R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(210907
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+ ,0
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+ ,21
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+ ,0
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+ ,56613
+ ,19
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+ ,1
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+ ,35
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+ ,0
+ ,28
+ ,15
+ ,60
+ ,72535
+ ,14
+ ,329
+ ,44
+ ,0
+ ,17
+ ,16
+ ,64)
+ ,dim=c(8
+ ,289)
+ ,dimnames=list(c('time_in_rfc'
+ ,'logins'
+ ,'compendium_views_info'
+ ,'compendium_views_pr'
+ ,'shared_compendiums'
+ ,'blogged_computations'
+ ,'compendiums_reviewed'
+ ,'feedback_messages_p1')
+ ,1:289))
> y <- array(NA,dim=c(8,289),dimnames=list(c('time_in_rfc','logins','compendium_views_info','compendium_views_pr','shared_compendiums','blogged_computations','compendiums_reviewed','feedback_messages_p1'),1:289))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '2'
> #'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, 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
logins time_in_rfc compendium_views_info compendium_views_pr
1 56 210907 396 81
2 56 120982 297 55
3 54 176508 559 50
4 89 179321 967 125
5 40 123185 270 40
6 25 52746 143 37
7 92 385534 1562 63
8 18 33170 109 44
9 63 101645 371 88
10 44 149061 656 66
11 33 165446 511 57
12 84 237213 655 74
13 88 173326 465 49
14 55 133131 525 52
15 60 258873 885 88
16 66 180083 497 36
17 154 324799 1436 108
18 53 230964 612 43
19 119 236785 865 75
20 41 135473 385 32
21 61 202925 567 44
22 58 215147 639 85
23 75 344297 963 86
24 33 153935 398 56
25 40 132943 410 50
26 92 174724 966 135
27 100 174415 801 63
28 112 225548 892 81
29 73 223632 513 52
30 40 124817 469 44
31 45 221698 683 113
32 60 210767 643 39
33 62 170266 535 73
34 75 260561 625 48
35 31 84853 264 33
36 77 294424 992 59
37 34 101011 238 41
38 46 215641 818 69
39 99 325107 937 64
40 17 7176 70 1
41 66 167542 507 59
42 30 106408 260 32
43 76 96560 503 129
44 146 265769 927 37
45 67 269651 1269 31
46 56 149112 537 65
47 107 175824 910 107
48 58 152871 532 74
49 34 111665 345 54
50 61 116408 918 76
51 119 362301 1635 715
52 42 78800 330 57
53 66 183167 557 66
54 89 277965 1178 106
55 44 150629 740 54
56 66 168809 452 32
57 24 24188 218 20
58 259 329267 764 71
59 17 65029 255 21
60 64 101097 454 70
61 41 218946 866 112
62 68 244052 574 66
63 168 341570 1276 190
64 43 103597 379 66
65 132 233328 825 165
66 105 256462 798 56
67 71 206161 663 61
68 112 311473 1069 53
69 94 235800 921 127
70 82 177939 858 63
71 70 207176 711 38
72 57 196553 503 50
73 53 174184 382 52
74 103 143246 464 42
75 121 187559 717 76
76 62 187681 690 67
77 52 119016 462 50
78 52 182192 657 53
79 32 73566 385 39
80 62 194979 577 50
81 45 167488 619 77
82 46 143756 479 57
83 63 275541 817 73
84 75 243199 752 34
85 88 182999 430 39
86 46 135649 451 46
87 53 152299 537 63
88 37 120221 519 35
89 90 346485 1000 106
90 63 145790 637 43
91 78 193339 465 47
92 25 80953 437 31
93 45 122774 711 162
94 46 130585 299 57
95 41 112611 248 36
96 144 286468 1162 263
97 82 241066 714 78
98 91 148446 905 63
99 71 204713 649 54
100 63 182079 512 63
101 53 140344 472 77
102 62 220516 905 79
103 63 243060 786 110
104 32 162765 489 56
105 39 182613 479 56
106 62 232138 617 43
107 117 265318 925 111
108 34 85574 351 71
109 92 310839 1144 62
110 93 225060 669 56
111 54 232317 707 74
112 144 144966 458 60
113 14 43287 214 43
114 61 155754 599 68
115 109 164709 572 53
116 38 201940 897 87
117 73 235454 819 46
118 75 220801 720 105
119 50 99466 273 32
120 61 92661 508 133
121 55 133328 506 79
122 77 61361 451 51
123 75 125930 699 207
124 72 100750 407 67
125 50 224549 465 47
126 32 82316 245 34
127 53 102010 370 66
128 42 101523 316 76
129 71 243511 603 65
130 10 22938 154 9
131 35 41566 229 42
132 65 152474 577 45
133 25 61857 192 25
134 66 99923 617 115
135 41 132487 411 97
136 86 317394 975 53
137 16 21054 146 2
138 42 209641 705 52
139 19 22648 184 44
140 19 31414 200 22
141 45 46698 274 35
142 65 131698 502 74
143 35 91735 382 103
144 95 244749 964 144
145 49 184510 537 60
146 37 79863 438 134
147 64 128423 369 89
148 38 97839 417 42
149 34 38214 276 52
150 32 151101 514 98
151 65 272458 822 99
152 52 172494 389 52
153 62 108043 466 29
154 65 328107 1255 125
155 83 250579 694 106
156 95 351067 1024 95
157 29 158015 400 40
158 18 98866 397 140
159 33 85439 350 43
160 247 229242 719 128
161 139 351619 1277 142
162 29 84207 356 73
163 118 120445 457 72
164 110 324598 1402 128
165 67 131069 600 61
166 42 204271 480 73
167 65 165543 595 148
168 94 141722 436 64
169 64 116048 230 45
170 81 250047 651 58
171 95 299775 1367 97
172 67 195838 564 50
173 63 173260 716 37
174 83 254488 747 50
175 45 104389 467 105
176 30 136084 671 69
177 70 199476 861 46
178 32 92499 319 57
179 83 224330 612 52
180 31 135781 433 98
181 67 74408 434 61
182 66 81240 503 89
183 10 14688 85 0
184 70 181633 564 48
185 103 271856 824 91
186 5 7199 74 0
187 20 46660 259 7
188 5 17547 69 3
189 36 133368 535 54
190 34 95227 239 70
191 48 152601 438 36
192 40 98146 459 37
193 43 79619 426 123
194 31 59194 288 247
195 42 139942 498 46
196 46 118612 454 72
197 33 72880 376 41
198 18 65475 225 24
199 55 99643 555 45
200 35 71965 252 33
201 59 77272 208 27
202 19 49289 130 36
203 66 135131 481 87
204 60 108446 389 90
205 36 89746 565 114
206 25 44296 173 31
207 47 77648 278 45
208 54 181528 609 69
209 53 134019 422 51
210 40 124064 445 34
211 40 92630 387 60
212 39 121848 339 45
213 14 52915 181 54
214 45 81872 245 25
215 36 58981 384 38
216 28 53515 212 52
217 44 60812 399 67
218 30 56375 229 74
219 22 65490 224 38
220 17 80949 203 30
221 31 76302 333 26
222 55 104011 384 67
223 54 98104 636 132
224 21 67989 185 42
225 14 30989 93 35
226 81 135458 581 118
227 35 73504 248 68
228 43 63123 304 43
229 46 61254 344 76
230 30 74914 407 64
231 23 31774 170 48
232 38 81437 312 64
233 54 87186 507 56
234 20 50090 224 71
235 53 65745 340 75
236 45 56653 168 39
237 39 158399 443 42
238 20 46455 204 39
239 24 73624 367 93
240 31 38395 210 38
241 35 91899 335 60
242 151 139526 364 71
243 52 52164 178 52
244 30 51567 206 27
245 31 70551 279 59
246 29 84856 387 40
247 57 102538 490 79
248 40 86678 238 44
249 44 85709 343 65
250 25 34662 232 10
251 77 150580 530 124
252 35 99611 291 81
253 11 19349 67 15
254 63 99373 397 92
255 44 86230 467 42
256 19 30837 178 10
257 13 31706 175 24
258 42 89806 299 64
259 38 62088 154 45
260 29 40151 106 22
261 20 27634 189 56
262 27 76990 194 94
263 20 37460 135 19
264 19 54157 201 35
265 37 49862 207 32
266 26 84337 280 35
267 42 64175 260 48
268 49 59382 227 49
269 30 119308 239 48
270 49 76702 333 62
271 67 103425 428 96
272 28 70344 230 45
273 19 43410 292 63
274 49 104838 350 71
275 27 62215 186 26
276 30 69304 326 48
277 22 53117 155 29
278 12 19764 75 19
279 31 86680 361 45
280 20 84105 261 45
281 20 77945 299 67
282 39 89113 300 30
283 29 91005 450 36
284 16 40248 183 34
285 27 64187 238 36
286 21 50857 165 34
287 19 56613 234 37
288 35 62792 176 46
289 14 72535 329 44
shared_compendiums blogged_computations compendiums_reviewed
1 3 79 30
2 4 58 28
3 12 60 38
4 2 108 30
5 1 49 22
6 3 0 26
7 0 121 25
8 0 1 18
9 0 20 11
10 5 43 26
11 0 69 25
12 0 78 38
13 7 86 44
14 7 44 30
15 3 104 40
16 9 63 34
17 0 158 47
18 4 102 30
19 3 77 31
20 0 82 23
21 7 115 36
22 0 101 36
23 1 80 30
24 5 50 25
25 7 83 39
26 0 123 34
27 0 73 31
28 5 81 31
29 0 105 33
30 0 47 25
31 0 105 33
32 3 94 35
33 4 44 42
34 1 114 43
35 4 38 30
36 2 107 33
37 0 30 13
38 0 71 32
39 0 84 36
40 0 0 0
41 2 59 28
42 1 33 14
43 0 42 17
44 2 96 32
45 10 106 30
46 6 56 35
47 0 57 20
48 5 59 28
49 4 39 28
50 1 34 39
51 2 76 34
52 2 20 26
53 0 91 39
54 8 115 39
55 3 85 33
56 0 76 28
57 0 8 4
58 8 79 39
59 5 21 18
60 3 30 14
61 1 76 29
62 5 101 44
63 1 94 21
64 1 27 16
65 5 92 28
66 0 123 35
67 12 75 28
68 8 128 38
69 8 105 23
70 8 55 36
71 8 56 32
72 2 41 29
73 0 72 25
74 5 67 27
75 8 75 36
76 2 114 28
77 5 118 23
78 12 77 40
79 6 22 23
80 7 66 40
81 2 69 28
82 0 105 34
83 4 116 33
84 3 88 28
85 6 73 34
86 2 99 30
87 0 62 33
88 1 53 22
89 0 118 38
90 5 30 26
91 2 100 35
92 0 49 8
93 0 24 24
94 5 67 29
95 0 46 20
96 1 57 29
97 0 75 45
98 1 135 37
99 1 68 33
100 2 124 33
101 6 33 25
102 1 98 32
103 4 58 29
104 2 68 28
105 3 81 28
106 0 131 31
107 10 110 52
108 0 37 21
109 9 130 24
110 7 93 41
111 0 118 33
112 0 39 32
113 4 13 19
114 4 74 20
115 0 81 31
116 0 109 31
117 0 151 32
118 1 51 18
119 0 28 23
120 1 40 17
121 0 56 20
122 0 27 12
123 4 37 17
124 0 83 30
125 4 54 31
126 4 27 10
127 3 28 13
128 0 59 22
129 0 133 42
130 0 12 1
131 5 0 9
132 0 106 32
133 4 23 11
134 0 44 25
135 0 71 36
136 1 116 31
137 0 4 0
138 5 62 24
139 0 12 13
140 0 18 8
141 0 14 13
142 0 60 19
143 0 7 18
144 2 98 33
145 7 64 40
146 1 29 22
147 8 32 38
148 2 25 24
149 0 16 8
150 2 48 35
151 0 100 43
152 0 46 43
153 1 45 14
154 3 129 41
155 0 130 38
156 3 136 45
157 0 59 31
158 0 25 13
159 0 32 28
160 4 63 31
161 4 95 40
162 11 14 30
163 0 36 16
164 0 113 37
165 4 47 30
166 0 92 35
167 1 70 32
168 0 19 27
169 0 50 20
170 0 41 18
171 9 91 31
172 1 111 31
173 3 41 21
174 10 120 39
175 5 135 41
176 0 27 13
177 2 87 32
178 0 25 18
179 1 131 39
180 2 45 14
181 4 29 7
182 0 58 17
183 0 4 0
184 2 47 30
185 1 109 37
186 0 7 0
187 0 12 5
188 0 0 1
189 1 37 16
190 0 37 32
191 2 46 24
192 0 15 17
193 3 42 11
194 6 7 24
195 0 54 22
196 2 54 12
197 0 14 19
198 2 16 13
199 1 33 17
200 1 32 15
201 2 21 16
202 1 15 24
203 0 38 15
204 1 22 17
205 3 28 18
206 0 10 20
207 0 31 16
208 0 32 16
209 0 32 18
210 1 43 22
211 4 27 8
212 0 37 17
213 0 20 18
214 0 32 16
215 7 0 23
216 2 5 22
217 0 26 13
218 7 10 13
219 3 27 16
220 0 11 16
221 0 29 20
222 6 25 22
223 2 55 17
224 0 23 18
225 0 5 17
226 3 43 12
227 0 23 7
228 1 34 17
229 1 36 14
230 0 35 23
231 1 0 17
232 0 37 14
233 0 28 15
234 0 16 17
235 0 26 21
236 0 38 18
237 0 23 18
238 0 22 17
239 0 30 17
240 0 16 16
241 0 18 15
242 0 28 21
243 0 32 16
244 2 21 14
245 0 23 15
246 1 29 17
247 1 50 15
248 0 12 15
249 0 21 10
250 0 18 6
251 0 27 22
252 0 41 21
253 0 13 1
254 1 12 18
255 0 21 17
256 0 8 4
257 0 26 10
258 0 27 16
259 1 13 16
260 0 16 9
261 0 2 16
262 0 42 17
263 0 5 7
264 0 37 15
265 0 17 14
266 0 38 14
267 0 37 18
268 0 29 12
269 0 32 16
270 0 35 21
271 1 17 19
272 0 20 16
273 0 7 1
274 1 46 16
275 0 24 10
276 6 40 19
277 3 3 12
278 1 10 2
279 2 37 14
280 0 17 17
281 0 28 19
282 0 19 14
283 3 29 11
284 1 8 4
285 0 10 16
286 0 15 20
287 1 15 12
288 0 28 15
289 0 17 16
feedback_messages_p1
1 115
2 109
3 146
4 116
5 68
6 101
7 96
8 67
9 44
10 100
11 93
12 140
13 166
14 99
15 139
16 130
17 181
18 116
19 116
20 88
21 139
22 135
23 108
24 89
25 156
26 129
27 118
28 118
29 125
30 95
31 126
32 135
33 154
34 165
35 113
36 127
37 52
38 121
39 136
40 0
41 108
42 46
43 54
44 124
45 115
46 128
47 80
48 97
49 104
50 59
51 125
52 82
53 149
54 149
55 122
56 118
57 12
58 144
59 67
60 52
61 108
62 166
63 80
64 60
65 107
66 127
67 107
68 146
69 84
70 141
71 123
72 111
73 98
74 105
75 135
76 107
77 85
78 155
79 88
80 155
81 104
82 132
83 127
84 108
85 129
86 116
87 122
88 85
89 147
90 99
91 87
92 28
93 90
94 109
95 78
96 111
97 158
98 141
99 122
100 124
101 93
102 124
103 112
104 108
105 99
106 117
107 199
108 78
109 91
110 158
111 126
112 122
113 71
114 75
115 115
116 119
117 124
118 72
119 91
120 45
121 78
122 39
123 68
124 119
125 117
126 39
127 50
128 88
129 155
130 0
131 36
132 123
133 32
134 99
135 136
136 117
137 0
138 88
139 39
140 25
141 52
142 75
143 71
144 124
145 151
146 71
147 145
148 87
149 27
150 131
151 162
152 165
153 54
154 159
155 147
156 170
157 119
158 49
159 104
160 120
161 150
162 112
163 59
164 136
165 107
166 130
167 115
168 107
169 75
170 71
171 120
172 116
173 79
174 150
175 156
176 51
177 118
178 71
179 144
180 47
181 28
182 68
183 0
184 110
185 147
186 0
187 15
188 4
189 64
190 111
191 85
192 68
193 40
194 80
195 88
196 48
197 76
198 51
199 67
200 59
201 61
202 76
203 60
204 68
205 71
206 76
207 62
208 61
209 67
210 88
211 30
212 64
213 68
214 64
215 91
216 88
217 52
218 49
219 62
220 61
221 76
222 88
223 66
224 71
225 68
226 48
227 25
228 68
229 41
230 90
231 66
232 54
233 59
234 60
235 77
236 68
237 72
238 67
239 64
240 63
241 59
242 84
243 64
244 56
245 54
246 67
247 58
248 59
249 40
250 22
251 83
252 81
253 2
254 72
255 61
256 15
257 32
258 62
259 58
260 36
261 59
262 68
263 21
264 55
265 54
266 55
267 72
268 41
269 61
270 67
271 76
272 64
273 3
274 63
275 40
276 69
277 48
278 8
279 52
280 66
281 76
282 43
283 39
284 14
285 61
286 71
287 44
288 60
289 64
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) time_in_rfc compendium_views_info
8.8941673 0.0002135 0.0251436
compendium_views_pr shared_compendiums blogged_computations
0.0516022 0.3801690 -0.0928666
compendiums_reviewed feedback_messages_p1
-0.2154781 0.1120692
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-50.107 -11.146 -3.821 7.654 162.033
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.894e+00 3.610e+00 2.464 0.0143 *
time_in_rfc 2.135e-04 4.785e-05 4.463 1.17e-05 ***
compendium_views_info 2.514e-02 1.219e-02 2.063 0.0400 *
compendium_views_pr 5.160e-02 3.223e-02 1.601 0.1105
shared_compendiums 3.802e-01 5.593e-01 0.680 0.4973
blogged_computations -9.287e-02 7.554e-02 -1.229 0.2200
compendiums_reviewed -2.155e-01 7.349e-01 -0.293 0.7696
feedback_messages_p1 1.121e-01 1.982e-01 0.566 0.5722
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 23.33 on 281 degrees of freedom
Multiple R-squared: 0.5371, Adjusted R-squared: 0.5256
F-statistic: 46.58 on 7 and 281 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,] 0.20632968 4.126594e-01 7.936703e-01
[2,] 0.22011585 4.402317e-01 7.798841e-01
[3,] 0.32500378 6.500076e-01 6.749962e-01
[4,] 0.21156830 4.231366e-01 7.884317e-01
[5,] 0.26652300 5.330460e-01 7.334770e-01
[6,] 0.19293096 3.858619e-01 8.070690e-01
[7,] 0.25541709 5.108342e-01 7.445829e-01
[8,] 0.20501650 4.100330e-01 7.949835e-01
[9,] 0.41420809 8.284162e-01 5.857919e-01
[10,] 0.32963370 6.592674e-01 6.703663e-01
[11,] 0.25623647 5.124729e-01 7.437635e-01
[12,] 0.25836581 5.167316e-01 7.416342e-01
[13,] 0.20864879 4.172976e-01 7.913512e-01
[14,] 0.16659999 3.332000e-01 8.334000e-01
[15,] 0.17894207 3.578841e-01 8.210579e-01
[16,] 0.14264204 2.852841e-01 8.573580e-01
[17,] 0.14684101 2.936820e-01 8.531590e-01
[18,] 0.18765555 3.753111e-01 8.123444e-01
[19,] 0.16182426 3.236485e-01 8.381757e-01
[20,] 0.13574666 2.714933e-01 8.642533e-01
[21,] 0.18417696 3.683539e-01 8.158230e-01
[22,] 0.14974400 2.994880e-01 8.502560e-01
[23,] 0.12416486 2.483297e-01 8.758351e-01
[24,] 0.09540970 1.908194e-01 9.045903e-01
[25,] 0.07703552 1.540710e-01 9.229645e-01
[26,] 0.06222810 1.244562e-01 9.377719e-01
[27,] 0.04799910 9.599820e-02 9.520009e-01
[28,] 0.06991065 1.398213e-01 9.300894e-01
[29,] 0.06023676 1.204735e-01 9.397632e-01
[30,] 0.04992256 9.984512e-02 9.500774e-01
[31,] 0.03923888 7.847776e-02 9.607611e-01
[32,] 0.02923645 5.847290e-02 9.707636e-01
[33,] 0.02962205 5.924409e-02 9.703780e-01
[34,] 0.18977575 3.795515e-01 8.102243e-01
[35,] 0.22158020 4.431604e-01 7.784198e-01
[36,] 0.18646149 3.729230e-01 8.135385e-01
[37,] 0.18965118 3.793024e-01 8.103488e-01
[38,] 0.15857725 3.171545e-01 8.414228e-01
[39,] 0.13901973 2.780395e-01 8.609803e-01
[40,] 0.11472614 2.294523e-01 8.852739e-01
[41,] 0.13591784 2.718357e-01 8.640822e-01
[42,] 0.11119700 2.223940e-01 8.888030e-01
[43,] 0.09141360 1.828272e-01 9.085864e-01
[44,] 0.07673875 1.534775e-01 9.232613e-01
[45,] 0.08865032 1.773006e-01 9.113497e-01
[46,] 0.07222997 1.444599e-01 9.277700e-01
[47,] 0.05776148 1.155230e-01 9.422385e-01
[48,] 0.99969115 6.177019e-04 3.088510e-04
[49,] 0.99960525 7.894988e-04 3.947494e-04
[50,] 0.99954967 9.006637e-04 4.503318e-04
[51,] 0.99975236 4.952883e-04 2.476442e-04
[52,] 0.99967446 6.510748e-04 3.255374e-04
[53,] 0.99987403 2.519493e-04 1.259746e-04
[54,] 0.99981492 3.701595e-04 1.850798e-04
[55,] 0.99992020 1.595964e-04 7.979820e-05
[56,] 0.99991478 1.704481e-04 8.522405e-05
[57,] 0.99988019 2.396168e-04 1.198084e-04
[58,] 0.99983144 3.371116e-04 1.685558e-04
[59,] 0.99976050 4.790002e-04 2.395001e-04
[60,] 0.99966283 6.743492e-04 3.371746e-04
[61,] 0.99954660 9.068038e-04 4.534019e-04
[62,] 0.99941740 1.165195e-03 5.825973e-04
[63,] 0.99919419 1.611615e-03 8.058073e-04
[64,] 0.99969453 6.109412e-04 3.054706e-04
[65,] 0.99987977 2.404538e-04 1.202269e-04
[66,] 0.99982844 3.431186e-04 1.715593e-04
[67,] 0.99976314 4.737239e-04 2.368620e-04
[68,] 0.99974209 5.158150e-04 2.579075e-04
[69,] 0.99964636 7.072771e-04 3.536386e-04
[70,] 0.99952910 9.417946e-04 4.708973e-04
[71,] 0.99945625 1.087490e-03 5.437452e-04
[72,] 0.99925803 1.483944e-03 7.419719e-04
[73,] 0.99934287 1.314251e-03 6.571257e-04
[74,] 0.99912523 1.749543e-03 8.747713e-04
[75,] 0.99914438 1.711231e-03 8.556155e-04
[76,] 0.99883738 2.325231e-03 1.162615e-03
[77,] 0.99844369 3.112621e-03 1.556311e-03
[78,] 0.99803307 3.933860e-03 1.966930e-03
[79,] 0.99791338 4.173245e-03 2.086622e-03
[80,] 0.99724095 5.518091e-03 2.759046e-03
[81,] 0.99716465 5.670695e-03 2.835348e-03
[82,] 0.99643687 7.126254e-03 3.563127e-03
[83,] 0.99606723 7.865541e-03 3.932771e-03
[84,] 0.99489296 1.021407e-02 5.107036e-03
[85,] 0.99340680 1.318640e-02 6.593202e-03
[86,] 0.99412616 1.174768e-02 5.873841e-03
[87,] 0.99247365 1.505271e-02 7.526355e-03
[88,] 0.99362640 1.274720e-02 6.373600e-03
[89,] 0.99184514 1.630972e-02 8.154859e-03
[90,] 0.98969133 2.061733e-02 1.030867e-02
[91,] 0.98710552 2.578895e-02 1.289448e-02
[92,] 0.98588081 2.823838e-02 1.411919e-02
[93,] 0.98643398 2.713205e-02 1.356602e-02
[94,] 0.98707401 2.585197e-02 1.292599e-02
[95,] 0.98649122 2.701755e-02 1.350878e-02
[96,] 0.98358058 3.283884e-02 1.641942e-02
[97,] 0.98201251 3.597498e-02 1.798749e-02
[98,] 0.97799232 4.401535e-02 2.200768e-02
[99,] 0.97501033 4.997934e-02 2.498967e-02
[100,] 0.97183148 5.633703e-02 2.816852e-02
[101,] 0.97091804 5.816391e-02 2.908196e-02
[102,] 0.99862335 2.753305e-03 1.376652e-03
[103,] 0.99833039 3.339226e-03 1.669613e-03
[104,] 0.99780947 4.381052e-03 2.190526e-03
[105,] 0.99922822 1.543562e-03 7.717811e-04
[106,] 0.99950793 9.841339e-04 4.920669e-04
[107,] 0.99933391 1.332174e-03 6.660872e-04
[108,] 0.99912621 1.747585e-03 8.737927e-04
[109,] 0.99888758 2.224849e-03 1.112425e-03
[110,] 0.99873549 2.529017e-03 1.264509e-03
[111,] 0.99833315 3.333708e-03 1.666854e-03
[112,] 0.99921696 1.566083e-03 7.830415e-04
[113,] 0.99899706 2.005873e-03 1.002936e-03
[114,] 0.99910856 1.782889e-03 8.914444e-04
[115,] 0.99909978 1.800431e-03 9.002154e-04
[116,] 0.99880780 2.384400e-03 1.192200e-03
[117,] 0.99846406 3.071879e-03 1.535940e-03
[118,] 0.99799265 4.014707e-03 2.007353e-03
[119,] 0.99738241 5.235172e-03 2.617586e-03
[120,] 0.99664123 6.717547e-03 3.358773e-03
[121,] 0.99571747 8.565051e-03 4.282525e-03
[122,] 0.99484827 1.030346e-02 5.151732e-03
[123,] 0.99345836 1.308329e-02 6.541644e-03
[124,] 0.99214146 1.571708e-02 7.858542e-03
[125,] 0.99072205 1.855591e-02 9.277953e-03
[126,] 0.98909334 2.181331e-02 1.090666e-02
[127,] 0.98636672 2.726656e-02 1.363328e-02
[128,] 0.98853784 2.292431e-02 1.146216e-02
[129,] 0.98582043 2.835914e-02 1.417957e-02
[130,] 0.98245545 3.508911e-02 1.754455e-02
[131,] 0.98048922 3.902155e-02 1.951078e-02
[132,] 0.97721261 4.557478e-02 2.278739e-02
[133,] 0.97429152 5.141696e-02 2.570848e-02
[134,] 0.96878895 6.242210e-02 3.121105e-02
[135,] 0.96687285 6.625430e-02 3.312715e-02
[136,] 0.96053812 7.892377e-02 3.946188e-02
[137,] 0.95308222 9.383556e-02 4.691778e-02
[138,] 0.94468366 1.106327e-01 5.531634e-02
[139,] 0.93598181 1.280364e-01 6.401819e-02
[140,] 0.94313194 1.137361e-01 5.686806e-02
[141,] 0.94781196 1.043761e-01 5.218804e-02
[142,] 0.94086731 1.182654e-01 5.913269e-02
[143,] 0.93791178 1.241764e-01 6.208822e-02
[144,] 0.97175460 5.649081e-02 2.824540e-02
[145,] 0.96681177 6.637647e-02 3.318823e-02
[146,] 0.96850037 6.299927e-02 3.149963e-02
[147,] 0.97293460 5.413080e-02 2.706540e-02
[148,] 0.98038902 3.922196e-02 1.961098e-02
[149,] 0.97616024 4.767953e-02 2.383976e-02
[150,] 1.00000000 2.903188e-10 1.451594e-10
[151,] 1.00000000 4.077619e-10 2.038810e-10
[152,] 1.00000000 5.959241e-10 2.979621e-10
[153,] 1.00000000 1.049399e-12 5.246994e-13
[154,] 1.00000000 1.915289e-12 9.576447e-13
[155,] 1.00000000 2.440555e-12 1.220277e-12
[156,] 1.00000000 1.005019e-12 5.025094e-13
[157,] 1.00000000 1.839182e-12 9.195910e-13
[158,] 1.00000000 4.210425e-13 2.105213e-13
[159,] 1.00000000 4.228749e-13 2.114375e-13
[160,] 1.00000000 8.306361e-13 4.153181e-13
[161,] 1.00000000 1.161026e-12 5.805132e-13
[162,] 1.00000000 2.237541e-12 1.118771e-12
[163,] 1.00000000 4.316315e-12 2.158157e-12
[164,] 1.00000000 8.147687e-12 4.073844e-12
[165,] 1.00000000 1.163602e-11 5.818008e-12
[166,] 1.00000000 5.601012e-12 2.800506e-12
[167,] 1.00000000 9.948785e-12 4.974392e-12
[168,] 1.00000000 1.595085e-11 7.975425e-12
[169,] 1.00000000 2.932847e-11 1.466424e-11
[170,] 1.00000000 2.009224e-11 1.004612e-11
[171,] 1.00000000 9.020067e-12 4.510033e-12
[172,] 1.00000000 8.976612e-12 4.488306e-12
[173,] 1.00000000 1.737714e-11 8.688568e-12
[174,] 1.00000000 3.317853e-11 1.658926e-11
[175,] 1.00000000 6.095648e-11 3.047824e-11
[176,] 1.00000000 1.141037e-10 5.705187e-11
[177,] 1.00000000 2.145664e-10 1.072832e-10
[178,] 1.00000000 3.779779e-10 1.889890e-10
[179,] 1.00000000 3.597501e-10 1.798750e-10
[180,] 1.00000000 6.282039e-10 3.141020e-10
[181,] 1.00000000 1.035737e-09 5.178685e-10
[182,] 1.00000000 1.860835e-09 9.304176e-10
[183,] 1.00000000 3.423648e-09 1.711824e-09
[184,] 1.00000000 2.585900e-09 1.292950e-09
[185,] 1.00000000 3.371845e-09 1.685923e-09
[186,] 1.00000000 5.660960e-09 2.830480e-09
[187,] 0.99999999 1.020887e-08 5.104433e-09
[188,] 0.99999999 1.544262e-08 7.721308e-09
[189,] 0.99999999 2.331967e-08 1.165983e-08
[190,] 0.99999998 4.156404e-08 2.078202e-08
[191,] 0.99999999 2.421032e-08 1.210516e-08
[192,] 0.99999998 4.182711e-08 2.091355e-08
[193,] 0.99999996 7.118463e-08 3.559231e-08
[194,] 0.99999994 1.177055e-07 5.885274e-08
[195,] 0.99999994 1.259116e-07 6.295579e-08
[196,] 0.99999989 2.216053e-07 1.108026e-07
[197,] 0.99999984 3.204967e-07 1.602484e-07
[198,] 0.99999983 3.335580e-07 1.667790e-07
[199,] 0.99999971 5.786435e-07 2.893218e-07
[200,] 0.99999958 8.473034e-07 4.236517e-07
[201,] 0.99999928 1.441320e-06 7.206600e-07
[202,] 0.99999899 2.023205e-06 1.011603e-06
[203,] 0.99999875 2.500473e-06 1.250237e-06
[204,] 0.99999820 3.599560e-06 1.799780e-06
[205,] 0.99999713 5.743565e-06 2.871783e-06
[206,] 0.99999521 9.578768e-06 4.789384e-06
[207,] 0.99999277 1.446904e-05 7.234521e-06
[208,] 0.99998822 2.356710e-05 1.178355e-05
[209,] 0.99998251 3.497789e-05 1.748894e-05
[210,] 0.99998099 3.801368e-05 1.900684e-05
[211,] 0.99996937 6.126793e-05 3.063396e-05
[212,] 0.99995175 9.649155e-05 4.824578e-05
[213,] 0.99992638 1.472333e-04 7.361664e-05
[214,] 0.99990098 1.980311e-04 9.901557e-05
[215,] 0.99984957 3.008553e-04 1.504276e-04
[216,] 0.99981410 3.718053e-04 1.859027e-04
[217,] 0.99971251 5.749843e-04 2.874922e-04
[218,] 0.99961834 7.633239e-04 3.816619e-04
[219,] 0.99949321 1.013585e-03 5.067926e-04
[220,] 0.99932357 1.352869e-03 6.764345e-04
[221,] 0.99898011 2.039779e-03 1.019890e-03
[222,] 0.99848608 3.027846e-03 1.513923e-03
[223,] 0.99807145 3.857097e-03 1.928549e-03
[224,] 0.99758878 4.822433e-03 2.411217e-03
[225,] 0.99703613 5.927739e-03 2.963870e-03
[226,] 0.99657935 6.841292e-03 3.420646e-03
[227,] 0.99809628 3.807430e-03 1.903715e-03
[228,] 0.99720978 5.580432e-03 2.790216e-03
[229,] 0.99670753 6.584943e-03 3.292471e-03
[230,] 0.99558430 8.831395e-03 4.415697e-03
[231,] 0.99444205 1.111590e-02 5.557951e-03
[232,] 1.00000000 7.085632e-09 3.542816e-09
[233,] 1.00000000 1.400350e-09 7.001748e-10
[234,] 1.00000000 2.434091e-09 1.217045e-09
[235,] 1.00000000 5.417018e-09 2.708509e-09
[236,] 0.99999999 1.204750e-08 6.023748e-09
[237,] 0.99999999 2.185850e-08 1.092925e-08
[238,] 0.99999998 4.836292e-08 2.418146e-08
[239,] 0.99999994 1.127146e-07 5.635731e-08
[240,] 0.99999990 1.913400e-07 9.566998e-08
[241,] 0.99999979 4.144847e-07 2.072424e-07
[242,] 0.99999964 7.289018e-07 3.644509e-07
[243,] 0.99999912 1.757393e-06 8.786964e-07
[244,] 0.99999913 1.743010e-06 8.715051e-07
[245,] 0.99999824 3.522337e-06 1.761169e-06
[246,] 0.99999638 7.234735e-06 3.617367e-06
[247,] 0.99999333 1.334286e-05 6.671431e-06
[248,] 0.99998555 2.889613e-05 1.444806e-05
[249,] 0.99997434 5.132763e-05 2.566381e-05
[250,] 0.99996735 6.530205e-05 3.265102e-05
[251,] 0.99993446 1.310873e-04 6.554365e-05
[252,] 0.99995784 8.431670e-05 4.215835e-05
[253,] 0.99990186 1.962887e-04 9.814435e-05
[254,] 0.99984337 3.132573e-04 1.566287e-04
[255,] 0.99986340 2.731931e-04 1.365966e-04
[256,] 0.99967215 6.556952e-04 3.278476e-04
[257,] 0.99963582 7.283540e-04 3.641770e-04
[258,] 0.99968926 6.214802e-04 3.107401e-04
[259,] 0.99990826 1.834868e-04 9.174341e-05
[260,] 0.99979236 4.152727e-04 2.076363e-04
[261,] 0.99999059 1.882003e-05 9.410016e-06
[262,] 0.99996558 6.884041e-05 3.442021e-05
[263,] 0.99991039 1.792167e-04 8.960836e-05
[264,] 0.99977676 4.464705e-04 2.232353e-04
[265,] 0.99907941 1.841175e-03 9.205873e-04
[266,] 0.99611491 7.770188e-03 3.885094e-03
[267,] 0.98627355 2.745290e-02 1.372645e-02
[268,] 0.96996324 6.007353e-02 3.003676e-02
> postscript(file="/var/wessaorg/rcomp/tmp/1wjcr1355562373.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/25s5n1355562373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3bbpi1355562373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4k0se1355562373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5mnqh1355562373.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 = 289
Frequency = 1
1 2 3 4 5 6
-12.29426000 8.64973892 -16.38395356 13.78415543 -2.76109057 -7.51910384
7 8 9 10 11 12
-35.87881751 -6.52539705 17.82845910 -20.13575831 -25.63988639 3.90727553
13 14 15 16 17 18
24.07747326 -1.41141051 -29.40627904 -0.51570189 38.58685938 -21.40355170
19 20 21 22 23 24
33.61484831 -5.44483174 -7.55437950 -15.28089200 -34.65428930 -23.50610137
25 26 27 28 29 30
-14.20339102 18.83324199 30.70627508 27.41282776 3.62396341 -10.50456170
31 32 33 34 35 36
-31.49727790 -12.07853842 -6.11363285 -6.74365959 -8.54516051 -20.69627198
37 38 39 40 41 42
-4.80368942 -33.14038909 -5.86102536 4.76185903 4.18630978 -9.25854745
43 44 45 46 47 48
28.69487440 56.29132077 -33.36213792 -5.47449470 32.79676072 -1.99130393
49 50 51 52 53 54
-13.72017575 4.81404520 -45.64793261 2.55040125 0.73893867 -14.99446636
55 56 57 58 59 60
-18.25985739 7.91029765 3.68754047 153.48349840 -16.85600240 17.32543536
61 62 63 64 65 66
-41.37727707 -12.48947687 48.19020673 -2.10007892 44.70943066 23.11972609
67 68 69 70 71 72
-5.28951742 7.65400467 7.29563068 4.30744562 -7.70127999 -12.23585462
73 74 75 76 77 78
-4.28595537 48.05589580 46.65741611 -3.90841609 7.98295678 -21.21549479
79 80 81 82 83 84
-9.43981373 -10.90034446 -19.17014036 -6.29193988 -26.91095052 -5.52618942
85 86 87 88 89 90
24.57269020 -3.67551884 -5.97215055 -12.66463290 -20.82163310 0.13209372
91 92 93 94 95 96
20.02236301 -10.63144855 -19.03318629 -2.88321021 -0.19369407 29.86937885
97 98 99 100 101 102
-1.39284994 28.72943662 -1.33894846 3.07065155 -5.95541269 -19.09386167
103 104 105 106 107 108
-25.67212172 -27.35044839 -22.50151363 -8.46274321 17.78250388 -6.43656792
109 110 111 112 113 114
-11.60813001 13.44018617 -22.14848985 86.38343568 -15.91327147 1.53293375
115 116 117 118 119 120
49.13188251 -37.59235629 -2.11631137 -4.39863371 8.70896419 14.63803661
121 122 123 124 125 126
1.60524589 41.75413989 8.91628981 28.73753774 -23.89821665 -3.61527449
127 128 129 130 131 132
8.27187771 -0.08235356 -4.37713774 -6.79886533 5.30887398 9.67215764
133 134 135 136 137 138
-3.82105816 12.69926480 -12.41473270 -13.95838689 -0.79261398 -32.90290747
139 140 141 142 143 144
-2.08228568 -2.17239587 15.71255382 12.80408712 -11.83081897 3.72926614
145 146 147 148 149 150
-20.91259249 -7.77864685 5.68110362 -7.45550024 7.50672259 -30.58224690
151 152 153 154 155 156
-27.45282944 -11.14564749 17.58539373 -50.10673941 1.46588816 -17.37402077
157 158 159 160 161 162
-26.93453415 -29.58023474 -7.80752041 162.03300710 14.69776808 -19.56242835
163 164 165 166 167 168
68.35952531 -6.83804518 9.20125945 -24.83236932 -1.71293879 36.16920587
169 170 171 172 173 174
22.76822901 -0.91986088 -19.02214725 3.13450087 -4.46455027 -2.66278927
175 176 177 178 179 180
-1.35709547 -28.79153600 -4.52120430 -9.36461279 12.18361581 -21.66410556
181 182 183 184 185 186
27.70001033 23.94719737 -3.79629303 3.40426177 11.88204719 -6.64195855
187 188 189 190 191 192
-5.22031511 -9.76356005 -18.27998302 -6.95814767 -7.19310829 -5.86643581
193 194 195 196 197 198
0.69361513 -15.94619764 -11.77851461 -1.89152775 -6.14924230 -13.95984652
199 200 201 202 203 204
7.39079858 1.91145846 24.78380462 -7.87833860 11.70427337 11.22919864
205 206 207 208 209 210
-14.76531331 -2.58140443 11.59155850 -12.94639004 1.58762541 -9.83788766
211 212 213 214 215 216
-2.15204547 -6.83185924 -15.41558928 10.42010246 -5.00811739 -5.75276446
217 218 219 220 221 222
8.01891473 -4.93127830 -10.60529995 -18.19877556 -5.41627072 5.70255153
223 224 225 226 227 228
1.96836912 -11.17335477 -9.14904333 22.54264082 1.50822452 9.58410229
229 230 231 232 233 234
12.83974707 -10.30673049 -3.54391702 0.96995098 10.07149231 -10.46115936
235 236 237 238 239 240
15.95830303 17.55871898 -19.07807123 -7.75810641 -15.36539407 4.53931631
241 242 243 244 245 246
-6.74526695 97.20777189 24.05512905 1.45236951 -3.70242313 -11.34100561
247 248 249 250 251 252
10.80887586 2.07695883 4.44787128 2.85392829 14.17329459 -7.40608962
253 254 255 256 257 258
-3.28588349 14.70077399 1.56055755 -1.54668759 -7.31995747 2.11549015
259 260 261 262 263 264
7.42846220 7.12244177 -5.41552461 -8.11978536 -1.64874740 -7.81403239
265 266 267 268 269 270
9.14630031 -9.36744094 9.63374250 19.87369032 -13.27353260 12.42193698
271 272 273 274 275 276
17.08120845 -5.88756758 -9.22720690 5.53433633 -1.29673279 -6.57167939
277 278 279 280 281 282
-7.28565843 -3.89776327 -7.93740182 -17.89271883 -18.33625874 1.94822557
283 284 285 286 287 288
-12.94693363 -8.18852523 -5.90202680 -6.91137916 -11.10832177 5.00692379
289
-23.07163479
> postscript(file="/var/wessaorg/rcomp/tmp/6sn5a1355562373.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 = 289
Frequency = 1
lag(myerror, k = 1) myerror
0 -12.29426000 NA
1 8.64973892 -12.29426000
2 -16.38395356 8.64973892
3 13.78415543 -16.38395356
4 -2.76109057 13.78415543
5 -7.51910384 -2.76109057
6 -35.87881751 -7.51910384
7 -6.52539705 -35.87881751
8 17.82845910 -6.52539705
9 -20.13575831 17.82845910
10 -25.63988639 -20.13575831
11 3.90727553 -25.63988639
12 24.07747326 3.90727553
13 -1.41141051 24.07747326
14 -29.40627904 -1.41141051
15 -0.51570189 -29.40627904
16 38.58685938 -0.51570189
17 -21.40355170 38.58685938
18 33.61484831 -21.40355170
19 -5.44483174 33.61484831
20 -7.55437950 -5.44483174
21 -15.28089200 -7.55437950
22 -34.65428930 -15.28089200
23 -23.50610137 -34.65428930
24 -14.20339102 -23.50610137
25 18.83324199 -14.20339102
26 30.70627508 18.83324199
27 27.41282776 30.70627508
28 3.62396341 27.41282776
29 -10.50456170 3.62396341
30 -31.49727790 -10.50456170
31 -12.07853842 -31.49727790
32 -6.11363285 -12.07853842
33 -6.74365959 -6.11363285
34 -8.54516051 -6.74365959
35 -20.69627198 -8.54516051
36 -4.80368942 -20.69627198
37 -33.14038909 -4.80368942
38 -5.86102536 -33.14038909
39 4.76185903 -5.86102536
40 4.18630978 4.76185903
41 -9.25854745 4.18630978
42 28.69487440 -9.25854745
43 56.29132077 28.69487440
44 -33.36213792 56.29132077
45 -5.47449470 -33.36213792
46 32.79676072 -5.47449470
47 -1.99130393 32.79676072
48 -13.72017575 -1.99130393
49 4.81404520 -13.72017575
50 -45.64793261 4.81404520
51 2.55040125 -45.64793261
52 0.73893867 2.55040125
53 -14.99446636 0.73893867
54 -18.25985739 -14.99446636
55 7.91029765 -18.25985739
56 3.68754047 7.91029765
57 153.48349840 3.68754047
58 -16.85600240 153.48349840
59 17.32543536 -16.85600240
60 -41.37727707 17.32543536
61 -12.48947687 -41.37727707
62 48.19020673 -12.48947687
63 -2.10007892 48.19020673
64 44.70943066 -2.10007892
65 23.11972609 44.70943066
66 -5.28951742 23.11972609
67 7.65400467 -5.28951742
68 7.29563068 7.65400467
69 4.30744562 7.29563068
70 -7.70127999 4.30744562
71 -12.23585462 -7.70127999
72 -4.28595537 -12.23585462
73 48.05589580 -4.28595537
74 46.65741611 48.05589580
75 -3.90841609 46.65741611
76 7.98295678 -3.90841609
77 -21.21549479 7.98295678
78 -9.43981373 -21.21549479
79 -10.90034446 -9.43981373
80 -19.17014036 -10.90034446
81 -6.29193988 -19.17014036
82 -26.91095052 -6.29193988
83 -5.52618942 -26.91095052
84 24.57269020 -5.52618942
85 -3.67551884 24.57269020
86 -5.97215055 -3.67551884
87 -12.66463290 -5.97215055
88 -20.82163310 -12.66463290
89 0.13209372 -20.82163310
90 20.02236301 0.13209372
91 -10.63144855 20.02236301
92 -19.03318629 -10.63144855
93 -2.88321021 -19.03318629
94 -0.19369407 -2.88321021
95 29.86937885 -0.19369407
96 -1.39284994 29.86937885
97 28.72943662 -1.39284994
98 -1.33894846 28.72943662
99 3.07065155 -1.33894846
100 -5.95541269 3.07065155
101 -19.09386167 -5.95541269
102 -25.67212172 -19.09386167
103 -27.35044839 -25.67212172
104 -22.50151363 -27.35044839
105 -8.46274321 -22.50151363
106 17.78250388 -8.46274321
107 -6.43656792 17.78250388
108 -11.60813001 -6.43656792
109 13.44018617 -11.60813001
110 -22.14848985 13.44018617
111 86.38343568 -22.14848985
112 -15.91327147 86.38343568
113 1.53293375 -15.91327147
114 49.13188251 1.53293375
115 -37.59235629 49.13188251
116 -2.11631137 -37.59235629
117 -4.39863371 -2.11631137
118 8.70896419 -4.39863371
119 14.63803661 8.70896419
120 1.60524589 14.63803661
121 41.75413989 1.60524589
122 8.91628981 41.75413989
123 28.73753774 8.91628981
124 -23.89821665 28.73753774
125 -3.61527449 -23.89821665
126 8.27187771 -3.61527449
127 -0.08235356 8.27187771
128 -4.37713774 -0.08235356
129 -6.79886533 -4.37713774
130 5.30887398 -6.79886533
131 9.67215764 5.30887398
132 -3.82105816 9.67215764
133 12.69926480 -3.82105816
134 -12.41473270 12.69926480
135 -13.95838689 -12.41473270
136 -0.79261398 -13.95838689
137 -32.90290747 -0.79261398
138 -2.08228568 -32.90290747
139 -2.17239587 -2.08228568
140 15.71255382 -2.17239587
141 12.80408712 15.71255382
142 -11.83081897 12.80408712
143 3.72926614 -11.83081897
144 -20.91259249 3.72926614
145 -7.77864685 -20.91259249
146 5.68110362 -7.77864685
147 -7.45550024 5.68110362
148 7.50672259 -7.45550024
149 -30.58224690 7.50672259
150 -27.45282944 -30.58224690
151 -11.14564749 -27.45282944
152 17.58539373 -11.14564749
153 -50.10673941 17.58539373
154 1.46588816 -50.10673941
155 -17.37402077 1.46588816
156 -26.93453415 -17.37402077
157 -29.58023474 -26.93453415
158 -7.80752041 -29.58023474
159 162.03300710 -7.80752041
160 14.69776808 162.03300710
161 -19.56242835 14.69776808
162 68.35952531 -19.56242835
163 -6.83804518 68.35952531
164 9.20125945 -6.83804518
165 -24.83236932 9.20125945
166 -1.71293879 -24.83236932
167 36.16920587 -1.71293879
168 22.76822901 36.16920587
169 -0.91986088 22.76822901
170 -19.02214725 -0.91986088
171 3.13450087 -19.02214725
172 -4.46455027 3.13450087
173 -2.66278927 -4.46455027
174 -1.35709547 -2.66278927
175 -28.79153600 -1.35709547
176 -4.52120430 -28.79153600
177 -9.36461279 -4.52120430
178 12.18361581 -9.36461279
179 -21.66410556 12.18361581
180 27.70001033 -21.66410556
181 23.94719737 27.70001033
182 -3.79629303 23.94719737
183 3.40426177 -3.79629303
184 11.88204719 3.40426177
185 -6.64195855 11.88204719
186 -5.22031511 -6.64195855
187 -9.76356005 -5.22031511
188 -18.27998302 -9.76356005
189 -6.95814767 -18.27998302
190 -7.19310829 -6.95814767
191 -5.86643581 -7.19310829
192 0.69361513 -5.86643581
193 -15.94619764 0.69361513
194 -11.77851461 -15.94619764
195 -1.89152775 -11.77851461
196 -6.14924230 -1.89152775
197 -13.95984652 -6.14924230
198 7.39079858 -13.95984652
199 1.91145846 7.39079858
200 24.78380462 1.91145846
201 -7.87833860 24.78380462
202 11.70427337 -7.87833860
203 11.22919864 11.70427337
204 -14.76531331 11.22919864
205 -2.58140443 -14.76531331
206 11.59155850 -2.58140443
207 -12.94639004 11.59155850
208 1.58762541 -12.94639004
209 -9.83788766 1.58762541
210 -2.15204547 -9.83788766
211 -6.83185924 -2.15204547
212 -15.41558928 -6.83185924
213 10.42010246 -15.41558928
214 -5.00811739 10.42010246
215 -5.75276446 -5.00811739
216 8.01891473 -5.75276446
217 -4.93127830 8.01891473
218 -10.60529995 -4.93127830
219 -18.19877556 -10.60529995
220 -5.41627072 -18.19877556
221 5.70255153 -5.41627072
222 1.96836912 5.70255153
223 -11.17335477 1.96836912
224 -9.14904333 -11.17335477
225 22.54264082 -9.14904333
226 1.50822452 22.54264082
227 9.58410229 1.50822452
228 12.83974707 9.58410229
229 -10.30673049 12.83974707
230 -3.54391702 -10.30673049
231 0.96995098 -3.54391702
232 10.07149231 0.96995098
233 -10.46115936 10.07149231
234 15.95830303 -10.46115936
235 17.55871898 15.95830303
236 -19.07807123 17.55871898
237 -7.75810641 -19.07807123
238 -15.36539407 -7.75810641
239 4.53931631 -15.36539407
240 -6.74526695 4.53931631
241 97.20777189 -6.74526695
242 24.05512905 97.20777189
243 1.45236951 24.05512905
244 -3.70242313 1.45236951
245 -11.34100561 -3.70242313
246 10.80887586 -11.34100561
247 2.07695883 10.80887586
248 4.44787128 2.07695883
249 2.85392829 4.44787128
250 14.17329459 2.85392829
251 -7.40608962 14.17329459
252 -3.28588349 -7.40608962
253 14.70077399 -3.28588349
254 1.56055755 14.70077399
255 -1.54668759 1.56055755
256 -7.31995747 -1.54668759
257 2.11549015 -7.31995747
258 7.42846220 2.11549015
259 7.12244177 7.42846220
260 -5.41552461 7.12244177
261 -8.11978536 -5.41552461
262 -1.64874740 -8.11978536
263 -7.81403239 -1.64874740
264 9.14630031 -7.81403239
265 -9.36744094 9.14630031
266 9.63374250 -9.36744094
267 19.87369032 9.63374250
268 -13.27353260 19.87369032
269 12.42193698 -13.27353260
270 17.08120845 12.42193698
271 -5.88756758 17.08120845
272 -9.22720690 -5.88756758
273 5.53433633 -9.22720690
274 -1.29673279 5.53433633
275 -6.57167939 -1.29673279
276 -7.28565843 -6.57167939
277 -3.89776327 -7.28565843
278 -7.93740182 -3.89776327
279 -17.89271883 -7.93740182
280 -18.33625874 -17.89271883
281 1.94822557 -18.33625874
282 -12.94693363 1.94822557
283 -8.18852523 -12.94693363
284 -5.90202680 -8.18852523
285 -6.91137916 -5.90202680
286 -11.10832177 -6.91137916
287 5.00692379 -11.10832177
288 -23.07163479 5.00692379
289 NA -23.07163479
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 8.64973892 -12.29426000
[2,] -16.38395356 8.64973892
[3,] 13.78415543 -16.38395356
[4,] -2.76109057 13.78415543
[5,] -7.51910384 -2.76109057
[6,] -35.87881751 -7.51910384
[7,] -6.52539705 -35.87881751
[8,] 17.82845910 -6.52539705
[9,] -20.13575831 17.82845910
[10,] -25.63988639 -20.13575831
[11,] 3.90727553 -25.63988639
[12,] 24.07747326 3.90727553
[13,] -1.41141051 24.07747326
[14,] -29.40627904 -1.41141051
[15,] -0.51570189 -29.40627904
[16,] 38.58685938 -0.51570189
[17,] -21.40355170 38.58685938
[18,] 33.61484831 -21.40355170
[19,] -5.44483174 33.61484831
[20,] -7.55437950 -5.44483174
[21,] -15.28089200 -7.55437950
[22,] -34.65428930 -15.28089200
[23,] -23.50610137 -34.65428930
[24,] -14.20339102 -23.50610137
[25,] 18.83324199 -14.20339102
[26,] 30.70627508 18.83324199
[27,] 27.41282776 30.70627508
[28,] 3.62396341 27.41282776
[29,] -10.50456170 3.62396341
[30,] -31.49727790 -10.50456170
[31,] -12.07853842 -31.49727790
[32,] -6.11363285 -12.07853842
[33,] -6.74365959 -6.11363285
[34,] -8.54516051 -6.74365959
[35,] -20.69627198 -8.54516051
[36,] -4.80368942 -20.69627198
[37,] -33.14038909 -4.80368942
[38,] -5.86102536 -33.14038909
[39,] 4.76185903 -5.86102536
[40,] 4.18630978 4.76185903
[41,] -9.25854745 4.18630978
[42,] 28.69487440 -9.25854745
[43,] 56.29132077 28.69487440
[44,] -33.36213792 56.29132077
[45,] -5.47449470 -33.36213792
[46,] 32.79676072 -5.47449470
[47,] -1.99130393 32.79676072
[48,] -13.72017575 -1.99130393
[49,] 4.81404520 -13.72017575
[50,] -45.64793261 4.81404520
[51,] 2.55040125 -45.64793261
[52,] 0.73893867 2.55040125
[53,] -14.99446636 0.73893867
[54,] -18.25985739 -14.99446636
[55,] 7.91029765 -18.25985739
[56,] 3.68754047 7.91029765
[57,] 153.48349840 3.68754047
[58,] -16.85600240 153.48349840
[59,] 17.32543536 -16.85600240
[60,] -41.37727707 17.32543536
[61,] -12.48947687 -41.37727707
[62,] 48.19020673 -12.48947687
[63,] -2.10007892 48.19020673
[64,] 44.70943066 -2.10007892
[65,] 23.11972609 44.70943066
[66,] -5.28951742 23.11972609
[67,] 7.65400467 -5.28951742
[68,] 7.29563068 7.65400467
[69,] 4.30744562 7.29563068
[70,] -7.70127999 4.30744562
[71,] -12.23585462 -7.70127999
[72,] -4.28595537 -12.23585462
[73,] 48.05589580 -4.28595537
[74,] 46.65741611 48.05589580
[75,] -3.90841609 46.65741611
[76,] 7.98295678 -3.90841609
[77,] -21.21549479 7.98295678
[78,] -9.43981373 -21.21549479
[79,] -10.90034446 -9.43981373
[80,] -19.17014036 -10.90034446
[81,] -6.29193988 -19.17014036
[82,] -26.91095052 -6.29193988
[83,] -5.52618942 -26.91095052
[84,] 24.57269020 -5.52618942
[85,] -3.67551884 24.57269020
[86,] -5.97215055 -3.67551884
[87,] -12.66463290 -5.97215055
[88,] -20.82163310 -12.66463290
[89,] 0.13209372 -20.82163310
[90,] 20.02236301 0.13209372
[91,] -10.63144855 20.02236301
[92,] -19.03318629 -10.63144855
[93,] -2.88321021 -19.03318629
[94,] -0.19369407 -2.88321021
[95,] 29.86937885 -0.19369407
[96,] -1.39284994 29.86937885
[97,] 28.72943662 -1.39284994
[98,] -1.33894846 28.72943662
[99,] 3.07065155 -1.33894846
[100,] -5.95541269 3.07065155
[101,] -19.09386167 -5.95541269
[102,] -25.67212172 -19.09386167
[103,] -27.35044839 -25.67212172
[104,] -22.50151363 -27.35044839
[105,] -8.46274321 -22.50151363
[106,] 17.78250388 -8.46274321
[107,] -6.43656792 17.78250388
[108,] -11.60813001 -6.43656792
[109,] 13.44018617 -11.60813001
[110,] -22.14848985 13.44018617
[111,] 86.38343568 -22.14848985
[112,] -15.91327147 86.38343568
[113,] 1.53293375 -15.91327147
[114,] 49.13188251 1.53293375
[115,] -37.59235629 49.13188251
[116,] -2.11631137 -37.59235629
[117,] -4.39863371 -2.11631137
[118,] 8.70896419 -4.39863371
[119,] 14.63803661 8.70896419
[120,] 1.60524589 14.63803661
[121,] 41.75413989 1.60524589
[122,] 8.91628981 41.75413989
[123,] 28.73753774 8.91628981
[124,] -23.89821665 28.73753774
[125,] -3.61527449 -23.89821665
[126,] 8.27187771 -3.61527449
[127,] -0.08235356 8.27187771
[128,] -4.37713774 -0.08235356
[129,] -6.79886533 -4.37713774
[130,] 5.30887398 -6.79886533
[131,] 9.67215764 5.30887398
[132,] -3.82105816 9.67215764
[133,] 12.69926480 -3.82105816
[134,] -12.41473270 12.69926480
[135,] -13.95838689 -12.41473270
[136,] -0.79261398 -13.95838689
[137,] -32.90290747 -0.79261398
[138,] -2.08228568 -32.90290747
[139,] -2.17239587 -2.08228568
[140,] 15.71255382 -2.17239587
[141,] 12.80408712 15.71255382
[142,] -11.83081897 12.80408712
[143,] 3.72926614 -11.83081897
[144,] -20.91259249 3.72926614
[145,] -7.77864685 -20.91259249
[146,] 5.68110362 -7.77864685
[147,] -7.45550024 5.68110362
[148,] 7.50672259 -7.45550024
[149,] -30.58224690 7.50672259
[150,] -27.45282944 -30.58224690
[151,] -11.14564749 -27.45282944
[152,] 17.58539373 -11.14564749
[153,] -50.10673941 17.58539373
[154,] 1.46588816 -50.10673941
[155,] -17.37402077 1.46588816
[156,] -26.93453415 -17.37402077
[157,] -29.58023474 -26.93453415
[158,] -7.80752041 -29.58023474
[159,] 162.03300710 -7.80752041
[160,] 14.69776808 162.03300710
[161,] -19.56242835 14.69776808
[162,] 68.35952531 -19.56242835
[163,] -6.83804518 68.35952531
[164,] 9.20125945 -6.83804518
[165,] -24.83236932 9.20125945
[166,] -1.71293879 -24.83236932
[167,] 36.16920587 -1.71293879
[168,] 22.76822901 36.16920587
[169,] -0.91986088 22.76822901
[170,] -19.02214725 -0.91986088
[171,] 3.13450087 -19.02214725
[172,] -4.46455027 3.13450087
[173,] -2.66278927 -4.46455027
[174,] -1.35709547 -2.66278927
[175,] -28.79153600 -1.35709547
[176,] -4.52120430 -28.79153600
[177,] -9.36461279 -4.52120430
[178,] 12.18361581 -9.36461279
[179,] -21.66410556 12.18361581
[180,] 27.70001033 -21.66410556
[181,] 23.94719737 27.70001033
[182,] -3.79629303 23.94719737
[183,] 3.40426177 -3.79629303
[184,] 11.88204719 3.40426177
[185,] -6.64195855 11.88204719
[186,] -5.22031511 -6.64195855
[187,] -9.76356005 -5.22031511
[188,] -18.27998302 -9.76356005
[189,] -6.95814767 -18.27998302
[190,] -7.19310829 -6.95814767
[191,] -5.86643581 -7.19310829
[192,] 0.69361513 -5.86643581
[193,] -15.94619764 0.69361513
[194,] -11.77851461 -15.94619764
[195,] -1.89152775 -11.77851461
[196,] -6.14924230 -1.89152775
[197,] -13.95984652 -6.14924230
[198,] 7.39079858 -13.95984652
[199,] 1.91145846 7.39079858
[200,] 24.78380462 1.91145846
[201,] -7.87833860 24.78380462
[202,] 11.70427337 -7.87833860
[203,] 11.22919864 11.70427337
[204,] -14.76531331 11.22919864
[205,] -2.58140443 -14.76531331
[206,] 11.59155850 -2.58140443
[207,] -12.94639004 11.59155850
[208,] 1.58762541 -12.94639004
[209,] -9.83788766 1.58762541
[210,] -2.15204547 -9.83788766
[211,] -6.83185924 -2.15204547
[212,] -15.41558928 -6.83185924
[213,] 10.42010246 -15.41558928
[214,] -5.00811739 10.42010246
[215,] -5.75276446 -5.00811739
[216,] 8.01891473 -5.75276446
[217,] -4.93127830 8.01891473
[218,] -10.60529995 -4.93127830
[219,] -18.19877556 -10.60529995
[220,] -5.41627072 -18.19877556
[221,] 5.70255153 -5.41627072
[222,] 1.96836912 5.70255153
[223,] -11.17335477 1.96836912
[224,] -9.14904333 -11.17335477
[225,] 22.54264082 -9.14904333
[226,] 1.50822452 22.54264082
[227,] 9.58410229 1.50822452
[228,] 12.83974707 9.58410229
[229,] -10.30673049 12.83974707
[230,] -3.54391702 -10.30673049
[231,] 0.96995098 -3.54391702
[232,] 10.07149231 0.96995098
[233,] -10.46115936 10.07149231
[234,] 15.95830303 -10.46115936
[235,] 17.55871898 15.95830303
[236,] -19.07807123 17.55871898
[237,] -7.75810641 -19.07807123
[238,] -15.36539407 -7.75810641
[239,] 4.53931631 -15.36539407
[240,] -6.74526695 4.53931631
[241,] 97.20777189 -6.74526695
[242,] 24.05512905 97.20777189
[243,] 1.45236951 24.05512905
[244,] -3.70242313 1.45236951
[245,] -11.34100561 -3.70242313
[246,] 10.80887586 -11.34100561
[247,] 2.07695883 10.80887586
[248,] 4.44787128 2.07695883
[249,] 2.85392829 4.44787128
[250,] 14.17329459 2.85392829
[251,] -7.40608962 14.17329459
[252,] -3.28588349 -7.40608962
[253,] 14.70077399 -3.28588349
[254,] 1.56055755 14.70077399
[255,] -1.54668759 1.56055755
[256,] -7.31995747 -1.54668759
[257,] 2.11549015 -7.31995747
[258,] 7.42846220 2.11549015
[259,] 7.12244177 7.42846220
[260,] -5.41552461 7.12244177
[261,] -8.11978536 -5.41552461
[262,] -1.64874740 -8.11978536
[263,] -7.81403239 -1.64874740
[264,] 9.14630031 -7.81403239
[265,] -9.36744094 9.14630031
[266,] 9.63374250 -9.36744094
[267,] 19.87369032 9.63374250
[268,] -13.27353260 19.87369032
[269,] 12.42193698 -13.27353260
[270,] 17.08120845 12.42193698
[271,] -5.88756758 17.08120845
[272,] -9.22720690 -5.88756758
[273,] 5.53433633 -9.22720690
[274,] -1.29673279 5.53433633
[275,] -6.57167939 -1.29673279
[276,] -7.28565843 -6.57167939
[277,] -3.89776327 -7.28565843
[278,] -7.93740182 -3.89776327
[279,] -17.89271883 -7.93740182
[280,] -18.33625874 -17.89271883
[281,] 1.94822557 -18.33625874
[282,] -12.94693363 1.94822557
[283,] -8.18852523 -12.94693363
[284,] -5.90202680 -8.18852523
[285,] -6.91137916 -5.90202680
[286,] -11.10832177 -6.91137916
[287,] 5.00692379 -11.10832177
[288,] -23.07163479 5.00692379
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 8.64973892 -12.29426000
2 -16.38395356 8.64973892
3 13.78415543 -16.38395356
4 -2.76109057 13.78415543
5 -7.51910384 -2.76109057
6 -35.87881751 -7.51910384
7 -6.52539705 -35.87881751
8 17.82845910 -6.52539705
9 -20.13575831 17.82845910
10 -25.63988639 -20.13575831
11 3.90727553 -25.63988639
12 24.07747326 3.90727553
13 -1.41141051 24.07747326
14 -29.40627904 -1.41141051
15 -0.51570189 -29.40627904
16 38.58685938 -0.51570189
17 -21.40355170 38.58685938
18 33.61484831 -21.40355170
19 -5.44483174 33.61484831
20 -7.55437950 -5.44483174
21 -15.28089200 -7.55437950
22 -34.65428930 -15.28089200
23 -23.50610137 -34.65428930
24 -14.20339102 -23.50610137
25 18.83324199 -14.20339102
26 30.70627508 18.83324199
27 27.41282776 30.70627508
28 3.62396341 27.41282776
29 -10.50456170 3.62396341
30 -31.49727790 -10.50456170
31 -12.07853842 -31.49727790
32 -6.11363285 -12.07853842
33 -6.74365959 -6.11363285
34 -8.54516051 -6.74365959
35 -20.69627198 -8.54516051
36 -4.80368942 -20.69627198
37 -33.14038909 -4.80368942
38 -5.86102536 -33.14038909
39 4.76185903 -5.86102536
40 4.18630978 4.76185903
41 -9.25854745 4.18630978
42 28.69487440 -9.25854745
43 56.29132077 28.69487440
44 -33.36213792 56.29132077
45 -5.47449470 -33.36213792
46 32.79676072 -5.47449470
47 -1.99130393 32.79676072
48 -13.72017575 -1.99130393
49 4.81404520 -13.72017575
50 -45.64793261 4.81404520
51 2.55040125 -45.64793261
52 0.73893867 2.55040125
53 -14.99446636 0.73893867
54 -18.25985739 -14.99446636
55 7.91029765 -18.25985739
56 3.68754047 7.91029765
57 153.48349840 3.68754047
58 -16.85600240 153.48349840
59 17.32543536 -16.85600240
60 -41.37727707 17.32543536
61 -12.48947687 -41.37727707
62 48.19020673 -12.48947687
63 -2.10007892 48.19020673
64 44.70943066 -2.10007892
65 23.11972609 44.70943066
66 -5.28951742 23.11972609
67 7.65400467 -5.28951742
68 7.29563068 7.65400467
69 4.30744562 7.29563068
70 -7.70127999 4.30744562
71 -12.23585462 -7.70127999
72 -4.28595537 -12.23585462
73 48.05589580 -4.28595537
74 46.65741611 48.05589580
75 -3.90841609 46.65741611
76 7.98295678 -3.90841609
77 -21.21549479 7.98295678
78 -9.43981373 -21.21549479
79 -10.90034446 -9.43981373
80 -19.17014036 -10.90034446
81 -6.29193988 -19.17014036
82 -26.91095052 -6.29193988
83 -5.52618942 -26.91095052
84 24.57269020 -5.52618942
85 -3.67551884 24.57269020
86 -5.97215055 -3.67551884
87 -12.66463290 -5.97215055
88 -20.82163310 -12.66463290
89 0.13209372 -20.82163310
90 20.02236301 0.13209372
91 -10.63144855 20.02236301
92 -19.03318629 -10.63144855
93 -2.88321021 -19.03318629
94 -0.19369407 -2.88321021
95 29.86937885 -0.19369407
96 -1.39284994 29.86937885
97 28.72943662 -1.39284994
98 -1.33894846 28.72943662
99 3.07065155 -1.33894846
100 -5.95541269 3.07065155
101 -19.09386167 -5.95541269
102 -25.67212172 -19.09386167
103 -27.35044839 -25.67212172
104 -22.50151363 -27.35044839
105 -8.46274321 -22.50151363
106 17.78250388 -8.46274321
107 -6.43656792 17.78250388
108 -11.60813001 -6.43656792
109 13.44018617 -11.60813001
110 -22.14848985 13.44018617
111 86.38343568 -22.14848985
112 -15.91327147 86.38343568
113 1.53293375 -15.91327147
114 49.13188251 1.53293375
115 -37.59235629 49.13188251
116 -2.11631137 -37.59235629
117 -4.39863371 -2.11631137
118 8.70896419 -4.39863371
119 14.63803661 8.70896419
120 1.60524589 14.63803661
121 41.75413989 1.60524589
122 8.91628981 41.75413989
123 28.73753774 8.91628981
124 -23.89821665 28.73753774
125 -3.61527449 -23.89821665
126 8.27187771 -3.61527449
127 -0.08235356 8.27187771
128 -4.37713774 -0.08235356
129 -6.79886533 -4.37713774
130 5.30887398 -6.79886533
131 9.67215764 5.30887398
132 -3.82105816 9.67215764
133 12.69926480 -3.82105816
134 -12.41473270 12.69926480
135 -13.95838689 -12.41473270
136 -0.79261398 -13.95838689
137 -32.90290747 -0.79261398
138 -2.08228568 -32.90290747
139 -2.17239587 -2.08228568
140 15.71255382 -2.17239587
141 12.80408712 15.71255382
142 -11.83081897 12.80408712
143 3.72926614 -11.83081897
144 -20.91259249 3.72926614
145 -7.77864685 -20.91259249
146 5.68110362 -7.77864685
147 -7.45550024 5.68110362
148 7.50672259 -7.45550024
149 -30.58224690 7.50672259
150 -27.45282944 -30.58224690
151 -11.14564749 -27.45282944
152 17.58539373 -11.14564749
153 -50.10673941 17.58539373
154 1.46588816 -50.10673941
155 -17.37402077 1.46588816
156 -26.93453415 -17.37402077
157 -29.58023474 -26.93453415
158 -7.80752041 -29.58023474
159 162.03300710 -7.80752041
160 14.69776808 162.03300710
161 -19.56242835 14.69776808
162 68.35952531 -19.56242835
163 -6.83804518 68.35952531
164 9.20125945 -6.83804518
165 -24.83236932 9.20125945
166 -1.71293879 -24.83236932
167 36.16920587 -1.71293879
168 22.76822901 36.16920587
169 -0.91986088 22.76822901
170 -19.02214725 -0.91986088
171 3.13450087 -19.02214725
172 -4.46455027 3.13450087
173 -2.66278927 -4.46455027
174 -1.35709547 -2.66278927
175 -28.79153600 -1.35709547
176 -4.52120430 -28.79153600
177 -9.36461279 -4.52120430
178 12.18361581 -9.36461279
179 -21.66410556 12.18361581
180 27.70001033 -21.66410556
181 23.94719737 27.70001033
182 -3.79629303 23.94719737
183 3.40426177 -3.79629303
184 11.88204719 3.40426177
185 -6.64195855 11.88204719
186 -5.22031511 -6.64195855
187 -9.76356005 -5.22031511
188 -18.27998302 -9.76356005
189 -6.95814767 -18.27998302
190 -7.19310829 -6.95814767
191 -5.86643581 -7.19310829
192 0.69361513 -5.86643581
193 -15.94619764 0.69361513
194 -11.77851461 -15.94619764
195 -1.89152775 -11.77851461
196 -6.14924230 -1.89152775
197 -13.95984652 -6.14924230
198 7.39079858 -13.95984652
199 1.91145846 7.39079858
200 24.78380462 1.91145846
201 -7.87833860 24.78380462
202 11.70427337 -7.87833860
203 11.22919864 11.70427337
204 -14.76531331 11.22919864
205 -2.58140443 -14.76531331
206 11.59155850 -2.58140443
207 -12.94639004 11.59155850
208 1.58762541 -12.94639004
209 -9.83788766 1.58762541
210 -2.15204547 -9.83788766
211 -6.83185924 -2.15204547
212 -15.41558928 -6.83185924
213 10.42010246 -15.41558928
214 -5.00811739 10.42010246
215 -5.75276446 -5.00811739
216 8.01891473 -5.75276446
217 -4.93127830 8.01891473
218 -10.60529995 -4.93127830
219 -18.19877556 -10.60529995
220 -5.41627072 -18.19877556
221 5.70255153 -5.41627072
222 1.96836912 5.70255153
223 -11.17335477 1.96836912
224 -9.14904333 -11.17335477
225 22.54264082 -9.14904333
226 1.50822452 22.54264082
227 9.58410229 1.50822452
228 12.83974707 9.58410229
229 -10.30673049 12.83974707
230 -3.54391702 -10.30673049
231 0.96995098 -3.54391702
232 10.07149231 0.96995098
233 -10.46115936 10.07149231
234 15.95830303 -10.46115936
235 17.55871898 15.95830303
236 -19.07807123 17.55871898
237 -7.75810641 -19.07807123
238 -15.36539407 -7.75810641
239 4.53931631 -15.36539407
240 -6.74526695 4.53931631
241 97.20777189 -6.74526695
242 24.05512905 97.20777189
243 1.45236951 24.05512905
244 -3.70242313 1.45236951
245 -11.34100561 -3.70242313
246 10.80887586 -11.34100561
247 2.07695883 10.80887586
248 4.44787128 2.07695883
249 2.85392829 4.44787128
250 14.17329459 2.85392829
251 -7.40608962 14.17329459
252 -3.28588349 -7.40608962
253 14.70077399 -3.28588349
254 1.56055755 14.70077399
255 -1.54668759 1.56055755
256 -7.31995747 -1.54668759
257 2.11549015 -7.31995747
258 7.42846220 2.11549015
259 7.12244177 7.42846220
260 -5.41552461 7.12244177
261 -8.11978536 -5.41552461
262 -1.64874740 -8.11978536
263 -7.81403239 -1.64874740
264 9.14630031 -7.81403239
265 -9.36744094 9.14630031
266 9.63374250 -9.36744094
267 19.87369032 9.63374250
268 -13.27353260 19.87369032
269 12.42193698 -13.27353260
270 17.08120845 12.42193698
271 -5.88756758 17.08120845
272 -9.22720690 -5.88756758
273 5.53433633 -9.22720690
274 -1.29673279 5.53433633
275 -6.57167939 -1.29673279
276 -7.28565843 -6.57167939
277 -3.89776327 -7.28565843
278 -7.93740182 -3.89776327
279 -17.89271883 -7.93740182
280 -18.33625874 -17.89271883
281 1.94822557 -18.33625874
282 -12.94693363 1.94822557
283 -8.18852523 -12.94693363
284 -5.90202680 -8.18852523
285 -6.91137916 -5.90202680
286 -11.10832177 -6.91137916
287 5.00692379 -11.10832177
288 -23.07163479 5.00692379
> 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/7h2na1355562373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/867qz1355562373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/999f31355562373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10c3jk1355562373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11i4j81355562373.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/12rcdj1355562373.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/1395971355562373.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/142aty1355562373.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/15wbia1355562373.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/169v571355562373.tab")
+ }
>
> try(system("convert tmp/1wjcr1355562373.ps tmp/1wjcr1355562373.png",intern=TRUE))
character(0)
> try(system("convert tmp/25s5n1355562373.ps tmp/25s5n1355562373.png",intern=TRUE))
character(0)
> try(system("convert tmp/3bbpi1355562373.ps tmp/3bbpi1355562373.png",intern=TRUE))
character(0)
> try(system("convert tmp/4k0se1355562373.ps tmp/4k0se1355562373.png",intern=TRUE))
character(0)
> try(system("convert tmp/5mnqh1355562373.ps tmp/5mnqh1355562373.png",intern=TRUE))
character(0)
> try(system("convert tmp/6sn5a1355562373.ps tmp/6sn5a1355562373.png",intern=TRUE))
character(0)
> try(system("convert tmp/7h2na1355562373.ps tmp/7h2na1355562373.png",intern=TRUE))
character(0)
> try(system("convert tmp/867qz1355562373.ps tmp/867qz1355562373.png",intern=TRUE))
character(0)
> try(system("convert tmp/999f31355562373.ps tmp/999f31355562373.png",intern=TRUE))
character(0)
> try(system("convert tmp/10c3jk1355562373.ps tmp/10c3jk1355562373.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
17.748 1.549 19.297