R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(4.3
+ ,10.8
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+ ,7.8
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+ ,7
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+ ,7.9
+ ,7.8
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+ ,13
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+ ,7.9
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+ ,7.9
+ ,9
+ ,8.2
+ ,8.2
+ ,8.2
+ ,8.1
+ ,7.3
+ ,9
+ ,7.9
+ ,8.2
+ ,8.2
+ ,8.2
+ ,6.9
+ ,9.8
+ ,7.3
+ ,7.9
+ ,8.2
+ ,8.2
+ ,6.6
+ ,10
+ ,6.9
+ ,7.3
+ ,7.9
+ ,8.2
+ ,6.7
+ ,9.8
+ ,6.6
+ ,6.9
+ ,7.3
+ ,7.9
+ ,6.9
+ ,9.3
+ ,6.7
+ ,6.6
+ ,6.9
+ ,7.3
+ ,7
+ ,9
+ ,6.9
+ ,6.7
+ ,6.6
+ ,6.9
+ ,7.1
+ ,9
+ ,7
+ ,6.9
+ ,6.7
+ ,6.6
+ ,7.2
+ ,9.1
+ ,7.1
+ ,7
+ ,6.9
+ ,6.7
+ ,7.1
+ ,9.1
+ ,7.2
+ ,7.1
+ ,7
+ ,6.9
+ ,6.9
+ ,9.1
+ ,7.1
+ ,7.2
+ ,7.1
+ ,7
+ ,7
+ ,9.2
+ ,6.9
+ ,7.1
+ ,7.2
+ ,7.1
+ ,6.8
+ ,8.8
+ ,7
+ ,6.9
+ ,7.1
+ ,7.2
+ ,6.4
+ ,8.3
+ ,6.8
+ ,7
+ ,6.9
+ ,7.1
+ ,6.7
+ ,8.4
+ ,6.4
+ ,6.8
+ ,7
+ ,6.9
+ ,6.6
+ ,8.1
+ ,6.7
+ ,6.4
+ ,6.8
+ ,7
+ ,6.4
+ ,7.7
+ ,6.6
+ ,6.7
+ ,6.4
+ ,6.8
+ ,6.3
+ ,7.9
+ ,6.4
+ ,6.6
+ ,6.7
+ ,6.4
+ ,6.2
+ ,7.9
+ ,6.3
+ ,6.4
+ ,6.6
+ ,6.7
+ ,6.5
+ ,8
+ ,6.2
+ ,6.3
+ ,6.4
+ ,6.6
+ ,6.8
+ ,7.9
+ ,6.5
+ ,6.2
+ ,6.3
+ ,6.4
+ ,6.8
+ ,7.6
+ ,6.8
+ ,6.5
+ ,6.2
+ ,6.3
+ ,6.4
+ ,7.1
+ ,6.8
+ ,6.8
+ ,6.5
+ ,6.2
+ ,6.1
+ ,6.8
+ ,6.4
+ ,6.8
+ ,6.8
+ ,6.5
+ ,5.8
+ ,6.5
+ ,6.1
+ ,6.4
+ ,6.8
+ ,6.8
+ ,6.1
+ ,6.9
+ ,5.8
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.2
+ ,8.2
+ ,6.1
+ ,5.8
+ ,6.1
+ ,6.4
+ ,7.3
+ ,8.7
+ ,7.2
+ ,6.1
+ ,5.8
+ ,6.1
+ ,6.9
+ ,8.3
+ ,7.3
+ ,7.2
+ ,6.1
+ ,5.8
+ ,6.1
+ ,7.9
+ ,6.9
+ ,7.3
+ ,7.2
+ ,6.1
+ ,5.8
+ ,7.5
+ ,6.1
+ ,6.9
+ ,7.3
+ ,7.2
+ ,6.2
+ ,7.8
+ ,5.8
+ ,6.1
+ ,6.9
+ ,7.3
+ ,7.1
+ ,8.3
+ ,6.2
+ ,5.8
+ ,6.1
+ ,6.9
+ ,7.7
+ ,8.4
+ ,7.1
+ ,6.2
+ ,5.8
+ ,6.1
+ ,7.9
+ ,8.2
+ ,7.7
+ ,7.1
+ ,6.2
+ ,5.8
+ ,7.7
+ ,7.7
+ ,7.9
+ ,7.7
+ ,7.1
+ ,6.2
+ ,7.4
+ ,7.2
+ ,7.7
+ ,7.9
+ ,7.7
+ ,7.1
+ ,7.5
+ ,7.3
+ ,7.4
+ ,7.7
+ ,7.9
+ ,7.7
+ ,8
+ ,8.1
+ ,7.5
+ ,7.4
+ ,7.7
+ ,7.9
+ ,8.1
+ ,8.5
+ ,8
+ ,7.5
+ ,7.4
+ ,7.7
+ ,8
+ ,8.4
+ ,8.1
+ ,8
+ ,7.5
+ ,7.4)
+ ,dim=c(6
+ ,236)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:236))
> y <- array(NA,dim=c(6,236),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:236))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly 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
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 4.3 10.8 4.4 4.4 4.5 4.6 1 0 0 0 0 0 0 0 0 0 0 1
2 4.1 10.4 4.3 4.4 4.4 4.5 0 1 0 0 0 0 0 0 0 0 0 2
3 3.9 10.1 4.1 4.3 4.4 4.4 0 0 1 0 0 0 0 0 0 0 0 3
4 3.7 9.8 3.9 4.1 4.3 4.4 0 0 0 1 0 0 0 0 0 0 0 4
5 3.6 9.7 3.7 3.9 4.1 4.3 0 0 0 0 1 0 0 0 0 0 0 5
6 3.9 10.3 3.6 3.7 3.9 4.1 0 0 0 0 0 1 0 0 0 0 0 6
7 4.2 10.9 3.9 3.6 3.7 3.9 0 0 0 0 0 0 1 0 0 0 0 7
8 4.2 10.8 4.2 3.9 3.6 3.7 0 0 0 0 0 0 0 1 0 0 0 8
9 4.1 10.6 4.2 4.2 3.9 3.6 0 0 0 0 0 0 0 0 1 0 0 9
10 4.1 10.4 4.1 4.2 4.2 3.9 0 0 0 0 0 0 0 0 0 1 0 10
11 4.1 10.3 4.1 4.1 4.2 4.2 0 0 0 0 0 0 0 0 0 0 1 11
12 4.1 10.2 4.1 4.1 4.1 4.2 0 0 0 0 0 0 0 0 0 0 0 12
13 4.1 10.0 4.1 4.1 4.1 4.1 1 0 0 0 0 0 0 0 0 0 0 13
14 4.0 9.7 4.1 4.1 4.1 4.1 0 1 0 0 0 0 0 0 0 0 0 14
15 3.9 9.4 4.0 4.1 4.1 4.1 0 0 1 0 0 0 0 0 0 0 0 15
16 3.8 9.2 3.9 4.0 4.1 4.1 0 0 0 1 0 0 0 0 0 0 0 16
17 3.8 9.1 3.8 3.9 4.0 4.1 0 0 0 0 1 0 0 0 0 0 0 17
18 4.0 9.6 3.8 3.8 3.9 4.0 0 0 0 0 0 1 0 0 0 0 0 18
19 4.4 10.2 4.0 3.8 3.8 3.9 0 0 0 0 0 0 1 0 0 0 0 19
20 4.6 10.2 4.4 4.0 3.8 3.8 0 0 0 0 0 0 0 1 0 0 0 20
21 4.6 10.0 4.6 4.4 4.0 3.8 0 0 0 0 0 0 0 0 1 0 0 21
22 4.6 9.9 4.6 4.6 4.4 4.0 0 0 0 0 0 0 0 0 0 1 0 22
23 4.7 9.9 4.6 4.6 4.6 4.4 0 0 0 0 0 0 0 0 0 0 1 23
24 4.8 9.9 4.7 4.6 4.6 4.6 0 0 0 0 0 0 0 0 0 0 0 24
25 4.8 9.7 4.8 4.7 4.6 4.6 1 0 0 0 0 0 0 0 0 0 0 25
26 4.7 9.5 4.8 4.8 4.7 4.6 0 1 0 0 0 0 0 0 0 0 0 26
27 4.7 9.4 4.7 4.8 4.8 4.7 0 0 1 0 0 0 0 0 0 0 0 27
28 4.7 9.3 4.7 4.7 4.8 4.8 0 0 0 1 0 0 0 0 0 0 0 28
29 4.6 9.3 4.7 4.7 4.7 4.8 0 0 0 0 1 0 0 0 0 0 0 29
30 5.0 9.9 4.6 4.7 4.7 4.7 0 0 0 0 0 1 0 0 0 0 0 30
31 5.4 10.5 5.0 4.6 4.7 4.7 0 0 0 0 0 0 1 0 0 0 0 31
32 5.5 10.6 5.4 5.0 4.6 4.7 0 0 0 0 0 0 0 1 0 0 0 32
33 5.6 10.6 5.5 5.4 5.0 4.6 0 0 0 0 0 0 0 0 1 0 0 33
34 5.6 10.5 5.6 5.5 5.4 5.0 0 0 0 0 0 0 0 0 0 1 0 34
35 5.8 10.6 5.6 5.6 5.5 5.4 0 0 0 0 0 0 0 0 0 0 1 35
36 6.0 10.8 5.8 5.6 5.6 5.5 0 0 0 0 0 0 0 0 0 0 0 36
37 6.1 10.8 6.0 5.8 5.6 5.6 1 0 0 0 0 0 0 0 0 0 0 37
38 6.1 10.7 6.1 6.0 5.8 5.6 0 1 0 0 0 0 0 0 0 0 0 38
39 6.0 10.6 6.1 6.1 6.0 5.8 0 0 1 0 0 0 0 0 0 0 0 39
40 6.0 10.6 6.0 6.1 6.1 6.0 0 0 0 1 0 0 0 0 0 0 0 40
41 6.1 10.8 6.0 6.0 6.1 6.1 0 0 0 0 1 0 0 0 0 0 0 41
42 6.5 11.4 6.1 6.0 6.0 6.1 0 0 0 0 0 1 0 0 0 0 0 42
43 7.1 12.2 6.5 6.1 6.0 6.0 0 0 0 0 0 0 1 0 0 0 0 43
44 7.4 12.4 7.1 6.5 6.1 6.0 0 0 0 0 0 0 0 1 0 0 0 44
45 7.4 12.4 7.4 7.1 6.5 6.1 0 0 0 0 0 0 0 0 1 0 0 45
46 7.5 12.3 7.4 7.4 7.1 6.5 0 0 0 0 0 0 0 0 0 1 0 46
47 7.6 12.4 7.5 7.4 7.4 7.1 0 0 0 0 0 0 0 0 0 0 1 47
48 7.8 12.5 7.6 7.5 7.4 7.4 0 0 0 0 0 0 0 0 0 0 0 48
49 7.8 12.5 7.8 7.6 7.5 7.4 1 0 0 0 0 0 0 0 0 0 0 49
50 7.7 12.4 7.8 7.8 7.6 7.5 0 1 0 0 0 0 0 0 0 0 0 50
51 7.6 12.3 7.7 7.8 7.8 7.6 0 0 1 0 0 0 0 0 0 0 0 51
52 7.5 12.2 7.6 7.7 7.8 7.8 0 0 0 1 0 0 0 0 0 0 0 52
53 7.3 12.1 7.5 7.6 7.7 7.8 0 0 0 0 1 0 0 0 0 0 0 53
54 7.6 12.6 7.3 7.5 7.6 7.7 0 0 0 0 0 1 0 0 0 0 0 54
55 8.0 13.2 7.6 7.3 7.5 7.6 0 0 0 0 0 0 1 0 0 0 0 55
56 8.0 13.4 8.0 7.6 7.3 7.5 0 0 0 0 0 0 0 1 0 0 0 56
57 7.9 13.2 8.0 8.0 7.6 7.3 0 0 0 0 0 0 0 0 1 0 0 57
58 7.8 12.9 7.9 8.0 8.0 7.6 0 0 0 0 0 0 0 0 0 1 0 58
59 7.7 12.8 7.8 7.9 8.0 8.0 0 0 0 0 0 0 0 0 0 0 1 59
60 7.8 12.7 7.7 7.8 7.9 8.0 0 0 0 0 0 0 0 0 0 0 0 60
61 7.7 12.6 7.8 7.7 7.8 7.9 1 0 0 0 0 0 0 0 0 0 0 61
62 7.5 12.4 7.7 7.8 7.7 7.8 0 1 0 0 0 0 0 0 0 0 0 62
63 7.3 12.1 7.5 7.7 7.8 7.7 0 0 1 0 0 0 0 0 0 0 0 63
64 7.1 12.0 7.3 7.5 7.7 7.8 0 0 0 1 0 0 0 0 0 0 0 64
65 7.0 11.9 7.1 7.3 7.5 7.7 0 0 0 0 1 0 0 0 0 0 0 65
66 7.3 12.5 7.0 7.1 7.3 7.5 0 0 0 0 0 1 0 0 0 0 0 66
67 7.8 13.2 7.3 7.0 7.1 7.3 0 0 0 0 0 0 1 0 0 0 0 67
68 7.9 13.4 7.8 7.3 7.0 7.1 0 0 0 0 0 0 0 1 0 0 0 68
69 7.9 13.3 7.9 7.8 7.3 7.0 0 0 0 0 0 0 0 0 1 0 0 69
70 7.8 13.0 7.9 7.9 7.8 7.3 0 0 0 0 0 0 0 0 0 1 0 70
71 7.8 12.9 7.8 7.9 7.9 7.8 0 0 0 0 0 0 0 0 0 0 1 71
72 7.9 13.0 7.8 7.8 7.9 7.9 0 0 0 0 0 0 0 0 0 0 0 72
73 7.8 12.9 7.9 7.8 7.8 7.9 1 0 0 0 0 0 0 0 0 0 0 73
74 7.6 12.6 7.8 7.9 7.8 7.8 0 1 0 0 0 0 0 0 0 0 0 74
75 7.4 12.4 7.6 7.8 7.9 7.8 0 0 1 0 0 0 0 0 0 0 0 75
76 7.2 12.1 7.4 7.6 7.8 7.9 0 0 0 1 0 0 0 0 0 0 0 76
77 6.9 11.9 7.2 7.4 7.6 7.8 0 0 0 0 1 0 0 0 0 0 0 77
78 7.1 12.3 6.9 7.2 7.4 7.6 0 0 0 0 0 1 0 0 0 0 0 78
79 7.5 13.0 7.1 6.9 7.2 7.4 0 0 0 0 0 0 1 0 0 0 0 79
80 7.6 13.0 7.5 7.1 6.9 7.2 0 0 0 0 0 0 0 1 0 0 0 80
81 7.4 12.6 7.6 7.5 7.1 6.9 0 0 0 0 0 0 0 0 1 0 0 81
82 7.3 12.2 7.4 7.6 7.5 7.1 0 0 0 0 0 0 0 0 0 1 0 82
83 7.2 12.1 7.3 7.4 7.6 7.5 0 0 0 0 0 0 0 0 0 0 1 83
84 7.3 12.0 7.2 7.3 7.4 7.6 0 0 0 0 0 0 0 0 0 0 0 84
85 7.2 11.8 7.3 7.2 7.3 7.4 1 0 0 0 0 0 0 0 0 0 0 85
86 7.1 11.6 7.2 7.3 7.2 7.3 0 1 0 0 0 0 0 0 0 0 0 86
87 7.0 11.4 7.1 7.2 7.3 7.2 0 0 1 0 0 0 0 0 0 0 0 87
88 6.9 11.2 7.0 7.1 7.2 7.3 0 0 0 1 0 0 0 0 0 0 0 88
89 6.8 11.2 6.9 7.0 7.1 7.2 0 0 0 0 1 0 0 0 0 0 0 89
90 7.2 11.8 6.8 6.9 7.0 7.1 0 0 0 0 0 1 0 0 0 0 0 90
91 7.6 12.5 7.2 6.8 6.9 7.0 0 0 0 0 0 0 1 0 0 0 0 91
92 7.7 12.6 7.6 7.2 6.8 6.9 0 0 0 0 0 0 0 1 0 0 0 92
93 7.6 12.4 7.7 7.6 7.2 6.8 0 0 0 0 0 0 0 0 1 0 0 93
94 7.5 12.1 7.6 7.7 7.6 7.2 0 0 0 0 0 0 0 0 0 1 0 94
95 7.5 12.0 7.5 7.6 7.7 7.6 0 0 0 0 0 0 0 0 0 0 1 95
96 7.6 12.0 7.5 7.5 7.6 7.7 0 0 0 0 0 0 0 0 0 0 0 96
97 7.6 11.9 7.6 7.5 7.5 7.6 1 0 0 0 0 0 0 0 0 0 0 97
98 7.6 11.8 7.6 7.6 7.5 7.5 0 1 0 0 0 0 0 0 0 0 0 98
99 7.5 11.5 7.6 7.6 7.6 7.5 0 0 1 0 0 0 0 0 0 0 0 99
100 7.3 11.3 7.5 7.6 7.6 7.6 0 0 0 1 0 0 0 0 0 0 0 100
101 7.2 11.2 7.3 7.5 7.6 7.6 0 0 0 0 1 0 0 0 0 0 0 101
102 7.4 11.6 7.2 7.3 7.5 7.6 0 0 0 0 0 1 0 0 0 0 0 102
103 8.0 12.2 7.4 7.2 7.3 7.5 0 0 0 0 0 0 1 0 0 0 0 103
104 8.2 12.2 8.0 7.4 7.2 7.3 0 0 0 0 0 0 0 1 0 0 0 104
105 8.0 11.7 8.2 8.0 7.4 7.2 0 0 0 0 0 0 0 0 1 0 0 105
106 7.7 11.2 8.0 8.2 8.0 7.4 0 0 0 0 0 0 0 0 0 1 0 106
107 7.7 11.0 7.7 8.0 8.2 8.0 0 0 0 0 0 0 0 0 0 0 1 107
108 7.8 10.9 7.7 7.7 8.0 8.2 0 0 0 0 0 0 0 0 0 0 0 108
109 7.8 10.8 7.8 7.7 7.7 8.0 1 0 0 0 0 0 0 0 0 0 0 109
110 7.7 10.5 7.8 7.8 7.7 7.7 0 1 0 0 0 0 0 0 0 0 0 110
111 7.5 10.2 7.7 7.8 7.8 7.7 0 0 1 0 0 0 0 0 0 0 0 111
112 7.3 10.0 7.5 7.7 7.8 7.8 0 0 0 1 0 0 0 0 0 0 0 112
113 7.1 9.9 7.3 7.5 7.7 7.8 0 0 0 0 1 0 0 0 0 0 0 113
114 7.1 10.3 7.1 7.3 7.5 7.7 0 0 0 0 0 1 0 0 0 0 0 114
115 7.2 10.7 7.1 7.1 7.3 7.5 0 0 0 0 0 0 1 0 0 0 0 115
116 6.8 10.4 7.2 7.1 7.1 7.3 0 0 0 0 0 0 0 1 0 0 0 116
117 6.6 10.1 6.8 7.2 7.1 7.1 0 0 0 0 0 0 0 0 1 0 0 117
118 6.4 9.7 6.6 6.8 7.2 7.1 0 0 0 0 0 0 0 0 0 1 0 118
119 6.4 9.4 6.4 6.6 6.8 7.2 0 0 0 0 0 0 0 0 0 0 1 119
120 6.5 8.9 6.4 6.4 6.6 6.8 0 0 0 0 0 0 0 0 0 0 0 120
121 6.3 8.4 6.5 6.4 6.4 6.6 1 0 0 0 0 0 0 0 0 0 0 121
122 5.9 8.1 6.3 6.5 6.4 6.4 0 1 0 0 0 0 0 0 0 0 0 122
123 5.5 8.3 5.9 6.3 6.5 6.4 0 0 1 0 0 0 0 0 0 0 0 123
124 5.2 8.1 5.5 5.9 6.3 6.5 0 0 0 1 0 0 0 0 0 0 0 124
125 4.9 8.0 5.2 5.5 5.9 6.3 0 0 0 0 1 0 0 0 0 0 0 125
126 5.4 8.7 4.9 5.2 5.5 5.9 0 0 0 0 0 1 0 0 0 0 0 126
127 5.8 9.2 5.4 4.9 5.2 5.5 0 0 0 0 0 0 1 0 0 0 0 127
128 5.7 9.0 5.8 5.4 4.9 5.2 0 0 0 0 0 0 0 1 0 0 0 128
129 5.6 8.9 5.7 5.8 5.4 4.9 0 0 0 0 0 0 0 0 1 0 0 129
130 5.5 8.5 5.6 5.7 5.8 5.4 0 0 0 0 0 0 0 0 0 1 0 130
131 5.4 8.1 5.5 5.6 5.7 5.8 0 0 0 0 0 0 0 0 0 0 1 131
132 5.4 7.5 5.4 5.5 5.6 5.7 0 0 0 0 0 0 0 0 0 0 0 132
133 5.4 7.1 5.4 5.4 5.5 5.6 1 0 0 0 0 0 0 0 0 0 0 133
134 5.5 6.9 5.4 5.4 5.4 5.5 0 1 0 0 0 0 0 0 0 0 0 134
135 5.8 7.1 5.5 5.4 5.4 5.4 0 0 1 0 0 0 0 0 0 0 0 135
136 5.7 7.0 5.8 5.5 5.4 5.4 0 0 0 1 0 0 0 0 0 0 0 136
137 5.4 6.7 5.7 5.8 5.5 5.4 0 0 0 0 1 0 0 0 0 0 0 137
138 5.6 7.0 5.4 5.7 5.8 5.5 0 0 0 0 0 1 0 0 0 0 0 138
139 5.8 7.3 5.6 5.4 5.7 5.8 0 0 0 0 0 0 1 0 0 0 0 139
140 6.2 7.7 5.8 5.6 5.4 5.7 0 0 0 0 0 0 0 1 0 0 0 140
141 6.8 8.4 6.2 5.8 5.6 5.4 0 0 0 0 0 0 0 0 1 0 0 141
142 6.7 8.4 6.8 6.2 5.8 5.6 0 0 0 0 0 0 0 0 0 1 0 142
143 6.7 8.8 6.7 6.8 6.2 5.8 0 0 0 0 0 0 0 0 0 0 1 143
144 6.4 9.1 6.7 6.7 6.8 6.2 0 0 0 0 0 0 0 0 0 0 0 144
145 6.3 9.0 6.4 6.7 6.7 6.8 1 0 0 0 0 0 0 0 0 0 0 145
146 6.3 8.6 6.3 6.4 6.7 6.7 0 1 0 0 0 0 0 0 0 0 0 146
147 6.4 7.9 6.3 6.3 6.4 6.7 0 0 1 0 0 0 0 0 0 0 0 147
148 6.3 7.7 6.4 6.3 6.3 6.4 0 0 0 1 0 0 0 0 0 0 0 148
149 6.0 7.8 6.3 6.4 6.3 6.3 0 0 0 0 1 0 0 0 0 0 0 149
150 6.3 9.2 6.0 6.3 6.4 6.3 0 0 0 0 0 1 0 0 0 0 0 150
151 6.3 9.4 6.3 6.0 6.3 6.4 0 0 0 0 0 0 1 0 0 0 0 151
152 6.6 9.2 6.3 6.3 6.0 6.3 0 0 0 0 0 0 0 1 0 0 0 152
153 7.5 8.7 6.6 6.3 6.3 6.0 0 0 0 0 0 0 0 0 1 0 0 153
154 7.8 8.4 7.5 6.6 6.3 6.3 0 0 0 0 0 0 0 0 0 1 0 154
155 7.9 8.6 7.8 7.5 6.6 6.3 0 0 0 0 0 0 0 0 0 0 1 155
156 7.8 9.0 7.9 7.8 7.5 6.6 0 0 0 0 0 0 0 0 0 0 0 156
157 7.6 9.1 7.8 7.9 7.8 7.5 1 0 0 0 0 0 0 0 0 0 0 157
158 7.5 8.7 7.6 7.8 7.9 7.8 0 1 0 0 0 0 0 0 0 0 0 158
159 7.6 8.2 7.5 7.6 7.8 7.9 0 0 1 0 0 0 0 0 0 0 0 159
160 7.5 7.9 7.6 7.5 7.6 7.8 0 0 0 1 0 0 0 0 0 0 0 160
161 7.3 7.9 7.5 7.6 7.5 7.6 0 0 0 0 1 0 0 0 0 0 0 161
162 7.6 9.1 7.3 7.5 7.6 7.5 0 0 0 0 0 1 0 0 0 0 0 162
163 7.5 9.4 7.6 7.3 7.5 7.6 0 0 0 0 0 0 1 0 0 0 0 163
164 7.6 9.4 7.5 7.6 7.3 7.5 0 0 0 0 0 0 0 1 0 0 0 164
165 7.9 9.1 7.6 7.5 7.6 7.3 0 0 0 0 0 0 0 0 1 0 0 165
166 7.9 9.0 7.9 7.6 7.5 7.6 0 0 0 0 0 0 0 0 0 1 0 166
167 8.1 9.3 7.9 7.9 7.6 7.5 0 0 0 0 0 0 0 0 0 0 1 167
168 8.2 9.9 8.1 7.9 7.9 7.6 0 0 0 0 0 0 0 0 0 0 0 168
169 8.0 9.8 8.2 8.1 7.9 7.9 1 0 0 0 0 0 0 0 0 0 0 169
170 7.5 9.3 8.0 8.2 8.1 7.9 0 1 0 0 0 0 0 0 0 0 0 170
171 6.8 8.3 7.5 8.0 8.2 8.1 0 0 1 0 0 0 0 0 0 0 0 171
172 6.5 8.0 6.8 7.5 8.0 8.2 0 0 0 1 0 0 0 0 0 0 0 172
173 6.6 8.5 6.5 6.8 7.5 8.0 0 0 0 0 1 0 0 0 0 0 0 173
174 7.6 10.4 6.6 6.5 6.8 7.5 0 0 0 0 0 1 0 0 0 0 0 174
175 8.0 11.1 7.6 6.6 6.5 6.8 0 0 0 0 0 0 1 0 0 0 0 175
176 8.1 10.9 8.0 7.6 6.6 6.5 0 0 0 0 0 0 0 1 0 0 0 176
177 7.7 10.0 8.1 8.0 7.6 6.6 0 0 0 0 0 0 0 0 1 0 0 177
178 7.5 9.2 7.7 8.1 8.0 7.6 0 0 0 0 0 0 0 0 0 1 0 178
179 7.6 9.2 7.5 7.7 8.1 8.0 0 0 0 0 0 0 0 0 0 0 1 179
180 7.8 9.5 7.6 7.5 7.7 8.1 0 0 0 0 0 0 0 0 0 0 0 180
181 7.8 9.6 7.8 7.6 7.5 7.7 1 0 0 0 0 0 0 0 0 0 0 181
182 7.8 9.5 7.8 7.8 7.6 7.5 0 1 0 0 0 0 0 0 0 0 0 182
183 7.5 9.1 7.8 7.8 7.8 7.6 0 0 1 0 0 0 0 0 0 0 0 183
184 7.5 8.9 7.5 7.8 7.8 7.8 0 0 0 1 0 0 0 0 0 0 0 184
185 7.1 9.0 7.5 7.5 7.8 7.8 0 0 0 0 1 0 0 0 0 0 0 185
186 7.5 10.1 7.1 7.5 7.5 7.8 0 0 0 0 0 1 0 0 0 0 0 186
187 7.5 10.3 7.5 7.1 7.5 7.5 0 0 0 0 0 0 1 0 0 0 0 187
188 7.6 10.2 7.5 7.5 7.1 7.5 0 0 0 0 0 0 0 1 0 0 0 188
189 7.7 9.6 7.6 7.5 7.5 7.1 0 0 0 0 0 0 0 0 1 0 0 189
190 7.7 9.2 7.7 7.6 7.5 7.5 0 0 0 0 0 0 0 0 0 1 0 190
191 7.9 9.3 7.7 7.7 7.6 7.5 0 0 0 0 0 0 0 0 0 0 1 191
192 8.1 9.4 7.9 7.7 7.7 7.6 0 0 0 0 0 0 0 0 0 0 0 192
193 8.2 9.4 8.1 7.9 7.7 7.7 1 0 0 0 0 0 0 0 0 0 0 193
194 8.2 9.2 8.2 8.1 7.9 7.7 0 1 0 0 0 0 0 0 0 0 0 194
195 8.2 9.0 8.2 8.2 8.1 7.9 0 0 1 0 0 0 0 0 0 0 0 195
196 7.9 9.0 8.2 8.2 8.2 8.1 0 0 0 1 0 0 0 0 0 0 0 196
197 7.3 9.0 7.9 8.2 8.2 8.2 0 0 0 0 1 0 0 0 0 0 0 197
198 6.9 9.8 7.3 7.9 8.2 8.2 0 0 0 0 0 1 0 0 0 0 0 198
199 6.6 10.0 6.9 7.3 7.9 8.2 0 0 0 0 0 0 1 0 0 0 0 199
200 6.7 9.8 6.6 6.9 7.3 7.9 0 0 0 0 0 0 0 1 0 0 0 200
201 6.9 9.3 6.7 6.6 6.9 7.3 0 0 0 0 0 0 0 0 1 0 0 201
202 7.0 9.0 6.9 6.7 6.6 6.9 0 0 0 0 0 0 0 0 0 1 0 202
203 7.1 9.0 7.0 6.9 6.7 6.6 0 0 0 0 0 0 0 0 0 0 1 203
204 7.2 9.1 7.1 7.0 6.9 6.7 0 0 0 0 0 0 0 0 0 0 0 204
205 7.1 9.1 7.2 7.1 7.0 6.9 1 0 0 0 0 0 0 0 0 0 0 205
206 6.9 9.1 7.1 7.2 7.1 7.0 0 1 0 0 0 0 0 0 0 0 0 206
207 7.0 9.2 6.9 7.1 7.2 7.1 0 0 1 0 0 0 0 0 0 0 0 207
208 6.8 8.8 7.0 6.9 7.1 7.2 0 0 0 1 0 0 0 0 0 0 0 208
209 6.4 8.3 6.8 7.0 6.9 7.1 0 0 0 0 1 0 0 0 0 0 0 209
210 6.7 8.4 6.4 6.8 7.0 6.9 0 0 0 0 0 1 0 0 0 0 0 210
211 6.6 8.1 6.7 6.4 6.8 7.0 0 0 0 0 0 0 1 0 0 0 0 211
212 6.4 7.7 6.6 6.7 6.4 6.8 0 0 0 0 0 0 0 1 0 0 0 212
213 6.3 7.9 6.4 6.6 6.7 6.4 0 0 0 0 0 0 0 0 1 0 0 213
214 6.2 7.9 6.3 6.4 6.6 6.7 0 0 0 0 0 0 0 0 0 1 0 214
215 6.5 8.0 6.2 6.3 6.4 6.6 0 0 0 0 0 0 0 0 0 0 1 215
216 6.8 7.9 6.5 6.2 6.3 6.4 0 0 0 0 0 0 0 0 0 0 0 216
217 6.8 7.6 6.8 6.5 6.2 6.3 1 0 0 0 0 0 0 0 0 0 0 217
218 6.4 7.1 6.8 6.8 6.5 6.2 0 1 0 0 0 0 0 0 0 0 0 218
219 6.1 6.8 6.4 6.8 6.8 6.5 0 0 1 0 0 0 0 0 0 0 0 219
220 5.8 6.5 6.1 6.4 6.8 6.8 0 0 0 1 0 0 0 0 0 0 0 220
221 6.1 6.9 5.8 6.1 6.4 6.8 0 0 0 0 1 0 0 0 0 0 0 221
222 7.2 8.2 6.1 5.8 6.1 6.4 0 0 0 0 0 1 0 0 0 0 0 222
223 7.3 8.7 7.2 6.1 5.8 6.1 0 0 0 0 0 0 1 0 0 0 0 223
224 6.9 8.3 7.3 7.2 6.1 5.8 0 0 0 0 0 0 0 1 0 0 0 224
225 6.1 7.9 6.9 7.3 7.2 6.1 0 0 0 0 0 0 0 0 1 0 0 225
226 5.8 7.5 6.1 6.9 7.3 7.2 0 0 0 0 0 0 0 0 0 1 0 226
227 6.2 7.8 5.8 6.1 6.9 7.3 0 0 0 0 0 0 0 0 0 0 1 227
228 7.1 8.3 6.2 5.8 6.1 6.9 0 0 0 0 0 0 0 0 0 0 0 228
229 7.7 8.4 7.1 6.2 5.8 6.1 1 0 0 0 0 0 0 0 0 0 0 229
230 7.9 8.2 7.7 7.1 6.2 5.8 0 1 0 0 0 0 0 0 0 0 0 230
231 7.7 7.7 7.9 7.7 7.1 6.2 0 0 1 0 0 0 0 0 0 0 0 231
232 7.4 7.2 7.7 7.9 7.7 7.1 0 0 0 1 0 0 0 0 0 0 0 232
233 7.5 7.3 7.4 7.7 7.9 7.7 0 0 0 0 1 0 0 0 0 0 0 233
234 8.0 8.1 7.5 7.4 7.7 7.9 0 0 0 0 0 1 0 0 0 0 0 234
235 8.1 8.5 8.0 7.5 7.4 7.7 0 0 0 0 0 0 1 0 0 0 0 235
236 8.0 8.4 8.1 8.0 7.5 7.4 0 0 0 0 0 0 0 1 0 0 0 236
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
0.0335157 0.0317668 1.4949443 -0.6694536 -0.1268620 0.2461681
M1 M2 M3 M4 M5 M6
-0.1690602 -0.1435000 -0.1012949 -0.1797672 -0.1857972 0.2691566
M7 M8 M9 M10 M11 t
-0.1149098 -0.1710358 -0.0089030 -0.0687879 0.0463263 0.0008433
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.528799 -0.099096 0.004607 0.091838 0.743133
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0335157 0.1323167 0.253 0.800276
X 0.0317668 0.0191353 1.660 0.098329 .
Y1 1.4949443 0.0669470 22.330 < 2e-16 ***
Y2 -0.6694536 0.1210430 -5.531 9.09e-08 ***
Y3 -0.1268620 0.1213258 -1.046 0.296889
Y4 0.2461681 0.0662099 3.718 0.000255 ***
M1 -0.1690602 0.0573165 -2.950 0.003529 **
M2 -0.1435000 0.0586628 -2.446 0.015230 *
M3 -0.1012949 0.0584632 -1.733 0.084576 .
M4 -0.1797672 0.0586731 -3.064 0.002460 **
M5 -0.1857972 0.0594494 -3.125 0.002018 **
M6 0.2691566 0.0588418 4.574 8.02e-06 ***
M7 -0.1149098 0.0605306 -1.898 0.058968 .
M8 -0.1710358 0.0651586 -2.625 0.009280 **
M9 -0.0089030 0.0617367 -0.144 0.885469
M10 -0.0687879 0.0587487 -1.171 0.242924
M11 0.0463263 0.0579017 0.800 0.424533
t 0.0008433 0.0005187 1.626 0.105408
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1753 on 218 degrees of freedom
Multiple R-squared: 0.9801, Adjusted R-squared: 0.9785
F-statistic: 630.4 on 17 and 218 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,] 6.442658e-03 1.288532e-02 0.99355734
[2,] 9.109792e-04 1.821958e-03 0.99908902
[3,] 2.090780e-04 4.181559e-04 0.99979092
[4,] 5.761631e-04 1.152326e-03 0.99942384
[5,] 6.563734e-04 1.312747e-03 0.99934363
[6,] 3.485169e-04 6.970339e-04 0.99965148
[7,] 8.963993e-05 1.792799e-04 0.99991036
[8,] 2.199636e-05 4.399272e-05 0.99997800
[9,] 1.906204e-05 3.812407e-05 0.99998094
[10,] 1.057217e-05 2.114435e-05 0.99998943
[11,] 5.393642e-06 1.078728e-05 0.99999461
[12,] 2.334167e-06 4.668333e-06 0.99999767
[13,] 8.236528e-07 1.647306e-06 0.99999918
[14,] 3.748299e-07 7.496598e-07 0.99999963
[15,] 2.479407e-07 4.958815e-07 0.99999975
[16,] 1.198190e-07 2.396380e-07 0.99999988
[17,] 4.029711e-08 8.059422e-08 0.99999996
[18,] 1.088057e-08 2.176113e-08 0.99999999
[19,] 1.482982e-08 2.965964e-08 0.99999999
[20,] 5.570623e-09 1.114125e-08 0.99999999
[21,] 1.600293e-09 3.200586e-09 1.00000000
[22,] 4.369672e-10 8.739344e-10 1.00000000
[23,] 1.384185e-09 2.768370e-09 1.00000000
[24,] 1.044010e-09 2.088020e-09 1.00000000
[25,] 4.872684e-10 9.745369e-10 1.00000000
[26,] 1.799021e-10 3.598043e-10 1.00000000
[27,] 2.394844e-10 4.789688e-10 1.00000000
[28,] 7.377247e-11 1.475449e-10 1.00000000
[29,] 1.423189e-10 2.846378e-10 1.00000000
[30,] 1.718917e-10 3.437833e-10 1.00000000
[31,] 1.760191e-10 3.520383e-10 1.00000000
[32,] 1.164360e-10 2.328720e-10 1.00000000
[33,] 6.435879e-10 1.287176e-09 1.00000000
[34,] 2.283646e-10 4.567291e-10 1.00000000
[35,] 1.036459e-10 2.072917e-10 1.00000000
[36,] 7.246038e-11 1.449208e-10 1.00000000
[37,] 3.695520e-11 7.391041e-11 1.00000000
[38,] 1.992409e-11 3.984819e-11 1.00000000
[39,] 1.238977e-11 2.477954e-11 1.00000000
[40,] 7.262033e-12 1.452407e-11 1.00000000
[41,] 2.997738e-12 5.995477e-12 1.00000000
[42,] 1.115048e-12 2.230097e-12 1.00000000
[43,] 6.263023e-13 1.252605e-12 1.00000000
[44,] 2.460686e-13 4.921372e-13 1.00000000
[45,] 9.483471e-14 1.896694e-13 1.00000000
[46,] 3.594557e-14 7.189113e-14 1.00000000
[47,] 4.809271e-14 9.618542e-14 1.00000000
[48,] 2.496459e-14 4.992917e-14 1.00000000
[49,] 8.889829e-15 1.777966e-14 1.00000000
[50,] 2.321356e-14 4.642712e-14 1.00000000
[51,] 9.434778e-15 1.886956e-14 1.00000000
[52,] 3.208702e-15 6.417404e-15 1.00000000
[53,] 1.396252e-15 2.792504e-15 1.00000000
[54,] 6.223682e-16 1.244736e-15 1.00000000
[55,] 2.513552e-16 5.027104e-16 1.00000000
[56,] 9.037022e-17 1.807404e-16 1.00000000
[57,] 2.581679e-16 5.163358e-16 1.00000000
[58,] 9.149457e-17 1.829891e-16 1.00000000
[59,] 8.874825e-17 1.774965e-16 1.00000000
[60,] 6.443463e-17 1.288693e-16 1.00000000
[61,] 1.314395e-16 2.628790e-16 1.00000000
[62,] 5.578848e-17 1.115770e-16 1.00000000
[63,] 4.247516e-17 8.495032e-17 1.00000000
[64,] 3.056872e-17 6.113744e-17 1.00000000
[65,] 1.173204e-17 2.346407e-17 1.00000000
[66,] 6.706646e-18 1.341329e-17 1.00000000
[67,] 2.432357e-18 4.864715e-18 1.00000000
[68,] 8.260445e-19 1.652089e-18 1.00000000
[69,] 2.925841e-19 5.851683e-19 1.00000000
[70,] 1.595412e-19 3.190824e-19 1.00000000
[71,] 1.275250e-19 2.550500e-19 1.00000000
[72,] 4.684905e-20 9.369810e-20 1.00000000
[73,] 2.742704e-20 5.485408e-20 1.00000000
[74,] 1.274323e-20 2.548646e-20 1.00000000
[75,] 4.303348e-21 8.606697e-21 1.00000000
[76,] 1.435068e-21 2.870136e-21 1.00000000
[77,] 5.847118e-22 1.169424e-21 1.00000000
[78,] 5.947985e-22 1.189597e-21 1.00000000
[79,] 2.021632e-22 4.043263e-22 1.00000000
[80,] 3.237414e-22 6.474828e-22 1.00000000
[81,] 1.283038e-22 2.566076e-22 1.00000000
[82,] 1.319939e-22 2.639877e-22 1.00000000
[83,] 4.212145e-20 8.424290e-20 1.00000000
[84,] 1.779971e-20 3.559942e-20 1.00000000
[85,] 1.474366e-19 2.948732e-19 1.00000000
[86,] 6.949550e-18 1.389910e-17 1.00000000
[87,] 4.589214e-18 9.178428e-18 1.00000000
[88,] 2.222211e-18 4.444421e-18 1.00000000
[89,] 1.243422e-18 2.486844e-18 1.00000000
[90,] 4.827706e-19 9.655412e-19 1.00000000
[91,] 2.976082e-19 5.952163e-19 1.00000000
[92,] 1.554259e-19 3.108519e-19 1.00000000
[93,] 7.623137e-20 1.524627e-19 1.00000000
[94,] 1.158559e-18 2.317118e-18 1.00000000
[95,] 4.549252e-18 9.098503e-18 1.00000000
[96,] 1.751591e-16 3.503182e-16 1.00000000
[97,] 1.563189e-15 3.126377e-15 1.00000000
[98,] 8.910555e-16 1.782111e-15 1.00000000
[99,] 1.212678e-15 2.425357e-15 1.00000000
[100,] 6.079082e-16 1.215816e-15 1.00000000
[101,] 8.147717e-16 1.629543e-15 1.00000000
[102,] 1.592117e-15 3.184233e-15 1.00000000
[103,] 1.153782e-15 2.307564e-15 1.00000000
[104,] 5.813641e-16 1.162728e-15 1.00000000
[105,] 4.664535e-16 9.329071e-16 1.00000000
[106,] 2.523137e-14 5.046273e-14 1.00000000
[107,] 2.032023e-14 4.064045e-14 1.00000000
[108,] 1.343273e-13 2.686547e-13 1.00000000
[109,] 6.948137e-14 1.389627e-13 1.00000000
[110,] 3.490624e-14 6.981248e-14 1.00000000
[111,] 8.873211e-14 1.774642e-13 1.00000000
[112,] 5.820966e-14 1.164193e-13 1.00000000
[113,] 6.358371e-14 1.271674e-13 1.00000000
[114,] 2.881628e-13 5.763255e-13 1.00000000
[115,] 4.475453e-12 8.950905e-12 1.00000000
[116,] 1.062485e-11 2.124969e-11 1.00000000
[117,] 1.462006e-11 2.924011e-11 1.00000000
[118,] 8.060993e-12 1.612199e-11 1.00000000
[119,] 8.239866e-12 1.647973e-11 1.00000000
[120,] 1.021546e-09 2.043092e-09 1.00000000
[121,] 4.710489e-08 9.420977e-08 0.99999995
[122,] 3.250131e-07 6.500262e-07 0.99999967
[123,] 2.015714e-07 4.031428e-07 0.99999980
[124,] 1.838029e-06 3.676058e-06 0.99999816
[125,] 1.504353e-06 3.008707e-06 0.99999850
[126,] 1.182830e-06 2.365661e-06 0.99999882
[127,] 9.958754e-07 1.991751e-06 0.99999900
[128,] 7.806895e-07 1.561379e-06 0.99999922
[129,] 8.624495e-07 1.724899e-06 0.99999914
[130,] 6.449150e-07 1.289830e-06 0.99999936
[131,] 2.065345e-06 4.130690e-06 0.99999793
[132,] 5.996620e-06 1.199324e-05 0.99999400
[133,] 5.646040e-03 1.129208e-02 0.99435396
[134,] 4.882507e-03 9.765014e-03 0.99511749
[135,] 3.644886e-03 7.289771e-03 0.99635511
[136,] 2.978101e-03 5.956203e-03 0.99702190
[137,] 2.338598e-03 4.677197e-03 0.99766140
[138,] 1.940303e-03 3.880607e-03 0.99805970
[139,] 1.923020e-03 3.846040e-03 0.99807698
[140,] 1.462796e-03 2.925593e-03 0.99853720
[141,] 1.027272e-03 2.054545e-03 0.99897273
[142,] 7.434963e-04 1.486993e-03 0.99925650
[143,] 1.420930e-03 2.841860e-03 0.99857907
[144,] 2.037416e-03 4.074832e-03 0.99796258
[145,] 4.953020e-03 9.906041e-03 0.99504698
[146,] 3.803934e-03 7.607868e-03 0.99619607
[147,] 4.041414e-03 8.082827e-03 0.99595859
[148,] 2.990516e-03 5.981033e-03 0.99700948
[149,] 2.275653e-03 4.551306e-03 0.99772435
[150,] 2.697668e-03 5.395337e-03 0.99730233
[151,] 7.460480e-03 1.492096e-02 0.99253952
[152,] 6.032506e-03 1.206501e-02 0.99396749
[153,] 5.655991e-03 1.131198e-02 0.99434401
[154,] 1.037642e-02 2.075283e-02 0.98962358
[155,] 1.177661e-02 2.355323e-02 0.98822339
[156,] 1.223474e-02 2.446947e-02 0.98776526
[157,] 1.273691e-02 2.547382e-02 0.98726309
[158,] 1.813921e-02 3.627841e-02 0.98186079
[159,] 1.412329e-02 2.824658e-02 0.98587671
[160,] 1.109316e-02 2.218632e-02 0.98890684
[161,] 8.085706e-03 1.617141e-02 0.99191429
[162,] 7.272636e-03 1.454527e-02 0.99272736
[163,] 8.606943e-03 1.721389e-02 0.99139306
[164,] 2.437764e-02 4.875527e-02 0.97562236
[165,] 4.553788e-02 9.107576e-02 0.95446212
[166,] 6.926276e-02 1.385255e-01 0.93073724
[167,] 6.721887e-02 1.344377e-01 0.93278113
[168,] 7.647860e-02 1.529572e-01 0.92352140
[169,] 9.349441e-02 1.869888e-01 0.90650559
[170,] 7.968581e-02 1.593716e-01 0.92031419
[171,] 8.397389e-02 1.679478e-01 0.91602611
[172,] 7.053949e-02 1.410790e-01 0.92946051
[173,] 8.662666e-02 1.732533e-01 0.91337334
[174,] 1.131044e-01 2.262088e-01 0.88689562
[175,] 1.340994e-01 2.681987e-01 0.86590065
[176,] 1.592255e-01 3.184509e-01 0.84077453
[177,] 1.535103e-01 3.070206e-01 0.84648970
[178,] 4.168683e-01 8.337367e-01 0.58313166
[179,] 3.865783e-01 7.731567e-01 0.61342165
[180,] 3.428171e-01 6.856341e-01 0.65718293
[181,] 2.927655e-01 5.855309e-01 0.70723454
[182,] 2.587145e-01 5.174291e-01 0.74128546
[183,] 2.201723e-01 4.403446e-01 0.77982770
[184,] 1.908557e-01 3.817114e-01 0.80914432
[185,] 1.490049e-01 2.980098e-01 0.85099510
[186,] 1.078472e-01 2.156943e-01 0.89215284
[187,] 1.215066e-01 2.430132e-01 0.87849342
[188,] 8.637888e-02 1.727578e-01 0.91362112
[189,] 5.812556e-01 8.374887e-01 0.41874435
[190,] 8.779998e-01 2.440004e-01 0.12200022
[191,] 9.600599e-01 7.988025e-02 0.03994013
[192,] 9.363237e-01 1.273526e-01 0.06367631
[193,] 9.261349e-01 1.477302e-01 0.07386512
[194,] 8.598370e-01 2.803259e-01 0.14016297
[195,] 7.423960e-01 5.152079e-01 0.25760397
> postscript(file="/var/www/html/rcomp/tmp/1wl241262084880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2mc5h1262084880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3cert1262084880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4fo7r1262084880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5y4ef1262084880.ps",horizontal=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 = 236
Frequency = 1
1 2 3 4 5 6
-0.102033352 -0.154305152 -0.131163237 -0.091592365 -0.018886405 -0.154278764
7 8 9 10 11 12
0.018316759 -0.134323681 -0.127434939 0.051662618 -0.201914002 -0.165940606
13 14 15 16 17 18
0.033246459 -0.083627060 -0.067651022 -0.001119696 0.077106588 -0.249588766
19 20 21 22 23 24
0.227515947 0.043328425 -0.119129346 0.078490838 -0.010561502 -0.063806634
25 26 27 28 29 30
0.028214554 -0.012204079 0.085487993 0.074731398 -0.032768066 0.066485899
31 32 33 34 35 36
0.165725776 -0.025050709 0.105621807 0.037568577 0.099598719 0.027808805
37 38 39 40 41 42
0.106310756 0.092852578 -0.003935026 0.186640890 0.193912096 -0.043125815
43 44 45 46 47 48
0.408268239 0.140698613 -0.042960626 0.297743709 0.019472841 0.105379585
49 50 51 52 53 54
0.054239183 0.052972426 0.063350700 0.077471729 -0.044301987 0.027991525
55 56 57 58 59 60
0.221711077 -0.127256845 -0.028805931 0.066154509 -0.162544487 0.055977977
61 62 63 64 65 66
-0.077137637 -0.068817432 -0.032989315 -0.024388610 0.048317349 -0.087075009
67 68 69 70 71 72
0.282343834 -0.078815510 0.009292847 0.034390420 -0.039293819 0.011450246
73 74 75 76 77 78
-0.079336811 -0.055153725 -0.047119096 -0.032165032 -0.156282393 -0.086332526
79 80 81 82 83 84
0.198690021 -0.098939299 -0.231698851 0.107494873 -0.175463285 0.005756168
85 86 87 88 89 90
-0.099565956 0.008754248 -0.008088747 0.021139568 0.020805982 0.040428381
91 92 93 94 95 96
0.148422184 -0.017737492 -0.080711617 0.056577378 -0.059435416 -0.018200873
97 98 99 100 101 102
0.015628878 0.083964172 -0.036868021 -0.028008139 0.112398780 -0.153187634
103 104 105 106 107 108
0.444285513 -0.026960269 -0.221380464 0.013307709 0.095967681 -0.030814835
109 110 111 112 113 114
0.002259320 0.026181591 -0.045156169 0.046252781 0.007028134 -0.297133241
115 116 117 118 119 120
0.063353576 -0.397466933 -0.036756307 -0.121114392 -0.137805342 -0.037234924
121 122 123 124 125 126
-0.178767847 -0.180473519 -0.153102108 -0.088912789 -0.201358720 0.115976957
127 128 129 130 131 132
-0.004583020 -0.270406080 0.024351800 0.006309938 -0.225545221 -0.066522550
133 134 135 136 137 138
0.059386309 0.151266717 0.276987299 -0.123745057 -0.046011553 0.083640941
139 140 141 142 143 144
0.070972367 0.335008385 0.384931329 -0.309073439 0.114940140 -0.238402367
145 146 147 148 149 150
0.121087455 0.080665765 0.054850118 -0.049497834 -0.106431170 0.087522253
151 152 153 154 155 156
-0.222230458 0.326799868 0.743132876 -0.106759729 0.063012986 0.087456237
157 158 159 160 161 162
0.085443581 0.142626019 0.193761678 -0.036274778 0.021899172 0.097328330
163 164 165 166 167 168
-0.248655696 0.256201905 0.273608246 -0.132248051 0.180403502 0.021279274
169 170 171 172 173 174
-0.096781279 -0.215994805 -0.350242422 0.098661512 0.153633208 0.321429200
175 176 177 178 179 180
-0.211324418 0.108324151 -0.205529621 0.148425219 0.077894086 -0.044899814
181 182 183 184 185 186
-0.038808267 0.133775402 -0.195810718 0.287421221 -0.311404813 0.157773637
187 188 189 190 191 192
-0.257265434 0.118230566 0.074032129 -0.035235870 0.125261506 0.056648272
193 194 195 196 197 198
0.135150224 0.124868725 0.131257801 -0.127660716 -0.298607489 -0.483687620
199 200 201 202 203 204
-0.248570988 0.091500098 -0.108967110 -0.112030359 -0.057054946 0.003457819
205 206 207 208 209 210
-0.047421767 -0.069316135 0.204571646 -0.225780909 -0.239532138 0.127500806
211 212 213 214 215 216
-0.346000072 -0.129191347 -0.029951489 -0.141842828 0.120816456 -0.009405194
217 218 219 220 221 222
-0.067374857 -0.214383510 0.014284007 -0.091705640 0.397676777 0.411671981
223 224 225 226 227 228
-0.528799216 -0.161996222 -0.381644735 0.060178882 0.172250102 0.300013415
229 230 231 232 233 234
0.146261057 0.156347772 0.047574640 0.128532465 0.422806646 0.016659464
235 236
-0.182175993 0.048052374
> postscript(file="/var/www/html/rcomp/tmp/67ih71262084880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 236
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.102033352 NA
1 -0.154305152 -0.102033352
2 -0.131163237 -0.154305152
3 -0.091592365 -0.131163237
4 -0.018886405 -0.091592365
5 -0.154278764 -0.018886405
6 0.018316759 -0.154278764
7 -0.134323681 0.018316759
8 -0.127434939 -0.134323681
9 0.051662618 -0.127434939
10 -0.201914002 0.051662618
11 -0.165940606 -0.201914002
12 0.033246459 -0.165940606
13 -0.083627060 0.033246459
14 -0.067651022 -0.083627060
15 -0.001119696 -0.067651022
16 0.077106588 -0.001119696
17 -0.249588766 0.077106588
18 0.227515947 -0.249588766
19 0.043328425 0.227515947
20 -0.119129346 0.043328425
21 0.078490838 -0.119129346
22 -0.010561502 0.078490838
23 -0.063806634 -0.010561502
24 0.028214554 -0.063806634
25 -0.012204079 0.028214554
26 0.085487993 -0.012204079
27 0.074731398 0.085487993
28 -0.032768066 0.074731398
29 0.066485899 -0.032768066
30 0.165725776 0.066485899
31 -0.025050709 0.165725776
32 0.105621807 -0.025050709
33 0.037568577 0.105621807
34 0.099598719 0.037568577
35 0.027808805 0.099598719
36 0.106310756 0.027808805
37 0.092852578 0.106310756
38 -0.003935026 0.092852578
39 0.186640890 -0.003935026
40 0.193912096 0.186640890
41 -0.043125815 0.193912096
42 0.408268239 -0.043125815
43 0.140698613 0.408268239
44 -0.042960626 0.140698613
45 0.297743709 -0.042960626
46 0.019472841 0.297743709
47 0.105379585 0.019472841
48 0.054239183 0.105379585
49 0.052972426 0.054239183
50 0.063350700 0.052972426
51 0.077471729 0.063350700
52 -0.044301987 0.077471729
53 0.027991525 -0.044301987
54 0.221711077 0.027991525
55 -0.127256845 0.221711077
56 -0.028805931 -0.127256845
57 0.066154509 -0.028805931
58 -0.162544487 0.066154509
59 0.055977977 -0.162544487
60 -0.077137637 0.055977977
61 -0.068817432 -0.077137637
62 -0.032989315 -0.068817432
63 -0.024388610 -0.032989315
64 0.048317349 -0.024388610
65 -0.087075009 0.048317349
66 0.282343834 -0.087075009
67 -0.078815510 0.282343834
68 0.009292847 -0.078815510
69 0.034390420 0.009292847
70 -0.039293819 0.034390420
71 0.011450246 -0.039293819
72 -0.079336811 0.011450246
73 -0.055153725 -0.079336811
74 -0.047119096 -0.055153725
75 -0.032165032 -0.047119096
76 -0.156282393 -0.032165032
77 -0.086332526 -0.156282393
78 0.198690021 -0.086332526
79 -0.098939299 0.198690021
80 -0.231698851 -0.098939299
81 0.107494873 -0.231698851
82 -0.175463285 0.107494873
83 0.005756168 -0.175463285
84 -0.099565956 0.005756168
85 0.008754248 -0.099565956
86 -0.008088747 0.008754248
87 0.021139568 -0.008088747
88 0.020805982 0.021139568
89 0.040428381 0.020805982
90 0.148422184 0.040428381
91 -0.017737492 0.148422184
92 -0.080711617 -0.017737492
93 0.056577378 -0.080711617
94 -0.059435416 0.056577378
95 -0.018200873 -0.059435416
96 0.015628878 -0.018200873
97 0.083964172 0.015628878
98 -0.036868021 0.083964172
99 -0.028008139 -0.036868021
100 0.112398780 -0.028008139
101 -0.153187634 0.112398780
102 0.444285513 -0.153187634
103 -0.026960269 0.444285513
104 -0.221380464 -0.026960269
105 0.013307709 -0.221380464
106 0.095967681 0.013307709
107 -0.030814835 0.095967681
108 0.002259320 -0.030814835
109 0.026181591 0.002259320
110 -0.045156169 0.026181591
111 0.046252781 -0.045156169
112 0.007028134 0.046252781
113 -0.297133241 0.007028134
114 0.063353576 -0.297133241
115 -0.397466933 0.063353576
116 -0.036756307 -0.397466933
117 -0.121114392 -0.036756307
118 -0.137805342 -0.121114392
119 -0.037234924 -0.137805342
120 -0.178767847 -0.037234924
121 -0.180473519 -0.178767847
122 -0.153102108 -0.180473519
123 -0.088912789 -0.153102108
124 -0.201358720 -0.088912789
125 0.115976957 -0.201358720
126 -0.004583020 0.115976957
127 -0.270406080 -0.004583020
128 0.024351800 -0.270406080
129 0.006309938 0.024351800
130 -0.225545221 0.006309938
131 -0.066522550 -0.225545221
132 0.059386309 -0.066522550
133 0.151266717 0.059386309
134 0.276987299 0.151266717
135 -0.123745057 0.276987299
136 -0.046011553 -0.123745057
137 0.083640941 -0.046011553
138 0.070972367 0.083640941
139 0.335008385 0.070972367
140 0.384931329 0.335008385
141 -0.309073439 0.384931329
142 0.114940140 -0.309073439
143 -0.238402367 0.114940140
144 0.121087455 -0.238402367
145 0.080665765 0.121087455
146 0.054850118 0.080665765
147 -0.049497834 0.054850118
148 -0.106431170 -0.049497834
149 0.087522253 -0.106431170
150 -0.222230458 0.087522253
151 0.326799868 -0.222230458
152 0.743132876 0.326799868
153 -0.106759729 0.743132876
154 0.063012986 -0.106759729
155 0.087456237 0.063012986
156 0.085443581 0.087456237
157 0.142626019 0.085443581
158 0.193761678 0.142626019
159 -0.036274778 0.193761678
160 0.021899172 -0.036274778
161 0.097328330 0.021899172
162 -0.248655696 0.097328330
163 0.256201905 -0.248655696
164 0.273608246 0.256201905
165 -0.132248051 0.273608246
166 0.180403502 -0.132248051
167 0.021279274 0.180403502
168 -0.096781279 0.021279274
169 -0.215994805 -0.096781279
170 -0.350242422 -0.215994805
171 0.098661512 -0.350242422
172 0.153633208 0.098661512
173 0.321429200 0.153633208
174 -0.211324418 0.321429200
175 0.108324151 -0.211324418
176 -0.205529621 0.108324151
177 0.148425219 -0.205529621
178 0.077894086 0.148425219
179 -0.044899814 0.077894086
180 -0.038808267 -0.044899814
181 0.133775402 -0.038808267
182 -0.195810718 0.133775402
183 0.287421221 -0.195810718
184 -0.311404813 0.287421221
185 0.157773637 -0.311404813
186 -0.257265434 0.157773637
187 0.118230566 -0.257265434
188 0.074032129 0.118230566
189 -0.035235870 0.074032129
190 0.125261506 -0.035235870
191 0.056648272 0.125261506
192 0.135150224 0.056648272
193 0.124868725 0.135150224
194 0.131257801 0.124868725
195 -0.127660716 0.131257801
196 -0.298607489 -0.127660716
197 -0.483687620 -0.298607489
198 -0.248570988 -0.483687620
199 0.091500098 -0.248570988
200 -0.108967110 0.091500098
201 -0.112030359 -0.108967110
202 -0.057054946 -0.112030359
203 0.003457819 -0.057054946
204 -0.047421767 0.003457819
205 -0.069316135 -0.047421767
206 0.204571646 -0.069316135
207 -0.225780909 0.204571646
208 -0.239532138 -0.225780909
209 0.127500806 -0.239532138
210 -0.346000072 0.127500806
211 -0.129191347 -0.346000072
212 -0.029951489 -0.129191347
213 -0.141842828 -0.029951489
214 0.120816456 -0.141842828
215 -0.009405194 0.120816456
216 -0.067374857 -0.009405194
217 -0.214383510 -0.067374857
218 0.014284007 -0.214383510
219 -0.091705640 0.014284007
220 0.397676777 -0.091705640
221 0.411671981 0.397676777
222 -0.528799216 0.411671981
223 -0.161996222 -0.528799216
224 -0.381644735 -0.161996222
225 0.060178882 -0.381644735
226 0.172250102 0.060178882
227 0.300013415 0.172250102
228 0.146261057 0.300013415
229 0.156347772 0.146261057
230 0.047574640 0.156347772
231 0.128532465 0.047574640
232 0.422806646 0.128532465
233 0.016659464 0.422806646
234 -0.182175993 0.016659464
235 0.048052374 -0.182175993
236 NA 0.048052374
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.154305152 -0.102033352
[2,] -0.131163237 -0.154305152
[3,] -0.091592365 -0.131163237
[4,] -0.018886405 -0.091592365
[5,] -0.154278764 -0.018886405
[6,] 0.018316759 -0.154278764
[7,] -0.134323681 0.018316759
[8,] -0.127434939 -0.134323681
[9,] 0.051662618 -0.127434939
[10,] -0.201914002 0.051662618
[11,] -0.165940606 -0.201914002
[12,] 0.033246459 -0.165940606
[13,] -0.083627060 0.033246459
[14,] -0.067651022 -0.083627060
[15,] -0.001119696 -0.067651022
[16,] 0.077106588 -0.001119696
[17,] -0.249588766 0.077106588
[18,] 0.227515947 -0.249588766
[19,] 0.043328425 0.227515947
[20,] -0.119129346 0.043328425
[21,] 0.078490838 -0.119129346
[22,] -0.010561502 0.078490838
[23,] -0.063806634 -0.010561502
[24,] 0.028214554 -0.063806634
[25,] -0.012204079 0.028214554
[26,] 0.085487993 -0.012204079
[27,] 0.074731398 0.085487993
[28,] -0.032768066 0.074731398
[29,] 0.066485899 -0.032768066
[30,] 0.165725776 0.066485899
[31,] -0.025050709 0.165725776
[32,] 0.105621807 -0.025050709
[33,] 0.037568577 0.105621807
[34,] 0.099598719 0.037568577
[35,] 0.027808805 0.099598719
[36,] 0.106310756 0.027808805
[37,] 0.092852578 0.106310756
[38,] -0.003935026 0.092852578
[39,] 0.186640890 -0.003935026
[40,] 0.193912096 0.186640890
[41,] -0.043125815 0.193912096
[42,] 0.408268239 -0.043125815
[43,] 0.140698613 0.408268239
[44,] -0.042960626 0.140698613
[45,] 0.297743709 -0.042960626
[46,] 0.019472841 0.297743709
[47,] 0.105379585 0.019472841
[48,] 0.054239183 0.105379585
[49,] 0.052972426 0.054239183
[50,] 0.063350700 0.052972426
[51,] 0.077471729 0.063350700
[52,] -0.044301987 0.077471729
[53,] 0.027991525 -0.044301987
[54,] 0.221711077 0.027991525
[55,] -0.127256845 0.221711077
[56,] -0.028805931 -0.127256845
[57,] 0.066154509 -0.028805931
[58,] -0.162544487 0.066154509
[59,] 0.055977977 -0.162544487
[60,] -0.077137637 0.055977977
[61,] -0.068817432 -0.077137637
[62,] -0.032989315 -0.068817432
[63,] -0.024388610 -0.032989315
[64,] 0.048317349 -0.024388610
[65,] -0.087075009 0.048317349
[66,] 0.282343834 -0.087075009
[67,] -0.078815510 0.282343834
[68,] 0.009292847 -0.078815510
[69,] 0.034390420 0.009292847
[70,] -0.039293819 0.034390420
[71,] 0.011450246 -0.039293819
[72,] -0.079336811 0.011450246
[73,] -0.055153725 -0.079336811
[74,] -0.047119096 -0.055153725
[75,] -0.032165032 -0.047119096
[76,] -0.156282393 -0.032165032
[77,] -0.086332526 -0.156282393
[78,] 0.198690021 -0.086332526
[79,] -0.098939299 0.198690021
[80,] -0.231698851 -0.098939299
[81,] 0.107494873 -0.231698851
[82,] -0.175463285 0.107494873
[83,] 0.005756168 -0.175463285
[84,] -0.099565956 0.005756168
[85,] 0.008754248 -0.099565956
[86,] -0.008088747 0.008754248
[87,] 0.021139568 -0.008088747
[88,] 0.020805982 0.021139568
[89,] 0.040428381 0.020805982
[90,] 0.148422184 0.040428381
[91,] -0.017737492 0.148422184
[92,] -0.080711617 -0.017737492
[93,] 0.056577378 -0.080711617
[94,] -0.059435416 0.056577378
[95,] -0.018200873 -0.059435416
[96,] 0.015628878 -0.018200873
[97,] 0.083964172 0.015628878
[98,] -0.036868021 0.083964172
[99,] -0.028008139 -0.036868021
[100,] 0.112398780 -0.028008139
[101,] -0.153187634 0.112398780
[102,] 0.444285513 -0.153187634
[103,] -0.026960269 0.444285513
[104,] -0.221380464 -0.026960269
[105,] 0.013307709 -0.221380464
[106,] 0.095967681 0.013307709
[107,] -0.030814835 0.095967681
[108,] 0.002259320 -0.030814835
[109,] 0.026181591 0.002259320
[110,] -0.045156169 0.026181591
[111,] 0.046252781 -0.045156169
[112,] 0.007028134 0.046252781
[113,] -0.297133241 0.007028134
[114,] 0.063353576 -0.297133241
[115,] -0.397466933 0.063353576
[116,] -0.036756307 -0.397466933
[117,] -0.121114392 -0.036756307
[118,] -0.137805342 -0.121114392
[119,] -0.037234924 -0.137805342
[120,] -0.178767847 -0.037234924
[121,] -0.180473519 -0.178767847
[122,] -0.153102108 -0.180473519
[123,] -0.088912789 -0.153102108
[124,] -0.201358720 -0.088912789
[125,] 0.115976957 -0.201358720
[126,] -0.004583020 0.115976957
[127,] -0.270406080 -0.004583020
[128,] 0.024351800 -0.270406080
[129,] 0.006309938 0.024351800
[130,] -0.225545221 0.006309938
[131,] -0.066522550 -0.225545221
[132,] 0.059386309 -0.066522550
[133,] 0.151266717 0.059386309
[134,] 0.276987299 0.151266717
[135,] -0.123745057 0.276987299
[136,] -0.046011553 -0.123745057
[137,] 0.083640941 -0.046011553
[138,] 0.070972367 0.083640941
[139,] 0.335008385 0.070972367
[140,] 0.384931329 0.335008385
[141,] -0.309073439 0.384931329
[142,] 0.114940140 -0.309073439
[143,] -0.238402367 0.114940140
[144,] 0.121087455 -0.238402367
[145,] 0.080665765 0.121087455
[146,] 0.054850118 0.080665765
[147,] -0.049497834 0.054850118
[148,] -0.106431170 -0.049497834
[149,] 0.087522253 -0.106431170
[150,] -0.222230458 0.087522253
[151,] 0.326799868 -0.222230458
[152,] 0.743132876 0.326799868
[153,] -0.106759729 0.743132876
[154,] 0.063012986 -0.106759729
[155,] 0.087456237 0.063012986
[156,] 0.085443581 0.087456237
[157,] 0.142626019 0.085443581
[158,] 0.193761678 0.142626019
[159,] -0.036274778 0.193761678
[160,] 0.021899172 -0.036274778
[161,] 0.097328330 0.021899172
[162,] -0.248655696 0.097328330
[163,] 0.256201905 -0.248655696
[164,] 0.273608246 0.256201905
[165,] -0.132248051 0.273608246
[166,] 0.180403502 -0.132248051
[167,] 0.021279274 0.180403502
[168,] -0.096781279 0.021279274
[169,] -0.215994805 -0.096781279
[170,] -0.350242422 -0.215994805
[171,] 0.098661512 -0.350242422
[172,] 0.153633208 0.098661512
[173,] 0.321429200 0.153633208
[174,] -0.211324418 0.321429200
[175,] 0.108324151 -0.211324418
[176,] -0.205529621 0.108324151
[177,] 0.148425219 -0.205529621
[178,] 0.077894086 0.148425219
[179,] -0.044899814 0.077894086
[180,] -0.038808267 -0.044899814
[181,] 0.133775402 -0.038808267
[182,] -0.195810718 0.133775402
[183,] 0.287421221 -0.195810718
[184,] -0.311404813 0.287421221
[185,] 0.157773637 -0.311404813
[186,] -0.257265434 0.157773637
[187,] 0.118230566 -0.257265434
[188,] 0.074032129 0.118230566
[189,] -0.035235870 0.074032129
[190,] 0.125261506 -0.035235870
[191,] 0.056648272 0.125261506
[192,] 0.135150224 0.056648272
[193,] 0.124868725 0.135150224
[194,] 0.131257801 0.124868725
[195,] -0.127660716 0.131257801
[196,] -0.298607489 -0.127660716
[197,] -0.483687620 -0.298607489
[198,] -0.248570988 -0.483687620
[199,] 0.091500098 -0.248570988
[200,] -0.108967110 0.091500098
[201,] -0.112030359 -0.108967110
[202,] -0.057054946 -0.112030359
[203,] 0.003457819 -0.057054946
[204,] -0.047421767 0.003457819
[205,] -0.069316135 -0.047421767
[206,] 0.204571646 -0.069316135
[207,] -0.225780909 0.204571646
[208,] -0.239532138 -0.225780909
[209,] 0.127500806 -0.239532138
[210,] -0.346000072 0.127500806
[211,] -0.129191347 -0.346000072
[212,] -0.029951489 -0.129191347
[213,] -0.141842828 -0.029951489
[214,] 0.120816456 -0.141842828
[215,] -0.009405194 0.120816456
[216,] -0.067374857 -0.009405194
[217,] -0.214383510 -0.067374857
[218,] 0.014284007 -0.214383510
[219,] -0.091705640 0.014284007
[220,] 0.397676777 -0.091705640
[221,] 0.411671981 0.397676777
[222,] -0.528799216 0.411671981
[223,] -0.161996222 -0.528799216
[224,] -0.381644735 -0.161996222
[225,] 0.060178882 -0.381644735
[226,] 0.172250102 0.060178882
[227,] 0.300013415 0.172250102
[228,] 0.146261057 0.300013415
[229,] 0.156347772 0.146261057
[230,] 0.047574640 0.156347772
[231,] 0.128532465 0.047574640
[232,] 0.422806646 0.128532465
[233,] 0.016659464 0.422806646
[234,] -0.182175993 0.016659464
[235,] 0.048052374 -0.182175993
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.154305152 -0.102033352
2 -0.131163237 -0.154305152
3 -0.091592365 -0.131163237
4 -0.018886405 -0.091592365
5 -0.154278764 -0.018886405
6 0.018316759 -0.154278764
7 -0.134323681 0.018316759
8 -0.127434939 -0.134323681
9 0.051662618 -0.127434939
10 -0.201914002 0.051662618
11 -0.165940606 -0.201914002
12 0.033246459 -0.165940606
13 -0.083627060 0.033246459
14 -0.067651022 -0.083627060
15 -0.001119696 -0.067651022
16 0.077106588 -0.001119696
17 -0.249588766 0.077106588
18 0.227515947 -0.249588766
19 0.043328425 0.227515947
20 -0.119129346 0.043328425
21 0.078490838 -0.119129346
22 -0.010561502 0.078490838
23 -0.063806634 -0.010561502
24 0.028214554 -0.063806634
25 -0.012204079 0.028214554
26 0.085487993 -0.012204079
27 0.074731398 0.085487993
28 -0.032768066 0.074731398
29 0.066485899 -0.032768066
30 0.165725776 0.066485899
31 -0.025050709 0.165725776
32 0.105621807 -0.025050709
33 0.037568577 0.105621807
34 0.099598719 0.037568577
35 0.027808805 0.099598719
36 0.106310756 0.027808805
37 0.092852578 0.106310756
38 -0.003935026 0.092852578
39 0.186640890 -0.003935026
40 0.193912096 0.186640890
41 -0.043125815 0.193912096
42 0.408268239 -0.043125815
43 0.140698613 0.408268239
44 -0.042960626 0.140698613
45 0.297743709 -0.042960626
46 0.019472841 0.297743709
47 0.105379585 0.019472841
48 0.054239183 0.105379585
49 0.052972426 0.054239183
50 0.063350700 0.052972426
51 0.077471729 0.063350700
52 -0.044301987 0.077471729
53 0.027991525 -0.044301987
54 0.221711077 0.027991525
55 -0.127256845 0.221711077
56 -0.028805931 -0.127256845
57 0.066154509 -0.028805931
58 -0.162544487 0.066154509
59 0.055977977 -0.162544487
60 -0.077137637 0.055977977
61 -0.068817432 -0.077137637
62 -0.032989315 -0.068817432
63 -0.024388610 -0.032989315
64 0.048317349 -0.024388610
65 -0.087075009 0.048317349
66 0.282343834 -0.087075009
67 -0.078815510 0.282343834
68 0.009292847 -0.078815510
69 0.034390420 0.009292847
70 -0.039293819 0.034390420
71 0.011450246 -0.039293819
72 -0.079336811 0.011450246
73 -0.055153725 -0.079336811
74 -0.047119096 -0.055153725
75 -0.032165032 -0.047119096
76 -0.156282393 -0.032165032
77 -0.086332526 -0.156282393
78 0.198690021 -0.086332526
79 -0.098939299 0.198690021
80 -0.231698851 -0.098939299
81 0.107494873 -0.231698851
82 -0.175463285 0.107494873
83 0.005756168 -0.175463285
84 -0.099565956 0.005756168
85 0.008754248 -0.099565956
86 -0.008088747 0.008754248
87 0.021139568 -0.008088747
88 0.020805982 0.021139568
89 0.040428381 0.020805982
90 0.148422184 0.040428381
91 -0.017737492 0.148422184
92 -0.080711617 -0.017737492
93 0.056577378 -0.080711617
94 -0.059435416 0.056577378
95 -0.018200873 -0.059435416
96 0.015628878 -0.018200873
97 0.083964172 0.015628878
98 -0.036868021 0.083964172
99 -0.028008139 -0.036868021
100 0.112398780 -0.028008139
101 -0.153187634 0.112398780
102 0.444285513 -0.153187634
103 -0.026960269 0.444285513
104 -0.221380464 -0.026960269
105 0.013307709 -0.221380464
106 0.095967681 0.013307709
107 -0.030814835 0.095967681
108 0.002259320 -0.030814835
109 0.026181591 0.002259320
110 -0.045156169 0.026181591
111 0.046252781 -0.045156169
112 0.007028134 0.046252781
113 -0.297133241 0.007028134
114 0.063353576 -0.297133241
115 -0.397466933 0.063353576
116 -0.036756307 -0.397466933
117 -0.121114392 -0.036756307
118 -0.137805342 -0.121114392
119 -0.037234924 -0.137805342
120 -0.178767847 -0.037234924
121 -0.180473519 -0.178767847
122 -0.153102108 -0.180473519
123 -0.088912789 -0.153102108
124 -0.201358720 -0.088912789
125 0.115976957 -0.201358720
126 -0.004583020 0.115976957
127 -0.270406080 -0.004583020
128 0.024351800 -0.270406080
129 0.006309938 0.024351800
130 -0.225545221 0.006309938
131 -0.066522550 -0.225545221
132 0.059386309 -0.066522550
133 0.151266717 0.059386309
134 0.276987299 0.151266717
135 -0.123745057 0.276987299
136 -0.046011553 -0.123745057
137 0.083640941 -0.046011553
138 0.070972367 0.083640941
139 0.335008385 0.070972367
140 0.384931329 0.335008385
141 -0.309073439 0.384931329
142 0.114940140 -0.309073439
143 -0.238402367 0.114940140
144 0.121087455 -0.238402367
145 0.080665765 0.121087455
146 0.054850118 0.080665765
147 -0.049497834 0.054850118
148 -0.106431170 -0.049497834
149 0.087522253 -0.106431170
150 -0.222230458 0.087522253
151 0.326799868 -0.222230458
152 0.743132876 0.326799868
153 -0.106759729 0.743132876
154 0.063012986 -0.106759729
155 0.087456237 0.063012986
156 0.085443581 0.087456237
157 0.142626019 0.085443581
158 0.193761678 0.142626019
159 -0.036274778 0.193761678
160 0.021899172 -0.036274778
161 0.097328330 0.021899172
162 -0.248655696 0.097328330
163 0.256201905 -0.248655696
164 0.273608246 0.256201905
165 -0.132248051 0.273608246
166 0.180403502 -0.132248051
167 0.021279274 0.180403502
168 -0.096781279 0.021279274
169 -0.215994805 -0.096781279
170 -0.350242422 -0.215994805
171 0.098661512 -0.350242422
172 0.153633208 0.098661512
173 0.321429200 0.153633208
174 -0.211324418 0.321429200
175 0.108324151 -0.211324418
176 -0.205529621 0.108324151
177 0.148425219 -0.205529621
178 0.077894086 0.148425219
179 -0.044899814 0.077894086
180 -0.038808267 -0.044899814
181 0.133775402 -0.038808267
182 -0.195810718 0.133775402
183 0.287421221 -0.195810718
184 -0.311404813 0.287421221
185 0.157773637 -0.311404813
186 -0.257265434 0.157773637
187 0.118230566 -0.257265434
188 0.074032129 0.118230566
189 -0.035235870 0.074032129
190 0.125261506 -0.035235870
191 0.056648272 0.125261506
192 0.135150224 0.056648272
193 0.124868725 0.135150224
194 0.131257801 0.124868725
195 -0.127660716 0.131257801
196 -0.298607489 -0.127660716
197 -0.483687620 -0.298607489
198 -0.248570988 -0.483687620
199 0.091500098 -0.248570988
200 -0.108967110 0.091500098
201 -0.112030359 -0.108967110
202 -0.057054946 -0.112030359
203 0.003457819 -0.057054946
204 -0.047421767 0.003457819
205 -0.069316135 -0.047421767
206 0.204571646 -0.069316135
207 -0.225780909 0.204571646
208 -0.239532138 -0.225780909
209 0.127500806 -0.239532138
210 -0.346000072 0.127500806
211 -0.129191347 -0.346000072
212 -0.029951489 -0.129191347
213 -0.141842828 -0.029951489
214 0.120816456 -0.141842828
215 -0.009405194 0.120816456
216 -0.067374857 -0.009405194
217 -0.214383510 -0.067374857
218 0.014284007 -0.214383510
219 -0.091705640 0.014284007
220 0.397676777 -0.091705640
221 0.411671981 0.397676777
222 -0.528799216 0.411671981
223 -0.161996222 -0.528799216
224 -0.381644735 -0.161996222
225 0.060178882 -0.381644735
226 0.172250102 0.060178882
227 0.300013415 0.172250102
228 0.146261057 0.300013415
229 0.156347772 0.146261057
230 0.047574640 0.156347772
231 0.128532465 0.047574640
232 0.422806646 0.128532465
233 0.016659464 0.422806646
234 -0.182175993 0.016659464
235 0.048052374 -0.182175993
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/70olg1262084880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/85fce1262084880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/92r4j1262084880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/104ook1262084880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11wcqh1262084880.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12qn9z1262084880.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13mi5q1262084881.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14ief21262084881.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15zoct1262084881.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16f4j71262084881.tab")
+ }
>
> try(system("convert tmp/1wl241262084880.ps tmp/1wl241262084880.png",intern=TRUE))
character(0)
> try(system("convert tmp/2mc5h1262084880.ps tmp/2mc5h1262084880.png",intern=TRUE))
character(0)
> try(system("convert tmp/3cert1262084880.ps tmp/3cert1262084880.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fo7r1262084880.ps tmp/4fo7r1262084880.png",intern=TRUE))
character(0)
> try(system("convert tmp/5y4ef1262084880.ps tmp/5y4ef1262084880.png",intern=TRUE))
character(0)
> try(system("convert tmp/67ih71262084880.ps tmp/67ih71262084880.png",intern=TRUE))
character(0)
> try(system("convert tmp/70olg1262084880.ps tmp/70olg1262084880.png",intern=TRUE))
character(0)
> try(system("convert tmp/85fce1262084880.ps tmp/85fce1262084880.png",intern=TRUE))
character(0)
> try(system("convert tmp/92r4j1262084880.ps tmp/92r4j1262084880.png",intern=TRUE))
character(0)
> try(system("convert tmp/104ook1262084880.ps tmp/104ook1262084880.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.403 1.834 8.529