1.6938811339
[182,] -3.8228083112 2.9916887886
[183,] 4.9470166962 -3.8228083112
[184,] -9.6709665748 4.9470166962
[185,] -2.1096580348 -9.6709665748
[186,] 2.5410286527 -2.1096580348
[187,] 6.3641229215 2.5410286527
[188,] -2.2905926159 6.3641229215
[189,] -2.1100943888 -2.2905926159
[190,] -0.3961449433 -2.1100943888
[191,] 1.6777769511 -0.3961449433
[192,] -1.0881535502 1.6777769511
[193,] 0.0156222686 -1.0881535502
[194,] -0.8189494295 0.0156222686
[195,] 1.4277587133 -0.8189494295
[196,] 1.7493621664 1.4277587133
[197,] -2.2403677449 1.7493621664
[198,] 0.4287472301 -2.2403677449
[199,] -6.5839322432 0.4287472301
[200,] -1.8549806094 -6.5839322432
[201,] -7.7211169425 -1.8549806094
[202,] 1.8317190904 -7.7211169425
[203,] 1.3568021824 1.8317190904
[204,] -0.6957843675 1.3568021824
[205,] -0.6276257361 -0.6957843675
[206,] -0.7011405676 -0.6276257361
[207,] 0.9641030357 -0.7011405676
[208,] 2.6856228912 0.9641030357
[209,] 0.5386485067 2.6856228912
[210,] 1.0536861989 0.5386485067
[211,] 2.3422494109 1.0536861989
[212,] -4.0285742067 2.3422494109
[213,] -2.9831230285 -4.0285742067
[214,] 1.2924453115 -2.9831230285
[215,] -1.8155129395 1.2924453115
[216,] -1.5855396168 -1.8155129395
[217,] 4.8604024091 -1.5855396168
[218,] 2.7629026435 4.8604024091
[219,] -0.3100582852 2.7629026435
[220,] 1.6933758564 -0.3100582852
[221,] 4.1424360195 1.6933758564
[222,] 0.4039306842 4.1424360195
[223,] -2.0355693434 0.4039306842
[224,] 2.0165531409 -2.0355693434
[225,] -2.6692152560 2.0165531409
[226,] -1.6533363287 -2.6692152560
[227,] 0.0008526166 -1.6533363287
[228,] 1.2245974436 0.0008526166
[229,] -0.0676251791 1.2245974436
[230,] 2.0722816937 -0.0676251791
[231,] -2.4730094286 2.0722816937
[232,] -1.6593846455 -2.4730094286
[233,] 7.6637808468 -1.6593846455
[234,] 0.9386693756 7.6637808468
[235,] 3.7135367665 0.9386693756
[236,] 4.7700636538 3.7135367665
[237,] 5.5228552553 4.7700636538
[238,] -3.3107159299 5.5228552553
[239,] 5.0607576105 -3.3107159299
[240,] -6.1718740779 5.0607576105
[241,] 1.4450287960 -6.1718740779
[242,] -2.5410454270 1.4450287960
[243,] 2.0825891835 -2.5410454270
[244,] 4.0693635085 2.0825891835
[245,] -4.6845239333 4.0693635085
[246,] -2.1191886664 -4.6845239333
[247,] 3.9514172234 -2.1191886664
[248,] -8.5185318013 3.9514172234
[249,] -5.7581919090 -8.5185318013
[250,] -2.5405416981 -5.7581919090
[251,] 0.3226311576 -2.5405416981
[252,] 2.1892159374 0.3226311576
[253,] 0.0660713380 2.1892159374
[254,] 3.7389617498 0.0660713380
[255,] 0.7108325708 3.7389617498
[256,] 0.6906325108 0.7108325708
[257,] -1.5440010185 0.6906325108
[258,] 3.4476885005 -1.5440010185
[259,] 2.5448168649 3.4476885005
[260,] -4.7240569046 2.5448168649
[261,] -1.3835078852 -4.7240569046
[262,] 6.0538173037 -1.3835078852
[263,] -3.7656328622 6.0538173037
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.4511606950 4.6673482414
2 -5.4068761460 4.4511606950
3 -3.4277531544 -5.4068761460
4 -2.0214947671 -3.4277531544
5 2.0233702703 -2.0214947671
6 5.5786409849 2.0233702703
7 -2.1447154359 5.5786409849
8 0.4028214660 -2.1447154359
9 0.1134991892 0.4028214660
10 3.9006498062 0.1134991892
11 1.0647578413 3.9006498062
12 2.7246245031 1.0647578413
13 2.6551730583 2.7246245031
14 -3.8262203833 2.6551730583
15 -2.2812643400 -3.8262203833
16 1.9687943555 -2.2812643400
17 0.7532520633 1.9687943555
18 2.0868727285 0.7532520633
19 -1.9805085596 2.0868727285
20 -2.7379016129 -1.9805085596
21 -2.7804666562 -2.7379016129
22 2.5953676150 -2.7804666562
23 -0.0654363646 2.5953676150
24 4.4994829804 -0.0654363646
25 6.8479819903 4.4994829804
26 0.7713778020 6.8479819903
27 -3.3711080954 0.7713778020
28 -1.3479312401 -3.3711080954
29 -0.7349322458 -1.3479312401
30 -3.0847866501 -0.7349322458
31 -6.6096094316 -3.0847866501
32 3.1987282341 -6.6096094316
33 -2.9711637253 3.1987282341
34 1.0835582843 -2.9711637253
35 -2.8407934037 1.0835582843
36 -3.8919087617 -2.8407934037
37 1.4455433805 -3.8919087617
38 -4.9002356145 1.4455433805
39 0.4244704981 -4.9002356145
40 2.6763377953 0.4244704981
41 -0.4018626254 2.6763377953
42 3.9795497061 -0.4018626254
43 1.0520404890 3.9795497061
44 5.6065184922 1.0520404890
45 1.8985498655 5.6065184922
46 0.3926568158 1.8985498655
47 -2.1779138470 0.3926568158
48 -0.3670480390 -2.1779138470
49 3.8443909284 -0.3670480390
50 -4.9189966860 3.8443909284
51 -0.9784114869 -4.9189966860
52 -1.9090946507 -0.9784114869
53 -3.3886430880 -1.9090946507
54 -1.6490755489 -3.3886430880
55 1.5037391253 -1.6490755489
56 3.9515880039 1.5037391253
57 -6.2072299082 3.9515880039
58 1.2161242167 -6.2072299082
59 0.1648988558 1.2161242167
60 -1.4723136432 0.1648988558
61 3.1178248405 -1.4723136432
62 -1.4877848874 3.1178248405
63 -3.8650355945 -1.4877848874
64 0.6743395116 -3.8650355945
65 -0.8609851222 0.6743395116
66 1.3651641624 -0.8609851222
67 -0.9087647977 1.3651641624
68 2.2556453446 -0.9087647977
69 -0.5585002956 2.2556453446
70 2.5421792789 -0.5585002956
71 -4.6672873387 2.5421792789
72 -2.5831038861 -4.6672873387
73 0.7430668343 -2.5831038861
74 2.5238351112 0.7430668343
75 6.5257825126 2.5238351112
76 1.2847689131 6.5257825126
77 1.0013446554 1.2847689131
78 -5.1710897703 1.0013446554
79 5.9122797056 -5.1710897703
80 -2.2212903068 5.9122797056
81 -1.0299192541 -2.2212903068
82 3.1586357303 -1.0299192541
83 -2.4177065566 3.1586357303
84 -0.1397193893 -2.4177065566
85 1.4938300969 -0.1397193893
86 -1.3204957879 1.4938300969
87 -3.9989520313 -1.3204957879
88 -0.3657627085 -3.9989520313
89 -4.4563279557 -0.3657627085
90 2.9842206688 -4.4563279557
91 -2.4104559855 2.9842206688
92 0.3786278834 -2.4104559855
93 -3.5672009027 0.3786278834
94 3.7034041856 -3.5672009027
95 2.3399544828 3.7034041856
96 1.9435203623 2.3399544828
97 2.4768248996 1.9435203623
98 -3.4047440057 2.4768248996
99 2.6489621241 -3.4047440057
100 2.3185803606 2.6489621241
101 -0.0476344543 2.3185803606
102 0.1643858206 -0.0476344543
103 -3.4166430169 0.1643858206
104 7.0289198847 -3.4166430169
105 2.9274658350 7.0289198847
106 4.6429325275 2.9274658350
107 1.7603696798 4.6429325275
108 6.0824868822 1.7603696798
109 -4.6150194358 6.0824868822
110 -2.6848556824 -4.6150194358
111 -5.9506271362 -2.6848556824
112 0.3115493509 -5.9506271362
113 2.3784067862 0.3115493509
114 2.9862271671 2.3784067862
115 -3.5538096007 2.9862271671
116 -3.2001459639 -3.5538096007
117 -1.8838054070 -3.2001459639
118 2.4794520781 -1.8838054070
119 -2.1510973038 2.4794520781
120 -0.3034041172 -2.1510973038
121 1.0411442803 -0.3034041172
122 0.4963184757 1.0411442803
123 2.7670018464 0.4963184757
124 -3.0075162042 2.7670018464
125 4.8614183582 -3.0075162042
126 7.3753416530 4.8614183582
127 -3.0000492431 7.3753416530
128 1.0623205569 -3.0000492431
129 -1.7978391683 1.0623205569
130 -4.9047290515 -1.7978391683
131 4.2209682489 -4.9047290515
132 -5.4267838958 4.2209682489
133 0.7016160478 -5.4267838958
134 -0.5737119551 0.7016160478
135 -0.2304408401 -0.5737119551
136 -0.9620285853 -0.2304408401
137 -4.4426213822 -0.9620285853
138 0.6198595849 -4.4426213822
139 -3.6026507259 0.6198595849
140 -2.1102116288 -3.6026507259
141 -5.8587167226 -2.1102116288
142 -5.2025389973 -5.8587167226
143 1.3911709043 -5.2025389973
144 6.4116320928 1.3911709043
145 0.4509530233 6.4116320928
146 0.9283532156 0.4509530233
147 -1.1832286715 0.9283532156
148 1.7138895523 -1.1832286715
149 1.0096288322 1.7138895523
150 6.5280515695 1.0096288322
151 -2.0030378787 6.5280515695
152 -1.5071110670 -2.0030378787
153 3.3642814447 -1.5071110670
154 -0.3018321963 3.3642814447
155 2.9842206688 -0.3018321963
156 -3.6211404065 2.9842206688
157 -3.0000492431 -3.6211404065
158 0.1461991604 -3.0000492431
159 -3.9138501061 0.1461991604
160 -1.4370741846 -3.9138501061
161 -2.8442479327 -1.4370741846
162 0.9272464285 -2.8442479327
163 4.7716277982 0.9272464285
164 -1.4730275962 4.7716277982
165 -7.0763884256 -1.4730275962
166 -3.4942146397 -7.0763884256
167 -3.6204911806 -3.4942146397
168 -1.4317784989 -3.6204911806
169 -7.3099618446 -1.4317784989
170 5.3051486288 -7.3099618446
171 0.8828820305 5.3051486288
172 6.8574396360 0.8828820305
173 -0.1823689077 6.8574396360
174 4.8475596295 -0.1823689077
175 -2.0127749211 4.8475596295
176 3.8826375820 -2.0127749211
177 -1.5048233745 3.8826375820
178 -1.4823396623 -1.5048233745
179 3.4224063556 -1.4823396623
180 1.6938811339 3.4224063556
181 2.9916887886 1.6938811339
182 -3.8228083112 2.9916887886
183 4.9470166962 -3.8228083112
184 -9.6709665748 4.9470166962
185 -2.1096580348 -9.6709665748
186 2.5410286527 -2.1096580348
187 6.3641229215 2.5410286527
188 -2.2905926159 6.3641229215
189 -2.1100943888 -2.2905926159
190 -0.3961449433 -2.1100943888
191 1.6777769511 -0.3961449433
192 -1.0881535502 1.6777769511
193 0.0156222686 -1.0881535502
194 -0.8189494295 0.0156222686
195 1.4277587133 -0.8189494295
196 1.7493621664 1.4277587133
197 -2.2403677449 1.7493621664
198 0.4287472301 -2.2403677449
199 -6.5839322432 0.4287472301
200 -1.8549806094 -6.5839322432
201 -7.7211169425 -1.8549806094
202 1.8317190904 -7.7211169425
203 1.3568021824 1.8317190904
204 -0.6957843675 1.3568021824
205 -0.6276257361 -0.6957843675
206 -0.7011405676 -0.6276257361
207 0.9641030357 -0.7011405676
208 2.6856228912 0.9641030357
209 0.5386485067 2.6856228912
210 1.0536861989 0.5386485067
211 2.3422494109 1.0536861989
212 -4.0285742067 2.3422494109
213 -2.9831230285 -4.0285742067
214 1.2924453115 -2.9831230285
215 -1.8155129395 1.2924453115
216 -1.5855396168 -1.8155129395
217 4.8604024091 -1.5855396168
218 2.7629026435 4.8604024091
219 -0.3100582852 2.7629026435
220 1.6933758564 -0.3100582852
221 4.1424360195 1.6933758564
222 0.4039306842 4.1424360195
223 -2.0355693434 0.4039306842
224 2.0165531409 -2.0355693434
225 -2.6692152560 2.0165531409
226 -1.6533363287 -2.6692152560
227 0.0008526166 -1.6533363287
228 1.2245974436 0.0008526166
229 -0.0676251791 1.2245974436
230 2.0722816937 -0.0676251791
231 -2.4730094286 2.0722816937
232 -1.6593846455 -2.4730094286
233 7.6637808468 -1.6593846455
234 0.9386693756 7.6637808468
235 3.7135367665 0.9386693756
236 4.7700636538 3.7135367665
237 5.5228552553 4.7700636538
238 -3.3107159299 5.5228552553
239 5.0607576105 -3.3107159299
240 -6.1718740779 5.0607576105
241 1.4450287960 -6.1718740779
242 -2.5410454270 1.4450287960
243 2.0825891835 -2.5410454270
244 4.0693635085 2.0825891835
245 -4.6845239333 4.0693635085
246 -2.1191886664 -4.6845239333
247 3.9514172234 -2.1191886664
248 -8.5185318013 3.9514172234
249 -5.7581919090 -8.5185318013
250 -2.5405416981 -5.7581919090
251 0.3226311576 -2.5405416981
252 2.1892159374 0.3226311576
253 0.0660713380 2.1892159374
254 3.7389617498 0.0660713380
255 0.7108325708 3.7389617498
256 0.6906325108 0.7108325708
257 -1.5440010185 0.6906325108
258 3.4476885005 -1.5440010185
259 2.5448168649 3.4476885005
260 -4.7240569046 2.5448168649
261 -1.3835078852 -4.7240569046
262 6.0538173037 -1.3835078852
263 -3.7656328622 6.0538173037
> 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/wessaorg/rcomp/tmp/7aj4f1384707620.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/821im1384707620.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9o0er1384707620.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10w0zx1384707620.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$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/wessaorg/rcomp/tmp/11akgs1384707620.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$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12nlq31384707620.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, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/134yvs1384707620.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,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14u7ht1384707620.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,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15386q1384707620.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,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ 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,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ 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,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ 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/wessaorg/rcomp/tmp/16w1uy1384707620.tab")
+ }
>
> try(system("convert tmp/1834c1384707620.ps tmp/1834c1384707620.png",intern=TRUE))
character(0)
> try(system("convert tmp/2d2751384707620.ps tmp/2d2751384707620.png",intern=TRUE))
character(0)
> try(system("convert tmp/36yu01384707620.ps tmp/36yu01384707620.png",intern=TRUE))
character(0)
> try(system("convert tmp/4nfxb1384707620.ps tmp/4nfxb1384707620.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ebmy1384707620.ps tmp/5ebmy1384707620.png",intern=TRUE))
character(0)
> try(system("convert tmp/61oiq1384707620.ps tmp/61oiq1384707620.png",intern=TRUE))
character(0)
> try(system("convert tmp/7aj4f1384707620.ps tmp/7aj4f1384707620.png",intern=TRUE))
character(0)
> try(system("convert tmp/821im1384707620.ps tmp/821im1384707620.png",intern=TRUE))
character(0)
> try(system("convert tmp/9o0er1384707620.ps tmp/9o0er1384707620.png",intern=TRUE))
character(0)
> try(system("convert tmp/10w0zx1384707620.ps tmp/10w0zx1384707620.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
15.925 3.089 19.119