R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(0
+ ,1
+ ,0
+ ,2
+ ,0
+ ,5
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,4
+ ,1
+ ,3
+ ,3
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,2
+ ,4
+ ,4
+ ,2
+ ,2
+ ,5
+ ,5
+ ,4
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,4
+ ,1
+ ,5
+ ,5
+ ,3
+ ,3
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,3
+ ,3
+ ,2
+ ,2
+ ,4
+ ,1
+ ,6
+ ,6
+ ,4
+ ,4
+ ,5
+ ,5
+ ,1
+ ,1
+ ,3
+ ,3
+ ,2
+ ,2
+ ,4
+ ,4
+ ,5
+ ,0
+ ,7
+ ,0
+ ,3
+ ,0
+ ,5
+ ,0
+ ,1
+ ,0
+ ,2
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,4
+ ,1
+ ,8
+ ,8
+ ,3
+ ,3
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,4
+ ,4
+ ,3
+ ,0
+ ,9
+ ,0
+ ,3
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,10
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,4
+ ,1
+ ,11
+ ,11
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,4
+ ,0
+ ,12
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,4
+ ,1
+ ,13
+ ,13
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,2
+ ,2
+ ,2
+ ,2
+ ,3
+ ,3
+ ,4
+ ,1
+ ,14
+ ,14
+ ,3
+ ,3
+ ,3
+ ,3
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,4
+ ,4
+ ,2
+ ,0
+ ,15
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,1
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,3
+ ,1
+ ,16
+ ,16
+ ,4
+ ,4
+ ,5
+ ,5
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,4
+ ,4
+ ,4
+ ,0
+ ,17
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,18
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,1
+ ,19
+ ,19
+ ,3
+ ,3
+ ,4
+ ,4
+ ,2
+ ,2
+ ,2
+ ,2
+ ,3
+ ,3
+ ,4
+ ,4
+ ,4
+ ,0
+ ,20
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,3
+ ,1
+ ,21
+ ,21
+ ,2
+ ,2
+ ,4
+ ,4
+ ,1
+ ,1
+ ,4
+ ,4
+ ,2
+ ,2
+ ,4
+ ,4
+ ,3
+ ,1
+ ,22
+ ,22
+ ,5
+ ,5
+ ,4
+ ,4
+ ,2
+ ,2
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,4
+ ,0
+ ,23
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,5
+ ,0
+ ,2
+ ,0
+ ,3
+ ,1
+ ,24
+ ,24
+ ,2
+ ,2
+ ,4
+ ,4
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,4
+ ,4
+ ,3
+ ,0
+ ,25
+ ,0
+ ,3
+ ,0
+ ,5
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,26
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,3
+ ,0
+ ,4
+ ,1
+ ,27
+ ,27
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,2
+ ,3
+ ,3
+ ,2
+ ,2
+ ,4
+ ,4
+ ,4
+ ,0
+ ,28
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,4
+ ,1
+ ,29
+ ,29
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,3
+ ,1
+ ,1
+ ,2
+ ,2
+ ,4
+ ,4
+ ,4
+ ,1
+ ,30
+ ,30
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,2
+ ,3
+ ,3
+ ,2
+ ,2
+ ,4
+ ,4
+ ,4
+ ,0
+ ,31
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,5
+ ,1
+ ,32
+ ,32
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,0
+ ,33
+ ,0
+ ,5
+ ,0
+ ,2
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,34
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,5
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,4
+ ,1
+ ,35
+ ,35
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,2
+ ,2
+ ,2
+ ,3
+ ,3
+ ,4
+ ,4
+ ,3
+ ,0
+ ,36
+ ,0
+ ,3
+ ,0
+ ,5
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,5
+ ,0
+ ,4
+ ,1
+ ,37
+ ,37
+ ,2
+ ,2
+ ,5
+ ,5
+ ,2
+ ,2
+ ,4
+ ,4
+ ,1
+ ,1
+ ,4
+ ,4
+ ,3
+ ,1
+ ,38
+ ,38
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,2
+ ,2
+ ,2
+ ,1
+ ,1
+ ,2
+ ,2
+ ,4
+ ,0
+ ,39
+ ,0
+ ,5
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,4
+ ,1
+ ,40
+ ,40
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,2
+ ,4
+ ,4
+ ,2
+ ,2
+ ,4
+ ,4
+ ,3
+ ,0
+ ,41
+ ,0
+ ,4
+ ,0
+ ,5
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,5
+ ,0
+ ,5
+ ,0
+ ,42
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,4
+ ,1
+ ,43
+ ,43
+ ,3
+ ,3
+ ,4
+ ,4
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,3
+ ,0
+ ,44
+ ,0
+ ,4
+ ,0
+ ,5
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,3
+ ,1
+ ,45
+ ,45
+ ,2
+ ,2
+ ,4
+ ,4
+ ,2
+ ,2
+ ,3
+ ,3
+ ,2
+ ,2
+ ,4
+ ,4
+ ,3
+ ,1
+ ,46
+ ,46
+ ,2
+ ,2
+ ,5
+ ,5
+ ,1
+ ,1
+ ,1
+ ,1
+ ,2
+ ,2
+ ,4
+ ,4
+ ,4
+ ,0
+ ,47
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,4
+ ,1
+ ,48
+ ,48
+ ,2
+ ,2
+ ,4
+ ,4
+ ,1
+ ,1
+ ,5
+ ,5
+ ,2
+ ,2
+ ,5
+ ,5
+ ,4
+ ,0
+ ,49
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,50
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,4
+ ,1
+ ,51
+ ,51
+ ,1
+ ,1
+ ,4
+ ,4
+ ,1
+ ,1
+ ,4
+ ,4
+ ,1
+ ,1
+ ,4
+ ,4
+ ,4
+ ,0
+ ,52
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,4
+ ,1
+ ,53
+ ,53
+ ,2
+ ,2
+ ,4
+ ,4
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,4
+ ,4
+ ,5
+ ,1
+ ,54
+ ,54
+ ,1
+ ,1
+ ,2
+ ,2
+ ,1
+ ,1
+ ,2
+ ,2
+ ,1
+ ,1
+ ,3
+ ,3
+ ,3
+ ,0
+ ,55
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,5
+ ,0
+ ,4
+ ,0
+ ,5
+ ,0
+ ,5
+ ,0
+ ,3
+ ,1
+ ,56
+ ,56
+ ,3
+ ,3
+ ,5
+ ,5
+ ,2
+ ,2
+ ,3
+ ,3
+ ,2
+ ,2
+ ,4
+ ,4
+ ,5
+ ,0
+ ,57
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,5
+ ,0
+ ,58
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,4
+ ,1
+ ,59
+ ,59
+ ,3
+ ,3
+ ,5
+ ,5
+ ,1
+ ,1
+ ,3
+ ,3
+ ,1
+ ,1
+ ,4
+ ,4
+ ,4
+ ,0
+ ,60
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,3
+ ,1
+ ,61
+ ,61
+ ,2
+ ,2
+ ,5
+ ,5
+ ,2
+ ,2
+ ,2
+ ,2
+ ,1
+ ,1
+ ,4
+ ,4
+ ,4
+ ,1
+ ,62
+ ,62
+ ,3
+ ,3
+ ,4
+ ,4
+ ,1
+ ,1
+ ,3
+ ,3
+ ,1
+ ,1
+ ,4
+ ,4
+ ,4
+ ,0
+ ,63
+ ,0
+ ,2
+ ,0
+ ,5
+ ,0
+ ,1
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,5
+ ,1
+ ,64
+ ,64
+ ,1
+ ,1
+ ,4
+ ,4
+ ,2
+ ,2
+ ,3
+ ,3
+ ,3
+ ,3
+ ,4
+ ,4
+ ,4
+ ,0
+ ,65
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,66
+ ,0
+ ,2
+ ,0
+ ,5
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,5
+ ,1
+ ,67
+ ,67
+ ,3
+ ,3
+ ,4
+ ,4
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,4
+ ,0
+ ,68
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,5
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,3
+ ,1
+ ,69
+ ,69
+ ,3
+ ,3
+ ,5
+ ,5
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,4
+ ,4
+ ,4
+ ,1
+ ,70
+ ,70
+ ,2
+ ,2
+ ,4
+ ,4
+ ,2
+ ,2
+ ,3
+ ,3
+ ,2
+ ,2
+ ,4
+ ,4
+ ,4
+ ,0
+ ,71
+ ,0
+ ,3
+ ,0
+ ,3
+ ,0
+ ,1
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,4
+ ,1
+ ,72
+ ,72
+ ,2
+ ,2
+ ,4
+ ,4
+ ,1
+ ,1
+ ,2
+ ,2
+ ,2
+ ,2
+ ,4
+ ,4
+ ,4
+ ,0
+ ,73
+ ,0
+ ,4
+ ,0
+ ,5
+ ,0
+ ,3
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,74
+ ,0
+ ,4
+ ,0
+ ,5
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,4
+ ,1
+ ,75
+ ,75
+ ,4
+ ,4
+ ,5
+ ,5
+ ,2
+ ,2
+ ,4
+ ,4
+ ,1
+ ,1
+ ,4
+ ,4
+ ,4
+ ,0
+ ,76
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,3
+ ,1
+ ,77
+ ,77
+ ,3
+ ,3
+ ,4
+ ,4
+ ,1
+ ,1
+ ,3
+ ,3
+ ,2
+ ,2
+ ,4
+ ,4
+ ,4
+ ,1
+ ,78
+ ,78
+ ,4
+ ,4
+ ,5
+ ,5
+ ,3
+ ,3
+ ,4
+ ,4
+ ,2
+ ,2
+ ,4
+ ,4
+ ,3
+ ,0
+ ,79
+ ,0
+ ,3
+ ,0
+ ,5
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,5
+ ,1
+ ,80
+ ,80
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,2
+ ,2
+ ,2
+ ,1
+ ,1
+ ,4
+ ,4
+ ,4
+ ,0
+ ,81
+ ,0
+ ,2
+ ,0
+ ,5
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,5
+ ,0
+ ,82
+ ,0
+ ,3
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,5
+ ,1
+ ,83
+ ,83
+ ,3
+ ,3
+ ,4
+ ,4
+ ,1
+ ,1
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,4
+ ,0
+ ,84
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,5
+ ,0
+ ,4
+ ,1
+ ,85
+ ,85
+ ,2
+ ,2
+ ,4
+ ,4
+ ,1
+ ,1
+ ,3
+ ,3
+ ,1
+ ,1
+ ,3
+ ,3
+ ,4
+ ,1
+ ,86
+ ,86
+ ,4
+ ,4
+ ,4
+ ,4
+ ,1
+ ,1
+ ,4
+ ,4
+ ,2
+ ,2
+ ,3
+ ,3
+ ,4
+ ,0
+ ,87
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,4
+ ,1
+ ,88
+ ,88
+ ,2
+ ,2
+ ,5
+ ,5
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,4
+ ,4
+ ,5
+ ,0
+ ,89
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,90
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,3
+ ,1
+ ,91
+ ,91
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,4
+ ,4
+ ,4
+ ,0
+ ,92
+ ,0
+ ,2
+ ,0
+ ,5
+ ,0
+ ,1
+ ,0
+ ,1
+ ,0
+ ,1
+ ,0
+ ,3
+ ,0
+ ,3
+ ,1
+ ,93
+ ,93
+ ,2
+ ,2
+ ,3
+ ,3
+ ,1
+ ,1
+ ,3
+ ,3
+ ,2
+ ,2
+ ,4
+ ,4
+ ,4
+ ,1
+ ,94
+ ,94
+ ,3
+ ,3
+ ,3
+ ,3
+ ,1
+ ,1
+ ,2
+ ,2
+ ,2
+ ,2
+ ,4
+ ,4
+ ,4
+ ,0
+ ,95
+ ,0
+ ,3
+ ,0
+ ,5
+ ,0
+ ,3
+ ,0
+ ,3
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,4
+ ,1
+ ,96
+ ,96
+ ,5
+ ,5
+ ,5
+ ,5
+ ,4
+ ,4
+ ,5
+ ,5
+ ,4
+ ,4
+ ,5
+ ,5
+ ,4
+ ,0
+ ,97
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,98
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,3
+ ,1
+ ,99
+ ,99
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,2
+ ,2
+ ,2
+ ,1
+ ,1
+ ,2
+ ,2
+ ,3
+ ,0
+ ,100
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,1
+ ,0
+ ,3
+ ,0
+ ,3
+ ,1
+ ,101
+ ,101
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,2
+ ,2
+ ,3
+ ,3
+ ,3
+ ,1
+ ,102
+ ,102
+ ,3
+ ,3
+ ,4
+ ,4
+ ,1
+ ,1
+ ,2
+ ,2
+ ,1
+ ,1
+ ,3
+ ,3
+ ,3
+ ,0
+ ,103
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,2
+ ,1
+ ,104
+ ,104
+ ,2
+ ,2
+ ,4
+ ,4
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,4
+ ,4
+ ,3
+ ,0
+ ,105
+ ,0
+ ,3
+ ,0
+ ,5
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,5
+ ,0
+ ,106
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,5
+ ,0
+ ,1
+ ,0
+ ,3
+ ,0
+ ,2
+ ,1
+ ,107
+ ,107
+ ,3
+ ,3
+ ,4
+ ,4
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,3
+ ,3
+ ,2
+ ,0
+ ,108
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,3
+ ,1
+ ,109
+ ,109
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,1
+ ,1
+ ,4
+ ,4
+ ,3
+ ,1
+ ,110
+ ,110
+ ,2
+ ,2
+ ,5
+ ,5
+ ,1
+ ,1
+ ,1
+ ,1
+ ,2
+ ,2
+ ,2
+ ,2
+ ,3
+ ,0
+ ,111
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,1
+ ,0
+ ,1
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,4
+ ,1
+ ,112
+ ,112
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,2
+ ,3
+ ,3
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,0
+ ,113
+ ,0
+ ,1
+ ,0
+ ,3
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,114
+ ,0
+ ,5
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,5
+ ,0
+ ,2
+ ,0
+ ,5
+ ,0
+ ,2
+ ,1
+ ,115
+ ,115
+ ,2
+ ,2
+ ,4
+ ,4
+ ,2
+ ,2
+ ,3
+ ,3
+ ,5
+ ,5
+ ,3
+ ,3
+ ,3
+ ,0
+ ,116
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,1
+ ,0
+ ,3
+ ,0
+ ,4
+ ,1
+ ,117
+ ,117
+ ,4
+ ,4
+ ,2
+ ,2
+ ,2
+ ,2
+ ,3
+ ,3
+ ,2
+ ,2
+ ,4
+ ,4
+ ,2
+ ,1
+ ,118
+ ,118
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,2
+ ,2
+ ,1
+ ,1
+ ,3
+ ,3
+ ,4
+ ,0
+ ,119
+ ,0
+ ,5
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,4
+ ,1
+ ,120
+ ,120
+ ,3
+ ,3
+ ,3
+ ,3
+ ,1
+ ,1
+ ,2
+ ,2
+ ,1
+ ,1
+ ,2
+ ,2
+ ,2
+ ,0
+ ,121
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,3
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,3
+ ,1
+ ,122
+ ,122
+ ,3
+ ,3
+ ,3
+ ,3
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,3
+ ,3
+ ,3
+ ,1
+ ,123
+ ,123
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,4
+ ,4
+ ,2
+ ,2
+ ,4
+ ,4
+ ,3
+ ,0
+ ,124
+ ,0
+ ,2
+ ,0
+ ,5
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,5
+ ,0
+ ,4
+ ,1
+ ,125
+ ,125
+ ,2
+ ,2
+ ,4
+ ,4
+ ,1
+ ,1
+ ,2
+ ,2
+ ,3
+ ,3
+ ,4
+ ,4
+ ,4
+ ,0
+ ,126
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,127
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,3
+ ,0
+ ,3
+ ,1
+ ,128
+ ,128
+ ,3
+ ,3
+ ,4
+ ,4
+ ,2
+ ,2
+ ,3
+ ,3
+ ,2
+ ,2
+ ,3
+ ,3
+ ,4
+ ,0
+ ,129
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,4
+ ,1
+ ,130
+ ,130
+ ,3
+ ,3
+ ,4
+ ,4
+ ,2
+ ,2
+ ,3
+ ,3
+ ,3
+ ,3
+ ,4
+ ,4
+ ,4
+ ,1
+ ,131
+ ,131
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,4
+ ,4
+ ,2
+ ,2
+ ,4
+ ,4
+ ,2
+ ,0
+ ,132
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,4
+ ,1
+ ,133
+ ,133
+ ,4
+ ,4
+ ,4
+ ,4
+ ,1
+ ,1
+ ,1
+ ,1
+ ,2
+ ,2
+ ,2
+ ,2
+ ,5
+ ,0
+ ,134
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,3
+ ,0
+ ,1
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,135
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,4
+ ,1
+ ,136
+ ,136
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,2
+ ,3
+ ,3
+ ,2
+ ,2
+ ,4
+ ,4
+ ,4
+ ,0
+ ,137
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,1
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,3
+ ,1
+ ,138
+ ,138
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,2
+ ,2
+ ,2
+ ,3
+ ,3
+ ,4
+ ,4
+ ,1
+ ,1
+ ,139
+ ,139
+ ,3
+ ,3
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,1
+ ,1
+ ,4
+ ,4
+ ,4
+ ,0
+ ,140
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,3
+ ,1
+ ,141
+ ,141
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,0
+ ,142
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,5
+ ,0
+ ,3
+ ,0
+ ,143
+ ,0
+ ,5
+ ,0
+ ,2
+ ,0
+ ,5
+ ,0
+ ,2
+ ,0
+ ,5
+ ,0
+ ,3
+ ,0
+ ,1
+ ,1
+ ,144
+ ,144
+ ,2
+ ,2
+ ,4
+ ,4
+ ,1
+ ,1
+ ,2
+ ,2
+ ,1
+ ,1
+ ,4
+ ,4
+ ,4
+ ,0
+ ,145
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,3
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,5
+ ,1
+ ,146
+ ,146
+ ,3
+ ,3
+ ,4
+ ,4
+ ,2
+ ,2
+ ,4
+ ,4
+ ,3
+ ,3
+ ,4
+ ,4
+ ,4
+ ,1
+ ,147
+ ,147
+ ,3
+ ,3
+ ,3
+ ,3
+ ,2
+ ,2
+ ,4
+ ,4
+ ,2
+ ,2
+ ,5
+ ,5
+ ,3
+ ,0
+ ,148
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,4
+ ,1
+ ,149
+ ,149
+ ,3
+ ,3
+ ,2
+ ,2
+ ,1
+ ,1
+ ,1
+ ,1
+ ,3
+ ,3
+ ,2
+ ,2
+ ,3
+ ,0
+ ,150
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,151
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,3
+ ,0
+ ,4
+ ,1
+ ,152
+ ,152
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,2
+ ,3
+ ,3
+ ,2
+ ,2
+ ,4
+ ,4
+ ,4
+ ,0
+ ,153
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,1
+ ,0
+ ,1
+ ,0
+ ,5
+ ,0
+ ,5
+ ,1
+ ,154
+ ,154
+ ,4
+ ,4
+ ,2
+ ,2
+ ,1
+ ,1
+ ,2
+ ,2
+ ,2
+ ,2
+ ,3
+ ,3
+ ,2
+ ,1
+ ,155
+ ,155
+ ,5
+ ,5
+ ,5
+ ,5
+ ,4
+ ,4
+ ,2
+ ,2
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,0
+ ,156
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,2
+ ,0
+ ,3
+ ,0
+ ,3
+ ,1
+ ,157
+ ,157
+ ,3
+ ,3
+ ,4
+ ,4
+ ,2
+ ,2
+ ,3
+ ,3
+ ,2
+ ,2
+ ,5
+ ,5
+ ,4
+ ,0
+ ,158
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,2
+ ,0
+ ,4
+ ,0
+ ,4
+ ,0
+ ,159
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,3
+ ,0
+ ,4
+ ,0
+ ,2
+ ,0
+ ,3)
+ ,dim=c(16
+ ,159)
+ ,dimnames=list(c('pop'
+ ,'t'
+ ,'pop_t'
+ ,'standards'
+ ,'standards_t'
+ ,'organization'
+ ,'organization_t'
+ ,'punished'
+ ,'punished_t'
+ ,'secondrate'
+ ,'secondrate_t'
+ ,'mistakes'
+ ,'mistakes_t'
+ ,'competent'
+ ,'competent_t'
+ ,'neat')
+ ,1:159))
> y <- array(NA,dim=c(16,159),dimnames=list(c('pop','t','pop_t','standards','standards_t','organization','organization_t','punished','punished_t','secondrate','secondrate_t','mistakes','mistakes_t','competent','competent_t','neat'),1:159))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '16'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
neat pop t pop_t standards standards_t organization organization_t
1 4 0 1 0 2 0 5 0
2 4 0 2 0 2 0 4 0
3 4 1 3 3 4 4 4 4
4 4 0 4 0 2 0 4 0
5 4 1 5 5 3 3 2 2
6 5 1 6 6 4 4 5 5
7 4 0 7 0 3 0 5 0
8 3 1 8 8 3 3 4 4
9 4 0 9 0 3 0 3 0
10 4 0 10 0 2 0 4 0
11 4 1 11 11 4 4 4 4
12 4 0 12 0 4 0 2 0
13 4 1 13 13 3 3 3 3
14 2 1 14 14 3 3 3 3
15 3 0 15 0 4 0 4 0
16 4 1 16 16 4 4 5 5
17 3 0 17 0 3 0 4 0
18 2 0 18 0 3 0 2 0
19 4 1 19 19 3 3 4 4
20 3 0 20 0 4 0 4 0
21 3 1 21 21 2 2 4 4
22 4 1 22 22 5 5 4 4
23 3 0 23 0 4 0 4 0
24 3 1 24 24 2 2 4 4
25 4 0 25 0 3 0 5 0
26 4 0 26 0 4 0 4 0
27 4 1 27 27 4 4 4 4
28 4 0 28 0 3 0 4 0
29 4 1 29 29 4 4 4 4
30 4 1 30 30 4 4 4 4
31 5 0 31 0 1 0 4 0
32 4 1 32 32 4 4 4 4
33 4 0 33 0 5 0 2 0
34 4 0 34 0 2 0 4 0
35 3 1 35 35 4 4 4 4
36 4 0 36 0 3 0 5 0
37 3 1 37 37 2 2 5 5
38 4 1 38 38 4 4 4 4
39 4 0 39 0 5 0 3 0
40 3 1 40 40 4 4 4 4
41 5 0 41 0 4 0 5 0
42 4 0 42 0 4 0 4 0
43 3 1 43 43 3 3 4 4
44 3 0 44 0 4 0 5 0
45 3 1 45 45 2 2 4 4
46 4 1 46 46 2 2 5 5
47 4 0 47 0 4 0 4 0
48 4 1 48 48 2 2 4 4
49 4 0 49 0 4 0 4 0
50 4 0 50 0 4 0 3 0
51 4 1 51 51 1 1 4 4
52 4 0 52 0 4 0 4 0
53 5 1 53 53 2 2 4 4
54 3 1 54 54 1 1 2 2
55 3 0 55 0 4 0 3 0
56 5 1 56 56 3 3 5 5
57 5 0 57 0 2 0 4 0
58 4 0 58 0 4 0 4 0
59 4 1 59 59 3 3 5 5
60 3 0 60 0 2 0 3 0
61 4 1 61 61 2 2 5 5
62 4 1 62 62 3 3 4 4
63 5 0 63 0 2 0 5 0
64 4 1 64 64 1 1 4 4
65 4 0 65 0 3 0 4 0
66 5 0 66 0 2 0 5 0
67 4 1 67 67 3 3 4 4
68 3 0 68 0 3 0 4 0
69 4 1 69 69 3 3 5 5
70 4 1 70 70 2 2 4 4
71 4 0 71 0 3 0 3 0
72 4 1 72 72 2 2 4 4
73 4 0 73 0 4 0 5 0
74 4 0 74 0 4 0 5 0
75 4 1 75 75 4 4 5 5
76 3 0 76 0 2 0 4 0
77 4 1 77 77 3 3 4 4
78 3 1 78 78 4 4 5 5
79 5 0 79 0 3 0 5 0
80 4 1 80 80 4 4 4 4
81 5 0 81 0 2 0 5 0
82 5 0 82 0 3 0 3 0
83 4 1 83 83 3 3 4 4
84 4 0 84 0 4 0 4 0
85 4 1 85 85 2 2 4 4
86 4 1 86 86 4 4 4 4
87 4 0 87 0 2 0 4 0
88 5 1 88 88 2 2 5 5
89 4 0 89 0 4 0 4 0
90 3 0 90 0 3 0 4 0
91 4 1 91 91 4 4 4 4
92 3 0 92 0 2 0 5 0
93 4 1 93 93 2 2 3 3
94 4 1 94 94 3 3 3 3
95 4 0 95 0 3 0 5 0
96 4 1 96 96 5 5 5 5
97 4 0 97 0 2 0 4 0
98 3 0 98 0 3 0 4 0
99 3 1 99 99 4 4 4 4
100 3 0 100 0 3 0 4 0
101 3 1 101 101 4 4 4 4
102 3 1 102 102 3 3 4 4
103 2 0 103 0 3 0 4 0
104 3 1 104 104 2 2 4 4
105 5 0 105 0 3 0 5 0
106 2 0 106 0 2 0 2 0
107 2 1 107 107 3 3 4 4
108 3 0 108 0 2 0 2 0
109 3 1 109 109 4 4 4 4
110 3 1 110 110 2 2 5 5
111 4 0 111 0 4 0 3 0
112 4 1 112 112 4 4 4 4
113 3 0 113 0 1 0 3 0
114 2 0 114 0 5 0 4 0
115 3 1 115 115 2 2 4 4
116 4 0 116 0 3 0 4 0
117 2 1 117 117 4 4 2 2
118 4 1 118 118 1 1 1 1
119 4 0 119 0 5 0 4 0
120 2 1 120 120 3 3 3 3
121 3 0 121 0 3 0 4 0
122 3 1 122 122 3 3 3 3
123 3 1 123 123 3 3 3 3
124 4 0 124 0 2 0 5 0
125 4 1 125 125 2 2 4 4
126 4 0 126 0 4 0 3 0
127 3 0 127 0 4 0 4 0
128 4 1 128 128 3 3 4 4
129 4 0 129 0 3 0 4 0
130 4 1 130 130 3 3 4 4
131 2 1 131 131 4 4 3 3
132 4 0 132 0 3 0 4 0
133 5 1 133 133 4 4 4 4
134 4 0 134 0 4 0 4 0
135 4 0 135 0 2 0 4 0
136 4 1 136 136 4 4 4 4
137 3 0 137 0 2 0 3 0
138 1 1 138 138 4 4 4 4
139 4 1 139 139 3 3 4 4
140 3 0 140 0 3 0 2 0
141 3 1 141 141 2 2 2 2
142 3 0 142 0 2 0 4 0
143 1 0 143 0 5 0 2 0
144 4 1 144 144 2 2 4 4
145 5 0 145 0 4 0 3 0
146 4 1 146 146 3 3 4 4
147 3 1 147 147 3 3 3 3
148 4 0 148 0 3 0 2 0
149 3 1 149 149 3 3 2 2
150 4 0 150 0 4 0 4 0
151 4 0 151 0 4 0 3 0
152 4 1 152 152 4 4 4 4
153 5 0 153 0 4 0 4 0
154 2 1 154 154 4 4 2 2
155 3 1 155 155 5 5 5 5
156 3 0 156 0 3 0 4 0
157 4 1 157 157 3 3 4 4
158 4 0 158 0 4 0 4 0
159 3 0 159 0 4 0 3 0
punished punished_t secondrate secondrate_t mistakes mistakes_t competent
1 2 0 3 0 3 0 4
2 2 0 4 0 3 0 4
3 2 2 4 4 2 2 5
4 2 0 2 0 2 0 2
5 2 2 2 2 3 3 2
6 1 1 3 3 2 2 4
7 1 0 2 0 1 0 4
8 3 3 3 3 3 3 4
9 2 0 3 0 2 0 4
10 1 0 3 0 2 0 2
11 4 4 3 3 3 3 3
12 2 0 4 0 2 0 4
13 3 3 2 2 2 2 3
14 2 2 2 2 2 2 4
15 1 0 1 0 3 0 4
16 1 1 1 1 1 1 4
17 2 0 3 0 3 0 4
18 2 0 2 0 2 0 2
19 2 2 2 2 3 3 4
20 2 0 3 0 4 0 4
21 1 1 4 4 2 2 4
22 2 2 4 4 3 3 3
23 4 0 3 0 5 0 2
24 2 2 2 2 2 2 4
25 2 0 3 0 2 0 2
26 2 0 4 0 3 0 3
27 2 2 3 3 2 2 4
28 2 0 2 0 2 0 3
29 3 3 1 1 2 2 4
30 2 2 3 3 2 2 4
31 1 0 2 0 3 0 4
32 4 4 4 4 4 4 4
33 1 0 4 0 1 0 4
34 2 0 5 0 3 0 4
35 2 2 2 2 3 3 4
36 2 0 4 0 2 0 5
37 2 2 4 4 1 1 4
38 2 2 2 2 1 1 2
39 2 0 4 0 2 0 4
40 2 2 4 4 2 2 4
41 2 0 2 0 2 0 5
42 2 0 3 0 1 0 4
43 2 2 2 2 2 2 2
44 2 0 4 0 1 0 4
45 2 2 3 3 2 2 4
46 1 1 1 1 2 2 4
47 2 0 2 0 4 0 2
48 1 1 5 5 2 2 5
49 2 0 2 0 2 0 4
50 1 0 4 0 2 0 4
51 1 1 4 4 1 1 4
52 2 0 2 0 2 0 4
53 2 2 2 2 2 2 4
54 1 1 2 2 1 1 3
55 5 0 4 0 5 0 5
56 2 2 3 3 2 2 4
57 2 0 4 0 2 0 4
58 1 0 2 0 2 0 4
59 1 1 3 3 1 1 4
60 2 0 2 0 3 0 2
61 2 2 2 2 1 1 4
62 1 1 3 3 1 1 4
63 1 0 2 0 2 0 4
64 2 2 3 3 3 3 4
65 1 0 2 0 2 0 3
66 1 0 4 0 2 0 4
67 2 2 2 2 2 2 2
68 1 0 5 0 4 0 4
69 1 1 1 1 1 1 4
70 2 2 3 3 2 2 4
71 1 0 2 0 2 0 4
72 1 1 2 2 2 2 4
73 3 0 3 0 2 0 4
74 3 0 4 0 2 0 3
75 2 2 4 4 1 1 4
76 2 0 2 0 2 0 4
77 1 1 3 3 2 2 4
78 3 3 4 4 2 2 4
79 2 0 2 0 2 0 4
80 2 2 2 2 1 1 4
81 2 0 4 0 4 0 4
82 2 0 2 0 2 0 2
83 1 1 4 4 3 3 3
84 4 0 2 0 2 0 5
85 1 1 3 3 1 1 3
86 1 1 4 4 2 2 3
87 1 0 3 0 2 0 4
88 1 1 1 1 1 1 4
89 4 0 3 0 2 0 4
90 2 0 2 0 1 0 4
91 2 2 2 2 2 2 4
92 1 0 1 0 1 0 3
93 1 1 3 3 2 2 4
94 1 1 2 2 2 2 4
95 3 0 3 0 3 0 4
96 4 4 5 5 4 4 5
97 4 0 3 0 1 0 4
98 3 0 4 0 3 0 4
99 2 2 2 2 1 1 2
100 2 0 2 0 1 0 3
101 3 3 3 3 2 2 3
102 1 1 2 2 1 1 3
103 3 0 2 0 3 0 4
104 2 2 2 2 2 2 4
105 2 0 3 0 2 0 2
106 2 0 5 0 1 0 3
107 2 2 2 2 2 2 3
108 4 0 3 0 2 0 4
109 3 3 3 3 1 1 4
110 1 1 1 1 2 2 2
111 1 0 1 0 2 0 3
112 2 2 3 3 4 4 4
113 1 0 4 0 3 0 4
114 3 0 5 0 2 0 5
115 2 2 3 3 5 5 3
116 2 0 3 0 1 0 3
117 2 2 3 3 2 2 4
118 1 1 2 2 1 1 3
119 3 0 3 0 2 0 3
120 1 1 2 2 1 1 2
121 1 0 3 0 1 0 4
122 2 2 2 2 2 2 3
123 3 3 4 4 2 2 4
124 2 0 2 0 2 0 5
125 1 1 2 2 3 3 4
126 2 0 4 0 2 0 3
127 1 0 4 0 1 0 3
128 2 2 3 3 2 2 3
129 1 0 3 0 2 0 3
130 2 2 3 3 3 3 4
131 3 3 4 4 2 2 4
132 2 0 2 0 2 0 3
133 1 1 1 1 2 2 2
134 1 0 3 0 1 0 3
135 2 0 2 0 2 0 2
136 2 2 3 3 2 2 4
137 1 0 2 0 2 0 4
138 2 2 2 2 3 3 4
139 3 3 3 3 1 1 4
140 4 0 2 0 3 0 4
141 2 2 4 4 4 4 4
142 4 0 4 0 2 0 5
143 5 0 2 0 5 0 3
144 1 1 2 2 1 1 4
145 3 0 3 0 2 0 4
146 2 2 4 4 3 3 4
147 2 2 4 4 2 2 5
148 2 0 4 0 2 0 3
149 1 1 1 1 3 3 2
150 4 0 4 0 2 0 4
151 2 0 4 0 1 0 3
152 2 2 3 3 2 2 4
153 3 0 1 0 1 0 5
154 1 1 2 2 2 2 3
155 4 4 2 2 3 3 3
156 2 0 2 0 2 0 3
157 2 2 3 3 2 2 5
158 4 0 3 0 2 0 4
159 4 0 3 0 4 0 2
competent_t
1 0
2 0
3 5
4 0
5 2
6 4
7 0
8 4
9 0
10 0
11 3
12 0
13 3
14 4
15 0
16 4
17 0
18 0
19 4
20 0
21 4
22 3
23 0
24 4
25 0
26 0
27 4
28 0
29 4
30 4
31 0
32 4
33 0
34 0
35 4
36 0
37 4
38 2
39 0
40 4
41 0
42 0
43 2
44 0
45 4
46 4
47 0
48 5
49 0
50 0
51 4
52 0
53 4
54 3
55 0
56 4
57 0
58 0
59 4
60 0
61 4
62 4
63 0
64 4
65 0
66 0
67 2
68 0
69 4
70 4
71 0
72 4
73 0
74 0
75 4
76 0
77 4
78 4
79 0
80 4
81 0
82 0
83 3
84 0
85 3
86 3
87 0
88 4
89 0
90 0
91 4
92 0
93 4
94 4
95 0
96 5
97 0
98 0
99 2
100 0
101 3
102 3
103 0
104 4
105 0
106 0
107 3
108 0
109 4
110 2
111 0
112 4
113 0
114 0
115 3
116 0
117 4
118 3
119 0
120 2
121 0
122 3
123 4
124 0
125 4
126 0
127 0
128 3
129 0
130 4
131 4
132 0
133 2
134 0
135 0
136 4
137 0
138 4
139 4
140 0
141 4
142 0
143 0
144 4
145 0
146 4
147 5
148 0
149 2
150 0
151 0
152 4
153 0
154 3
155 3
156 0
157 5
158 0
159 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) pop t pop_t standards
3.114962 -0.356878 -0.001194 -0.001795 -0.002195
standards_t organization organization_t punished punished_t
-0.034934 0.304511 -0.002635 -0.097553 -0.091054
secondrate secondrate_t mistakes mistakes_t competent
-0.004497 0.001896 -0.145120 0.186359 0.026159
competent_t
0.052098
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.4588 -0.5218 0.1222 0.4456 1.6452
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.114962 0.707599 4.402 2.08e-05 ***
pop -0.356878 0.943670 -0.378 0.70586
t -0.001194 0.002018 -0.592 0.55503
pop_t -0.001795 0.002780 -0.646 0.51943
standards -0.002195 0.091643 -0.024 0.98093
standards_t -0.034934 0.136461 -0.256 0.79832
organization 0.304511 0.102162 2.981 0.00338 **
organization_t -0.002635 0.150280 -0.018 0.98603
punished -0.097553 0.100025 -0.975 0.33107
punished_t -0.091054 0.163360 -0.557 0.57814
secondrate -0.004497 0.085287 -0.053 0.95802
secondrate_t 0.001896 0.131187 0.014 0.98849
mistakes -0.145120 0.101501 -1.430 0.15497
mistakes_t 0.186359 0.147917 1.260 0.20976
competent 0.026159 0.103439 0.253 0.80072
competent_t 0.052098 0.163981 0.318 0.75117
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7435 on 143 degrees of freedom
Multiple R-squared: 0.2094, Adjusted R-squared: 0.1265
F-statistic: 2.525 on 15 and 143 DF, p-value: 0.002393
> 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,] 0.688810286 0.622379429 0.3111897
[2,] 0.536650010 0.926699980 0.4633500
[3,] 0.571779371 0.856441258 0.4282206
[4,] 0.500041805 0.999916389 0.4999582
[5,] 0.383825778 0.767651555 0.6161742
[6,] 0.296712463 0.593424927 0.7032875
[7,] 0.237055919 0.474111839 0.7629441
[8,] 0.165618909 0.331237818 0.8343811
[9,] 0.179344099 0.358688198 0.8206559
[10,] 0.259775621 0.519551241 0.7402244
[11,] 0.228592403 0.457184806 0.7714076
[12,] 0.182264790 0.364529580 0.8177352
[13,] 0.236091967 0.472183934 0.7639080
[14,] 0.197939014 0.395878028 0.8020610
[15,] 0.149816161 0.299632322 0.8501838
[16,] 0.181396251 0.362792502 0.8186037
[17,] 0.174341143 0.348682287 0.8256589
[18,] 0.157011463 0.314022926 0.8429885
[19,] 0.133684754 0.267369508 0.8663152
[20,] 0.105739637 0.211479274 0.8942604
[21,] 0.085151200 0.170302401 0.9148488
[22,] 0.067799576 0.135599153 0.9322004
[23,] 0.077237893 0.154475786 0.9227621
[24,] 0.060774836 0.121549671 0.9392252
[25,] 0.051477502 0.102955003 0.9485225
[26,] 0.124441615 0.248883230 0.8755584
[27,] 0.116352989 0.232705978 0.8836470
[28,] 0.120116285 0.240232571 0.8798837
[29,] 0.104803317 0.209606635 0.8951967
[30,] 0.157347993 0.314695985 0.8426520
[31,] 0.124097620 0.248195240 0.8759024
[32,] 0.099065553 0.198131105 0.9009344
[33,] 0.116669988 0.233339976 0.8833300
[34,] 0.090633749 0.181267499 0.9093663
[35,] 0.197712734 0.395425468 0.8022873
[36,] 0.162817448 0.325634897 0.8371826
[37,] 0.135192717 0.270385435 0.8648073
[38,] 0.157052979 0.314105959 0.8429470
[39,] 0.156980386 0.313960771 0.8430196
[40,] 0.129932650 0.259865300 0.8700673
[41,] 0.105951895 0.211903789 0.8940481
[42,] 0.111516944 0.223033887 0.8884831
[43,] 0.088556288 0.177112575 0.9114437
[44,] 0.069544302 0.139088604 0.9304557
[45,] 0.059928430 0.119856860 0.9400716
[46,] 0.046578371 0.093156743 0.9534216
[47,] 0.035846023 0.071692046 0.9641540
[48,] 0.031716518 0.063433035 0.9682835
[49,] 0.026271236 0.052542473 0.9737288
[50,] 0.034846716 0.069693433 0.9651533
[51,] 0.026798361 0.053596722 0.9732016
[52,] 0.020090045 0.040180090 0.9799100
[53,] 0.016089153 0.032178306 0.9839108
[54,] 0.011576071 0.023152143 0.9884239
[55,] 0.008281484 0.016562968 0.9917185
[56,] 0.005794567 0.011589134 0.9942054
[57,] 0.004079423 0.008158845 0.9959206
[58,] 0.006809311 0.013618622 0.9931907
[59,] 0.004741880 0.009483759 0.9952581
[60,] 0.006325570 0.012651140 0.9936744
[61,] 0.006898992 0.013797983 0.9931010
[62,] 0.005167478 0.010334957 0.9948325
[63,] 0.010378450 0.020756900 0.9896215
[64,] 0.024712722 0.049425444 0.9752873
[65,] 0.018401456 0.036802912 0.9815985
[66,] 0.015474762 0.030949525 0.9845252
[67,] 0.011331328 0.022662655 0.9886687
[68,] 0.008166187 0.016332374 0.9918338
[69,] 0.008768757 0.017537514 0.9912312
[70,] 0.008981157 0.017962314 0.9910188
[71,] 0.007985369 0.015970738 0.9920146
[72,] 0.011714530 0.023429060 0.9882855
[73,] 0.010128865 0.020257730 0.9898711
[74,] 0.021100559 0.042201118 0.9788994
[75,] 0.016623107 0.033246214 0.9833769
[76,] 0.016707947 0.033415895 0.9832921
[77,] 0.015977538 0.031955075 0.9840225
[78,] 0.016323524 0.032647048 0.9836765
[79,] 0.012417344 0.024834688 0.9875827
[80,] 0.013431745 0.026863489 0.9865683
[81,] 0.012548839 0.025097679 0.9874512
[82,] 0.015121862 0.030243724 0.9848781
[83,] 0.013062322 0.026124644 0.9869377
[84,] 0.011808384 0.023616767 0.9881916
[85,] 0.021767877 0.043535754 0.9782321
[86,] 0.018577444 0.037154888 0.9814226
[87,] 0.028572854 0.057145707 0.9714271
[88,] 0.051257382 0.102514764 0.9487426
[89,] 0.088184792 0.176369585 0.9118152
[90,] 0.073709017 0.147418034 0.9262910
[91,] 0.056648434 0.113296868 0.9433516
[92,] 0.089398168 0.178796335 0.9106018
[93,] 0.076667345 0.153334690 0.9233327
[94,] 0.107501190 0.215002381 0.8924988
[95,] 0.108218762 0.216437524 0.8917812
[96,] 0.138453906 0.276907812 0.8615461
[97,] 0.127463761 0.254927521 0.8725362
[98,] 0.102651353 0.205302707 0.8973486
[99,] 0.101181979 0.202363958 0.8988180
[100,] 0.150694569 0.301389138 0.8493054
[101,] 0.125332739 0.250665478 0.8746673
[102,] 0.337302602 0.674605204 0.6626974
[103,] 0.384563302 0.769126604 0.6154367
[104,] 0.324315287 0.648630574 0.6756847
[105,] 0.270045356 0.540090712 0.7299546
[106,] 0.250653858 0.501307715 0.7493461
[107,] 0.207281454 0.414562908 0.7927185
[108,] 0.194903674 0.389807348 0.8050963
[109,] 0.256317113 0.512634227 0.7436829
[110,] 0.270960357 0.541920715 0.7290396
[111,] 0.260662439 0.521324877 0.7393376
[112,] 0.242091743 0.484183485 0.7579083
[113,] 0.233570852 0.467141705 0.7664291
[114,] 0.194969189 0.389938377 0.8050308
[115,] 0.262106705 0.524213409 0.7378933
[116,] 0.199013306 0.398026613 0.8009867
[117,] 0.230475258 0.460950517 0.7695247
[118,] 0.434114316 0.868228631 0.5658857
[119,] 0.326741429 0.653482858 0.6732586
[120,] 0.278394161 0.556788322 0.7216058
[121,] 0.308092156 0.616184313 0.6919078
[122,] 0.313951992 0.627903983 0.6860480
> postscript(file="/var/www/html/rcomp/tmp/1lhon1291229903.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2lhon1291229903.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3v9581291229903.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4v9581291229903.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5v9581291229903.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 = 159
Frequency = 1
1 2 3 4 5 6
-0.092610142 0.217592081 0.105752077 0.118183589 0.866683127 0.699894083
7 8 9 10 11 12
-0.475540818 -0.693405290 0.383040024 0.032292900 0.619555202 0.697825379
13 14 15 16 17 18
0.740313484 -1.523559801 -0.873537408 -0.234171921 -0.766796917 -1.253880537
19 20 21 22 23 24
0.148273330 -0.615899152 -1.025042427 0.314957212 -0.219772923 -0.832668234
25 26 27 28 29 30
-0.154557341 0.276801325 0.253158700 0.122880781 0.442543271 0.262127568
31 32 33 34 35 36
1.143482855 0.565442562 0.482425275 0.260303894 -0.766763991 -0.215400073
37 38 39 40 41 42
-1.049238775 0.481195579 0.427752812 -0.705375570 0.783771850 -0.024987669
43 44 45 46 47 48
-0.582224061 -1.322613391 -0.767285522 -0.259979709 0.464165081 -0.019978292
49 50 51 52 53 54
0.123995326 0.341141568 0.068756577 0.127577972 1.254030825 -0.245467623
55 56 57 58 59 60
0.146526851 1.000853099 1.138153498 0.037190355 -0.137545590 -0.365308864
61 62 63 64 65 66
0.017310900 0.173299223 0.734260947 0.211149556 0.069513834 0.746837448
67 68 69 70 71 72
0.489526885 -0.649330579 -0.112850633 0.307455046 0.355031529 0.122227061
73 74 75 76 77 78
-0.049804753 -0.017954935 0.138624319 -0.848150269 0.176904526 -0.705039256
79 80 81 82 83 84
0.853116000 0.450247107 1.152544417 1.518038155 0.234460172 0.334739998
85 86 87 88 89 90
0.283188143 0.321796794 0.071930118 0.906823483 0.371366677 -0.974356971
91 92 93 94 95 96
0.441893919 -1.354565483 0.489485718 0.527003421 0.119393695 0.416375509
97 98 99 100 101 102
0.231410577 -0.568015652 -0.336437435 -0.936256144 -0.258746310 -0.631460190
103 104 105 106 107 108
-1.571038432 -0.593498417 0.940979873 -1.308772613 -1.469144520 -0.001310481
109 110 111 112 113 114
-0.271846604 -0.912131229 0.426656586 0.424798555 -0.445086566 -1.711300969
115 116 117 118 119 120
-0.603472733 0.087348227 -0.874023366 1.247744176 0.337993602 -1.197514723
121 122 123 124 125 126
-1.030392281 -0.122424234 -0.003883060 -0.121497695 0.239438027 0.555613505
127 128 129 130 131 132
-0.990376684 0.596238192 0.150440533 0.482722087 -0.942837362 0.247079159
133 134 135 136 137 138
1.532763470 0.013485894 0.274625777 0.579027577 -0.568344971 -2.458832851
139 140 141 142 143 144
0.780713362 0.179722597 0.043592707 -0.591391071 -1.398352936 0.378718935
145 146 147 148 149 150
1.645200898 0.533156685 -0.198995023 0.884202616 0.105981569 0.448710730
151 152 153 154 155 156
0.440348467 0.626861540 1.169970593 -0.876358242 -0.217286991 -0.724259677
157 158 159
0.526424625 0.453767524 0.102031003
> postscript(file="/var/www/html/rcomp/tmp/6o0mb1291229903.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.092610142 NA
1 0.217592081 -0.092610142
2 0.105752077 0.217592081
3 0.118183589 0.105752077
4 0.866683127 0.118183589
5 0.699894083 0.866683127
6 -0.475540818 0.699894083
7 -0.693405290 -0.475540818
8 0.383040024 -0.693405290
9 0.032292900 0.383040024
10 0.619555202 0.032292900
11 0.697825379 0.619555202
12 0.740313484 0.697825379
13 -1.523559801 0.740313484
14 -0.873537408 -1.523559801
15 -0.234171921 -0.873537408
16 -0.766796917 -0.234171921
17 -1.253880537 -0.766796917
18 0.148273330 -1.253880537
19 -0.615899152 0.148273330
20 -1.025042427 -0.615899152
21 0.314957212 -1.025042427
22 -0.219772923 0.314957212
23 -0.832668234 -0.219772923
24 -0.154557341 -0.832668234
25 0.276801325 -0.154557341
26 0.253158700 0.276801325
27 0.122880781 0.253158700
28 0.442543271 0.122880781
29 0.262127568 0.442543271
30 1.143482855 0.262127568
31 0.565442562 1.143482855
32 0.482425275 0.565442562
33 0.260303894 0.482425275
34 -0.766763991 0.260303894
35 -0.215400073 -0.766763991
36 -1.049238775 -0.215400073
37 0.481195579 -1.049238775
38 0.427752812 0.481195579
39 -0.705375570 0.427752812
40 0.783771850 -0.705375570
41 -0.024987669 0.783771850
42 -0.582224061 -0.024987669
43 -1.322613391 -0.582224061
44 -0.767285522 -1.322613391
45 -0.259979709 -0.767285522
46 0.464165081 -0.259979709
47 -0.019978292 0.464165081
48 0.123995326 -0.019978292
49 0.341141568 0.123995326
50 0.068756577 0.341141568
51 0.127577972 0.068756577
52 1.254030825 0.127577972
53 -0.245467623 1.254030825
54 0.146526851 -0.245467623
55 1.000853099 0.146526851
56 1.138153498 1.000853099
57 0.037190355 1.138153498
58 -0.137545590 0.037190355
59 -0.365308864 -0.137545590
60 0.017310900 -0.365308864
61 0.173299223 0.017310900
62 0.734260947 0.173299223
63 0.211149556 0.734260947
64 0.069513834 0.211149556
65 0.746837448 0.069513834
66 0.489526885 0.746837448
67 -0.649330579 0.489526885
68 -0.112850633 -0.649330579
69 0.307455046 -0.112850633
70 0.355031529 0.307455046
71 0.122227061 0.355031529
72 -0.049804753 0.122227061
73 -0.017954935 -0.049804753
74 0.138624319 -0.017954935
75 -0.848150269 0.138624319
76 0.176904526 -0.848150269
77 -0.705039256 0.176904526
78 0.853116000 -0.705039256
79 0.450247107 0.853116000
80 1.152544417 0.450247107
81 1.518038155 1.152544417
82 0.234460172 1.518038155
83 0.334739998 0.234460172
84 0.283188143 0.334739998
85 0.321796794 0.283188143
86 0.071930118 0.321796794
87 0.906823483 0.071930118
88 0.371366677 0.906823483
89 -0.974356971 0.371366677
90 0.441893919 -0.974356971
91 -1.354565483 0.441893919
92 0.489485718 -1.354565483
93 0.527003421 0.489485718
94 0.119393695 0.527003421
95 0.416375509 0.119393695
96 0.231410577 0.416375509
97 -0.568015652 0.231410577
98 -0.336437435 -0.568015652
99 -0.936256144 -0.336437435
100 -0.258746310 -0.936256144
101 -0.631460190 -0.258746310
102 -1.571038432 -0.631460190
103 -0.593498417 -1.571038432
104 0.940979873 -0.593498417
105 -1.308772613 0.940979873
106 -1.469144520 -1.308772613
107 -0.001310481 -1.469144520
108 -0.271846604 -0.001310481
109 -0.912131229 -0.271846604
110 0.426656586 -0.912131229
111 0.424798555 0.426656586
112 -0.445086566 0.424798555
113 -1.711300969 -0.445086566
114 -0.603472733 -1.711300969
115 0.087348227 -0.603472733
116 -0.874023366 0.087348227
117 1.247744176 -0.874023366
118 0.337993602 1.247744176
119 -1.197514723 0.337993602
120 -1.030392281 -1.197514723
121 -0.122424234 -1.030392281
122 -0.003883060 -0.122424234
123 -0.121497695 -0.003883060
124 0.239438027 -0.121497695
125 0.555613505 0.239438027
126 -0.990376684 0.555613505
127 0.596238192 -0.990376684
128 0.150440533 0.596238192
129 0.482722087 0.150440533
130 -0.942837362 0.482722087
131 0.247079159 -0.942837362
132 1.532763470 0.247079159
133 0.013485894 1.532763470
134 0.274625777 0.013485894
135 0.579027577 0.274625777
136 -0.568344971 0.579027577
137 -2.458832851 -0.568344971
138 0.780713362 -2.458832851
139 0.179722597 0.780713362
140 0.043592707 0.179722597
141 -0.591391071 0.043592707
142 -1.398352936 -0.591391071
143 0.378718935 -1.398352936
144 1.645200898 0.378718935
145 0.533156685 1.645200898
146 -0.198995023 0.533156685
147 0.884202616 -0.198995023
148 0.105981569 0.884202616
149 0.448710730 0.105981569
150 0.440348467 0.448710730
151 0.626861540 0.440348467
152 1.169970593 0.626861540
153 -0.876358242 1.169970593
154 -0.217286991 -0.876358242
155 -0.724259677 -0.217286991
156 0.526424625 -0.724259677
157 0.453767524 0.526424625
158 0.102031003 0.453767524
159 NA 0.102031003
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.217592081 -0.092610142
[2,] 0.105752077 0.217592081
[3,] 0.118183589 0.105752077
[4,] 0.866683127 0.118183589
[5,] 0.699894083 0.866683127
[6,] -0.475540818 0.699894083
[7,] -0.693405290 -0.475540818
[8,] 0.383040024 -0.693405290
[9,] 0.032292900 0.383040024
[10,] 0.619555202 0.032292900
[11,] 0.697825379 0.619555202
[12,] 0.740313484 0.697825379
[13,] -1.523559801 0.740313484
[14,] -0.873537408 -1.523559801
[15,] -0.234171921 -0.873537408
[16,] -0.766796917 -0.234171921
[17,] -1.253880537 -0.766796917
[18,] 0.148273330 -1.253880537
[19,] -0.615899152 0.148273330
[20,] -1.025042427 -0.615899152
[21,] 0.314957212 -1.025042427
[22,] -0.219772923 0.314957212
[23,] -0.832668234 -0.219772923
[24,] -0.154557341 -0.832668234
[25,] 0.276801325 -0.154557341
[26,] 0.253158700 0.276801325
[27,] 0.122880781 0.253158700
[28,] 0.442543271 0.122880781
[29,] 0.262127568 0.442543271
[30,] 1.143482855 0.262127568
[31,] 0.565442562 1.143482855
[32,] 0.482425275 0.565442562
[33,] 0.260303894 0.482425275
[34,] -0.766763991 0.260303894
[35,] -0.215400073 -0.766763991
[36,] -1.049238775 -0.215400073
[37,] 0.481195579 -1.049238775
[38,] 0.427752812 0.481195579
[39,] -0.705375570 0.427752812
[40,] 0.783771850 -0.705375570
[41,] -0.024987669 0.783771850
[42,] -0.582224061 -0.024987669
[43,] -1.322613391 -0.582224061
[44,] -0.767285522 -1.322613391
[45,] -0.259979709 -0.767285522
[46,] 0.464165081 -0.259979709
[47,] -0.019978292 0.464165081
[48,] 0.123995326 -0.019978292
[49,] 0.341141568 0.123995326
[50,] 0.068756577 0.341141568
[51,] 0.127577972 0.068756577
[52,] 1.254030825 0.127577972
[53,] -0.245467623 1.254030825
[54,] 0.146526851 -0.245467623
[55,] 1.000853099 0.146526851
[56,] 1.138153498 1.000853099
[57,] 0.037190355 1.138153498
[58,] -0.137545590 0.037190355
[59,] -0.365308864 -0.137545590
[60,] 0.017310900 -0.365308864
[61,] 0.173299223 0.017310900
[62,] 0.734260947 0.173299223
[63,] 0.211149556 0.734260947
[64,] 0.069513834 0.211149556
[65,] 0.746837448 0.069513834
[66,] 0.489526885 0.746837448
[67,] -0.649330579 0.489526885
[68,] -0.112850633 -0.649330579
[69,] 0.307455046 -0.112850633
[70,] 0.355031529 0.307455046
[71,] 0.122227061 0.355031529
[72,] -0.049804753 0.122227061
[73,] -0.017954935 -0.049804753
[74,] 0.138624319 -0.017954935
[75,] -0.848150269 0.138624319
[76,] 0.176904526 -0.848150269
[77,] -0.705039256 0.176904526
[78,] 0.853116000 -0.705039256
[79,] 0.450247107 0.853116000
[80,] 1.152544417 0.450247107
[81,] 1.518038155 1.152544417
[82,] 0.234460172 1.518038155
[83,] 0.334739998 0.234460172
[84,] 0.283188143 0.334739998
[85,] 0.321796794 0.283188143
[86,] 0.071930118 0.321796794
[87,] 0.906823483 0.071930118
[88,] 0.371366677 0.906823483
[89,] -0.974356971 0.371366677
[90,] 0.441893919 -0.974356971
[91,] -1.354565483 0.441893919
[92,] 0.489485718 -1.354565483
[93,] 0.527003421 0.489485718
[94,] 0.119393695 0.527003421
[95,] 0.416375509 0.119393695
[96,] 0.231410577 0.416375509
[97,] -0.568015652 0.231410577
[98,] -0.336437435 -0.568015652
[99,] -0.936256144 -0.336437435
[100,] -0.258746310 -0.936256144
[101,] -0.631460190 -0.258746310
[102,] -1.571038432 -0.631460190
[103,] -0.593498417 -1.571038432
[104,] 0.940979873 -0.593498417
[105,] -1.308772613 0.940979873
[106,] -1.469144520 -1.308772613
[107,] -0.001310481 -1.469144520
[108,] -0.271846604 -0.001310481
[109,] -0.912131229 -0.271846604
[110,] 0.426656586 -0.912131229
[111,] 0.424798555 0.426656586
[112,] -0.445086566 0.424798555
[113,] -1.711300969 -0.445086566
[114,] -0.603472733 -1.711300969
[115,] 0.087348227 -0.603472733
[116,] -0.874023366 0.087348227
[117,] 1.247744176 -0.874023366
[118,] 0.337993602 1.247744176
[119,] -1.197514723 0.337993602
[120,] -1.030392281 -1.197514723
[121,] -0.122424234 -1.030392281
[122,] -0.003883060 -0.122424234
[123,] -0.121497695 -0.003883060
[124,] 0.239438027 -0.121497695
[125,] 0.555613505 0.239438027
[126,] -0.990376684 0.555613505
[127,] 0.596238192 -0.990376684
[128,] 0.150440533 0.596238192
[129,] 0.482722087 0.150440533
[130,] -0.942837362 0.482722087
[131,] 0.247079159 -0.942837362
[132,] 1.532763470 0.247079159
[133,] 0.013485894 1.532763470
[134,] 0.274625777 0.013485894
[135,] 0.579027577 0.274625777
[136,] -0.568344971 0.579027577
[137,] -2.458832851 -0.568344971
[138,] 0.780713362 -2.458832851
[139,] 0.179722597 0.780713362
[140,] 0.043592707 0.179722597
[141,] -0.591391071 0.043592707
[142,] -1.398352936 -0.591391071
[143,] 0.378718935 -1.398352936
[144,] 1.645200898 0.378718935
[145,] 0.533156685 1.645200898
[146,] -0.198995023 0.533156685
[147,] 0.884202616 -0.198995023
[148,] 0.105981569 0.884202616
[149,] 0.448710730 0.105981569
[150,] 0.440348467 0.448710730
[151,] 0.626861540 0.440348467
[152,] 1.169970593 0.626861540
[153,] -0.876358242 1.169970593
[154,] -0.217286991 -0.876358242
[155,] -0.724259677 -0.217286991
[156,] 0.526424625 -0.724259677
[157,] 0.453767524 0.526424625
[158,] 0.102031003 0.453767524
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.217592081 -0.092610142
2 0.105752077 0.217592081
3 0.118183589 0.105752077
4 0.866683127 0.118183589
5 0.699894083 0.866683127
6 -0.475540818 0.699894083
7 -0.693405290 -0.475540818
8 0.383040024 -0.693405290
9 0.032292900 0.383040024
10 0.619555202 0.032292900
11 0.697825379 0.619555202
12 0.740313484 0.697825379
13 -1.523559801 0.740313484
14 -0.873537408 -1.523559801
15 -0.234171921 -0.873537408
16 -0.766796917 -0.234171921
17 -1.253880537 -0.766796917
18 0.148273330 -1.253880537
19 -0.615899152 0.148273330
20 -1.025042427 -0.615899152
21 0.314957212 -1.025042427
22 -0.219772923 0.314957212
23 -0.832668234 -0.219772923
24 -0.154557341 -0.832668234
25 0.276801325 -0.154557341
26 0.253158700 0.276801325
27 0.122880781 0.253158700
28 0.442543271 0.122880781
29 0.262127568 0.442543271
30 1.143482855 0.262127568
31 0.565442562 1.143482855
32 0.482425275 0.565442562
33 0.260303894 0.482425275
34 -0.766763991 0.260303894
35 -0.215400073 -0.766763991
36 -1.049238775 -0.215400073
37 0.481195579 -1.049238775
38 0.427752812 0.481195579
39 -0.705375570 0.427752812
40 0.783771850 -0.705375570
41 -0.024987669 0.783771850
42 -0.582224061 -0.024987669
43 -1.322613391 -0.582224061
44 -0.767285522 -1.322613391
45 -0.259979709 -0.767285522
46 0.464165081 -0.259979709
47 -0.019978292 0.464165081
48 0.123995326 -0.019978292
49 0.341141568 0.123995326
50 0.068756577 0.341141568
51 0.127577972 0.068756577
52 1.254030825 0.127577972
53 -0.245467623 1.254030825
54 0.146526851 -0.245467623
55 1.000853099 0.146526851
56 1.138153498 1.000853099
57 0.037190355 1.138153498
58 -0.137545590 0.037190355
59 -0.365308864 -0.137545590
60 0.017310900 -0.365308864
61 0.173299223 0.017310900
62 0.734260947 0.173299223
63 0.211149556 0.734260947
64 0.069513834 0.211149556
65 0.746837448 0.069513834
66 0.489526885 0.746837448
67 -0.649330579 0.489526885
68 -0.112850633 -0.649330579
69 0.307455046 -0.112850633
70 0.355031529 0.307455046
71 0.122227061 0.355031529
72 -0.049804753 0.122227061
73 -0.017954935 -0.049804753
74 0.138624319 -0.017954935
75 -0.848150269 0.138624319
76 0.176904526 -0.848150269
77 -0.705039256 0.176904526
78 0.853116000 -0.705039256
79 0.450247107 0.853116000
80 1.152544417 0.450247107
81 1.518038155 1.152544417
82 0.234460172 1.518038155
83 0.334739998 0.234460172
84 0.283188143 0.334739998
85 0.321796794 0.283188143
86 0.071930118 0.321796794
87 0.906823483 0.071930118
88 0.371366677 0.906823483
89 -0.974356971 0.371366677
90 0.441893919 -0.974356971
91 -1.354565483 0.441893919
92 0.489485718 -1.354565483
93 0.527003421 0.489485718
94 0.119393695 0.527003421
95 0.416375509 0.119393695
96 0.231410577 0.416375509
97 -0.568015652 0.231410577
98 -0.336437435 -0.568015652
99 -0.936256144 -0.336437435
100 -0.258746310 -0.936256144
101 -0.631460190 -0.258746310
102 -1.571038432 -0.631460190
103 -0.593498417 -1.571038432
104 0.940979873 -0.593498417
105 -1.308772613 0.940979873
106 -1.469144520 -1.308772613
107 -0.001310481 -1.469144520
108 -0.271846604 -0.001310481
109 -0.912131229 -0.271846604
110 0.426656586 -0.912131229
111 0.424798555 0.426656586
112 -0.445086566 0.424798555
113 -1.711300969 -0.445086566
114 -0.603472733 -1.711300969
115 0.087348227 -0.603472733
116 -0.874023366 0.087348227
117 1.247744176 -0.874023366
118 0.337993602 1.247744176
119 -1.197514723 0.337993602
120 -1.030392281 -1.197514723
121 -0.122424234 -1.030392281
122 -0.003883060 -0.122424234
123 -0.121497695 -0.003883060
124 0.239438027 -0.121497695
125 0.555613505 0.239438027
126 -0.990376684 0.555613505
127 0.596238192 -0.990376684
128 0.150440533 0.596238192
129 0.482722087 0.150440533
130 -0.942837362 0.482722087
131 0.247079159 -0.942837362
132 1.532763470 0.247079159
133 0.013485894 1.532763470
134 0.274625777 0.013485894
135 0.579027577 0.274625777
136 -0.568344971 0.579027577
137 -2.458832851 -0.568344971
138 0.780713362 -2.458832851
139 0.179722597 0.780713362
140 0.043592707 0.179722597
141 -0.591391071 0.043592707
142 -1.398352936 -0.591391071
143 0.378718935 -1.398352936
144 1.645200898 0.378718935
145 0.533156685 1.645200898
146 -0.198995023 0.533156685
147 0.884202616 -0.198995023
148 0.105981569 0.884202616
149 0.448710730 0.105981569
150 0.440348467 0.448710730
151 0.626861540 0.440348467
152 1.169970593 0.626861540
153 -0.876358242 1.169970593
154 -0.217286991 -0.876358242
155 -0.724259677 -0.217286991
156 0.526424625 -0.724259677
157 0.453767524 0.526424625
158 0.102031003 0.453767524
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7zr3e1291229903.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8zr3e1291229903.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9zr3e1291229903.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10silh1291229903.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11djj41291229903.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12y1is1291229903.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13cbf11291229903.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14ycep1291229903.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/151cdv1291229903.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16nvbj1291229903.tab")
+ }
>
> try(system("convert tmp/1lhon1291229903.ps tmp/1lhon1291229903.png",intern=TRUE))
character(0)
> try(system("convert tmp/2lhon1291229903.ps tmp/2lhon1291229903.png",intern=TRUE))
character(0)
> try(system("convert tmp/3v9581291229903.ps tmp/3v9581291229903.png",intern=TRUE))
character(0)
> try(system("convert tmp/4v9581291229903.ps tmp/4v9581291229903.png",intern=TRUE))
character(0)
> try(system("convert tmp/5v9581291229903.ps tmp/5v9581291229903.png",intern=TRUE))
character(0)
> try(system("convert tmp/6o0mb1291229903.ps tmp/6o0mb1291229903.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zr3e1291229903.ps tmp/7zr3e1291229903.png",intern=TRUE))
character(0)
> try(system("convert tmp/8zr3e1291229903.ps tmp/8zr3e1291229903.png",intern=TRUE))
character(0)
> try(system("convert tmp/9zr3e1291229903.ps tmp/9zr3e1291229903.png",intern=TRUE))
character(0)
> try(system("convert tmp/10silh1291229903.ps tmp/10silh1291229903.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.245 1.759 13.387