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(-45.6
+ ,16.1
+ ,23.9
+ ,39.3
+ ,-39.4
+ ,-0.3
+ ,17.3
+ ,17.7
+ ,31.4
+ ,-28.6
+ ,-17.2
+ ,-79
+ ,-47.9
+ ,9.1
+ ,10.6
+ ,-23.9
+ ,-45
+ ,-42.2
+ ,43.2
+ ,32.1
+ ,-15.3
+ ,21.8
+ ,-12
+ ,-95.8
+ ,-14.3
+ ,47.8
+ ,64.8
+ ,40.2
+ ,-28.8
+ ,23.5
+ ,70.3
+ ,12.3
+ ,43.5
+ ,-30.1
+ ,-5.3
+ ,-24
+ ,11.1
+ ,21.5
+ ,38.5
+ ,16.8
+ ,-36.2
+ ,6
+ ,26.6
+ ,-8
+ ,13.2
+ ,-23.6
+ ,19.4
+ ,-46.2
+ ,-8.2
+ ,33.8
+ ,16.6
+ ,5.4
+ ,-25
+ ,-5.3
+ ,16.7
+ ,19
+ ,24.8
+ ,-11.4
+ ,4.9
+ ,-58.7
+ ,16.8
+ ,13.6
+ ,6.4
+ ,22.8
+ ,-19.6
+ ,2.2
+ ,19.8
+ ,-10.7
+ ,4.7
+ ,-44.5
+ ,-34.7
+ ,-119.7
+ ,-42.2
+ ,-5.4
+ ,19.1
+ ,18.8
+ ,-2.3
+ ,0.2
+ ,20.9
+ ,3.7
+ ,50.4
+ ,-18.6
+ ,10.6
+ ,-66
+ ,10
+ ,27.2
+ ,13.5
+ ,47.2
+ ,-20.3
+ ,23.1
+ ,12.6
+ ,19.8
+ ,5.4
+ ,-25.2
+ ,-6.5
+ ,-46.5
+ ,-2.6
+ ,-0.3
+ ,38.5
+ ,-8.9
+ ,-38
+ ,19.5
+ ,51.7
+ ,19.4
+ ,18.2
+ ,-50.8
+ ,-6.1
+ ,-54.6
+ ,12.1
+ ,26.3
+ ,19.5
+ ,-0.8
+ ,-49.6
+ ,28.8
+ ,31.7
+ ,2.3
+ ,3.8
+ ,-66.2
+ ,-20.5
+ ,-113.2
+ ,-65.2
+ ,-3.9
+ ,9.1
+ ,23.2
+ ,-39.1
+ ,12.5
+ ,49.1
+ ,54.9
+ ,30.8
+ ,-3.5
+ ,-28.3
+ ,-61
+ ,-2
+ ,40
+ ,74
+ ,23.1
+ ,-45.3
+ ,17.5
+ ,25.8
+ ,15.2
+ ,-3.6
+ ,-40.5
+ ,11.5
+ ,-59.8
+ ,23.3
+ ,-27.8
+ ,55.7
+ ,22.7
+ ,-79.2
+ ,28.8
+ ,17.3
+ ,39.6
+ ,-22.2
+ ,-43
+ ,-50.3
+ ,-86.5
+ ,-31.9
+ ,23.1
+ ,53.6
+ ,21.6
+ ,-64.2
+ ,35.2
+ ,52.1
+ ,40.6
+ ,17.1
+ ,-7.8
+ ,-10
+ ,-58
+ ,14
+ ,15.8
+ ,46
+ ,-8.9
+ ,-26.7
+ ,39
+ ,-1.3
+ ,38.7
+ ,22.1
+ ,-49.2
+ ,-3.4
+ ,-86.7
+ ,-24.3
+ ,42.8
+ ,44.9
+ ,4.4
+ ,-60.5
+ ,41.4
+ ,38.5
+ ,28.5
+ ,7.6
+ ,-46.4
+ ,7
+ ,-73
+ ,5.7
+ ,23.6
+ ,39.4
+ ,30.3
+ ,-92.5
+ ,77.8
+ ,12.4
+ ,28.9
+ ,6.4
+ ,-12
+ ,-9.1
+ ,-53.2
+ ,-23.1
+ ,47.3
+ ,20.7
+ ,27.8
+ ,-84.3
+ ,62.8
+ ,26.4
+ ,32.3
+ ,13.3
+ ,-17.9
+ ,10
+ ,-45.6
+ ,13.5
+ ,11.9
+ ,26
+ ,-6.3
+ ,-79.9
+ ,54.2
+ ,22.9
+ ,31.8
+ ,3.8
+ ,-11.4
+ ,-8.6
+ ,-49.4
+ ,-2.5
+ ,23
+ ,29
+ ,20.6
+ ,-117
+ ,37.9
+ ,30.7
+ ,4.7
+ ,-5.7
+ ,4.9
+ ,18.3
+ ,-35.4
+ ,-21.3
+ ,35.8
+ ,43.8
+ ,18.7
+ ,-131.1
+ ,39.8
+ ,44.5
+ ,16.5
+ ,9.7
+ ,-6.6
+ ,15.8
+ ,-45.7
+ ,-4.8
+ ,17.6
+ ,20.5
+ ,24.2
+ ,-109
+ ,20.8
+ ,31.2
+ ,-8.8
+ ,11.8
+ ,13
+ ,8.3
+ ,-77.9
+ ,-38.8
+ ,6.1
+ ,18.1
+ ,16.8
+ ,-128.5
+ ,15.9
+ ,29
+ ,-7.2
+ ,3.3
+ ,-34.8
+ ,-2.9
+ ,-77.8
+ ,-2.8
+ ,26.7
+ ,48.1
+ ,30
+ ,-109.6
+ ,16
+ ,26.9
+ ,22.1
+ ,27
+ ,-24.5
+ ,12
+ ,-75.2
+ ,3.5
+ ,19.7
+ ,51.8
+ ,35.3
+ ,-108.2
+ ,25.3
+ ,31.6
+ ,19.9
+ ,18.8
+ ,20.4
+ ,15
+ ,-55.9
+ ,-17
+ ,33.3
+ ,33.8
+ ,37.5
+ ,-104.8
+ ,29.7
+ ,34.2
+ ,4.3
+ ,40.2
+ ,-29.3
+ ,-0.2
+ ,-95
+ ,-13.2
+ ,38.5
+ ,45.4
+ ,15.7
+ ,-123.6
+ ,12
+ ,37.5
+ ,-31.7
+ ,15.8
+ ,-64.1
+ ,-42.1
+ ,-207.4
+ ,-12.9
+ ,-5
+ ,53.9
+ ,19.7
+ ,-94.6
+ ,36
+ ,51.3
+ ,17.4
+ ,27.8
+ ,1.3
+ ,3.6
+ ,-97.9
+ ,14.1
+ ,50.8
+ ,63.5
+ ,58.6
+ ,-135.1
+ ,7.8
+ ,25.5
+ ,29.6
+ ,19.3
+ ,-26.2
+ ,7.3
+ ,-82.6
+ ,-26.1
+ ,55.3
+ ,98.8
+ ,41.7
+ ,-130.2
+ ,51.2
+ ,18.4
+ ,32
+ ,21.6
+ ,-12.5
+ ,46.6
+ ,-101.7
+ ,15.8
+ ,26
+ ,79.1
+ ,23.1
+ ,-86.9
+ ,-11.2
+ ,50.7
+ ,13.4
+ ,33.7
+ ,-16.9
+ ,-9.6)
+ ,dim=c(1
+ ,371)
+ ,dimnames=list(c('y')
+ ,1:371))
> y <- array(NA,dim=c(1,371),dimnames=list(c('y'),1:371))
> 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
> 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 -45.6 1 0 0 0 0 0 0 0 0 0 0 1
2 16.1 0 1 0 0 0 0 0 0 0 0 0 2
3 23.9 0 0 1 0 0 0 0 0 0 0 0 3
4 39.3 0 0 0 1 0 0 0 0 0 0 0 4
5 -39.4 0 0 0 0 1 0 0 0 0 0 0 5
6 -0.3 0 0 0 0 0 1 0 0 0 0 0 6
7 17.3 0 0 0 0 0 0 1 0 0 0 0 7
8 17.7 0 0 0 0 0 0 0 1 0 0 0 8
9 31.4 0 0 0 0 0 0 0 0 1 0 0 9
10 -28.6 0 0 0 0 0 0 0 0 0 1 0 10
11 -17.2 0 0 0 0 0 0 0 0 0 0 1 11
12 -79.0 0 0 0 0 0 0 0 0 0 0 0 12
13 -47.9 1 0 0 0 0 0 0 0 0 0 0 13
14 9.1 0 1 0 0 0 0 0 0 0 0 0 14
15 10.6 0 0 1 0 0 0 0 0 0 0 0 15
16 -23.9 0 0 0 1 0 0 0 0 0 0 0 16
17 -45.0 0 0 0 0 1 0 0 0 0 0 0 17
18 -42.2 0 0 0 0 0 1 0 0 0 0 0 18
19 43.2 0 0 0 0 0 0 1 0 0 0 0 19
20 32.1 0 0 0 0 0 0 0 1 0 0 0 20
21 -15.3 0 0 0 0 0 0 0 0 1 0 0 21
22 21.8 0 0 0 0 0 0 0 0 0 1 0 22
23 -12.0 0 0 0 0 0 0 0 0 0 0 1 23
24 -95.8 0 0 0 0 0 0 0 0 0 0 0 24
25 -14.3 1 0 0 0 0 0 0 0 0 0 0 25
26 47.8 0 1 0 0 0 0 0 0 0 0 0 26
27 64.8 0 0 1 0 0 0 0 0 0 0 0 27
28 40.2 0 0 0 1 0 0 0 0 0 0 0 28
29 -28.8 0 0 0 0 1 0 0 0 0 0 0 29
30 23.5 0 0 0 0 0 1 0 0 0 0 0 30
31 70.3 0 0 0 0 0 0 1 0 0 0 0 31
32 12.3 0 0 0 0 0 0 0 1 0 0 0 32
33 43.5 0 0 0 0 0 0 0 0 1 0 0 33
34 -30.1 0 0 0 0 0 0 0 0 0 1 0 34
35 -5.3 0 0 0 0 0 0 0 0 0 0 1 35
36 -24.0 0 0 0 0 0 0 0 0 0 0 0 36
37 11.1 1 0 0 0 0 0 0 0 0 0 0 37
38 21.5 0 1 0 0 0 0 0 0 0 0 0 38
39 38.5 0 0 1 0 0 0 0 0 0 0 0 39
40 16.8 0 0 0 1 0 0 0 0 0 0 0 40
41 -36.2 0 0 0 0 1 0 0 0 0 0 0 41
42 6.0 0 0 0 0 0 1 0 0 0 0 0 42
43 26.6 0 0 0 0 0 0 1 0 0 0 0 43
44 -8.0 0 0 0 0 0 0 0 1 0 0 0 44
45 13.2 0 0 0 0 0 0 0 0 1 0 0 45
46 -23.6 0 0 0 0 0 0 0 0 0 1 0 46
47 19.4 0 0 0 0 0 0 0 0 0 0 1 47
48 -46.2 0 0 0 0 0 0 0 0 0 0 0 48
49 -8.2 1 0 0 0 0 0 0 0 0 0 0 49
50 33.8 0 1 0 0 0 0 0 0 0 0 0 50
51 16.6 0 0 1 0 0 0 0 0 0 0 0 51
52 5.4 0 0 0 1 0 0 0 0 0 0 0 52
53 -25.0 0 0 0 0 1 0 0 0 0 0 0 53
54 -5.3 0 0 0 0 0 1 0 0 0 0 0 54
55 16.7 0 0 0 0 0 0 1 0 0 0 0 55
56 19.0 0 0 0 0 0 0 0 1 0 0 0 56
57 24.8 0 0 0 0 0 0 0 0 1 0 0 57
58 -11.4 0 0 0 0 0 0 0 0 0 1 0 58
59 4.9 0 0 0 0 0 0 0 0 0 0 1 59
60 -58.7 0 0 0 0 0 0 0 0 0 0 0 60
61 16.8 1 0 0 0 0 0 0 0 0 0 0 61
62 13.6 0 1 0 0 0 0 0 0 0 0 0 62
63 6.4 0 0 1 0 0 0 0 0 0 0 0 63
64 22.8 0 0 0 1 0 0 0 0 0 0 0 64
65 -19.6 0 0 0 0 1 0 0 0 0 0 0 65
66 2.2 0 0 0 0 0 1 0 0 0 0 0 66
67 19.8 0 0 0 0 0 0 1 0 0 0 0 67
68 -10.7 0 0 0 0 0 0 0 1 0 0 0 68
69 4.7 0 0 0 0 0 0 0 0 1 0 0 69
70 -44.5 0 0 0 0 0 0 0 0 0 1 0 70
71 -34.7 0 0 0 0 0 0 0 0 0 0 1 71
72 -119.7 0 0 0 0 0 0 0 0 0 0 0 72
73 -42.2 1 0 0 0 0 0 0 0 0 0 0 73
74 -5.4 0 1 0 0 0 0 0 0 0 0 0 74
75 19.1 0 0 1 0 0 0 0 0 0 0 0 75
76 18.8 0 0 0 1 0 0 0 0 0 0 0 76
77 -2.3 0 0 0 0 1 0 0 0 0 0 0 77
78 0.2 0 0 0 0 0 1 0 0 0 0 0 78
79 20.9 0 0 0 0 0 0 1 0 0 0 0 79
80 3.7 0 0 0 0 0 0 0 1 0 0 0 80
81 50.4 0 0 0 0 0 0 0 0 1 0 0 81
82 -18.6 0 0 0 0 0 0 0 0 0 1 0 82
83 10.6 0 0 0 0 0 0 0 0 0 0 1 83
84 -66.0 0 0 0 0 0 0 0 0 0 0 0 84
85 10.0 1 0 0 0 0 0 0 0 0 0 0 85
86 27.2 0 1 0 0 0 0 0 0 0 0 0 86
87 13.5 0 0 1 0 0 0 0 0 0 0 0 87
88 47.2 0 0 0 1 0 0 0 0 0 0 0 88
89 -20.3 0 0 0 0 1 0 0 0 0 0 0 89
90 23.1 0 0 0 0 0 1 0 0 0 0 0 90
91 12.6 0 0 0 0 0 0 1 0 0 0 0 91
92 19.8 0 0 0 0 0 0 0 1 0 0 0 92
93 5.4 0 0 0 0 0 0 0 0 1 0 0 93
94 -25.2 0 0 0 0 0 0 0 0 0 1 0 94
95 -6.5 0 0 0 0 0 0 0 0 0 0 1 95
96 -46.5 0 0 0 0 0 0 0 0 0 0 0 96
97 -2.6 1 0 0 0 0 0 0 0 0 0 0 97
98 -0.3 0 1 0 0 0 0 0 0 0 0 0 98
99 38.5 0 0 1 0 0 0 0 0 0 0 0 99
100 -8.9 0 0 0 1 0 0 0 0 0 0 0 100
101 -38.0 0 0 0 0 1 0 0 0 0 0 0 101
102 19.5 0 0 0 0 0 1 0 0 0 0 0 102
103 51.7 0 0 0 0 0 0 1 0 0 0 0 103
104 19.4 0 0 0 0 0 0 0 1 0 0 0 104
105 18.2 0 0 0 0 0 0 0 0 1 0 0 105
106 -50.8 0 0 0 0 0 0 0 0 0 1 0 106
107 -6.1 0 0 0 0 0 0 0 0 0 0 1 107
108 -54.6 0 0 0 0 0 0 0 0 0 0 0 108
109 12.1 1 0 0 0 0 0 0 0 0 0 0 109
110 26.3 0 1 0 0 0 0 0 0 0 0 0 110
111 19.5 0 0 1 0 0 0 0 0 0 0 0 111
112 -0.8 0 0 0 1 0 0 0 0 0 0 0 112
113 -49.6 0 0 0 0 1 0 0 0 0 0 0 113
114 28.8 0 0 0 0 0 1 0 0 0 0 0 114
115 31.7 0 0 0 0 0 0 1 0 0 0 0 115
116 2.3 0 0 0 0 0 0 0 1 0 0 0 116
117 3.8 0 0 0 0 0 0 0 0 1 0 0 117
118 -66.2 0 0 0 0 0 0 0 0 0 1 0 118
119 -20.5 0 0 0 0 0 0 0 0 0 0 1 119
120 -113.2 0 0 0 0 0 0 0 0 0 0 0 120
121 -65.2 1 0 0 0 0 0 0 0 0 0 0 121
122 -3.9 0 1 0 0 0 0 0 0 0 0 0 122
123 9.1 0 0 1 0 0 0 0 0 0 0 0 123
124 23.2 0 0 0 1 0 0 0 0 0 0 0 124
125 -39.1 0 0 0 0 1 0 0 0 0 0 0 125
126 12.5 0 0 0 0 0 1 0 0 0 0 0 126
127 49.1 0 0 0 0 0 0 1 0 0 0 0 127
128 54.9 0 0 0 0 0 0 0 1 0 0 0 128
129 30.8 0 0 0 0 0 0 0 0 1 0 0 129
130 -3.5 0 0 0 0 0 0 0 0 0 1 0 130
131 -28.3 0 0 0 0 0 0 0 0 0 0 1 131
132 -61.0 0 0 0 0 0 0 0 0 0 0 0 132
133 -2.0 1 0 0 0 0 0 0 0 0 0 0 133
134 40.0 0 1 0 0 0 0 0 0 0 0 0 134
135 74.0 0 0 1 0 0 0 0 0 0 0 0 135
136 23.1 0 0 0 1 0 0 0 0 0 0 0 136
137 -45.3 0 0 0 0 1 0 0 0 0 0 0 137
138 17.5 0 0 0 0 0 1 0 0 0 0 0 138
139 25.8 0 0 0 0 0 0 1 0 0 0 0 139
140 15.2 0 0 0 0 0 0 0 1 0 0 0 140
141 -3.6 0 0 0 0 0 0 0 0 1 0 0 141
142 -40.5 0 0 0 0 0 0 0 0 0 1 0 142
143 11.5 0 0 0 0 0 0 0 0 0 0 1 143
144 -59.8 0 0 0 0 0 0 0 0 0 0 0 144
145 23.3 1 0 0 0 0 0 0 0 0 0 0 145
146 -27.8 0 1 0 0 0 0 0 0 0 0 0 146
147 55.7 0 0 1 0 0 0 0 0 0 0 0 147
148 22.7 0 0 0 1 0 0 0 0 0 0 0 148
149 -79.2 0 0 0 0 1 0 0 0 0 0 0 149
150 28.8 0 0 0 0 0 1 0 0 0 0 0 150
151 17.3 0 0 0 0 0 0 1 0 0 0 0 151
152 39.6 0 0 0 0 0 0 0 1 0 0 0 152
153 -22.2 0 0 0 0 0 0 0 0 1 0 0 153
154 -43.0 0 0 0 0 0 0 0 0 0 1 0 154
155 -50.3 0 0 0 0 0 0 0 0 0 0 1 155
156 -86.5 0 0 0 0 0 0 0 0 0 0 0 156
157 -31.9 1 0 0 0 0 0 0 0 0 0 0 157
158 23.1 0 1 0 0 0 0 0 0 0 0 0 158
159 53.6 0 0 1 0 0 0 0 0 0 0 0 159
160 21.6 0 0 0 1 0 0 0 0 0 0 0 160
161 -64.2 0 0 0 0 1 0 0 0 0 0 0 161
162 35.2 0 0 0 0 0 1 0 0 0 0 0 162
163 52.1 0 0 0 0 0 0 1 0 0 0 0 163
164 40.6 0 0 0 0 0 0 0 1 0 0 0 164
165 17.1 0 0 0 0 0 0 0 0 1 0 0 165
166 -7.8 0 0 0 0 0 0 0 0 0 1 0 166
167 -10.0 0 0 0 0 0 0 0 0 0 0 1 167
168 -58.0 0 0 0 0 0 0 0 0 0 0 0 168
169 14.0 1 0 0 0 0 0 0 0 0 0 0 169
170 15.8 0 1 0 0 0 0 0 0 0 0 0 170
171 46.0 0 0 1 0 0 0 0 0 0 0 0 171
172 -8.9 0 0 0 1 0 0 0 0 0 0 0 172
173 -26.7 0 0 0 0 1 0 0 0 0 0 0 173
174 39.0 0 0 0 0 0 1 0 0 0 0 0 174
175 -1.3 0 0 0 0 0 0 1 0 0 0 0 175
176 38.7 0 0 0 0 0 0 0 1 0 0 0 176
177 22.1 0 0 0 0 0 0 0 0 1 0 0 177
178 -49.2 0 0 0 0 0 0 0 0 0 1 0 178
179 -3.4 0 0 0 0 0 0 0 0 0 0 1 179
180 -86.7 0 0 0 0 0 0 0 0 0 0 0 180
181 -24.3 1 0 0 0 0 0 0 0 0 0 0 181
182 42.8 0 1 0 0 0 0 0 0 0 0 0 182
183 44.9 0 0 1 0 0 0 0 0 0 0 0 183
184 4.4 0 0 0 1 0 0 0 0 0 0 0 184
185 -60.5 0 0 0 0 1 0 0 0 0 0 0 185
186 41.4 0 0 0 0 0 1 0 0 0 0 0 186
187 38.5 0 0 0 0 0 0 1 0 0 0 0 187
188 28.5 0 0 0 0 0 0 0 1 0 0 0 188
189 7.6 0 0 0 0 0 0 0 0 1 0 0 189
190 -46.4 0 0 0 0 0 0 0 0 0 1 0 190
191 7.0 0 0 0 0 0 0 0 0 0 0 1 191
192 -73.0 0 0 0 0 0 0 0 0 0 0 0 192
193 5.7 1 0 0 0 0 0 0 0 0 0 0 193
194 23.6 0 1 0 0 0 0 0 0 0 0 0 194
195 39.4 0 0 1 0 0 0 0 0 0 0 0 195
196 30.3 0 0 0 1 0 0 0 0 0 0 0 196
197 -92.5 0 0 0 0 1 0 0 0 0 0 0 197
198 77.8 0 0 0 0 0 1 0 0 0 0 0 198
199 12.4 0 0 0 0 0 0 1 0 0 0 0 199
200 28.9 0 0 0 0 0 0 0 1 0 0 0 200
201 6.4 0 0 0 0 0 0 0 0 1 0 0 201
202 -12.0 0 0 0 0 0 0 0 0 0 1 0 202
203 -9.1 0 0 0 0 0 0 0 0 0 0 1 203
204 -53.2 0 0 0 0 0 0 0 0 0 0 0 204
205 -23.1 1 0 0 0 0 0 0 0 0 0 0 205
206 47.3 0 1 0 0 0 0 0 0 0 0 0 206
207 20.7 0 0 1 0 0 0 0 0 0 0 0 207
208 27.8 0 0 0 1 0 0 0 0 0 0 0 208
209 -84.3 0 0 0 0 1 0 0 0 0 0 0 209
210 62.8 0 0 0 0 0 1 0 0 0 0 0 210
211 26.4 0 0 0 0 0 0 1 0 0 0 0 211
212 32.3 0 0 0 0 0 0 0 1 0 0 0 212
213 13.3 0 0 0 0 0 0 0 0 1 0 0 213
214 -17.9 0 0 0 0 0 0 0 0 0 1 0 214
215 10.0 0 0 0 0 0 0 0 0 0 0 1 215
216 -45.6 0 0 0 0 0 0 0 0 0 0 0 216
217 13.5 1 0 0 0 0 0 0 0 0 0 0 217
218 11.9 0 1 0 0 0 0 0 0 0 0 0 218
219 26.0 0 0 1 0 0 0 0 0 0 0 0 219
220 -6.3 0 0 0 1 0 0 0 0 0 0 0 220
221 -79.9 0 0 0 0 1 0 0 0 0 0 0 221
222 54.2 0 0 0 0 0 1 0 0 0 0 0 222
223 22.9 0 0 0 0 0 0 1 0 0 0 0 223
224 31.8 0 0 0 0 0 0 0 1 0 0 0 224
225 3.8 0 0 0 0 0 0 0 0 1 0 0 225
226 -11.4 0 0 0 0 0 0 0 0 0 1 0 226
227 -8.6 0 0 0 0 0 0 0 0 0 0 1 227
228 -49.4 0 0 0 0 0 0 0 0 0 0 0 228
229 -2.5 1 0 0 0 0 0 0 0 0 0 0 229
230 23.0 0 1 0 0 0 0 0 0 0 0 0 230
231 29.0 0 0 1 0 0 0 0 0 0 0 0 231
232 20.6 0 0 0 1 0 0 0 0 0 0 0 232
233 -117.0 0 0 0 0 1 0 0 0 0 0 0 233
234 37.9 0 0 0 0 0 1 0 0 0 0 0 234
235 30.7 0 0 0 0 0 0 1 0 0 0 0 235
236 4.7 0 0 0 0 0 0 0 1 0 0 0 236
237 -5.7 0 0 0 0 0 0 0 0 1 0 0 237
238 4.9 0 0 0 0 0 0 0 0 0 1 0 238
239 18.3 0 0 0 0 0 0 0 0 0 0 1 239
240 -35.4 0 0 0 0 0 0 0 0 0 0 0 240
241 -21.3 1 0 0 0 0 0 0 0 0 0 0 241
242 35.8 0 1 0 0 0 0 0 0 0 0 0 242
243 43.8 0 0 1 0 0 0 0 0 0 0 0 243
244 18.7 0 0 0 1 0 0 0 0 0 0 0 244
245 -131.1 0 0 0 0 1 0 0 0 0 0 0 245
246 39.8 0 0 0 0 0 1 0 0 0 0 0 246
247 44.5 0 0 0 0 0 0 1 0 0 0 0 247
248 16.5 0 0 0 0 0 0 0 1 0 0 0 248
249 9.7 0 0 0 0 0 0 0 0 1 0 0 249
250 -6.6 0 0 0 0 0 0 0 0 0 1 0 250
251 15.8 0 0 0 0 0 0 0 0 0 0 1 251
252 -45.7 0 0 0 0 0 0 0 0 0 0 0 252
253 -4.8 1 0 0 0 0 0 0 0 0 0 0 253
254 17.6 0 1 0 0 0 0 0 0 0 0 0 254
255 20.5 0 0 1 0 0 0 0 0 0 0 0 255
256 24.2 0 0 0 1 0 0 0 0 0 0 0 256
257 -109.0 0 0 0 0 1 0 0 0 0 0 0 257
258 20.8 0 0 0 0 0 1 0 0 0 0 0 258
259 31.2 0 0 0 0 0 0 1 0 0 0 0 259
260 -8.8 0 0 0 0 0 0 0 1 0 0 0 260
261 11.8 0 0 0 0 0 0 0 0 1 0 0 261
262 13.0 0 0 0 0 0 0 0 0 0 1 0 262
263 8.3 0 0 0 0 0 0 0 0 0 0 1 263
264 -77.9 0 0 0 0 0 0 0 0 0 0 0 264
265 -38.8 1 0 0 0 0 0 0 0 0 0 0 265
266 6.1 0 1 0 0 0 0 0 0 0 0 0 266
267 18.1 0 0 1 0 0 0 0 0 0 0 0 267
268 16.8 0 0 0 1 0 0 0 0 0 0 0 268
269 -128.5 0 0 0 0 1 0 0 0 0 0 0 269
270 15.9 0 0 0 0 0 1 0 0 0 0 0 270
271 29.0 0 0 0 0 0 0 1 0 0 0 0 271
272 -7.2 0 0 0 0 0 0 0 1 0 0 0 272
273 3.3 0 0 0 0 0 0 0 0 1 0 0 273
274 -34.8 0 0 0 0 0 0 0 0 0 1 0 274
275 -2.9 0 0 0 0 0 0 0 0 0 0 1 275
276 -77.8 0 0 0 0 0 0 0 0 0 0 0 276
277 -2.8 1 0 0 0 0 0 0 0 0 0 0 277
278 26.7 0 1 0 0 0 0 0 0 0 0 0 278
279 48.1 0 0 1 0 0 0 0 0 0 0 0 279
280 30.0 0 0 0 1 0 0 0 0 0 0 0 280
281 -109.6 0 0 0 0 1 0 0 0 0 0 0 281
282 16.0 0 0 0 0 0 1 0 0 0 0 0 282
283 26.9 0 0 0 0 0 0 1 0 0 0 0 283
284 22.1 0 0 0 0 0 0 0 1 0 0 0 284
285 27.0 0 0 0 0 0 0 0 0 1 0 0 285
286 -24.5 0 0 0 0 0 0 0 0 0 1 0 286
287 12.0 0 0 0 0 0 0 0 0 0 0 1 287
288 -75.2 0 0 0 0 0 0 0 0 0 0 0 288
289 3.5 1 0 0 0 0 0 0 0 0 0 0 289
290 19.7 0 1 0 0 0 0 0 0 0 0 0 290
291 51.8 0 0 1 0 0 0 0 0 0 0 0 291
292 35.3 0 0 0 1 0 0 0 0 0 0 0 292
293 -108.2 0 0 0 0 1 0 0 0 0 0 0 293
294 25.3 0 0 0 0 0 1 0 0 0 0 0 294
295 31.6 0 0 0 0 0 0 1 0 0 0 0 295
296 19.9 0 0 0 0 0 0 0 1 0 0 0 296
297 18.8 0 0 0 0 0 0 0 0 1 0 0 297
298 20.4 0 0 0 0 0 0 0 0 0 1 0 298
299 15.0 0 0 0 0 0 0 0 0 0 0 1 299
300 -55.9 0 0 0 0 0 0 0 0 0 0 0 300
301 -17.0 1 0 0 0 0 0 0 0 0 0 0 301
302 33.3 0 1 0 0 0 0 0 0 0 0 0 302
303 33.8 0 0 1 0 0 0 0 0 0 0 0 303
304 37.5 0 0 0 1 0 0 0 0 0 0 0 304
305 -104.8 0 0 0 0 1 0 0 0 0 0 0 305
306 29.7 0 0 0 0 0 1 0 0 0 0 0 306
307 34.2 0 0 0 0 0 0 1 0 0 0 0 307
308 4.3 0 0 0 0 0 0 0 1 0 0 0 308
309 40.2 0 0 0 0 0 0 0 0 1 0 0 309
310 -29.3 0 0 0 0 0 0 0 0 0 1 0 310
311 -0.2 0 0 0 0 0 0 0 0 0 0 1 311
312 -95.0 0 0 0 0 0 0 0 0 0 0 0 312
313 -13.2 1 0 0 0 0 0 0 0 0 0 0 313
314 38.5 0 1 0 0 0 0 0 0 0 0 0 314
315 45.4 0 0 1 0 0 0 0 0 0 0 0 315
316 15.7 0 0 0 1 0 0 0 0 0 0 0 316
317 -123.6 0 0 0 0 1 0 0 0 0 0 0 317
318 12.0 0 0 0 0 0 1 0 0 0 0 0 318
319 37.5 0 0 0 0 0 0 1 0 0 0 0 319
320 -31.7 0 0 0 0 0 0 0 1 0 0 0 320
321 15.8 0 0 0 0 0 0 0 0 1 0 0 321
322 -64.1 0 0 0 0 0 0 0 0 0 1 0 322
323 -42.1 0 0 0 0 0 0 0 0 0 0 1 323
324 -207.4 0 0 0 0 0 0 0 0 0 0 0 324
325 -12.9 1 0 0 0 0 0 0 0 0 0 0 325
326 -5.0 0 1 0 0 0 0 0 0 0 0 0 326
327 53.9 0 0 1 0 0 0 0 0 0 0 0 327
328 19.7 0 0 0 1 0 0 0 0 0 0 0 328
329 -94.6 0 0 0 0 1 0 0 0 0 0 0 329
330 36.0 0 0 0 0 0 1 0 0 0 0 0 330
331 51.3 0 0 0 0 0 0 1 0 0 0 0 331
332 17.4 0 0 0 0 0 0 0 1 0 0 0 332
333 27.8 0 0 0 0 0 0 0 0 1 0 0 333
334 1.3 0 0 0 0 0 0 0 0 0 1 0 334
335 3.6 0 0 0 0 0 0 0 0 0 0 1 335
336 -97.9 0 0 0 0 0 0 0 0 0 0 0 336
337 14.1 1 0 0 0 0 0 0 0 0 0 0 337
338 50.8 0 1 0 0 0 0 0 0 0 0 0 338
339 63.5 0 0 1 0 0 0 0 0 0 0 0 339
340 58.6 0 0 0 1 0 0 0 0 0 0 0 340
341 -135.1 0 0 0 0 1 0 0 0 0 0 0 341
342 7.8 0 0 0 0 0 1 0 0 0 0 0 342
343 25.5 0 0 0 0 0 0 1 0 0 0 0 343
344 29.6 0 0 0 0 0 0 0 1 0 0 0 344
345 19.3 0 0 0 0 0 0 0 0 1 0 0 345
346 -26.2 0 0 0 0 0 0 0 0 0 1 0 346
347 7.3 0 0 0 0 0 0 0 0 0 0 1 347
348 -82.6 0 0 0 0 0 0 0 0 0 0 0 348
349 -26.1 1 0 0 0 0 0 0 0 0 0 0 349
350 55.3 0 1 0 0 0 0 0 0 0 0 0 350
351 98.8 0 0 1 0 0 0 0 0 0 0 0 351
352 41.7 0 0 0 1 0 0 0 0 0 0 0 352
353 -130.2 0 0 0 0 1 0 0 0 0 0 0 353
354 51.2 0 0 0 0 0 1 0 0 0 0 0 354
355 18.4 0 0 0 0 0 0 1 0 0 0 0 355
356 32.0 0 0 0 0 0 0 0 1 0 0 0 356
357 21.6 0 0 0 0 0 0 0 0 1 0 0 357
358 -12.5 0 0 0 0 0 0 0 0 0 1 0 358
359 46.6 0 0 0 0 0 0 0 0 0 0 1 359
360 -101.7 0 0 0 0 0 0 0 0 0 0 0 360
361 15.8 1 0 0 0 0 0 0 0 0 0 0 361
362 26.0 0 1 0 0 0 0 0 0 0 0 0 362
363 79.1 0 0 1 0 0 0 0 0 0 0 0 363
364 23.1 0 0 0 1 0 0 0 0 0 0 0 364
365 -86.9 0 0 0 0 1 0 0 0 0 0 0 365
366 -11.2 0 0 0 0 0 1 0 0 0 0 0 366
367 50.7 0 0 0 0 0 0 1 0 0 0 0 367
368 13.4 0 0 0 0 0 0 0 1 0 0 0 368
369 33.7 0 0 0 0 0 0 0 0 1 0 0 369
370 -16.9 0 0 0 0 0 0 0 0 0 1 0 370
371 -9.6 0 0 0 0 0 0 0 0 0 0 1 371
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
-7.429e+01 6.442e+01 9.658e+01 1.132e+02 9.485e+01 1.587e+00
M6 M7 M8 M9 M10 M11
9.708e+01 1.055e+02 9.142e+01 8.976e+01 5.224e+01 7.185e+01
t
-1.126e-04
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-133.071 -13.053 0.479 14.944 70.414
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.429e+01 4.823e+00 -15.405 < 2e-16 ***
M1 6.442e+01 6.065e+00 10.620 < 2e-16 ***
M2 9.658e+01 6.065e+00 15.923 < 2e-16 ***
M3 1.132e+02 6.065e+00 18.670 < 2e-16 ***
M4 9.485e+01 6.065e+00 15.639 < 2e-16 ***
M5 1.587e+00 6.065e+00 0.262 0.794
M6 9.708e+01 6.065e+00 16.007 < 2e-16 ***
M7 1.055e+02 6.065e+00 17.388 < 2e-16 ***
M8 9.142e+01 6.065e+00 15.073 < 2e-16 ***
M9 8.976e+01 6.065e+00 14.798 < 2e-16 ***
M10 5.224e+01 6.065e+00 8.613 2.3e-16 ***
M11 7.185e+01 6.065e+00 11.845 < 2e-16 ***
t -1.126e-04 1.148e-02 -0.010 0.992
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 23.68 on 358 degrees of freedom
Multiple R-squared: 0.7075, Adjusted R-squared: 0.6977
F-statistic: 72.17 on 12 and 358 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,] 0.45342936 0.9068587 0.54657064
[2,] 0.32124826 0.6424965 0.67875174
[3,] 0.26755716 0.5351143 0.73244284
[4,] 0.40223434 0.8044687 0.59776566
[5,] 0.37465580 0.7493116 0.62534420
[6,] 0.38587868 0.7717574 0.61412132
[7,] 0.64081294 0.7183741 0.35918706
[8,] 0.56385193 0.8722961 0.43614807
[9,] 0.48168450 0.9633690 0.51831550
[10,] 0.55987397 0.8802521 0.44012603
[11,] 0.59623078 0.8075384 0.40376922
[12,] 0.66397490 0.6720502 0.33602510
[13,] 0.63112702 0.7377460 0.36887298
[14,] 0.57200137 0.8559973 0.42799863
[15,] 0.57681845 0.8463631 0.42318155
[16,] 0.55999484 0.8800103 0.44000516
[17,] 0.55723654 0.8855269 0.44276346
[18,] 0.52446862 0.9510628 0.47553138
[19,] 0.58759891 0.8248022 0.41240109
[20,] 0.52619933 0.9476013 0.47380067
[21,] 0.64055803 0.7188839 0.35944197
[22,] 0.62479386 0.7504123 0.37520614
[23,] 0.61579167 0.7684167 0.38420833
[24,] 0.57672593 0.8465481 0.42327407
[25,] 0.55204751 0.8959050 0.44795249
[26,] 0.52869865 0.9426027 0.47130135
[27,] 0.47785251 0.9557050 0.52214749
[28,] 0.49825064 0.9965013 0.50174936
[29,] 0.56787760 0.8642448 0.43212240
[30,] 0.53648764 0.9270247 0.46351236
[31,] 0.51419898 0.9716020 0.48580102
[32,] 0.49379478 0.9875896 0.50620522
[33,] 0.45596785 0.9119357 0.54403215
[34,] 0.40619269 0.8123854 0.59380731
[35,] 0.36039965 0.7207993 0.63960035
[36,] 0.37864465 0.7572893 0.62135535
[37,] 0.36982912 0.7396582 0.63017088
[38,] 0.35136526 0.7027305 0.64863474
[39,] 0.32547402 0.6509480 0.67452598
[40,] 0.34333752 0.6866750 0.65666248
[41,] 0.30127724 0.6025545 0.69872276
[42,] 0.26258430 0.5251686 0.73741570
[43,] 0.22793657 0.4558731 0.77206343
[44,] 0.19491331 0.3898266 0.80508669
[45,] 0.16801000 0.3360200 0.83199000
[46,] 0.17754526 0.3550905 0.82245474
[47,] 0.16998115 0.3399623 0.83001885
[48,] 0.19628780 0.3925756 0.80371220
[49,] 0.16704328 0.3340866 0.83295672
[50,] 0.16886182 0.3377236 0.83113818
[51,] 0.14696041 0.2939208 0.85303959
[52,] 0.14167354 0.2833471 0.85832646
[53,] 0.15945937 0.3189187 0.84054063
[54,] 0.14977865 0.2995573 0.85022135
[55,] 0.17174750 0.3434950 0.82825250
[56,] 0.20352790 0.4070558 0.79647210
[57,] 0.36184280 0.7236856 0.63815720
[58,] 0.37622978 0.7524596 0.62377022
[59,] 0.38685801 0.7737160 0.61314199
[60,] 0.35746508 0.7149302 0.64253492
[61,] 0.32216993 0.6443399 0.67783007
[62,] 0.42260317 0.8452063 0.57739683
[63,] 0.39763639 0.7952728 0.60236361
[64,] 0.36716315 0.7343263 0.63283685
[65,] 0.33578420 0.6715684 0.66421580
[66,] 0.37741898 0.7548380 0.62258102
[67,] 0.34156849 0.6831370 0.65843151
[68,] 0.32496801 0.6499360 0.67503199
[69,] 0.29319501 0.5863900 0.70680499
[70,] 0.30098041 0.6019608 0.69901959
[71,] 0.27073167 0.5414633 0.72926833
[72,] 0.25937146 0.5187429 0.74062854
[73,] 0.27305761 0.5461152 0.72694239
[74,] 0.30171397 0.6034279 0.69828603
[75,] 0.29429460 0.5885892 0.70570540
[76,] 0.28751552 0.5750310 0.71248448
[77,] 0.25960332 0.5192066 0.74039668
[78,] 0.24622857 0.4924571 0.75377143
[79,] 0.22083255 0.4416651 0.77916745
[80,] 0.19543152 0.3908630 0.80456848
[81,] 0.19713320 0.3942664 0.80286680
[82,] 0.17656750 0.3531350 0.82343250
[83,] 0.17810544 0.3562109 0.82189456
[84,] 0.16058937 0.3211787 0.83941063
[85,] 0.18153422 0.3630684 0.81846578
[86,] 0.19349104 0.3869821 0.80650896
[87,] 0.18143960 0.3628792 0.81856040
[88,] 0.17838943 0.3567789 0.82161057
[89,] 0.15756961 0.3151392 0.84243039
[90,] 0.13800568 0.2760114 0.86199432
[91,] 0.15794892 0.3158978 0.84205108
[92,] 0.13821799 0.2764360 0.86178201
[93,] 0.12778997 0.2555799 0.87221003
[94,] 0.12877308 0.2575462 0.87122692
[95,] 0.11227903 0.2245581 0.88772097
[96,] 0.10348083 0.2069617 0.89651917
[97,] 0.10214891 0.2042978 0.89785109
[98,] 0.11415734 0.2283147 0.88584266
[99,] 0.11199373 0.2239875 0.88800627
[100,] 0.09665048 0.1933010 0.90334952
[101,] 0.08751635 0.1750327 0.91248365
[102,] 0.08092594 0.1618519 0.91907406
[103,] 0.12529658 0.2505932 0.87470342
[104,] 0.11809367 0.2361873 0.88190633
[105,] 0.17382973 0.3476595 0.82617027
[106,] 0.29695968 0.5939194 0.70304032
[107,] 0.30045777 0.6009155 0.69954223
[108,] 0.30703231 0.6140646 0.69296769
[109,] 0.28347790 0.5669558 0.71652210
[110,] 0.30830218 0.6166044 0.69169782
[111,] 0.29258122 0.5851624 0.70741878
[112,] 0.28667833 0.5733567 0.71332167
[113,] 0.36599318 0.7319864 0.63400682
[114,] 0.34906155 0.6981231 0.65093845
[115,] 0.34943453 0.6988691 0.65056547
[116,] 0.35544278 0.7108856 0.64455722
[117,] 0.33436443 0.6687289 0.66563557
[118,] 0.31429660 0.6285932 0.68570340
[119,] 0.31092396 0.6218479 0.68907604
[120,] 0.39317871 0.7863574 0.60682129
[121,] 0.36404721 0.7280944 0.63595279
[122,] 0.40168172 0.8033634 0.59831828
[123,] 0.38072992 0.7614598 0.61927008
[124,] 0.35374658 0.7074932 0.64625342
[125,] 0.32499338 0.6499868 0.67500662
[126,] 0.32381259 0.6476252 0.67618741
[127,] 0.31434264 0.6286853 0.68565736
[128,] 0.30286168 0.6057234 0.69713832
[129,] 0.28472802 0.5694560 0.71527198
[130,] 0.32595780 0.6519156 0.67404220
[131,] 0.45326962 0.9065392 0.54673038
[132,] 0.45252414 0.9050483 0.54747586
[133,] 0.42252771 0.8450554 0.57747229
[134,] 0.48539380 0.9707876 0.51460620
[135,] 0.47174962 0.9434992 0.52825038
[136,] 0.45479728 0.9095946 0.54520272
[137,] 0.45689319 0.9137864 0.54310681
[138,] 0.52286086 0.9542783 0.47713914
[139,] 0.51931842 0.9613632 0.48068158
[140,] 0.63102038 0.7379592 0.36897962
[141,] 0.61400718 0.7719856 0.38599282
[142,] 0.61501485 0.7699703 0.38498515
[143,] 0.58977514 0.8204497 0.41022486
[144,] 0.58199155 0.8360169 0.41800845
[145,] 0.55309668 0.8938066 0.44690332
[146,] 0.57922433 0.8415513 0.42077567
[147,] 0.57506170 0.8498766 0.42493830
[148,] 0.57164942 0.8567012 0.42835058
[149,] 0.57509336 0.8498133 0.42490664
[150,] 0.54512323 0.9097535 0.45487677
[151,] 0.53049845 0.9390031 0.46950155
[152,] 0.50675846 0.9864831 0.49324154
[153,] 0.49093982 0.9818796 0.50906018
[154,] 0.49655633 0.9931127 0.50344367
[155,] 0.47136916 0.9427383 0.52863084
[156,] 0.44684192 0.8936838 0.55315808
[157,] 0.47661343 0.9532269 0.52338657
[158,] 0.62567267 0.7486547 0.37432733
[159,] 0.62100203 0.7579959 0.37899797
[160,] 0.66299332 0.6740134 0.33700668
[161,] 0.66129099 0.6774180 0.33870901
[162,] 0.63508356 0.7298329 0.36491644
[163,] 0.65660587 0.6867883 0.34339413
[164,] 0.63179210 0.7364158 0.36820790
[165,] 0.61580894 0.7683821 0.38419106
[166,] 0.60245587 0.7950883 0.39754413
[167,] 0.60021826 0.7995635 0.39978174
[168,] 0.57453330 0.8509334 0.42546670
[169,] 0.56683840 0.8663232 0.43316160
[170,] 0.62361235 0.7527753 0.37638765
[171,] 0.62047244 0.7590551 0.37952756
[172,] 0.59383010 0.8123398 0.40616990
[173,] 0.57205626 0.8558875 0.42794374
[174,] 0.54717165 0.9056567 0.45282835
[175,] 0.56433630 0.8713274 0.43566370
[176,] 0.54205238 0.9158952 0.45794762
[177,] 0.51264441 0.9747112 0.48735559
[178,] 0.49638241 0.9927648 0.50361759
[179,] 0.46756726 0.9351345 0.53243274
[180,] 0.43959352 0.8791870 0.56040648
[181,] 0.41552309 0.8310462 0.58447691
[182,] 0.48451535 0.9690307 0.51548465
[183,] 0.64330589 0.7133882 0.35669411
[184,] 0.63915996 0.7216801 0.36084004
[185,] 0.61988711 0.7602258 0.38011289
[186,] 0.59700239 0.8059952 0.40299761
[187,] 0.57408436 0.8518313 0.42591564
[188,] 0.55153973 0.8969205 0.44846027
[189,] 0.55304335 0.8939133 0.44695665
[190,] 0.53697231 0.9260554 0.46302769
[191,] 0.54275760 0.9144848 0.45724240
[192,] 0.54347979 0.9130404 0.45652021
[193,] 0.51557020 0.9688596 0.48442980
[194,] 0.56477198 0.8704560 0.43522802
[195,] 0.63146482 0.7370704 0.36853518
[196,] 0.60413228 0.7917354 0.39586772
[197,] 0.59366468 0.8126706 0.40633532
[198,] 0.56398691 0.8720262 0.43601309
[199,] 0.53538951 0.9292210 0.46461049
[200,] 0.51309698 0.9738060 0.48690302
[201,] 0.55041211 0.8991758 0.44958789
[202,] 0.55432586 0.8913483 0.44567414
[203,] 0.53316647 0.9336671 0.46683353
[204,] 0.52161395 0.9567721 0.47838605
[205,] 0.55051797 0.8989641 0.44948203
[206,] 0.60495744 0.7900851 0.39504256
[207,] 0.63845850 0.7230830 0.36154150
[208,] 0.61395820 0.7720836 0.38604180
[209,] 0.60858102 0.7828380 0.39141898
[210,] 0.58797085 0.8240583 0.41202915
[211,] 0.56309465 0.8738107 0.43690535
[212,] 0.53835112 0.9232978 0.46164888
[213,] 0.57547867 0.8490427 0.42452133
[214,] 0.54880378 0.9023924 0.45119622
[215,] 0.51758504 0.9648299 0.48241496
[216,] 0.50175569 0.9964886 0.49824431
[217,] 0.47193560 0.9438712 0.52806440
[218,] 0.56731717 0.8653657 0.43268283
[219,] 0.55359615 0.8928077 0.44640385
[220,] 0.52202443 0.9559511 0.47797557
[221,] 0.49884950 0.9976990 0.50115050
[222,] 0.50072085 0.9985583 0.49927915
[223,] 0.51217138 0.9756572 0.48782862
[224,] 0.50415449 0.9916910 0.49584551
[225,] 0.63836026 0.7232795 0.36163974
[226,] 0.61512683 0.7697463 0.38487317
[227,] 0.59504347 0.8099131 0.40495653
[228,] 0.56551790 0.8689642 0.43448210
[229,] 0.53592370 0.9281526 0.46407630
[230,] 0.65658824 0.6868235 0.34341176
[231,] 0.65216210 0.6956758 0.34783790
[232,] 0.63425046 0.7314991 0.36574954
[233,] 0.60980584 0.7803883 0.39019416
[234,] 0.58083268 0.8383346 0.41916732
[235,] 0.56430266 0.8713947 0.43569734
[236,] 0.55255283 0.8948943 0.44744717
[237,] 0.66372928 0.6725414 0.33627072
[238,] 0.63745534 0.7250893 0.36254466
[239,] 0.60692575 0.7861485 0.39307425
[240,] 0.61430913 0.7713817 0.38569087
[241,] 0.58261869 0.8347626 0.41738131
[242,] 0.61723692 0.7655262 0.38276308
[243,] 0.58700943 0.8259811 0.41299057
[244,] 0.55436582 0.8912684 0.44563418
[245,] 0.54808234 0.9038353 0.45191766
[246,] 0.51581398 0.9683720 0.48418602
[247,] 0.57934681 0.8413064 0.42065319
[248,] 0.55495742 0.8900852 0.44504258
[249,] 0.56172724 0.8765455 0.43827276
[250,] 0.57852280 0.8429544 0.42147720
[251,] 0.56453951 0.8709210 0.43546049
[252,] 0.59534692 0.8093062 0.40465308
[253,] 0.56720689 0.8655862 0.43279311
[254,] 0.63120186 0.7375963 0.36879814
[255,] 0.59870573 0.8025885 0.40129427
[256,] 0.56440336 0.8711933 0.43559664
[257,] 0.55476377 0.8904725 0.44523623
[258,] 0.53807526 0.9238495 0.46192474
[259,] 0.51319654 0.9736069 0.48680346
[260,] 0.47864358 0.9572872 0.52135642
[261,] 0.48080498 0.9616100 0.51919502
[262,] 0.44795090 0.8959018 0.55204910
[263,] 0.41361463 0.8272293 0.58638537
[264,] 0.38760568 0.7752114 0.61239432
[265,] 0.35627450 0.7125490 0.64372550
[266,] 0.36185985 0.7237197 0.63814015
[267,] 0.32917641 0.6583528 0.67082359
[268,] 0.29825490 0.5965098 0.70174510
[269,] 0.27401805 0.5480361 0.72598195
[270,] 0.24811047 0.4962209 0.75188953
[271,] 0.22006523 0.4401305 0.77993477
[272,] 0.20200380 0.4040076 0.79799620
[273,] 0.21904883 0.4380977 0.78095117
[274,] 0.20200606 0.4040121 0.79799394
[275,] 0.17889706 0.3577941 0.82110294
[276,] 0.15986996 0.3197399 0.84013004
[277,] 0.14207564 0.2841513 0.85792436
[278,] 0.14180751 0.2836150 0.85819249
[279,] 0.12359787 0.2471957 0.87640213
[280,] 0.10502943 0.2100589 0.89497057
[281,] 0.09272409 0.1854482 0.90727591
[282,] 0.07787337 0.1557467 0.92212663
[283,] 0.13260478 0.2652096 0.86739522
[284,] 0.12836775 0.2567355 0.87163225
[285,] 0.27692482 0.5538496 0.72307518
[286,] 0.24473313 0.4894663 0.75526687
[287,] 0.22043789 0.4408758 0.77956211
[288,] 0.21300241 0.4260048 0.78699759
[289,] 0.19741066 0.3948213 0.80258934
[290,] 0.20149280 0.4029856 0.79850720
[291,] 0.19253621 0.3850724 0.80746379
[292,] 0.16899227 0.3379845 0.83100773
[293,] 0.14628577 0.2925715 0.85371423
[294,] 0.15809933 0.3161987 0.84190067
[295,] 0.13700130 0.2740026 0.86299870
[296,] 0.12079485 0.2415897 0.87920515
[297,] 0.15682836 0.3136567 0.84317164
[298,] 0.13256530 0.2651306 0.86743470
[299,] 0.12709312 0.2541862 0.87290688
[300,] 0.10967839 0.2193568 0.89032161
[301,] 0.09036689 0.1807338 0.90963311
[302,] 0.08980729 0.1796146 0.91019271
[303,] 0.07430133 0.1486027 0.92569867
[304,] 0.06537799 0.1307560 0.93462201
[305,] 0.08846302 0.1769260 0.91153698
[306,] 0.07167478 0.1433496 0.92832522
[307,] 0.08915409 0.1783082 0.91084591
[308,] 0.12374630 0.2474926 0.87625370
[309,] 0.87271208 0.2545758 0.12728792
[310,] 0.84945816 0.3010837 0.15054184
[311,] 0.92063484 0.1587303 0.07936516
[312,] 0.92821896 0.1435621 0.07178104
[313,] 0.92871519 0.1425696 0.07128481
[314,] 0.92172340 0.1565532 0.07827660
[315,] 0.91091441 0.1781712 0.08908559
[316,] 0.90138021 0.1972396 0.09861979
[317,] 0.87578771 0.2484246 0.12421229
[318,] 0.84252653 0.3149469 0.15747347
[319,] 0.83048396 0.3390321 0.16951604
[320,] 0.79556755 0.4088649 0.20443245
[321,] 0.75205141 0.4958972 0.24794859
[322,] 0.72899898 0.5420020 0.27100102
[323,] 0.68682947 0.6263411 0.31317053
[324,] 0.67878669 0.6424266 0.32121331
[325,] 0.68414107 0.6317179 0.31585893
[326,] 0.69622554 0.6075489 0.30377446
[327,] 0.64459053 0.7108189 0.35540947
[328,] 0.58338949 0.8332210 0.41661051
[329,] 0.50848784 0.9830243 0.49151216
[330,] 0.44162732 0.8832546 0.55837268
[331,] 0.38851273 0.7770255 0.61148727
[332,] 0.34774846 0.6954969 0.65225154
[333,] 0.27759108 0.5551822 0.72240892
[334,] 0.37327349 0.7465470 0.62672651
[335,] 0.31130581 0.6226116 0.68869419
[336,] 0.24512051 0.4902410 0.75487949
[337,] 0.17335005 0.3467001 0.82664995
[338,] 0.28595881 0.5719176 0.71404119
[339,] 0.41244945 0.8248989 0.58755055
[340,] 0.52084059 0.9583188 0.47915941
> postscript(file="/var/wessaorg/rcomp/tmp/194rh1322153799.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2n61x1322153799.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/38aax1322153799.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/42srt1322153799.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ttml1322153799.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 = 371
Frequency = 1
1 2 3 4 5
-35.72349505 -6.18801118 -15.04284989 18.74102108 33.30553721
6 7 8 9 10
-23.09123698 -13.86543053 0.57327915 15.93779528 -6.54930150
11 12 13 14 15
-14.75252731 -4.70626027 -38.02214376 -13.18665989 -28.34149860
16 17 18 19 20
-44.45762764 27.70688849 -64.98988570 12.03592075 14.97463043
21 22 23 24 25
-30.76085344 43.85204978 -9.55117602 -21.50490898 -4.42079248
26 27 28 29 30
25.51469139 25.85985268 19.64372365 43.90823978 0.71146558
31 32 33 34 35
39.13727203 -4.82401829 28.04049784 -8.04659893 -2.84982474
36 37 38 39 40
50.29644230 20.98055880 -0.78395733 -0.43879604 -3.75492507
41 42 43 44 45
36.50959106 -16.78718313 -4.56137668 -25.12266701 -2.25815088
46 47 48 49 50
-1.54524765 21.85152654 28.09779358 1.68191008 11.51739395
51 52 53 54 55
-22.33744475 -15.15357379 47.71094234 -28.08583185 -14.46002540
56 57 58 59 60
1.87868428 9.34320041 10.65610363 7.35287783 15.59914486
61 62 63 64 65
26.68326137 -8.68125476 -32.53609347 2.24777750 53.11229362
66 67 68 69 70
-20.58448057 -11.35867412 -27.81996444 -10.75544831 -22.44254509
71 72 73 74 75
-32.24577089 -45.39950385 -32.31538735 -27.67990348 -19.83474219
76 77 78 79 80
-1.75087122 70.41364491 -22.58312929 -10.25732283 -13.41861316
81 82 83 84 85
34.94590297 3.45880620 13.05558039 8.30184743 19.88596393
86 87 88 89 90
4.92144780 -25.43339091 26.65048006 52.41499619 0.31822200
91 92 93 94 95
-18.55597155 2.68273813 -10.05274575 -3.13984252 -4.04306833
96 97 98 99 100
27.80319871 7.28731521 -22.57720091 -0.43203962 -29.44816866
101 102 103 104 105
34.71634747 -3.28042672 20.54537973 2.28408941 2.74860554
106 107 108 109 110
-28.73849124 -3.64171704 19.70455000 21.98866650 4.02415037
111 112 113 114 115
-19.43068834 -21.34681737 23.11769876 6.02092456 0.54673101
116 117 118 119 120
-14.81455931 -11.65004318 -44.13713995 -18.04036576 -38.89409872
121 122 123 124 125
-55.30998222 -26.17449835 -29.82933706 2.65453391 33.61905004
126 127 128 129 130
-10.27772416 17.94808230 37.78679197 15.35130810 18.56421133
131 132 133 134 135
-25.83901448 13.30725256 7.89136906 17.72685293 35.07201422
136 137 138 139 140
2.55588519 27.42040132 -5.27637287 -5.35056642 -1.91185674
141 142 143 144 145
-19.04734061 -18.43443739 13.96233680 14.50860384 33.19272035
146 147 148 149 150
-50.07179578 16.77336551 2.15723647 -6.47824740 6.02497841
151 152 153 154 155
-13.84921514 22.48949454 -37.64598933 -20.93308611 -47.83631191
156 157 158 159 160
-12.19004487 -22.00592837 0.82955550 14.67471679 1.05858776
161 162 163 164 165
8.52310389 12.42632969 20.95213614 23.49084582 1.65536195
166 167 168 169 170
14.26826518 -7.53496063 16.31130641 23.89542291 -6.46909322
171 172 173 174 175
7.07606807 -29.44006096 46.02445517 16.22768098 -32.44651257
176 177 178 179 180
21.59219710 6.65671323 -27.13038354 -0.93360935 -12.38734231
181 182 183 184 185
-14.40322581 20.53225806 5.97741935 -16.13870968 12.22580645
186 187 188 189 190
18.62903226 7.35483871 11.39354839 -7.84193548 -24.32903226
191 192 193 194 195
9.46774194 1.31400897 15.59812548 1.33360935 0.47877064
196 197 198 199 200
9.76264161 -19.77284227 55.03038354 -18.74381001 11.79489967
201 202 203 204 205
-9.04058420 10.07231902 -6.63090678 21.11536026 -13.20052324
206 207 208 209 210
25.03496063 -18.21987808 7.26399289 -11.57149098 40.03173482
211 212 213 214 215
-4.74245872 15.19625095 -2.13923292 4.17367031 12.47044450
216 217 218 219 220
28.71671154 23.40082804 -10.36368809 -12.91852680 -26.83465583
221 222 223 224 225
-7.17013970 31.43308611 -8.24110744 14.69760224 -11.63788164
226 227 228 229 230
10.67502159 -6.12820422 24.91806282 7.40217932 0.73766320
231 232 233 234 235
-9.91717551 0.06669545 -44.26878842 15.13443739 -0.43975616
236 237 238 239 240
-12.40104648 -21.13653035 26.97637287 20.77314707 38.91941411
241 242 243 244 245
-11.39646939 13.53901448 4.88417577 -1.83195326 -58.36743713
246 247 248 249 250
17.03578867 13.36159512 -0.59969520 -5.73517907 15.47772416
251 252 253 254 255
18.27449835 28.62076539 5.10488189 -4.65963424 -18.41447295
256 257 258 259 260
3.66939802 -36.26608585 -1.96286005 0.06294641 -25.89834392
261 262 263 264 265
-3.63382779 35.07907544 10.77584963 -3.57788333 -28.89376683
266 267 268 269 270
-16.15828296 -20.81312167 -3.72925070 -55.76473457 -6.86150876
271 272 273 274 275
-2.13570231 -24.29699263 -12.13247650 -12.71957328 -0.42279909
276 277 278 279 280
-3.47653205 7.10758446 4.44306833 9.18822962 9.47210058
281 282 283 284 285
-36.86338329 -6.76015748 -4.23435103 5.00435865 11.56887478
286 287 288 289 290
-2.41822200 14.47855220 -0.87518076 13.40893574 -2.55558039
291 292 293 294 295
12.88958090 14.77345187 -35.46203200 2.54119380 0.46700025
296 297 298 299 300
2.80570993 3.37022606 42.48312929 17.47990348 18.42617052
301 302 303 304 305
-7.08971298 11.04577089 -5.10906782 16.97480315 -32.06068072
306 307 308 309 310
6.94254509 3.06835154 -12.79293879 24.77157734 -7.21551943
311 312 313 314 315
2.28125476 -20.67247820 -3.28836170 16.24712217 6.49228346
316 317 318 319 320
-4.82384557 -50.85932944 -10.75610363 6.36970282 -48.79158750
321 322 323 324 325
0.37292863 -42.01416815 -39.61739395 -133.07112692 -2.98701041
326 327 328 329 330
-27.25152654 14.99363475 -0.82249428 -21.85797816 13.24524765
331 332 333 334 335
20.17105410 0.30976378 12.37427991 23.38718313 6.08395733
336 337 338 339 340
-23.56977563 24.01434087 28.54982474 24.59498603 38.07885700
341 342 343 344 345
-62.35662687 -14.95340107 -5.62759462 12.51111506 3.87563119
346 347 348 349 350
-4.11146558 9.78530861 -8.26842435 -16.18430785 33.05117602
351 352 353 354 355
59.89633731 21.18020828 -57.45527559 28.44795022 -12.72624333
356 357 358 359 360
14.91246634 6.17698247 9.58988570 49.08665989 -27.36707307
361 362 363 364 365
25.71704343 3.75252731 40.19768860 2.58155956 -14.15392431
366 367 368 369 370
-33.95069850 19.57510795 -3.68618237 18.27833376 5.19123698
371
-7.11198882
> postscript(file="/var/wessaorg/rcomp/tmp/6wedt1322153799.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 = 371
Frequency = 1
lag(myerror, k = 1) myerror
0 -35.72349505 NA
1 -6.18801118 -35.72349505
2 -15.04284989 -6.18801118
3 18.74102108 -15.04284989
4 33.30553721 18.74102108
5 -23.09123698 33.30553721
6 -13.86543053 -23.09123698
7 0.57327915 -13.86543053
8 15.93779528 0.57327915
9 -6.54930150 15.93779528
10 -14.75252731 -6.54930150
11 -4.70626027 -14.75252731
12 -38.02214376 -4.70626027
13 -13.18665989 -38.02214376
14 -28.34149860 -13.18665989
15 -44.45762764 -28.34149860
16 27.70688849 -44.45762764
17 -64.98988570 27.70688849
18 12.03592075 -64.98988570
19 14.97463043 12.03592075
20 -30.76085344 14.97463043
21 43.85204978 -30.76085344
22 -9.55117602 43.85204978
23 -21.50490898 -9.55117602
24 -4.42079248 -21.50490898
25 25.51469139 -4.42079248
26 25.85985268 25.51469139
27 19.64372365 25.85985268
28 43.90823978 19.64372365
29 0.71146558 43.90823978
30 39.13727203 0.71146558
31 -4.82401829 39.13727203
32 28.04049784 -4.82401829
33 -8.04659893 28.04049784
34 -2.84982474 -8.04659893
35 50.29644230 -2.84982474
36 20.98055880 50.29644230
37 -0.78395733 20.98055880
38 -0.43879604 -0.78395733
39 -3.75492507 -0.43879604
40 36.50959106 -3.75492507
41 -16.78718313 36.50959106
42 -4.56137668 -16.78718313
43 -25.12266701 -4.56137668
44 -2.25815088 -25.12266701
45 -1.54524765 -2.25815088
46 21.85152654 -1.54524765
47 28.09779358 21.85152654
48 1.68191008 28.09779358
49 11.51739395 1.68191008
50 -22.33744475 11.51739395
51 -15.15357379 -22.33744475
52 47.71094234 -15.15357379
53 -28.08583185 47.71094234
54 -14.46002540 -28.08583185
55 1.87868428 -14.46002540
56 9.34320041 1.87868428
57 10.65610363 9.34320041
58 7.35287783 10.65610363
59 15.59914486 7.35287783
60 26.68326137 15.59914486
61 -8.68125476 26.68326137
62 -32.53609347 -8.68125476
63 2.24777750 -32.53609347
64 53.11229362 2.24777750
65 -20.58448057 53.11229362
66 -11.35867412 -20.58448057
67 -27.81996444 -11.35867412
68 -10.75544831 -27.81996444
69 -22.44254509 -10.75544831
70 -32.24577089 -22.44254509
71 -45.39950385 -32.24577089
72 -32.31538735 -45.39950385
73 -27.67990348 -32.31538735
74 -19.83474219 -27.67990348
75 -1.75087122 -19.83474219
76 70.41364491 -1.75087122
77 -22.58312929 70.41364491
78 -10.25732283 -22.58312929
79 -13.41861316 -10.25732283
80 34.94590297 -13.41861316
81 3.45880620 34.94590297
82 13.05558039 3.45880620
83 8.30184743 13.05558039
84 19.88596393 8.30184743
85 4.92144780 19.88596393
86 -25.43339091 4.92144780
87 26.65048006 -25.43339091
88 52.41499619 26.65048006
89 0.31822200 52.41499619
90 -18.55597155 0.31822200
91 2.68273813 -18.55597155
92 -10.05274575 2.68273813
93 -3.13984252 -10.05274575
94 -4.04306833 -3.13984252
95 27.80319871 -4.04306833
96 7.28731521 27.80319871
97 -22.57720091 7.28731521
98 -0.43203962 -22.57720091
99 -29.44816866 -0.43203962
100 34.71634747 -29.44816866
101 -3.28042672 34.71634747
102 20.54537973 -3.28042672
103 2.28408941 20.54537973
104 2.74860554 2.28408941
105 -28.73849124 2.74860554
106 -3.64171704 -28.73849124
107 19.70455000 -3.64171704
108 21.98866650 19.70455000
109 4.02415037 21.98866650
110 -19.43068834 4.02415037
111 -21.34681737 -19.43068834
112 23.11769876 -21.34681737
113 6.02092456 23.11769876
114 0.54673101 6.02092456
115 -14.81455931 0.54673101
116 -11.65004318 -14.81455931
117 -44.13713995 -11.65004318
118 -18.04036576 -44.13713995
119 -38.89409872 -18.04036576
120 -55.30998222 -38.89409872
121 -26.17449835 -55.30998222
122 -29.82933706 -26.17449835
123 2.65453391 -29.82933706
124 33.61905004 2.65453391
125 -10.27772416 33.61905004
126 17.94808230 -10.27772416
127 37.78679197 17.94808230
128 15.35130810 37.78679197
129 18.56421133 15.35130810
130 -25.83901448 18.56421133
131 13.30725256 -25.83901448
132 7.89136906 13.30725256
133 17.72685293 7.89136906
134 35.07201422 17.72685293
135 2.55588519 35.07201422
136 27.42040132 2.55588519
137 -5.27637287 27.42040132
138 -5.35056642 -5.27637287
139 -1.91185674 -5.35056642
140 -19.04734061 -1.91185674
141 -18.43443739 -19.04734061
142 13.96233680 -18.43443739
143 14.50860384 13.96233680
144 33.19272035 14.50860384
145 -50.07179578 33.19272035
146 16.77336551 -50.07179578
147 2.15723647 16.77336551
148 -6.47824740 2.15723647
149 6.02497841 -6.47824740
150 -13.84921514 6.02497841
151 22.48949454 -13.84921514
152 -37.64598933 22.48949454
153 -20.93308611 -37.64598933
154 -47.83631191 -20.93308611
155 -12.19004487 -47.83631191
156 -22.00592837 -12.19004487
157 0.82955550 -22.00592837
158 14.67471679 0.82955550
159 1.05858776 14.67471679
160 8.52310389 1.05858776
161 12.42632969 8.52310389
162 20.95213614 12.42632969
163 23.49084582 20.95213614
164 1.65536195 23.49084582
165 14.26826518 1.65536195
166 -7.53496063 14.26826518
167 16.31130641 -7.53496063
168 23.89542291 16.31130641
169 -6.46909322 23.89542291
170 7.07606807 -6.46909322
171 -29.44006096 7.07606807
172 46.02445517 -29.44006096
173 16.22768098 46.02445517
174 -32.44651257 16.22768098
175 21.59219710 -32.44651257
176 6.65671323 21.59219710
177 -27.13038354 6.65671323
178 -0.93360935 -27.13038354
179 -12.38734231 -0.93360935
180 -14.40322581 -12.38734231
181 20.53225806 -14.40322581
182 5.97741935 20.53225806
183 -16.13870968 5.97741935
184 12.22580645 -16.13870968
185 18.62903226 12.22580645
186 7.35483871 18.62903226
187 11.39354839 7.35483871
188 -7.84193548 11.39354839
189 -24.32903226 -7.84193548
190 9.46774194 -24.32903226
191 1.31400897 9.46774194
192 15.59812548 1.31400897
193 1.33360935 15.59812548
194 0.47877064 1.33360935
195 9.76264161 0.47877064
196 -19.77284227 9.76264161
197 55.03038354 -19.77284227
198 -18.74381001 55.03038354
199 11.79489967 -18.74381001
200 -9.04058420 11.79489967
201 10.07231902 -9.04058420
202 -6.63090678 10.07231902
203 21.11536026 -6.63090678
204 -13.20052324 21.11536026
205 25.03496063 -13.20052324
206 -18.21987808 25.03496063
207 7.26399289 -18.21987808
208 -11.57149098 7.26399289
209 40.03173482 -11.57149098
210 -4.74245872 40.03173482
211 15.19625095 -4.74245872
212 -2.13923292 15.19625095
213 4.17367031 -2.13923292
214 12.47044450 4.17367031
215 28.71671154 12.47044450
216 23.40082804 28.71671154
217 -10.36368809 23.40082804
218 -12.91852680 -10.36368809
219 -26.83465583 -12.91852680
220 -7.17013970 -26.83465583
221 31.43308611 -7.17013970
222 -8.24110744 31.43308611
223 14.69760224 -8.24110744
224 -11.63788164 14.69760224
225 10.67502159 -11.63788164
226 -6.12820422 10.67502159
227 24.91806282 -6.12820422
228 7.40217932 24.91806282
229 0.73766320 7.40217932
230 -9.91717551 0.73766320
231 0.06669545 -9.91717551
232 -44.26878842 0.06669545
233 15.13443739 -44.26878842
234 -0.43975616 15.13443739
235 -12.40104648 -0.43975616
236 -21.13653035 -12.40104648
237 26.97637287 -21.13653035
238 20.77314707 26.97637287
239 38.91941411 20.77314707
240 -11.39646939 38.91941411
241 13.53901448 -11.39646939
242 4.88417577 13.53901448
243 -1.83195326 4.88417577
244 -58.36743713 -1.83195326
245 17.03578867 -58.36743713
246 13.36159512 17.03578867
247 -0.59969520 13.36159512
248 -5.73517907 -0.59969520
249 15.47772416 -5.73517907
250 18.27449835 15.47772416
251 28.62076539 18.27449835
252 5.10488189 28.62076539
253 -4.65963424 5.10488189
254 -18.41447295 -4.65963424
255 3.66939802 -18.41447295
256 -36.26608585 3.66939802
257 -1.96286005 -36.26608585
258 0.06294641 -1.96286005
259 -25.89834392 0.06294641
260 -3.63382779 -25.89834392
261 35.07907544 -3.63382779
262 10.77584963 35.07907544
263 -3.57788333 10.77584963
264 -28.89376683 -3.57788333
265 -16.15828296 -28.89376683
266 -20.81312167 -16.15828296
267 -3.72925070 -20.81312167
268 -55.76473457 -3.72925070
269 -6.86150876 -55.76473457
270 -2.13570231 -6.86150876
271 -24.29699263 -2.13570231
272 -12.13247650 -24.29699263
273 -12.71957328 -12.13247650
274 -0.42279909 -12.71957328
275 -3.47653205 -0.42279909
276 7.10758446 -3.47653205
277 4.44306833 7.10758446
278 9.18822962 4.44306833
279 9.47210058 9.18822962
280 -36.86338329 9.47210058
281 -6.76015748 -36.86338329
282 -4.23435103 -6.76015748
283 5.00435865 -4.23435103
284 11.56887478 5.00435865
285 -2.41822200 11.56887478
286 14.47855220 -2.41822200
287 -0.87518076 14.47855220
288 13.40893574 -0.87518076
289 -2.55558039 13.40893574
290 12.88958090 -2.55558039
291 14.77345187 12.88958090
292 -35.46203200 14.77345187
293 2.54119380 -35.46203200
294 0.46700025 2.54119380
295 2.80570993 0.46700025
296 3.37022606 2.80570993
297 42.48312929 3.37022606
298 17.47990348 42.48312929
299 18.42617052 17.47990348
300 -7.08971298 18.42617052
301 11.04577089 -7.08971298
302 -5.10906782 11.04577089
303 16.97480315 -5.10906782
304 -32.06068072 16.97480315
305 6.94254509 -32.06068072
306 3.06835154 6.94254509
307 -12.79293879 3.06835154
308 24.77157734 -12.79293879
309 -7.21551943 24.77157734
310 2.28125476 -7.21551943
311 -20.67247820 2.28125476
312 -3.28836170 -20.67247820
313 16.24712217 -3.28836170
314 6.49228346 16.24712217
315 -4.82384557 6.49228346
316 -50.85932944 -4.82384557
317 -10.75610363 -50.85932944
318 6.36970282 -10.75610363
319 -48.79158750 6.36970282
320 0.37292863 -48.79158750
321 -42.01416815 0.37292863
322 -39.61739395 -42.01416815
323 -133.07112692 -39.61739395
324 -2.98701041 -133.07112692
325 -27.25152654 -2.98701041
326 14.99363475 -27.25152654
327 -0.82249428 14.99363475
328 -21.85797816 -0.82249428
329 13.24524765 -21.85797816
330 20.17105410 13.24524765
331 0.30976378 20.17105410
332 12.37427991 0.30976378
333 23.38718313 12.37427991
334 6.08395733 23.38718313
335 -23.56977563 6.08395733
336 24.01434087 -23.56977563
337 28.54982474 24.01434087
338 24.59498603 28.54982474
339 38.07885700 24.59498603
340 -62.35662687 38.07885700
341 -14.95340107 -62.35662687
342 -5.62759462 -14.95340107
343 12.51111506 -5.62759462
344 3.87563119 12.51111506
345 -4.11146558 3.87563119
346 9.78530861 -4.11146558
347 -8.26842435 9.78530861
348 -16.18430785 -8.26842435
349 33.05117602 -16.18430785
350 59.89633731 33.05117602
351 21.18020828 59.89633731
352 -57.45527559 21.18020828
353 28.44795022 -57.45527559
354 -12.72624333 28.44795022
355 14.91246634 -12.72624333
356 6.17698247 14.91246634
357 9.58988570 6.17698247
358 49.08665989 9.58988570
359 -27.36707307 49.08665989
360 25.71704343 -27.36707307
361 3.75252731 25.71704343
362 40.19768860 3.75252731
363 2.58155956 40.19768860
364 -14.15392431 2.58155956
365 -33.95069850 -14.15392431
366 19.57510795 -33.95069850
367 -3.68618237 19.57510795
368 18.27833376 -3.68618237
369 5.19123698 18.27833376
370 -7.11198882 5.19123698
371 NA -7.11198882
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -6.18801118 -35.72349505
[2,] -15.04284989 -6.18801118
[3,] 18.74102108 -15.04284989
[4,] 33.30553721 18.74102108
[5,] -23.09123698 33.30553721
[6,] -13.86543053 -23.09123698
[7,] 0.57327915 -13.86543053
[8,] 15.93779528 0.57327915
[9,] -6.54930150 15.93779528
[10,] -14.75252731 -6.54930150
[11,] -4.70626027 -14.75252731
[12,] -38.02214376 -4.70626027
[13,] -13.18665989 -38.02214376
[14,] -28.34149860 -13.18665989
[15,] -44.45762764 -28.34149860
[16,] 27.70688849 -44.45762764
[17,] -64.98988570 27.70688849
[18,] 12.03592075 -64.98988570
[19,] 14.97463043 12.03592075
[20,] -30.76085344 14.97463043
[21,] 43.85204978 -30.76085344
[22,] -9.55117602 43.85204978
[23,] -21.50490898 -9.55117602
[24,] -4.42079248 -21.50490898
[25,] 25.51469139 -4.42079248
[26,] 25.85985268 25.51469139
[27,] 19.64372365 25.85985268
[28,] 43.90823978 19.64372365
[29,] 0.71146558 43.90823978
[30,] 39.13727203 0.71146558
[31,] -4.82401829 39.13727203
[32,] 28.04049784 -4.82401829
[33,] -8.04659893 28.04049784
[34,] -2.84982474 -8.04659893
[35,] 50.29644230 -2.84982474
[36,] 20.98055880 50.29644230
[37,] -0.78395733 20.98055880
[38,] -0.43879604 -0.78395733
[39,] -3.75492507 -0.43879604
[40,] 36.50959106 -3.75492507
[41,] -16.78718313 36.50959106
[42,] -4.56137668 -16.78718313
[43,] -25.12266701 -4.56137668
[44,] -2.25815088 -25.12266701
[45,] -1.54524765 -2.25815088
[46,] 21.85152654 -1.54524765
[47,] 28.09779358 21.85152654
[48,] 1.68191008 28.09779358
[49,] 11.51739395 1.68191008
[50,] -22.33744475 11.51739395
[51,] -15.15357379 -22.33744475
[52,] 47.71094234 -15.15357379
[53,] -28.08583185 47.71094234
[54,] -14.46002540 -28.08583185
[55,] 1.87868428 -14.46002540
[56,] 9.34320041 1.87868428
[57,] 10.65610363 9.34320041
[58,] 7.35287783 10.65610363
[59,] 15.59914486 7.35287783
[60,] 26.68326137 15.59914486
[61,] -8.68125476 26.68326137
[62,] -32.53609347 -8.68125476
[63,] 2.24777750 -32.53609347
[64,] 53.11229362 2.24777750
[65,] -20.58448057 53.11229362
[66,] -11.35867412 -20.58448057
[67,] -27.81996444 -11.35867412
[68,] -10.75544831 -27.81996444
[69,] -22.44254509 -10.75544831
[70,] -32.24577089 -22.44254509
[71,] -45.39950385 -32.24577089
[72,] -32.31538735 -45.39950385
[73,] -27.67990348 -32.31538735
[74,] -19.83474219 -27.67990348
[75,] -1.75087122 -19.83474219
[76,] 70.41364491 -1.75087122
[77,] -22.58312929 70.41364491
[78,] -10.25732283 -22.58312929
[79,] -13.41861316 -10.25732283
[80,] 34.94590297 -13.41861316
[81,] 3.45880620 34.94590297
[82,] 13.05558039 3.45880620
[83,] 8.30184743 13.05558039
[84,] 19.88596393 8.30184743
[85,] 4.92144780 19.88596393
[86,] -25.43339091 4.92144780
[87,] 26.65048006 -25.43339091
[88,] 52.41499619 26.65048006
[89,] 0.31822200 52.41499619
[90,] -18.55597155 0.31822200
[91,] 2.68273813 -18.55597155
[92,] -10.05274575 2.68273813
[93,] -3.13984252 -10.05274575
[94,] -4.04306833 -3.13984252
[95,] 27.80319871 -4.04306833
[96,] 7.28731521 27.80319871
[97,] -22.57720091 7.28731521
[98,] -0.43203962 -22.57720091
[99,] -29.44816866 -0.43203962
[100,] 34.71634747 -29.44816866
[101,] -3.28042672 34.71634747
[102,] 20.54537973 -3.28042672
[103,] 2.28408941 20.54537973
[104,] 2.74860554 2.28408941
[105,] -28.73849124 2.74860554
[106,] -3.64171704 -28.73849124
[107,] 19.70455000 -3.64171704
[108,] 21.98866650 19.70455000
[109,] 4.02415037 21.98866650
[110,] -19.43068834 4.02415037
[111,] -21.34681737 -19.43068834
[112,] 23.11769876 -21.34681737
[113,] 6.02092456 23.11769876
[114,] 0.54673101 6.02092456
[115,] -14.81455931 0.54673101
[116,] -11.65004318 -14.81455931
[117,] -44.13713995 -11.65004318
[118,] -18.04036576 -44.13713995
[119,] -38.89409872 -18.04036576
[120,] -55.30998222 -38.89409872
[121,] -26.17449835 -55.30998222
[122,] -29.82933706 -26.17449835
[123,] 2.65453391 -29.82933706
[124,] 33.61905004 2.65453391
[125,] -10.27772416 33.61905004
[126,] 17.94808230 -10.27772416
[127,] 37.78679197 17.94808230
[128,] 15.35130810 37.78679197
[129,] 18.56421133 15.35130810
[130,] -25.83901448 18.56421133
[131,] 13.30725256 -25.83901448
[132,] 7.89136906 13.30725256
[133,] 17.72685293 7.89136906
[134,] 35.07201422 17.72685293
[135,] 2.55588519 35.07201422
[136,] 27.42040132 2.55588519
[137,] -5.27637287 27.42040132
[138,] -5.35056642 -5.27637287
[139,] -1.91185674 -5.35056642
[140,] -19.04734061 -1.91185674
[141,] -18.43443739 -19.04734061
[142,] 13.96233680 -18.43443739
[143,] 14.50860384 13.96233680
[144,] 33.19272035 14.50860384
[145,] -50.07179578 33.19272035
[146,] 16.77336551 -50.07179578
[147,] 2.15723647 16.77336551
[148,] -6.47824740 2.15723647
[149,] 6.02497841 -6.47824740
[150,] -13.84921514 6.02497841
[151,] 22.48949454 -13.84921514
[152,] -37.64598933 22.48949454
[153,] -20.93308611 -37.64598933
[154,] -47.83631191 -20.93308611
[155,] -12.19004487 -47.83631191
[156,] -22.00592837 -12.19004487
[157,] 0.82955550 -22.00592837
[158,] 14.67471679 0.82955550
[159,] 1.05858776 14.67471679
[160,] 8.52310389 1.05858776
[161,] 12.42632969 8.52310389
[162,] 20.95213614 12.42632969
[163,] 23.49084582 20.95213614
[164,] 1.65536195 23.49084582
[165,] 14.26826518 1.65536195
[166,] -7.53496063 14.26826518
[167,] 16.31130641 -7.53496063
[168,] 23.89542291 16.31130641
[169,] -6.46909322 23.89542291
[170,] 7.07606807 -6.46909322
[171,] -29.44006096 7.07606807
[172,] 46.02445517 -29.44006096
[173,] 16.22768098 46.02445517
[174,] -32.44651257 16.22768098
[175,] 21.59219710 -32.44651257
[176,] 6.65671323 21.59219710
[177,] -27.13038354 6.65671323
[178,] -0.93360935 -27.13038354
[179,] -12.38734231 -0.93360935
[180,] -14.40322581 -12.38734231
[181,] 20.53225806 -14.40322581
[182,] 5.97741935 20.53225806
[183,] -16.13870968 5.97741935
[184,] 12.22580645 -16.13870968
[185,] 18.62903226 12.22580645
[186,] 7.35483871 18.62903226
[187,] 11.39354839 7.35483871
[188,] -7.84193548 11.39354839
[189,] -24.32903226 -7.84193548
[190,] 9.46774194 -24.32903226
[191,] 1.31400897 9.46774194
[192,] 15.59812548 1.31400897
[193,] 1.33360935 15.59812548
[194,] 0.47877064 1.33360935
[195,] 9.76264161 0.47877064
[196,] -19.77284227 9.76264161
[197,] 55.03038354 -19.77284227
[198,] -18.74381001 55.03038354
[199,] 11.79489967 -18.74381001
[200,] -9.04058420 11.79489967
[201,] 10.07231902 -9.04058420
[202,] -6.63090678 10.07231902
[203,] 21.11536026 -6.63090678
[204,] -13.20052324 21.11536026
[205,] 25.03496063 -13.20052324
[206,] -18.21987808 25.03496063
[207,] 7.26399289 -18.21987808
[208,] -11.57149098 7.26399289
[209,] 40.03173482 -11.57149098
[210,] -4.74245872 40.03173482
[211,] 15.19625095 -4.74245872
[212,] -2.13923292 15.19625095
[213,] 4.17367031 -2.13923292
[214,] 12.47044450 4.17367031
[215,] 28.71671154 12.47044450
[216,] 23.40082804 28.71671154
[217,] -10.36368809 23.40082804
[218,] -12.91852680 -10.36368809
[219,] -26.83465583 -12.91852680
[220,] -7.17013970 -26.83465583
[221,] 31.43308611 -7.17013970
[222,] -8.24110744 31.43308611
[223,] 14.69760224 -8.24110744
[224,] -11.63788164 14.69760224
[225,] 10.67502159 -11.63788164
[226,] -6.12820422 10.67502159
[227,] 24.91806282 -6.12820422
[228,] 7.40217932 24.91806282
[229,] 0.73766320 7.40217932
[230,] -9.91717551 0.73766320
[231,] 0.06669545 -9.91717551
[232,] -44.26878842 0.06669545
[233,] 15.13443739 -44.26878842
[234,] -0.43975616 15.13443739
[235,] -12.40104648 -0.43975616
[236,] -21.13653035 -12.40104648
[237,] 26.97637287 -21.13653035
[238,] 20.77314707 26.97637287
[239,] 38.91941411 20.77314707
[240,] -11.39646939 38.91941411
[241,] 13.53901448 -11.39646939
[242,] 4.88417577 13.53901448
[243,] -1.83195326 4.88417577
[244,] -58.36743713 -1.83195326
[245,] 17.03578867 -58.36743713
[246,] 13.36159512 17.03578867
[247,] -0.59969520 13.36159512
[248,] -5.73517907 -0.59969520
[249,] 15.47772416 -5.73517907
[250,] 18.27449835 15.47772416
[251,] 28.62076539 18.27449835
[252,] 5.10488189 28.62076539
[253,] -4.65963424 5.10488189
[254,] -18.41447295 -4.65963424
[255,] 3.66939802 -18.41447295
[256,] -36.26608585 3.66939802
[257,] -1.96286005 -36.26608585
[258,] 0.06294641 -1.96286005
[259,] -25.89834392 0.06294641
[260,] -3.63382779 -25.89834392
[261,] 35.07907544 -3.63382779
[262,] 10.77584963 35.07907544
[263,] -3.57788333 10.77584963
[264,] -28.89376683 -3.57788333
[265,] -16.15828296 -28.89376683
[266,] -20.81312167 -16.15828296
[267,] -3.72925070 -20.81312167
[268,] -55.76473457 -3.72925070
[269,] -6.86150876 -55.76473457
[270,] -2.13570231 -6.86150876
[271,] -24.29699263 -2.13570231
[272,] -12.13247650 -24.29699263
[273,] -12.71957328 -12.13247650
[274,] -0.42279909 -12.71957328
[275,] -3.47653205 -0.42279909
[276,] 7.10758446 -3.47653205
[277,] 4.44306833 7.10758446
[278,] 9.18822962 4.44306833
[279,] 9.47210058 9.18822962
[280,] -36.86338329 9.47210058
[281,] -6.76015748 -36.86338329
[282,] -4.23435103 -6.76015748
[283,] 5.00435865 -4.23435103
[284,] 11.56887478 5.00435865
[285,] -2.41822200 11.56887478
[286,] 14.47855220 -2.41822200
[287,] -0.87518076 14.47855220
[288,] 13.40893574 -0.87518076
[289,] -2.55558039 13.40893574
[290,] 12.88958090 -2.55558039
[291,] 14.77345187 12.88958090
[292,] -35.46203200 14.77345187
[293,] 2.54119380 -35.46203200
[294,] 0.46700025 2.54119380
[295,] 2.80570993 0.46700025
[296,] 3.37022606 2.80570993
[297,] 42.48312929 3.37022606
[298,] 17.47990348 42.48312929
[299,] 18.42617052 17.47990348
[300,] -7.08971298 18.42617052
[301,] 11.04577089 -7.08971298
[302,] -5.10906782 11.04577089
[303,] 16.97480315 -5.10906782
[304,] -32.06068072 16.97480315
[305,] 6.94254509 -32.06068072
[306,] 3.06835154 6.94254509
[307,] -12.79293879 3.06835154
[308,] 24.77157734 -12.79293879
[309,] -7.21551943 24.77157734
[310,] 2.28125476 -7.21551943
[311,] -20.67247820 2.28125476
[312,] -3.28836170 -20.67247820
[313,] 16.24712217 -3.28836170
[314,] 6.49228346 16.24712217
[315,] -4.82384557 6.49228346
[316,] -50.85932944 -4.82384557
[317,] -10.75610363 -50.85932944
[318,] 6.36970282 -10.75610363
[319,] -48.79158750 6.36970282
[320,] 0.37292863 -48.79158750
[321,] -42.01416815 0.37292863
[322,] -39.61739395 -42.01416815
[323,] -133.07112692 -39.61739395
[324,] -2.98701041 -133.07112692
[325,] -27.25152654 -2.98701041
[326,] 14.99363475 -27.25152654
[327,] -0.82249428 14.99363475
[328,] -21.85797816 -0.82249428
[329,] 13.24524765 -21.85797816
[330,] 20.17105410 13.24524765
[331,] 0.30976378 20.17105410
[332,] 12.37427991 0.30976378
[333,] 23.38718313 12.37427991
[334,] 6.08395733 23.38718313
[335,] -23.56977563 6.08395733
[336,] 24.01434087 -23.56977563
[337,] 28.54982474 24.01434087
[338,] 24.59498603 28.54982474
[339,] 38.07885700 24.59498603
[340,] -62.35662687 38.07885700
[341,] -14.95340107 -62.35662687
[342,] -5.62759462 -14.95340107
[343,] 12.51111506 -5.62759462
[344,] 3.87563119 12.51111506
[345,] -4.11146558 3.87563119
[346,] 9.78530861 -4.11146558
[347,] -8.26842435 9.78530861
[348,] -16.18430785 -8.26842435
[349,] 33.05117602 -16.18430785
[350,] 59.89633731 33.05117602
[351,] 21.18020828 59.89633731
[352,] -57.45527559 21.18020828
[353,] 28.44795022 -57.45527559
[354,] -12.72624333 28.44795022
[355,] 14.91246634 -12.72624333
[356,] 6.17698247 14.91246634
[357,] 9.58988570 6.17698247
[358,] 49.08665989 9.58988570
[359,] -27.36707307 49.08665989
[360,] 25.71704343 -27.36707307
[361,] 3.75252731 25.71704343
[362,] 40.19768860 3.75252731
[363,] 2.58155956 40.19768860
[364,] -14.15392431 2.58155956
[365,] -33.95069850 -14.15392431
[366,] 19.57510795 -33.95069850
[367,] -3.68618237 19.57510795
[368,] 18.27833376 -3.68618237
[369,] 5.19123698 18.27833376
[370,] -7.11198882 5.19123698
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -6.18801118 -35.72349505
2 -15.04284989 -6.18801118
3 18.74102108 -15.04284989
4 33.30553721 18.74102108
5 -23.09123698 33.30553721
6 -13.86543053 -23.09123698
7 0.57327915 -13.86543053
8 15.93779528 0.57327915
9 -6.54930150 15.93779528
10 -14.75252731 -6.54930150
11 -4.70626027 -14.75252731
12 -38.02214376 -4.70626027
13 -13.18665989 -38.02214376
14 -28.34149860 -13.18665989
15 -44.45762764 -28.34149860
16 27.70688849 -44.45762764
17 -64.98988570 27.70688849
18 12.03592075 -64.98988570
19 14.97463043 12.03592075
20 -30.76085344 14.97463043
21 43.85204978 -30.76085344
22 -9.55117602 43.85204978
23 -21.50490898 -9.55117602
24 -4.42079248 -21.50490898
25 25.51469139 -4.42079248
26 25.85985268 25.51469139
27 19.64372365 25.85985268
28 43.90823978 19.64372365
29 0.71146558 43.90823978
30 39.13727203 0.71146558
31 -4.82401829 39.13727203
32 28.04049784 -4.82401829
33 -8.04659893 28.04049784
34 -2.84982474 -8.04659893
35 50.29644230 -2.84982474
36 20.98055880 50.29644230
37 -0.78395733 20.98055880
38 -0.43879604 -0.78395733
39 -3.75492507 -0.43879604
40 36.50959106 -3.75492507
41 -16.78718313 36.50959106
42 -4.56137668 -16.78718313
43 -25.12266701 -4.56137668
44 -2.25815088 -25.12266701
45 -1.54524765 -2.25815088
46 21.85152654 -1.54524765
47 28.09779358 21.85152654
48 1.68191008 28.09779358
49 11.51739395 1.68191008
50 -22.33744475 11.51739395
51 -15.15357379 -22.33744475
52 47.71094234 -15.15357379
53 -28.08583185 47.71094234
54 -14.46002540 -28.08583185
55 1.87868428 -14.46002540
56 9.34320041 1.87868428
57 10.65610363 9.34320041
58 7.35287783 10.65610363
59 15.59914486 7.35287783
60 26.68326137 15.59914486
61 -8.68125476 26.68326137
62 -32.53609347 -8.68125476
63 2.24777750 -32.53609347
64 53.11229362 2.24777750
65 -20.58448057 53.11229362
66 -11.35867412 -20.58448057
67 -27.81996444 -11.35867412
68 -10.75544831 -27.81996444
69 -22.44254509 -10.75544831
70 -32.24577089 -22.44254509
71 -45.39950385 -32.24577089
72 -32.31538735 -45.39950385
73 -27.67990348 -32.31538735
74 -19.83474219 -27.67990348
75 -1.75087122 -19.83474219
76 70.41364491 -1.75087122
77 -22.58312929 70.41364491
78 -10.25732283 -22.58312929
79 -13.41861316 -10.25732283
80 34.94590297 -13.41861316
81 3.45880620 34.94590297
82 13.05558039 3.45880620
83 8.30184743 13.05558039
84 19.88596393 8.30184743
85 4.92144780 19.88596393
86 -25.43339091 4.92144780
87 26.65048006 -25.43339091
88 52.41499619 26.65048006
89 0.31822200 52.41499619
90 -18.55597155 0.31822200
91 2.68273813 -18.55597155
92 -10.05274575 2.68273813
93 -3.13984252 -10.05274575
94 -4.04306833 -3.13984252
95 27.80319871 -4.04306833
96 7.28731521 27.80319871
97 -22.57720091 7.28731521
98 -0.43203962 -22.57720091
99 -29.44816866 -0.43203962
100 34.71634747 -29.44816866
101 -3.28042672 34.71634747
102 20.54537973 -3.28042672
103 2.28408941 20.54537973
104 2.74860554 2.28408941
105 -28.73849124 2.74860554
106 -3.64171704 -28.73849124
107 19.70455000 -3.64171704
108 21.98866650 19.70455000
109 4.02415037 21.98866650
110 -19.43068834 4.02415037
111 -21.34681737 -19.43068834
112 23.11769876 -21.34681737
113 6.02092456 23.11769876
114 0.54673101 6.02092456
115 -14.81455931 0.54673101
116 -11.65004318 -14.81455931
117 -44.13713995 -11.65004318
118 -18.04036576 -44.13713995
119 -38.89409872 -18.04036576
120 -55.30998222 -38.89409872
121 -26.17449835 -55.30998222
122 -29.82933706 -26.17449835
123 2.65453391 -29.82933706
124 33.61905004 2.65453391
125 -10.27772416 33.61905004
126 17.94808230 -10.27772416
127 37.78679197 17.94808230
128 15.35130810 37.78679197
129 18.56421133 15.35130810
130 -25.83901448 18.56421133
131 13.30725256 -25.83901448
132 7.89136906 13.30725256
133 17.72685293 7.89136906
134 35.07201422 17.72685293
135 2.55588519 35.07201422
136 27.42040132 2.55588519
137 -5.27637287 27.42040132
138 -5.35056642 -5.27637287
139 -1.91185674 -5.35056642
140 -19.04734061 -1.91185674
141 -18.43443739 -19.04734061
142 13.96233680 -18.43443739
143 14.50860384 13.96233680
144 33.19272035 14.50860384
145 -50.07179578 33.19272035
146 16.77336551 -50.07179578
147 2.15723647 16.77336551
148 -6.47824740 2.15723647
149 6.02497841 -6.47824740
150 -13.84921514 6.02497841
151 22.48949454 -13.84921514
152 -37.64598933 22.48949454
153 -20.93308611 -37.64598933
154 -47.83631191 -20.93308611
155 -12.19004487 -47.83631191
156 -22.00592837 -12.19004487
157 0.82955550 -22.00592837
158 14.67471679 0.82955550
159 1.05858776 14.67471679
160 8.52310389 1.05858776
161 12.42632969 8.52310389
162 20.95213614 12.42632969
163 23.49084582 20.95213614
164 1.65536195 23.49084582
165 14.26826518 1.65536195
166 -7.53496063 14.26826518
167 16.31130641 -7.53496063
168 23.89542291 16.31130641
169 -6.46909322 23.89542291
170 7.07606807 -6.46909322
171 -29.44006096 7.07606807
172 46.02445517 -29.44006096
173 16.22768098 46.02445517
174 -32.44651257 16.22768098
175 21.59219710 -32.44651257
176 6.65671323 21.59219710
177 -27.13038354 6.65671323
178 -0.93360935 -27.13038354
179 -12.38734231 -0.93360935
180 -14.40322581 -12.38734231
181 20.53225806 -14.40322581
182 5.97741935 20.53225806
183 -16.13870968 5.97741935
184 12.22580645 -16.13870968
185 18.62903226 12.22580645
186 7.35483871 18.62903226
187 11.39354839 7.35483871
188 -7.84193548 11.39354839
189 -24.32903226 -7.84193548
190 9.46774194 -24.32903226
191 1.31400897 9.46774194
192 15.59812548 1.31400897
193 1.33360935 15.59812548
194 0.47877064 1.33360935
195 9.76264161 0.47877064
196 -19.77284227 9.76264161
197 55.03038354 -19.77284227
198 -18.74381001 55.03038354
199 11.79489967 -18.74381001
200 -9.04058420 11.79489967
201 10.07231902 -9.04058420
202 -6.63090678 10.07231902
203 21.11536026 -6.63090678
204 -13.20052324 21.11536026
205 25.03496063 -13.20052324
206 -18.21987808 25.03496063
207 7.26399289 -18.21987808
208 -11.57149098 7.26399289
209 40.03173482 -11.57149098
210 -4.74245872 40.03173482
211 15.19625095 -4.74245872
212 -2.13923292 15.19625095
213 4.17367031 -2.13923292
214 12.47044450 4.17367031
215 28.71671154 12.47044450
216 23.40082804 28.71671154
217 -10.36368809 23.40082804
218 -12.91852680 -10.36368809
219 -26.83465583 -12.91852680
220 -7.17013970 -26.83465583
221 31.43308611 -7.17013970
222 -8.24110744 31.43308611
223 14.69760224 -8.24110744
224 -11.63788164 14.69760224
225 10.67502159 -11.63788164
226 -6.12820422 10.67502159
227 24.91806282 -6.12820422
228 7.40217932 24.91806282
229 0.73766320 7.40217932
230 -9.91717551 0.73766320
231 0.06669545 -9.91717551
232 -44.26878842 0.06669545
233 15.13443739 -44.26878842
234 -0.43975616 15.13443739
235 -12.40104648 -0.43975616
236 -21.13653035 -12.40104648
237 26.97637287 -21.13653035
238 20.77314707 26.97637287
239 38.91941411 20.77314707
240 -11.39646939 38.91941411
241 13.53901448 -11.39646939
242 4.88417577 13.53901448
243 -1.83195326 4.88417577
244 -58.36743713 -1.83195326
245 17.03578867 -58.36743713
246 13.36159512 17.03578867
247 -0.59969520 13.36159512
248 -5.73517907 -0.59969520
249 15.47772416 -5.73517907
250 18.27449835 15.47772416
251 28.62076539 18.27449835
252 5.10488189 28.62076539
253 -4.65963424 5.10488189
254 -18.41447295 -4.65963424
255 3.66939802 -18.41447295
256 -36.26608585 3.66939802
257 -1.96286005 -36.26608585
258 0.06294641 -1.96286005
259 -25.89834392 0.06294641
260 -3.63382779 -25.89834392
261 35.07907544 -3.63382779
262 10.77584963 35.07907544
263 -3.57788333 10.77584963
264 -28.89376683 -3.57788333
265 -16.15828296 -28.89376683
266 -20.81312167 -16.15828296
267 -3.72925070 -20.81312167
268 -55.76473457 -3.72925070
269 -6.86150876 -55.76473457
270 -2.13570231 -6.86150876
271 -24.29699263 -2.13570231
272 -12.13247650 -24.29699263
273 -12.71957328 -12.13247650
274 -0.42279909 -12.71957328
275 -3.47653205 -0.42279909
276 7.10758446 -3.47653205
277 4.44306833 7.10758446
278 9.18822962 4.44306833
279 9.47210058 9.18822962
280 -36.86338329 9.47210058
281 -6.76015748 -36.86338329
282 -4.23435103 -6.76015748
283 5.00435865 -4.23435103
284 11.56887478 5.00435865
285 -2.41822200 11.56887478
286 14.47855220 -2.41822200
287 -0.87518076 14.47855220
288 13.40893574 -0.87518076
289 -2.55558039 13.40893574
290 12.88958090 -2.55558039
291 14.77345187 12.88958090
292 -35.46203200 14.77345187
293 2.54119380 -35.46203200
294 0.46700025 2.54119380
295 2.80570993 0.46700025
296 3.37022606 2.80570993
297 42.48312929 3.37022606
298 17.47990348 42.48312929
299 18.42617052 17.47990348
300 -7.08971298 18.42617052
301 11.04577089 -7.08971298
302 -5.10906782 11.04577089
303 16.97480315 -5.10906782
304 -32.06068072 16.97480315
305 6.94254509 -32.06068072
306 3.06835154 6.94254509
307 -12.79293879 3.06835154
308 24.77157734 -12.79293879
309 -7.21551943 24.77157734
310 2.28125476 -7.21551943
311 -20.67247820 2.28125476
312 -3.28836170 -20.67247820
313 16.24712217 -3.28836170
314 6.49228346 16.24712217
315 -4.82384557 6.49228346
316 -50.85932944 -4.82384557
317 -10.75610363 -50.85932944
318 6.36970282 -10.75610363
319 -48.79158750 6.36970282
320 0.37292863 -48.79158750
321 -42.01416815 0.37292863
322 -39.61739395 -42.01416815
323 -133.07112692 -39.61739395
324 -2.98701041 -133.07112692
325 -27.25152654 -2.98701041
326 14.99363475 -27.25152654
327 -0.82249428 14.99363475
328 -21.85797816 -0.82249428
329 13.24524765 -21.85797816
330 20.17105410 13.24524765
331 0.30976378 20.17105410
332 12.37427991 0.30976378
333 23.38718313 12.37427991
334 6.08395733 23.38718313
335 -23.56977563 6.08395733
336 24.01434087 -23.56977563
337 28.54982474 24.01434087
338 24.59498603 28.54982474
339 38.07885700 24.59498603
340 -62.35662687 38.07885700
341 -14.95340107 -62.35662687
342 -5.62759462 -14.95340107
343 12.51111506 -5.62759462
344 3.87563119 12.51111506
345 -4.11146558 3.87563119
346 9.78530861 -4.11146558
347 -8.26842435 9.78530861
348 -16.18430785 -8.26842435
349 33.05117602 -16.18430785
350 59.89633731 33.05117602
351 21.18020828 59.89633731
352 -57.45527559 21.18020828
353 28.44795022 -57.45527559
354 -12.72624333 28.44795022
355 14.91246634 -12.72624333
356 6.17698247 14.91246634
357 9.58988570 6.17698247
358 49.08665989 9.58988570
359 -27.36707307 49.08665989
360 25.71704343 -27.36707307
361 3.75252731 25.71704343
362 40.19768860 3.75252731
363 2.58155956 40.19768860
364 -14.15392431 2.58155956
365 -33.95069850 -14.15392431
366 19.57510795 -33.95069850
367 -3.68618237 19.57510795
368 18.27833376 -3.68618237
369 5.19123698 18.27833376
370 -7.11198882 5.19123698
> 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/7v8yu1322153799.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/86hkf1322153799.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9lbx91322153799.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10iz7q1322153799.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/119hoc1322153799.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/12af541322153799.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/138rp41322153799.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/1463y11322153799.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/15bwwi1322153799.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/16ehy01322153799.tab")
+ }
>
> try(system("convert tmp/194rh1322153799.ps tmp/194rh1322153799.png",intern=TRUE))
character(0)
> try(system("convert tmp/2n61x1322153799.ps tmp/2n61x1322153799.png",intern=TRUE))
character(0)
> try(system("convert tmp/38aax1322153799.ps tmp/38aax1322153799.png",intern=TRUE))
character(0)
> try(system("convert tmp/42srt1322153799.ps tmp/42srt1322153799.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ttml1322153799.ps tmp/5ttml1322153799.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wedt1322153799.ps tmp/6wedt1322153799.png",intern=TRUE))
character(0)
> try(system("convert tmp/7v8yu1322153799.ps tmp/7v8yu1322153799.png",intern=TRUE))
character(0)
> try(system("convert tmp/86hkf1322153799.ps tmp/86hkf1322153799.png",intern=TRUE))
character(0)
> try(system("convert tmp/9lbx91322153799.ps tmp/9lbx91322153799.png",intern=TRUE))
character(0)
> try(system("convert tmp/10iz7q1322153799.ps tmp/10iz7q1322153799.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.236 0.550 10.917