R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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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(51.220
+ ,50.487
+ ,49.415
+ ,49.398
+ ,48.196
+ ,47.348
+ ,49.331
+ ,49.644
+ ,49.588
+ ,49.567
+ ,49.010
+ ,49.563
+ ,49.741
+ ,49.487
+ ,48.278
+ ,47.478
+ ,46.985
+ ,45.216
+ ,46.581
+ ,49.266
+ ,48.121
+ ,46.412
+ ,46.285
+ ,46.824
+ ,46.949
+ ,45.355
+ ,44.924
+ ,45.059
+ ,44.202
+ ,44.149
+ ,46.151
+ ,47.703
+ ,48.436
+ ,47.089
+ ,47.492
+ ,49.295
+ ,49.127
+ ,50.041
+ ,48.857
+ ,48.428
+ ,48.788
+ ,48.820
+ ,50.743
+ ,52.590
+ ,51.959
+ ,53.451
+ ,55.674
+ ,56.120
+ ,55.685
+ ,56.714
+ ,54.882
+ ,55.173
+ ,53.574
+ ,53.954
+ ,58.055
+ ,61.062
+ ,58.353
+ ,59.693
+ ,58.833
+ ,60.417
+ ,61.696
+ ,62.515
+ ,62.687
+ ,61.794
+ ,63.014
+ ,63.134
+ ,68.057
+ ,67.327
+ ,68.310
+ ,69.780
+ ,69.944
+ ,69.881
+ ,71.397
+ ,70.631
+ ,70.452
+ ,69.862
+ ,69.114
+ ,69.358
+ ,71.133
+ ,73.128
+ ,73.528
+ ,73.677
+ ,72.273
+ ,71.962
+ ,73.654
+ ,73.305
+ ,73.355
+ ,73.346
+ ,72.881
+ ,72.424
+ ,74.540
+ ,74.847
+ ,75.904
+ ,76.870
+ ,76.370
+ ,77.631
+ ,78.335
+ ,77.926
+ ,77.236
+ ,76.755
+ ,74.710
+ ,73.486
+ ,76.034
+ ,76.389
+ ,77.767
+ ,78.124
+ ,76.696
+ ,77.375
+ ,77.431
+ ,77.347
+ ,77.013
+ ,76.666
+ ,75.225
+ ,75.579
+ ,77.100
+ ,78.592
+ ,79.502
+ ,78.528
+ ,77.775
+ ,77.271
+ ,78.738
+ ,77.885
+ ,76.896
+ ,75.813
+ ,74.958
+ ,75.340
+ ,77.187
+ ,78.602
+ ,81.653
+ ,78.125
+ ,76.092
+ ,74.870
+ ,75.615
+ ,74.776
+ ,72.528
+ ,71.894
+ ,71.641
+ ,71.145
+ ,73.320
+ ,72.186
+ ,72.854
+ ,74.243
+ ,74.628
+ ,72.368
+ ,75.361
+ ,72.746
+ ,70.536
+ ,69.410
+ ,66.219
+ ,66.739
+ ,67.626
+ ,70.602
+ ,71.758
+ ,71.786
+ ,69.641
+ ,68.055
+ ,70.148
+ ,69.390
+ ,68.562
+ ,68.622
+ ,68.120
+ ,68.308
+ ,70.421
+ ,69.766
+ ,72.157
+ ,72.928
+ ,75.340
+ ,74.812
+ ,74.593
+ ,76.003
+ ,75.112
+ ,75.452
+ ,75.634
+ ,75.653
+ ,78.645
+ ,73.100
+ ,79.699
+ ,82.848
+ ,81.834
+ ,81.736
+ ,82.267
+ ,84.120
+ ,83.819
+ ,82.734
+ ,81.842
+ ,81.735
+ ,83.227
+ ,81.934
+ ,89.521
+ ,88.827
+ ,85.874
+ ,85.211
+ ,87.130
+ ,88.620
+ ,89.563
+ ,89.056
+ ,88.542
+ ,89.504
+ ,89.428
+ ,86.040
+ ,96.240
+ ,94.423
+ ,93.028
+ ,92.285
+ ,91.685
+ ,94.260
+ ,93.858
+ ,92.437
+ ,92.980
+ ,92.099
+ ,92.803
+ ,88.551
+ ,98.334
+ ,98.329
+ ,96.455
+ ,97.109
+ ,97.687
+ ,98.512
+ ,98.673
+ ,96.028
+ ,98.014
+ ,95.580
+ ,97.838
+ ,97.760
+ ,99.913
+ ,97.588
+ ,93.942
+ ,93.656
+ ,93.365
+ ,92.881
+ ,93.120
+ ,91.063
+ ,90.930
+ ,91.946
+ ,94.624
+ ,95.484
+ ,95.862
+ ,95.530
+ ,94.574
+ ,94.677
+ ,93.845
+ ,91.533
+ ,91.214
+ ,90.922
+ ,89.563
+ ,89.945
+ ,91.850
+ ,92.505
+ ,92.437
+ ,93.876)
+ ,dim=c(1
+ ,250)
+ ,dimnames=list(c('Werkloosheid')
+ ,1:250))
> y <- array(NA,dim=c(1,250),dimnames=list(c('Werkloosheid'),1:250))
> 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
Werkloosheid M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 51.220 1 0 0 0 0 0 0 0 0 0 0 1
2 50.487 0 1 0 0 0 0 0 0 0 0 0 2
3 49.415 0 0 1 0 0 0 0 0 0 0 0 3
4 49.398 0 0 0 1 0 0 0 0 0 0 0 4
5 48.196 0 0 0 0 1 0 0 0 0 0 0 5
6 47.348 0 0 0 0 0 1 0 0 0 0 0 6
7 49.331 0 0 0 0 0 0 1 0 0 0 0 7
8 49.644 0 0 0 0 0 0 0 1 0 0 0 8
9 49.588 0 0 0 0 0 0 0 0 1 0 0 9
10 49.567 0 0 0 0 0 0 0 0 0 1 0 10
11 49.010 0 0 0 0 0 0 0 0 0 0 1 11
12 49.563 0 0 0 0 0 0 0 0 0 0 0 12
13 49.741 1 0 0 0 0 0 0 0 0 0 0 13
14 49.487 0 1 0 0 0 0 0 0 0 0 0 14
15 48.278 0 0 1 0 0 0 0 0 0 0 0 15
16 47.478 0 0 0 1 0 0 0 0 0 0 0 16
17 46.985 0 0 0 0 1 0 0 0 0 0 0 17
18 45.216 0 0 0 0 0 1 0 0 0 0 0 18
19 46.581 0 0 0 0 0 0 1 0 0 0 0 19
20 49.266 0 0 0 0 0 0 0 1 0 0 0 20
21 48.121 0 0 0 0 0 0 0 0 1 0 0 21
22 46.412 0 0 0 0 0 0 0 0 0 1 0 22
23 46.285 0 0 0 0 0 0 0 0 0 0 1 23
24 46.824 0 0 0 0 0 0 0 0 0 0 0 24
25 46.949 1 0 0 0 0 0 0 0 0 0 0 25
26 45.355 0 1 0 0 0 0 0 0 0 0 0 26
27 44.924 0 0 1 0 0 0 0 0 0 0 0 27
28 45.059 0 0 0 1 0 0 0 0 0 0 0 28
29 44.202 0 0 0 0 1 0 0 0 0 0 0 29
30 44.149 0 0 0 0 0 1 0 0 0 0 0 30
31 46.151 0 0 0 0 0 0 1 0 0 0 0 31
32 47.703 0 0 0 0 0 0 0 1 0 0 0 32
33 48.436 0 0 0 0 0 0 0 0 1 0 0 33
34 47.089 0 0 0 0 0 0 0 0 0 1 0 34
35 47.492 0 0 0 0 0 0 0 0 0 0 1 35
36 49.295 0 0 0 0 0 0 0 0 0 0 0 36
37 49.127 1 0 0 0 0 0 0 0 0 0 0 37
38 50.041 0 1 0 0 0 0 0 0 0 0 0 38
39 48.857 0 0 1 0 0 0 0 0 0 0 0 39
40 48.428 0 0 0 1 0 0 0 0 0 0 0 40
41 48.788 0 0 0 0 1 0 0 0 0 0 0 41
42 48.820 0 0 0 0 0 1 0 0 0 0 0 42
43 50.743 0 0 0 0 0 0 1 0 0 0 0 43
44 52.590 0 0 0 0 0 0 0 1 0 0 0 44
45 51.959 0 0 0 0 0 0 0 0 1 0 0 45
46 53.451 0 0 0 0 0 0 0 0 0 1 0 46
47 55.674 0 0 0 0 0 0 0 0 0 0 1 47
48 56.120 0 0 0 0 0 0 0 0 0 0 0 48
49 55.685 1 0 0 0 0 0 0 0 0 0 0 49
50 56.714 0 1 0 0 0 0 0 0 0 0 0 50
51 54.882 0 0 1 0 0 0 0 0 0 0 0 51
52 55.173 0 0 0 1 0 0 0 0 0 0 0 52
53 53.574 0 0 0 0 1 0 0 0 0 0 0 53
54 53.954 0 0 0 0 0 1 0 0 0 0 0 54
55 58.055 0 0 0 0 0 0 1 0 0 0 0 55
56 61.062 0 0 0 0 0 0 0 1 0 0 0 56
57 58.353 0 0 0 0 0 0 0 0 1 0 0 57
58 59.693 0 0 0 0 0 0 0 0 0 1 0 58
59 58.833 0 0 0 0 0 0 0 0 0 0 1 59
60 60.417 0 0 0 0 0 0 0 0 0 0 0 60
61 61.696 1 0 0 0 0 0 0 0 0 0 0 61
62 62.515 0 1 0 0 0 0 0 0 0 0 0 62
63 62.687 0 0 1 0 0 0 0 0 0 0 0 63
64 61.794 0 0 0 1 0 0 0 0 0 0 0 64
65 63.014 0 0 0 0 1 0 0 0 0 0 0 65
66 63.134 0 0 0 0 0 1 0 0 0 0 0 66
67 68.057 0 0 0 0 0 0 1 0 0 0 0 67
68 67.327 0 0 0 0 0 0 0 1 0 0 0 68
69 68.310 0 0 0 0 0 0 0 0 1 0 0 69
70 69.780 0 0 0 0 0 0 0 0 0 1 0 70
71 69.944 0 0 0 0 0 0 0 0 0 0 1 71
72 69.881 0 0 0 0 0 0 0 0 0 0 0 72
73 71.397 1 0 0 0 0 0 0 0 0 0 0 73
74 70.631 0 1 0 0 0 0 0 0 0 0 0 74
75 70.452 0 0 1 0 0 0 0 0 0 0 0 75
76 69.862 0 0 0 1 0 0 0 0 0 0 0 76
77 69.114 0 0 0 0 1 0 0 0 0 0 0 77
78 69.358 0 0 0 0 0 1 0 0 0 0 0 78
79 71.133 0 0 0 0 0 0 1 0 0 0 0 79
80 73.128 0 0 0 0 0 0 0 1 0 0 0 80
81 73.528 0 0 0 0 0 0 0 0 1 0 0 81
82 73.677 0 0 0 0 0 0 0 0 0 1 0 82
83 72.273 0 0 0 0 0 0 0 0 0 0 1 83
84 71.962 0 0 0 0 0 0 0 0 0 0 0 84
85 73.654 1 0 0 0 0 0 0 0 0 0 0 85
86 73.305 0 1 0 0 0 0 0 0 0 0 0 86
87 73.355 0 0 1 0 0 0 0 0 0 0 0 87
88 73.346 0 0 0 1 0 0 0 0 0 0 0 88
89 72.881 0 0 0 0 1 0 0 0 0 0 0 89
90 72.424 0 0 0 0 0 1 0 0 0 0 0 90
91 74.540 0 0 0 0 0 0 1 0 0 0 0 91
92 74.847 0 0 0 0 0 0 0 1 0 0 0 92
93 75.904 0 0 0 0 0 0 0 0 1 0 0 93
94 76.870 0 0 0 0 0 0 0 0 0 1 0 94
95 76.370 0 0 0 0 0 0 0 0 0 0 1 95
96 77.631 0 0 0 0 0 0 0 0 0 0 0 96
97 78.335 1 0 0 0 0 0 0 0 0 0 0 97
98 77.926 0 1 0 0 0 0 0 0 0 0 0 98
99 77.236 0 0 1 0 0 0 0 0 0 0 0 99
100 76.755 0 0 0 1 0 0 0 0 0 0 0 100
101 74.710 0 0 0 0 1 0 0 0 0 0 0 101
102 73.486 0 0 0 0 0 1 0 0 0 0 0 102
103 76.034 0 0 0 0 0 0 1 0 0 0 0 103
104 76.389 0 0 0 0 0 0 0 1 0 0 0 104
105 77.767 0 0 0 0 0 0 0 0 1 0 0 105
106 78.124 0 0 0 0 0 0 0 0 0 1 0 106
107 76.696 0 0 0 0 0 0 0 0 0 0 1 107
108 77.375 0 0 0 0 0 0 0 0 0 0 0 108
109 77.431 1 0 0 0 0 0 0 0 0 0 0 109
110 77.347 0 1 0 0 0 0 0 0 0 0 0 110
111 77.013 0 0 1 0 0 0 0 0 0 0 0 111
112 76.666 0 0 0 1 0 0 0 0 0 0 0 112
113 75.225 0 0 0 0 1 0 0 0 0 0 0 113
114 75.579 0 0 0 0 0 1 0 0 0 0 0 114
115 77.100 0 0 0 0 0 0 1 0 0 0 0 115
116 78.592 0 0 0 0 0 0 0 1 0 0 0 116
117 79.502 0 0 0 0 0 0 0 0 1 0 0 117
118 78.528 0 0 0 0 0 0 0 0 0 1 0 118
119 77.775 0 0 0 0 0 0 0 0 0 0 1 119
120 77.271 0 0 0 0 0 0 0 0 0 0 0 120
121 78.738 1 0 0 0 0 0 0 0 0 0 0 121
122 77.885 0 1 0 0 0 0 0 0 0 0 0 122
123 76.896 0 0 1 0 0 0 0 0 0 0 0 123
124 75.813 0 0 0 1 0 0 0 0 0 0 0 124
125 74.958 0 0 0 0 1 0 0 0 0 0 0 125
126 75.340 0 0 0 0 0 1 0 0 0 0 0 126
127 77.187 0 0 0 0 0 0 1 0 0 0 0 127
128 78.602 0 0 0 0 0 0 0 1 0 0 0 128
129 81.653 0 0 0 0 0 0 0 0 1 0 0 129
130 78.125 0 0 0 0 0 0 0 0 0 1 0 130
131 76.092 0 0 0 0 0 0 0 0 0 0 1 131
132 74.870 0 0 0 0 0 0 0 0 0 0 0 132
133 75.615 1 0 0 0 0 0 0 0 0 0 0 133
134 74.776 0 1 0 0 0 0 0 0 0 0 0 134
135 72.528 0 0 1 0 0 0 0 0 0 0 0 135
136 71.894 0 0 0 1 0 0 0 0 0 0 0 136
137 71.641 0 0 0 0 1 0 0 0 0 0 0 137
138 71.145 0 0 0 0 0 1 0 0 0 0 0 138
139 73.320 0 0 0 0 0 0 1 0 0 0 0 139
140 72.186 0 0 0 0 0 0 0 1 0 0 0 140
141 72.854 0 0 0 0 0 0 0 0 1 0 0 141
142 74.243 0 0 0 0 0 0 0 0 0 1 0 142
143 74.628 0 0 0 0 0 0 0 0 0 0 1 143
144 72.368 0 0 0 0 0 0 0 0 0 0 0 144
145 75.361 1 0 0 0 0 0 0 0 0 0 0 145
146 72.746 0 1 0 0 0 0 0 0 0 0 0 146
147 70.536 0 0 1 0 0 0 0 0 0 0 0 147
148 69.410 0 0 0 1 0 0 0 0 0 0 0 148
149 66.219 0 0 0 0 1 0 0 0 0 0 0 149
150 66.739 0 0 0 0 0 1 0 0 0 0 0 150
151 67.626 0 0 0 0 0 0 1 0 0 0 0 151
152 70.602 0 0 0 0 0 0 0 1 0 0 0 152
153 71.758 0 0 0 0 0 0 0 0 1 0 0 153
154 71.786 0 0 0 0 0 0 0 0 0 1 0 154
155 69.641 0 0 0 0 0 0 0 0 0 0 1 155
156 68.055 0 0 0 0 0 0 0 0 0 0 0 156
157 70.148 1 0 0 0 0 0 0 0 0 0 0 157
158 69.390 0 1 0 0 0 0 0 0 0 0 0 158
159 68.562 0 0 1 0 0 0 0 0 0 0 0 159
160 68.622 0 0 0 1 0 0 0 0 0 0 0 160
161 68.120 0 0 0 0 1 0 0 0 0 0 0 161
162 68.308 0 0 0 0 0 1 0 0 0 0 0 162
163 70.421 0 0 0 0 0 0 1 0 0 0 0 163
164 69.766 0 0 0 0 0 0 0 1 0 0 0 164
165 72.157 0 0 0 0 0 0 0 0 1 0 0 165
166 72.928 0 0 0 0 0 0 0 0 0 1 0 166
167 75.340 0 0 0 0 0 0 0 0 0 0 1 167
168 74.812 0 0 0 0 0 0 0 0 0 0 0 168
169 74.593 1 0 0 0 0 0 0 0 0 0 0 169
170 76.003 0 1 0 0 0 0 0 0 0 0 0 170
171 75.112 0 0 1 0 0 0 0 0 0 0 0 171
172 75.452 0 0 0 1 0 0 0 0 0 0 0 172
173 75.634 0 0 0 0 1 0 0 0 0 0 0 173
174 75.653 0 0 0 0 0 1 0 0 0 0 0 174
175 78.645 0 0 0 0 0 0 1 0 0 0 0 175
176 73.100 0 0 0 0 0 0 0 1 0 0 0 176
177 79.699 0 0 0 0 0 0 0 0 1 0 0 177
178 82.848 0 0 0 0 0 0 0 0 0 1 0 178
179 81.834 0 0 0 0 0 0 0 0 0 0 1 179
180 81.736 0 0 0 0 0 0 0 0 0 0 0 180
181 82.267 1 0 0 0 0 0 0 0 0 0 0 181
182 84.120 0 1 0 0 0 0 0 0 0 0 0 182
183 83.819 0 0 1 0 0 0 0 0 0 0 0 183
184 82.734 0 0 0 1 0 0 0 0 0 0 0 184
185 81.842 0 0 0 0 1 0 0 0 0 0 0 185
186 81.735 0 0 0 0 0 1 0 0 0 0 0 186
187 83.227 0 0 0 0 0 0 1 0 0 0 0 187
188 81.934 0 0 0 0 0 0 0 1 0 0 0 188
189 89.521 0 0 0 0 0 0 0 0 1 0 0 189
190 88.827 0 0 0 0 0 0 0 0 0 1 0 190
191 85.874 0 0 0 0 0 0 0 0 0 0 1 191
192 85.211 0 0 0 0 0 0 0 0 0 0 0 192
193 87.130 1 0 0 0 0 0 0 0 0 0 0 193
194 88.620 0 1 0 0 0 0 0 0 0 0 0 194
195 89.563 0 0 1 0 0 0 0 0 0 0 0 195
196 89.056 0 0 0 1 0 0 0 0 0 0 0 196
197 88.542 0 0 0 0 1 0 0 0 0 0 0 197
198 89.504 0 0 0 0 0 1 0 0 0 0 0 198
199 89.428 0 0 0 0 0 0 1 0 0 0 0 199
200 86.040 0 0 0 0 0 0 0 1 0 0 0 200
201 96.240 0 0 0 0 0 0 0 0 1 0 0 201
202 94.423 0 0 0 0 0 0 0 0 0 1 0 202
203 93.028 0 0 0 0 0 0 0 0 0 0 1 203
204 92.285 0 0 0 0 0 0 0 0 0 0 0 204
205 91.685 1 0 0 0 0 0 0 0 0 0 0 205
206 94.260 0 1 0 0 0 0 0 0 0 0 0 206
207 93.858 0 0 1 0 0 0 0 0 0 0 0 207
208 92.437 0 0 0 1 0 0 0 0 0 0 0 208
209 92.980 0 0 0 0 1 0 0 0 0 0 0 209
210 92.099 0 0 0 0 0 1 0 0 0 0 0 210
211 92.803 0 0 0 0 0 0 1 0 0 0 0 211
212 88.551 0 0 0 0 0 0 0 1 0 0 0 212
213 98.334 0 0 0 0 0 0 0 0 1 0 0 213
214 98.329 0 0 0 0 0 0 0 0 0 1 0 214
215 96.455 0 0 0 0 0 0 0 0 0 0 1 215
216 97.109 0 0 0 0 0 0 0 0 0 0 0 216
217 97.687 1 0 0 0 0 0 0 0 0 0 0 217
218 98.512 0 1 0 0 0 0 0 0 0 0 0 218
219 98.673 0 0 1 0 0 0 0 0 0 0 0 219
220 96.028 0 0 0 1 0 0 0 0 0 0 0 220
221 98.014 0 0 0 0 1 0 0 0 0 0 0 221
222 95.580 0 0 0 0 0 1 0 0 0 0 0 222
223 97.838 0 0 0 0 0 0 1 0 0 0 0 223
224 97.760 0 0 0 0 0 0 0 1 0 0 0 224
225 99.913 0 0 0 0 0 0 0 0 1 0 0 225
226 97.588 0 0 0 0 0 0 0 0 0 1 0 226
227 93.942 0 0 0 0 0 0 0 0 0 0 1 227
228 93.656 0 0 0 0 0 0 0 0 0 0 0 228
229 93.365 1 0 0 0 0 0 0 0 0 0 0 229
230 92.881 0 1 0 0 0 0 0 0 0 0 0 230
231 93.120 0 0 1 0 0 0 0 0 0 0 0 231
232 91.063 0 0 0 1 0 0 0 0 0 0 0 232
233 90.930 0 0 0 0 1 0 0 0 0 0 0 233
234 91.946 0 0 0 0 0 1 0 0 0 0 0 234
235 94.624 0 0 0 0 0 0 1 0 0 0 0 235
236 95.484 0 0 0 0 0 0 0 1 0 0 0 236
237 95.862 0 0 0 0 0 0 0 0 1 0 0 237
238 95.530 0 0 0 0 0 0 0 0 0 1 0 238
239 94.574 0 0 0 0 0 0 0 0 0 0 1 239
240 94.677 0 0 0 0 0 0 0 0 0 0 0 240
241 93.845 1 0 0 0 0 0 0 0 0 0 0 241
242 91.533 0 1 0 0 0 0 0 0 0 0 0 242
243 91.214 0 0 1 0 0 0 0 0 0 0 0 243
244 90.922 0 0 0 1 0 0 0 0 0 0 0 244
245 89.563 0 0 0 0 1 0 0 0 0 0 0 245
246 89.945 0 0 0 0 0 1 0 0 0 0 0 246
247 91.850 0 0 0 0 0 0 1 0 0 0 0 247
248 92.505 0 0 0 0 0 0 0 1 0 0 0 248
249 92.437 0 0 0 0 0 0 0 0 1 0 0 249
250 93.876 0 0 0 0 0 0 0 0 0 1 0 250
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
48.4257 0.5684 0.3149 -0.5299 -1.3765 -2.1597
M6 M7 M8 M9 M10 M11
-2.5339 -0.6747 -0.7606 1.1741 0.9651 0.2315
t
0.1994
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.4843 -4.9552 -0.7691 5.4175 10.0585
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 48.425738 1.486566 32.576 <2e-16 ***
M1 0.568424 1.862372 0.305 0.760
M2 0.314931 1.862306 0.169 0.866
M3 -0.529944 1.862255 -0.285 0.776
M4 -1.376532 1.862218 -0.739 0.461
M5 -2.159692 1.862196 -1.160 0.247
M6 -2.533900 1.862189 -1.361 0.175
M7 -0.674679 1.862196 -0.362 0.717
M8 -0.760601 1.862218 -0.408 0.683
M9 1.174144 1.862255 0.630 0.529
M10 0.965079 1.862306 0.518 0.605
M11 0.231546 1.884769 0.123 0.902
t 0.199446 0.005227 38.159 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.96 on 237 degrees of freedom
Multiple R-squared: 0.861, Adjusted R-squared: 0.854
F-statistic: 122.3 on 12 and 237 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,] 1.447970e-04 2.895940e-04 9.998552e-01
[2,] 6.082547e-06 1.216509e-05 9.999939e-01
[3,] 1.168731e-06 2.337461e-06 9.999988e-01
[4,] 6.764574e-07 1.352915e-06 9.999993e-01
[5,] 2.233834e-07 4.467667e-07 9.999998e-01
[6,] 1.746693e-08 3.493386e-08 1.000000e+00
[7,] 1.200454e-08 2.400909e-08 1.000000e+00
[8,] 2.319615e-09 4.639230e-09 1.000000e+00
[9,] 4.059837e-10 8.119673e-10 1.000000e+00
[10,] 6.121756e-11 1.224351e-10 1.000000e+00
[11,] 4.619724e-11 9.239449e-11 1.000000e+00
[12,] 7.327187e-12 1.465437e-11 1.000000e+00
[13,] 8.286774e-13 1.657355e-12 1.000000e+00
[14,] 9.243924e-14 1.848785e-13 1.000000e+00
[15,] 2.888612e-14 5.777224e-14 1.000000e+00
[16,] 1.281763e-14 2.563526e-14 1.000000e+00
[17,] 4.659603e-15 9.319205e-15 1.000000e+00
[18,] 1.909836e-14 3.819673e-14 1.000000e+00
[19,] 1.413769e-14 2.827538e-14 1.000000e+00
[20,] 2.478175e-14 4.956350e-14 1.000000e+00
[21,] 1.993137e-13 3.986274e-13 1.000000e+00
[22,] 3.244121e-13 6.488242e-13 1.000000e+00
[23,] 3.307854e-12 6.615707e-12 1.000000e+00
[24,] 7.653521e-12 1.530704e-11 1.000000e+00
[25,] 9.060242e-12 1.812048e-11 1.000000e+00
[26,] 2.389201e-11 4.778402e-11 1.000000e+00
[27,] 9.314935e-11 1.862987e-10 1.000000e+00
[28,] 2.407119e-10 4.814238e-10 1.000000e+00
[29,] 5.129118e-10 1.025824e-09 1.000000e+00
[30,] 6.901983e-10 1.380397e-09 1.000000e+00
[31,] 4.195123e-09 8.390245e-09 1.000000e+00
[32,] 7.266413e-08 1.453283e-07 9.999999e-01
[33,] 3.364612e-07 6.729224e-07 9.999997e-01
[34,] 5.779378e-07 1.155876e-06 9.999994e-01
[35,] 1.435579e-06 2.871159e-06 9.999986e-01
[36,] 2.015754e-06 4.031508e-06 9.999980e-01
[37,] 2.897913e-06 5.795826e-06 9.999971e-01
[38,] 2.840249e-06 5.680499e-06 9.999972e-01
[39,] 3.438529e-06 6.877058e-06 9.999966e-01
[40,] 7.557288e-06 1.511458e-05 9.999924e-01
[41,] 2.077678e-05 4.155355e-05 9.999792e-01
[42,] 2.530960e-05 5.061920e-05 9.999747e-01
[43,] 4.324004e-05 8.648009e-05 9.999568e-01
[44,] 4.785391e-05 9.570782e-05 9.999521e-01
[45,] 5.588940e-05 1.117788e-04 9.999441e-01
[46,] 6.477477e-05 1.295495e-04 9.999352e-01
[47,] 8.406197e-05 1.681239e-04 9.999159e-01
[48,] 1.338269e-04 2.676537e-04 9.998662e-01
[49,] 1.602391e-04 3.204782e-04 9.998398e-01
[50,] 2.681285e-04 5.362570e-04 9.997319e-01
[51,] 4.453557e-04 8.907114e-04 9.995546e-01
[52,] 1.199587e-03 2.399174e-03 9.988004e-01
[53,] 1.505799e-03 3.011598e-03 9.984942e-01
[54,] 2.393427e-03 4.786854e-03 9.976066e-01
[55,] 4.459839e-03 8.919679e-03 9.955402e-01
[56,] 7.154356e-03 1.430871e-02 9.928456e-01
[57,] 8.957453e-03 1.791491e-02 9.910425e-01
[58,] 1.065917e-02 2.131834e-02 9.893408e-01
[59,] 1.099422e-02 2.198844e-02 9.890058e-01
[60,] 1.187097e-02 2.374194e-02 9.881290e-01
[61,] 1.205662e-02 2.411324e-02 9.879434e-01
[62,] 1.160394e-02 2.320788e-02 9.883961e-01
[63,] 1.156824e-02 2.313648e-02 9.884318e-01
[64,] 1.050129e-02 2.100257e-02 9.894987e-01
[65,] 1.027921e-02 2.055842e-02 9.897208e-01
[66,] 1.008521e-02 2.017042e-02 9.899148e-01
[67,] 9.708447e-03 1.941689e-02 9.902916e-01
[68,] 8.226463e-03 1.645293e-02 9.917735e-01
[69,] 6.565943e-03 1.313189e-02 9.934341e-01
[70,] 5.271870e-03 1.054374e-02 9.947281e-01
[71,] 4.141462e-03 8.282923e-03 9.958585e-01
[72,] 3.389711e-03 6.779422e-03 9.966103e-01
[73,] 2.863068e-03 5.726135e-03 9.971369e-01
[74,] 2.423670e-03 4.847340e-03 9.975763e-01
[75,] 2.006997e-03 4.013995e-03 9.979930e-01
[76,] 1.644786e-03 3.289571e-03 9.983552e-01
[77,] 1.354807e-03 2.709614e-03 9.986452e-01
[78,] 1.068579e-03 2.137159e-03 9.989314e-01
[79,] 9.032639e-04 1.806528e-03 9.990967e-01
[80,] 7.601838e-04 1.520368e-03 9.992398e-01
[81,] 7.190433e-04 1.438087e-03 9.992810e-01
[82,] 6.543091e-04 1.308618e-03 9.993457e-01
[83,] 5.836071e-04 1.167214e-03 9.994164e-01
[84,] 5.243401e-04 1.048680e-03 9.994757e-01
[85,] 4.875020e-04 9.750040e-04 9.995125e-01
[86,] 4.181926e-04 8.363853e-04 9.995818e-01
[87,] 3.467224e-04 6.934448e-04 9.996533e-01
[88,] 3.021881e-04 6.043763e-04 9.996978e-01
[89,] 2.970682e-04 5.941364e-04 9.997029e-01
[90,] 2.446263e-04 4.892527e-04 9.997554e-01
[91,] 2.099073e-04 4.198145e-04 9.997901e-01
[92,] 1.829906e-04 3.659812e-04 9.998170e-01
[93,] 1.757233e-04 3.514466e-04 9.998243e-01
[94,] 1.798228e-04 3.596457e-04 9.998202e-01
[95,] 1.832257e-04 3.664513e-04 9.998168e-01
[96,] 1.878391e-04 3.756782e-04 9.998122e-01
[97,] 2.056799e-04 4.113597e-04 9.997943e-01
[98,] 2.240165e-04 4.480330e-04 9.997760e-01
[99,] 2.432783e-04 4.865565e-04 9.997567e-01
[100,] 2.836961e-04 5.673922e-04 9.997163e-01
[101,] 4.228199e-04 8.456398e-04 9.995772e-01
[102,] 4.581209e-04 9.162418e-04 9.995419e-01
[103,] 5.122380e-04 1.024476e-03 9.994878e-01
[104,] 6.026317e-04 1.205263e-03 9.993974e-01
[105,] 8.094942e-04 1.618988e-03 9.991905e-01
[106,] 1.172231e-03 2.344462e-03 9.988278e-01
[107,] 1.672534e-03 3.345067e-03 9.983275e-01
[108,] 2.376188e-03 4.752377e-03 9.976238e-01
[109,] 3.583500e-03 7.166999e-03 9.964165e-01
[110,] 5.226570e-03 1.045314e-02 9.947734e-01
[111,] 7.435345e-03 1.487069e-02 9.925647e-01
[112,] 1.125234e-02 2.250467e-02 9.887477e-01
[113,] 2.197466e-02 4.394932e-02 9.780253e-01
[114,] 3.379597e-02 6.759194e-02 9.662040e-01
[115,] 4.575086e-02 9.150172e-02 9.542491e-01
[116,] 6.220686e-02 1.244137e-01 9.377931e-01
[117,] 9.107493e-02 1.821499e-01 9.089251e-01
[118,] 1.324006e-01 2.648011e-01 8.675994e-01
[119,] 1.812829e-01 3.625658e-01 8.187171e-01
[120,] 2.439628e-01 4.879256e-01 7.560372e-01
[121,] 3.146345e-01 6.292689e-01 6.853655e-01
[122,] 3.788518e-01 7.577037e-01 6.211482e-01
[123,] 4.404897e-01 8.809794e-01 5.595103e-01
[124,] 5.054148e-01 9.891704e-01 4.945852e-01
[125,] 6.020633e-01 7.958733e-01 3.979367e-01
[126,] 6.584940e-01 6.830120e-01 3.415060e-01
[127,] 6.920874e-01 6.158251e-01 3.079126e-01
[128,] 7.166945e-01 5.666109e-01 2.833055e-01
[129,] 7.551282e-01 4.897437e-01 2.448718e-01
[130,] 7.826719e-01 4.346561e-01 2.173281e-01
[131,] 8.111403e-01 3.777194e-01 1.888597e-01
[132,] 8.419570e-01 3.160859e-01 1.580430e-01
[133,] 8.701273e-01 2.597453e-01 1.298727e-01
[134,] 9.097897e-01 1.804206e-01 9.021032e-02
[135,] 9.321018e-01 1.357964e-01 6.789822e-02
[136,] 9.537375e-01 9.252493e-02 4.626246e-02
[137,] 9.611744e-01 7.765120e-02 3.882560e-02
[138,] 9.666598e-01 6.668032e-02 3.334016e-02
[139,] 9.707871e-01 5.842589e-02 2.921294e-02
[140,] 9.783196e-01 4.336075e-02 2.168037e-02
[141,] 9.867402e-01 2.651963e-02 1.325981e-02
[142,] 9.901676e-01 1.966488e-02 9.832439e-03
[143,] 9.935364e-01 1.292721e-02 6.463607e-03
[144,] 9.960655e-01 7.869016e-03 3.934508e-03
[145,] 9.972881e-01 5.423866e-03 2.711933e-03
[146,] 9.982158e-01 3.568434e-03 1.784217e-03
[147,] 9.987903e-01 2.419379e-03 1.209689e-03
[148,] 9.991714e-01 1.657266e-03 8.286332e-04
[149,] 9.994250e-01 1.150081e-03 5.750405e-04
[150,] 9.997178e-01 5.644782e-04 2.822391e-04
[151,] 9.998500e-01 2.999911e-04 1.499955e-04
[152,] 9.998617e-01 2.765154e-04 1.382577e-04
[153,] 9.998841e-01 2.317582e-04 1.158791e-04
[154,] 9.999166e-01 1.667531e-04 8.337653e-05
[155,] 9.999313e-01 1.374204e-04 6.871020e-05
[156,] 9.999548e-01 9.049264e-05 4.524632e-05
[157,] 9.999616e-01 7.678104e-05 3.839052e-05
[158,] 9.999671e-01 6.587938e-05 3.293969e-05
[159,] 9.999723e-01 5.535407e-05 2.767704e-05
[160,] 9.999713e-01 5.739104e-05 2.869552e-05
[161,] 9.999916e-01 1.680611e-05 8.403054e-06
[162,] 9.999963e-01 7.333125e-06 3.666563e-06
[163,] 9.999963e-01 7.316809e-06 3.658405e-06
[164,] 9.999964e-01 7.141269e-06 3.570635e-06
[165,] 9.999966e-01 6.717599e-06 3.358799e-06
[166,] 9.999968e-01 6.432680e-06 3.216340e-06
[167,] 9.999961e-01 7.753596e-06 3.876798e-06
[168,] 9.999959e-01 8.295870e-06 4.147935e-06
[169,] 9.999955e-01 8.972735e-06 4.486367e-06
[170,] 9.999964e-01 7.276572e-06 3.638286e-06
[171,] 9.999973e-01 5.417281e-06 2.708641e-06
[172,] 9.999982e-01 3.519076e-06 1.759538e-06
[173,] 9.999991e-01 1.851037e-06 9.255186e-07
[174,] 9.999990e-01 2.017298e-06 1.008649e-06
[175,] 9.999990e-01 2.032068e-06 1.016034e-06
[176,] 9.999994e-01 1.105601e-06 5.528005e-07
[177,] 9.999998e-01 3.682102e-07 1.841051e-07
[178,] 9.999999e-01 2.382281e-07 1.191140e-07
[179,] 9.999999e-01 2.140005e-07 1.070002e-07
[180,] 9.999999e-01 2.356493e-07 1.178247e-07
[181,] 9.999998e-01 3.268823e-07 1.634411e-07
[182,] 9.999998e-01 3.455423e-07 1.727712e-07
[183,] 9.999998e-01 4.914226e-07 2.457113e-07
[184,] 9.999998e-01 3.639647e-07 1.819823e-07
[185,] 1.000000e+00 4.000141e-08 2.000071e-08
[186,] 1.000000e+00 7.281522e-08 3.640761e-08
[187,] 1.000000e+00 9.116054e-08 4.558027e-08
[188,] 9.999999e-01 1.234519e-07 6.172597e-08
[189,] 9.999999e-01 1.122800e-07 5.614002e-08
[190,] 1.000000e+00 7.168819e-08 3.584409e-08
[191,] 9.999999e-01 1.334270e-07 6.671348e-08
[192,] 9.999999e-01 2.117885e-07 1.058942e-07
[193,] 9.999998e-01 3.464381e-07 1.732191e-07
[194,] 9.999997e-01 5.845745e-07 2.922872e-07
[195,] 9.999996e-01 7.881554e-07 3.940777e-07
[196,] 9.999998e-01 4.222128e-07 2.111064e-07
[197,] 1.000000e+00 3.007434e-11 1.503717e-11
[198,] 1.000000e+00 4.014335e-11 2.007168e-11
[199,] 1.000000e+00 5.716568e-11 2.858284e-11
[200,] 1.000000e+00 1.434711e-10 7.173554e-11
[201,] 1.000000e+00 4.837461e-10 2.418731e-10
[202,] 1.000000e+00 2.026144e-09 1.013072e-09
[203,] 1.000000e+00 4.305819e-09 2.152910e-09
[204,] 1.000000e+00 6.796668e-09 3.398334e-09
[205,] 1.000000e+00 2.323036e-08 1.161518e-08
[206,] 1.000000e+00 4.057764e-09 2.028882e-09
[207,] 1.000000e+00 1.325604e-08 6.628020e-09
[208,] 1.000000e+00 3.898875e-08 1.949438e-08
[209,] 9.999999e-01 1.652518e-07 8.262588e-08
[210,] 1.000000e+00 5.684093e-08 2.842046e-08
[211,] 9.999999e-01 2.831771e-07 1.415885e-07
[212,] 9.999996e-01 7.001520e-07 3.500760e-07
[213,] 9.999996e-01 7.159256e-07 3.579628e-07
[214,] 9.999997e-01 6.948759e-07 3.474379e-07
[215,] 9.999974e-01 5.142437e-06 2.571219e-06
[216,] 9.999781e-01 4.388970e-05 2.194485e-05
[217,] 9.999774e-01 4.529469e-05 2.264734e-05
[218,] 9.998975e-01 2.050173e-04 1.025086e-04
[219,] 9.990578e-01 1.884343e-03 9.421714e-04
> postscript(file="/var/www/html/rcomp/tmp/1vu001229074522.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2uj271229074522.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3st411229074522.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/40pmo1229074522.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5nwsw1229074522.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 = 250
Frequency = 1
1 2 3 4 5
2.026392027 1.347439646 0.920868217 1.551011074 0.932725360
6 7 8 9 10
0.259487265 0.183820598 0.383296788 -1.806893688 -1.818274640
11 12 13 14 15
-1.841187099 -1.256087099 -1.845956700 -2.045909081 -2.609480509
16 17 18 19 20
-2.762337652 -2.671623367 -4.265861462 -4.959528128 -2.388051938
21 22 23 24 25
-5.667242414 -7.366623367 -6.959535825 -6.388435825 -7.031305426
26 27 28 29 30
-8.571257807 -8.356829236 -7.574686379 -7.847972093 -7.726210188
31 32 33 34 35
-7.782876855 -6.344400664 -7.745591141 -9.082972093 -8.145884551
36 37 38 39 40
-6.310784551 -7.246654153 -6.278606534 -6.817177962 -6.599035105
41 42 43 44 45
-5.655320819 -5.448558915 -5.584225581 -3.850749391 -6.615939867
46 47 48 49 50
-5.114320819 -2.357233278 -1.879133278 -3.082002879 -1.998955260
51 52 53 54 55
-3.185526689 -2.247383832 -3.262669546 -2.707907641 -0.665574308
56 57 58 59 60
2.227901883 -2.615288594 -1.265669546 -1.591582004 0.024517996
61 62 63 64 65
0.535648394 1.408696013 2.226124585 1.980267442 3.783981728
66 67 68 69 70
4.078743632 6.943076966 6.099553156 4.948362680 6.427981728
71 72 73 74 75
7.126069269 7.095169269 7.843299668 7.131347287 7.597775858
76 77 78 79 80
7.654918715 7.490633001 7.909394906 7.625728239 9.507204430
81 82 83 84 85
7.773013953 7.931633001 7.061720543 6.782820543 7.706950941
86 87 88 89 90
7.411998560 8.107427132 8.745569989 8.864284275 8.582046179
91 92 93 94 95
8.639379513 8.832855703 7.755665227 8.731284275 8.765371816
96 97 98 99 100
10.058471816 9.994602215 9.639649834 9.595078405 9.761221262
101 102 103 104 105
8.299935548 7.250697453 7.740030786 7.981506977 7.225316501
106 107 108 109 110
7.591935548 6.698023090 7.409123090 6.697253488 6.667301107
111 112 113 114 115
6.978729679 7.278872536 6.421586822 6.950348726 6.412682060
116 117 118 119 120
7.791158250 6.566967774 5.602586822 5.383674363 4.911774363
121 122 123 124 125
5.610904762 4.811952381 4.468380952 4.032523810 3.761238095
126 127 128 129 130
4.318000000 4.106333333 5.407809524 6.324619048 2.806238095
131 132 133 134 135
1.307325637 0.117425637 0.094556035 -0.690396346 -2.292967774
136 137 138 139 140
-2.279824917 -1.949110631 -2.270348726 -2.154015393 -3.401539203
141 142 143 144 145
-4.867729679 -3.469110631 -2.550023090 -4.777923090 -2.552792691
146 147 148 149 150
-5.113745072 -6.678316501 -7.157173643 -9.764459358 -9.069697453
151 152 153 154 155
-10.241364120 -7.378887929 -8.357078405 -8.319459358 -9.930371816
156 157 158 159 160
-11.484271816 -10.159141417 -10.863093798 -11.045665227 -10.338522370
161 162 163 164 165
-10.256808084 -9.894046179 -9.839712846 -10.608236656 -10.351427132
166 167 168 169 170
-9.570808084 -6.624720543 -7.120620543 -8.107490144 -6.643442525
171 172 173 174 175
-6.889013953 -5.901871096 -5.136156811 -4.942394906 -4.009061573
176 177 178 179 180
-9.667585382 -5.202775858 -2.044156811 -2.524069269 -2.589969269
181 182 183 184 185
-2.826838870 -0.919791251 -0.575362680 -1.013219823 -1.321505537
186 187 188 189 190
-1.253743632 -1.820410299 -3.226934109 2.225875415 1.541494463
191 192 193 194 195
-0.877417996 -1.508317996 -0.357187597 1.186860022 2.775288594
196 197 198 199 200
2.915431451 2.985145736 4.121907641 1.987240975 -1.514282835
201 202 203 204 205
6.551526689 4.744145736 3.883233278 3.172333278 1.804463677
206 207 208 209 210
4.433511296 4.676939867 3.903082724 5.029797010 4.323558915
211 212 213 214 215
2.968892248 -1.396631561 6.252177962 6.256797010 4.916884551
216 217 218 219 220
5.602984551 5.413114950 6.292162569 7.098591141 5.100733998
221 222 223 224 225
7.670448283 5.411210188 5.610543522 5.419019712 5.437829236
226 227 228 229 230
3.122448283 0.010535825 -0.243364175 -1.302233776 -1.732186157
231 232 233 234 235
-0.847757586 -2.257614729 -1.806900443 -0.616138538 0.003194795
236 237 238 239 240
0.749670986 -1.006519491 -1.328900443 -1.750812901 -1.615712901
241 242 243 244 245
-3.215582503 -5.473534884 -5.147106312 -4.791963455 -5.567249169
246 247 248 249 250
-5.010487265 -5.164153931 -4.622677741 -6.824868217 -5.376249169
> postscript(file="/var/www/html/rcomp/tmp/6ogm41229074522.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 = 250
Frequency = 1
lag(myerror, k = 1) myerror
0 2.026392027 NA
1 1.347439646 2.026392027
2 0.920868217 1.347439646
3 1.551011074 0.920868217
4 0.932725360 1.551011074
5 0.259487265 0.932725360
6 0.183820598 0.259487265
7 0.383296788 0.183820598
8 -1.806893688 0.383296788
9 -1.818274640 -1.806893688
10 -1.841187099 -1.818274640
11 -1.256087099 -1.841187099
12 -1.845956700 -1.256087099
13 -2.045909081 -1.845956700
14 -2.609480509 -2.045909081
15 -2.762337652 -2.609480509
16 -2.671623367 -2.762337652
17 -4.265861462 -2.671623367
18 -4.959528128 -4.265861462
19 -2.388051938 -4.959528128
20 -5.667242414 -2.388051938
21 -7.366623367 -5.667242414
22 -6.959535825 -7.366623367
23 -6.388435825 -6.959535825
24 -7.031305426 -6.388435825
25 -8.571257807 -7.031305426
26 -8.356829236 -8.571257807
27 -7.574686379 -8.356829236
28 -7.847972093 -7.574686379
29 -7.726210188 -7.847972093
30 -7.782876855 -7.726210188
31 -6.344400664 -7.782876855
32 -7.745591141 -6.344400664
33 -9.082972093 -7.745591141
34 -8.145884551 -9.082972093
35 -6.310784551 -8.145884551
36 -7.246654153 -6.310784551
37 -6.278606534 -7.246654153
38 -6.817177962 -6.278606534
39 -6.599035105 -6.817177962
40 -5.655320819 -6.599035105
41 -5.448558915 -5.655320819
42 -5.584225581 -5.448558915
43 -3.850749391 -5.584225581
44 -6.615939867 -3.850749391
45 -5.114320819 -6.615939867
46 -2.357233278 -5.114320819
47 -1.879133278 -2.357233278
48 -3.082002879 -1.879133278
49 -1.998955260 -3.082002879
50 -3.185526689 -1.998955260
51 -2.247383832 -3.185526689
52 -3.262669546 -2.247383832
53 -2.707907641 -3.262669546
54 -0.665574308 -2.707907641
55 2.227901883 -0.665574308
56 -2.615288594 2.227901883
57 -1.265669546 -2.615288594
58 -1.591582004 -1.265669546
59 0.024517996 -1.591582004
60 0.535648394 0.024517996
61 1.408696013 0.535648394
62 2.226124585 1.408696013
63 1.980267442 2.226124585
64 3.783981728 1.980267442
65 4.078743632 3.783981728
66 6.943076966 4.078743632
67 6.099553156 6.943076966
68 4.948362680 6.099553156
69 6.427981728 4.948362680
70 7.126069269 6.427981728
71 7.095169269 7.126069269
72 7.843299668 7.095169269
73 7.131347287 7.843299668
74 7.597775858 7.131347287
75 7.654918715 7.597775858
76 7.490633001 7.654918715
77 7.909394906 7.490633001
78 7.625728239 7.909394906
79 9.507204430 7.625728239
80 7.773013953 9.507204430
81 7.931633001 7.773013953
82 7.061720543 7.931633001
83 6.782820543 7.061720543
84 7.706950941 6.782820543
85 7.411998560 7.706950941
86 8.107427132 7.411998560
87 8.745569989 8.107427132
88 8.864284275 8.745569989
89 8.582046179 8.864284275
90 8.639379513 8.582046179
91 8.832855703 8.639379513
92 7.755665227 8.832855703
93 8.731284275 7.755665227
94 8.765371816 8.731284275
95 10.058471816 8.765371816
96 9.994602215 10.058471816
97 9.639649834 9.994602215
98 9.595078405 9.639649834
99 9.761221262 9.595078405
100 8.299935548 9.761221262
101 7.250697453 8.299935548
102 7.740030786 7.250697453
103 7.981506977 7.740030786
104 7.225316501 7.981506977
105 7.591935548 7.225316501
106 6.698023090 7.591935548
107 7.409123090 6.698023090
108 6.697253488 7.409123090
109 6.667301107 6.697253488
110 6.978729679 6.667301107
111 7.278872536 6.978729679
112 6.421586822 7.278872536
113 6.950348726 6.421586822
114 6.412682060 6.950348726
115 7.791158250 6.412682060
116 6.566967774 7.791158250
117 5.602586822 6.566967774
118 5.383674363 5.602586822
119 4.911774363 5.383674363
120 5.610904762 4.911774363
121 4.811952381 5.610904762
122 4.468380952 4.811952381
123 4.032523810 4.468380952
124 3.761238095 4.032523810
125 4.318000000 3.761238095
126 4.106333333 4.318000000
127 5.407809524 4.106333333
128 6.324619048 5.407809524
129 2.806238095 6.324619048
130 1.307325637 2.806238095
131 0.117425637 1.307325637
132 0.094556035 0.117425637
133 -0.690396346 0.094556035
134 -2.292967774 -0.690396346
135 -2.279824917 -2.292967774
136 -1.949110631 -2.279824917
137 -2.270348726 -1.949110631
138 -2.154015393 -2.270348726
139 -3.401539203 -2.154015393
140 -4.867729679 -3.401539203
141 -3.469110631 -4.867729679
142 -2.550023090 -3.469110631
143 -4.777923090 -2.550023090
144 -2.552792691 -4.777923090
145 -5.113745072 -2.552792691
146 -6.678316501 -5.113745072
147 -7.157173643 -6.678316501
148 -9.764459358 -7.157173643
149 -9.069697453 -9.764459358
150 -10.241364120 -9.069697453
151 -7.378887929 -10.241364120
152 -8.357078405 -7.378887929
153 -8.319459358 -8.357078405
154 -9.930371816 -8.319459358
155 -11.484271816 -9.930371816
156 -10.159141417 -11.484271816
157 -10.863093798 -10.159141417
158 -11.045665227 -10.863093798
159 -10.338522370 -11.045665227
160 -10.256808084 -10.338522370
161 -9.894046179 -10.256808084
162 -9.839712846 -9.894046179
163 -10.608236656 -9.839712846
164 -10.351427132 -10.608236656
165 -9.570808084 -10.351427132
166 -6.624720543 -9.570808084
167 -7.120620543 -6.624720543
168 -8.107490144 -7.120620543
169 -6.643442525 -8.107490144
170 -6.889013953 -6.643442525
171 -5.901871096 -6.889013953
172 -5.136156811 -5.901871096
173 -4.942394906 -5.136156811
174 -4.009061573 -4.942394906
175 -9.667585382 -4.009061573
176 -5.202775858 -9.667585382
177 -2.044156811 -5.202775858
178 -2.524069269 -2.044156811
179 -2.589969269 -2.524069269
180 -2.826838870 -2.589969269
181 -0.919791251 -2.826838870
182 -0.575362680 -0.919791251
183 -1.013219823 -0.575362680
184 -1.321505537 -1.013219823
185 -1.253743632 -1.321505537
186 -1.820410299 -1.253743632
187 -3.226934109 -1.820410299
188 2.225875415 -3.226934109
189 1.541494463 2.225875415
190 -0.877417996 1.541494463
191 -1.508317996 -0.877417996
192 -0.357187597 -1.508317996
193 1.186860022 -0.357187597
194 2.775288594 1.186860022
195 2.915431451 2.775288594
196 2.985145736 2.915431451
197 4.121907641 2.985145736
198 1.987240975 4.121907641
199 -1.514282835 1.987240975
200 6.551526689 -1.514282835
201 4.744145736 6.551526689
202 3.883233278 4.744145736
203 3.172333278 3.883233278
204 1.804463677 3.172333278
205 4.433511296 1.804463677
206 4.676939867 4.433511296
207 3.903082724 4.676939867
208 5.029797010 3.903082724
209 4.323558915 5.029797010
210 2.968892248 4.323558915
211 -1.396631561 2.968892248
212 6.252177962 -1.396631561
213 6.256797010 6.252177962
214 4.916884551 6.256797010
215 5.602984551 4.916884551
216 5.413114950 5.602984551
217 6.292162569 5.413114950
218 7.098591141 6.292162569
219 5.100733998 7.098591141
220 7.670448283 5.100733998
221 5.411210188 7.670448283
222 5.610543522 5.411210188
223 5.419019712 5.610543522
224 5.437829236 5.419019712
225 3.122448283 5.437829236
226 0.010535825 3.122448283
227 -0.243364175 0.010535825
228 -1.302233776 -0.243364175
229 -1.732186157 -1.302233776
230 -0.847757586 -1.732186157
231 -2.257614729 -0.847757586
232 -1.806900443 -2.257614729
233 -0.616138538 -1.806900443
234 0.003194795 -0.616138538
235 0.749670986 0.003194795
236 -1.006519491 0.749670986
237 -1.328900443 -1.006519491
238 -1.750812901 -1.328900443
239 -1.615712901 -1.750812901
240 -3.215582503 -1.615712901
241 -5.473534884 -3.215582503
242 -5.147106312 -5.473534884
243 -4.791963455 -5.147106312
244 -5.567249169 -4.791963455
245 -5.010487265 -5.567249169
246 -5.164153931 -5.010487265
247 -4.622677741 -5.164153931
248 -6.824868217 -4.622677741
249 -5.376249169 -6.824868217
250 NA -5.376249169
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.347439646 2.026392027
[2,] 0.920868217 1.347439646
[3,] 1.551011074 0.920868217
[4,] 0.932725360 1.551011074
[5,] 0.259487265 0.932725360
[6,] 0.183820598 0.259487265
[7,] 0.383296788 0.183820598
[8,] -1.806893688 0.383296788
[9,] -1.818274640 -1.806893688
[10,] -1.841187099 -1.818274640
[11,] -1.256087099 -1.841187099
[12,] -1.845956700 -1.256087099
[13,] -2.045909081 -1.845956700
[14,] -2.609480509 -2.045909081
[15,] -2.762337652 -2.609480509
[16,] -2.671623367 -2.762337652
[17,] -4.265861462 -2.671623367
[18,] -4.959528128 -4.265861462
[19,] -2.388051938 -4.959528128
[20,] -5.667242414 -2.388051938
[21,] -7.366623367 -5.667242414
[22,] -6.959535825 -7.366623367
[23,] -6.388435825 -6.959535825
[24,] -7.031305426 -6.388435825
[25,] -8.571257807 -7.031305426
[26,] -8.356829236 -8.571257807
[27,] -7.574686379 -8.356829236
[28,] -7.847972093 -7.574686379
[29,] -7.726210188 -7.847972093
[30,] -7.782876855 -7.726210188
[31,] -6.344400664 -7.782876855
[32,] -7.745591141 -6.344400664
[33,] -9.082972093 -7.745591141
[34,] -8.145884551 -9.082972093
[35,] -6.310784551 -8.145884551
[36,] -7.246654153 -6.310784551
[37,] -6.278606534 -7.246654153
[38,] -6.817177962 -6.278606534
[39,] -6.599035105 -6.817177962
[40,] -5.655320819 -6.599035105
[41,] -5.448558915 -5.655320819
[42,] -5.584225581 -5.448558915
[43,] -3.850749391 -5.584225581
[44,] -6.615939867 -3.850749391
[45,] -5.114320819 -6.615939867
[46,] -2.357233278 -5.114320819
[47,] -1.879133278 -2.357233278
[48,] -3.082002879 -1.879133278
[49,] -1.998955260 -3.082002879
[50,] -3.185526689 -1.998955260
[51,] -2.247383832 -3.185526689
[52,] -3.262669546 -2.247383832
[53,] -2.707907641 -3.262669546
[54,] -0.665574308 -2.707907641
[55,] 2.227901883 -0.665574308
[56,] -2.615288594 2.227901883
[57,] -1.265669546 -2.615288594
[58,] -1.591582004 -1.265669546
[59,] 0.024517996 -1.591582004
[60,] 0.535648394 0.024517996
[61,] 1.408696013 0.535648394
[62,] 2.226124585 1.408696013
[63,] 1.980267442 2.226124585
[64,] 3.783981728 1.980267442
[65,] 4.078743632 3.783981728
[66,] 6.943076966 4.078743632
[67,] 6.099553156 6.943076966
[68,] 4.948362680 6.099553156
[69,] 6.427981728 4.948362680
[70,] 7.126069269 6.427981728
[71,] 7.095169269 7.126069269
[72,] 7.843299668 7.095169269
[73,] 7.131347287 7.843299668
[74,] 7.597775858 7.131347287
[75,] 7.654918715 7.597775858
[76,] 7.490633001 7.654918715
[77,] 7.909394906 7.490633001
[78,] 7.625728239 7.909394906
[79,] 9.507204430 7.625728239
[80,] 7.773013953 9.507204430
[81,] 7.931633001 7.773013953
[82,] 7.061720543 7.931633001
[83,] 6.782820543 7.061720543
[84,] 7.706950941 6.782820543
[85,] 7.411998560 7.706950941
[86,] 8.107427132 7.411998560
[87,] 8.745569989 8.107427132
[88,] 8.864284275 8.745569989
[89,] 8.582046179 8.864284275
[90,] 8.639379513 8.582046179
[91,] 8.832855703 8.639379513
[92,] 7.755665227 8.832855703
[93,] 8.731284275 7.755665227
[94,] 8.765371816 8.731284275
[95,] 10.058471816 8.765371816
[96,] 9.994602215 10.058471816
[97,] 9.639649834 9.994602215
[98,] 9.595078405 9.639649834
[99,] 9.761221262 9.595078405
[100,] 8.299935548 9.761221262
[101,] 7.250697453 8.299935548
[102,] 7.740030786 7.250697453
[103,] 7.981506977 7.740030786
[104,] 7.225316501 7.981506977
[105,] 7.591935548 7.225316501
[106,] 6.698023090 7.591935548
[107,] 7.409123090 6.698023090
[108,] 6.697253488 7.409123090
[109,] 6.667301107 6.697253488
[110,] 6.978729679 6.667301107
[111,] 7.278872536 6.978729679
[112,] 6.421586822 7.278872536
[113,] 6.950348726 6.421586822
[114,] 6.412682060 6.950348726
[115,] 7.791158250 6.412682060
[116,] 6.566967774 7.791158250
[117,] 5.602586822 6.566967774
[118,] 5.383674363 5.602586822
[119,] 4.911774363 5.383674363
[120,] 5.610904762 4.911774363
[121,] 4.811952381 5.610904762
[122,] 4.468380952 4.811952381
[123,] 4.032523810 4.468380952
[124,] 3.761238095 4.032523810
[125,] 4.318000000 3.761238095
[126,] 4.106333333 4.318000000
[127,] 5.407809524 4.106333333
[128,] 6.324619048 5.407809524
[129,] 2.806238095 6.324619048
[130,] 1.307325637 2.806238095
[131,] 0.117425637 1.307325637
[132,] 0.094556035 0.117425637
[133,] -0.690396346 0.094556035
[134,] -2.292967774 -0.690396346
[135,] -2.279824917 -2.292967774
[136,] -1.949110631 -2.279824917
[137,] -2.270348726 -1.949110631
[138,] -2.154015393 -2.270348726
[139,] -3.401539203 -2.154015393
[140,] -4.867729679 -3.401539203
[141,] -3.469110631 -4.867729679
[142,] -2.550023090 -3.469110631
[143,] -4.777923090 -2.550023090
[144,] -2.552792691 -4.777923090
[145,] -5.113745072 -2.552792691
[146,] -6.678316501 -5.113745072
[147,] -7.157173643 -6.678316501
[148,] -9.764459358 -7.157173643
[149,] -9.069697453 -9.764459358
[150,] -10.241364120 -9.069697453
[151,] -7.378887929 -10.241364120
[152,] -8.357078405 -7.378887929
[153,] -8.319459358 -8.357078405
[154,] -9.930371816 -8.319459358
[155,] -11.484271816 -9.930371816
[156,] -10.159141417 -11.484271816
[157,] -10.863093798 -10.159141417
[158,] -11.045665227 -10.863093798
[159,] -10.338522370 -11.045665227
[160,] -10.256808084 -10.338522370
[161,] -9.894046179 -10.256808084
[162,] -9.839712846 -9.894046179
[163,] -10.608236656 -9.839712846
[164,] -10.351427132 -10.608236656
[165,] -9.570808084 -10.351427132
[166,] -6.624720543 -9.570808084
[167,] -7.120620543 -6.624720543
[168,] -8.107490144 -7.120620543
[169,] -6.643442525 -8.107490144
[170,] -6.889013953 -6.643442525
[171,] -5.901871096 -6.889013953
[172,] -5.136156811 -5.901871096
[173,] -4.942394906 -5.136156811
[174,] -4.009061573 -4.942394906
[175,] -9.667585382 -4.009061573
[176,] -5.202775858 -9.667585382
[177,] -2.044156811 -5.202775858
[178,] -2.524069269 -2.044156811
[179,] -2.589969269 -2.524069269
[180,] -2.826838870 -2.589969269
[181,] -0.919791251 -2.826838870
[182,] -0.575362680 -0.919791251
[183,] -1.013219823 -0.575362680
[184,] -1.321505537 -1.013219823
[185,] -1.253743632 -1.321505537
[186,] -1.820410299 -1.253743632
[187,] -3.226934109 -1.820410299
[188,] 2.225875415 -3.226934109
[189,] 1.541494463 2.225875415
[190,] -0.877417996 1.541494463
[191,] -1.508317996 -0.877417996
[192,] -0.357187597 -1.508317996
[193,] 1.186860022 -0.357187597
[194,] 2.775288594 1.186860022
[195,] 2.915431451 2.775288594
[196,] 2.985145736 2.915431451
[197,] 4.121907641 2.985145736
[198,] 1.987240975 4.121907641
[199,] -1.514282835 1.987240975
[200,] 6.551526689 -1.514282835
[201,] 4.744145736 6.551526689
[202,] 3.883233278 4.744145736
[203,] 3.172333278 3.883233278
[204,] 1.804463677 3.172333278
[205,] 4.433511296 1.804463677
[206,] 4.676939867 4.433511296
[207,] 3.903082724 4.676939867
[208,] 5.029797010 3.903082724
[209,] 4.323558915 5.029797010
[210,] 2.968892248 4.323558915
[211,] -1.396631561 2.968892248
[212,] 6.252177962 -1.396631561
[213,] 6.256797010 6.252177962
[214,] 4.916884551 6.256797010
[215,] 5.602984551 4.916884551
[216,] 5.413114950 5.602984551
[217,] 6.292162569 5.413114950
[218,] 7.098591141 6.292162569
[219,] 5.100733998 7.098591141
[220,] 7.670448283 5.100733998
[221,] 5.411210188 7.670448283
[222,] 5.610543522 5.411210188
[223,] 5.419019712 5.610543522
[224,] 5.437829236 5.419019712
[225,] 3.122448283 5.437829236
[226,] 0.010535825 3.122448283
[227,] -0.243364175 0.010535825
[228,] -1.302233776 -0.243364175
[229,] -1.732186157 -1.302233776
[230,] -0.847757586 -1.732186157
[231,] -2.257614729 -0.847757586
[232,] -1.806900443 -2.257614729
[233,] -0.616138538 -1.806900443
[234,] 0.003194795 -0.616138538
[235,] 0.749670986 0.003194795
[236,] -1.006519491 0.749670986
[237,] -1.328900443 -1.006519491
[238,] -1.750812901 -1.328900443
[239,] -1.615712901 -1.750812901
[240,] -3.215582503 -1.615712901
[241,] -5.473534884 -3.215582503
[242,] -5.147106312 -5.473534884
[243,] -4.791963455 -5.147106312
[244,] -5.567249169 -4.791963455
[245,] -5.010487265 -5.567249169
[246,] -5.164153931 -5.010487265
[247,] -4.622677741 -5.164153931
[248,] -6.824868217 -4.622677741
[249,] -5.376249169 -6.824868217
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.347439646 2.026392027
2 0.920868217 1.347439646
3 1.551011074 0.920868217
4 0.932725360 1.551011074
5 0.259487265 0.932725360
6 0.183820598 0.259487265
7 0.383296788 0.183820598
8 -1.806893688 0.383296788
9 -1.818274640 -1.806893688
10 -1.841187099 -1.818274640
11 -1.256087099 -1.841187099
12 -1.845956700 -1.256087099
13 -2.045909081 -1.845956700
14 -2.609480509 -2.045909081
15 -2.762337652 -2.609480509
16 -2.671623367 -2.762337652
17 -4.265861462 -2.671623367
18 -4.959528128 -4.265861462
19 -2.388051938 -4.959528128
20 -5.667242414 -2.388051938
21 -7.366623367 -5.667242414
22 -6.959535825 -7.366623367
23 -6.388435825 -6.959535825
24 -7.031305426 -6.388435825
25 -8.571257807 -7.031305426
26 -8.356829236 -8.571257807
27 -7.574686379 -8.356829236
28 -7.847972093 -7.574686379
29 -7.726210188 -7.847972093
30 -7.782876855 -7.726210188
31 -6.344400664 -7.782876855
32 -7.745591141 -6.344400664
33 -9.082972093 -7.745591141
34 -8.145884551 -9.082972093
35 -6.310784551 -8.145884551
36 -7.246654153 -6.310784551
37 -6.278606534 -7.246654153
38 -6.817177962 -6.278606534
39 -6.599035105 -6.817177962
40 -5.655320819 -6.599035105
41 -5.448558915 -5.655320819
42 -5.584225581 -5.448558915
43 -3.850749391 -5.584225581
44 -6.615939867 -3.850749391
45 -5.114320819 -6.615939867
46 -2.357233278 -5.114320819
47 -1.879133278 -2.357233278
48 -3.082002879 -1.879133278
49 -1.998955260 -3.082002879
50 -3.185526689 -1.998955260
51 -2.247383832 -3.185526689
52 -3.262669546 -2.247383832
53 -2.707907641 -3.262669546
54 -0.665574308 -2.707907641
55 2.227901883 -0.665574308
56 -2.615288594 2.227901883
57 -1.265669546 -2.615288594
58 -1.591582004 -1.265669546
59 0.024517996 -1.591582004
60 0.535648394 0.024517996
61 1.408696013 0.535648394
62 2.226124585 1.408696013
63 1.980267442 2.226124585
64 3.783981728 1.980267442
65 4.078743632 3.783981728
66 6.943076966 4.078743632
67 6.099553156 6.943076966
68 4.948362680 6.099553156
69 6.427981728 4.948362680
70 7.126069269 6.427981728
71 7.095169269 7.126069269
72 7.843299668 7.095169269
73 7.131347287 7.843299668
74 7.597775858 7.131347287
75 7.654918715 7.597775858
76 7.490633001 7.654918715
77 7.909394906 7.490633001
78 7.625728239 7.909394906
79 9.507204430 7.625728239
80 7.773013953 9.507204430
81 7.931633001 7.773013953
82 7.061720543 7.931633001
83 6.782820543 7.061720543
84 7.706950941 6.782820543
85 7.411998560 7.706950941
86 8.107427132 7.411998560
87 8.745569989 8.107427132
88 8.864284275 8.745569989
89 8.582046179 8.864284275
90 8.639379513 8.582046179
91 8.832855703 8.639379513
92 7.755665227 8.832855703
93 8.731284275 7.755665227
94 8.765371816 8.731284275
95 10.058471816 8.765371816
96 9.994602215 10.058471816
97 9.639649834 9.994602215
98 9.595078405 9.639649834
99 9.761221262 9.595078405
100 8.299935548 9.761221262
101 7.250697453 8.299935548
102 7.740030786 7.250697453
103 7.981506977 7.740030786
104 7.225316501 7.981506977
105 7.591935548 7.225316501
106 6.698023090 7.591935548
107 7.409123090 6.698023090
108 6.697253488 7.409123090
109 6.667301107 6.697253488
110 6.978729679 6.667301107
111 7.278872536 6.978729679
112 6.421586822 7.278872536
113 6.950348726 6.421586822
114 6.412682060 6.950348726
115 7.791158250 6.412682060
116 6.566967774 7.791158250
117 5.602586822 6.566967774
118 5.383674363 5.602586822
119 4.911774363 5.383674363
120 5.610904762 4.911774363
121 4.811952381 5.610904762
122 4.468380952 4.811952381
123 4.032523810 4.468380952
124 3.761238095 4.032523810
125 4.318000000 3.761238095
126 4.106333333 4.318000000
127 5.407809524 4.106333333
128 6.324619048 5.407809524
129 2.806238095 6.324619048
130 1.307325637 2.806238095
131 0.117425637 1.307325637
132 0.094556035 0.117425637
133 -0.690396346 0.094556035
134 -2.292967774 -0.690396346
135 -2.279824917 -2.292967774
136 -1.949110631 -2.279824917
137 -2.270348726 -1.949110631
138 -2.154015393 -2.270348726
139 -3.401539203 -2.154015393
140 -4.867729679 -3.401539203
141 -3.469110631 -4.867729679
142 -2.550023090 -3.469110631
143 -4.777923090 -2.550023090
144 -2.552792691 -4.777923090
145 -5.113745072 -2.552792691
146 -6.678316501 -5.113745072
147 -7.157173643 -6.678316501
148 -9.764459358 -7.157173643
149 -9.069697453 -9.764459358
150 -10.241364120 -9.069697453
151 -7.378887929 -10.241364120
152 -8.357078405 -7.378887929
153 -8.319459358 -8.357078405
154 -9.930371816 -8.319459358
155 -11.484271816 -9.930371816
156 -10.159141417 -11.484271816
157 -10.863093798 -10.159141417
158 -11.045665227 -10.863093798
159 -10.338522370 -11.045665227
160 -10.256808084 -10.338522370
161 -9.894046179 -10.256808084
162 -9.839712846 -9.894046179
163 -10.608236656 -9.839712846
164 -10.351427132 -10.608236656
165 -9.570808084 -10.351427132
166 -6.624720543 -9.570808084
167 -7.120620543 -6.624720543
168 -8.107490144 -7.120620543
169 -6.643442525 -8.107490144
170 -6.889013953 -6.643442525
171 -5.901871096 -6.889013953
172 -5.136156811 -5.901871096
173 -4.942394906 -5.136156811
174 -4.009061573 -4.942394906
175 -9.667585382 -4.009061573
176 -5.202775858 -9.667585382
177 -2.044156811 -5.202775858
178 -2.524069269 -2.044156811
179 -2.589969269 -2.524069269
180 -2.826838870 -2.589969269
181 -0.919791251 -2.826838870
182 -0.575362680 -0.919791251
183 -1.013219823 -0.575362680
184 -1.321505537 -1.013219823
185 -1.253743632 -1.321505537
186 -1.820410299 -1.253743632
187 -3.226934109 -1.820410299
188 2.225875415 -3.226934109
189 1.541494463 2.225875415
190 -0.877417996 1.541494463
191 -1.508317996 -0.877417996
192 -0.357187597 -1.508317996
193 1.186860022 -0.357187597
194 2.775288594 1.186860022
195 2.915431451 2.775288594
196 2.985145736 2.915431451
197 4.121907641 2.985145736
198 1.987240975 4.121907641
199 -1.514282835 1.987240975
200 6.551526689 -1.514282835
201 4.744145736 6.551526689
202 3.883233278 4.744145736
203 3.172333278 3.883233278
204 1.804463677 3.172333278
205 4.433511296 1.804463677
206 4.676939867 4.433511296
207 3.903082724 4.676939867
208 5.029797010 3.903082724
209 4.323558915 5.029797010
210 2.968892248 4.323558915
211 -1.396631561 2.968892248
212 6.252177962 -1.396631561
213 6.256797010 6.252177962
214 4.916884551 6.256797010
215 5.602984551 4.916884551
216 5.413114950 5.602984551
217 6.292162569 5.413114950
218 7.098591141 6.292162569
219 5.100733998 7.098591141
220 7.670448283 5.100733998
221 5.411210188 7.670448283
222 5.610543522 5.411210188
223 5.419019712 5.610543522
224 5.437829236 5.419019712
225 3.122448283 5.437829236
226 0.010535825 3.122448283
227 -0.243364175 0.010535825
228 -1.302233776 -0.243364175
229 -1.732186157 -1.302233776
230 -0.847757586 -1.732186157
231 -2.257614729 -0.847757586
232 -1.806900443 -2.257614729
233 -0.616138538 -1.806900443
234 0.003194795 -0.616138538
235 0.749670986 0.003194795
236 -1.006519491 0.749670986
237 -1.328900443 -1.006519491
238 -1.750812901 -1.328900443
239 -1.615712901 -1.750812901
240 -3.215582503 -1.615712901
241 -5.473534884 -3.215582503
242 -5.147106312 -5.473534884
243 -4.791963455 -5.147106312
244 -5.567249169 -4.791963455
245 -5.010487265 -5.567249169
246 -5.164153931 -5.010487265
247 -4.622677741 -5.164153931
248 -6.824868217 -4.622677741
249 -5.376249169 -6.824868217
> 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/71i4u1229074522.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8e0071229074522.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9prk41229074522.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/106lta1229074522.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/114bgo1229074522.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/12znpn1229074522.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/13pnqy1229074522.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/14y9fu1229074523.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/152ecx1229074523.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/1615fz1229074523.tab")
+ }
>
> system("convert tmp/1vu001229074522.ps tmp/1vu001229074522.png")
> system("convert tmp/2uj271229074522.ps tmp/2uj271229074522.png")
> system("convert tmp/3st411229074522.ps tmp/3st411229074522.png")
> system("convert tmp/40pmo1229074522.ps tmp/40pmo1229074522.png")
> system("convert tmp/5nwsw1229074522.ps tmp/5nwsw1229074522.png")
> system("convert tmp/6ogm41229074522.ps tmp/6ogm41229074522.png")
> system("convert tmp/71i4u1229074522.ps tmp/71i4u1229074522.png")
> system("convert tmp/8e0071229074522.ps tmp/8e0071229074522.png")
> system("convert tmp/9prk41229074522.ps tmp/9prk41229074522.png")
> system("convert tmp/106lta1229074522.ps tmp/106lta1229074522.png")
>
>
> proc.time()
user system elapsed
6.374 1.941 8.839