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(0.98
+ ,1.34
+ ,1.98
+ ,1.97
+ ,2.62
+ ,5.05
+ ,8.02
+ ,8.47
+ ,2.07
+ ,1.78
+ ,1.25
+ ,5.87
+ ,1.45
+ ,3.91
+ ,9.77
+ ,4.06
+ ,0.98
+ ,1.34
+ ,1.97
+ ,1.98
+ ,2.62
+ ,5.04
+ ,7.98
+ ,8.46
+ ,2.06
+ ,1.77
+ ,1.24
+ ,5.89
+ ,1.46
+ ,3.93
+ ,9.73
+ ,4.12
+ ,0.98
+ ,1.34
+ ,1.98
+ ,1.98
+ ,2.61
+ ,5.02
+ ,7.98
+ ,8.43
+ ,2.06
+ ,1.76
+ ,1.24
+ ,5.88
+ ,1.47
+ ,3.93
+ ,9.74
+ ,4.06
+ ,0.97
+ ,1.34
+ ,1.98
+ ,1.98
+ ,2.61
+ ,5.03
+ ,7.97
+ ,8.41
+ ,2.05
+ ,1.76
+ ,1.24
+ ,5.89
+ ,1.47
+ ,3.93
+ ,9.71
+ ,4.07
+ ,1.04
+ ,1.34
+ ,1.98
+ ,1.98
+ ,2.6
+ ,5.01
+ ,7.96
+ ,8.33
+ ,2.05
+ ,1.75
+ ,1.24
+ ,5.85
+ ,1.47
+ ,4.01
+ ,9.69
+ ,4.05
+ ,1.05
+ ,1.33
+ ,1.97
+ ,1.98
+ ,2.59
+ ,5
+ ,7.95
+ ,8.26
+ ,2.03
+ ,1.74
+ ,1.23
+ ,5.72
+ ,1.45
+ ,4.07
+ ,9.66
+ ,4.07
+ ,1.07
+ ,1.33
+ ,1.97
+ ,1.98
+ ,2.59
+ ,5
+ ,7.94
+ ,8.25
+ ,2.02
+ ,1.74
+ ,1.23
+ ,5.69
+ ,1.42
+ ,4.08
+ ,9.65
+ ,4.08
+ ,1.06
+ ,1.33
+ ,1.97
+ ,1.97
+ ,2.59
+ ,5
+ ,7.91
+ ,8.25
+ ,2.02
+ ,1.73
+ ,1.22
+ ,5.72
+ ,1.42
+ ,4.05
+ ,9.63
+ ,4.07
+ ,1.07
+ ,1.33
+ ,1.97
+ ,1.97
+ ,2.58
+ ,5
+ ,7.9
+ ,8.25
+ ,2.02
+ ,1.73
+ ,1.23
+ ,5.76
+ ,1.41
+ ,3.96
+ ,9.63
+ ,4.03
+ ,1.03
+ ,1.33
+ ,1.96
+ ,1.97
+ ,2.58
+ ,4.97
+ ,7.9
+ ,8.25
+ ,2.02
+ ,1.73
+ ,1.22
+ ,5.8
+ ,1.41
+ ,3.85
+ ,9.6
+ ,3.97
+ ,1.02
+ ,1.33
+ ,1.96
+ ,1.97
+ ,2.58
+ ,4.97
+ ,7.88
+ ,8.25
+ ,2.01
+ ,1.72
+ ,1.22
+ ,5.87
+ ,1.41
+ ,3.77
+ ,9.59
+ ,3.89
+ ,1.02
+ ,1.33
+ ,1.96
+ ,1.97
+ ,2.57
+ ,4.96
+ ,7.88
+ ,8.25
+ ,2
+ ,1.72
+ ,1.22
+ ,5.88
+ ,1.4
+ ,3.75
+ ,9.57
+ ,3.91
+ ,1.01
+ ,1.32
+ ,1.95
+ ,1.97
+ ,2.56
+ ,4.93
+ ,7.86
+ ,8.22
+ ,2
+ ,1.72
+ ,1.21
+ ,5.79
+ ,1.39
+ ,3.71
+ ,9.54
+ ,3.89
+ ,1.01
+ ,1.32
+ ,1.95
+ ,1.96
+ ,2.57
+ ,4.93
+ ,7.86
+ ,8.21
+ ,2
+ ,1.71
+ ,1.21
+ ,5.83
+ ,1.39
+ ,3.73
+ ,9.54
+ ,3.88
+ ,1
+ ,1.32
+ ,1.95
+ ,1.96
+ ,2.56
+ ,4.92
+ ,7.86
+ ,8.21
+ ,2
+ ,1.71
+ ,1.21
+ ,5.8
+ ,1.38
+ ,3.74
+ ,9.53
+ ,3.86
+ ,1
+ ,1.32
+ ,1.95
+ ,1.96
+ ,2.56
+ ,4.92
+ ,7.84
+ ,8.2
+ ,2
+ ,1.71
+ ,1.21
+ ,5.66
+ ,1.37
+ ,3.73
+ ,9.52
+ ,3.83
+ ,1
+ ,1.32
+ ,1.94
+ ,1.96
+ ,2.57
+ ,4.92
+ ,7.79
+ ,8.2
+ ,1.99
+ ,1.72
+ ,1.2
+ ,5.32
+ ,1.3
+ ,3.77
+ ,9.47
+ ,3.77
+ ,0.98
+ ,1.31
+ ,1.93
+ ,1.96
+ ,2.55
+ ,4.91
+ ,7.62
+ ,8.15
+ ,1.99
+ ,1.71
+ ,1.19
+ ,5.3
+ ,1.31
+ ,3.84
+ ,9.38
+ ,3.64
+ ,0.87
+ ,1.3
+ ,1.93
+ ,1.95
+ ,2.53
+ ,4.88
+ ,7.6
+ ,8.1
+ ,1.98
+ ,1.7
+ ,1.18
+ ,5.3
+ ,1.31
+ ,3.75
+ ,9.35
+ ,3.66
+ ,0.82
+ ,1.27
+ ,1.9
+ ,1.92
+ ,2.5
+ ,4.83
+ ,7.55
+ ,8.03
+ ,1.97
+ ,1.69
+ ,1.17
+ ,5.28
+ ,1.32
+ ,3.69
+ ,9.3
+ ,3.62
+ ,0.8
+ ,1.27
+ ,1.9
+ ,1.93
+ ,2.49
+ ,4.82
+ ,7.53
+ ,8.08
+ ,1.97
+ ,1.69
+ ,1.17
+ ,5.3
+ ,1.32
+ ,3.73
+ ,9.27
+ ,3.61
+ ,0.81
+ ,1.27
+ ,1.9
+ ,1.92
+ ,2.48
+ ,4.81
+ ,7.5
+ ,8.04
+ ,1.96
+ ,1.69
+ ,1.17
+ ,5.28
+ ,1.32
+ ,3.73
+ ,9.25
+ ,3.61
+ ,0.81
+ ,1.26
+ ,1.88
+ ,1.9
+ ,2.46
+ ,4.77
+ ,7.4
+ ,7.98
+ ,1.96
+ ,1.67
+ ,1.16
+ ,5.28
+ ,1.29
+ ,3.72
+ ,9.19
+ ,3.59
+ ,0.81
+ ,1.26
+ ,1.88
+ ,1.9
+ ,2.44
+ ,4.74
+ ,7.35
+ ,7.95
+ ,1.95
+ ,1.67
+ ,1.16
+ ,5.25
+ ,1.26
+ ,3.7
+ ,9.15
+ ,3.56
+ ,0.81
+ ,1.25
+ ,1.87
+ ,1.89
+ ,2.43
+ ,4.77
+ ,7.31
+ ,7.88
+ ,1.95
+ ,1.67
+ ,1.16
+ ,5.27
+ ,1.25
+ ,3.69
+ ,9.11
+ ,3.56
+ ,0.81
+ ,1.25
+ ,1.88
+ ,1.89
+ ,2.43
+ ,4.75
+ ,7.35
+ ,7.92
+ ,1.95
+ ,1.67
+ ,1.15
+ ,5.29
+ ,1.24
+ ,3.7
+ ,9.09
+ ,3.55
+ ,0.79
+ ,1.25
+ ,1.87
+ ,1.89
+ ,2.44
+ ,4.76
+ ,7.38
+ ,7.88
+ ,1.95
+ ,1.67
+ ,1.16
+ ,5.26
+ ,1.24
+ ,3.72
+ ,9.07
+ ,3.53
+ ,0.78
+ ,1.25
+ ,1.88
+ ,1.89
+ ,2.43
+ ,4.76
+ ,7.37
+ ,7.95
+ ,1.94
+ ,1.67
+ ,1.15
+ ,5.27
+ ,1.23
+ ,3.71
+ ,9.07
+ ,3.55
+ ,0.78
+ ,1.25
+ ,1.87
+ ,1.89
+ ,2.43
+ ,4.75
+ ,7.37
+ ,7.93
+ ,1.94
+ ,1.66
+ ,1.15
+ ,5.21
+ ,1.22
+ ,3.75
+ ,9.07
+ ,3.55
+ ,0.77
+ ,1.25
+ ,1.87
+ ,1.89
+ ,2.44
+ ,4.73
+ ,7.32
+ ,7.95
+ ,1.92
+ ,1.66
+ ,1.15
+ ,5.23
+ ,1.22
+ ,3.76
+ ,9.03
+ ,3.56
+ ,0.78
+ ,1.25
+ ,1.87
+ ,1.89
+ ,2.43
+ ,4.74
+ ,7.24
+ ,7.85
+ ,1.93
+ ,1.65
+ ,1.15
+ ,5.27
+ ,1.21
+ ,3.78
+ ,8.95
+ ,3.53
+ ,0.77
+ ,1.25
+ ,1.87
+ ,1.89
+ ,2.43
+ ,4.74
+ ,7.21
+ ,7.85
+ ,1.93
+ ,1.65
+ ,1.15
+ ,5.26
+ ,1.2
+ ,3.76
+ ,8.95
+ ,3.53
+ ,0.78
+ ,1.25
+ ,1.87
+ ,1.89
+ ,2.43
+ ,4.74
+ ,7.21
+ ,7.85
+ ,1.92
+ ,1.65
+ ,1.15
+ ,5.27
+ ,1.19
+ ,3.77
+ ,8.89
+ ,3.51
+ ,0.79
+ ,1.25
+ ,1.87
+ ,1.89
+ ,2.43
+ ,4.72
+ ,7.19
+ ,7.83
+ ,1.92
+ ,1.65
+ ,1.14
+ ,5.27
+ ,1.19
+ ,3.78
+ ,8.87
+ ,3.53
+ ,0.79
+ ,1.24
+ ,1.87
+ ,1.89
+ ,2.43
+ ,4.71
+ ,7.14
+ ,7.81
+ ,1.91
+ ,1.65
+ ,1.14
+ ,5.3
+ ,1.19
+ ,3.77
+ ,8.84
+ ,3.53
+ ,0.79
+ ,1.25
+ ,1.87
+ ,1.89
+ ,2.42
+ ,4.7
+ ,7.13
+ ,7.85
+ ,1.91
+ ,1.64
+ ,1.14
+ ,5.29
+ ,1.19
+ ,3.79
+ ,8.83
+ ,3.54
+ ,0.79
+ ,1.25
+ ,1.87
+ ,1.89
+ ,2.43
+ ,4.71
+ ,7.12
+ ,7.83
+ ,1.91
+ ,1.64
+ ,1.14
+ ,5.31
+ ,1.19
+ ,3.78
+ ,8.81
+ ,3.56
+ ,0.79
+ ,1.24
+ ,1.87
+ ,1.89
+ ,2.44
+ ,4.72
+ ,7.08
+ ,7.8
+ ,1.9
+ ,1.64
+ ,1.14
+ ,5.32
+ ,1.19
+ ,3.85
+ ,8.74
+ ,3.58
+ ,0.8
+ ,1.24
+ ,1.87
+ ,1.89
+ ,2.44
+ ,4.7
+ ,7.04
+ ,7.81
+ ,1.89
+ ,1.64
+ ,1.14
+ ,5.26
+ ,1.19
+ ,3.8
+ ,8.72
+ ,3.56
+ ,0.8
+ ,1.24
+ ,1.87
+ ,1.89
+ ,2.44
+ ,4.7
+ ,7.04
+ ,7.79
+ ,1.9
+ ,1.64
+ ,1.13
+ ,5.28
+ ,1.18
+ ,3.86
+ ,8.71
+ ,3.55
+ ,0.8
+ ,1.24
+ ,1.87
+ ,1.89
+ ,2.44
+ ,4.7
+ ,7.03
+ ,7.75
+ ,1.88
+ ,1.63
+ ,1.13
+ ,5.27
+ ,1.18
+ ,3.84
+ ,8.7
+ ,3.52
+ ,0.8
+ ,1.24
+ ,1.87
+ ,1.89
+ ,2.44
+ ,4.68
+ ,7.03
+ ,7.77
+ ,1.88
+ ,1.62
+ ,1.13
+ ,5.28
+ ,1.18
+ ,3.82
+ ,8.69
+ ,3.52
+ ,0.81
+ ,1.25
+ ,1.87
+ ,1.89
+ ,2.43
+ ,4.68
+ ,6.99
+ ,7.72
+ ,1.87
+ ,1.62
+ ,1.13
+ ,5.25
+ ,1.18
+ ,3.82
+ ,8.62
+ ,3.49
+ ,0.8
+ ,1.26
+ ,1.88
+ ,1.89
+ ,2.44
+ ,4.67
+ ,7
+ ,7.68
+ ,1.87
+ ,1.61
+ ,1.13
+ ,5.13
+ ,1.17
+ ,3.8
+ ,8.55
+ ,3.46
+ ,0.82
+ ,1.26
+ ,1.88
+ ,1.9
+ ,2.44
+ ,4.67
+ ,6.97
+ ,7.7
+ ,1.87
+ ,1.62
+ ,1.12
+ ,5.12
+ ,1.16
+ ,3.79
+ ,8.57
+ ,3.46
+ ,0.85
+ ,1.26
+ ,1.87
+ ,1.89
+ ,2.43
+ ,4.67
+ ,6.91
+ ,7.69
+ ,1.86
+ ,1.62
+ ,1.11
+ ,5.12
+ ,1.16
+ ,3.78
+ ,8.54
+ ,3.45
+ ,0.85
+ ,1.26
+ ,1.87
+ ,1.89
+ ,2.42
+ ,4.62
+ ,6.83
+ ,7.64
+ ,1.85
+ ,1.61
+ ,1.1
+ ,5.11
+ ,1.16
+ ,3.8
+ ,8.45
+ ,3.48
+ ,0.86
+ ,1.26
+ ,1.87
+ ,1.89
+ ,2.42
+ ,4.62
+ ,6.8
+ ,7.66
+ ,1.84
+ ,1.61
+ ,1.1
+ ,5.09
+ ,1.15
+ ,3.78
+ ,8.4
+ ,3.48
+ ,0.85
+ ,1.26
+ ,1.87
+ ,1.88
+ ,2.41
+ ,4.61
+ ,6.79
+ ,7.63
+ ,1.83
+ ,1.61
+ ,1.1
+ ,5.05
+ ,1.15
+ ,3.75
+ ,8.37
+ ,3.48
+ ,0.83
+ ,1.26
+ ,1.87
+ ,1.88
+ ,2.41
+ ,4.61
+ ,6.77
+ ,7.64
+ ,1.83
+ ,1.6
+ ,1.1
+ ,5.1
+ ,1.15
+ ,3.77
+ ,8.36
+ ,3.48
+ ,0.81
+ ,1.26
+ ,1.87
+ ,1.88
+ ,2.41
+ ,4.61
+ ,6.78
+ ,7.63
+ ,1.83
+ ,1.59
+ ,1.1
+ ,5.07
+ ,1.15
+ ,3.75
+ ,8.36
+ ,3.46
+ ,0.82
+ ,1.26
+ ,1.87
+ ,1.88
+ ,2.41
+ ,4.61
+ ,6.75
+ ,7.6
+ ,1.82
+ ,1.6
+ ,1.09
+ ,5.09
+ ,1.15
+ ,3.74
+ ,8.35
+ ,3.44
+ ,0.82
+ ,1.25
+ ,1.86
+ ,1.87
+ ,2.38
+ ,4.6
+ ,6.73
+ ,7.58
+ ,1.81
+ ,1.59
+ ,1.09
+ ,5.1
+ ,1.16
+ ,3.71
+ ,8.34
+ ,3.41
+ ,0.78
+ ,1.25
+ ,1.86
+ ,1.87
+ ,2.38
+ ,4.62
+ ,6.68
+ ,7.55
+ ,1.81
+ ,1.58
+ ,1.09
+ ,5.1
+ ,1.14
+ ,3.71
+ ,8.28
+ ,3.4
+ ,0.78
+ ,1.25
+ ,1.85
+ ,1.87
+ ,2.37
+ ,4.61
+ ,6.64
+ ,7.54
+ ,1.8
+ ,1.57
+ ,1.08
+ ,5.07
+ ,1.12
+ ,3.69
+ ,8.24
+ ,3.34
+ ,0.73
+ ,1.24
+ ,1.84
+ ,1.85
+ ,2.35
+ ,4.59
+ ,6.52
+ ,7.49
+ ,1.79
+ ,1.56
+ ,1.08
+ ,5.06
+ ,1.11
+ ,3.65
+ ,8.16
+ ,3.34
+ ,0.68
+ ,1.24
+ ,1.83
+ ,1.84
+ ,2.33
+ ,4.58
+ ,6.44
+ ,7.45
+ ,1.78
+ ,1.56
+ ,1.07
+ ,5.05
+ ,1.09
+ ,3.56
+ ,8.09
+ ,3.34
+ ,0.65
+ ,1.23
+ ,1.82
+ ,1.83
+ ,2.33
+ ,4.54
+ ,6.37
+ ,7.31
+ ,1.77
+ ,1.54
+ ,1.06
+ ,4.95
+ ,1.07
+ ,3.44
+ ,8.04
+ ,3.3
+ ,0.62
+ ,1.2
+ ,1.78
+ ,1.79
+ ,2.254
+ ,4.46
+ ,6.11
+ ,7.23
+ ,1.75
+ ,1.52
+ ,1.04
+ ,4.94
+ ,1.07
+ ,3.39
+ ,7.84
+ ,3.27
+ ,0.6
+ ,1.18
+ ,1.75
+ ,1.76
+ ,2.22
+ ,4.43
+ ,5.98
+ ,7.12
+ ,1.73
+ ,1.5
+ ,1.03
+ ,4.94
+ ,1.07
+ ,3.38
+ ,7.73
+ ,3.26
+ ,0.6
+ ,1.17
+ ,1.74
+ ,1.75
+ ,2.212
+ ,4.4
+ ,5.94
+ ,7.09
+ ,1.72
+ ,1.49
+ ,1.03
+ ,4.95
+ ,1.06
+ ,3.38
+ ,7.7
+ ,3.28
+ ,0.59
+ ,1.18
+ ,1.74
+ ,1.75
+ ,2.2
+ ,4.39
+ ,5.94
+ ,7.06
+ ,1.71
+ ,1.49
+ ,1.03
+ ,4.96
+ ,1.07
+ ,3.37
+ ,7.68
+ ,3.3
+ ,0.6
+ ,1.17
+ ,1.74
+ ,1.75
+ ,2.2
+ ,4.39
+ ,5.93
+ ,7.06
+ ,1.71
+ ,1.49
+ ,1.03
+ ,4.95
+ ,1.06
+ ,3.35
+ ,7.68
+ ,3.29
+ ,0.6
+ ,1.17
+ ,1.73
+ ,1.74
+ ,2.2
+ ,4.39
+ ,5.92
+ ,7.05
+ ,1.71
+ ,1.49
+ ,1.03
+ ,4.97
+ ,1.06
+ ,3.31
+ ,7.66
+ ,3.29
+ ,0.6
+ ,1.17
+ ,1.73
+ ,1.74
+ ,2.19
+ ,4.4
+ ,5.91
+ ,7.02
+ ,1.71
+ ,1.47
+ ,1.03
+ ,4.9
+ ,1.06
+ ,3.25
+ ,7.66
+ ,3.25
+ ,0.59
+ ,1.17
+ ,1.73
+ ,1.73
+ ,2.2
+ ,4.38
+ ,5.89
+ ,6.99
+ ,1.7
+ ,1.47
+ ,1.03
+ ,4.9
+ ,1.06
+ ,3.22
+ ,7.62
+ ,3.26
+ ,0.58
+ ,1.16
+ ,1.71
+ ,1.72
+ ,2.16
+ ,4.33
+ ,5.82
+ ,6.92
+ ,1.68
+ ,1.47
+ ,1.01
+ ,4.68
+ ,1.03
+ ,3.25
+ ,7.57
+ ,3.26
+ ,0.56
+ ,1.14
+ ,1.7
+ ,1.71
+ ,2.17
+ ,4.32
+ ,5.77
+ ,6.92
+ ,1.68
+ ,1.46
+ ,1.02
+ ,4.63
+ ,1.03
+ ,3.21
+ ,7.5
+ ,3.24
+ ,0.55
+ ,1.14
+ ,1.7
+ ,1.7
+ ,2.16
+ ,4.32
+ ,5.76
+ ,6.88
+ ,1.67
+ ,1.46
+ ,1.02
+ ,4.62
+ ,1.02
+ ,3.2
+ ,7.49
+ ,3.24
+ ,0.54
+ ,1.13
+ ,1.69
+ ,1.7
+ ,2.15
+ ,4.28
+ ,5.73
+ ,6.88
+ ,1.66
+ ,1.45
+ ,1.01
+ ,4.6
+ ,1.01
+ ,3.17
+ ,7.46
+ ,3.25
+ ,0.55
+ ,1.13
+ ,1.68
+ ,1.69
+ ,2.14
+ ,4.26
+ ,5.72
+ ,6.86
+ ,1.65
+ ,1.44
+ ,1
+ ,4.64
+ ,1.02
+ ,3.17
+ ,7.42
+ ,3.21
+ ,0.55
+ ,1.12
+ ,1.68
+ ,1.68
+ ,2.13
+ ,4.26
+ ,5.7
+ ,6.87
+ ,1.64
+ ,1.44
+ ,1
+ ,4.64
+ ,1.02
+ ,3.18
+ ,7.38
+ ,3.2
+ ,0.54
+ ,1.12
+ ,1.68
+ ,1.68
+ ,2.134
+ ,4.26
+ ,5.68
+ ,6.81
+ ,1.63
+ ,1.43
+ ,1
+ ,4.65
+ ,1.02
+ ,3.19
+ ,7.37
+ ,3.21
+ ,0.54
+ ,1.12
+ ,1.68
+ ,1.68
+ ,2.132
+ ,4.25
+ ,5.68
+ ,6.81
+ ,1.63
+ ,1.43
+ ,1
+ ,4.65
+ ,1.01
+ ,3.17
+ ,7.36
+ ,3.23
+ ,0.54
+ ,1.12
+ ,1.67
+ ,1.68
+ ,2.13
+ ,4.27
+ ,5.69
+ ,6.81
+ ,1.63
+ ,1.43
+ ,1
+ ,4.63
+ ,1.02
+ ,3.16
+ ,7.36
+ ,3.2
+ ,0.53
+ ,1.12
+ ,1.66
+ ,1.67
+ ,2.117
+ ,4.25
+ ,5.65
+ ,6.77
+ ,1.63
+ ,1.43
+ ,0.99
+ ,4.65
+ ,1.01
+ ,3.14
+ ,7.33
+ ,3.2
+ ,0.53
+ ,1.11
+ ,1.65
+ ,1.66
+ ,2.11
+ ,4.26
+ ,5.66
+ ,6.75
+ ,1.62
+ ,1.43
+ ,0.99
+ ,4.67
+ ,1.01
+ ,3.13
+ ,7.32
+ ,3.19
+ ,0.53
+ ,1.11
+ ,1.65
+ ,1.66
+ ,2.104
+ ,4.26
+ ,5.64
+ ,6.69
+ ,1.62
+ ,1.42
+ ,0.99
+ ,4.64
+ ,0.99
+ ,3.1
+ ,7.3
+ ,3.16
+ ,0.53
+ ,1.1
+ ,1.65
+ ,1.65
+ ,2.1
+ ,4.24
+ ,5.61
+ ,6.69
+ ,1.61
+ ,1.41
+ ,0.98
+ ,4.64
+ ,0.98
+ ,3.11
+ ,7.27
+ ,3.11)
+ ,dim=c(16
+ ,79)
+ ,dimnames=list(c('Flour_1kg'
+ ,'Speciality_bread_400g'
+ ,'Speciality_bread_800g'
+ ,'Brown_bread_800g'
+ ,'Multigrain_bread_800g'
+ ,'Currant_1kg'
+ ,'Roll_1kg'
+ ,'Rice_tart_1kg'
+ ,'Mocha_tart'
+ ,'Fruit_tart'
+ ,'Eclair'
+ ,'Biscuits_1kg'
+ ,'Penny_wafer_200g'
+ ,'Spekulatius_1kg'
+ ,'Garibaldi'
+ ,'biscuit_1kg')
+ ,1:79))
> y <- array(NA,dim=c(16,79),dimnames=list(c('Flour_1kg','Speciality_bread_400g','Speciality_bread_800g','Brown_bread_800g','Multigrain_bread_800g','Currant_1kg','Roll_1kg','Rice_tart_1kg','Mocha_tart','Fruit_tart','Eclair','Biscuits_1kg','Penny_wafer_200g','Spekulatius_1kg','Garibaldi','biscuit_1kg'),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
Flour_1kg Speciality_bread_400g Speciality_bread_800g Brown_bread_800g
1 0.98 1.34 1.98 1.97
2 0.98 1.34 1.97 1.98
3 0.98 1.34 1.98 1.98
4 0.97 1.34 1.98 1.98
5 1.04 1.34 1.98 1.98
6 1.05 1.33 1.97 1.98
7 1.07 1.33 1.97 1.98
8 1.06 1.33 1.97 1.97
9 1.07 1.33 1.97 1.97
10 1.03 1.33 1.96 1.97
11 1.02 1.33 1.96 1.97
12 1.02 1.33 1.96 1.97
13 1.01 1.32 1.95 1.97
14 1.01 1.32 1.95 1.96
15 1.00 1.32 1.95 1.96
16 1.00 1.32 1.95 1.96
17 1.00 1.32 1.94 1.96
18 0.98 1.31 1.93 1.96
19 0.87 1.30 1.93 1.95
20 0.82 1.27 1.90 1.92
21 0.80 1.27 1.90 1.93
22 0.81 1.27 1.90 1.92
23 0.81 1.26 1.88 1.90
24 0.81 1.26 1.88 1.90
25 0.81 1.25 1.87 1.89
26 0.81 1.25 1.88 1.89
27 0.79 1.25 1.87 1.89
28 0.78 1.25 1.88 1.89
29 0.78 1.25 1.87 1.89
30 0.77 1.25 1.87 1.89
31 0.78 1.25 1.87 1.89
32 0.77 1.25 1.87 1.89
33 0.78 1.25 1.87 1.89
34 0.79 1.25 1.87 1.89
35 0.79 1.24 1.87 1.89
36 0.79 1.25 1.87 1.89
37 0.79 1.25 1.87 1.89
38 0.79 1.24 1.87 1.89
39 0.80 1.24 1.87 1.89
40 0.80 1.24 1.87 1.89
41 0.80 1.24 1.87 1.89
42 0.80 1.24 1.87 1.89
43 0.81 1.25 1.87 1.89
44 0.80 1.26 1.88 1.89
45 0.82 1.26 1.88 1.90
46 0.85 1.26 1.87 1.89
47 0.85 1.26 1.87 1.89
48 0.86 1.26 1.87 1.89
49 0.85 1.26 1.87 1.88
50 0.83 1.26 1.87 1.88
51 0.81 1.26 1.87 1.88
52 0.82 1.26 1.87 1.88
53 0.82 1.25 1.86 1.87
54 0.78 1.25 1.86 1.87
55 0.78 1.25 1.85 1.87
56 0.73 1.24 1.84 1.85
57 0.68 1.24 1.83 1.84
58 0.65 1.23 1.82 1.83
59 0.62 1.20 1.78 1.79
60 0.60 1.18 1.75 1.76
61 0.60 1.17 1.74 1.75
62 0.59 1.18 1.74 1.75
63 0.60 1.17 1.74 1.75
64 0.60 1.17 1.73 1.74
65 0.60 1.17 1.73 1.74
66 0.59 1.17 1.73 1.73
67 0.58 1.16 1.71 1.72
68 0.56 1.14 1.70 1.71
69 0.55 1.14 1.70 1.70
70 0.54 1.13 1.69 1.70
71 0.55 1.13 1.68 1.69
72 0.55 1.12 1.68 1.68
73 0.54 1.12 1.68 1.68
74 0.54 1.12 1.68 1.68
75 0.54 1.12 1.67 1.68
76 0.53 1.12 1.66 1.67
77 0.53 1.11 1.65 1.66
78 0.53 1.11 1.65 1.66
79 0.53 1.10 1.65 1.65
Multigrain_bread_800g Currant_1kg Roll_1kg Rice_tart_1kg Mocha_tart
1 2.620 5.05 8.02 8.47 2.07
2 2.620 5.04 7.98 8.46 2.06
3 2.610 5.02 7.98 8.43 2.06
4 2.610 5.03 7.97 8.41 2.05
5 2.600 5.01 7.96 8.33 2.05
6 2.590 5.00 7.95 8.26 2.03
7 2.590 5.00 7.94 8.25 2.02
8 2.590 5.00 7.91 8.25 2.02
9 2.580 5.00 7.90 8.25 2.02
10 2.580 4.97 7.90 8.25 2.02
11 2.580 4.97 7.88 8.25 2.01
12 2.570 4.96 7.88 8.25 2.00
13 2.560 4.93 7.86 8.22 2.00
14 2.570 4.93 7.86 8.21 2.00
15 2.560 4.92 7.86 8.21 2.00
16 2.560 4.92 7.84 8.20 2.00
17 2.570 4.92 7.79 8.20 1.99
18 2.550 4.91 7.62 8.15 1.99
19 2.530 4.88 7.60 8.10 1.98
20 2.500 4.83 7.55 8.03 1.97
21 2.490 4.82 7.53 8.08 1.97
22 2.480 4.81 7.50 8.04 1.96
23 2.460 4.77 7.40 7.98 1.96
24 2.440 4.74 7.35 7.95 1.95
25 2.430 4.77 7.31 7.88 1.95
26 2.430 4.75 7.35 7.92 1.95
27 2.440 4.76 7.38 7.88 1.95
28 2.430 4.76 7.37 7.95 1.94
29 2.430 4.75 7.37 7.93 1.94
30 2.440 4.73 7.32 7.95 1.92
31 2.430 4.74 7.24 7.85 1.93
32 2.430 4.74 7.21 7.85 1.93
33 2.430 4.74 7.21 7.85 1.92
34 2.430 4.72 7.19 7.83 1.92
35 2.430 4.71 7.14 7.81 1.91
36 2.420 4.70 7.13 7.85 1.91
37 2.430 4.71 7.12 7.83 1.91
38 2.440 4.72 7.08 7.80 1.90
39 2.440 4.70 7.04 7.81 1.89
40 2.440 4.70 7.04 7.79 1.90
41 2.440 4.70 7.03 7.75 1.88
42 2.440 4.68 7.03 7.77 1.88
43 2.430 4.68 6.99 7.72 1.87
44 2.440 4.67 7.00 7.68 1.87
45 2.440 4.67 6.97 7.70 1.87
46 2.430 4.67 6.91 7.69 1.86
47 2.420 4.62 6.83 7.64 1.85
48 2.420 4.62 6.80 7.66 1.84
49 2.410 4.61 6.79 7.63 1.83
50 2.410 4.61 6.77 7.64 1.83
51 2.410 4.61 6.78 7.63 1.83
52 2.410 4.61 6.75 7.60 1.82
53 2.380 4.60 6.73 7.58 1.81
54 2.380 4.62 6.68 7.55 1.81
55 2.370 4.61 6.64 7.54 1.80
56 2.350 4.59 6.52 7.49 1.79
57 2.330 4.58 6.44 7.45 1.78
58 2.330 4.54 6.37 7.31 1.77
59 2.254 4.46 6.11 7.23 1.75
60 2.220 4.43 5.98 7.12 1.73
61 2.212 4.40 5.94 7.09 1.72
62 2.200 4.39 5.94 7.06 1.71
63 2.200 4.39 5.93 7.06 1.71
64 2.200 4.39 5.92 7.05 1.71
65 2.190 4.40 5.91 7.02 1.71
66 2.200 4.38 5.89 6.99 1.70
67 2.160 4.33 5.82 6.92 1.68
68 2.170 4.32 5.77 6.92 1.68
69 2.160 4.32 5.76 6.88 1.67
70 2.150 4.28 5.73 6.88 1.66
71 2.140 4.26 5.72 6.86 1.65
72 2.130 4.26 5.70 6.87 1.64
73 2.134 4.26 5.68 6.81 1.63
74 2.132 4.25 5.68 6.81 1.63
75 2.130 4.27 5.69 6.81 1.63
76 2.117 4.25 5.65 6.77 1.63
77 2.110 4.26 5.66 6.75 1.62
78 2.104 4.26 5.64 6.69 1.62
79 2.100 4.24 5.61 6.69 1.61
Fruit_tart Eclair Biscuits_1kg Penny_wafer_200g Spekulatius_1kg Garibaldi
1 1.78 1.25 5.87 1.45 3.91 9.77
2 1.77 1.24 5.89 1.46 3.93 9.73
3 1.76 1.24 5.88 1.47 3.93 9.74
4 1.76 1.24 5.89 1.47 3.93 9.71
5 1.75 1.24 5.85 1.47 4.01 9.69
6 1.74 1.23 5.72 1.45 4.07 9.66
7 1.74 1.23 5.69 1.42 4.08 9.65
8 1.73 1.22 5.72 1.42 4.05 9.63
9 1.73 1.23 5.76 1.41 3.96 9.63
10 1.73 1.22 5.80 1.41 3.85 9.60
11 1.72 1.22 5.87 1.41 3.77 9.59
12 1.72 1.22 5.88 1.40 3.75 9.57
13 1.72 1.21 5.79 1.39 3.71 9.54
14 1.71 1.21 5.83 1.39 3.73 9.54
15 1.71 1.21 5.80 1.38 3.74 9.53
16 1.71 1.21 5.66 1.37 3.73 9.52
17 1.72 1.20 5.32 1.30 3.77 9.47
18 1.71 1.19 5.30 1.31 3.84 9.38
19 1.70 1.18 5.30 1.31 3.75 9.35
20 1.69 1.17 5.28 1.32 3.69 9.30
21 1.69 1.17 5.30 1.32 3.73 9.27
22 1.69 1.17 5.28 1.32 3.73 9.25
23 1.67 1.16 5.28 1.29 3.72 9.19
24 1.67 1.16 5.25 1.26 3.70 9.15
25 1.67 1.16 5.27 1.25 3.69 9.11
26 1.67 1.15 5.29 1.24 3.70 9.09
27 1.67 1.16 5.26 1.24 3.72 9.07
28 1.67 1.15 5.27 1.23 3.71 9.07
29 1.66 1.15 5.21 1.22 3.75 9.07
30 1.66 1.15 5.23 1.22 3.76 9.03
31 1.65 1.15 5.27 1.21 3.78 8.95
32 1.65 1.15 5.26 1.20 3.76 8.95
33 1.65 1.15 5.27 1.19 3.77 8.89
34 1.65 1.14 5.27 1.19 3.78 8.87
35 1.65 1.14 5.30 1.19 3.77 8.84
36 1.64 1.14 5.29 1.19 3.79 8.83
37 1.64 1.14 5.31 1.19 3.78 8.81
38 1.64 1.14 5.32 1.19 3.85 8.74
39 1.64 1.14 5.26 1.19 3.80 8.72
40 1.64 1.13 5.28 1.18 3.86 8.71
41 1.63 1.13 5.27 1.18 3.84 8.70
42 1.62 1.13 5.28 1.18 3.82 8.69
43 1.62 1.13 5.25 1.18 3.82 8.62
44 1.61 1.13 5.13 1.17 3.80 8.55
45 1.62 1.12 5.12 1.16 3.79 8.57
46 1.62 1.11 5.12 1.16 3.78 8.54
47 1.61 1.10 5.11 1.16 3.80 8.45
48 1.61 1.10 5.09 1.15 3.78 8.40
49 1.61 1.10 5.05 1.15 3.75 8.37
50 1.60 1.10 5.10 1.15 3.77 8.36
51 1.59 1.10 5.07 1.15 3.75 8.36
52 1.60 1.09 5.09 1.15 3.74 8.35
53 1.59 1.09 5.10 1.16 3.71 8.34
54 1.58 1.09 5.10 1.14 3.71 8.28
55 1.57 1.08 5.07 1.12 3.69 8.24
56 1.56 1.08 5.06 1.11 3.65 8.16
57 1.56 1.07 5.05 1.09 3.56 8.09
58 1.54 1.06 4.95 1.07 3.44 8.04
59 1.52 1.04 4.94 1.07 3.39 7.84
60 1.50 1.03 4.94 1.07 3.38 7.73
61 1.49 1.03 4.95 1.06 3.38 7.70
62 1.49 1.03 4.96 1.07 3.37 7.68
63 1.49 1.03 4.95 1.06 3.35 7.68
64 1.49 1.03 4.97 1.06 3.31 7.66
65 1.47 1.03 4.90 1.06 3.25 7.66
66 1.47 1.03 4.90 1.06 3.22 7.62
67 1.47 1.01 4.68 1.03 3.25 7.57
68 1.46 1.02 4.63 1.03 3.21 7.50
69 1.46 1.02 4.62 1.02 3.20 7.49
70 1.45 1.01 4.60 1.01 3.17 7.46
71 1.44 1.00 4.64 1.02 3.17 7.42
72 1.44 1.00 4.64 1.02 3.18 7.38
73 1.43 1.00 4.65 1.02 3.19 7.37
74 1.43 1.00 4.65 1.01 3.17 7.36
75 1.43 1.00 4.63 1.02 3.16 7.36
76 1.43 0.99 4.65 1.01 3.14 7.33
77 1.43 0.99 4.67 1.01 3.13 7.32
78 1.42 0.99 4.64 0.99 3.10 7.30
79 1.41 0.98 4.64 0.98 3.11 7.27
biscuit_1kg
1 4.06
2 4.12
3 4.06
4 4.07
5 4.05
6 4.07
7 4.08
8 4.07
9 4.03
10 3.97
11 3.89
12 3.91
13 3.89
14 3.88
15 3.86
16 3.83
17 3.77
18 3.64
19 3.66
20 3.62
21 3.61
22 3.61
23 3.59
24 3.56
25 3.56
26 3.55
27 3.53
28 3.55
29 3.55
30 3.56
31 3.53
32 3.53
33 3.51
34 3.53
35 3.53
36 3.54
37 3.56
38 3.58
39 3.56
40 3.55
41 3.52
42 3.52
43 3.49
44 3.46
45 3.46
46 3.45
47 3.48
48 3.48
49 3.48
50 3.48
51 3.46
52 3.44
53 3.41
54 3.40
55 3.34
56 3.34
57 3.34
58 3.30
59 3.27
60 3.26
61 3.28
62 3.30
63 3.29
64 3.29
65 3.25
66 3.26
67 3.26
68 3.24
69 3.24
70 3.25
71 3.21
72 3.20
73 3.21
74 3.23
75 3.20
76 3.20
77 3.19
78 3.16
79 3.11
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Speciality_bread_400g Speciality_bread_800g
-0.16782 2.32198 -0.98397
Brown_bread_800g Multigrain_bread_800g Currant_1kg
0.29506 0.78795 -0.51359
Roll_1kg Rice_tart_1kg Mocha_tart
0.12373 -0.37771 -1.39522
Fruit_tart Eclair Biscuits_1kg
0.38031 0.36147 0.06012
Penny_wafer_200g Spekulatius_1kg Garibaldi
-0.10538 0.15556 0.25924
biscuit_1kg
0.13647
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.072582 -0.010057 -0.000651 0.011863 0.064378
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.16782 0.38666 -0.434 0.665760
Speciality_bread_400g 2.32198 0.58586 3.963 0.000191 ***
Speciality_bread_800g -0.98397 0.67374 -1.460 0.149132
Brown_bread_800g 0.29506 0.66390 0.444 0.658253
Multigrain_bread_800g 0.78795 0.35955 2.191 0.032123 *
Currant_1kg -0.51359 0.19597 -2.621 0.010982 *
Roll_1kg 0.12373 0.10592 1.168 0.247144
Rice_tart_1kg -0.37771 0.10712 -3.526 0.000793 ***
Mocha_tart -1.39522 0.47920 -2.912 0.004968 **
Fruit_tart 0.38031 0.57697 0.659 0.512202
Eclair 0.36147 0.68079 0.531 0.597316
Biscuits_1kg 0.06012 0.04011 1.499 0.138891
Penny_wafer_200g -0.10538 0.17475 -0.603 0.548663
Spekulatius_1kg 0.15556 0.06085 2.557 0.012991 *
Garibaldi 0.25924 0.14442 1.795 0.077455 .
biscuit_1kg 0.13647 0.09020 1.513 0.135266
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.02293 on 63 degrees of freedom
Multiple R-squared: 0.984, Adjusted R-squared: 0.9802
F-statistic: 258.8 on 15 and 63 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,] 0.7677744 0.464451203 0.232225601
[2,] 0.7973989 0.405202298 0.202601149
[3,] 0.7094920 0.581015967 0.290507983
[4,] 0.6991904 0.601619158 0.300809579
[5,] 0.6745782 0.650843642 0.325421821
[6,] 0.5758447 0.848310516 0.424155258
[7,] 0.5448207 0.910358683 0.455179342
[8,] 0.5834201 0.833159714 0.416579857
[9,] 0.7265885 0.546822981 0.273411491
[10,] 0.6492488 0.701502346 0.350751173
[11,] 0.7619576 0.476084729 0.238042364
[12,] 0.7688151 0.462369824 0.231184912
[13,] 0.7126182 0.574763697 0.287381849
[14,] 0.6451497 0.709700669 0.354850334
[15,] 0.6449591 0.710081789 0.355040895
[16,] 0.6071471 0.785705750 0.392852875
[17,] 0.6203094 0.759381284 0.379690642
[18,] 0.6561567 0.687686578 0.343843289
[19,] 0.6255533 0.748893352 0.374446676
[20,] 0.6671934 0.665613220 0.332806610
[21,] 0.7172204 0.565559277 0.282779639
[22,] 0.6739693 0.652061346 0.326030673
[23,] 0.6365423 0.726915363 0.363457681
[24,] 0.6048515 0.790297009 0.395148504
[25,] 0.5721318 0.855736469 0.427868234
[26,] 0.7051511 0.589697878 0.294848939
[27,] 0.9292482 0.141503562 0.070751781
[28,] 0.9495804 0.100839163 0.050419581
[29,] 0.9567801 0.086439774 0.043219887
[30,] 0.9770948 0.045810316 0.022905158
[31,] 0.9852402 0.029519636 0.014759818
[32,] 0.9842382 0.031523564 0.015761782
[33,] 0.9876941 0.024611781 0.012305891
[34,] 0.9954261 0.009147737 0.004573868
[35,] 0.9905005 0.018999069 0.009499534
[36,] 0.9904865 0.019026922 0.009513461
[37,] 0.9906418 0.018716385 0.009358193
[38,] 0.9901357 0.019728640 0.009864320
[39,] 0.9847521 0.030495766 0.015247883
[40,] 0.9695269 0.060946154 0.030473077
[41,] 0.9464816 0.107036738 0.053518369
[42,] 0.9443672 0.111265669 0.055632834
> postscript(file="/var/wessaorg/rcomp/tmp/1cnt61353439920.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/229w71353439920.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/36pea1353439920.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4gpeh1353439920.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/51sre1353439920.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
3.569507e-03 -2.079742e-02 -1.362729e-02 -3.294916e-02 7.356290e-03
6 7 8 9 10
-1.078705e-02 -8.964770e-03 4.528353e-03 3.603131e-02 5.071040e-03
11 12 13 14 15
9.143401e-03 1.846352e-03 1.354215e-02 4.488043e-03 1.747578e-03
16 17 18 19 20
1.605003e-02 1.836831e-02 6.437809e-02 -2.299729e-02 -2.282313e-02
21 22 23 24 25
-1.995297e-02 -2.322026e-02 -9.537940e-04 -3.482311e-03 2.431426e-02
26 27 28 29 30
4.039360e-02 -8.018151e-03 1.421167e-02 -8.184144e-03 -4.425217e-02
31 32 33 34 35
-1.309029e-02 -1.671971e-02 -5.598730e-03 -6.435673e-03 3.857700e-03
36 37 38 39 40
2.247864e-03 -4.003992e-03 6.488393e-05 1.386660e-02 1.624661e-02
41 42 43 44 45
-1.132670e-02 -5.138834e-03 -1.432308e-02 -3.174873e-02 -7.703318e-03
46 47 48 49 50
2.729391e-02 1.069984e-02 3.423550e-02 2.073222e-02 7.262322e-03
51 52 53 54 55
-6.304777e-03 -1.238956e-02 1.902105e-02 2.762765e-03 1.166953e-02
56 57 58 59 60
-1.239828e-03 -3.241239e-02 -7.258224e-02 1.152344e-02 1.796853e-02
61 62 63 64 65
1.205674e-02 -3.766349e-02 8.169602e-04 1.592910e-03 3.112139e-02
66 67 68 69 70
-3.216510e-03 -2.098077e-02 2.203379e-02 -1.263982e-03 -2.142624e-03
71 72 73 74 75
2.021907e-04 3.673024e-02 -7.687618e-03 -9.327281e-03 -6.506370e-04
76 77 78 79
-1.548040e-02 -6.931488e-03 -4.946852e-03 2.327246e-02
> postscript(file="/var/wessaorg/rcomp/tmp/6hotk1353439920.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 3.569507e-03 NA
1 -2.079742e-02 3.569507e-03
2 -1.362729e-02 -2.079742e-02
3 -3.294916e-02 -1.362729e-02
4 7.356290e-03 -3.294916e-02
5 -1.078705e-02 7.356290e-03
6 -8.964770e-03 -1.078705e-02
7 4.528353e-03 -8.964770e-03
8 3.603131e-02 4.528353e-03
9 5.071040e-03 3.603131e-02
10 9.143401e-03 5.071040e-03
11 1.846352e-03 9.143401e-03
12 1.354215e-02 1.846352e-03
13 4.488043e-03 1.354215e-02
14 1.747578e-03 4.488043e-03
15 1.605003e-02 1.747578e-03
16 1.836831e-02 1.605003e-02
17 6.437809e-02 1.836831e-02
18 -2.299729e-02 6.437809e-02
19 -2.282313e-02 -2.299729e-02
20 -1.995297e-02 -2.282313e-02
21 -2.322026e-02 -1.995297e-02
22 -9.537940e-04 -2.322026e-02
23 -3.482311e-03 -9.537940e-04
24 2.431426e-02 -3.482311e-03
25 4.039360e-02 2.431426e-02
26 -8.018151e-03 4.039360e-02
27 1.421167e-02 -8.018151e-03
28 -8.184144e-03 1.421167e-02
29 -4.425217e-02 -8.184144e-03
30 -1.309029e-02 -4.425217e-02
31 -1.671971e-02 -1.309029e-02
32 -5.598730e-03 -1.671971e-02
33 -6.435673e-03 -5.598730e-03
34 3.857700e-03 -6.435673e-03
35 2.247864e-03 3.857700e-03
36 -4.003992e-03 2.247864e-03
37 6.488393e-05 -4.003992e-03
38 1.386660e-02 6.488393e-05
39 1.624661e-02 1.386660e-02
40 -1.132670e-02 1.624661e-02
41 -5.138834e-03 -1.132670e-02
42 -1.432308e-02 -5.138834e-03
43 -3.174873e-02 -1.432308e-02
44 -7.703318e-03 -3.174873e-02
45 2.729391e-02 -7.703318e-03
46 1.069984e-02 2.729391e-02
47 3.423550e-02 1.069984e-02
48 2.073222e-02 3.423550e-02
49 7.262322e-03 2.073222e-02
50 -6.304777e-03 7.262322e-03
51 -1.238956e-02 -6.304777e-03
52 1.902105e-02 -1.238956e-02
53 2.762765e-03 1.902105e-02
54 1.166953e-02 2.762765e-03
55 -1.239828e-03 1.166953e-02
56 -3.241239e-02 -1.239828e-03
57 -7.258224e-02 -3.241239e-02
58 1.152344e-02 -7.258224e-02
59 1.796853e-02 1.152344e-02
60 1.205674e-02 1.796853e-02
61 -3.766349e-02 1.205674e-02
62 8.169602e-04 -3.766349e-02
63 1.592910e-03 8.169602e-04
64 3.112139e-02 1.592910e-03
65 -3.216510e-03 3.112139e-02
66 -2.098077e-02 -3.216510e-03
67 2.203379e-02 -2.098077e-02
68 -1.263982e-03 2.203379e-02
69 -2.142624e-03 -1.263982e-03
70 2.021907e-04 -2.142624e-03
71 3.673024e-02 2.021907e-04
72 -7.687618e-03 3.673024e-02
73 -9.327281e-03 -7.687618e-03
74 -6.506370e-04 -9.327281e-03
75 -1.548040e-02 -6.506370e-04
76 -6.931488e-03 -1.548040e-02
77 -4.946852e-03 -6.931488e-03
78 2.327246e-02 -4.946852e-03
79 NA 2.327246e-02
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.079742e-02 3.569507e-03
[2,] -1.362729e-02 -2.079742e-02
[3,] -3.294916e-02 -1.362729e-02
[4,] 7.356290e-03 -3.294916e-02
[5,] -1.078705e-02 7.356290e-03
[6,] -8.964770e-03 -1.078705e-02
[7,] 4.528353e-03 -8.964770e-03
[8,] 3.603131e-02 4.528353e-03
[9,] 5.071040e-03 3.603131e-02
[10,] 9.143401e-03 5.071040e-03
[11,] 1.846352e-03 9.143401e-03
[12,] 1.354215e-02 1.846352e-03
[13,] 4.488043e-03 1.354215e-02
[14,] 1.747578e-03 4.488043e-03
[15,] 1.605003e-02 1.747578e-03
[16,] 1.836831e-02 1.605003e-02
[17,] 6.437809e-02 1.836831e-02
[18,] -2.299729e-02 6.437809e-02
[19,] -2.282313e-02 -2.299729e-02
[20,] -1.995297e-02 -2.282313e-02
[21,] -2.322026e-02 -1.995297e-02
[22,] -9.537940e-04 -2.322026e-02
[23,] -3.482311e-03 -9.537940e-04
[24,] 2.431426e-02 -3.482311e-03
[25,] 4.039360e-02 2.431426e-02
[26,] -8.018151e-03 4.039360e-02
[27,] 1.421167e-02 -8.018151e-03
[28,] -8.184144e-03 1.421167e-02
[29,] -4.425217e-02 -8.184144e-03
[30,] -1.309029e-02 -4.425217e-02
[31,] -1.671971e-02 -1.309029e-02
[32,] -5.598730e-03 -1.671971e-02
[33,] -6.435673e-03 -5.598730e-03
[34,] 3.857700e-03 -6.435673e-03
[35,] 2.247864e-03 3.857700e-03
[36,] -4.003992e-03 2.247864e-03
[37,] 6.488393e-05 -4.003992e-03
[38,] 1.386660e-02 6.488393e-05
[39,] 1.624661e-02 1.386660e-02
[40,] -1.132670e-02 1.624661e-02
[41,] -5.138834e-03 -1.132670e-02
[42,] -1.432308e-02 -5.138834e-03
[43,] -3.174873e-02 -1.432308e-02
[44,] -7.703318e-03 -3.174873e-02
[45,] 2.729391e-02 -7.703318e-03
[46,] 1.069984e-02 2.729391e-02
[47,] 3.423550e-02 1.069984e-02
[48,] 2.073222e-02 3.423550e-02
[49,] 7.262322e-03 2.073222e-02
[50,] -6.304777e-03 7.262322e-03
[51,] -1.238956e-02 -6.304777e-03
[52,] 1.902105e-02 -1.238956e-02
[53,] 2.762765e-03 1.902105e-02
[54,] 1.166953e-02 2.762765e-03
[55,] -1.239828e-03 1.166953e-02
[56,] -3.241239e-02 -1.239828e-03
[57,] -7.258224e-02 -3.241239e-02
[58,] 1.152344e-02 -7.258224e-02
[59,] 1.796853e-02 1.152344e-02
[60,] 1.205674e-02 1.796853e-02
[61,] -3.766349e-02 1.205674e-02
[62,] 8.169602e-04 -3.766349e-02
[63,] 1.592910e-03 8.169602e-04
[64,] 3.112139e-02 1.592910e-03
[65,] -3.216510e-03 3.112139e-02
[66,] -2.098077e-02 -3.216510e-03
[67,] 2.203379e-02 -2.098077e-02
[68,] -1.263982e-03 2.203379e-02
[69,] -2.142624e-03 -1.263982e-03
[70,] 2.021907e-04 -2.142624e-03
[71,] 3.673024e-02 2.021907e-04
[72,] -7.687618e-03 3.673024e-02
[73,] -9.327281e-03 -7.687618e-03
[74,] -6.506370e-04 -9.327281e-03
[75,] -1.548040e-02 -6.506370e-04
[76,] -6.931488e-03 -1.548040e-02
[77,] -4.946852e-03 -6.931488e-03
[78,] 2.327246e-02 -4.946852e-03
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.079742e-02 3.569507e-03
2 -1.362729e-02 -2.079742e-02
3 -3.294916e-02 -1.362729e-02
4 7.356290e-03 -3.294916e-02
5 -1.078705e-02 7.356290e-03
6 -8.964770e-03 -1.078705e-02
7 4.528353e-03 -8.964770e-03
8 3.603131e-02 4.528353e-03
9 5.071040e-03 3.603131e-02
10 9.143401e-03 5.071040e-03
11 1.846352e-03 9.143401e-03
12 1.354215e-02 1.846352e-03
13 4.488043e-03 1.354215e-02
14 1.747578e-03 4.488043e-03
15 1.605003e-02 1.747578e-03
16 1.836831e-02 1.605003e-02
17 6.437809e-02 1.836831e-02
18 -2.299729e-02 6.437809e-02
19 -2.282313e-02 -2.299729e-02
20 -1.995297e-02 -2.282313e-02
21 -2.322026e-02 -1.995297e-02
22 -9.537940e-04 -2.322026e-02
23 -3.482311e-03 -9.537940e-04
24 2.431426e-02 -3.482311e-03
25 4.039360e-02 2.431426e-02
26 -8.018151e-03 4.039360e-02
27 1.421167e-02 -8.018151e-03
28 -8.184144e-03 1.421167e-02
29 -4.425217e-02 -8.184144e-03
30 -1.309029e-02 -4.425217e-02
31 -1.671971e-02 -1.309029e-02
32 -5.598730e-03 -1.671971e-02
33 -6.435673e-03 -5.598730e-03
34 3.857700e-03 -6.435673e-03
35 2.247864e-03 3.857700e-03
36 -4.003992e-03 2.247864e-03
37 6.488393e-05 -4.003992e-03
38 1.386660e-02 6.488393e-05
39 1.624661e-02 1.386660e-02
40 -1.132670e-02 1.624661e-02
41 -5.138834e-03 -1.132670e-02
42 -1.432308e-02 -5.138834e-03
43 -3.174873e-02 -1.432308e-02
44 -7.703318e-03 -3.174873e-02
45 2.729391e-02 -7.703318e-03
46 1.069984e-02 2.729391e-02
47 3.423550e-02 1.069984e-02
48 2.073222e-02 3.423550e-02
49 7.262322e-03 2.073222e-02
50 -6.304777e-03 7.262322e-03
51 -1.238956e-02 -6.304777e-03
52 1.902105e-02 -1.238956e-02
53 2.762765e-03 1.902105e-02
54 1.166953e-02 2.762765e-03
55 -1.239828e-03 1.166953e-02
56 -3.241239e-02 -1.239828e-03
57 -7.258224e-02 -3.241239e-02
58 1.152344e-02 -7.258224e-02
59 1.796853e-02 1.152344e-02
60 1.205674e-02 1.796853e-02
61 -3.766349e-02 1.205674e-02
62 8.169602e-04 -3.766349e-02
63 1.592910e-03 8.169602e-04
64 3.112139e-02 1.592910e-03
65 -3.216510e-03 3.112139e-02
66 -2.098077e-02 -3.216510e-03
67 2.203379e-02 -2.098077e-02
68 -1.263982e-03 2.203379e-02
69 -2.142624e-03 -1.263982e-03
70 2.021907e-04 -2.142624e-03
71 3.673024e-02 2.021907e-04
72 -7.687618e-03 3.673024e-02
73 -9.327281e-03 -7.687618e-03
74 -6.506370e-04 -9.327281e-03
75 -1.548040e-02 -6.506370e-04
76 -6.931488e-03 -1.548040e-02
77 -4.946852e-03 -6.931488e-03
78 2.327246e-02 -4.946852e-03
> 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/7umba1353439920.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/8ncg11353439920.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/99npr1353439920.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/102rys1353439920.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11sqd11353439920.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12yvfh1353439920.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13lsc71353439920.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14dm5d1353439920.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/15k0r11353439920.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/16tky41353439920.tab")
+ }
>
> try(system("convert tmp/1cnt61353439920.ps tmp/1cnt61353439920.png",intern=TRUE))
character(0)
> try(system("convert tmp/229w71353439920.ps tmp/229w71353439920.png",intern=TRUE))
character(0)
> try(system("convert tmp/36pea1353439920.ps tmp/36pea1353439920.png",intern=TRUE))
character(0)
> try(system("convert tmp/4gpeh1353439920.ps tmp/4gpeh1353439920.png",intern=TRUE))
character(0)
> try(system("convert tmp/51sre1353439920.ps tmp/51sre1353439920.png",intern=TRUE))
character(0)
> try(system("convert tmp/6hotk1353439920.ps tmp/6hotk1353439920.png",intern=TRUE))
character(0)
> try(system("convert tmp/7umba1353439920.ps tmp/7umba1353439920.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ncg11353439920.ps tmp/8ncg11353439920.png",intern=TRUE))
character(0)
> try(system("convert tmp/99npr1353439920.ps tmp/99npr1353439920.png",intern=TRUE))
character(0)
> try(system("convert tmp/102rys1353439920.ps tmp/102rys1353439920.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.539 1.385 8.958