R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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(9.11
+ ,15.13
+ ,9.24
+ ,19.31
+ ,9.84
+ ,7.66
+ ,6.11
+ ,8.11
+ ,2.58
+ ,1.55
+ ,1.55
+ ,1.64
+ ,2.07
+ ,2.39
+ ,1.32
+ ,3.16
+ ,0.89
+ ,0.66
+ ,9.06
+ ,15.25
+ ,9.29
+ ,19.47
+ ,9.87
+ ,7.53
+ ,6.13
+ ,8.13
+ ,2.59
+ ,1.56
+ ,1.56
+ ,1.65
+ ,2.08
+ ,2.4
+ ,1.33
+ ,3.2
+ ,0.89
+ ,0.67
+ ,9.11
+ ,15.33
+ ,9.39
+ ,19.7
+ ,9.9
+ ,7.54
+ ,6.15
+ ,8.16
+ ,2.6
+ ,1.56
+ ,1.56
+ ,1.65
+ ,2.08
+ ,2.42
+ ,1.33
+ ,3.2
+ ,0.89
+ ,0.67
+ ,9.13
+ ,15.36
+ ,9.42
+ ,19.76
+ ,9.9
+ ,7.56
+ ,6.15
+ ,8.17
+ ,2.6
+ ,1.57
+ ,1.56
+ ,1.65
+ ,2.08
+ ,2.42
+ ,1.33
+ ,3.21
+ ,0.89
+ ,0.67
+ ,9.13
+ ,15.4
+ ,9.42
+ ,19.9
+ ,9.87
+ ,7.57
+ ,6.16
+ ,8.22
+ ,2.61
+ ,1.57
+ ,1.57
+ ,1.66
+ ,2.09
+ ,2.44
+ ,1.33
+ ,3.22
+ ,0.89
+ ,0.67
+ ,9.19
+ ,15.4
+ ,9.43
+ ,19.97
+ ,9.87
+ ,7.56
+ ,6.18
+ ,8.23
+ ,2.62
+ ,1.58
+ ,1.57
+ ,1.66
+ ,2.09
+ ,2.44
+ ,1.33
+ ,3.22
+ ,0.89
+ ,0.67
+ ,9.2
+ ,15.41
+ ,9.5
+ ,20.1
+ ,9.88
+ ,7.57
+ ,6.21
+ ,8.28
+ ,2.64
+ ,1.58
+ ,1.57
+ ,1.67
+ ,2.09
+ ,2.44
+ ,1.34
+ ,3.23
+ ,0.89
+ ,0.67
+ ,9.23
+ ,15.47
+ ,9.53
+ ,20.26
+ ,9.76
+ ,7.61
+ ,6.22
+ ,8.28
+ ,2.65
+ ,1.58
+ ,1.57
+ ,1.67
+ ,2.1
+ ,2.45
+ ,1.34
+ ,3.24
+ ,0.9
+ ,0.67
+ ,9.24
+ ,15.54
+ ,9.58
+ ,20.44
+ ,9.76
+ ,7.61
+ ,6.23
+ ,8.29
+ ,2.66
+ ,1.58
+ ,1.58
+ ,1.68
+ ,2.1
+ ,2.46
+ ,1.34
+ ,3.25
+ ,0.9
+ ,0.67
+ ,9.28
+ ,15.55
+ ,9.58
+ ,20.43
+ ,9.76
+ ,7.6
+ ,6.26
+ ,8.31
+ ,2.67
+ ,1.59
+ ,1.59
+ ,1.68
+ ,2.1
+ ,2.47
+ ,1.34
+ ,3.25
+ ,0.9
+ ,0.67
+ ,9.32
+ ,15.59
+ ,9.6
+ ,20.57
+ ,9.77
+ ,7.61
+ ,6.28
+ ,8.33
+ ,2.68
+ ,1.59
+ ,1.59
+ ,1.68
+ ,2.11
+ ,2.48
+ ,1.35
+ ,3.26
+ ,0.9
+ ,0.67
+ ,9.32
+ ,15.65
+ ,9.61
+ ,20.6
+ ,9.77
+ ,7.61
+ ,6.28
+ ,8.36
+ ,2.69
+ ,1.6
+ ,1.59
+ ,1.68
+ ,2.11
+ ,2.48
+ ,1.35
+ ,3.26
+ ,0.9
+ ,0.67
+ ,9.32
+ ,15.75
+ ,9.65
+ ,20.69
+ ,9.77
+ ,7.62
+ ,6.29
+ ,8.36
+ ,2.69
+ ,1.6
+ ,1.6
+ ,1.69
+ ,2.11
+ ,2.49
+ ,1.34
+ ,3.29
+ ,0.9
+ ,0.67
+ ,9.36
+ ,15.86
+ ,9.71
+ ,20.93
+ ,9.83
+ ,7.7
+ ,6.32
+ ,8.39
+ ,2.71
+ ,1.61
+ ,1.6
+ ,1.69
+ ,2.13
+ ,2.5
+ ,1.35
+ ,3.31
+ ,0.91
+ ,0.69
+ ,9.37
+ ,15.89
+ ,9.78
+ ,20.98
+ ,9.85
+ ,7.73
+ ,6.36
+ ,8.46
+ ,2.72
+ ,1.62
+ ,1.61
+ ,1.7
+ ,2.18
+ ,2.51
+ ,1.35
+ ,3.33
+ ,0.91
+ ,0.7
+ ,9.38
+ ,15.94
+ ,9.79
+ ,21.11
+ ,9.85
+ ,7.75
+ ,6.37
+ ,8.48
+ ,2.73
+ ,1.62
+ ,1.62
+ ,1.7
+ ,2.2
+ ,2.52
+ ,1.36
+ ,3.33
+ ,0.91
+ ,0.7
+ ,9.41
+ ,15.93
+ ,9.84
+ ,21.14
+ ,9.89
+ ,7.76
+ ,6.38
+ ,8.52
+ ,2.73
+ ,1.63
+ ,1.62
+ ,1.71
+ ,2.21
+ ,2.52
+ ,1.36
+ ,3.33
+ ,0.91
+ ,0.7
+ ,9.44
+ ,15.95
+ ,9.87
+ ,21.16
+ ,9.9
+ ,7.76
+ ,6.38
+ ,8.53
+ ,2.74
+ ,1.63
+ ,1.62
+ ,1.71
+ ,2.21
+ ,2.52
+ ,1.37
+ ,3.33
+ ,0.91
+ ,0.7
+ ,9.44
+ ,15.99
+ ,9.9
+ ,21.32
+ ,9.92
+ ,7.77
+ ,6.4
+ ,8.57
+ ,2.74
+ ,1.63
+ ,1.62
+ ,1.71
+ ,2.22
+ ,2.54
+ ,1.37
+ ,3.33
+ ,0.91
+ ,0.7
+ ,9.44
+ ,15.99
+ ,9.95
+ ,21.32
+ ,9.91
+ ,7.79
+ ,6.41
+ ,8.58
+ ,2.74
+ ,1.63
+ ,1.63
+ ,1.71
+ ,2.22
+ ,2.54
+ ,1.37
+ ,3.33
+ ,0.91
+ ,0.7
+ ,9.47
+ ,16.06
+ ,9.96
+ ,21.48
+ ,9.92
+ ,7.79
+ ,6.42
+ ,8.58
+ ,2.74
+ ,1.63
+ ,1.63
+ ,1.72
+ ,2.23
+ ,2.54
+ ,1.38
+ ,3.35
+ ,0.91
+ ,0.71
+ ,9.48
+ ,16.08
+ ,9.98
+ ,21.58
+ ,9.92
+ ,7.79
+ ,6.43
+ ,8.63
+ ,2.74
+ ,1.63
+ ,1.63
+ ,1.72
+ ,2.23
+ ,2.56
+ ,1.38
+ ,3.37
+ ,0.91
+ ,0.71
+ ,9.56
+ ,16.07
+ ,10.01
+ ,21.74
+ ,9.96
+ ,7.83
+ ,6.44
+ ,8.66
+ ,2.75
+ ,1.64
+ ,1.63
+ ,1.72
+ ,2.23
+ ,2.57
+ ,1.38
+ ,3.4
+ ,0.91
+ ,0.71
+ ,9.58
+ ,16.11
+ ,10
+ ,21.75
+ ,9.97
+ ,7.83
+ ,6.47
+ ,8.69
+ ,2.75
+ ,1.64
+ ,1.64
+ ,1.73
+ ,2.23
+ ,2.58
+ ,1.38
+ ,3.42
+ ,0.91
+ ,0.71
+ ,9.56
+ ,16.15
+ ,10.03
+ ,21.81
+ ,9.98
+ ,7.88
+ ,6.47
+ ,8.72
+ ,2.75
+ ,1.64
+ ,1.64
+ ,1.73
+ ,2.24
+ ,2.58
+ ,1.39
+ ,3.46
+ ,0.91
+ ,0.71
+ ,9.58
+ ,16.18
+ ,10.05
+ ,21.89
+ ,10.06
+ ,7.95
+ ,6.48
+ ,8.74
+ ,2.75
+ ,1.65
+ ,1.64
+ ,1.73
+ ,2.25
+ ,2.58
+ ,1.39
+ ,3.46
+ ,0.91
+ ,0.71
+ ,9.7
+ ,16.3
+ ,10.06
+ ,22.21
+ ,10.07
+ ,8.01
+ ,6.51
+ ,8.8
+ ,2.77
+ ,1.65
+ ,1.65
+ ,1.74
+ ,2.26
+ ,2.58
+ ,1.4
+ ,3.47
+ ,0.92
+ ,0.71
+ ,9.74
+ ,16.42
+ ,10.09
+ ,22.37
+ ,10.12
+ ,8.05
+ ,6.54
+ ,8.86
+ ,2.78
+ ,1.66
+ ,1.66
+ ,1.75
+ ,2.27
+ ,2.59
+ ,1.4
+ ,3.48
+ ,0.92
+ ,0.71
+ ,9.76
+ ,16.49
+ ,10.24
+ ,22.47
+ ,10.1
+ ,8.1
+ ,6.56
+ ,8.91
+ ,2.79
+ ,1.67
+ ,1.67
+ ,1.75
+ ,2.28
+ ,2.6
+ ,1.41
+ ,3.49
+ ,0.92
+ ,0.72
+ ,9.78
+ ,16.5
+ ,10.23
+ ,22.51
+ ,10.1
+ ,8.1
+ ,6.57
+ ,8.94
+ ,2.8
+ ,1.67
+ ,1.67
+ ,1.76
+ ,2.29
+ ,2.61
+ ,1.42
+ ,3.49
+ ,0.92
+ ,0.72
+ ,9.84
+ ,16.58
+ ,10.27
+ ,22.55
+ ,10.1
+ ,8.16
+ ,6.6
+ ,8.97
+ ,2.82
+ ,1.68
+ ,1.68
+ ,1.76
+ ,2.3
+ ,2.61
+ ,1.43
+ ,3.5
+ ,0.93
+ ,0.72
+ ,9.88
+ ,16.64
+ ,10.28
+ ,22.61
+ ,10.19
+ ,8.18
+ ,6.62
+ ,8.99
+ ,2.83
+ ,1.68
+ ,1.68
+ ,1.77
+ ,2.3
+ ,2.62
+ ,1.44
+ ,3.5
+ ,0.93
+ ,0.72
+ ,9.96
+ ,16.66
+ ,10.29
+ ,22.58
+ ,10.21
+ ,8.2
+ ,6.65
+ ,8.99
+ ,2.84
+ ,1.69
+ ,1.69
+ ,1.77
+ ,2.3
+ ,2.63
+ ,1.44
+ ,3.5
+ ,0.94
+ ,0.73
+ ,9.97
+ ,16.81
+ ,10.44
+ ,22.85
+ ,10.2
+ ,7.99
+ ,6.71
+ ,9.06
+ ,2.87
+ ,1.7
+ ,1.7
+ ,1.78
+ ,2.32
+ ,2.65
+ ,1.45
+ ,3.51
+ ,0.95
+ ,0.73
+ ,9.96
+ ,16.91
+ ,10.51
+ ,22.93
+ ,10.39
+ ,8.01
+ ,6.76
+ ,9.12
+ ,2.89
+ ,1.71
+ ,1.71
+ ,1.79
+ ,2.32
+ ,2.67
+ ,1.46
+ ,3.48
+ ,0.95
+ ,0.73
+ ,9.96
+ ,16.92
+ ,10.52
+ ,22.98
+ ,10.39
+ ,8.02
+ ,6.78
+ ,9.14
+ ,2.9
+ ,1.72
+ ,1.71
+ ,1.8
+ ,2.32
+ ,2.68
+ ,1.46
+ ,3.48
+ ,0.95
+ ,0.73
+ ,9.96
+ ,16.95
+ ,10.57
+ ,23.01
+ ,10.39
+ ,8.03
+ ,6.8
+ ,9.15
+ ,2.9
+ ,1.72
+ ,1.72
+ ,1.8
+ ,2.33
+ ,2.67
+ ,1.46
+ ,3.48
+ ,0.95
+ ,0.73
+ ,10.02
+ ,17.11
+ ,10.62
+ ,23.11
+ ,10.45
+ ,8.04
+ ,6.83
+ ,9.19
+ ,2.91
+ ,1.73
+ ,1.72
+ ,1.81
+ ,2.34
+ ,2.68
+ ,1.46
+ ,3.49
+ ,0.96
+ ,0.73
+ ,10.08
+ ,17.16
+ ,10.71
+ ,23.18
+ ,10.49
+ ,8.07
+ ,6.86
+ ,9.21
+ ,2.92
+ ,1.73
+ ,1.73
+ ,1.81
+ ,2.34
+ ,2.68
+ ,1.46
+ ,3.51
+ ,0.96
+ ,0.73
+ ,10.09
+ ,17.16
+ ,10.73
+ ,23.18
+ ,10.48
+ ,8.08
+ ,6.86
+ ,9.22
+ ,2.92
+ ,1.73
+ ,1.73
+ ,1.81
+ ,2.34
+ ,2.68
+ ,1.46
+ ,3.51
+ ,0.96
+ ,0.73
+ ,10.12
+ ,17.27
+ ,10.74
+ ,23.21
+ ,10.49
+ ,8.08
+ ,6.87
+ ,9.23
+ ,2.92
+ ,1.73
+ ,1.73
+ ,1.81
+ ,2.35
+ ,2.68
+ ,1.46
+ ,3.52
+ ,0.97
+ ,0.73
+ ,10.14
+ ,17.34
+ ,10.75
+ ,23.22
+ ,10.49
+ ,8.1
+ ,6.88
+ ,9.24
+ ,2.92
+ ,1.74
+ ,1.74
+ ,1.82
+ ,2.35
+ ,2.69
+ ,1.47
+ ,3.52
+ ,0.97
+ ,0.73
+ ,10.17
+ ,17.39
+ ,10.79
+ ,23.12
+ ,10.5
+ ,8.11
+ ,6.9
+ ,9.27
+ ,2.94
+ ,1.75
+ ,1.75
+ ,1.82
+ ,2.36
+ ,2.69
+ ,1.47
+ ,3.54
+ ,0.97
+ ,0.73
+ ,10.22
+ ,17.43
+ ,10.81
+ ,23.15
+ ,10.51
+ ,8.15
+ ,6.92
+ ,9.29
+ ,2.95
+ ,1.75
+ ,1.75
+ ,1.82
+ ,2.37
+ ,2.69
+ ,1.47
+ ,3.55
+ ,0.97
+ ,0.73
+ ,10.25
+ ,17.45
+ ,10.87
+ ,23.16
+ ,10.51
+ ,8.16
+ ,6.93
+ ,9.31
+ ,2.95
+ ,1.75
+ ,1.75
+ ,1.83
+ ,2.37
+ ,2.7
+ ,1.48
+ ,3.55
+ ,0.98
+ ,0.74
+ ,10.25
+ ,17.5
+ ,10.92
+ ,23.21
+ ,10.53
+ ,8.17
+ ,6.94
+ ,9.34
+ ,2.97
+ ,1.76
+ ,1.76
+ ,1.83
+ ,2.37
+ ,2.71
+ ,1.48
+ ,3.55
+ ,0.98
+ ,0.75
+ ,10.26
+ ,17.56
+ ,10.95
+ ,23.21
+ ,10.54
+ ,8.18
+ ,6.96
+ ,9.35
+ ,2.99
+ ,1.76
+ ,1.76
+ ,1.83
+ ,2.38
+ ,2.72
+ ,1.48
+ ,3.55
+ ,0.98
+ ,0.75
+ ,10.34
+ ,17.65
+ ,10.94
+ ,23.22
+ ,10.54
+ ,8.15
+ ,6.98
+ ,9.38
+ ,3
+ ,1.76
+ ,1.77
+ ,1.84
+ ,2.38
+ ,2.71
+ ,1.48
+ ,3.55
+ ,0.99
+ ,0.75
+ ,10.33
+ ,17.62
+ ,10.97
+ ,23.25
+ ,10.55
+ ,8.15
+ ,6.99
+ ,9.4
+ ,3
+ ,1.77
+ ,1.77
+ ,1.84
+ ,2.38
+ ,2.72
+ ,1.48
+ ,3.56
+ ,0.99
+ ,0.75
+ ,10.3
+ ,17.7
+ ,10.99
+ ,23.39
+ ,10.58
+ ,8.17
+ ,7.01
+ ,9.44
+ ,3.01
+ ,1.78
+ ,1.78
+ ,1.85
+ ,2.39
+ ,2.73
+ ,1.49
+ ,3.56
+ ,0.99
+ ,0.76
+ ,10.33
+ ,17.72
+ ,11.04
+ ,23.41
+ ,10.59
+ ,8.16
+ ,7.06
+ ,9.47
+ ,3.03
+ ,1.78
+ ,1.79
+ ,1.85
+ ,2.4
+ ,2.74
+ ,1.5
+ ,3.57
+ ,1
+ ,0.76
+ ,10.33
+ ,17.71
+ ,11.09
+ ,23.45
+ ,10.56
+ ,8.15
+ ,7.07
+ ,9.48
+ ,3.03
+ ,1.79
+ ,1.79
+ ,1.86
+ ,2.41
+ ,2.74
+ ,1.5
+ ,3.57
+ ,1.01
+ ,0.76
+ ,10.37
+ ,17.74
+ ,11.12
+ ,23.46
+ ,10.57
+ ,8.16
+ ,7.08
+ ,9.5
+ ,3.04
+ ,1.79
+ ,1.79
+ ,1.86
+ ,2.42
+ ,2.75
+ ,1.5
+ ,3.57
+ ,1.02
+ ,0.77
+ ,10.44
+ ,17.75
+ ,11.11
+ ,23.44
+ ,10.59
+ ,8.15
+ ,7.08
+ ,9.52
+ ,3.04
+ ,1.79
+ ,1.79
+ ,1.86
+ ,2.43
+ ,2.75
+ ,1.5
+ ,3.57
+ ,1.02
+ ,0.77
+ ,10.45
+ ,17.78
+ ,11.14
+ ,23.54
+ ,10.63
+ ,8.18
+ ,7.1
+ ,9.54
+ ,3.05
+ ,1.79
+ ,1.79
+ ,1.86
+ ,2.43
+ ,2.76
+ ,1.5
+ ,3.57
+ ,1.02
+ ,0.78
+ ,10.45
+ ,17.8
+ ,11.2
+ ,23.62
+ ,10.63
+ ,8.19
+ ,7.11
+ ,9.53
+ ,3.05
+ ,1.79
+ ,1.8
+ ,1.86
+ ,2.43
+ ,2.75
+ ,1.5
+ ,3.58
+ ,1.02
+ ,0.78
+ ,10.44
+ ,17.86
+ ,11.25
+ ,23.86
+ ,10.66
+ ,8.18
+ ,7.22
+ ,9.74
+ ,3.09
+ ,1.83
+ ,1.82
+ ,1.89
+ ,2.43
+ ,2.78
+ ,1.51
+ ,3.64
+ ,1.02
+ ,0.78
+ ,10.43
+ ,17.88
+ ,11.3
+ ,24.07
+ ,10.69
+ ,8.2
+ ,7.24
+ ,9.75
+ ,3.09
+ ,1.83
+ ,1.83
+ ,1.89
+ ,2.44
+ ,2.79
+ ,1.52
+ ,3.64
+ ,1.03
+ ,0.78
+ ,10.4
+ ,17.89
+ ,11.31
+ ,24.13
+ ,10.72
+ ,8.21
+ ,7.25
+ ,9.75
+ ,3.09
+ ,1.83
+ ,1.83
+ ,1.89
+ ,2.44
+ ,2.8
+ ,1.52
+ ,3.64
+ ,1.03
+ ,0.79
+ ,10.43
+ ,17.94
+ ,11.31
+ ,24.12
+ ,10.72
+ ,8.22
+ ,7.26
+ ,9.78
+ ,3.1
+ ,1.83
+ ,1.83
+ ,1.89
+ ,2.45
+ ,2.81
+ ,1.53
+ ,3.64
+ ,1.03
+ ,0.79
+ ,10.47
+ ,17.98
+ ,11.33
+ ,24.17
+ ,10.73
+ ,8.23
+ ,7.27
+ ,9.8
+ ,3.1
+ ,1.84
+ ,1.83
+ ,1.9
+ ,2.45
+ ,2.81
+ ,1.53
+ ,3.65
+ ,1.03
+ ,0.79
+ ,10.52
+ ,18.1
+ ,11.41
+ ,24.23
+ ,10.75
+ ,8.25
+ ,7.3
+ ,9.84
+ ,3.11
+ ,1.84
+ ,1.84
+ ,1.91
+ ,2.48
+ ,2.82
+ ,1.54
+ ,3.67
+ ,1.03
+ ,0.8
+ ,10.55
+ ,18.14
+ ,11.46
+ ,24.28
+ ,10.78
+ ,8.28
+ ,7.32
+ ,9.88
+ ,3.12
+ ,1.84
+ ,1.84
+ ,1.91
+ ,2.49
+ ,2.82
+ ,1.55
+ ,3.68
+ ,1.03
+ ,0.8
+ ,10.5
+ ,18.19
+ ,11.48
+ ,24.12
+ ,10.79
+ ,8.28
+ ,7.34
+ ,9.91
+ ,3.12
+ ,1.85
+ ,1.85
+ ,1.92
+ ,2.49
+ ,2.83
+ ,1.55
+ ,3.68
+ ,1.03
+ ,0.8
+ ,10.44
+ ,18.23
+ ,11.58
+ ,24.14
+ ,10.83
+ ,8.29
+ ,7.35
+ ,9.92
+ ,3.12
+ ,1.85
+ ,1.85
+ ,1.92
+ ,2.5
+ ,2.83
+ ,1.55
+ ,3.68
+ ,1.03
+ ,0.8
+ ,10.47
+ ,18.24
+ ,11.63
+ ,24.17
+ ,10.83
+ ,8.3
+ ,7.36
+ ,9.92
+ ,3.13
+ ,1.85
+ ,1.85
+ ,1.92
+ ,2.51
+ ,2.84
+ ,1.55
+ ,3.68
+ ,1.03
+ ,0.81
+ ,10.5
+ ,18.27
+ ,11.69
+ ,24.2
+ ,10.85
+ ,8.34
+ ,7.39
+ ,9.97
+ ,3.15
+ ,1.86
+ ,1.86
+ ,1.93
+ ,2.52
+ ,2.84
+ ,1.56
+ ,3.68
+ ,1.03
+ ,0.8
+ ,10.54
+ ,18.3
+ ,11.74
+ ,24.36
+ ,10.88
+ ,8.38
+ ,7.41
+ ,9.99
+ ,3.16
+ ,1.86
+ ,1.86
+ ,1.93
+ ,2.53
+ ,2.84
+ ,1.56
+ ,3.69
+ ,1.03
+ ,0.81
+ ,10.55
+ ,18.34
+ ,11.68
+ ,24.34
+ ,10.97
+ ,8.39
+ ,7.43
+ ,10.02
+ ,3.16
+ ,1.86
+ ,1.86
+ ,1.94
+ ,2.54
+ ,2.86
+ ,1.56
+ ,3.69
+ ,1.03
+ ,0.82
+ ,10.53
+ ,18.36
+ ,11.69
+ ,24.38
+ ,10.98
+ ,8.44
+ ,7.46
+ ,10.05
+ ,3.18
+ ,1.87
+ ,1.87
+ ,1.94
+ ,2.54
+ ,2.87
+ ,1.57
+ ,3.71
+ ,1.03
+ ,0.82
+ ,10.54
+ ,18.36
+ ,11.71
+ ,24.46
+ ,11
+ ,8.46
+ ,7.47
+ ,10.07
+ ,3.19
+ ,1.87
+ ,1.87
+ ,1.94
+ ,2.56
+ ,2.88
+ ,1.58
+ ,3.71
+ ,1.04
+ ,0.82
+ ,10.54
+ ,18.4
+ ,11.75
+ ,24.6
+ ,11.04
+ ,8.46
+ ,7.5
+ ,10.11
+ ,3.19
+ ,1.88
+ ,1.88
+ ,1.95
+ ,2.56
+ ,2.88
+ ,1.58
+ ,3.71
+ ,1.04
+ ,0.82
+ ,10.54
+ ,18.43
+ ,11.76
+ ,24.63
+ ,11.08
+ ,8.49
+ ,7.51
+ ,10.11
+ ,3.2
+ ,1.88
+ ,1.88
+ ,1.95
+ ,2.56
+ ,2.89
+ ,1.58
+ ,3.71
+ ,1.04
+ ,0.82
+ ,10.59
+ ,18.47
+ ,11.79
+ ,24.75
+ ,11.16
+ ,8.5
+ ,7.52
+ ,10.13
+ ,3.21
+ ,1.88
+ ,1.88
+ ,1.95
+ ,2.57
+ ,2.89
+ ,1.58
+ ,3.72
+ ,1.04
+ ,0.82
+ ,10.72
+ ,18.56
+ ,11.89
+ ,24.64
+ ,11.19
+ ,8.51
+ ,7.58
+ ,10.23
+ ,3.26
+ ,1.89
+ ,1.9
+ ,1.97
+ ,2.58
+ ,2.9
+ ,1.58
+ ,3.73
+ ,1.05
+ ,0.83
+ ,10.76
+ ,18.58
+ ,11.94
+ ,24.69
+ ,11.2
+ ,8.51
+ ,7.59
+ ,10.24
+ ,3.27
+ ,1.89
+ ,1.9
+ ,1.98
+ ,2.58
+ ,2.9
+ ,1.59
+ ,3.74
+ ,1.05
+ ,0.83
+ ,10.78
+ ,18.61
+ ,11.97
+ ,24.7
+ ,11.22
+ ,8.52
+ ,7.63
+ ,10.32
+ ,3.28
+ ,1.9
+ ,1.91
+ ,1.99
+ ,2.59
+ ,2.9
+ ,1.6
+ ,3.74
+ ,1.05
+ ,0.83
+ ,10.78
+ ,18.61
+ ,11.99
+ ,24.74
+ ,11.26
+ ,8.53
+ ,7.64
+ ,10.33
+ ,3.29
+ ,1.91
+ ,1.91
+ ,1.99
+ ,2.59
+ ,2.92
+ ,1.6
+ ,3.75
+ ,1.05
+ ,0.83
+ ,10.78
+ ,18.69
+ ,12.02
+ ,24.87
+ ,11.29
+ ,8.53
+ ,7.64
+ ,10.34
+ ,3.29
+ ,1.91
+ ,1.91
+ ,1.99
+ ,2.6
+ ,2.93
+ ,1.62
+ ,3.76
+ ,1.05
+ ,0.84)
+ ,dim=c(18
+ ,79)
+ ,dimnames=list(c('Restaurant'
+ ,'Pepersteak'
+ ,'Salade'
+ ,'Tong'
+ ,'Chinees'
+ ,'Pizza'
+ ,'Bier'
+ ,'SpecBier'
+ ,'Aperitief'
+ ,'Water'
+ ,'Limonade'
+ ,'Expresso'
+ ,'Frieten'
+ ,'Broodje'
+ ,'vleessnack'
+ ,'Hamburger'
+ ,'Frisdrank'
+ ,'Candybar')
+ ,1:79))
> y <- array(NA,dim=c(18,79),dimnames=list(c('Restaurant','Pepersteak','Salade','Tong','Chinees','Pizza','Bier','SpecBier','Aperitief','Water','Limonade','Expresso','Frieten','Broodje','vleessnack','Hamburger','Frisdrank','Candybar'),1:79))
> 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 = '1'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) 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
Restaurant Pepersteak Salade Tong Chinees Pizza Bier SpecBier Aperitief
1 9.11 15.13 9.24 19.31 9.84 7.66 6.11 8.11 2.58
2 9.06 15.25 9.29 19.47 9.87 7.53 6.13 8.13 2.59
3 9.11 15.33 9.39 19.70 9.90 7.54 6.15 8.16 2.60
4 9.13 15.36 9.42 19.76 9.90 7.56 6.15 8.17 2.60
5 9.13 15.40 9.42 19.90 9.87 7.57 6.16 8.22 2.61
6 9.19 15.40 9.43 19.97 9.87 7.56 6.18 8.23 2.62
7 9.20 15.41 9.50 20.10 9.88 7.57 6.21 8.28 2.64
8 9.23 15.47 9.53 20.26 9.76 7.61 6.22 8.28 2.65
9 9.24 15.54 9.58 20.44 9.76 7.61 6.23 8.29 2.66
10 9.28 15.55 9.58 20.43 9.76 7.60 6.26 8.31 2.67
11 9.32 15.59 9.60 20.57 9.77 7.61 6.28 8.33 2.68
12 9.32 15.65 9.61 20.60 9.77 7.61 6.28 8.36 2.69
13 9.32 15.75 9.65 20.69 9.77 7.62 6.29 8.36 2.69
14 9.36 15.86 9.71 20.93 9.83 7.70 6.32 8.39 2.71
15 9.37 15.89 9.78 20.98 9.85 7.73 6.36 8.46 2.72
16 9.38 15.94 9.79 21.11 9.85 7.75 6.37 8.48 2.73
17 9.41 15.93 9.84 21.14 9.89 7.76 6.38 8.52 2.73
18 9.44 15.95 9.87 21.16 9.90 7.76 6.38 8.53 2.74
19 9.44 15.99 9.90 21.32 9.92 7.77 6.40 8.57 2.74
20 9.44 15.99 9.95 21.32 9.91 7.79 6.41 8.58 2.74
21 9.47 16.06 9.96 21.48 9.92 7.79 6.42 8.58 2.74
22 9.48 16.08 9.98 21.58 9.92 7.79 6.43 8.63 2.74
23 9.56 16.07 10.01 21.74 9.96 7.83 6.44 8.66 2.75
24 9.58 16.11 10.00 21.75 9.97 7.83 6.47 8.69 2.75
25 9.56 16.15 10.03 21.81 9.98 7.88 6.47 8.72 2.75
26 9.58 16.18 10.05 21.89 10.06 7.95 6.48 8.74 2.75
27 9.70 16.30 10.06 22.21 10.07 8.01 6.51 8.80 2.77
28 9.74 16.42 10.09 22.37 10.12 8.05 6.54 8.86 2.78
29 9.76 16.49 10.24 22.47 10.10 8.10 6.56 8.91 2.79
30 9.78 16.50 10.23 22.51 10.10 8.10 6.57 8.94 2.80
31 9.84 16.58 10.27 22.55 10.10 8.16 6.60 8.97 2.82
32 9.88 16.64 10.28 22.61 10.19 8.18 6.62 8.99 2.83
33 9.96 16.66 10.29 22.58 10.21 8.20 6.65 8.99 2.84
34 9.97 16.81 10.44 22.85 10.20 7.99 6.71 9.06 2.87
35 9.96 16.91 10.51 22.93 10.39 8.01 6.76 9.12 2.89
36 9.96 16.92 10.52 22.98 10.39 8.02 6.78 9.14 2.90
37 9.96 16.95 10.57 23.01 10.39 8.03 6.80 9.15 2.90
38 10.02 17.11 10.62 23.11 10.45 8.04 6.83 9.19 2.91
39 10.08 17.16 10.71 23.18 10.49 8.07 6.86 9.21 2.92
40 10.09 17.16 10.73 23.18 10.48 8.08 6.86 9.22 2.92
41 10.12 17.27 10.74 23.21 10.49 8.08 6.87 9.23 2.92
42 10.14 17.34 10.75 23.22 10.49 8.10 6.88 9.24 2.92
43 10.17 17.39 10.79 23.12 10.50 8.11 6.90 9.27 2.94
44 10.22 17.43 10.81 23.15 10.51 8.15 6.92 9.29 2.95
45 10.25 17.45 10.87 23.16 10.51 8.16 6.93 9.31 2.95
46 10.25 17.50 10.92 23.21 10.53 8.17 6.94 9.34 2.97
47 10.26 17.56 10.95 23.21 10.54 8.18 6.96 9.35 2.99
48 10.34 17.65 10.94 23.22 10.54 8.15 6.98 9.38 3.00
49 10.33 17.62 10.97 23.25 10.55 8.15 6.99 9.40 3.00
50 10.30 17.70 10.99 23.39 10.58 8.17 7.01 9.44 3.01
51 10.33 17.72 11.04 23.41 10.59 8.16 7.06 9.47 3.03
52 10.33 17.71 11.09 23.45 10.56 8.15 7.07 9.48 3.03
53 10.37 17.74 11.12 23.46 10.57 8.16 7.08 9.50 3.04
54 10.44 17.75 11.11 23.44 10.59 8.15 7.08 9.52 3.04
55 10.45 17.78 11.14 23.54 10.63 8.18 7.10 9.54 3.05
56 10.45 17.80 11.20 23.62 10.63 8.19 7.11 9.53 3.05
57 10.44 17.86 11.25 23.86 10.66 8.18 7.22 9.74 3.09
58 10.43 17.88 11.30 24.07 10.69 8.20 7.24 9.75 3.09
59 10.40 17.89 11.31 24.13 10.72 8.21 7.25 9.75 3.09
60 10.43 17.94 11.31 24.12 10.72 8.22 7.26 9.78 3.10
61 10.47 17.98 11.33 24.17 10.73 8.23 7.27 9.80 3.10
62 10.52 18.10 11.41 24.23 10.75 8.25 7.30 9.84 3.11
63 10.55 18.14 11.46 24.28 10.78 8.28 7.32 9.88 3.12
64 10.50 18.19 11.48 24.12 10.79 8.28 7.34 9.91 3.12
65 10.44 18.23 11.58 24.14 10.83 8.29 7.35 9.92 3.12
66 10.47 18.24 11.63 24.17 10.83 8.30 7.36 9.92 3.13
67 10.50 18.27 11.69 24.20 10.85 8.34 7.39 9.97 3.15
68 10.54 18.30 11.74 24.36 10.88 8.38 7.41 9.99 3.16
69 10.55 18.34 11.68 24.34 10.97 8.39 7.43 10.02 3.16
70 10.53 18.36 11.69 24.38 10.98 8.44 7.46 10.05 3.18
71 10.54 18.36 11.71 24.46 11.00 8.46 7.47 10.07 3.19
72 10.54 18.40 11.75 24.60 11.04 8.46 7.50 10.11 3.19
73 10.54 18.43 11.76 24.63 11.08 8.49 7.51 10.11 3.20
74 10.59 18.47 11.79 24.75 11.16 8.50 7.52 10.13 3.21
75 10.72 18.56 11.89 24.64 11.19 8.51 7.58 10.23 3.26
76 10.76 18.58 11.94 24.69 11.20 8.51 7.59 10.24 3.27
77 10.78 18.61 11.97 24.70 11.22 8.52 7.63 10.32 3.28
78 10.78 18.61 11.99 24.74 11.26 8.53 7.64 10.33 3.29
79 10.78 18.69 12.02 24.87 11.29 8.53 7.64 10.34 3.29
Water Limonade Expresso Frieten Broodje vleessnack Hamburger Frisdrank
1 1.55 1.55 1.64 2.07 2.39 1.32 3.16 0.89
2 1.56 1.56 1.65 2.08 2.40 1.33 3.20 0.89
3 1.56 1.56 1.65 2.08 2.42 1.33 3.20 0.89
4 1.57 1.56 1.65 2.08 2.42 1.33 3.21 0.89
5 1.57 1.57 1.66 2.09 2.44 1.33 3.22 0.89
6 1.58 1.57 1.66 2.09 2.44 1.33 3.22 0.89
7 1.58 1.57 1.67 2.09 2.44 1.34 3.23 0.89
8 1.58 1.57 1.67 2.10 2.45 1.34 3.24 0.90
9 1.58 1.58 1.68 2.10 2.46 1.34 3.25 0.90
10 1.59 1.59 1.68 2.10 2.47 1.34 3.25 0.90
11 1.59 1.59 1.68 2.11 2.48 1.35 3.26 0.90
12 1.60 1.59 1.68 2.11 2.48 1.35 3.26 0.90
13 1.60 1.60 1.69 2.11 2.49 1.34 3.29 0.90
14 1.61 1.60 1.69 2.13 2.50 1.35 3.31 0.91
15 1.62 1.61 1.70 2.18 2.51 1.35 3.33 0.91
16 1.62 1.62 1.70 2.20 2.52 1.36 3.33 0.91
17 1.63 1.62 1.71 2.21 2.52 1.36 3.33 0.91
18 1.63 1.62 1.71 2.21 2.52 1.37 3.33 0.91
19 1.63 1.62 1.71 2.22 2.54 1.37 3.33 0.91
20 1.63 1.63 1.71 2.22 2.54 1.37 3.33 0.91
21 1.63 1.63 1.72 2.23 2.54 1.38 3.35 0.91
22 1.63 1.63 1.72 2.23 2.56 1.38 3.37 0.91
23 1.64 1.63 1.72 2.23 2.57 1.38 3.40 0.91
24 1.64 1.64 1.73 2.23 2.58 1.38 3.42 0.91
25 1.64 1.64 1.73 2.24 2.58 1.39 3.46 0.91
26 1.65 1.64 1.73 2.25 2.58 1.39 3.46 0.91
27 1.65 1.65 1.74 2.26 2.58 1.40 3.47 0.92
28 1.66 1.66 1.75 2.27 2.59 1.40 3.48 0.92
29 1.67 1.67 1.75 2.28 2.60 1.41 3.49 0.92
30 1.67 1.67 1.76 2.29 2.61 1.42 3.49 0.92
31 1.68 1.68 1.76 2.30 2.61 1.43 3.50 0.93
32 1.68 1.68 1.77 2.30 2.62 1.44 3.50 0.93
33 1.69 1.69 1.77 2.30 2.63 1.44 3.50 0.94
34 1.70 1.70 1.78 2.32 2.65 1.45 3.51 0.95
35 1.71 1.71 1.79 2.32 2.67 1.46 3.48 0.95
36 1.72 1.71 1.80 2.32 2.68 1.46 3.48 0.95
37 1.72 1.72 1.80 2.33 2.67 1.46 3.48 0.95
38 1.73 1.72 1.81 2.34 2.68 1.46 3.49 0.96
39 1.73 1.73 1.81 2.34 2.68 1.46 3.51 0.96
40 1.73 1.73 1.81 2.34 2.68 1.46 3.51 0.96
41 1.73 1.73 1.81 2.35 2.68 1.46 3.52 0.97
42 1.74 1.74 1.82 2.35 2.69 1.47 3.52 0.97
43 1.75 1.75 1.82 2.36 2.69 1.47 3.54 0.97
44 1.75 1.75 1.82 2.37 2.69 1.47 3.55 0.97
45 1.75 1.75 1.83 2.37 2.70 1.48 3.55 0.98
46 1.76 1.76 1.83 2.37 2.71 1.48 3.55 0.98
47 1.76 1.76 1.83 2.38 2.72 1.48 3.55 0.98
48 1.76 1.77 1.84 2.38 2.71 1.48 3.55 0.99
49 1.77 1.77 1.84 2.38 2.72 1.48 3.56 0.99
50 1.78 1.78 1.85 2.39 2.73 1.49 3.56 0.99
51 1.78 1.79 1.85 2.40 2.74 1.50 3.57 1.00
52 1.79 1.79 1.86 2.41 2.74 1.50 3.57 1.01
53 1.79 1.79 1.86 2.42 2.75 1.50 3.57 1.02
54 1.79 1.79 1.86 2.43 2.75 1.50 3.57 1.02
55 1.79 1.79 1.86 2.43 2.76 1.50 3.57 1.02
56 1.79 1.80 1.86 2.43 2.75 1.50 3.58 1.02
57 1.83 1.82 1.89 2.43 2.78 1.51 3.64 1.02
58 1.83 1.83 1.89 2.44 2.79 1.52 3.64 1.03
59 1.83 1.83 1.89 2.44 2.80 1.52 3.64 1.03
60 1.83 1.83 1.89 2.45 2.81 1.53 3.64 1.03
61 1.84 1.83 1.90 2.45 2.81 1.53 3.65 1.03
62 1.84 1.84 1.91 2.48 2.82 1.54 3.67 1.03
63 1.84 1.84 1.91 2.49 2.82 1.55 3.68 1.03
64 1.85 1.85 1.92 2.49 2.83 1.55 3.68 1.03
65 1.85 1.85 1.92 2.50 2.83 1.55 3.68 1.03
66 1.85 1.85 1.92 2.51 2.84 1.55 3.68 1.03
67 1.86 1.86 1.93 2.52 2.84 1.56 3.68 1.03
68 1.86 1.86 1.93 2.53 2.84 1.56 3.69 1.03
69 1.86 1.86 1.94 2.54 2.86 1.56 3.69 1.03
70 1.87 1.87 1.94 2.54 2.87 1.57 3.71 1.03
71 1.87 1.87 1.94 2.56 2.88 1.58 3.71 1.04
72 1.88 1.88 1.95 2.56 2.88 1.58 3.71 1.04
73 1.88 1.88 1.95 2.56 2.89 1.58 3.71 1.04
74 1.88 1.88 1.95 2.57 2.89 1.58 3.72 1.04
75 1.89 1.90 1.97 2.58 2.90 1.58 3.73 1.05
76 1.89 1.90 1.98 2.58 2.90 1.59 3.74 1.05
77 1.90 1.91 1.99 2.59 2.90 1.60 3.74 1.05
78 1.91 1.91 1.99 2.59 2.92 1.60 3.75 1.05
79 1.91 1.91 1.99 2.60 2.93 1.62 3.76 1.05
Candybar
1 0.66
2 0.67
3 0.67
4 0.67
5 0.67
6 0.67
7 0.67
8 0.67
9 0.67
10 0.67
11 0.67
12 0.67
13 0.67
14 0.69
15 0.70
16 0.70
17 0.70
18 0.70
19 0.70
20 0.70
21 0.71
22 0.71
23 0.71
24 0.71
25 0.71
26 0.71
27 0.71
28 0.71
29 0.72
30 0.72
31 0.72
32 0.72
33 0.73
34 0.73
35 0.73
36 0.73
37 0.73
38 0.73
39 0.73
40 0.73
41 0.73
42 0.73
43 0.73
44 0.73
45 0.74
46 0.75
47 0.75
48 0.75
49 0.75
50 0.76
51 0.76
52 0.76
53 0.77
54 0.77
55 0.78
56 0.78
57 0.78
58 0.78
59 0.79
60 0.79
61 0.79
62 0.80
63 0.80
64 0.80
65 0.80
66 0.81
67 0.80
68 0.81
69 0.82
70 0.82
71 0.82
72 0.82
73 0.82
74 0.82
75 0.83
76 0.83
77 0.83
78 0.83
79 0.84
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pepersteak Salade Tong Chinees Pizza
0.612279 0.348061 0.028637 0.064630 0.207910 0.061020
Bier SpecBier Aperitief Water Limonade Expresso
-1.785940 0.647469 1.983065 -2.102036 0.034559 0.001853
Frieten Broodje vleessnack Hamburger Frisdrank Candybar
-0.231369 -0.493662 0.074073 0.586575 4.565107 -2.210762
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.060241 -0.020247 -0.002122 0.017727 0.079936
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.612279 0.996757 0.614 0.541320
Pepersteak 0.348061 0.088766 3.921 0.000226 ***
Salade 0.028637 0.133889 0.214 0.831351
Tong 0.064630 0.034669 1.864 0.067107 .
Chinees 0.207910 0.084196 2.469 0.016351 *
Pizza 0.061020 0.108240 0.564 0.574994
Bier -1.785940 0.410215 -4.354 5.20e-05 ***
SpecBier 0.647469 0.291350 2.222 0.029981 *
Aperitief 1.983065 0.384192 5.162 2.83e-06 ***
Water -2.102036 1.040022 -2.021 0.047659 *
Limonade 0.034559 1.293181 0.027 0.978767
Expresso 0.001853 1.255684 0.001 0.998827
Frieten -0.231369 0.406491 -0.569 0.571319
Broodje -0.493662 0.673275 -0.733 0.466229
vleessnack 0.074073 0.711228 0.104 0.917393
Hamburger 0.586575 0.333187 1.760 0.083336 .
Frisdrank 4.565107 0.801880 5.693 3.81e-07 ***
Candybar -2.210762 0.891291 -2.480 0.015898 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.03344 on 61 degrees of freedom
Multiple R-squared: 0.9968, Adjusted R-squared: 0.9959
F-statistic: 1125 on 17 and 61 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.09932691 0.19865382 0.90067309
[2,] 0.08676025 0.17352051 0.91323975
[3,] 0.04358887 0.08717774 0.95641113
[4,] 0.02559489 0.05118978 0.97440511
[5,] 0.03253237 0.06506475 0.96746763
[6,] 0.03390023 0.06780046 0.96609977
[7,] 0.24996574 0.49993148 0.75003426
[8,] 0.23360421 0.46720842 0.76639579
[9,] 0.17394733 0.34789466 0.82605267
[10,] 0.11482640 0.22965280 0.88517360
[11,] 0.07836349 0.15672697 0.92163651
[12,] 0.08701732 0.17403465 0.91298268
[13,] 0.05639772 0.11279545 0.94360228
[14,] 0.04727229 0.09454457 0.95272771
[15,] 0.25188218 0.50376436 0.74811782
[16,] 0.35715392 0.71430783 0.64284608
[17,] 0.30004798 0.60009595 0.69995202
[18,] 0.38050643 0.76101287 0.61949357
[19,] 0.48410425 0.96820849 0.51589575
[20,] 0.44413614 0.88827228 0.55586386
[21,] 0.65714602 0.68570795 0.34285398
[22,] 0.60147069 0.79705861 0.39852931
[23,] 0.55344496 0.89311009 0.44655504
[24,] 0.55188822 0.89622356 0.44811178
[25,] 0.49945755 0.99891510 0.50054245
[26,] 0.44224514 0.88449028 0.55775486
[27,] 0.35993986 0.71987971 0.64006014
[28,] 0.33433590 0.66867180 0.66566410
[29,] 0.56688679 0.86622642 0.43311321
[30,] 0.68075971 0.63848057 0.31924029
[31,] 0.72746560 0.54506880 0.27253440
[32,] 0.72965752 0.54068495 0.27034248
[33,] 0.81901861 0.36196279 0.18098139
[34,] 0.77160631 0.45678738 0.22839369
[35,] 0.68317083 0.63365834 0.31682917
[36,] 0.96430408 0.07139183 0.03569592
[37,] 0.96187848 0.07624305 0.03812152
[38,] 0.91185377 0.17629246 0.08814623
> postscript(file="/var/wessaorg/rcomp/tmp/1lk9l1353075040.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/26d7w1353075040.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3ca651353075040.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4z1sw1353075040.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/58p6f1353075040.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 79
Frequency = 1
1 2 3 4 5
0.0556454108 -0.0174501663 -0.0135339212 -0.0012531572 -0.0469844024
6 7 8 9 10
0.0392491127 0.0075911053 -0.0182219250 -0.0553907218 0.0287947256
11 12 13 14 15
0.0461445820 0.0048012602 -0.0320018879 -0.0537051794 -0.0115503240
16 17 18 19 20
-0.0353050517 0.0011543904 -0.0070837195 -0.0149675627 -0.0045015755
21 22 23 24 25
0.0182177457 -0.0021515562 0.0463363357 0.0769699798 -0.0081335773
26 27 28 29 30
0.0030213274 -0.0206936545 -0.0295570519 -0.0166317652 -0.0173154982
31 32 33 34 35
-0.0273285430 -0.0251925701 0.0799356415 0.0161148643 -0.0184159893
36 37 38 39 40
0.0028525296 0.0147053278 -0.0173800505 0.0166895423 0.0211110183
41 42 43 44 45
-0.0292985343 0.0004209455 0.0035365563 0.0319692777 0.0375772647
46 47 48 49 50
0.0172354885 -0.0003634732 -0.0047060903 0.0158598986 -0.0198008627
51 52 53 54 55
-0.0160966469 -0.0207364380 -0.0265867532 0.0273279258 0.0393999616
56 57 58 59 60
0.0381260278 0.0280437326 -0.0216223813 -0.0212110395 -0.0234637408
61 62 63 64 65
0.0161663556 0.0418493962 0.0308922967 0.0130658568 -0.0602410677
66 67 68 69 70
-0.0103159461 -0.0193488601 0.0112528406 0.0415898757 0.0142589757
71 72 73 74 75
-0.0386112105 -0.0227081105 -0.0425568447 -0.0508097174 -0.0133475891
76 77 78 79
-0.0021222271 0.0232007568 0.0276981398 -0.0001090879
> postscript(file="/var/wessaorg/rcomp/tmp/6t4bv1353075040.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 = 79
Frequency = 1
lag(myerror, k = 1) myerror
0 0.0556454108 NA
1 -0.0174501663 0.0556454108
2 -0.0135339212 -0.0174501663
3 -0.0012531572 -0.0135339212
4 -0.0469844024 -0.0012531572
5 0.0392491127 -0.0469844024
6 0.0075911053 0.0392491127
7 -0.0182219250 0.0075911053
8 -0.0553907218 -0.0182219250
9 0.0287947256 -0.0553907218
10 0.0461445820 0.0287947256
11 0.0048012602 0.0461445820
12 -0.0320018879 0.0048012602
13 -0.0537051794 -0.0320018879
14 -0.0115503240 -0.0537051794
15 -0.0353050517 -0.0115503240
16 0.0011543904 -0.0353050517
17 -0.0070837195 0.0011543904
18 -0.0149675627 -0.0070837195
19 -0.0045015755 -0.0149675627
20 0.0182177457 -0.0045015755
21 -0.0021515562 0.0182177457
22 0.0463363357 -0.0021515562
23 0.0769699798 0.0463363357
24 -0.0081335773 0.0769699798
25 0.0030213274 -0.0081335773
26 -0.0206936545 0.0030213274
27 -0.0295570519 -0.0206936545
28 -0.0166317652 -0.0295570519
29 -0.0173154982 -0.0166317652
30 -0.0273285430 -0.0173154982
31 -0.0251925701 -0.0273285430
32 0.0799356415 -0.0251925701
33 0.0161148643 0.0799356415
34 -0.0184159893 0.0161148643
35 0.0028525296 -0.0184159893
36 0.0147053278 0.0028525296
37 -0.0173800505 0.0147053278
38 0.0166895423 -0.0173800505
39 0.0211110183 0.0166895423
40 -0.0292985343 0.0211110183
41 0.0004209455 -0.0292985343
42 0.0035365563 0.0004209455
43 0.0319692777 0.0035365563
44 0.0375772647 0.0319692777
45 0.0172354885 0.0375772647
46 -0.0003634732 0.0172354885
47 -0.0047060903 -0.0003634732
48 0.0158598986 -0.0047060903
49 -0.0198008627 0.0158598986
50 -0.0160966469 -0.0198008627
51 -0.0207364380 -0.0160966469
52 -0.0265867532 -0.0207364380
53 0.0273279258 -0.0265867532
54 0.0393999616 0.0273279258
55 0.0381260278 0.0393999616
56 0.0280437326 0.0381260278
57 -0.0216223813 0.0280437326
58 -0.0212110395 -0.0216223813
59 -0.0234637408 -0.0212110395
60 0.0161663556 -0.0234637408
61 0.0418493962 0.0161663556
62 0.0308922967 0.0418493962
63 0.0130658568 0.0308922967
64 -0.0602410677 0.0130658568
65 -0.0103159461 -0.0602410677
66 -0.0193488601 -0.0103159461
67 0.0112528406 -0.0193488601
68 0.0415898757 0.0112528406
69 0.0142589757 0.0415898757
70 -0.0386112105 0.0142589757
71 -0.0227081105 -0.0386112105
72 -0.0425568447 -0.0227081105
73 -0.0508097174 -0.0425568447
74 -0.0133475891 -0.0508097174
75 -0.0021222271 -0.0133475891
76 0.0232007568 -0.0021222271
77 0.0276981398 0.0232007568
78 -0.0001090879 0.0276981398
79 NA -0.0001090879
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.0174501663 0.0556454108
[2,] -0.0135339212 -0.0174501663
[3,] -0.0012531572 -0.0135339212
[4,] -0.0469844024 -0.0012531572
[5,] 0.0392491127 -0.0469844024
[6,] 0.0075911053 0.0392491127
[7,] -0.0182219250 0.0075911053
[8,] -0.0553907218 -0.0182219250
[9,] 0.0287947256 -0.0553907218
[10,] 0.0461445820 0.0287947256
[11,] 0.0048012602 0.0461445820
[12,] -0.0320018879 0.0048012602
[13,] -0.0537051794 -0.0320018879
[14,] -0.0115503240 -0.0537051794
[15,] -0.0353050517 -0.0115503240
[16,] 0.0011543904 -0.0353050517
[17,] -0.0070837195 0.0011543904
[18,] -0.0149675627 -0.0070837195
[19,] -0.0045015755 -0.0149675627
[20,] 0.0182177457 -0.0045015755
[21,] -0.0021515562 0.0182177457
[22,] 0.0463363357 -0.0021515562
[23,] 0.0769699798 0.0463363357
[24,] -0.0081335773 0.0769699798
[25,] 0.0030213274 -0.0081335773
[26,] -0.0206936545 0.0030213274
[27,] -0.0295570519 -0.0206936545
[28,] -0.0166317652 -0.0295570519
[29,] -0.0173154982 -0.0166317652
[30,] -0.0273285430 -0.0173154982
[31,] -0.0251925701 -0.0273285430
[32,] 0.0799356415 -0.0251925701
[33,] 0.0161148643 0.0799356415
[34,] -0.0184159893 0.0161148643
[35,] 0.0028525296 -0.0184159893
[36,] 0.0147053278 0.0028525296
[37,] -0.0173800505 0.0147053278
[38,] 0.0166895423 -0.0173800505
[39,] 0.0211110183 0.0166895423
[40,] -0.0292985343 0.0211110183
[41,] 0.0004209455 -0.0292985343
[42,] 0.0035365563 0.0004209455
[43,] 0.0319692777 0.0035365563
[44,] 0.0375772647 0.0319692777
[45,] 0.0172354885 0.0375772647
[46,] -0.0003634732 0.0172354885
[47,] -0.0047060903 -0.0003634732
[48,] 0.0158598986 -0.0047060903
[49,] -0.0198008627 0.0158598986
[50,] -0.0160966469 -0.0198008627
[51,] -0.0207364380 -0.0160966469
[52,] -0.0265867532 -0.0207364380
[53,] 0.0273279258 -0.0265867532
[54,] 0.0393999616 0.0273279258
[55,] 0.0381260278 0.0393999616
[56,] 0.0280437326 0.0381260278
[57,] -0.0216223813 0.0280437326
[58,] -0.0212110395 -0.0216223813
[59,] -0.0234637408 -0.0212110395
[60,] 0.0161663556 -0.0234637408
[61,] 0.0418493962 0.0161663556
[62,] 0.0308922967 0.0418493962
[63,] 0.0130658568 0.0308922967
[64,] -0.0602410677 0.0130658568
[65,] -0.0103159461 -0.0602410677
[66,] -0.0193488601 -0.0103159461
[67,] 0.0112528406 -0.0193488601
[68,] 0.0415898757 0.0112528406
[69,] 0.0142589757 0.0415898757
[70,] -0.0386112105 0.0142589757
[71,] -0.0227081105 -0.0386112105
[72,] -0.0425568447 -0.0227081105
[73,] -0.0508097174 -0.0425568447
[74,] -0.0133475891 -0.0508097174
[75,] -0.0021222271 -0.0133475891
[76,] 0.0232007568 -0.0021222271
[77,] 0.0276981398 0.0232007568
[78,] -0.0001090879 0.0276981398
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.0174501663 0.0556454108
2 -0.0135339212 -0.0174501663
3 -0.0012531572 -0.0135339212
4 -0.0469844024 -0.0012531572
5 0.0392491127 -0.0469844024
6 0.0075911053 0.0392491127
7 -0.0182219250 0.0075911053
8 -0.0553907218 -0.0182219250
9 0.0287947256 -0.0553907218
10 0.0461445820 0.0287947256
11 0.0048012602 0.0461445820
12 -0.0320018879 0.0048012602
13 -0.0537051794 -0.0320018879
14 -0.0115503240 -0.0537051794
15 -0.0353050517 -0.0115503240
16 0.0011543904 -0.0353050517
17 -0.0070837195 0.0011543904
18 -0.0149675627 -0.0070837195
19 -0.0045015755 -0.0149675627
20 0.0182177457 -0.0045015755
21 -0.0021515562 0.0182177457
22 0.0463363357 -0.0021515562
23 0.0769699798 0.0463363357
24 -0.0081335773 0.0769699798
25 0.0030213274 -0.0081335773
26 -0.0206936545 0.0030213274
27 -0.0295570519 -0.0206936545
28 -0.0166317652 -0.0295570519
29 -0.0173154982 -0.0166317652
30 -0.0273285430 -0.0173154982
31 -0.0251925701 -0.0273285430
32 0.0799356415 -0.0251925701
33 0.0161148643 0.0799356415
34 -0.0184159893 0.0161148643
35 0.0028525296 -0.0184159893
36 0.0147053278 0.0028525296
37 -0.0173800505 0.0147053278
38 0.0166895423 -0.0173800505
39 0.0211110183 0.0166895423
40 -0.0292985343 0.0211110183
41 0.0004209455 -0.0292985343
42 0.0035365563 0.0004209455
43 0.0319692777 0.0035365563
44 0.0375772647 0.0319692777
45 0.0172354885 0.0375772647
46 -0.0003634732 0.0172354885
47 -0.0047060903 -0.0003634732
48 0.0158598986 -0.0047060903
49 -0.0198008627 0.0158598986
50 -0.0160966469 -0.0198008627
51 -0.0207364380 -0.0160966469
52 -0.0265867532 -0.0207364380
53 0.0273279258 -0.0265867532
54 0.0393999616 0.0273279258
55 0.0381260278 0.0393999616
56 0.0280437326 0.0381260278
57 -0.0216223813 0.0280437326
58 -0.0212110395 -0.0216223813
59 -0.0234637408 -0.0212110395
60 0.0161663556 -0.0234637408
61 0.0418493962 0.0161663556
62 0.0308922967 0.0418493962
63 0.0130658568 0.0308922967
64 -0.0602410677 0.0130658568
65 -0.0103159461 -0.0602410677
66 -0.0193488601 -0.0103159461
67 0.0112528406 -0.0193488601
68 0.0415898757 0.0112528406
69 0.0142589757 0.0415898757
70 -0.0386112105 0.0142589757
71 -0.0227081105 -0.0386112105
72 -0.0425568447 -0.0227081105
73 -0.0508097174 -0.0425568447
74 -0.0133475891 -0.0508097174
75 -0.0021222271 -0.0133475891
76 0.0232007568 -0.0021222271
77 0.0276981398 0.0232007568
78 -0.0001090879 0.0276981398
> 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/79e101353075040.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/8xerq1353075040.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/9td5k1353075040.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/10b2ga1353075040.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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/117h651353075040.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12dnxp1353075040.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13zenq1353075041.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14y8i01353075041.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/wessaorg/rcomp/tmp/15atb31353075041.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/wessaorg/rcomp/tmp/16s8z61353075041.tab")
+ }
>
> try(system("convert tmp/1lk9l1353075040.ps tmp/1lk9l1353075040.png",intern=TRUE))
character(0)
> try(system("convert tmp/26d7w1353075040.ps tmp/26d7w1353075040.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ca651353075040.ps tmp/3ca651353075040.png",intern=TRUE))
character(0)
> try(system("convert tmp/4z1sw1353075040.ps tmp/4z1sw1353075040.png",intern=TRUE))
character(0)
> try(system("convert tmp/58p6f1353075040.ps tmp/58p6f1353075040.png",intern=TRUE))
character(0)
> try(system("convert tmp/6t4bv1353075040.ps tmp/6t4bv1353075040.png",intern=TRUE))
character(0)
> try(system("convert tmp/79e101353075040.ps tmp/79e101353075040.png",intern=TRUE))
character(0)
> try(system("convert tmp/8xerq1353075040.ps tmp/8xerq1353075040.png",intern=TRUE))
character(0)
> try(system("convert tmp/9td5k1353075040.ps tmp/9td5k1353075040.png",intern=TRUE))
character(0)
> try(system("convert tmp/10b2ga1353075040.ps tmp/10b2ga1353075040.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.132 1.519 11.693