R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-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(19.72
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+ ,4.25
+ ,4.75
+ ,5.01
+ ,20.21
+ ,20.47
+ ,19.42
+ ,4.64
+ ,5.27
+ ,5.21
+ ,4.99
+ ,5.36
+ ,5.46
+ ,4.43
+ ,4.28
+ ,4.26
+ ,4.73
+ ,4.99
+ ,20.21
+ ,20.42
+ ,19.48
+ ,4.65
+ ,5.29
+ ,5.21
+ ,4.99
+ ,5.37
+ ,5.46
+ ,4.44
+ ,4.29
+ ,4.26
+ ,4.72
+ ,4.99
+ ,20.25
+ ,20.44
+ ,19.53
+ ,4.66
+ ,5.28
+ ,5.22
+ ,4.99
+ ,5.37
+ ,5.47
+ ,4.43
+ ,4.29
+ ,4.24
+ ,4.71
+ ,4.97
+ ,20.33
+ ,20.52
+ ,19.56
+ ,4.65
+ ,5.27
+ ,5.23
+ ,4.99
+ ,5.37
+ ,5.46
+ ,4.43
+ ,4.29
+ ,4.24
+ ,4.72
+ ,4.95
+ ,20.31
+ ,20.5
+ ,19.53
+ ,4.65
+ ,5.26
+ ,5.24
+ ,4.99
+ ,5.37
+ ,5.46
+ ,4.42
+ ,4.28
+ ,4.23
+ ,4.72
+ ,4.96
+ ,20.32
+ ,20.47
+ ,19.52
+ ,4.68
+ ,5.28
+ ,5.26
+ ,5.01
+ ,5.39
+ ,5.46
+ ,4.43
+ ,4.29
+ ,4.26
+ ,4.72
+ ,4.9
+ ,20.31
+ ,20.65
+ ,19.52
+ ,4.66
+ ,5.26
+ ,5.23
+ ,4.97
+ ,5.37
+ ,5.43
+ ,4.4
+ ,4.28
+ ,4.26
+ ,4.71
+ ,4.94
+ ,20.29
+ ,20.47
+ ,19.63
+ ,4.65
+ ,5.26
+ ,5.23
+ ,4.98
+ ,5.38
+ ,5.44
+ ,4.4
+ ,4.27
+ ,4.24
+ ,4.69
+ ,4.93
+ ,20.4
+ ,20.5
+ ,19.63
+ ,4.65
+ ,5.27
+ ,5.18
+ ,4.97
+ ,5.38
+ ,5.44
+ ,4.41
+ ,4.29
+ ,4.27
+ ,4.71
+ ,4.97
+ ,20.41
+ ,20.54
+ ,19.57
+ ,4.64
+ ,5.37
+ ,5.15
+ ,4.97
+ ,5.38
+ ,5.45
+ ,4.42
+ ,4.28
+ ,4.26
+ ,4.7
+ ,4.95
+ ,20.37
+ ,20.46
+ ,19.6
+ ,4.64
+ ,5.31
+ ,5.16
+ ,4.98
+ ,5.38
+ ,5.44
+ ,4.41
+ ,4.28
+ ,4.26
+ ,4.7
+ ,4.96
+ ,20.37
+ ,20.48
+ ,19.74
+ ,4.67
+ ,5.36
+ ,5.16
+ ,5.03
+ ,5.4
+ ,5.44
+ ,4.44
+ ,4.28
+ ,4.28
+ ,4.71
+ ,4.94
+ ,20.5
+ ,20.74
+ ,19.63
+ ,4.68
+ ,5.39
+ ,5.17
+ ,5.08
+ ,5.41
+ ,5.46
+ ,4.45
+ ,4.3
+ ,4.27
+ ,4.73
+ ,4.95
+ ,20.41
+ ,20.81
+ ,19.59
+ ,4.66
+ ,5.38
+ ,5.17
+ ,4.98
+ ,5.38
+ ,5.44
+ ,4.41
+ ,4.3
+ ,4.27
+ ,4.71
+ ,4.94
+ ,20.38
+ ,20.62
+ ,19.7
+ ,4.66
+ ,5.39
+ ,5.17
+ ,4.91
+ ,5.35
+ ,5.43
+ ,4.4
+ ,4.29
+ ,4.27
+ ,4.7
+ ,4.94
+ ,20.47
+ ,20.63
+ ,19.88
+ ,4.66
+ ,5.38
+ ,5.17
+ ,4.93
+ ,5.34
+ ,5.42
+ ,4.4
+ ,4.26
+ ,4.24
+ ,4.7
+ ,4.92
+ ,20.63
+ ,20.64
+ ,19.72
+ ,4.66
+ ,5.29
+ ,5.21
+ ,4.94
+ ,5.34
+ ,5.44
+ ,4.4
+ ,4.3
+ ,4.26
+ ,4.72
+ ,4.96
+ ,20.49
+ ,20.55
+ ,19.62
+ ,4.65
+ ,5.27
+ ,5.23
+ ,4.93
+ ,5.33
+ ,5.44
+ ,4.4
+ ,4.33
+ ,4.29
+ ,4.77
+ ,4.99
+ ,20.4
+ ,20.73
+ ,19.78
+ ,4.65
+ ,5.28
+ ,5.22
+ ,4.94
+ ,5.33
+ ,5.42
+ ,4.4
+ ,4.32
+ ,4.27
+ ,4.74
+ ,4.97
+ ,20.56
+ ,20.75
+ ,19.61
+ ,4.64
+ ,5.31
+ ,5.22
+ ,4.94
+ ,5.33
+ ,5.4
+ ,4.4
+ ,4.32
+ ,4.25
+ ,4.73
+ ,4.97
+ ,20.4
+ ,20.56
+ ,19.7
+ ,4.67
+ ,5.34
+ ,5.24
+ ,4.99
+ ,5.36
+ ,5.45
+ ,4.4
+ ,4.33
+ ,4.27
+ ,4.76
+ ,5.01
+ ,20.48
+ ,20.85
+ ,19.65
+ ,4.66
+ ,5.33
+ ,5.24
+ ,4.99
+ ,5.37
+ ,5.45
+ ,4.4
+ ,4.35
+ ,4.29
+ ,4.78
+ ,5
+ ,20.44
+ ,21
+ ,19.61
+ ,4.64
+ ,5.31
+ ,5.24
+ ,4.99
+ ,5.35
+ ,5.44
+ ,4.39
+ ,4.34
+ ,4.26
+ ,4.76
+ ,4.99
+ ,20.38
+ ,20.7
+ ,19.62
+ ,4.64
+ ,5.3
+ ,5.22
+ ,4.98
+ ,5.34
+ ,5.43
+ ,4.39
+ ,4.32
+ ,4.22
+ ,4.72
+ ,4.99
+ ,20.4
+ ,20.56
+ ,19.58
+ ,4.67
+ ,5.32
+ ,5.19
+ ,5.02
+ ,5.37
+ ,5.46
+ ,4.43
+ ,4.33
+ ,4.25
+ ,4.75
+ ,5
+ ,20.37
+ ,20.6
+ ,19.69
+ ,4.64
+ ,5.3
+ ,5.14
+ ,5
+ ,5.32
+ ,5.44
+ ,4.39
+ ,4.31
+ ,4.24
+ ,4.72
+ ,5
+ ,20.47
+ ,20.55
+ ,19.63
+ ,4.62
+ ,5.27
+ ,5.13
+ ,4.99
+ ,5.32
+ ,5.42
+ ,4.38
+ ,4.31
+ ,4.24
+ ,4.71
+ ,5
+ ,20.4
+ ,20.49
+ ,19.54
+ ,4.61
+ ,5.27
+ ,5.19
+ ,4.98
+ ,5.33
+ ,5.43
+ ,4.4
+ ,4.35
+ ,4.28
+ ,4.72
+ ,5.05
+ ,20.3
+ ,20.63
+ ,19.56
+ ,4.61
+ ,5.28
+ ,5.21
+ ,5.01
+ ,5.32
+ ,5.43
+ ,4.42
+ ,4.34
+ ,4.26
+ ,4.71
+ ,5.06
+ ,20.3
+ ,20.56
+ ,19.55
+ ,4.62
+ ,5.29
+ ,5.23
+ ,5.03
+ ,5.34
+ ,5.43
+ ,4.44
+ ,4.32
+ ,4.25
+ ,4.74
+ ,5.07
+ ,20.3
+ ,20.57
+ ,19.49
+ ,4.62
+ ,5.27
+ ,5.21
+ ,5.03
+ ,5.33
+ ,5.42
+ ,4.43
+ ,4.32
+ ,4.24
+ ,4.72
+ ,5.06
+ ,20.25
+ ,20.45
+ ,19.53
+ ,4.62
+ ,5.26
+ ,5.23
+ ,5.04
+ ,5.35
+ ,5.43
+ ,4.44
+ ,4.32
+ ,4.25
+ ,4.74
+ ,5.06
+ ,20.31
+ ,20.49
+ ,19.48
+ ,4.6
+ ,5.24
+ ,5.21
+ ,5.03
+ ,5.35
+ ,5.45
+ ,4.42
+ ,4.29
+ ,4.22
+ ,4.71
+ ,5.04
+ ,20.29
+ ,20.38
+ ,19.58
+ ,4.6
+ ,5.24
+ ,5.23
+ ,5.02
+ ,5.35
+ ,5.43
+ ,4.42
+ ,4.29
+ ,4.21
+ ,4.71
+ ,5.05
+ ,20.37
+ ,20.46
+ ,19.48
+ ,4.61
+ ,5.26
+ ,5.22
+ ,5.03
+ ,5.33
+ ,5.43
+ ,4.43
+ ,4.3
+ ,4.23
+ ,4.73
+ ,5.05
+ ,20.28
+ ,20.5
+ ,19.46
+ ,4.6
+ ,5.23
+ ,5.14
+ ,5.01
+ ,5.32
+ ,5.42
+ ,4.41
+ ,4.3
+ ,4.24
+ ,4.72
+ ,5.04
+ ,20.27
+ ,20.41
+ ,19.45
+ ,4.61
+ ,5.25
+ ,5.01
+ ,5.03
+ ,5.33
+ ,5.44
+ ,4.4
+ ,4.31
+ ,4.23
+ ,4.72
+ ,5.03
+ ,20.29
+ ,20.41
+ ,19.39
+ ,4.63
+ ,5.27
+ ,5.1
+ ,5.05
+ ,5.36
+ ,5.48
+ ,4.41
+ ,4.32
+ ,4.24
+ ,4.72
+ ,5.04
+ ,20.22
+ ,20.47
+ ,19.46
+ ,4.63
+ ,5.29
+ ,5.12
+ ,5.04
+ ,5.36
+ ,5.5
+ ,4.41
+ ,4.3
+ ,4.22
+ ,4.73
+ ,5.05
+ ,20.29
+ ,20.47
+ ,19.41
+ ,4.62
+ ,5.29
+ ,5.18
+ ,5.05
+ ,5.36
+ ,5.53
+ ,4.43
+ ,4.29
+ ,4.22
+ ,4.69
+ ,5.03
+ ,20.27
+ ,20.42
+ ,19.45
+ ,4.64
+ ,5.3
+ ,5.26
+ ,5.06
+ ,5.41
+ ,5.55
+ ,4.44
+ ,4.28
+ ,4.22
+ ,4.67
+ ,5.05
+ ,20.35
+ ,20.64
+ ,19.52
+ ,4.6
+ ,5.27
+ ,5.25
+ ,5.02
+ ,5.4
+ ,5.54
+ ,4.4
+ ,4.23
+ ,4.18
+ ,4.62
+ ,4.99
+ ,20.47
+ ,20.47
+ ,19.47
+ ,4.6
+ ,5.27
+ ,5.23
+ ,5.02
+ ,5.36
+ ,5.48
+ ,4.39
+ ,4.25
+ ,4.18
+ ,4.63
+ ,4.99
+ ,20.37
+ ,20.34
+ ,19.37
+ ,4.61
+ ,5.3
+ ,5.25
+ ,5.05
+ ,5.37
+ ,5.48
+ ,4.4
+ ,4.31
+ ,4.22
+ ,4.75
+ ,5.02
+ ,20.15
+ ,20.46
+ ,19.37
+ ,4.6
+ ,5.27
+ ,5.25
+ ,5.04
+ ,5.36
+ ,5.47
+ ,4.4
+ ,4.31
+ ,4.24
+ ,4.76
+ ,5.02
+ ,20.14
+ ,20.42
+ ,19.4
+ ,4.61
+ ,5.31
+ ,5.25
+ ,5.03
+ ,5.35
+ ,5.47
+ ,4.42
+ ,4.31
+ ,4.23
+ ,4.75
+ ,5.01
+ ,20.17
+ ,20.42
+ ,19.42
+ ,4.61
+ ,5.32
+ ,5.25
+ ,5.05
+ ,5.37
+ ,5.47
+ ,4.44
+ ,4.3
+ ,4.22
+ ,4.78
+ ,5.03
+ ,20.18
+ ,20.47
+ ,19.45
+ ,4.61
+ ,5.3
+ ,5.23
+ ,5.04
+ ,5.36
+ ,5.46
+ ,4.43
+ ,4.31
+ ,4.23
+ ,4.79
+ ,5.02
+ ,20.23
+ ,20.52)
+ ,dim=c(14
+ ,130)
+ ,dimnames=list(c('LNDEFQPILS'
+ ,'LNDEFPBEPIL'
+ ,'LNDEFPBELUX'
+ ,'LNDEFPBEABD'
+ ,'LNDEFPBEWIT'
+ ,'LNDEFPBEZWB'
+ ,'LNDEFPBEREG'
+ ,'LNDEFPBETAF'
+ ,'LNDEFPSOORA'
+ ,'LNDEFPSOLEM'
+ ,'LNDEFPICET'
+ ,'LNDEFPSPORT'
+ ,'LNDEFBUDBEER'
+ ,'LNDEFBUDSISSS
')
+ ,1:130))
> y <- array(NA,dim=c(14,130),dimnames=list(c('LNDEFQPILS','LNDEFPBEPIL','LNDEFPBELUX','LNDEFPBEABD','LNDEFPBEWIT','LNDEFPBEZWB','LNDEFPBEREG','LNDEFPBETAF','LNDEFPSOORA','LNDEFPSOLEM','LNDEFPICET','LNDEFPSPORT','LNDEFBUDBEER','LNDEFBUDSISSS
'),1:130))
> 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'
> #'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
> 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
LNDEFQPILS LNDEFPBEPIL LNDEFPBELUX LNDEFPBEABD LNDEFPBEWIT LNDEFPBEZWB
1 19.72 4.66 5.35 5.31 5.04 5.41
2 19.65 4.67 5.37 5.31 5.05 5.42
3 19.59 4.65 5.35 5.31 5.03 5.40
4 19.59 4.65 5.34 5.28 5.04 5.41
5 19.55 4.64 5.34 5.27 5.03 5.41
6 19.61 4.64 5.32 5.28 5.01 5.40
7 19.57 4.66 5.35 5.29 5.03 5.42
8 19.55 4.65 5.34 5.25 5.03 5.41
9 19.57 4.64 5.35 5.28 5.04 5.42
10 19.51 4.63 5.32 5.30 5.01 5.43
11 19.48 4.63 5.33 5.31 5.01 5.47
12 19.49 4.63 5.33 5.26 4.99 5.45
13 19.58 4.62 5.31 5.28 4.99 5.43
14 19.48 4.63 5.33 5.27 5.02 5.43
15 19.46 4.64 5.30 5.27 5.02 5.44
16 19.48 4.63 5.30 5.28 5.02 5.44
17 19.49 4.64 5.31 5.35 5.03 5.49
18 19.44 4.63 5.30 5.34 5.01 5.48
19 19.57 4.63 5.30 5.35 5.01 5.49
20 19.50 4.64 5.33 5.35 5.03 5.48
21 19.34 4.63 5.30 5.33 5.03 5.45
22 19.40 4.67 5.34 5.35 5.05 5.47
23 19.40 4.67 5.35 5.33 5.04 5.47
24 19.43 4.66 5.31 5.23 5.04 5.44
25 19.44 4.66 5.32 5.27 5.06 5.46
26 19.49 4.64 5.31 5.23 5.03 5.42
27 19.48 4.65 5.32 5.26 5.03 5.43
28 19.48 4.64 5.31 5.27 5.03 5.43
29 19.45 4.65 5.33 5.27 5.01 5.44
30 19.48 4.64 5.32 5.29 5.04 5.42
31 19.44 4.65 5.33 5.30 5.02 5.42
32 19.51 4.65 5.33 5.30 5.04 5.42
33 19.49 4.65 5.32 5.29 5.04 5.41
34 19.56 4.67 5.34 5.29 5.04 5.39
35 19.57 4.66 5.31 5.27 5.02 5.38
36 19.49 4.67 5.33 5.27 5.02 5.39
37 19.54 4.69 5.31 5.28 5.00 5.39
38 19.62 4.66 5.26 5.25 4.97 5.36
39 19.56 4.68 5.31 5.27 5.00 5.38
40 19.64 4.69 5.34 5.29 5.01 5.40
41 19.59 4.65 5.32 5.26 4.99 5.39
42 19.60 4.66 5.33 5.27 4.98 5.39
43 19.63 4.65 5.32 5.27 4.99 5.40
44 19.67 4.65 5.31 5.26 5.00 5.40
45 19.61 4.67 5.32 5.29 5.00 5.41
46 19.62 4.67 5.32 5.29 4.97 5.40
47 19.67 4.67 5.32 5.27 4.99 5.38
48 19.63 4.65 5.33 5.24 4.99 5.38
49 19.62 4.65 5.34 5.21 5.01 5.39
50 19.70 4.66 5.35 5.24 5.02 5.39
51 19.83 4.66 5.33 5.26 5.00 5.39
52 19.85 4.64 5.31 5.24 4.98 5.37
53 19.73 4.67 5.35 5.28 5.01 5.40
54 19.61 4.66 5.35 5.28 4.99 5.37
55 19.63 4.66 5.38 5.30 5.02 5.41
56 19.68 4.65 5.35 5.28 5.02 5.39
57 19.66 4.63 5.32 5.25 5.01 5.37
58 19.56 4.63 5.32 5.24 5.00 5.37
59 19.50 4.63 5.31 5.25 4.99 5.37
60 19.55 4.64 5.32 5.26 4.99 5.37
61 19.53 4.63 5.31 5.25 4.99 5.37
62 19.49 4.63 5.30 5.25 4.98 5.36
63 19.47 4.62 5.29 5.27 4.98 5.39
64 19.57 4.62 5.30 5.26 4.99 5.38
65 19.53 4.63 5.30 5.27 4.99 5.38
66 19.43 4.62 5.30 5.23 4.98 5.36
67 19.43 4.64 5.31 5.21 5.02 5.39
68 19.43 4.64 5.31 5.21 5.06 5.40
69 19.45 4.64 5.29 5.23 5.06 5.40
70 19.41 4.63 5.30 5.26 5.02 5.41
71 19.48 4.67 5.32 5.29 4.99 5.44
72 19.48 4.64 5.29 5.27 4.98 5.44
73 19.48 4.62 5.28 5.23 4.96 5.40
74 19.37 4.66 5.31 5.28 5.01 5.41
75 19.35 4.67 5.29 5.26 5.00 5.38
76 19.38 4.66 5.30 5.24 4.99 5.38
77 19.41 4.66 5.30 5.20 5.00 5.39
78 19.48 4.66 5.30 5.21 4.98 5.38
79 19.44 4.64 5.29 5.21 4.97 5.37
80 19.41 4.64 5.29 5.18 4.97 5.36
81 19.42 4.66 5.30 5.22 4.99 5.37
82 19.42 4.64 5.28 5.23 4.98 5.37
83 19.42 4.64 5.27 5.21 4.99 5.36
84 19.48 4.65 5.29 5.21 4.99 5.37
85 19.53 4.66 5.28 5.22 4.99 5.37
86 19.56 4.65 5.27 5.23 4.99 5.37
87 19.53 4.65 5.26 5.24 4.99 5.37
88 19.52 4.68 5.28 5.26 5.01 5.39
89 19.52 4.66 5.26 5.23 4.97 5.37
90 19.63 4.65 5.26 5.23 4.98 5.38
91 19.63 4.65 5.27 5.18 4.97 5.38
92 19.57 4.64 5.37 5.15 4.97 5.38
93 19.60 4.64 5.31 5.16 4.98 5.38
94 19.74 4.67 5.36 5.16 5.03 5.40
95 19.63 4.68 5.39 5.17 5.08 5.41
96 19.59 4.66 5.38 5.17 4.98 5.38
97 19.70 4.66 5.39 5.17 4.91 5.35
98 19.88 4.66 5.38 5.17 4.93 5.34
99 19.72 4.66 5.29 5.21 4.94 5.34
100 19.62 4.65 5.27 5.23 4.93 5.33
101 19.78 4.65 5.28 5.22 4.94 5.33
102 19.61 4.64 5.31 5.22 4.94 5.33
103 19.70 4.67 5.34 5.24 4.99 5.36
104 19.65 4.66 5.33 5.24 4.99 5.37
105 19.61 4.64 5.31 5.24 4.99 5.35
106 19.62 4.64 5.30 5.22 4.98 5.34
107 19.58 4.67 5.32 5.19 5.02 5.37
108 19.69 4.64 5.30 5.14 5.00 5.32
109 19.63 4.62 5.27 5.13 4.99 5.32
110 19.54 4.61 5.27 5.19 4.98 5.33
111 19.56 4.61 5.28 5.21 5.01 5.32
112 19.55 4.62 5.29 5.23 5.03 5.34
113 19.49 4.62 5.27 5.21 5.03 5.33
114 19.53 4.62 5.26 5.23 5.04 5.35
115 19.48 4.60 5.24 5.21 5.03 5.35
116 19.58 4.60 5.24 5.23 5.02 5.35
117 19.48 4.61 5.26 5.22 5.03 5.33
118 19.46 4.60 5.23 5.14 5.01 5.32
119 19.45 4.61 5.25 5.01 5.03 5.33
120 19.39 4.63 5.27 5.10 5.05 5.36
121 19.46 4.63 5.29 5.12 5.04 5.36
122 19.41 4.62 5.29 5.18 5.05 5.36
123 19.45 4.64 5.30 5.26 5.06 5.41
124 19.52 4.60 5.27 5.25 5.02 5.40
125 19.47 4.60 5.27 5.23 5.02 5.36
126 19.37 4.61 5.30 5.25 5.05 5.37
127 19.37 4.60 5.27 5.25 5.04 5.36
128 19.40 4.61 5.31 5.25 5.03 5.35
129 19.42 4.61 5.32 5.25 5.05 5.37
130 19.45 4.61 5.30 5.23 5.04 5.36
LNDEFPBEREG LNDEFPBETAF LNDEFPSOORA LNDEFPSOLEM LNDEFPICET LNDEFPSPORT
1 5.50 4.44 4.25 4.24 4.73 5.03
2 5.51 4.45 4.26 4.26 4.75 5.06
3 5.49 4.44 4.26 4.25 4.73 5.03
4 5.49 4.45 4.24 4.24 4.75 5.03
5 5.49 4.43 4.25 4.24 4.74 5.05
6 5.47 4.41 4.24 4.24 4.69 5.04
7 5.48 4.44 4.25 4.25 4.75 5.07
8 5.48 4.44 4.23 4.24 4.75 5.07
9 5.50 4.45 4.23 4.23 4.79 5.04
10 5.50 4.43 4.23 4.23 4.77 5.04
11 5.52 4.43 4.23 4.24 4.74 5.04
12 5.48 4.43 4.24 4.22 4.73 5.03
13 5.44 4.42 4.23 4.23 4.75 5.04
14 5.48 4.44 4.23 4.24 4.77 5.05
15 5.50 4.40 4.22 4.21 4.76 5.03
16 5.53 4.40 4.22 4.23 4.77 5.04
17 5.59 4.42 4.21 4.21 4.77 5.04
18 5.59 4.42 4.19 4.20 4.74 5.03
19 5.61 4.41 4.17 4.17 4.71 5.04
20 5.59 4.42 4.19 4.19 4.72 5.04
21 5.55 4.41 4.24 4.22 4.74 5.04
22 5.54 4.45 4.28 4.27 4.78 5.04
23 5.53 4.46 4.27 4.26 4.77 5.06
24 5.51 4.45 4.24 4.23 4.76 5.04
25 5.51 4.47 4.23 4.23 4.74 5.03
26 5.50 4.46 4.25 4.25 4.72 5.02
27 5.51 4.45 4.27 4.27 4.75 5.02
28 5.47 4.44 4.25 4.25 4.74 5.02
29 5.49 4.45 4.26 4.25 4.75 5.03
30 5.48 4.45 4.26 4.26 4.74 5.02
31 5.49 4.45 4.24 4.24 4.74 5.03
32 5.50 4.45 4.23 4.22 4.74 5.01
33 5.51 4.46 4.26 4.24 4.77 5.03
34 5.49 4.43 4.24 4.22 4.73 4.98
35 5.47 4.42 4.24 4.20 4.72 5.00
36 5.47 4.43 4.26 4.23 4.72 4.93
37 5.48 4.43 4.26 4.23 4.71 4.96
38 5.47 4.39 4.25 4.22 4.69 4.97
39 5.47 4.43 4.27 4.23 4.71 5.01
40 5.47 4.45 4.28 4.24 4.74 5.02
41 5.45 4.43 4.26 4.24 4.76 5.00
42 5.44 4.42 4.28 4.25 4.76 5.01
43 5.44 4.43 4.28 4.24 4.76 4.97
44 5.44 4.42 4.28 4.24 4.75 5.01
45 5.47 4.45 4.30 4.26 4.75 5.02
46 5.46 4.44 4.27 4.24 4.73 5.00
47 5.44 4.42 4.28 4.23 4.73 4.99
48 5.44 4.41 4.29 4.24 4.73 4.97
49 5.46 4.43 4.28 4.24 4.74 4.98
50 5.46 4.44 4.28 4.26 4.75 4.98
51 5.46 4.41 4.27 4.24 4.73 4.98
52 5.42 4.39 4.25 4.22 4.72 4.98
53 5.45 4.43 4.29 4.26 4.76 4.99
54 5.44 4.43 4.29 4.25 4.73 4.99
55 5.47 4.46 4.30 4.27 4.78 5.01
56 5.45 4.43 4.29 4.25 4.76 5.00
57 5.44 4.42 4.29 4.24 4.75 4.92
58 5.43 4.43 4.29 4.23 4.75 4.97
59 5.43 4.43 4.29 4.24 4.75 5.01
60 5.45 4.44 4.30 4.25 4.75 5.02
61 5.43 4.43 4.29 4.25 4.76 5.02
62 5.45 4.43 4.29 4.24 4.76 5.03
63 5.43 4.43 4.28 4.23 4.74 5.03
64 5.46 4.44 4.26 4.21 4.74 5.01
65 5.46 4.45 4.27 4.22 4.75 5.02
66 5.44 4.45 4.27 4.22 4.75 5.01
67 5.46 4.42 4.27 4.21 4.75 5.01
68 5.49 4.43 4.26 4.21 4.74 5.01
69 5.50 4.43 4.24 4.20 4.73 5.01
70 5.51 4.43 4.25 4.20 4.72 5.01
71 5.56 4.45 4.25 4.21 4.73 5.04
72 5.55 4.43 4.23 4.19 4.70 5.01
73 5.49 4.40 4.24 4.20 4.71 4.99
74 5.50 4.44 4.29 4.23 4.76 5.03
75 5.46 4.43 4.29 4.23 4.74 5.02
76 5.45 4.43 4.28 4.23 4.74 5.01
77 5.45 4.45 4.28 4.22 4.76 5.02
78 5.45 4.45 4.28 4.22 4.76 5.03
79 5.46 4.44 4.29 4.22 4.77 5.01
80 5.45 4.40 4.29 4.23 4.76 5.01
81 5.46 4.44 4.30 4.24 4.77 5.00
82 5.46 4.45 4.28 4.25 4.75 5.01
83 5.46 4.43 4.28 4.26 4.73 4.99
84 5.46 4.44 4.29 4.26 4.72 4.99
85 5.47 4.43 4.29 4.24 4.71 4.97
86 5.46 4.43 4.29 4.24 4.72 4.95
87 5.46 4.42 4.28 4.23 4.72 4.96
88 5.46 4.43 4.29 4.26 4.72 4.90
89 5.43 4.40 4.28 4.26 4.71 4.94
90 5.44 4.40 4.27 4.24 4.69 4.93
91 5.44 4.41 4.29 4.27 4.71 4.97
92 5.45 4.42 4.28 4.26 4.70 4.95
93 5.44 4.41 4.28 4.26 4.70 4.96
94 5.44 4.44 4.28 4.28 4.71 4.94
95 5.46 4.45 4.30 4.27 4.73 4.95
96 5.44 4.41 4.30 4.27 4.71 4.94
97 5.43 4.40 4.29 4.27 4.70 4.94
98 5.42 4.40 4.26 4.24 4.70 4.92
99 5.44 4.40 4.30 4.26 4.72 4.96
100 5.44 4.40 4.33 4.29 4.77 4.99
101 5.42 4.40 4.32 4.27 4.74 4.97
102 5.40 4.40 4.32 4.25 4.73 4.97
103 5.45 4.40 4.33 4.27 4.76 5.01
104 5.45 4.40 4.35 4.29 4.78 5.00
105 5.44 4.39 4.34 4.26 4.76 4.99
106 5.43 4.39 4.32 4.22 4.72 4.99
107 5.46 4.43 4.33 4.25 4.75 5.00
108 5.44 4.39 4.31 4.24 4.72 5.00
109 5.42 4.38 4.31 4.24 4.71 5.00
110 5.43 4.40 4.35 4.28 4.72 5.05
111 5.43 4.42 4.34 4.26 4.71 5.06
112 5.43 4.44 4.32 4.25 4.74 5.07
113 5.42 4.43 4.32 4.24 4.72 5.06
114 5.43 4.44 4.32 4.25 4.74 5.06
115 5.45 4.42 4.29 4.22 4.71 5.04
116 5.43 4.42 4.29 4.21 4.71 5.05
117 5.43 4.43 4.30 4.23 4.73 5.05
118 5.42 4.41 4.30 4.24 4.72 5.04
119 5.44 4.40 4.31 4.23 4.72 5.03
120 5.48 4.41 4.32 4.24 4.72 5.04
121 5.50 4.41 4.30 4.22 4.73 5.05
122 5.53 4.43 4.29 4.22 4.69 5.03
123 5.55 4.44 4.28 4.22 4.67 5.05
124 5.54 4.40 4.23 4.18 4.62 4.99
125 5.48 4.39 4.25 4.18 4.63 4.99
126 5.48 4.40 4.31 4.22 4.75 5.02
127 5.47 4.40 4.31 4.24 4.76 5.02
128 5.47 4.42 4.31 4.23 4.75 5.01
129 5.47 4.44 4.30 4.22 4.78 5.03
130 5.46 4.43 4.31 4.23 4.79 5.02
LNDEFBUDBEER LNDEFBUDSISSS\r
1 20.46 20.58
2 20.39 20.71
3 20.32 20.52
4 20.32 20.37
5 20.27 20.32
6 20.33 20.29
7 20.29 20.39
8 20.29 20.41
9 20.27 20.42
10 20.25 20.37
11 20.25 20.37
12 20.27 20.35
13 20.33 20.35
14 20.24 20.33
15 20.23 20.29
16 20.25 20.30
17 20.28 20.41
18 20.27 20.34
19 20.48 20.51
20 20.36 20.44
21 20.13 20.30
22 20.15 20.43
23 20.15 20.40
24 20.19 20.34
25 20.19 20.42
26 20.24 20.41
27 20.22 20.41
28 20.23 20.35
29 20.20 20.42
30 20.23 20.43
31 20.19 20.37
32 20.25 20.40
33 20.24 20.45
34 20.33 20.53
35 20.36 20.45
36 20.26 20.40
37 20.30 20.46
38 20.40 20.44
39 20.34 20.45
40 20.40 20.59
41 20.36 20.43
42 20.39 20.56
43 20.40 20.55
44 20.44 20.55
45 20.37 20.66
46 20.38 20.58
47 20.43 20.47
48 20.38 20.46
49 20.38 20.46
50 20.46 20.63
51 20.58 20.66
52 20.60 20.51
53 20.49 20.71
54 20.38 20.53
55 20.36 20.61
56 20.40 20.54
57 20.38 20.47
58 20.30 20.43
59 20.25 20.44
60 20.30 20.51
61 20.27 20.42
62 20.24 20.39
63 20.25 20.40
64 20.36 20.43
65 20.30 20.47
66 20.23 20.36
67 20.23 20.37
68 20.24 20.42
69 20.28 20.40
70 20.26 20.38
71 20.38 20.62
72 20.39 20.49
73 20.32 20.27
74 20.17 20.40
75 20.16 20.37
76 20.18 20.37
77 20.19 20.40
78 20.26 20.43
79 20.23 20.39
80 20.20 20.40
81 20.21 20.48
82 20.21 20.47
83 20.21 20.42
84 20.25 20.44
85 20.33 20.52
86 20.31 20.50
87 20.32 20.47
88 20.31 20.65
89 20.29 20.47
90 20.40 20.50
91 20.41 20.54
92 20.37 20.46
93 20.37 20.48
94 20.50 20.74
95 20.41 20.81
96 20.38 20.62
97 20.47 20.63
98 20.63 20.64
99 20.49 20.55
100 20.40 20.73
101 20.56 20.75
102 20.40 20.56
103 20.48 20.85
104 20.44 21.00
105 20.38 20.70
106 20.40 20.56
107 20.37 20.60
108 20.47 20.55
109 20.40 20.49
110 20.30 20.63
111 20.30 20.56
112 20.30 20.57
113 20.25 20.45
114 20.31 20.49
115 20.29 20.38
116 20.37 20.46
117 20.28 20.50
118 20.27 20.41
119 20.29 20.41
120 20.22 20.47
121 20.29 20.47
122 20.27 20.42
123 20.35 20.64
124 20.47 20.47
125 20.37 20.34
126 20.15 20.46
127 20.14 20.42
128 20.17 20.42
129 20.18 20.47
130 20.23 20.52
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) LNDEFPBEPIL LNDEFPBELUX LNDEFPBEABD
-4.91995 0.17800 0.22345 0.27790
LNDEFPBEWIT LNDEFPBEZWB LNDEFPBEREG LNDEFPBETAF
0.43353 -0.39559 -0.48909 -0.11634
LNDEFPSOORA LNDEFPSOLEM LNDEFPICET LNDEFPSPORT
-0.42192 1.23161 0.50122 -0.01325
LNDEFBUDBEER `LNDEFBUDSISSS\r`
1.08061 -0.17272
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.047355 -0.011150 -0.001021 0.012569 0.049521
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.91995 1.40157 -3.510 0.000638 ***
LNDEFPBEPIL 0.17800 0.12103 1.471 0.144065
LNDEFPBELUX 0.22345 0.07668 2.914 0.004280 **
LNDEFPBEABD 0.27790 0.04725 5.881 4.01e-08 ***
LNDEFPBEWIT 0.43353 0.07634 5.679 1.02e-07 ***
LNDEFPBEZWB -0.39559 0.12611 -3.137 0.002164 **
LNDEFPBEREG -0.48909 0.09856 -4.962 2.41e-06 ***
LNDEFPBETAF -0.11634 0.12716 -0.915 0.362127
LNDEFPSOORA -0.42192 0.14545 -2.901 0.004453 **
LNDEFPSOLEM 1.23161 0.12527 9.831 < 2e-16 ***
LNDEFPICET 0.50122 0.08636 5.804 5.74e-08 ***
LNDEFPSPORT -0.01325 0.06816 -0.194 0.846194
LNDEFBUDBEER 1.08061 0.03823 28.265 < 2e-16 ***
`LNDEFBUDSISSS\r` -0.17272 0.03447 -5.011 1.96e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.01873 on 116 degrees of freedom
Multiple R-squared: 0.9722, Adjusted R-squared: 0.969
F-statistic: 311.6 on 13 and 116 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,] 0.2542932 0.508586317 0.745706841
[2,] 0.4257362 0.851472494 0.574263753
[3,] 0.5893149 0.821370210 0.410685105
[4,] 0.6744584 0.651083224 0.325541612
[5,] 0.5754138 0.849172304 0.424586152
[6,] 0.5903309 0.819338118 0.409669059
[7,] 0.4863475 0.972694993 0.513652504
[8,] 0.5030821 0.993835853 0.496917926
[9,] 0.6012715 0.797457057 0.398728528
[10,] 0.5553937 0.889212538 0.444606269
[11,] 0.4938671 0.987734189 0.506132905
[12,] 0.4916890 0.983378059 0.508310971
[13,] 0.4458649 0.891729791 0.554135105
[14,] 0.4599349 0.919869871 0.540065065
[15,] 0.4278601 0.855720219 0.572139890
[16,] 0.4261166 0.852233289 0.573883355
[17,] 0.3984707 0.796941379 0.601529310
[18,] 0.4615852 0.923170494 0.538414753
[19,] 0.4213046 0.842609220 0.578695390
[20,] 0.3834947 0.766989461 0.616505270
[21,] 0.4566735 0.913346921 0.543326540
[22,] 0.4895305 0.979060954 0.510469523
[23,] 0.4912297 0.982459346 0.508770327
[24,] 0.4420561 0.884112152 0.557943924
[25,] 0.5277382 0.944523620 0.472261810
[26,] 0.6709309 0.658138100 0.329069050
[27,] 0.6488994 0.702201299 0.351100649
[28,] 0.6011392 0.797721592 0.398860796
[29,] 0.5907202 0.818559657 0.409279828
[30,] 0.6710798 0.657840392 0.328920196
[31,] 0.6199763 0.760047321 0.380023660
[32,] 0.5816258 0.836748328 0.418374164
[33,] 0.5254976 0.949004766 0.474502383
[34,] 0.4894534 0.978906834 0.510546583
[35,] 0.6144402 0.771119685 0.385559843
[36,] 0.6165024 0.766995112 0.383497556
[37,] 0.5723632 0.855273508 0.427636754
[38,] 0.6290834 0.741833155 0.370916577
[39,] 0.5796801 0.840639809 0.420319904
[40,] 0.5627327 0.874534695 0.437267348
[41,] 0.5686420 0.862715905 0.431357952
[42,] 0.5164417 0.967116606 0.483558303
[43,] 0.4663619 0.932723718 0.533638141
[44,] 0.4201327 0.840265390 0.579867305
[45,] 0.3740089 0.748017843 0.625991078
[46,] 0.3345956 0.669191174 0.665404413
[47,] 0.3043419 0.608683705 0.695658147
[48,] 0.2670526 0.534105224 0.732947388
[49,] 0.2655386 0.531077233 0.734461384
[50,] 0.3631031 0.726206128 0.636896936
[51,] 0.3568577 0.713715397 0.643142301
[52,] 0.3643226 0.728645113 0.635677444
[53,] 0.4022051 0.804410241 0.597794880
[54,] 0.4068827 0.813765454 0.593117273
[55,] 0.4163703 0.832740565 0.583629718
[56,] 0.3818334 0.763666763 0.618166619
[57,] 0.3339785 0.667957053 0.666021473
[58,] 0.2850445 0.570088944 0.714955528
[59,] 0.3398059 0.679611883 0.660194059
[60,] 0.3665848 0.733169590 0.633415205
[61,] 0.3536886 0.707377204 0.646311398
[62,] 0.3413607 0.682721435 0.658639283
[63,] 0.3049048 0.609809662 0.695095169
[64,] 0.2628926 0.525785148 0.737107426
[65,] 0.2357980 0.471596100 0.764201950
[66,] 0.2073951 0.414790258 0.792604871
[67,] 0.2169807 0.433961450 0.783019275
[68,] 0.1759179 0.351835740 0.824082130
[69,] 0.1442779 0.288555893 0.855722054
[70,] 0.3903760 0.780752020 0.609623990
[71,] 0.3647461 0.729492228 0.635253886
[72,] 0.3292724 0.658544860 0.670727570
[73,] 0.2769828 0.553965560 0.723017220
[74,] 0.4720125 0.944025087 0.527987457
[75,] 0.4823831 0.964766278 0.517616861
[76,] 0.5912229 0.817554214 0.408777107
[77,] 0.6115656 0.776868737 0.388434369
[78,] 0.6414184 0.717163116 0.358581558
[79,] 0.7306299 0.538740172 0.269370086
[80,] 0.6680828 0.663834406 0.331917203
[81,] 0.6664439 0.667112193 0.333556097
[82,] 0.8606260 0.278747929 0.139373964
[83,] 0.9500065 0.099986967 0.049993484
[84,] 0.9263519 0.147296186 0.073648093
[85,] 0.9532744 0.093451235 0.046725618
[86,] 0.9501489 0.099702143 0.049851072
[87,] 0.9398960 0.120208031 0.060104015
[88,] 0.9084599 0.183080228 0.091540114
[89,] 0.9141811 0.171637791 0.085818895
[90,] 0.8950792 0.209841605 0.104920803
[91,] 0.8374542 0.325091502 0.162545751
[92,] 0.7651628 0.469674319 0.234837159
[93,] 0.9943628 0.011274493 0.005637247
[94,] 0.9979031 0.004193887 0.002096944
[95,] 0.9961912 0.007617569 0.003808784
[96,] 0.9863882 0.027223576 0.013611788
[97,] 0.9597405 0.080518924 0.040259462
> postscript(file="/var/wessaorg/rcomp/tmp/1igad1333289296.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2npz11333289296.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3vt7u1333289296.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4jtgy1333289296.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5kmtr1333289296.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 = 130
Frequency = 1
1 2 3 4 5
0.0134042334 0.0108867524 0.0134981843 -0.0072011381 0.0142572546
6 7 8 9 10
0.0192442970 -0.0034518956 -0.0049452688 0.0320381794 0.0125990020
11 12 13 14 15
0.0059113324 0.0196660413 -0.0095455310 -0.0325383011 0.0028423953
16 17 18 19 20
-0.0128807452 0.0277617621 0.0067673331 -0.0073243807 -0.0106755526
21 22 23 24 25
0.0092544271 -0.0172682708 -0.0051444618 0.0160006979 0.0337085693
26 27 28 29 30
0.0296876528 0.0054014005 -0.0140535379 0.0171173527 -0.0183620388
31 32 33 34 35
-0.0024067390 0.0243064615 0.0041144597 -0.0029556979 -0.0014713866
36 37 38 39 40
-0.0126132868 0.0216265415 0.0248078411 -0.0027310152 0.0054741372
41 42 43 44 45
-0.0351325067 -0.0473548418 -0.0133036101 -0.0114710059 0.0150970854
46 47 48 49 50
0.0252028133 -0.0045570185 0.0078836011 0.0022811875 -0.0199728775
51 52 53 54 55
0.0200639395 0.0062049594 -0.0198932772 -0.0310709869 -0.0121308686
56 57 58 59 60
0.0057167263 0.0204763784 0.0163818221 0.0041442468 0.0083915368
61 62 63 64 65
-0.0081181148 0.0039627973 -0.0064525627 0.0038800402 0.0192533435
66 67 68 69 70
-0.0246978499 -0.0100731004 -0.0089993291 -0.0229865266 -0.0182009209
71 72 73 74 75
-0.0216234245 -0.0093314079 -0.0081391708 -0.0098713784 -0.0343673573
76 77 78 79 80
-0.0257833621 0.0140797588 0.0156868144 0.0100396838 0.0017178650
81 82 83 84 85
-0.0105927404 -0.0121688600 -0.0261870461 0.0021447313 0.0102957032
86 87 88 89 90
0.0495208765 0.0100545357 -0.0065361226 -0.0070581497 0.0258526155
91 92 93 94 95
0.0011104742 -0.0228119926 0.0110131141 -0.0012550412 -0.0090847801
96 97 98 99 100
-0.0167473052 0.0087084314 0.0262806278 -0.0007876289 -0.0203267490
101 102 103 104 105
-0.0081580020 -0.0231379807 -0.0073732392 -0.0066210575 0.0030833267
106 107 108 109 110
0.0314459057 -0.0044791021 0.0058978013 0.0226220260 0.0287417849
111 112 113 114 115
0.0397817268 0.0124779778 0.0080068050 -0.0259542456 -0.0089020128
116 117 118 119 120
0.0199108202 -0.0209162585 -0.0248789685 -0.0063066470 0.0026190028
121 122 123 124 125
0.0123803383 -0.0113085993 -0.0153260798 -0.0314846025 -0.0332251221
126 127 128 129 130
0.0196097986 -0.0021652905 0.0046005506 0.0150892657 -0.0091947781
> postscript(file="/var/wessaorg/rcomp/tmp/63ikg1333289296.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 = 130
Frequency = 1
lag(myerror, k = 1) myerror
0 0.0134042334 NA
1 0.0108867524 0.0134042334
2 0.0134981843 0.0108867524
3 -0.0072011381 0.0134981843
4 0.0142572546 -0.0072011381
5 0.0192442970 0.0142572546
6 -0.0034518956 0.0192442970
7 -0.0049452688 -0.0034518956
8 0.0320381794 -0.0049452688
9 0.0125990020 0.0320381794
10 0.0059113324 0.0125990020
11 0.0196660413 0.0059113324
12 -0.0095455310 0.0196660413
13 -0.0325383011 -0.0095455310
14 0.0028423953 -0.0325383011
15 -0.0128807452 0.0028423953
16 0.0277617621 -0.0128807452
17 0.0067673331 0.0277617621
18 -0.0073243807 0.0067673331
19 -0.0106755526 -0.0073243807
20 0.0092544271 -0.0106755526
21 -0.0172682708 0.0092544271
22 -0.0051444618 -0.0172682708
23 0.0160006979 -0.0051444618
24 0.0337085693 0.0160006979
25 0.0296876528 0.0337085693
26 0.0054014005 0.0296876528
27 -0.0140535379 0.0054014005
28 0.0171173527 -0.0140535379
29 -0.0183620388 0.0171173527
30 -0.0024067390 -0.0183620388
31 0.0243064615 -0.0024067390
32 0.0041144597 0.0243064615
33 -0.0029556979 0.0041144597
34 -0.0014713866 -0.0029556979
35 -0.0126132868 -0.0014713866
36 0.0216265415 -0.0126132868
37 0.0248078411 0.0216265415
38 -0.0027310152 0.0248078411
39 0.0054741372 -0.0027310152
40 -0.0351325067 0.0054741372
41 -0.0473548418 -0.0351325067
42 -0.0133036101 -0.0473548418
43 -0.0114710059 -0.0133036101
44 0.0150970854 -0.0114710059
45 0.0252028133 0.0150970854
46 -0.0045570185 0.0252028133
47 0.0078836011 -0.0045570185
48 0.0022811875 0.0078836011
49 -0.0199728775 0.0022811875
50 0.0200639395 -0.0199728775
51 0.0062049594 0.0200639395
52 -0.0198932772 0.0062049594
53 -0.0310709869 -0.0198932772
54 -0.0121308686 -0.0310709869
55 0.0057167263 -0.0121308686
56 0.0204763784 0.0057167263
57 0.0163818221 0.0204763784
58 0.0041442468 0.0163818221
59 0.0083915368 0.0041442468
60 -0.0081181148 0.0083915368
61 0.0039627973 -0.0081181148
62 -0.0064525627 0.0039627973
63 0.0038800402 -0.0064525627
64 0.0192533435 0.0038800402
65 -0.0246978499 0.0192533435
66 -0.0100731004 -0.0246978499
67 -0.0089993291 -0.0100731004
68 -0.0229865266 -0.0089993291
69 -0.0182009209 -0.0229865266
70 -0.0216234245 -0.0182009209
71 -0.0093314079 -0.0216234245
72 -0.0081391708 -0.0093314079
73 -0.0098713784 -0.0081391708
74 -0.0343673573 -0.0098713784
75 -0.0257833621 -0.0343673573
76 0.0140797588 -0.0257833621
77 0.0156868144 0.0140797588
78 0.0100396838 0.0156868144
79 0.0017178650 0.0100396838
80 -0.0105927404 0.0017178650
81 -0.0121688600 -0.0105927404
82 -0.0261870461 -0.0121688600
83 0.0021447313 -0.0261870461
84 0.0102957032 0.0021447313
85 0.0495208765 0.0102957032
86 0.0100545357 0.0495208765
87 -0.0065361226 0.0100545357
88 -0.0070581497 -0.0065361226
89 0.0258526155 -0.0070581497
90 0.0011104742 0.0258526155
91 -0.0228119926 0.0011104742
92 0.0110131141 -0.0228119926
93 -0.0012550412 0.0110131141
94 -0.0090847801 -0.0012550412
95 -0.0167473052 -0.0090847801
96 0.0087084314 -0.0167473052
97 0.0262806278 0.0087084314
98 -0.0007876289 0.0262806278
99 -0.0203267490 -0.0007876289
100 -0.0081580020 -0.0203267490
101 -0.0231379807 -0.0081580020
102 -0.0073732392 -0.0231379807
103 -0.0066210575 -0.0073732392
104 0.0030833267 -0.0066210575
105 0.0314459057 0.0030833267
106 -0.0044791021 0.0314459057
107 0.0058978013 -0.0044791021
108 0.0226220260 0.0058978013
109 0.0287417849 0.0226220260
110 0.0397817268 0.0287417849
111 0.0124779778 0.0397817268
112 0.0080068050 0.0124779778
113 -0.0259542456 0.0080068050
114 -0.0089020128 -0.0259542456
115 0.0199108202 -0.0089020128
116 -0.0209162585 0.0199108202
117 -0.0248789685 -0.0209162585
118 -0.0063066470 -0.0248789685
119 0.0026190028 -0.0063066470
120 0.0123803383 0.0026190028
121 -0.0113085993 0.0123803383
122 -0.0153260798 -0.0113085993
123 -0.0314846025 -0.0153260798
124 -0.0332251221 -0.0314846025
125 0.0196097986 -0.0332251221
126 -0.0021652905 0.0196097986
127 0.0046005506 -0.0021652905
128 0.0150892657 0.0046005506
129 -0.0091947781 0.0150892657
130 NA -0.0091947781
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.0108867524 0.0134042334
[2,] 0.0134981843 0.0108867524
[3,] -0.0072011381 0.0134981843
[4,] 0.0142572546 -0.0072011381
[5,] 0.0192442970 0.0142572546
[6,] -0.0034518956 0.0192442970
[7,] -0.0049452688 -0.0034518956
[8,] 0.0320381794 -0.0049452688
[9,] 0.0125990020 0.0320381794
[10,] 0.0059113324 0.0125990020
[11,] 0.0196660413 0.0059113324
[12,] -0.0095455310 0.0196660413
[13,] -0.0325383011 -0.0095455310
[14,] 0.0028423953 -0.0325383011
[15,] -0.0128807452 0.0028423953
[16,] 0.0277617621 -0.0128807452
[17,] 0.0067673331 0.0277617621
[18,] -0.0073243807 0.0067673331
[19,] -0.0106755526 -0.0073243807
[20,] 0.0092544271 -0.0106755526
[21,] -0.0172682708 0.0092544271
[22,] -0.0051444618 -0.0172682708
[23,] 0.0160006979 -0.0051444618
[24,] 0.0337085693 0.0160006979
[25,] 0.0296876528 0.0337085693
[26,] 0.0054014005 0.0296876528
[27,] -0.0140535379 0.0054014005
[28,] 0.0171173527 -0.0140535379
[29,] -0.0183620388 0.0171173527
[30,] -0.0024067390 -0.0183620388
[31,] 0.0243064615 -0.0024067390
[32,] 0.0041144597 0.0243064615
[33,] -0.0029556979 0.0041144597
[34,] -0.0014713866 -0.0029556979
[35,] -0.0126132868 -0.0014713866
[36,] 0.0216265415 -0.0126132868
[37,] 0.0248078411 0.0216265415
[38,] -0.0027310152 0.0248078411
[39,] 0.0054741372 -0.0027310152
[40,] -0.0351325067 0.0054741372
[41,] -0.0473548418 -0.0351325067
[42,] -0.0133036101 -0.0473548418
[43,] -0.0114710059 -0.0133036101
[44,] 0.0150970854 -0.0114710059
[45,] 0.0252028133 0.0150970854
[46,] -0.0045570185 0.0252028133
[47,] 0.0078836011 -0.0045570185
[48,] 0.0022811875 0.0078836011
[49,] -0.0199728775 0.0022811875
[50,] 0.0200639395 -0.0199728775
[51,] 0.0062049594 0.0200639395
[52,] -0.0198932772 0.0062049594
[53,] -0.0310709869 -0.0198932772
[54,] -0.0121308686 -0.0310709869
[55,] 0.0057167263 -0.0121308686
[56,] 0.0204763784 0.0057167263
[57,] 0.0163818221 0.0204763784
[58,] 0.0041442468 0.0163818221
[59,] 0.0083915368 0.0041442468
[60,] -0.0081181148 0.0083915368
[61,] 0.0039627973 -0.0081181148
[62,] -0.0064525627 0.0039627973
[63,] 0.0038800402 -0.0064525627
[64,] 0.0192533435 0.0038800402
[65,] -0.0246978499 0.0192533435
[66,] -0.0100731004 -0.0246978499
[67,] -0.0089993291 -0.0100731004
[68,] -0.0229865266 -0.0089993291
[69,] -0.0182009209 -0.0229865266
[70,] -0.0216234245 -0.0182009209
[71,] -0.0093314079 -0.0216234245
[72,] -0.0081391708 -0.0093314079
[73,] -0.0098713784 -0.0081391708
[74,] -0.0343673573 -0.0098713784
[75,] -0.0257833621 -0.0343673573
[76,] 0.0140797588 -0.0257833621
[77,] 0.0156868144 0.0140797588
[78,] 0.0100396838 0.0156868144
[79,] 0.0017178650 0.0100396838
[80,] -0.0105927404 0.0017178650
[81,] -0.0121688600 -0.0105927404
[82,] -0.0261870461 -0.0121688600
[83,] 0.0021447313 -0.0261870461
[84,] 0.0102957032 0.0021447313
[85,] 0.0495208765 0.0102957032
[86,] 0.0100545357 0.0495208765
[87,] -0.0065361226 0.0100545357
[88,] -0.0070581497 -0.0065361226
[89,] 0.0258526155 -0.0070581497
[90,] 0.0011104742 0.0258526155
[91,] -0.0228119926 0.0011104742
[92,] 0.0110131141 -0.0228119926
[93,] -0.0012550412 0.0110131141
[94,] -0.0090847801 -0.0012550412
[95,] -0.0167473052 -0.0090847801
[96,] 0.0087084314 -0.0167473052
[97,] 0.0262806278 0.0087084314
[98,] -0.0007876289 0.0262806278
[99,] -0.0203267490 -0.0007876289
[100,] -0.0081580020 -0.0203267490
[101,] -0.0231379807 -0.0081580020
[102,] -0.0073732392 -0.0231379807
[103,] -0.0066210575 -0.0073732392
[104,] 0.0030833267 -0.0066210575
[105,] 0.0314459057 0.0030833267
[106,] -0.0044791021 0.0314459057
[107,] 0.0058978013 -0.0044791021
[108,] 0.0226220260 0.0058978013
[109,] 0.0287417849 0.0226220260
[110,] 0.0397817268 0.0287417849
[111,] 0.0124779778 0.0397817268
[112,] 0.0080068050 0.0124779778
[113,] -0.0259542456 0.0080068050
[114,] -0.0089020128 -0.0259542456
[115,] 0.0199108202 -0.0089020128
[116,] -0.0209162585 0.0199108202
[117,] -0.0248789685 -0.0209162585
[118,] -0.0063066470 -0.0248789685
[119,] 0.0026190028 -0.0063066470
[120,] 0.0123803383 0.0026190028
[121,] -0.0113085993 0.0123803383
[122,] -0.0153260798 -0.0113085993
[123,] -0.0314846025 -0.0153260798
[124,] -0.0332251221 -0.0314846025
[125,] 0.0196097986 -0.0332251221
[126,] -0.0021652905 0.0196097986
[127,] 0.0046005506 -0.0021652905
[128,] 0.0150892657 0.0046005506
[129,] -0.0091947781 0.0150892657
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.0108867524 0.0134042334
2 0.0134981843 0.0108867524
3 -0.0072011381 0.0134981843
4 0.0142572546 -0.0072011381
5 0.0192442970 0.0142572546
6 -0.0034518956 0.0192442970
7 -0.0049452688 -0.0034518956
8 0.0320381794 -0.0049452688
9 0.0125990020 0.0320381794
10 0.0059113324 0.0125990020
11 0.0196660413 0.0059113324
12 -0.0095455310 0.0196660413
13 -0.0325383011 -0.0095455310
14 0.0028423953 -0.0325383011
15 -0.0128807452 0.0028423953
16 0.0277617621 -0.0128807452
17 0.0067673331 0.0277617621
18 -0.0073243807 0.0067673331
19 -0.0106755526 -0.0073243807
20 0.0092544271 -0.0106755526
21 -0.0172682708 0.0092544271
22 -0.0051444618 -0.0172682708
23 0.0160006979 -0.0051444618
24 0.0337085693 0.0160006979
25 0.0296876528 0.0337085693
26 0.0054014005 0.0296876528
27 -0.0140535379 0.0054014005
28 0.0171173527 -0.0140535379
29 -0.0183620388 0.0171173527
30 -0.0024067390 -0.0183620388
31 0.0243064615 -0.0024067390
32 0.0041144597 0.0243064615
33 -0.0029556979 0.0041144597
34 -0.0014713866 -0.0029556979
35 -0.0126132868 -0.0014713866
36 0.0216265415 -0.0126132868
37 0.0248078411 0.0216265415
38 -0.0027310152 0.0248078411
39 0.0054741372 -0.0027310152
40 -0.0351325067 0.0054741372
41 -0.0473548418 -0.0351325067
42 -0.0133036101 -0.0473548418
43 -0.0114710059 -0.0133036101
44 0.0150970854 -0.0114710059
45 0.0252028133 0.0150970854
46 -0.0045570185 0.0252028133
47 0.0078836011 -0.0045570185
48 0.0022811875 0.0078836011
49 -0.0199728775 0.0022811875
50 0.0200639395 -0.0199728775
51 0.0062049594 0.0200639395
52 -0.0198932772 0.0062049594
53 -0.0310709869 -0.0198932772
54 -0.0121308686 -0.0310709869
55 0.0057167263 -0.0121308686
56 0.0204763784 0.0057167263
57 0.0163818221 0.0204763784
58 0.0041442468 0.0163818221
59 0.0083915368 0.0041442468
60 -0.0081181148 0.0083915368
61 0.0039627973 -0.0081181148
62 -0.0064525627 0.0039627973
63 0.0038800402 -0.0064525627
64 0.0192533435 0.0038800402
65 -0.0246978499 0.0192533435
66 -0.0100731004 -0.0246978499
67 -0.0089993291 -0.0100731004
68 -0.0229865266 -0.0089993291
69 -0.0182009209 -0.0229865266
70 -0.0216234245 -0.0182009209
71 -0.0093314079 -0.0216234245
72 -0.0081391708 -0.0093314079
73 -0.0098713784 -0.0081391708
74 -0.0343673573 -0.0098713784
75 -0.0257833621 -0.0343673573
76 0.0140797588 -0.0257833621
77 0.0156868144 0.0140797588
78 0.0100396838 0.0156868144
79 0.0017178650 0.0100396838
80 -0.0105927404 0.0017178650
81 -0.0121688600 -0.0105927404
82 -0.0261870461 -0.0121688600
83 0.0021447313 -0.0261870461
84 0.0102957032 0.0021447313
85 0.0495208765 0.0102957032
86 0.0100545357 0.0495208765
87 -0.0065361226 0.0100545357
88 -0.0070581497 -0.0065361226
89 0.0258526155 -0.0070581497
90 0.0011104742 0.0258526155
91 -0.0228119926 0.0011104742
92 0.0110131141 -0.0228119926
93 -0.0012550412 0.0110131141
94 -0.0090847801 -0.0012550412
95 -0.0167473052 -0.0090847801
96 0.0087084314 -0.0167473052
97 0.0262806278 0.0087084314
98 -0.0007876289 0.0262806278
99 -0.0203267490 -0.0007876289
100 -0.0081580020 -0.0203267490
101 -0.0231379807 -0.0081580020
102 -0.0073732392 -0.0231379807
103 -0.0066210575 -0.0073732392
104 0.0030833267 -0.0066210575
105 0.0314459057 0.0030833267
106 -0.0044791021 0.0314459057
107 0.0058978013 -0.0044791021
108 0.0226220260 0.0058978013
109 0.0287417849 0.0226220260
110 0.0397817268 0.0287417849
111 0.0124779778 0.0397817268
112 0.0080068050 0.0124779778
113 -0.0259542456 0.0080068050
114 -0.0089020128 -0.0259542456
115 0.0199108202 -0.0089020128
116 -0.0209162585 0.0199108202
117 -0.0248789685 -0.0209162585
118 -0.0063066470 -0.0248789685
119 0.0026190028 -0.0063066470
120 0.0123803383 0.0026190028
121 -0.0113085993 0.0123803383
122 -0.0153260798 -0.0113085993
123 -0.0314846025 -0.0153260798
124 -0.0332251221 -0.0314846025
125 0.0196097986 -0.0332251221
126 -0.0021652905 0.0196097986
127 0.0046005506 -0.0021652905
128 0.0150892657 0.0046005506
129 -0.0091947781 0.0150892657
> 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/7g8k21333289296.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/862ko1333289296.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9ju271333289296.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10j18e1333289296.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ug8e1333289296.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/129pct1333289296.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/13b5lf1333289296.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/14n5v41333289296.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/15ef6k1333289296.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/16iq911333289296.tab")
+ }
>
> try(system("convert tmp/1igad1333289296.ps tmp/1igad1333289296.png",intern=TRUE))
character(0)
> try(system("convert tmp/2npz11333289296.ps tmp/2npz11333289296.png",intern=TRUE))
character(0)
> try(system("convert tmp/3vt7u1333289296.ps tmp/3vt7u1333289296.png",intern=TRUE))
character(0)
> try(system("convert tmp/4jtgy1333289296.ps tmp/4jtgy1333289296.png",intern=TRUE))
character(0)
> try(system("convert tmp/5kmtr1333289296.ps tmp/5kmtr1333289296.png",intern=TRUE))
character(0)
> try(system("convert tmp/63ikg1333289296.ps tmp/63ikg1333289296.png",intern=TRUE))
character(0)
> try(system("convert tmp/7g8k21333289296.ps tmp/7g8k21333289296.png",intern=TRUE))
character(0)
> try(system("convert tmp/862ko1333289296.ps tmp/862ko1333289296.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ju271333289296.ps tmp/9ju271333289296.png",intern=TRUE))
character(0)
> try(system("convert tmp/10j18e1333289296.ps tmp/10j18e1333289296.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.083 0.615 5.748