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(41
+ ,38
+ ,13
+ ,12
+ ,14
+ ,12
+ ,53
+ ,32
+ ,39
+ ,32
+ ,16
+ ,11
+ ,18
+ ,11
+ ,83
+ ,51
+ ,30
+ ,35
+ ,19
+ ,15
+ ,11
+ ,14
+ ,66
+ ,42
+ ,31
+ ,33
+ ,15
+ ,6
+ ,12
+ ,12
+ ,67
+ ,41
+ ,34
+ ,37
+ ,14
+ ,13
+ ,16
+ ,21
+ ,76
+ ,46
+ ,35
+ ,29
+ ,13
+ ,10
+ ,18
+ ,12
+ ,78
+ ,47
+ ,39
+ ,31
+ ,19
+ ,12
+ ,14
+ ,22
+ ,53
+ ,37
+ ,34
+ ,36
+ ,15
+ ,14
+ ,14
+ ,11
+ ,80
+ ,49
+ ,36
+ ,35
+ ,14
+ ,12
+ ,15
+ ,10
+ ,74
+ ,45
+ ,37
+ ,38
+ ,15
+ ,9
+ ,15
+ ,13
+ ,76
+ ,47
+ ,38
+ ,31
+ ,16
+ ,10
+ ,17
+ ,10
+ ,79
+ ,49
+ ,36
+ ,34
+ ,16
+ ,12
+ ,19
+ ,8
+ ,54
+ ,33
+ ,38
+ ,35
+ ,16
+ ,12
+ ,10
+ ,15
+ ,67
+ ,42
+ ,39
+ ,38
+ ,16
+ ,11
+ ,16
+ ,14
+ ,54
+ ,33
+ ,33
+ ,37
+ ,17
+ ,15
+ ,18
+ ,10
+ ,87
+ ,53
+ ,32
+ ,33
+ ,15
+ ,12
+ ,14
+ ,14
+ ,58
+ ,36
+ ,36
+ ,32
+ ,15
+ ,10
+ ,14
+ ,14
+ ,75
+ ,45
+ ,38
+ ,38
+ ,20
+ ,12
+ ,17
+ ,11
+ ,88
+ ,54
+ ,39
+ ,38
+ ,18
+ ,11
+ ,14
+ ,10
+ ,64
+ ,41
+ ,32
+ ,32
+ ,16
+ ,12
+ ,16
+ ,13
+ ,57
+ ,36
+ ,32
+ ,33
+ ,16
+ ,11
+ ,18
+ ,9.5
+ ,66
+ ,41
+ ,31
+ ,31
+ ,16
+ ,12
+ ,11
+ ,14
+ ,68
+ ,44
+ ,39
+ ,38
+ ,19
+ ,13
+ ,14
+ ,12
+ ,54
+ ,33
+ ,37
+ ,39
+ ,16
+ ,11
+ ,12
+ ,14
+ ,56
+ ,37
+ ,39
+ ,32
+ ,17
+ ,12
+ ,17
+ ,11
+ ,86
+ ,52
+ ,41
+ ,32
+ ,17
+ ,13
+ ,9
+ ,9
+ ,80
+ ,47
+ ,36
+ ,35
+ ,16
+ ,10
+ ,16
+ ,11
+ ,76
+ ,43
+ ,33
+ ,37
+ ,15
+ ,14
+ ,14
+ ,15
+ ,69
+ ,44
+ ,33
+ ,33
+ ,16
+ ,12
+ ,15
+ ,14
+ ,78
+ ,45
+ ,34
+ ,33
+ ,14
+ ,10
+ ,11
+ ,13
+ ,67
+ ,44
+ ,31
+ ,31
+ ,15
+ ,12
+ ,16
+ ,9
+ ,80
+ ,49
+ ,27
+ ,32
+ ,12
+ ,8
+ ,13
+ ,15
+ ,54
+ ,33
+ ,37
+ ,31
+ ,14
+ ,10
+ ,17
+ ,10
+ ,71
+ ,43
+ ,34
+ ,37
+ ,16
+ ,12
+ ,15
+ ,11
+ ,84
+ ,54
+ ,34
+ ,30
+ ,14
+ ,12
+ ,14
+ ,13
+ ,74
+ ,42
+ ,32
+ ,33
+ ,10
+ ,7
+ ,16
+ ,8
+ ,71
+ ,44
+ ,29
+ ,31
+ ,10
+ ,9
+ ,9
+ ,20
+ ,63
+ ,37
+ ,36
+ ,33
+ ,14
+ ,12
+ ,15
+ ,12
+ ,71
+ ,43
+ ,29
+ ,31
+ ,16
+ ,10
+ ,17
+ ,10
+ ,76
+ ,46
+ ,35
+ ,33
+ ,16
+ ,10
+ ,13
+ ,10
+ ,69
+ ,42
+ ,37
+ ,32
+ ,16
+ ,10
+ ,15
+ ,9
+ ,74
+ ,45
+ ,34
+ ,33
+ ,14
+ ,12
+ ,16
+ ,14
+ ,75
+ ,44
+ ,38
+ ,32
+ ,20
+ ,15
+ ,16
+ ,8
+ ,54
+ ,33
+ ,35
+ ,33
+ ,14
+ ,10
+ ,12
+ ,14
+ ,52
+ ,31
+ ,38
+ ,28
+ ,14
+ ,10
+ ,15
+ ,11
+ ,69
+ ,42
+ ,37
+ ,35
+ ,11
+ ,12
+ ,11
+ ,13
+ ,68
+ ,40
+ ,38
+ ,39
+ ,14
+ ,13
+ ,15
+ ,9
+ ,65
+ ,43
+ ,33
+ ,34
+ ,15
+ ,11
+ ,15
+ ,11
+ ,75
+ ,46
+ ,36
+ ,38
+ ,16
+ ,11
+ ,17
+ ,15
+ ,74
+ ,42
+ ,38
+ ,32
+ ,14
+ ,12
+ ,13
+ ,11
+ ,75
+ ,45
+ ,32
+ ,38
+ ,16
+ ,14
+ ,16
+ ,10
+ ,72
+ ,44
+ ,32
+ ,30
+ ,14
+ ,10
+ ,14
+ ,14
+ ,67
+ ,40
+ ,32
+ ,33
+ ,12
+ ,12
+ ,11
+ ,18
+ ,63
+ ,37
+ ,34
+ ,38
+ ,16
+ ,13
+ ,12
+ ,14
+ ,62
+ ,46
+ ,32
+ ,32
+ ,9
+ ,5
+ ,12
+ ,11
+ ,63
+ ,36
+ ,37
+ ,35
+ ,14
+ ,6
+ ,15
+ ,14.5
+ ,76
+ ,47
+ ,39
+ ,34
+ ,16
+ ,12
+ ,16
+ ,13
+ ,74
+ ,45
+ ,29
+ ,34
+ ,16
+ ,12
+ ,15
+ ,9
+ ,67
+ ,42
+ ,37
+ ,36
+ ,15
+ ,11
+ ,12
+ ,10
+ ,73
+ ,43
+ ,35
+ ,34
+ ,16
+ ,10
+ ,12
+ ,15
+ ,70
+ ,43
+ ,30
+ ,28
+ ,12
+ ,7
+ ,8
+ ,20
+ ,53
+ ,32
+ ,38
+ ,34
+ ,16
+ ,12
+ ,13
+ ,12
+ ,77
+ ,45
+ ,34
+ ,35
+ ,16
+ ,14
+ ,11
+ ,12
+ ,80
+ ,48
+ ,31
+ ,35
+ ,14
+ ,11
+ ,14
+ ,14
+ ,52
+ ,31
+ ,34
+ ,31
+ ,16
+ ,12
+ ,15
+ ,13
+ ,54
+ ,33
+ ,35
+ ,37
+ ,17
+ ,13
+ ,10
+ ,11
+ ,80
+ ,49
+ ,36
+ ,35
+ ,18
+ ,14
+ ,11
+ ,17
+ ,66
+ ,42
+ ,30
+ ,27
+ ,18
+ ,11
+ ,12
+ ,12
+ ,73
+ ,41
+ ,39
+ ,40
+ ,12
+ ,12
+ ,15
+ ,13
+ ,63
+ ,38
+ ,35
+ ,37
+ ,16
+ ,12
+ ,15
+ ,14
+ ,69
+ ,42
+ ,38
+ ,36
+ ,10
+ ,8
+ ,14
+ ,13
+ ,67
+ ,44
+ ,31
+ ,38
+ ,14
+ ,11
+ ,16
+ ,15
+ ,54
+ ,33
+ ,34
+ ,39
+ ,18
+ ,14
+ ,15
+ ,13
+ ,81
+ ,48
+ ,38
+ ,41
+ ,18
+ ,14
+ ,15
+ ,10
+ ,69
+ ,40
+ ,34
+ ,27
+ ,16
+ ,12
+ ,13
+ ,11
+ ,84
+ ,50
+ ,39
+ ,30
+ ,17
+ ,9
+ ,12
+ ,19
+ ,80
+ ,49
+ ,37
+ ,37
+ ,16
+ ,13
+ ,17
+ ,13
+ ,70
+ ,43
+ ,34
+ ,31
+ ,16
+ ,11
+ ,13
+ ,17
+ ,69
+ ,44
+ ,28
+ ,31
+ ,13
+ ,12
+ ,15
+ ,13
+ ,77
+ ,47
+ ,37
+ ,27
+ ,16
+ ,12
+ ,13
+ ,9
+ ,54
+ ,33
+ ,33
+ ,36
+ ,16
+ ,12
+ ,15
+ ,11
+ ,79
+ ,46
+ ,35
+ ,37
+ ,16
+ ,12
+ ,15
+ ,9
+ ,71
+ ,45
+ ,37
+ ,33
+ ,15
+ ,12
+ ,16
+ ,12
+ ,73
+ ,43
+ ,32
+ ,34
+ ,15
+ ,11
+ ,15
+ ,12
+ ,72
+ ,44
+ ,33
+ ,31
+ ,16
+ ,10
+ ,14
+ ,13
+ ,77
+ ,47
+ ,38
+ ,39
+ ,14
+ ,9
+ ,15
+ ,13
+ ,75
+ ,45
+ ,33
+ ,34
+ ,16
+ ,12
+ ,14
+ ,12
+ ,69
+ ,42
+ ,29
+ ,32
+ ,16
+ ,12
+ ,13
+ ,15
+ ,54
+ ,33
+ ,33
+ ,33
+ ,15
+ ,12
+ ,7
+ ,22
+ ,70
+ ,43
+ ,31
+ ,36
+ ,12
+ ,9
+ ,17
+ ,13
+ ,73
+ ,46
+ ,36
+ ,32
+ ,17
+ ,15
+ ,13
+ ,15
+ ,54
+ ,33
+ ,35
+ ,41
+ ,16
+ ,12
+ ,15
+ ,13
+ ,77
+ ,46
+ ,32
+ ,28
+ ,15
+ ,12
+ ,14
+ ,15
+ ,82
+ ,48
+ ,29
+ ,30
+ ,13
+ ,12
+ ,13
+ ,12.5
+ ,80
+ ,47
+ ,39
+ ,36
+ ,16
+ ,10
+ ,16
+ ,11
+ ,80
+ ,47
+ ,37
+ ,35
+ ,16
+ ,13
+ ,12
+ ,16
+ ,69
+ ,43
+ ,35
+ ,31
+ ,16
+ ,9
+ ,14
+ ,11
+ ,78
+ ,46
+ ,37
+ ,34
+ ,16
+ ,12
+ ,17
+ ,11
+ ,81
+ ,48
+ ,32
+ ,36
+ ,14
+ ,10
+ ,15
+ ,10
+ ,76
+ ,46
+ ,38
+ ,36
+ ,16
+ ,14
+ ,17
+ ,10
+ ,76
+ ,45
+ ,37
+ ,35
+ ,16
+ ,11
+ ,12
+ ,16
+ ,73
+ ,45
+ ,36
+ ,37
+ ,20
+ ,15
+ ,16
+ ,12
+ ,85
+ ,52
+ ,32
+ ,28
+ ,15
+ ,11
+ ,11
+ ,11
+ ,66
+ ,42
+ ,33
+ ,39
+ ,16
+ ,11
+ ,15
+ ,16
+ ,79
+ ,47
+ ,40
+ ,32
+ ,13
+ ,12
+ ,9
+ ,19
+ ,68
+ ,41
+ ,38
+ ,35
+ ,17
+ ,12
+ ,16
+ ,11
+ ,76
+ ,47
+ ,41
+ ,39
+ ,16
+ ,12
+ ,15
+ ,16
+ ,71
+ ,43
+ ,36
+ ,35
+ ,16
+ ,11
+ ,10
+ ,15
+ ,54
+ ,33
+ ,43
+ ,42
+ ,12
+ ,7
+ ,10
+ ,24
+ ,46
+ ,30
+ ,30
+ ,34
+ ,16
+ ,12
+ ,15
+ ,14
+ ,85
+ ,52
+ ,31
+ ,33
+ ,16
+ ,14
+ ,11
+ ,15
+ ,74
+ ,44
+ ,32
+ ,41
+ ,17
+ ,11
+ ,13
+ ,11
+ ,88
+ ,55
+ ,32
+ ,33
+ ,13
+ ,11
+ ,14
+ ,15
+ ,38
+ ,11
+ ,37
+ ,34
+ ,12
+ ,10
+ ,18
+ ,12
+ ,76
+ ,47
+ ,37
+ ,32
+ ,18
+ ,13
+ ,16
+ ,10
+ ,86
+ ,53
+ ,33
+ ,40
+ ,14
+ ,13
+ ,14
+ ,14
+ ,54
+ ,33
+ ,34
+ ,40
+ ,14
+ ,8
+ ,14
+ ,13
+ ,67
+ ,44
+ ,33
+ ,35
+ ,13
+ ,11
+ ,14
+ ,9
+ ,69
+ ,42
+ ,38
+ ,36
+ ,16
+ ,12
+ ,14
+ ,15
+ ,90
+ ,55
+ ,33
+ ,37
+ ,13
+ ,11
+ ,12
+ ,15
+ ,54
+ ,33
+ ,31
+ ,27
+ ,16
+ ,13
+ ,14
+ ,14
+ ,76
+ ,46
+ ,38
+ ,39
+ ,13
+ ,12
+ ,15
+ ,11
+ ,89
+ ,54
+ ,37
+ ,38
+ ,16
+ ,14
+ ,15
+ ,8
+ ,76
+ ,47
+ ,36
+ ,31
+ ,15
+ ,13
+ ,15
+ ,11
+ ,73
+ ,45
+ ,31
+ ,33
+ ,16
+ ,15
+ ,13
+ ,11
+ ,79
+ ,47
+ ,39
+ ,32
+ ,15
+ ,10
+ ,17
+ ,8
+ ,90
+ ,55
+ ,44
+ ,39
+ ,17
+ ,11
+ ,17
+ ,10
+ ,74
+ ,44
+ ,33
+ ,36
+ ,15
+ ,9
+ ,19
+ ,11
+ ,81
+ ,53
+ ,35
+ ,33
+ ,12
+ ,11
+ ,15
+ ,13
+ ,72
+ ,44
+ ,32
+ ,33
+ ,16
+ ,10
+ ,13
+ ,11
+ ,71
+ ,42
+ ,28
+ ,32
+ ,10
+ ,11
+ ,9
+ ,20
+ ,66
+ ,40
+ ,40
+ ,37
+ ,16
+ ,8
+ ,15
+ ,10
+ ,77
+ ,46
+ ,27
+ ,30
+ ,12
+ ,11
+ ,15
+ ,15
+ ,65
+ ,40
+ ,37
+ ,38
+ ,14
+ ,12
+ ,15
+ ,12
+ ,74
+ ,46
+ ,32
+ ,29
+ ,15
+ ,12
+ ,16
+ ,14
+ ,85
+ ,53
+ ,28
+ ,22
+ ,13
+ ,9
+ ,11
+ ,23
+ ,54
+ ,33
+ ,34
+ ,35
+ ,15
+ ,11
+ ,14
+ ,14
+ ,63
+ ,42
+ ,30
+ ,35
+ ,11
+ ,10
+ ,11
+ ,16
+ ,54
+ ,35
+ ,35
+ ,34
+ ,12
+ ,8
+ ,15
+ ,11
+ ,64
+ ,40
+ ,31
+ ,35
+ ,11
+ ,9
+ ,13
+ ,12
+ ,69
+ ,41
+ ,32
+ ,34
+ ,16
+ ,8
+ ,15
+ ,10
+ ,54
+ ,33
+ ,30
+ ,37
+ ,15
+ ,9
+ ,16
+ ,14
+ ,84
+ ,51
+ ,30
+ ,35
+ ,17
+ ,15
+ ,14
+ ,12
+ ,86
+ ,53
+ ,31
+ ,23
+ ,16
+ ,11
+ ,15
+ ,12
+ ,77
+ ,46
+ ,40
+ ,31
+ ,10
+ ,8
+ ,16
+ ,11
+ ,89
+ ,55
+ ,32
+ ,27
+ ,18
+ ,13
+ ,16
+ ,12
+ ,76
+ ,47
+ ,36
+ ,36
+ ,13
+ ,12
+ ,11
+ ,13
+ ,60
+ ,38
+ ,32
+ ,31
+ ,16
+ ,12
+ ,12
+ ,11
+ ,75
+ ,46
+ ,35
+ ,32
+ ,13
+ ,9
+ ,9
+ ,19
+ ,73
+ ,46
+ ,38
+ ,39
+ ,10
+ ,7
+ ,16
+ ,12
+ ,85
+ ,53
+ ,42
+ ,37
+ ,15
+ ,13
+ ,13
+ ,17
+ ,79
+ ,47
+ ,34
+ ,38
+ ,16
+ ,9
+ ,16
+ ,9
+ ,71
+ ,41
+ ,35
+ ,39
+ ,16
+ ,6
+ ,12
+ ,12
+ ,72
+ ,44
+ ,38
+ ,34
+ ,14
+ ,8
+ ,9
+ ,19
+ ,69
+ ,43
+ ,33
+ ,31
+ ,10
+ ,8
+ ,13
+ ,18
+ ,78
+ ,51
+ ,36
+ ,32
+ ,17
+ ,15
+ ,13
+ ,15
+ ,54
+ ,33
+ ,32
+ ,37
+ ,13
+ ,6
+ ,14
+ ,14
+ ,69
+ ,43
+ ,33
+ ,36
+ ,15
+ ,9
+ ,19
+ ,11
+ ,81
+ ,53
+ ,34
+ ,32
+ ,16
+ ,11
+ ,13
+ ,9
+ ,84
+ ,51
+ ,32
+ ,38
+ ,12
+ ,8
+ ,12
+ ,18
+ ,84
+ ,50
+ ,34
+ ,36
+ ,13
+ ,8
+ ,13
+ ,16
+ ,69
+ ,46
+ ,27
+ ,26
+ ,13
+ ,10
+ ,10
+ ,24
+ ,66
+ ,43
+ ,31
+ ,26
+ ,12
+ ,8
+ ,14
+ ,14
+ ,81
+ ,47
+ ,38
+ ,33
+ ,17
+ ,14
+ ,16
+ ,20
+ ,82
+ ,50
+ ,34
+ ,39
+ ,15
+ ,10
+ ,10
+ ,18
+ ,72
+ ,43
+ ,24
+ ,30
+ ,10
+ ,8
+ ,11
+ ,23
+ ,54
+ ,33
+ ,30
+ ,33
+ ,14
+ ,11
+ ,14
+ ,12
+ ,78
+ ,48
+ ,26
+ ,25
+ ,11
+ ,12
+ ,12
+ ,14
+ ,74
+ ,44
+ ,34
+ ,38
+ ,13
+ ,12
+ ,9
+ ,16
+ ,82
+ ,50
+ ,27
+ ,37
+ ,16
+ ,12
+ ,9
+ ,18
+ ,73
+ ,41
+ ,37
+ ,31
+ ,12
+ ,5
+ ,11
+ ,20
+ ,55
+ ,34
+ ,36
+ ,37
+ ,16
+ ,12
+ ,16
+ ,12
+ ,72
+ ,44
+ ,41
+ ,35
+ ,12
+ ,10
+ ,9
+ ,12
+ ,78
+ ,47
+ ,29
+ ,25
+ ,9
+ ,7
+ ,13
+ ,17
+ ,59
+ ,35
+ ,36
+ ,28
+ ,12
+ ,12
+ ,16
+ ,13
+ ,72
+ ,44
+ ,32
+ ,35
+ ,15
+ ,11
+ ,13
+ ,9
+ ,78
+ ,44
+ ,37
+ ,33
+ ,12
+ ,8
+ ,9
+ ,16
+ ,68
+ ,43
+ ,30
+ ,30
+ ,12
+ ,9
+ ,12
+ ,18
+ ,69
+ ,41
+ ,31
+ ,31
+ ,14
+ ,10
+ ,16
+ ,10
+ ,67
+ ,41
+ ,38
+ ,37
+ ,12
+ ,9
+ ,11
+ ,14
+ ,74
+ ,42
+ ,36
+ ,36
+ ,16
+ ,12
+ ,14
+ ,11
+ ,54
+ ,33
+ ,35
+ ,30
+ ,11
+ ,6
+ ,13
+ ,9
+ ,67
+ ,41
+ ,31
+ ,36
+ ,19
+ ,15
+ ,15
+ ,11
+ ,70
+ ,44
+ ,38
+ ,32
+ ,15
+ ,12
+ ,14
+ ,10
+ ,80
+ ,48
+ ,22
+ ,28
+ ,8
+ ,12
+ ,16
+ ,11
+ ,89
+ ,55
+ ,32
+ ,36
+ ,16
+ ,12
+ ,13
+ ,19
+ ,76
+ ,44
+ ,36
+ ,34
+ ,17
+ ,11
+ ,14
+ ,14
+ ,74
+ ,43
+ ,39
+ ,31
+ ,12
+ ,7
+ ,15
+ ,12
+ ,87
+ ,52
+ ,28
+ ,28
+ ,11
+ ,7
+ ,13
+ ,14
+ ,54
+ ,30
+ ,32
+ ,36
+ ,11
+ ,5
+ ,11
+ ,21
+ ,61
+ ,39
+ ,32
+ ,36
+ ,14
+ ,12
+ ,11
+ ,13
+ ,38
+ ,11
+ ,38
+ ,40
+ ,16
+ ,12
+ ,14
+ ,10
+ ,75
+ ,44
+ ,32
+ ,33
+ ,12
+ ,3
+ ,15
+ ,15
+ ,69
+ ,42
+ ,35
+ ,37
+ ,16
+ ,11
+ ,11
+ ,16
+ ,62
+ ,41
+ ,32
+ ,32
+ ,13
+ ,10
+ ,15
+ ,14
+ ,72
+ ,44
+ ,37
+ ,38
+ ,15
+ ,12
+ ,12
+ ,12
+ ,70
+ ,44
+ ,34
+ ,31
+ ,16
+ ,9
+ ,14
+ ,19
+ ,79
+ ,48
+ ,33
+ ,37
+ ,16
+ ,12
+ ,14
+ ,15
+ ,87
+ ,53
+ ,33
+ ,33
+ ,14
+ ,9
+ ,8
+ ,19
+ ,62
+ ,37
+ ,26
+ ,32
+ ,16
+ ,12
+ ,13
+ ,13
+ ,77
+ ,44
+ ,30
+ ,30
+ ,16
+ ,12
+ ,9
+ ,17
+ ,69
+ ,44
+ ,24
+ ,30
+ ,14
+ ,10
+ ,15
+ ,12
+ ,69
+ ,40
+ ,34
+ ,31
+ ,11
+ ,9
+ ,17
+ ,11
+ ,75
+ ,42
+ ,34
+ ,32
+ ,12
+ ,12
+ ,13
+ ,14
+ ,54
+ ,35
+ ,33
+ ,34
+ ,15
+ ,8
+ ,15
+ ,11
+ ,72
+ ,43
+ ,34
+ ,36
+ ,15
+ ,11
+ ,15
+ ,13
+ ,74
+ ,45
+ ,35
+ ,37
+ ,16
+ ,11
+ ,14
+ ,12
+ ,85
+ ,55
+ ,35
+ ,36
+ ,16
+ ,12
+ ,16
+ ,15
+ ,52
+ ,31
+ ,36
+ ,33
+ ,11
+ ,10
+ ,13
+ ,14
+ ,70
+ ,44
+ ,34
+ ,33
+ ,15
+ ,10
+ ,16
+ ,12
+ ,84
+ ,50
+ ,34
+ ,33
+ ,12
+ ,12
+ ,9
+ ,17
+ ,64
+ ,40
+ ,41
+ ,44
+ ,12
+ ,12
+ ,16
+ ,11
+ ,84
+ ,53
+ ,32
+ ,39
+ ,15
+ ,11
+ ,11
+ ,18
+ ,87
+ ,54
+ ,30
+ ,32
+ ,15
+ ,8
+ ,10
+ ,13
+ ,79
+ ,49
+ ,35
+ ,35
+ ,16
+ ,12
+ ,11
+ ,17
+ ,67
+ ,40
+ ,28
+ ,25
+ ,14
+ ,10
+ ,15
+ ,13
+ ,65
+ ,41
+ ,33
+ ,35
+ ,17
+ ,11
+ ,17
+ ,11
+ ,85
+ ,52
+ ,39
+ ,34
+ ,14
+ ,10
+ ,14
+ ,12
+ ,83
+ ,52
+ ,36
+ ,35
+ ,13
+ ,8
+ ,8
+ ,22
+ ,61
+ ,36
+ ,36
+ ,39
+ ,15
+ ,12
+ ,15
+ ,14
+ ,82
+ ,52
+ ,35
+ ,33
+ ,13
+ ,12
+ ,11
+ ,12
+ ,76
+ ,46
+ ,38
+ ,36
+ ,14
+ ,10
+ ,16
+ ,12
+ ,58
+ ,31
+ ,33
+ ,32
+ ,15
+ ,12
+ ,10
+ ,17
+ ,72
+ ,44
+ ,31
+ ,32
+ ,12
+ ,9
+ ,15
+ ,9
+ ,72
+ ,44
+ ,34
+ ,36
+ ,13
+ ,9
+ ,9
+ ,21
+ ,38
+ ,11
+ ,32
+ ,36
+ ,8
+ ,6
+ ,16
+ ,10
+ ,78
+ ,46
+ ,31
+ ,32
+ ,14
+ ,10
+ ,19
+ ,11
+ ,54
+ ,33
+ ,33
+ ,34
+ ,14
+ ,9
+ ,12
+ ,12
+ ,63
+ ,34
+ ,34
+ ,33
+ ,11
+ ,9
+ ,8
+ ,23
+ ,66
+ ,42
+ ,34
+ ,35
+ ,12
+ ,9
+ ,11
+ ,13
+ ,70
+ ,43
+ ,34
+ ,30
+ ,13
+ ,6
+ ,14
+ ,12
+ ,71
+ ,43
+ ,33
+ ,38
+ ,10
+ ,10
+ ,9
+ ,16
+ ,67
+ ,44
+ ,32
+ ,34
+ ,16
+ ,6
+ ,15
+ ,9
+ ,58
+ ,36
+ ,41
+ ,33
+ ,18
+ ,14
+ ,13
+ ,17
+ ,72
+ ,46
+ ,34
+ ,32
+ ,13
+ ,10
+ ,16
+ ,9
+ ,72
+ ,44
+ ,36
+ ,31
+ ,11
+ ,10
+ ,11
+ ,14
+ ,70
+ ,43
+ ,37
+ ,30
+ ,4
+ ,6
+ ,12
+ ,17
+ ,76
+ ,50
+ ,36
+ ,27
+ ,13
+ ,12
+ ,13
+ ,13
+ ,50
+ ,33
+ ,29
+ ,31
+ ,16
+ ,12
+ ,10
+ ,11
+ ,72
+ ,43
+ ,37
+ ,30
+ ,10
+ ,7
+ ,11
+ ,12
+ ,72
+ ,44
+ ,27
+ ,32
+ ,12
+ ,8
+ ,12
+ ,10
+ ,88
+ ,53
+ ,35
+ ,35
+ ,12
+ ,11
+ ,8
+ ,19
+ ,53
+ ,34
+ ,28
+ ,28
+ ,10
+ ,3
+ ,12
+ ,16
+ ,58
+ ,35
+ ,35
+ ,33
+ ,13
+ ,6
+ ,12
+ ,16
+ ,66
+ ,40
+ ,37
+ ,31
+ ,15
+ ,10
+ ,15
+ ,14
+ ,82
+ ,53
+ ,29
+ ,35
+ ,12
+ ,8
+ ,11
+ ,20
+ ,69
+ ,42
+ ,32
+ ,35
+ ,14
+ ,9
+ ,13
+ ,15
+ ,68
+ ,43
+ ,36
+ ,32
+ ,10
+ ,9
+ ,14
+ ,23
+ ,44
+ ,29
+ ,19
+ ,21
+ ,12
+ ,8
+ ,10
+ ,20
+ ,56
+ ,36
+ ,21
+ ,20
+ ,12
+ ,9
+ ,12
+ ,16
+ ,53
+ ,30
+ ,31
+ ,34
+ ,11
+ ,7
+ ,15
+ ,14
+ ,70
+ ,42
+ ,33
+ ,32
+ ,10
+ ,7
+ ,13
+ ,17
+ ,78
+ ,47
+ ,36
+ ,34
+ ,12
+ ,6
+ ,13
+ ,11
+ ,71
+ ,44
+ ,33
+ ,32
+ ,16
+ ,9
+ ,13
+ ,13
+ ,72
+ ,45
+ ,37
+ ,33
+ ,12
+ ,10
+ ,12
+ ,17
+ ,68
+ ,44
+ ,34
+ ,33
+ ,14
+ ,11
+ ,12
+ ,15
+ ,67
+ ,43
+ ,35
+ ,37
+ ,16
+ ,12
+ ,9
+ ,21
+ ,75
+ ,43
+ ,31
+ ,32
+ ,14
+ ,8
+ ,9
+ ,18
+ ,62
+ ,40
+ ,37
+ ,34
+ ,13
+ ,11
+ ,15
+ ,15
+ ,67
+ ,41
+ ,35
+ ,30
+ ,4
+ ,3
+ ,10
+ ,8
+ ,83
+ ,52
+ ,27
+ ,30
+ ,15
+ ,11
+ ,14
+ ,12
+ ,64
+ ,38
+ ,34
+ ,38
+ ,11
+ ,12
+ ,15
+ ,12
+ ,68
+ ,41
+ ,40
+ ,36
+ ,11
+ ,7
+ ,7
+ ,22
+ ,62
+ ,39
+ ,29
+ ,32
+ ,14
+ ,9
+ ,14
+ ,12
+ ,72
+ ,43)
+ ,dim=c(8
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Sport1'
+ ,'Sport2')
+ ,1:264))
> y <- array(NA,dim=c(8,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Sport1','Sport2'),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 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'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 Learning Software Happiness Depression Sport1 Sport2 t
1 41 38 13 12 14 12.0 53 32 1
2 39 32 16 11 18 11.0 83 51 2
3 30 35 19 15 11 14.0 66 42 3
4 31 33 15 6 12 12.0 67 41 4
5 34 37 14 13 16 21.0 76 46 5
6 35 29 13 10 18 12.0 78 47 6
7 39 31 19 12 14 22.0 53 37 7
8 34 36 15 14 14 11.0 80 49 8
9 36 35 14 12 15 10.0 74 45 9
10 37 38 15 9 15 13.0 76 47 10
11 38 31 16 10 17 10.0 79 49 11
12 36 34 16 12 19 8.0 54 33 12
13 38 35 16 12 10 15.0 67 42 13
14 39 38 16 11 16 14.0 54 33 14
15 33 37 17 15 18 10.0 87 53 15
16 32 33 15 12 14 14.0 58 36 16
17 36 32 15 10 14 14.0 75 45 17
18 38 38 20 12 17 11.0 88 54 18
19 39 38 18 11 14 10.0 64 41 19
20 32 32 16 12 16 13.0 57 36 20
21 32 33 16 11 18 9.5 66 41 21
22 31 31 16 12 11 14.0 68 44 22
23 39 38 19 13 14 12.0 54 33 23
24 37 39 16 11 12 14.0 56 37 24
25 39 32 17 12 17 11.0 86 52 25
26 41 32 17 13 9 9.0 80 47 26
27 36 35 16 10 16 11.0 76 43 27
28 33 37 15 14 14 15.0 69 44 28
29 33 33 16 12 15 14.0 78 45 29
30 34 33 14 10 11 13.0 67 44 30
31 31 31 15 12 16 9.0 80 49 31
32 27 32 12 8 13 15.0 54 33 32
33 37 31 14 10 17 10.0 71 43 33
34 34 37 16 12 15 11.0 84 54 34
35 34 30 14 12 14 13.0 74 42 35
36 32 33 10 7 16 8.0 71 44 36
37 29 31 10 9 9 20.0 63 37 37
38 36 33 14 12 15 12.0 71 43 38
39 29 31 16 10 17 10.0 76 46 39
40 35 33 16 10 13 10.0 69 42 40
41 37 32 16 10 15 9.0 74 45 41
42 34 33 14 12 16 14.0 75 44 42
43 38 32 20 15 16 8.0 54 33 43
44 35 33 14 10 12 14.0 52 31 44
45 38 28 14 10 15 11.0 69 42 45
46 37 35 11 12 11 13.0 68 40 46
47 38 39 14 13 15 9.0 65 43 47
48 33 34 15 11 15 11.0 75 46 48
49 36 38 16 11 17 15.0 74 42 49
50 38 32 14 12 13 11.0 75 45 50
51 32 38 16 14 16 10.0 72 44 51
52 32 30 14 10 14 14.0 67 40 52
53 32 33 12 12 11 18.0 63 37 53
54 34 38 16 13 12 14.0 62 46 54
55 32 32 9 5 12 11.0 63 36 55
56 37 35 14 6 15 14.5 76 47 56
57 39 34 16 12 16 13.0 74 45 57
58 29 34 16 12 15 9.0 67 42 58
59 37 36 15 11 12 10.0 73 43 59
60 35 34 16 10 12 15.0 70 43 60
61 30 28 12 7 8 20.0 53 32 61
62 38 34 16 12 13 12.0 77 45 62
63 34 35 16 14 11 12.0 80 48 63
64 31 35 14 11 14 14.0 52 31 64
65 34 31 16 12 15 13.0 54 33 65
66 35 37 17 13 10 11.0 80 49 66
67 36 35 18 14 11 17.0 66 42 67
68 30 27 18 11 12 12.0 73 41 68
69 39 40 12 12 15 13.0 63 38 69
70 35 37 16 12 15 14.0 69 42 70
71 38 36 10 8 14 13.0 67 44 71
72 31 38 14 11 16 15.0 54 33 72
73 34 39 18 14 15 13.0 81 48 73
74 38 41 18 14 15 10.0 69 40 74
75 34 27 16 12 13 11.0 84 50 75
76 39 30 17 9 12 19.0 80 49 76
77 37 37 16 13 17 13.0 70 43 77
78 34 31 16 11 13 17.0 69 44 78
79 28 31 13 12 15 13.0 77 47 79
80 37 27 16 12 13 9.0 54 33 80
81 33 36 16 12 15 11.0 79 46 81
82 35 37 16 12 15 9.0 71 45 82
83 37 33 15 12 16 12.0 73 43 83
84 32 34 15 11 15 12.0 72 44 84
85 33 31 16 10 14 13.0 77 47 85
86 38 39 14 9 15 13.0 75 45 86
87 33 34 16 12 14 12.0 69 42 87
88 29 32 16 12 13 15.0 54 33 88
89 33 33 15 12 7 22.0 70 43 89
90 31 36 12 9 17 13.0 73 46 90
91 36 32 17 15 13 15.0 54 33 91
92 35 41 16 12 15 13.0 77 46 92
93 32 28 15 12 14 15.0 82 48 93
94 29 30 13 12 13 12.5 80 47 94
95 39 36 16 10 16 11.0 80 47 95
96 37 35 16 13 12 16.0 69 43 96
97 35 31 16 9 14 11.0 78 46 97
98 37 34 16 12 17 11.0 81 48 98
99 32 36 14 10 15 10.0 76 46 99
100 38 36 16 14 17 10.0 76 45 100
101 37 35 16 11 12 16.0 73 45 101
102 36 37 20 15 16 12.0 85 52 102
103 32 28 15 11 11 11.0 66 42 103
104 33 39 16 11 15 16.0 79 47 104
105 40 32 13 12 9 19.0 68 41 105
106 38 35 17 12 16 11.0 76 47 106
107 41 39 16 12 15 16.0 71 43 107
108 36 35 16 11 10 15.0 54 33 108
109 43 42 12 7 10 24.0 46 30 109
110 30 34 16 12 15 14.0 85 52 110
111 31 33 16 14 11 15.0 74 44 111
112 32 41 17 11 13 11.0 88 55 112
113 32 33 13 11 14 15.0 38 11 113
114 37 34 12 10 18 12.0 76 47 114
115 37 32 18 13 16 10.0 86 53 115
116 33 40 14 13 14 14.0 54 33 116
117 34 40 14 8 14 13.0 67 44 117
118 33 35 13 11 14 9.0 69 42 118
119 38 36 16 12 14 15.0 90 55 119
120 33 37 13 11 12 15.0 54 33 120
121 31 27 16 13 14 14.0 76 46 121
122 38 39 13 12 15 11.0 89 54 122
123 37 38 16 14 15 8.0 76 47 123
124 36 31 15 13 15 11.0 73 45 124
125 31 33 16 15 13 11.0 79 47 125
126 39 32 15 10 17 8.0 90 55 126
127 44 39 17 11 17 10.0 74 44 127
128 33 36 15 9 19 11.0 81 53 128
129 35 33 12 11 15 13.0 72 44 129
130 32 33 16 10 13 11.0 71 42 130
131 28 32 10 11 9 20.0 66 40 131
132 40 37 16 8 15 10.0 77 46 132
133 27 30 12 11 15 15.0 65 40 133
134 37 38 14 12 15 12.0 74 46 134
135 32 29 15 12 16 14.0 85 53 135
136 28 22 13 9 11 23.0 54 33 136
137 34 35 15 11 14 14.0 63 42 137
138 30 35 11 10 11 16.0 54 35 138
139 35 34 12 8 15 11.0 64 40 139
140 31 35 11 9 13 12.0 69 41 140
141 32 34 16 8 15 10.0 54 33 141
142 30 37 15 9 16 14.0 84 51 142
143 30 35 17 15 14 12.0 86 53 143
144 31 23 16 11 15 12.0 77 46 144
145 40 31 10 8 16 11.0 89 55 145
146 32 27 18 13 16 12.0 76 47 146
147 36 36 13 12 11 13.0 60 38 147
148 32 31 16 12 12 11.0 75 46 148
149 35 32 13 9 9 19.0 73 46 149
150 38 39 10 7 16 12.0 85 53 150
151 42 37 15 13 13 17.0 79 47 151
152 34 38 16 9 16 9.0 71 41 152
153 35 39 16 6 12 12.0 72 44 153
154 38 34 14 8 9 19.0 69 43 154
155 33 31 10 8 13 18.0 78 51 155
156 36 32 17 15 13 15.0 54 33 156
157 32 37 13 6 14 14.0 69 43 157
158 33 36 15 9 19 11.0 81 53 158
159 34 32 16 11 13 9.0 84 51 159
160 32 38 12 8 12 18.0 84 50 160
161 34 36 13 8 13 16.0 69 46 161
162 27 26 13 10 10 24.0 66 43 162
163 31 26 12 8 14 14.0 81 47 163
164 38 33 17 14 16 20.0 82 50 164
165 34 39 15 10 10 18.0 72 43 165
166 24 30 10 8 11 23.0 54 33 166
167 30 33 14 11 14 12.0 78 48 167
168 26 25 11 12 12 14.0 74 44 168
169 34 38 13 12 9 16.0 82 50 169
170 27 37 16 12 9 18.0 73 41 170
171 37 31 12 5 11 20.0 55 34 171
172 36 37 16 12 16 12.0 72 44 172
173 41 35 12 10 9 12.0 78 47 173
174 29 25 9 7 13 17.0 59 35 174
175 36 28 12 12 16 13.0 72 44 175
176 32 35 15 11 13 9.0 78 44 176
177 37 33 12 8 9 16.0 68 43 177
178 30 30 12 9 12 18.0 69 41 178
179 31 31 14 10 16 10.0 67 41 179
180 38 37 12 9 11 14.0 74 42 180
181 36 36 16 12 14 11.0 54 33 181
182 35 30 11 6 13 9.0 67 41 182
183 31 36 19 15 15 11.0 70 44 183
184 38 32 15 12 14 10.0 80 48 184
185 22 28 8 12 16 11.0 89 55 185
186 32 36 16 12 13 19.0 76 44 186
187 36 34 17 11 14 14.0 74 43 187
188 39 31 12 7 15 12.0 87 52 188
189 28 28 11 7 13 14.0 54 30 189
190 32 36 11 5 11 21.0 61 39 190
191 32 36 14 12 11 13.0 38 11 191
192 38 40 16 12 14 10.0 75 44 192
193 32 33 12 3 15 15.0 69 42 193
194 35 37 16 11 11 16.0 62 41 194
195 32 32 13 10 15 14.0 72 44 195
196 37 38 15 12 12 12.0 70 44 196
197 34 31 16 9 14 19.0 79 48 197
198 33 37 16 12 14 15.0 87 53 198
199 33 33 14 9 8 19.0 62 37 199
200 26 32 16 12 13 13.0 77 44 200
201 30 30 16 12 9 17.0 69 44 201
202 24 30 14 10 15 12.0 69 40 202
203 34 31 11 9 17 11.0 75 42 203
204 34 32 12 12 13 14.0 54 35 204
205 33 34 15 8 15 11.0 72 43 205
206 34 36 15 11 15 13.0 74 45 206
207 35 37 16 11 14 12.0 85 55 207
208 35 36 16 12 16 15.0 52 31 208
209 36 33 11 10 13 14.0 70 44 209
210 34 33 15 10 16 12.0 84 50 210
211 34 33 12 12 9 17.0 64 40 211
212 41 44 12 12 16 11.0 84 53 212
213 32 39 15 11 11 18.0 87 54 213
214 30 32 15 8 10 13.0 79 49 214
215 35 35 16 12 11 17.0 67 40 215
216 28 25 14 10 15 13.0 65 41 216
217 33 35 17 11 17 11.0 85 52 217
218 39 34 14 10 14 12.0 83 52 218
219 36 35 13 8 8 22.0 61 36 219
220 36 39 15 12 15 14.0 82 52 220
221 35 33 13 12 11 12.0 76 46 221
222 38 36 14 10 16 12.0 58 31 222
223 33 32 15 12 10 17.0 72 44 223
224 31 32 12 9 15 9.0 72 44 224
225 34 36 13 9 9 21.0 38 11 225
226 32 36 8 6 16 10.0 78 46 226
227 31 32 14 10 19 11.0 54 33 227
228 33 34 14 9 12 12.0 63 34 228
229 34 33 11 9 8 23.0 66 42 229
230 34 35 12 9 11 13.0 70 43 230
231 34 30 13 6 14 12.0 71 43 231
232 33 38 10 10 9 16.0 67 44 232
233 32 34 16 6 15 9.0 58 36 233
234 41 33 18 14 13 17.0 72 46 234
235 34 32 13 10 16 9.0 72 44 235
236 36 31 11 10 11 14.0 70 43 236
237 37 30 4 6 12 17.0 76 50 237
238 36 27 13 12 13 13.0 50 33 238
239 29 31 16 12 10 11.0 72 43 239
240 37 30 10 7 11 12.0 72 44 240
241 27 32 12 8 12 10.0 88 53 241
242 35 35 12 11 8 19.0 53 34 242
243 28 28 10 3 12 16.0 58 35 243
244 35 33 13 6 12 16.0 66 40 244
245 37 31 15 10 15 14.0 82 53 245
246 29 35 12 8 11 20.0 69 42 246
247 32 35 14 9 13 15.0 68 43 247
248 36 32 10 9 14 23.0 44 29 248
249 19 21 12 8 10 20.0 56 36 249
250 21 20 12 9 12 16.0 53 30 250
251 31 34 11 7 15 14.0 70 42 251
252 33 32 10 7 13 17.0 78 47 252
253 36 34 12 6 13 11.0 71 44 253
254 33 32 16 9 13 13.0 72 45 254
255 37 33 12 10 12 17.0 68 44 255
256 34 33 14 11 12 15.0 67 43 256
257 35 37 16 12 9 21.0 75 43 257
258 31 32 14 8 9 18.0 62 40 258
259 37 34 13 11 15 15.0 67 41 259
260 35 30 4 3 10 8.0 83 52 260
261 27 30 15 11 14 12.0 64 38 261
262 34 38 11 12 15 12.0 68 41 262
263 40 36 11 7 7 22.0 62 39 263
264 29 32 14 9 14 12.0 72 43 264
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Separate Learning Software Happiness Depression
18.848968 0.428972 0.109175 -0.071774 0.005533 -0.048308
Sport1 Sport2 t
-0.061854 0.131947 -0.005921
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.0861 -2.3477 0.0876 2.2558 7.5534
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.848968 3.398828 5.546 7.31e-08 ***
Separate 0.428972 0.057728 7.431 1.63e-12 ***
Learning 0.109175 0.112958 0.967 0.335
Software -0.071774 0.115901 -0.619 0.536
Happiness 0.005533 0.104952 0.053 0.958
Depression -0.048308 0.076069 -0.635 0.526
Sport1 -0.061854 0.067501 -0.916 0.360
Sport2 0.131947 0.100284 1.316 0.189
t -0.005921 0.003071 -1.928 0.055 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.356 on 255 degrees of freedom
Multiple R-squared: 0.2424, Adjusted R-squared: 0.2186
F-statistic: 10.2 on 8 and 255 DF, p-value: 2.368e-12
> 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,] 0.17752406 0.35504812 0.8224759
[2,] 0.69508375 0.60983250 0.3049163
[3,] 0.65877115 0.68245770 0.3412289
[4,] 0.56450050 0.87099899 0.4354995
[5,] 0.58990560 0.82018881 0.4100944
[6,] 0.65453065 0.69093870 0.3454694
[7,] 0.64588592 0.70822815 0.3541141
[8,] 0.55498407 0.89003187 0.4450159
[9,] 0.58954989 0.82090022 0.4104501
[10,] 0.62097723 0.75804555 0.3790228
[11,] 0.55590456 0.88819089 0.4440954
[12,] 0.56855086 0.86289828 0.4314491
[13,] 0.49143763 0.98287526 0.5085624
[14,] 0.58269234 0.83461531 0.4173077
[15,] 0.74298925 0.51402151 0.2570108
[16,] 0.71765850 0.56468299 0.2823415
[17,] 0.67081484 0.65837033 0.3291852
[18,] 0.65039126 0.69921747 0.3496087
[19,] 0.58936433 0.82127134 0.4106357
[20,] 0.56172330 0.87655339 0.4382767
[21,] 0.64665902 0.70668196 0.3533410
[22,] 0.67039185 0.65921629 0.3296081
[23,] 0.61894625 0.76210750 0.3810538
[24,] 0.56804562 0.86390876 0.4319544
[25,] 0.51369752 0.97260495 0.4863025
[26,] 0.46538599 0.93077197 0.5346140
[27,] 0.45217240 0.90434480 0.5478276
[28,] 0.51773191 0.96453618 0.4822681
[29,] 0.46627253 0.93254507 0.5337275
[30,] 0.45175850 0.90351700 0.5482415
[31,] 0.40378391 0.80756782 0.5962161
[32,] 0.37223279 0.74446559 0.6277672
[33,] 0.33454540 0.66909079 0.6654546
[34,] 0.42519988 0.85039977 0.5748001
[35,] 0.44687131 0.89374261 0.5531287
[36,] 0.42649751 0.85299503 0.5735025
[37,] 0.39016577 0.78033155 0.6098342
[38,] 0.34312094 0.68624187 0.6568791
[39,] 0.36047193 0.72094386 0.6395281
[40,] 0.39176739 0.78353479 0.6082326
[41,] 0.35016392 0.70032785 0.6498361
[42,] 0.30834072 0.61668144 0.6916593
[43,] 0.27487368 0.54974736 0.7251263
[44,] 0.23764508 0.47529016 0.7623549
[45,] 0.23600999 0.47201997 0.7639900
[46,] 0.25815192 0.51630385 0.7418481
[47,] 0.35577725 0.71155450 0.6442227
[48,] 0.32209870 0.64419741 0.6779013
[49,] 0.28284327 0.56568654 0.7171567
[50,] 0.25071506 0.50143013 0.7492849
[51,] 0.24122119 0.48244238 0.7587788
[52,] 0.21243162 0.42486324 0.7875684
[53,] 0.20906241 0.41812481 0.7909376
[54,] 0.17969431 0.35938861 0.8203057
[55,] 0.15500951 0.31001901 0.8449905
[56,] 0.13401654 0.26803309 0.8659835
[57,] 0.14410653 0.28821306 0.8558935
[58,] 0.15203327 0.30406654 0.8479667
[59,] 0.12869972 0.25739944 0.8713003
[60,] 0.14490669 0.28981337 0.8550933
[61,] 0.16149554 0.32299108 0.8385045
[62,] 0.15094134 0.30188267 0.8490587
[63,] 0.12873921 0.25747843 0.8712608
[64,] 0.11390327 0.22780654 0.8860967
[65,] 0.16127884 0.32255768 0.8387212
[66,] 0.14177798 0.28355597 0.8582220
[67,] 0.12051862 0.24103723 0.8794814
[68,] 0.14729153 0.29458305 0.8527085
[69,] 0.17572240 0.35144480 0.8242776
[70,] 0.16361958 0.32723917 0.8363804
[71,] 0.14144588 0.28289177 0.8585541
[72,] 0.13777013 0.27554026 0.8622299
[73,] 0.12804205 0.25608410 0.8719579
[74,] 0.10976610 0.21953220 0.8902339
[75,] 0.09988736 0.19977472 0.9001126
[76,] 0.08679801 0.17359602 0.9132020
[77,] 0.09791232 0.19582463 0.9020877
[78,] 0.08221613 0.16443226 0.9177839
[79,] 0.08333589 0.16667177 0.9166641
[80,] 0.07987094 0.15974189 0.9201291
[81,] 0.07022555 0.14045109 0.9297745
[82,] 0.05835751 0.11671503 0.9416425
[83,] 0.05745940 0.11491880 0.9425406
[84,] 0.06047730 0.12095460 0.9395227
[85,] 0.05767883 0.11535765 0.9423212
[86,] 0.04927994 0.09855987 0.9507201
[87,] 0.04513572 0.09027144 0.9548643
[88,] 0.04336473 0.08672946 0.9566353
[89,] 0.04166338 0.08332676 0.9583366
[90,] 0.03809711 0.07619422 0.9619029
[91,] 0.03116706 0.06233411 0.9688329
[92,] 0.02520622 0.05041244 0.9747938
[93,] 0.02440742 0.04881483 0.9755926
[94,] 0.06060904 0.12121808 0.9393910
[95,] 0.05811123 0.11622246 0.9418888
[96,] 0.07321246 0.14642491 0.9267875
[97,] 0.06367084 0.12734167 0.9363292
[98,] 0.10381088 0.20762175 0.8961891
[99,] 0.11876185 0.23752369 0.8812382
[100,] 0.11268697 0.22537394 0.8873130
[101,] 0.14483163 0.28966325 0.8551684
[102,] 0.13401657 0.26803315 0.8659834
[103,] 0.13268536 0.26537072 0.8673146
[104,] 0.12814925 0.25629850 0.8718507
[105,] 0.12384817 0.24769634 0.8761518
[106,] 0.11898746 0.23797492 0.8810125
[107,] 0.10402004 0.20804007 0.8959800
[108,] 0.09958135 0.19916270 0.9004186
[109,] 0.08842629 0.17685259 0.9115737
[110,] 0.07654633 0.15309265 0.9234537
[111,] 0.07073144 0.14146289 0.9292686
[112,] 0.06081581 0.12163162 0.9391842
[113,] 0.05904400 0.11808799 0.9409560
[114,] 0.05494677 0.10989353 0.9450532
[115,] 0.06963886 0.13927771 0.9303611
[116,] 0.12819254 0.25638509 0.8718075
[117,] 0.12463085 0.24926170 0.8753692
[118,] 0.11156314 0.22312629 0.8884369
[119,] 0.10229536 0.20459071 0.8977046
[120,] 0.10877466 0.21754932 0.8912253
[121,] 0.11844179 0.23688358 0.8815582
[122,] 0.13916204 0.27832409 0.8608380
[123,] 0.12349842 0.24699684 0.8765016
[124,] 0.10661892 0.21323785 0.8933811
[125,] 0.09280953 0.18561907 0.9071905
[126,] 0.07922394 0.15844787 0.9207761
[127,] 0.08475858 0.16951716 0.9152414
[128,] 0.07270164 0.14540328 0.9272984
[129,] 0.07052949 0.14105898 0.9294705
[130,] 0.06656817 0.13313635 0.9334318
[131,] 0.09028294 0.18056589 0.9097171
[132,] 0.10516553 0.21033107 0.8948345
[133,] 0.09786514 0.19573028 0.9021349
[134,] 0.17691008 0.35382016 0.8230899
[135,] 0.16064398 0.32128796 0.8393560
[136,] 0.14587912 0.29175824 0.8541209
[137,] 0.12731020 0.25462041 0.8726898
[138,] 0.11738357 0.23476715 0.8826164
[139,] 0.10509881 0.21019763 0.8949012
[140,] 0.17676471 0.35352942 0.8232353
[141,] 0.16198109 0.32396218 0.8380189
[142,] 0.14599159 0.29198318 0.8540084
[143,] 0.16011247 0.32022494 0.8398875
[144,] 0.13934184 0.27868369 0.8606582
[145,] 0.14062666 0.28125332 0.8593733
[146,] 0.14228412 0.28456824 0.8577159
[147,] 0.13535562 0.27071125 0.8646444
[148,] 0.11942818 0.23885635 0.8805718
[149,] 0.11980869 0.23961739 0.8801913
[150,] 0.10690391 0.21380782 0.8930961
[151,] 0.10086800 0.20173600 0.8991320
[152,] 0.09224097 0.18448194 0.9077590
[153,] 0.12112057 0.24224115 0.8788794
[154,] 0.10890491 0.21780982 0.8910951
[155,] 0.18825367 0.37650733 0.8117463
[156,] 0.18862003 0.37724006 0.8113800
[157,] 0.17822219 0.35644437 0.8217778
[158,] 0.16298797 0.32597594 0.8370120
[159,] 0.27672218 0.55344437 0.7232778
[160,] 0.30156642 0.60313285 0.6984336
[161,] 0.27024863 0.54049725 0.7297514
[162,] 0.36915383 0.73830767 0.6308462
[163,] 0.33368703 0.66737406 0.6663130
[164,] 0.40011272 0.80022544 0.5998873
[165,] 0.37352631 0.74705263 0.6264737
[166,] 0.37709208 0.75418416 0.6229079
[167,] 0.34461841 0.68923682 0.6553816
[168,] 0.31469443 0.62938886 0.6853056
[169,] 0.31297756 0.62595512 0.6870224
[170,] 0.28284107 0.56568215 0.7171589
[171,] 0.28222815 0.56445629 0.7177719
[172,] 0.29418080 0.58836160 0.7058192
[173,] 0.37141344 0.74282689 0.6285866
[174,] 0.60022010 0.79955979 0.3997799
[175,] 0.58012622 0.83974757 0.4198738
[176,] 0.57672917 0.84654165 0.4232708
[177,] 0.73473910 0.53052179 0.2652609
[178,] 0.70915044 0.58169911 0.2908496
[179,] 0.70829543 0.58340913 0.2917046
[180,] 0.67256007 0.65487986 0.3274399
[181,] 0.64416375 0.71167249 0.3558362
[182,] 0.60915654 0.78168692 0.3908435
[183,] 0.57432938 0.85134125 0.4256706
[184,] 0.53430743 0.93138514 0.4656926
[185,] 0.49638718 0.99277435 0.5036128
[186,] 0.49134617 0.98269235 0.5086538
[187,] 0.46112050 0.92224099 0.5388795
[188,] 0.42041036 0.84082071 0.5795896
[189,] 0.48834137 0.97668273 0.5116586
[190,] 0.45341398 0.90682796 0.5465860
[191,] 0.60819392 0.78361215 0.3918061
[192,] 0.58535997 0.82928007 0.4146400
[193,] 0.55177165 0.89645671 0.4482284
[194,] 0.50819436 0.98361127 0.4918056
[195,] 0.46834609 0.93669217 0.5316539
[196,] 0.43022384 0.86044767 0.5697762
[197,] 0.39044723 0.78089447 0.6095528
[198,] 0.36256049 0.72512098 0.6374395
[199,] 0.32779738 0.65559475 0.6722026
[200,] 0.29349495 0.58698991 0.7065050
[201,] 0.27010030 0.54020059 0.7298997
[202,] 0.33241426 0.66482853 0.6675857
[203,] 0.32005543 0.64011086 0.6799446
[204,] 0.28020652 0.56041304 0.7197935
[205,] 0.24685430 0.49370860 0.7531457
[206,] 0.22247317 0.44494633 0.7775268
[207,] 0.23760948 0.47521896 0.7623905
[208,] 0.20824677 0.41649355 0.7917532
[209,] 0.19828222 0.39656443 0.8017178
[210,] 0.16801966 0.33603932 0.8319803
[211,] 0.17543587 0.35087174 0.8245641
[212,] 0.14462931 0.28925862 0.8553707
[213,] 0.12651391 0.25302781 0.8734861
[214,] 0.17727720 0.35455440 0.8227228
[215,] 0.16937117 0.33874235 0.8306288
[216,] 0.15553324 0.31106649 0.8444668
[217,] 0.14862012 0.29724024 0.8513799
[218,] 0.12209480 0.24418960 0.8779052
[219,] 0.09745941 0.19491881 0.9025406
[220,] 0.09089091 0.18178183 0.9091091
[221,] 0.25858577 0.51717154 0.7414142
[222,] 0.22496610 0.44993219 0.7750339
[223,] 0.30985042 0.61970083 0.6901496
[224,] 0.25679039 0.51358078 0.7432096
[225,] 0.24828714 0.49657427 0.7517129
[226,] 0.21446220 0.42892439 0.7855378
[227,] 0.29230240 0.58460480 0.7076976
[228,] 0.24097027 0.48194053 0.7590297
[229,] 0.47378641 0.94757282 0.5262136
[230,] 0.47792337 0.95584674 0.5220766
[231,] 0.41158091 0.82316183 0.5884191
[232,] 0.33643238 0.67286476 0.6635676
[233,] 0.37276724 0.74553449 0.6272328
[234,] 0.42330623 0.84661246 0.5766938
[235,] 0.54956129 0.90087742 0.4504387
[236,] 0.53410798 0.93178403 0.4658920
[237,] 0.46187945 0.92375889 0.5381206
[238,] 0.67478966 0.65042069 0.3252103
[239,] 0.61562600 0.76874800 0.3843740
[240,] 0.56282497 0.87435007 0.4371750
[241,] 0.79232450 0.41535100 0.2076755
> postscript(file="/var/wessaorg/rcomp/tmp/1pi2p1383515159.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2y4nb1383515159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/31au81383515159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4ydf41383515159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5qb131383515159.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 6
4.856191326 4.314825077 -5.686933022 -3.940682168 -1.729470849 2.148005789
7 8 9 10 11 12
5.062819958 -1.940574077 0.562771632 -0.037984509 3.699020324 1.018702084
13 14 15 16 17 18
2.600177410 2.549327507 -3.639924422 -2.250379592 1.904955680 0.389768668
19 20 21 22 23 24
1.741379335 -2.028124170 -2.806144862 -2.886522427 2.333094621 -0.202380901
25 26 27 28 29 30
4.472763805 6.786721770 0.737836930 -3.078543192 -1.238557822 -0.732456823
31 32 33 34 35 36
-2.910750067 -6.483969715 3.344493505 -2.886167903 1.407888875 -2.497340999
37 38 39 40 41 42
-3.442700025 1.767385566 -4.924900182 0.340020979 2.628968037 -0.002377936
43 44 45 46 47 48
3.855432751 1.180725682 5.770080962 2.565056442 0.802570398 -1.980056222
49 50 51 52 53 54
-0.151097721 4.213693653 -4.547541916 -0.755770401 -1.316611565 -3.268622552
55 56 57 58 59 60
-1.262431505 1.487630783 4.197011919 -6.021900210 1.467560114 0.206455133
61 62 63 64 65 66
-1.328835759 3.380471985 -1.098245472 -3.498083449 0.883124371 -1.294097109
67 68 69 70 71 72
0.874359914 -1.585408860 2.379704730 -0.872513592 2.499954032 -4.840574930
73 74 75 76 77 78
-2.885240896 0.431144717 2.185189157 5.856224212 1.111242649 0.569012623
79 80 81 82 83 84
-5.131076572 5.505669476 -2.432580060 -1.315130858 3.042848199 -2.640245261
85 86 87 88 89 90
-0.561087168 1.294284172 -1.576015452 -4.301974298 -0.574309180 -4.443485295
91 92 93 94 95 96
2.821937407 -2.539399011 0.299861838 -3.440809348 3.431144645 2.192428747
97 98 99 100 101 102
1.535381133 2.374775131 -3.485063650 2.710486099 2.062008357 -0.336366157
103 104 105 106 107 108
-0.087308757 -3.545487617 7.151952070 2.712210032 4.577009032 1.474361325
109 110 111 112 113 114
5.962859815 -4.678552727 -2.654484158 -6.194603633 0.580486502 2.628120290
115 116 117 118 119 120
2.793565783 -3.331672359 -3.380250284 -1.710598640 2.484063088 -1.996069095
121 122 123 124 125 126
-0.298304010 1.413762926 0.639288579 2.908674153 -2.790681013 4.852274743
127 128 129 130 131 132
7.267189144 -3.082479195 1.431021300 -1.955038425 -4.381794583 3.981326308
133 134 135 136 137 138
-5.066947819 0.980696032 -0.413961924 -0.218292896 -0.946025866 -4.095018187
139 140 141 142 143 144
0.782283303 -3.223122851 -2.385787243 -5.817572615 -4.967148039 1.369934587
145 146 147 148 149 150
6.884681208 0.391752097 1.284858340 -1.121801675 1.846703187 1.475521888
151 152 153 154 155 156
6.902859104 -2.022671410 -1.827977327 3.985843336 0.146045892 3.206827574
157 158 159 160 161 162
-3.586887812 -2.904837580 0.237386354 -3.536899081 -1.284375443 -3.231839121
163 164 165 166 167 168
0.634517093 4.467195364 -1.927789057 -7.216680753 -3.761756356 -3.536701729
169 170 171 172 173 174
-1.509409446 -7.674581827 4.735265225 0.550985960 6.722015102 -0.242585601
175 176 177 178 179 180
4.914508161 -2.287183874 3.562579361 -1.667042474 -1.768971696 3.331617251
181 182 183 184 185 186
1.334060562 2.686483821 -4.233590764 4.757571229 -9.086100612 -2.334972231
187 188 189 190 191 192
2.109112537 6.175153073 -2.453487102 -2.428221797 -0.361979657 1.482508684
193 194 195 196 197 198
-1.589253474 -0.392317796 -0.881835549 1.271725797 1.311941713 -2.398786575
199 200 201 202 203 204
0.117292642 -6.764176407 -2.179778069 -7.846003220 2.034550263 1.509402250
205 206 207 208 209 210
-1.055439946 -0.735712589 -0.949794608 0.816290461 2.877798928 0.408077176
211 212 213 214 215 216
1.247737318 2.728145068 -4.100934761 -3.378629003 1.151275280 -1.949297150
217 218 219 220 221 222
-1.810875084 4.820967664 2.630193272 -0.248449395 1.895733494 4.200191081
223 224 225 226 227 228
0.381751024 -1.914254071 2.130728980 -2.246921139 -1.630530129 -0.042550379
229 230 231 232 233 234
1.403367877 0.057960303 1.881193870 -2.088510479 -2.181302305 7.553443967
235 236 237 238 239 240
1.107947567 4.038634806 5.537536469 5.714588197 -3.361608679 5.280289887
241 242 243 244 245 246
-6.018321874 1.715021819 -2.621826708 1.962125003 4.055868963 -4.510822963
247 248 249 250 251 252
-2.097883626 4.375353613 -8.494328299 -5.585834875 -2.264968507 0.699156095
253 254 255 256 257 258
2.230027066 -0.100960871 4.067756877 0.900580901 0.845315346 -1.625830558
259 260 261 262 263 264
3.845843096 3.203784161 -4.573883614 -0.645228733 6.279881602 -3.613343687
> postscript(file="/var/wessaorg/rcomp/tmp/6e4fx1383515159.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 4.856191326 NA
1 4.314825077 4.856191326
2 -5.686933022 4.314825077
3 -3.940682168 -5.686933022
4 -1.729470849 -3.940682168
5 2.148005789 -1.729470849
6 5.062819958 2.148005789
7 -1.940574077 5.062819958
8 0.562771632 -1.940574077
9 -0.037984509 0.562771632
10 3.699020324 -0.037984509
11 1.018702084 3.699020324
12 2.600177410 1.018702084
13 2.549327507 2.600177410
14 -3.639924422 2.549327507
15 -2.250379592 -3.639924422
16 1.904955680 -2.250379592
17 0.389768668 1.904955680
18 1.741379335 0.389768668
19 -2.028124170 1.741379335
20 -2.806144862 -2.028124170
21 -2.886522427 -2.806144862
22 2.333094621 -2.886522427
23 -0.202380901 2.333094621
24 4.472763805 -0.202380901
25 6.786721770 4.472763805
26 0.737836930 6.786721770
27 -3.078543192 0.737836930
28 -1.238557822 -3.078543192
29 -0.732456823 -1.238557822
30 -2.910750067 -0.732456823
31 -6.483969715 -2.910750067
32 3.344493505 -6.483969715
33 -2.886167903 3.344493505
34 1.407888875 -2.886167903
35 -2.497340999 1.407888875
36 -3.442700025 -2.497340999
37 1.767385566 -3.442700025
38 -4.924900182 1.767385566
39 0.340020979 -4.924900182
40 2.628968037 0.340020979
41 -0.002377936 2.628968037
42 3.855432751 -0.002377936
43 1.180725682 3.855432751
44 5.770080962 1.180725682
45 2.565056442 5.770080962
46 0.802570398 2.565056442
47 -1.980056222 0.802570398
48 -0.151097721 -1.980056222
49 4.213693653 -0.151097721
50 -4.547541916 4.213693653
51 -0.755770401 -4.547541916
52 -1.316611565 -0.755770401
53 -3.268622552 -1.316611565
54 -1.262431505 -3.268622552
55 1.487630783 -1.262431505
56 4.197011919 1.487630783
57 -6.021900210 4.197011919
58 1.467560114 -6.021900210
59 0.206455133 1.467560114
60 -1.328835759 0.206455133
61 3.380471985 -1.328835759
62 -1.098245472 3.380471985
63 -3.498083449 -1.098245472
64 0.883124371 -3.498083449
65 -1.294097109 0.883124371
66 0.874359914 -1.294097109
67 -1.585408860 0.874359914
68 2.379704730 -1.585408860
69 -0.872513592 2.379704730
70 2.499954032 -0.872513592
71 -4.840574930 2.499954032
72 -2.885240896 -4.840574930
73 0.431144717 -2.885240896
74 2.185189157 0.431144717
75 5.856224212 2.185189157
76 1.111242649 5.856224212
77 0.569012623 1.111242649
78 -5.131076572 0.569012623
79 5.505669476 -5.131076572
80 -2.432580060 5.505669476
81 -1.315130858 -2.432580060
82 3.042848199 -1.315130858
83 -2.640245261 3.042848199
84 -0.561087168 -2.640245261
85 1.294284172 -0.561087168
86 -1.576015452 1.294284172
87 -4.301974298 -1.576015452
88 -0.574309180 -4.301974298
89 -4.443485295 -0.574309180
90 2.821937407 -4.443485295
91 -2.539399011 2.821937407
92 0.299861838 -2.539399011
93 -3.440809348 0.299861838
94 3.431144645 -3.440809348
95 2.192428747 3.431144645
96 1.535381133 2.192428747
97 2.374775131 1.535381133
98 -3.485063650 2.374775131
99 2.710486099 -3.485063650
100 2.062008357 2.710486099
101 -0.336366157 2.062008357
102 -0.087308757 -0.336366157
103 -3.545487617 -0.087308757
104 7.151952070 -3.545487617
105 2.712210032 7.151952070
106 4.577009032 2.712210032
107 1.474361325 4.577009032
108 5.962859815 1.474361325
109 -4.678552727 5.962859815
110 -2.654484158 -4.678552727
111 -6.194603633 -2.654484158
112 0.580486502 -6.194603633
113 2.628120290 0.580486502
114 2.793565783 2.628120290
115 -3.331672359 2.793565783
116 -3.380250284 -3.331672359
117 -1.710598640 -3.380250284
118 2.484063088 -1.710598640
119 -1.996069095 2.484063088
120 -0.298304010 -1.996069095
121 1.413762926 -0.298304010
122 0.639288579 1.413762926
123 2.908674153 0.639288579
124 -2.790681013 2.908674153
125 4.852274743 -2.790681013
126 7.267189144 4.852274743
127 -3.082479195 7.267189144
128 1.431021300 -3.082479195
129 -1.955038425 1.431021300
130 -4.381794583 -1.955038425
131 3.981326308 -4.381794583
132 -5.066947819 3.981326308
133 0.980696032 -5.066947819
134 -0.413961924 0.980696032
135 -0.218292896 -0.413961924
136 -0.946025866 -0.218292896
137 -4.095018187 -0.946025866
138 0.782283303 -4.095018187
139 -3.223122851 0.782283303
140 -2.385787243 -3.223122851
141 -5.817572615 -2.385787243
142 -4.967148039 -5.817572615
143 1.369934587 -4.967148039
144 6.884681208 1.369934587
145 0.391752097 6.884681208
146 1.284858340 0.391752097
147 -1.121801675 1.284858340
148 1.846703187 -1.121801675
149 1.475521888 1.846703187
150 6.902859104 1.475521888
151 -2.022671410 6.902859104
152 -1.827977327 -2.022671410
153 3.985843336 -1.827977327
154 0.146045892 3.985843336
155 3.206827574 0.146045892
156 -3.586887812 3.206827574
157 -2.904837580 -3.586887812
158 0.237386354 -2.904837580
159 -3.536899081 0.237386354
160 -1.284375443 -3.536899081
161 -3.231839121 -1.284375443
162 0.634517093 -3.231839121
163 4.467195364 0.634517093
164 -1.927789057 4.467195364
165 -7.216680753 -1.927789057
166 -3.761756356 -7.216680753
167 -3.536701729 -3.761756356
168 -1.509409446 -3.536701729
169 -7.674581827 -1.509409446
170 4.735265225 -7.674581827
171 0.550985960 4.735265225
172 6.722015102 0.550985960
173 -0.242585601 6.722015102
174 4.914508161 -0.242585601
175 -2.287183874 4.914508161
176 3.562579361 -2.287183874
177 -1.667042474 3.562579361
178 -1.768971696 -1.667042474
179 3.331617251 -1.768971696
180 1.334060562 3.331617251
181 2.686483821 1.334060562
182 -4.233590764 2.686483821
183 4.757571229 -4.233590764
184 -9.086100612 4.757571229
185 -2.334972231 -9.086100612
186 2.109112537 -2.334972231
187 6.175153073 2.109112537
188 -2.453487102 6.175153073
189 -2.428221797 -2.453487102
190 -0.361979657 -2.428221797
191 1.482508684 -0.361979657
192 -1.589253474 1.482508684
193 -0.392317796 -1.589253474
194 -0.881835549 -0.392317796
195 1.271725797 -0.881835549
196 1.311941713 1.271725797
197 -2.398786575 1.311941713
198 0.117292642 -2.398786575
199 -6.764176407 0.117292642
200 -2.179778069 -6.764176407
201 -7.846003220 -2.179778069
202 2.034550263 -7.846003220
203 1.509402250 2.034550263
204 -1.055439946 1.509402250
205 -0.735712589 -1.055439946
206 -0.949794608 -0.735712589
207 0.816290461 -0.949794608
208 2.877798928 0.816290461
209 0.408077176 2.877798928
210 1.247737318 0.408077176
211 2.728145068 1.247737318
212 -4.100934761 2.728145068
213 -3.378629003 -4.100934761
214 1.151275280 -3.378629003
215 -1.949297150 1.151275280
216 -1.810875084 -1.949297150
217 4.820967664 -1.810875084
218 2.630193272 4.820967664
219 -0.248449395 2.630193272
220 1.895733494 -0.248449395
221 4.200191081 1.895733494
222 0.381751024 4.200191081
223 -1.914254071 0.381751024
224 2.130728980 -1.914254071
225 -2.246921139 2.130728980
226 -1.630530129 -2.246921139
227 -0.042550379 -1.630530129
228 1.403367877 -0.042550379
229 0.057960303 1.403367877
230 1.881193870 0.057960303
231 -2.088510479 1.881193870
232 -2.181302305 -2.088510479
233 7.553443967 -2.181302305
234 1.107947567 7.553443967
235 4.038634806 1.107947567
236 5.537536469 4.038634806
237 5.714588197 5.537536469
238 -3.361608679 5.714588197
239 5.280289887 -3.361608679
240 -6.018321874 5.280289887
241 1.715021819 -6.018321874
242 -2.621826708 1.715021819
243 1.962125003 -2.621826708
244 4.055868963 1.962125003
245 -4.510822963 4.055868963
246 -2.097883626 -4.510822963
247 4.375353613 -2.097883626
248 -8.494328299 4.375353613
249 -5.585834875 -8.494328299
250 -2.264968507 -5.585834875
251 0.699156095 -2.264968507
252 2.230027066 0.699156095
253 -0.100960871 2.230027066
254 4.067756877 -0.100960871
255 0.900580901 4.067756877
256 0.845315346 0.900580901
257 -1.625830558 0.845315346
258 3.845843096 -1.625830558
259 3.203784161 3.845843096
260 -4.573883614 3.203784161
261 -0.645228733 -4.573883614
262 6.279881602 -0.645228733
263 -3.613343687 6.279881602
264 NA -3.613343687
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.314825077 4.856191326
[2,] -5.686933022 4.314825077
[3,] -3.940682168 -5.686933022
[4,] -1.729470849 -3.940682168
[5,] 2.148005789 -1.729470849
[6,] 5.062819958 2.148005789
[7,] -1.940574077 5.062819958
[8,] 0.562771632 -1.940574077
[9,] -0.037984509 0.562771632
[10,] 3.699020324 -0.037984509
[11,] 1.018702084 3.699020324
[12,] 2.600177410 1.018702084
[13,] 2.549327507 2.600177410
[14,] -3.639924422 2.549327507
[15,] -2.250379592 -3.639924422
[16,] 1.904955680 -2.250379592
[17,] 0.389768668 1.904955680
[18,] 1.741379335 0.389768668
[19,] -2.028124170 1.741379335
[20,] -2.806144862 -2.028124170
[21,] -2.886522427 -2.806144862
[22,] 2.333094621 -2.886522427
[23,] -0.202380901 2.333094621
[24,] 4.472763805 -0.202380901
[25,] 6.786721770 4.472763805
[26,] 0.737836930 6.786721770
[27,] -3.078543192 0.737836930
[28,] -1.238557822 -3.078543192
[29,] -0.732456823 -1.238557822
[30,] -2.910750067 -0.732456823
[31,] -6.483969715 -2.910750067
[32,] 3.344493505 -6.483969715
[33,] -2.886167903 3.344493505
[34,] 1.407888875 -2.886167903
[35,] -2.497340999 1.407888875
[36,] -3.442700025 -2.497340999
[37,] 1.767385566 -3.442700025
[38,] -4.924900182 1.767385566
[39,] 0.340020979 -4.924900182
[40,] 2.628968037 0.340020979
[41,] -0.002377936 2.628968037
[42,] 3.855432751 -0.002377936
[43,] 1.180725682 3.855432751
[44,] 5.770080962 1.180725682
[45,] 2.565056442 5.770080962
[46,] 0.802570398 2.565056442
[47,] -1.980056222 0.802570398
[48,] -0.151097721 -1.980056222
[49,] 4.213693653 -0.151097721
[50,] -4.547541916 4.213693653
[51,] -0.755770401 -4.547541916
[52,] -1.316611565 -0.755770401
[53,] -3.268622552 -1.316611565
[54,] -1.262431505 -3.268622552
[55,] 1.487630783 -1.262431505
[56,] 4.197011919 1.487630783
[57,] -6.021900210 4.197011919
[58,] 1.467560114 -6.021900210
[59,] 0.206455133 1.467560114
[60,] -1.328835759 0.206455133
[61,] 3.380471985 -1.328835759
[62,] -1.098245472 3.380471985
[63,] -3.498083449 -1.098245472
[64,] 0.883124371 -3.498083449
[65,] -1.294097109 0.883124371
[66,] 0.874359914 -1.294097109
[67,] -1.585408860 0.874359914
[68,] 2.379704730 -1.585408860
[69,] -0.872513592 2.379704730
[70,] 2.499954032 -0.872513592
[71,] -4.840574930 2.499954032
[72,] -2.885240896 -4.840574930
[73,] 0.431144717 -2.885240896
[74,] 2.185189157 0.431144717
[75,] 5.856224212 2.185189157
[76,] 1.111242649 5.856224212
[77,] 0.569012623 1.111242649
[78,] -5.131076572 0.569012623
[79,] 5.505669476 -5.131076572
[80,] -2.432580060 5.505669476
[81,] -1.315130858 -2.432580060
[82,] 3.042848199 -1.315130858
[83,] -2.640245261 3.042848199
[84,] -0.561087168 -2.640245261
[85,] 1.294284172 -0.561087168
[86,] -1.576015452 1.294284172
[87,] -4.301974298 -1.576015452
[88,] -0.574309180 -4.301974298
[89,] -4.443485295 -0.574309180
[90,] 2.821937407 -4.443485295
[91,] -2.539399011 2.821937407
[92,] 0.299861838 -2.539399011
[93,] -3.440809348 0.299861838
[94,] 3.431144645 -3.440809348
[95,] 2.192428747 3.431144645
[96,] 1.535381133 2.192428747
[97,] 2.374775131 1.535381133
[98,] -3.485063650 2.374775131
[99,] 2.710486099 -3.485063650
[100,] 2.062008357 2.710486099
[101,] -0.336366157 2.062008357
[102,] -0.087308757 -0.336366157
[103,] -3.545487617 -0.087308757
[104,] 7.151952070 -3.545487617
[105,] 2.712210032 7.151952070
[106,] 4.577009032 2.712210032
[107,] 1.474361325 4.577009032
[108,] 5.962859815 1.474361325
[109,] -4.678552727 5.962859815
[110,] -2.654484158 -4.678552727
[111,] -6.194603633 -2.654484158
[112,] 0.580486502 -6.194603633
[113,] 2.628120290 0.580486502
[114,] 2.793565783 2.628120290
[115,] -3.331672359 2.793565783
[116,] -3.380250284 -3.331672359
[117,] -1.710598640 -3.380250284
[118,] 2.484063088 -1.710598640
[119,] -1.996069095 2.484063088
[120,] -0.298304010 -1.996069095
[121,] 1.413762926 -0.298304010
[122,] 0.639288579 1.413762926
[123,] 2.908674153 0.639288579
[124,] -2.790681013 2.908674153
[125,] 4.852274743 -2.790681013
[126,] 7.267189144 4.852274743
[127,] -3.082479195 7.267189144
[128,] 1.431021300 -3.082479195
[129,] -1.955038425 1.431021300
[130,] -4.381794583 -1.955038425
[131,] 3.981326308 -4.381794583
[132,] -5.066947819 3.981326308
[133,] 0.980696032 -5.066947819
[134,] -0.413961924 0.980696032
[135,] -0.218292896 -0.413961924
[136,] -0.946025866 -0.218292896
[137,] -4.095018187 -0.946025866
[138,] 0.782283303 -4.095018187
[139,] -3.223122851 0.782283303
[140,] -2.385787243 -3.223122851
[141,] -5.817572615 -2.385787243
[142,] -4.967148039 -5.817572615
[143,] 1.369934587 -4.967148039
[144,] 6.884681208 1.369934587
[145,] 0.391752097 6.884681208
[146,] 1.284858340 0.391752097
[147,] -1.121801675 1.284858340
[148,] 1.846703187 -1.121801675
[149,] 1.475521888 1.846703187
[150,] 6.902859104 1.475521888
[151,] -2.022671410 6.902859104
[152,] -1.827977327 -2.022671410
[153,] 3.985843336 -1.827977327
[154,] 0.146045892 3.985843336
[155,] 3.206827574 0.146045892
[156,] -3.586887812 3.206827574
[157,] -2.904837580 -3.586887812
[158,] 0.237386354 -2.904837580
[159,] -3.536899081 0.237386354
[160,] -1.284375443 -3.536899081
[161,] -3.231839121 -1.284375443
[162,] 0.634517093 -3.231839121
[163,] 4.467195364 0.634517093
[164,] -1.927789057 4.467195364
[165,] -7.216680753 -1.927789057
[166,] -3.761756356 -7.216680753
[167,] -3.536701729 -3.761756356
[168,] -1.509409446 -3.536701729
[169,] -7.674581827 -1.509409446
[170,] 4.735265225 -7.674581827
[171,] 0.550985960 4.735265225
[172,] 6.722015102 0.550985960
[173,] -0.242585601 6.722015102
[174,] 4.914508161 -0.242585601
[175,] -2.287183874 4.914508161
[176,] 3.562579361 -2.287183874
[177,] -1.667042474 3.562579361
[178,] -1.768971696 -1.667042474
[179,] 3.331617251 -1.768971696
[180,] 1.334060562 3.331617251
[181,] 2.686483821 1.334060562
[182,] -4.233590764 2.686483821
[183,] 4.757571229 -4.233590764
[184,] -9.086100612 4.757571229
[185,] -2.334972231 -9.086100612
[186,] 2.109112537 -2.334972231
[187,] 6.175153073 2.109112537
[188,] -2.453487102 6.175153073
[189,] -2.428221797 -2.453487102
[190,] -0.361979657 -2.428221797
[191,] 1.482508684 -0.361979657
[192,] -1.589253474 1.482508684
[193,] -0.392317796 -1.589253474
[194,] -0.881835549 -0.392317796
[195,] 1.271725797 -0.881835549
[196,] 1.311941713 1.271725797
[197,] -2.398786575 1.311941713
[198,] 0.117292642 -2.398786575
[199,] -6.764176407 0.117292642
[200,] -2.179778069 -6.764176407
[201,] -7.846003220 -2.179778069
[202,] 2.034550263 -7.846003220
[203,] 1.509402250 2.034550263
[204,] -1.055439946 1.509402250
[205,] -0.735712589 -1.055439946
[206,] -0.949794608 -0.735712589
[207,] 0.816290461 -0.949794608
[208,] 2.877798928 0.816290461
[209,] 0.408077176 2.877798928
[210,] 1.247737318 0.408077176
[211,] 2.728145068 1.247737318
[212,] -4.100934761 2.728145068
[213,] -3.378629003 -4.100934761
[214,] 1.151275280 -3.378629003
[215,] -1.949297150 1.151275280
[216,] -1.810875084 -1.949297150
[217,] 4.820967664 -1.810875084
[218,] 2.630193272 4.820967664
[219,] -0.248449395 2.630193272
[220,] 1.895733494 -0.248449395
[221,] 4.200191081 1.895733494
[222,] 0.381751024 4.200191081
[223,] -1.914254071 0.381751024
[224,] 2.130728980 -1.914254071
[225,] -2.246921139 2.130728980
[226,] -1.630530129 -2.246921139
[227,] -0.042550379 -1.630530129
[228,] 1.403367877 -0.042550379
[229,] 0.057960303 1.403367877
[230,] 1.881193870 0.057960303
[231,] -2.088510479 1.881193870
[232,] -2.181302305 -2.088510479
[233,] 7.553443967 -2.181302305
[234,] 1.107947567 7.553443967
[235,] 4.038634806 1.107947567
[236,] 5.537536469 4.038634806
[237,] 5.714588197 5.537536469
[238,] -3.361608679 5.714588197
[239,] 5.280289887 -3.361608679
[240,] -6.018321874 5.280289887
[241,] 1.715021819 -6.018321874
[242,] -2.621826708 1.715021819
[243,] 1.962125003 -2.621826708
[244,] 4.055868963 1.962125003
[245,] -4.510822963 4.055868963
[246,] -2.097883626 -4.510822963
[247,] 4.375353613 -2.097883626
[248,] -8.494328299 4.375353613
[249,] -5.585834875 -8.494328299
[250,] -2.264968507 -5.585834875
[251,] 0.699156095 -2.264968507
[252,] 2.230027066 0.699156095
[253,] -0.100960871 2.230027066
[254,] 4.067756877 -0.100960871
[255,] 0.900580901 4.067756877
[256,] 0.845315346 0.900580901
[257,] -1.625830558 0.845315346
[258,] 3.845843096 -1.625830558
[259,] 3.203784161 3.845843096
[260,] -4.573883614 3.203784161
[261,] -0.645228733 -4.573883614
[262,] 6.279881602 -0.645228733
[263,] -3.613343687 6.279881602
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.314825077 4.856191326
2 -5.686933022 4.314825077
3 -3.940682168 -5.686933022
4 -1.729470849 -3.940682168
5 2.148005789 -1.729470849
6 5.062819958 2.148005789
7 -1.940574077 5.062819958
8 0.562771632 -1.940574077
9 -0.037984509 0.562771632
10 3.699020324 -0.037984509
11 1.018702084 3.699020324
12 2.600177410 1.018702084
13 2.549327507 2.600177410
14 -3.639924422 2.549327507
15 -2.250379592 -3.639924422
16 1.904955680 -2.250379592
17 0.389768668 1.904955680
18 1.741379335 0.389768668
19 -2.028124170 1.741379335
20 -2.806144862 -2.028124170
21 -2.886522427 -2.806144862
22 2.333094621 -2.886522427
23 -0.202380901 2.333094621
24 4.472763805 -0.202380901
25 6.786721770 4.472763805
26 0.737836930 6.786721770
27 -3.078543192 0.737836930
28 -1.238557822 -3.078543192
29 -0.732456823 -1.238557822
30 -2.910750067 -0.732456823
31 -6.483969715 -2.910750067
32 3.344493505 -6.483969715
33 -2.886167903 3.344493505
34 1.407888875 -2.886167903
35 -2.497340999 1.407888875
36 -3.442700025 -2.497340999
37 1.767385566 -3.442700025
38 -4.924900182 1.767385566
39 0.340020979 -4.924900182
40 2.628968037 0.340020979
41 -0.002377936 2.628968037
42 3.855432751 -0.002377936
43 1.180725682 3.855432751
44 5.770080962 1.180725682
45 2.565056442 5.770080962
46 0.802570398 2.565056442
47 -1.980056222 0.802570398
48 -0.151097721 -1.980056222
49 4.213693653 -0.151097721
50 -4.547541916 4.213693653
51 -0.755770401 -4.547541916
52 -1.316611565 -0.755770401
53 -3.268622552 -1.316611565
54 -1.262431505 -3.268622552
55 1.487630783 -1.262431505
56 4.197011919 1.487630783
57 -6.021900210 4.197011919
58 1.467560114 -6.021900210
59 0.206455133 1.467560114
60 -1.328835759 0.206455133
61 3.380471985 -1.328835759
62 -1.098245472 3.380471985
63 -3.498083449 -1.098245472
64 0.883124371 -3.498083449
65 -1.294097109 0.883124371
66 0.874359914 -1.294097109
67 -1.585408860 0.874359914
68 2.379704730 -1.585408860
69 -0.872513592 2.379704730
70 2.499954032 -0.872513592
71 -4.840574930 2.499954032
72 -2.885240896 -4.840574930
73 0.431144717 -2.885240896
74 2.185189157 0.431144717
75 5.856224212 2.185189157
76 1.111242649 5.856224212
77 0.569012623 1.111242649
78 -5.131076572 0.569012623
79 5.505669476 -5.131076572
80 -2.432580060 5.505669476
81 -1.315130858 -2.432580060
82 3.042848199 -1.315130858
83 -2.640245261 3.042848199
84 -0.561087168 -2.640245261
85 1.294284172 -0.561087168
86 -1.576015452 1.294284172
87 -4.301974298 -1.576015452
88 -0.574309180 -4.301974298
89 -4.443485295 -0.574309180
90 2.821937407 -4.443485295
91 -2.539399011 2.821937407
92 0.299861838 -2.539399011
93 -3.440809348 0.299861838
94 3.431144645 -3.440809348
95 2.192428747 3.431144645
96 1.535381133 2.192428747
97 2.374775131 1.535381133
98 -3.485063650 2.374775131
99 2.710486099 -3.485063650
100 2.062008357 2.710486099
101 -0.336366157 2.062008357
102 -0.087308757 -0.336366157
103 -3.545487617 -0.087308757
104 7.151952070 -3.545487617
105 2.712210032 7.151952070
106 4.577009032 2.712210032
107 1.474361325 4.577009032
108 5.962859815 1.474361325
109 -4.678552727 5.962859815
110 -2.654484158 -4.678552727
111 -6.194603633 -2.654484158
112 0.580486502 -6.194603633
113 2.628120290 0.580486502
114 2.793565783 2.628120290
115 -3.331672359 2.793565783
116 -3.380250284 -3.331672359
117 -1.710598640 -3.380250284
118 2.484063088 -1.710598640
119 -1.996069095 2.484063088
120 -0.298304010 -1.996069095
121 1.413762926 -0.298304010
122 0.639288579 1.413762926
123 2.908674153 0.639288579
124 -2.790681013 2.908674153
125 4.852274743 -2.790681013
126 7.267189144 4.852274743
127 -3.082479195 7.267189144
128 1.431021300 -3.082479195
129 -1.955038425 1.431021300
130 -4.381794583 -1.955038425
131 3.981326308 -4.381794583
132 -5.066947819 3.981326308
133 0.980696032 -5.066947819
134 -0.413961924 0.980696032
135 -0.218292896 -0.413961924
136 -0.946025866 -0.218292896
137 -4.095018187 -0.946025866
138 0.782283303 -4.095018187
139 -3.223122851 0.782283303
140 -2.385787243 -3.223122851
141 -5.817572615 -2.385787243
142 -4.967148039 -5.817572615
143 1.369934587 -4.967148039
144 6.884681208 1.369934587
145 0.391752097 6.884681208
146 1.284858340 0.391752097
147 -1.121801675 1.284858340
148 1.846703187 -1.121801675
149 1.475521888 1.846703187
150 6.902859104 1.475521888
151 -2.022671410 6.902859104
152 -1.827977327 -2.022671410
153 3.985843336 -1.827977327
154 0.146045892 3.985843336
155 3.206827574 0.146045892
156 -3.586887812 3.206827574
157 -2.904837580 -3.586887812
158 0.237386354 -2.904837580
159 -3.536899081 0.237386354
160 -1.284375443 -3.536899081
161 -3.231839121 -1.284375443
162 0.634517093 -3.231839121
163 4.467195364 0.634517093
164 -1.927789057 4.467195364
165 -7.216680753 -1.927789057
166 -3.761756356 -7.216680753
167 -3.536701729 -3.761756356
168 -1.509409446 -3.536701729
169 -7.674581827 -1.509409446
170 4.735265225 -7.674581827
171 0.550985960 4.735265225
172 6.722015102 0.550985960
173 -0.242585601 6.722015102
174 4.914508161 -0.242585601
175 -2.287183874 4.914508161
176 3.562579361 -2.287183874
177 -1.667042474 3.562579361
178 -1.768971696 -1.667042474
179 3.331617251 -1.768971696
180 1.334060562 3.331617251
181 2.686483821 1.334060562
182 -4.233590764 2.686483821
183 4.757571229 -4.233590764
184 -9.086100612 4.757571229
185 -2.334972231 -9.086100612
186 2.109112537 -2.334972231
187 6.175153073 2.109112537
188 -2.453487102 6.175153073
189 -2.428221797 -2.453487102
190 -0.361979657 -2.428221797
191 1.482508684 -0.361979657
192 -1.589253474 1.482508684
193 -0.392317796 -1.589253474
194 -0.881835549 -0.392317796
195 1.271725797 -0.881835549
196 1.311941713 1.271725797
197 -2.398786575 1.311941713
198 0.117292642 -2.398786575
199 -6.764176407 0.117292642
200 -2.179778069 -6.764176407
201 -7.846003220 -2.179778069
202 2.034550263 -7.846003220
203 1.509402250 2.034550263
204 -1.055439946 1.509402250
205 -0.735712589 -1.055439946
206 -0.949794608 -0.735712589
207 0.816290461 -0.949794608
208 2.877798928 0.816290461
209 0.408077176 2.877798928
210 1.247737318 0.408077176
211 2.728145068 1.247737318
212 -4.100934761 2.728145068
213 -3.378629003 -4.100934761
214 1.151275280 -3.378629003
215 -1.949297150 1.151275280
216 -1.810875084 -1.949297150
217 4.820967664 -1.810875084
218 2.630193272 4.820967664
219 -0.248449395 2.630193272
220 1.895733494 -0.248449395
221 4.200191081 1.895733494
222 0.381751024 4.200191081
223 -1.914254071 0.381751024
224 2.130728980 -1.914254071
225 -2.246921139 2.130728980
226 -1.630530129 -2.246921139
227 -0.042550379 -1.630530129
228 1.403367877 -0.042550379
229 0.057960303 1.403367877
230 1.881193870 0.057960303
231 -2.088510479 1.881193870
232 -2.181302305 -2.088510479
233 7.553443967 -2.181302305
234 1.107947567 7.553443967
235 4.038634806 1.107947567
236 5.537536469 4.038634806
237 5.714588197 5.537536469
238 -3.361608679 5.714588197
239 5.280289887 -3.361608679
240 -6.018321874 5.280289887
241 1.715021819 -6.018321874
242 -2.621826708 1.715021819
243 1.962125003 -2.621826708
244 4.055868963 1.962125003
245 -4.510822963 4.055868963
246 -2.097883626 -4.510822963
247 4.375353613 -2.097883626
248 -8.494328299 4.375353613
249 -5.585834875 -8.494328299
250 -2.264968507 -5.585834875
251 0.699156095 -2.264968507
252 2.230027066 0.699156095
253 -0.100960871 2.230027066
254 4.067756877 -0.100960871
255 0.900580901 4.067756877
256 0.845315346 0.900580901
257 -1.625830558 0.845315346
258 3.845843096 -1.625830558
259 3.203784161 3.845843096
260 -4.573883614 3.203784161
261 -0.645228733 -4.573883614
262 6.279881602 -0.645228733
263 -3.613343687 6.279881602
> 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/74gl61383515159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8jfqc1383515159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9k1vg1383515159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10jd961383515159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11lkeu1383515159.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/12klmk1383515159.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/13x8vy1383515159.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/144ggl1383515159.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/15ijxb1383515159.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/16q9ra1383515159.tab")
+ }
>
> try(system("convert tmp/1pi2p1383515159.ps tmp/1pi2p1383515159.png",intern=TRUE))
character(0)
> try(system("convert tmp/2y4nb1383515159.ps tmp/2y4nb1383515159.png",intern=TRUE))
character(0)
> try(system("convert tmp/31au81383515159.ps tmp/31au81383515159.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ydf41383515159.ps tmp/4ydf41383515159.png",intern=TRUE))
character(0)
> try(system("convert tmp/5qb131383515159.ps tmp/5qb131383515159.png",intern=TRUE))
character(0)
> try(system("convert tmp/6e4fx1383515159.ps tmp/6e4fx1383515159.png",intern=TRUE))
character(0)
> try(system("convert tmp/74gl61383515159.ps tmp/74gl61383515159.png",intern=TRUE))
character(0)
> try(system("convert tmp/8jfqc1383515159.ps tmp/8jfqc1383515159.png",intern=TRUE))
character(0)
> try(system("convert tmp/9k1vg1383515159.ps tmp/9k1vg1383515159.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jd961383515159.ps tmp/10jd961383515159.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
14.599 2.536 17.122