R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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+ ,15
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+ ,16
+ ,65
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+ ,13
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+ ,74
+ ,15
+ ,65
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+ ,13
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+ ,13
+ ,71
+ ,84
+ ,14
+ ,62
+ ,73
+ ,12
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+ ,13
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+ ,123
+ ,15
+ ,94
+ ,107
+ ,14
+ ,91
+ ,106
+ ,14
+ ,102
+ ,119
+ ,16
+ ,73
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+ ,15
+ ,107
+ ,123
+ ,17
+ ,74
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+ ,14
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+ ,127
+ ,16
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+ ,15
+ ,78
+ ,89
+ ,13
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+ ,84
+ ,15
+ ,88
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+ ,11
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+ ,122
+ ,16
+ ,56
+ ,68
+ ,14
+ ,118
+ ,134
+ ,15
+ ,90
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+ ,16
+ ,76
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+ ,13
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+ ,13
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+ ,14
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+ ,15
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+ ,14
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+ ,15
+ ,59
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+ ,13
+ ,92
+ ,104
+ ,13
+ ,58
+ ,70
+ ,13
+ ,115
+ ,133
+ ,17
+ ,96
+ ,110
+ ,14
+ ,120
+ ,136
+ ,17
+ ,87
+ ,101
+ ,14
+ ,118
+ ,133
+ ,16
+ ,87
+ ,102
+ ,15
+ ,101
+ ,116
+ ,16
+ ,55
+ ,65
+ ,12
+ ,98
+ ,113
+ ,16
+ ,83
+ ,97
+ ,15
+ ,102
+ ,115
+ ,15
+ ,79
+ ,94
+ ,15)
+ ,dim=c(3
+ ,653)
+ ,dimnames=list(c('Gewicht'
+ ,'cholesterol'
+ ,'Bloeddruk')
+ ,1:653))
> y <- array(NA,dim=c(3,653),dimnames=list(c('Gewicht','cholesterol','Bloeddruk'),1:653))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Gewicht cholesterol Bloeddruk
1 83 96 14
2 79 91 13
3 92 108 16
4 83 95 13
5 92 105 14
6 103 117 15
7 82 94 14
8 86 98 13
9 106 120 15
10 79 91 13
11 86 100 13
12 76 90 13
13 108 123 16
14 82 97 16
15 108 120 12
16 118 133 15
17 127 141 16
18 123 138 16
19 72 85 12
20 105 121 16
21 63 74 13
22 86 100 14
23 58 67 11
24 59 73 12
25 100 116 15
26 100 115 15
27 78 92 14
28 94 109 15
29 105 120 15
30 89 105 15
31 101 115 16
32 92 105 15
33 105 122 16
34 76 87 12
35 80 94 16
36 66 78 12
37 117 135 18
38 94 113 18
39 107 123 15
40 110 126 17
41 110 127 17
42 106 120 16
43 94 108 14
44 71 83 14
45 101 117 15
46 84 96 14
47 89 103 14
48 119 134 17
49 97 112 15
50 82 93 14
51 89 103 15
52 70 84 14
53 101 116 15
54 81 97 15
55 74 87 14
56 107 122 15
57 97 111 14
58 83 101 16
59 95 109 15
60 82 96 15
61 88 100 14
62 74 90 16
63 104 120 16
64 73 86 13
65 73 85 13
66 81 96 15
67 79 93 14
68 83 95 13
69 111 127 17
70 138 156 18
71 81 94 13
72 107 123 16
73 66 80 13
74 81 98 15
75 74 88 15
76 96 109 13
77 86 98 14
78 69 83 14
79 73 85 14
80 71 85 15
81 64 76 12
82 79 96 16
83 60 72 13
84 111 125 15
85 107 121 16
86 90 103 14
87 98 113 17
88 77 89 12
89 93 108 16
90 68 80 13
91 74 87 13
92 70 84 14
93 80 93 14
94 81 94 13
95 72 87 15
96 81 95 15
97 92 106 15
98 81 94 12
99 78 91 12
100 92 106 14
101 92 103 12
102 107 121 15
103 98 112 14
104 86 98 15
105 77 91 13
106 96 112 15
107 104 118 14
108 77 90 13
109 65 80 14
110 61 73 14
111 117 135 17
112 84 97 14
113 69 82 12
114 85 99 15
115 116 132 16
116 115 130 16
117 55 67 13
118 64 78 14
119 117 131 15
120 68 80 13
121 104 121 17
122 66 79 13
123 70 82 13
124 89 101 12
125 79 91 14
126 70 83 15
127 63 76 13
128 79 93 15
129 62 73 13
130 94 107 15
131 83 97 14
132 118 133 16
133 62 74 13
134 78 93 15
135 83 99 15
136 91 105 14
137 84 95 13
138 76 90 15
139 100 115 16
140 80 95 15
141 98 115 16
142 89 105 16
143 98 115 16
144 88 102 14
145 81 92 12
146 88 101 13
147 75 90 15
148 77 93 15
149 88 102 15
150 65 79 14
151 69 83 14
152 76 89 14
153 53 65 13
154 82 97 15
155 67 78 13
156 84 99 15
157 112 126 16
158 91 106 15
159 104 115 13
160 79 90 13
161 90 104 14
162 60 76 15
163 79 95 15
164 99 114 16
165 68 81 15
166 79 91 12
167 107 120 14
168 114 132 16
169 81 96 14
170 83 95 14
171 61 74 14
172 82 93 12
173 134 152 18
174 102 116 14
175 132 148 16
176 72 84 12
177 72 87 14
178 102 114 13
179 92 106 14
180 94 110 16
181 86 99 13
182 84 96 13
183 66 80 13
184 129 144 14
185 88 102 13
186 109 125 14
187 84 98 14
188 73 87 14
189 86 100 15
190 113 128 15
191 88 102 14
192 90 104 13
193 82 96 14
194 111 127 17
195 73 85 13
196 91 106 15
197 72 85 14
198 111 126 16
199 108 121 14
200 83 94 14
201 81 94 14
202 111 128 17
203 69 82 13
204 106 118 14
205 115 128 15
206 132 149 18
207 78 92 15
208 90 105 15
209 132 146 16
210 91 106 15
211 115 130 15
212 65 77 12
213 77 92 14
214 71 83 14
215 74 88 15
216 76 89 14
217 115 132 17
218 100 113 15
219 70 83 14
220 71 84 13
221 74 88 14
222 60 74 14
223 58 71 13
224 105 119 14
225 105 120 14
226 100 113 14
227 74 88 14
228 77 92 15
229 77 91 14
230 109 123 15
231 68 80 13
232 67 79 12
233 96 109 14
234 86 98 14
235 85 98 14
236 91 106 15
237 104 117 15
238 94 108 15
239 67 79 13
240 79 93 13
241 73 86 13
242 93 106 14
243 87 100 13
244 66 78 12
245 79 92 14
246 94 111 15
247 84 97 15
248 67 77 10
249 121 134 14
250 82 95 14
251 116 134 18
252 83 96 15
253 66 78 12
254 66 79 13
255 71 87 17
256 83 95 14
257 93 110 16
258 83 96 14
259 112 129 17
260 79 93 15
261 135 154 19
262 85 97 13
263 91 109 17
264 103 118 16
265 77 91 15
266 70 83 15
267 53 65 14
268 85 99 14
269 88 103 14
270 65 75 12
271 119 135 16
272 93 109 17
273 84 98 14
274 70 81 13
275 64 78 14
276 63 75 13
277 152 171 18
278 83 96 14
279 66 78 12
280 83 96 15
281 106 121 14
282 85 100 16
283 85 97 13
284 84 99 15
285 78 92 16
286 94 108 13
287 82 96 15
288 66 77 13
289 69 82 13
290 83 94 12
291 83 98 14
292 124 139 15
293 101 116 14
294 113 126 15
295 107 121 15
296 83 96 15
297 79 91 14
298 85 99 15
299 62 74 13
300 83 95 13
301 101 118 17
302 60 74 13
303 86 96 11
304 101 117 16
305 73 85 13
306 70 84 15
307 88 100 13
308 74 88 14
309 105 120 15
310 82 97 15
311 83 94 13
312 90 105 15
313 70 81 13
314 56 70 13
315 70 83 14
316 79 93 14
317 127 141 16
318 96 109 15
319 101 115 15
320 81 97 15
321 93 109 16
322 92 103 13
323 79 95 14
324 78 92 15
325 68 80 13
326 92 109 17
327 73 86 13
328 61 74 12
329 73 84 13
330 108 121 15
331 88 99 12
332 66 76 12
333 59 73 14
334 61 73 13
335 55 66 13
336 119 134 15
337 89 102 14
338 68 81 13
339 125 144 17
340 66 78 13
341 82 95 16
342 101 116 16
343 104 118 13
344 63 77 14
345 63 77 13
346 63 72 12
347 98 113 15
348 90 104 15
349 97 111 16
350 74 86 13
351 63 76 13
352 102 117 15
353 90 103 14
354 79 95 15
355 74 87 15
356 89 105 16
357 70 84 14
358 77 90 13
359 78 90 13
360 70 84 15
361 95 109 15
362 100 113 13
363 64 73 11
364 90 102 13
365 109 124 16
366 89 102 13
367 66 78 13
368 88 103 15
369 108 122 15
370 97 112 14
371 66 76 12
372 85 97 14
373 79 95 15
374 95 106 13
375 62 77 13
376 76 88 14
377 69 85 15
378 105 121 16
379 74 86 13
380 96 113 16
381 83 95 13
382 76 92 16
383 83 97 16
384 68 83 14
385 68 81 13
386 76 90 14
387 73 89 14
388 71 83 14
389 74 89 15
390 78 90 14
391 95 110 16
392 103 116 15
393 82 95 14
394 67 79 13
395 77 87 12
396 93 109 16
397 76 90 13
398 85 96 13
399 68 81 13
400 81 93 12
401 87 101 14
402 67 80 12
403 78 90 13
404 93 108 15
405 87 102 15
406 77 90 13
407 79 93 14
408 84 97 13
409 114 128 14
410 84 99 16
411 105 118 16
412 117 131 15
413 94 110 16
414 65 79 15
415 75 89 13
416 73 84 13
417 117 132 16
418 75 89 14
419 85 100 14
420 83 97 14
421 63 75 11
422 80 94 14
423 73 86 14
424 86 97 13
425 103 117 15
426 67 79 12
427 83 96 14
428 55 71 15
429 90 103 15
430 123 138 15
431 98 113 16
432 188 207 19
433 76 91 16
434 72 85 14
435 65 78 13
436 99 114 16
437 86 98 12
438 81 97 16
439 99 113 15
440 58 69 11
441 90 107 16
442 97 113 16
443 85 100 15
444 79 92 15
445 110 126 17
446 92 106 15
447 88 100 14
448 56 68 13
449 71 85 14
450 110 124 16
451 77 89 14
452 66 80 14
453 114 132 17
454 76 89 14
455 109 122 15
456 125 139 15
457 74 87 14
458 89 103 14
459 100 116 16
460 77 90 13
461 99 115 15
462 88 101 14
463 96 110 14
464 87 103 16
465 101 116 15
466 78 92 14
467 119 136 17
468 105 122 16
469 80 93 15
470 69 81 13
471 89 105 15
472 69 86 14
473 66 75 10
474 70 84 15
475 83 96 14
476 81 96 16
477 73 87 14
478 100 115 15
479 67 81 15
480 108 124 15
481 105 118 14
482 119 136 17
483 91 104 15
484 102 116 15
485 100 114 15
486 76 90 15
487 100 115 16
488 86 98 13
489 103 119 15
490 93 110 16
491 57 69 13
492 115 135 17
493 82 92 12
494 99 112 15
495 63 77 15
496 166 182 17
497 74 85 13
498 86 102 17
499 87 100 13
500 101 118 15
501 73 88 15
502 102 115 14
503 97 114 16
504 86 101 15
505 116 133 16
506 81 93 14
507 140 156 17
508 93 105 14
509 111 128 16
510 115 131 16
511 70 88 15
512 68 81 13
513 77 91 15
514 81 95 14
515 64 78 14
516 111 125 15
517 126 140 17
518 97 114 16
519 72 86 15
520 88 103 15
521 78 92 14
522 94 107 14
523 78 92 14
524 70 83 13
525 96 109 14
526 99 116 16
527 79 93 14
528 102 118 16
529 89 101 13
530 79 91 13
531 94 110 16
532 89 105 15
533 94 111 16
534 63 78 14
535 76 88 12
536 55 70 15
537 83 95 14
538 70 85 15
539 81 95 14
540 130 146 17
541 64 75 13
542 77 89 14
543 85 100 15
544 104 120 15
545 72 85 15
546 70 83 15
547 84 99 15
548 106 121 16
549 118 136 17
550 70 85 16
551 74 88 14
552 75 87 13
553 95 107 14
554 71 87 16
555 81 96 16
556 103 116 14
557 56 67 14
558 91 105 15
559 54 67 13
560 76 90 15
561 101 119 17
562 83 97 14
563 62 72 10
564 67 80 14
565 91 102 15
566 91 105 15
567 97 111 15
568 63 74 12
569 80 96 16
570 71 84 14
571 83 97 16
572 88 102 15
573 74 85 13
574 89 101 13
575 65 77 12
576 103 116 13
577 122 138 17
578 123 140 18
579 98 114 15
580 97 112 15
581 69 81 12
582 90 103 14
583 85 97 13
584 60 73 14
585 97 112 15
586 111 129 16
587 105 117 14
588 72 88 15
589 123 140 17
590 129 146 18
591 83 97 15
592 86 99 14
593 88 104 15
594 50 60 12
595 62 75 12
596 79 91 13
597 99 114 15
598 82 94 13
599 71 82 11
600 124 138 15
601 81 95 14
602 83 98 14
603 121 136 16
604 65 76 13
605 61 74 15
606 65 77 13
607 71 84 13
608 71 84 14
609 62 73 12
610 74 88 15
611 63 75 13
612 75 90 15
613 82 96 14
614 94 110 16
615 107 123 15
616 94 107 14
617 91 106 14
618 102 119 16
619 73 87 15
620 107 123 17
621 74 88 14
622 80 93 14
623 112 127 16
624 71 86 15
625 78 89 13
626 69 84 15
627 88 98 11
628 106 122 16
629 56 68 14
630 118 134 15
631 90 105 16
632 76 88 13
633 83 95 13
634 77 90 14
635 122 137 15
636 85 99 14
637 67 80 14
638 93 108 15
639 59 72 13
640 92 104 13
641 58 70 13
642 115 133 17
643 96 110 14
644 120 136 17
645 87 101 14
646 118 133 16
647 87 102 15
648 101 116 16
649 55 65 12
650 98 113 16
651 83 97 15
652 102 115 15
653 79 94 15
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) cholesterol Bloeddruk
0.9720 0.9928 -0.9824
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.5992 -0.5817 0.2696 0.5785 3.5020
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.971973 0.420160 2.313 0.021 *
cholesterol 0.992766 0.002895 342.898 <2e-16 ***
Bloeddruk -0.982410 0.038833 -25.298 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.039 on 650 degrees of freedom
Multiple R-squared: 0.9968, Adjusted R-squared: 0.9967
F-statistic: 9.978e+04 on 2 and 650 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,] 0.01137625 0.022752501 0.988623749
[2,] 0.13951815 0.279036298 0.860481851
[3,] 0.07252614 0.145052278 0.927473861
[4,] 0.03576823 0.071536465 0.964231768
[5,] 0.01659615 0.033192304 0.983403848
[6,] 0.23938236 0.478764727 0.760617637
[7,] 0.44863561 0.897271220 0.551364390
[8,] 0.35523541 0.710470826 0.644764587
[9,] 0.27188826 0.543776520 0.728111740
[10,] 0.20442096 0.408841919 0.795579041
[11,] 0.16160346 0.323206923 0.838396538
[12,] 0.20095923 0.401918451 0.799040774
[13,] 0.14901728 0.298034550 0.850982725
[14,] 0.17426313 0.348526267 0.825736867
[15,] 0.18590594 0.371811882 0.814094059
[16,] 0.25653992 0.513079837 0.743460082
[17,] 0.22672670 0.453453410 0.773273295
[18,] 0.31301595 0.626031899 0.686984050
[19,] 0.62989367 0.740212654 0.370106327
[20,] 0.70868822 0.582623563 0.291311781
[21,] 0.67253091 0.654938178 0.327469089
[22,] 0.63320001 0.733599989 0.366799994
[23,] 0.59491597 0.810168061 0.405084030
[24,] 0.55127652 0.897446969 0.448723485
[25,] 0.61293560 0.774128802 0.387064401
[26,] 0.64801745 0.703965091 0.351982546
[27,] 0.68112252 0.637754969 0.318877485
[28,] 0.74241146 0.515177088 0.257588544
[29,] 0.71869311 0.562613772 0.281306886
[30,] 0.72020316 0.559593680 0.279796840
[31,] 0.68218252 0.635634953 0.317817476
[32,] 0.66453768 0.670924642 0.335462321
[33,] 0.74132840 0.517343204 0.258671602
[34,] 0.76199475 0.476010493 0.238005246
[35,] 0.73435814 0.531283712 0.265641856
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[384,] 0.57948619 0.841027621 0.420513810
[385,] 0.59901807 0.801963858 0.400981929
[386,] 0.58327677 0.833446464 0.416723232
[387,] 0.61340999 0.773180027 0.386590013
[388,] 0.59586973 0.808260546 0.404130273
[389,] 0.57636864 0.847262730 0.423631365
[390,] 0.59579465 0.808410704 0.404205352
[391,] 0.57775528 0.844489437 0.422244718
[392,] 0.60549274 0.789014521 0.394507260
[393,] 0.62787387 0.744252254 0.372126127
[394,] 0.61388591 0.772228175 0.386114088
[395,] 0.59776870 0.804462608 0.402231304
[396,] 0.58052900 0.838941990 0.419470995
[397,] 0.61324439 0.773511213 0.386755607
[398,] 0.59490020 0.810199608 0.405099804
[399,] 0.57678287 0.846434259 0.423217129
[400,] 0.55930019 0.881399623 0.440699812
[401,] 0.54355316 0.912893672 0.456446836
[402,] 0.52723921 0.945521577 0.472760788
[403,] 0.51038086 0.979238280 0.489619140
[404,] 0.48976027 0.979520548 0.510239726
[405,] 0.47193453 0.943869052 0.528065474
[406,] 0.59374320 0.812513595 0.406256798
[407,] 0.58145307 0.837093863 0.418546932
[408,] 0.56270764 0.874584728 0.437292364
[409,] 0.54262571 0.914748572 0.457374286
[410,] 0.57345514 0.853089722 0.426544861
[411,] 0.59045593 0.819088148 0.409544074
[412,] 0.57862233 0.842755342 0.421377671
[413,] 0.56296740 0.874065202 0.437032601
[414,] 0.58845972 0.823080557 0.411540278
[415,] 0.57174770 0.856504592 0.428252296
[416,] 0.61248760 0.775024801 0.387512401
[417,] 0.59667816 0.806643685 0.403321843
[418,] 0.57709507 0.845809863 0.422904931
[419,] 0.59807439 0.803851211 0.401925606
[420,] 0.58309141 0.833817188 0.416908594
[421,] 0.57075738 0.858485236 0.429242618
[422,] 0.55202940 0.895941209 0.447970604
[423,] 0.59055389 0.818892211 0.409446105
[424,] 0.61613927 0.767721464 0.383860732
[425,] 0.59477470 0.810450596 0.405225298
[426,] 0.57964593 0.840708140 0.420354070
[427,] 0.55955247 0.880895069 0.440447534
[428,] 0.54076662 0.918466756 0.459233378
[429,] 0.52037165 0.959256708 0.479628354
[430,] 0.50745076 0.985098471 0.492549235
[431,] 0.49237169 0.984743383 0.507628309
[432,] 0.47664426 0.953288516 0.523355742
[433,] 0.45855312 0.917106232 0.541446884
[434,] 0.44265133 0.885302661 0.557348669
[435,] 0.43475104 0.869502087 0.565248957
[436,] 0.45540742 0.910814839 0.544592580
[437,] 0.43523637 0.870472742 0.564763629
[438,] 0.41738104 0.834762073 0.582618963
[439,] 0.43880392 0.877607842 0.561196079
[440,] 0.42758837 0.855176743 0.572411628
[441,] 0.41062950 0.821259004 0.589370498
[442,] 0.43418640 0.868372803 0.565813599
[443,] 0.41195138 0.823902755 0.588048623
[444,] 0.39716604 0.794332088 0.602833956
[445,] 0.43713005 0.874260094 0.562869953
[446,] 0.45658893 0.913177870 0.543411065
[447,] 0.44237686 0.884753721 0.557623140
[448,] 0.45066108 0.901322163 0.549338919
[449,] 0.43036849 0.860736979 0.569631510
[450,] 0.46831206 0.936624126 0.531687937
[451,] 0.45902402 0.918048045 0.540975978
[452,] 0.43835454 0.876709090 0.561645455
[453,] 0.41968730 0.839374600 0.580312700
[454,] 0.39870938 0.797418760 0.601290620
[455,] 0.38358128 0.767162562 0.616418719
[456,] 0.40092808 0.801856159 0.599071920
[457,] 0.38263044 0.765260884 0.617369558
[458,] 0.36362549 0.727250981 0.636374509
[459,] 0.34501307 0.690026147 0.654986927
[460,] 0.32550983 0.651019656 0.674490172
[461,] 0.31039835 0.620796693 0.689601654
[462,] 0.28995890 0.579917807 0.710041097
[463,] 0.30182005 0.603640104 0.698179948
[464,] 0.32215384 0.644307684 0.677846158
[465,] 0.30232550 0.604650992 0.697674504
[466,] 0.32295144 0.645902888 0.677048556
[467,] 0.59401618 0.811967638 0.405983819
[468,] 0.57130815 0.857383695 0.428691847
[469,] 0.54975984 0.900480322 0.450240161
[470,] 0.52919755 0.941604903 0.470802451
[471,] 0.51004385 0.979912306 0.489956153
[472,] 0.49437609 0.988752190 0.505623905
[473,] 0.47287848 0.945756959 0.527121521
[474,] 0.45076188 0.901523757 0.549238121
[475,] 0.46690485 0.933809693 0.533095154
[476,] 0.44963884 0.899277680 0.550361160
[477,] 0.42565995 0.851319899 0.574340050
[478,] 0.45428520 0.908570394 0.545714803
[479,] 0.43791212 0.875824231 0.562087885
[480,] 0.42131170 0.842623407 0.578688296
[481,] 0.40078996 0.801579918 0.599210041
[482,] 0.38608704 0.772174078 0.613912961
[483,] 0.36574404 0.731488082 0.634255959
[484,] 0.38243301 0.764866011 0.617566994
[485,] 0.40133288 0.802665760 0.598667120
[486,] 0.37773517 0.755470337 0.622264831
[487,] 0.59379071 0.812418578 0.406209289
[488,] 0.61190815 0.776183690 0.388091845
[489,] 0.64404915 0.711901705 0.355950852
[490,] 0.62194725 0.756105490 0.378052745
[491,] 0.62787390 0.744252197 0.372126098
[492,] 0.64519635 0.709607298 0.354803649
[493,] 0.62730897 0.745382060 0.372691030
[494,] 0.60879056 0.782418875 0.391209438
[495,] 0.71142556 0.577148879 0.288574439
[496,] 0.69606551 0.607868988 0.303934494
[497,] 0.68004603 0.639907934 0.319953967
[498,] 0.69929396 0.601412083 0.300706041
[499,] 0.68147249 0.637055021 0.318527511
[500,] 0.69340656 0.613186883 0.306593442
[501,] 0.71398847 0.572023058 0.286011529
[502,] 0.70957040 0.580859199 0.290429600
[503,] 0.73630345 0.527393102 0.263696551
[504,] 0.74856095 0.502878092 0.251439046
[505,] 0.72795149 0.544097024 0.272048512
[506,] 0.92076908 0.158461847 0.079230923
[507,] 0.91536518 0.169269649 0.084634825
[508,] 0.90563704 0.188725918 0.094362959
[509,] 0.89763504 0.204729924 0.102364962
[510,] 0.89199497 0.216010056 0.108005028
[511,] 0.88493212 0.230135758 0.115067879
[512,] 0.95534262 0.089314757 0.044657379
[513,] 0.96121196 0.077576070 0.038788035
[514,] 0.95545923 0.089081546 0.044540773
[515,] 0.95028679 0.099426424 0.049713212
[516,] 0.94569209 0.108615827 0.054307913
[517,] 0.93994392 0.120112152 0.060056076
[518,] 0.93459974 0.130800511 0.065400255
[519,] 0.93018461 0.139630775 0.069815388
[520,] 0.92339154 0.153216920 0.076608460
[521,] 0.93233083 0.135338337 0.067669168
[522,] 0.92642685 0.147146296 0.073573148
[523,] 0.91728986 0.165420280 0.082710140
[524,] 0.90848763 0.183024747 0.091512373
[525,] 0.89771537 0.204569262 0.102284631
[526,] 0.88722026 0.225559480 0.112779740
[527,] 0.90409170 0.191816609 0.095908305
[528,] 0.91844015 0.163119704 0.081559852
[529,] 0.94253373 0.114932537 0.057466269
[530,] 0.93736859 0.125262818 0.062631409
[531,] 0.93865919 0.122681628 0.061340814
[532,] 0.94603197 0.107936069 0.053968034
[533,] 0.94403993 0.111920139 0.055960069
[534,] 0.93901329 0.121973426 0.060986713
[535,] 0.93851523 0.122969543 0.061484771
[536,] 0.94063330 0.118733395 0.059366698
[537,] 0.94617818 0.107643648 0.053821824
[538,] 0.94088270 0.118234597 0.059117298
[539,] 0.94752659 0.104946822 0.052473411
[540,] 0.95048097 0.099038060 0.049519030
[541,] 0.95329236 0.093415276 0.046707638
[542,] 0.94848810 0.103023804 0.051511902
[543,] 0.94335448 0.113291033 0.056645516
[544,] 0.94789622 0.104207556 0.052103778
[545,] 0.93917491 0.121650179 0.060825090
[546,] 0.93469805 0.130603895 0.065301947
[547,] 0.92500936 0.149981290 0.074990645
[548,] 0.93937610 0.121247806 0.060623903
[549,] 0.93749495 0.125010103 0.062505052
[550,] 0.92761063 0.144778739 0.072389369
[551,] 0.92169245 0.156615106 0.078307553
[552,] 0.95175898 0.096482039 0.048241020
[553,] 0.94502021 0.109959585 0.054979793
[554,] 0.94397761 0.112044776 0.056022388
[555,] 0.93473620 0.130527603 0.065263801
[556,] 0.94707684 0.105846313 0.052923157
[557,] 0.94107783 0.117844350 0.058922175
[558,] 0.93562572 0.128748560 0.064374280
[559,] 0.92466473 0.150670533 0.075335267
[560,] 0.99198834 0.016023313 0.008011656
[561,] 0.99044442 0.019111167 0.009555583
[562,] 0.98888442 0.022231157 0.011115578
[563,] 0.98620532 0.027589364 0.013794682
[564,] 0.98460521 0.030789584 0.015394792
[565,] 0.98110892 0.037782151 0.018891076
[566,] 0.98472449 0.030551012 0.015275506
[567,] 0.98191049 0.036179022 0.018089511
[568,] 0.98537003 0.029259933 0.014629967
[569,] 0.98296343 0.034073142 0.017036571
[570,] 0.98111469 0.037770613 0.018885306
[571,] 0.97690431 0.046191384 0.023095692
[572,] 0.97610357 0.047792850 0.023896425
[573,] 0.97603895 0.047922107 0.023961053
[574,] 0.98057482 0.038850358 0.019425179
[575,] 0.97648426 0.047031482 0.023515741
[576,] 0.97451596 0.050968072 0.025484036
[577,] 0.97010636 0.059787282 0.029893641
[578,] 0.96452054 0.070958917 0.035479458
[579,] 0.95658245 0.086835091 0.043417545
[580,] 0.94844771 0.103104585 0.051552293
[581,] 0.97976060 0.040478798 0.020239399
[582,] 0.98783792 0.024324152 0.012162076
[583,] 0.99370570 0.012588598 0.006294299
[584,] 0.99169277 0.016614460 0.008307230
[585,] 0.99185564 0.016288710 0.008144355
[586,] 0.98976620 0.020467604 0.010233802
[587,] 0.98745312 0.025093751 0.012546875
[588,] 0.99196278 0.016074442 0.008037221
[589,] 0.99226008 0.015479837 0.007739919
[590,] 0.99691078 0.006178436 0.003089218
[591,] 0.99590719 0.008185614 0.004092807
[592,] 0.99465705 0.010685901 0.005342950
[593,] 0.99310017 0.013799655 0.006899827
[594,] 0.99244580 0.015108392 0.007554196
[595,] 0.99290829 0.014183429 0.007091714
[596,] 0.99165763 0.016684736 0.008342368
[597,] 0.99585477 0.008290470 0.004145235
[598,] 0.99625271 0.007494575 0.003747287
[599,] 0.99665781 0.006684383 0.003342191
[600,] 0.99709049 0.005819028 0.002909514
[601,] 0.99583814 0.008323719 0.004161859
[602,] 0.99557360 0.008852804 0.004426402
[603,] 0.99380565 0.012388709 0.006194354
[604,] 0.99127333 0.017453345 0.008726673
[605,] 0.98828173 0.023436530 0.011718265
[606,] 0.98383989 0.032320221 0.016160110
[607,] 0.98127428 0.037451436 0.018725718
[608,] 0.97800889 0.043982229 0.021991115
[609,] 0.97171987 0.056560258 0.028280129
[610,] 0.97864808 0.042703847 0.021351924
[611,] 0.97299013 0.054019734 0.027009867
[612,] 0.98615107 0.027697868 0.013848934
[613,] 0.99176392 0.016472162 0.008236081
[614,] 0.98818993 0.023620131 0.011810065
[615,] 0.98663893 0.026722132 0.013361066
[616,] 0.98590755 0.028184907 0.014092453
[617,] 0.97998321 0.040033588 0.020016794
[618,] 0.97787482 0.044250354 0.022125177
[619,] 0.97633670 0.047326607 0.023663303
[620,] 0.98142243 0.037155138 0.018577569
[621,] 0.98215786 0.035684277 0.017842139
[622,] 0.97618545 0.047629106 0.023814553
[623,] 0.96705361 0.065892783 0.032946392
[624,] 0.96410026 0.071799483 0.035899741
[625,] 0.97681557 0.046368857 0.023184429
[626,] 0.96878309 0.062433819 0.031216910
[627,] 0.95373728 0.092525442 0.046262721
[628,] 0.93357636 0.132847273 0.066423636
[629,] 0.90783142 0.184337165 0.092168583
[630,] 0.88170658 0.236586840 0.118293420
[631,] 0.86561221 0.268775577 0.134387789
[632,] 0.82090656 0.358186881 0.179093440
[633,] 0.78532875 0.429342493 0.214671246
[634,] 0.78357151 0.432856971 0.216428486
[635,] 0.70779661 0.584406783 0.292203391
[636,] 0.61790305 0.764193894 0.382096947
[637,] 0.74251917 0.514961652 0.257480826
[638,] 0.78589025 0.428219505 0.214109753
[639,] 0.69464631 0.610707382 0.305353691
[640,] 0.86812765 0.263744700 0.131872350
[641,] 0.85053168 0.298936634 0.149468317
[642,] 0.94535090 0.109298205 0.054649103
> postscript(file="/var/wessaorg/rcomp/tmp/1rxjg1321619915.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2ygf91321619915.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3b7471321619915.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/49ffz1321619915.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5nt0f1321619915.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 = 653
Frequency = 1
1 2 3 4 5 6 7
0.4762113 0.4576325 -0.4721632 0.4865678 0.5413159 0.6105316 1.4617437
8 9 10 11 12 13 14
0.5082693 0.6322331 0.4576325 -1.4772630 -1.5496014 0.6363443 0.4482646
15 16 17 18 19 20 21
-0.3149959 -0.2737270 1.7665534 0.7448519 -1.5681802 -0.3781233 1.3346572
22 23 24 25 26 27 28
-0.4948533 1.3192010 -2.6549863 -1.3967022 -0.4039360 -0.5527240 -0.4473391
29 30 31 32 33 34 35
-0.3677669 -1.4762744 1.5784736 1.5237256 -1.3708895 0.4462874 1.4265630
36 37 38 39 40 41 42
-0.6188171 -0.3120302 -1.4711747 -1.3460653 0.6404555 -0.3523106 1.6146428
43 44 45 46 47 48 49
-0.4369826 1.3821715 -1.3894684 1.4762113 -0.4731518 1.6983262 -0.4256376
50 51 52 53 54 55 56
2.4545098 0.5092579 -0.6105947 -0.3967022 -1.5341451 0.4111068 -0.3532992
57 58 59 60 61 62 63
-0.4152811 -2.5228001 0.5526609 0.4586210 1.5051467 -0.6023723 -0.3853572
64 65 66 67 68 69 70
-0.5785367 0.4142294 -0.5413790 -0.5454902 0.4865678 0.6476894 -0.1601196
71 72 73 74 75 76 77
-0.5206660 -0.3636557 -1.6219397 -2.5269113 0.4007503 -0.4121585 1.4906790
78 79 80 81 82 83 84
-0.6178285 1.3966391 0.3790488 -0.6332848 -1.5589693 0.3201895 0.6684023
85 86 87 88 89 90 91
1.6218767 0.5268482 1.5464157 -0.5392449 0.5278368 0.3780603 -0.5713029
92 93 94 95 96 97 98
-0.6105947 0.4545098 -0.5206660 -0.6064835 0.4513872 0.5309594 -1.5030757
99 100 101 102 103 104 105
-1.5247772 -0.4514503 0.5620288 0.6394670 -0.4080473 2.4730887 -1.5423675
106 107 108 109 110 111 112
-1.4256376 -0.3646442 -0.5496014 -1.6395301 1.3098331 -1.2944399 0.4834452
113 114 115 116 117 118 119
-1.5898818 0.4803225 -0.2985511 0.6869812 0.2840204 -0.6539977 0.7118054
120 121 122 123 124 125 126
0.3780603 -0.3957136 -0.6291736 0.3925279 -0.4524388 1.4400422 1.3645811
127 128 129 130 131 132 133
-0.6508751 0.4369195 1.3274234 1.5381932 -0.5165548 0.7086827 0.3346572
134 135 136 137 138 139 140
-0.5630805 -1.5196775 -0.4586841 1.4865678 0.4152180 0.5784736 -0.5486128
141 142 143 144 145 146 147
-1.4215264 -0.4938647 -1.4215264 -0.4803856 0.4824566 -0.4700292 -0.5847820
148 149 150 151 152 153 154
-1.5630805 0.5020241 -0.6467639 -0.6178285 0.4255745 0.2695527 -0.5341451
155 156 157 158 159 160 161
1.3635926 -0.5196775 1.6580459 -0.4690406 1.6312446 1.4503986 -0.4659180
162 163 164 165 166 167 168
-1.6860557 -1.5486128 0.5712398 1.3501135 -0.5247772 0.6498235 -2.2985511
169 170 171 172 173 174 175
-1.5237887 1.4689775 0.3170669 0.4896905 -0.1890550 -0.3791119 -0.1828097
176 177 178 179 180 181 182
-0.5754141 -1.5888932 0.6240107 -0.4514503 -0.4576956 -0.4844968 0.4938017
183 184 185 186 187 188 189
-1.6219397 -1.1765644 -1.4627953 -2.3140074 -0.5093210 -0.5888932 0.4875564
190 191 192 193 194 195 196
-0.3098962 -0.4803856 -1.4483276 -0.5237887 0.6476894 0.4142294 -0.4690406
197 198 199 200 201 202 203
0.3966391 0.6580459 0.6570573 2.4617437 0.4617437 -0.3450768 -0.6074721
204 205 206 207 208 209 210
1.6353558 1.6901038 0.7892435 0.4296857 -0.4762744 1.8027226 -0.4690406
211 212 213 214 215 216 217
-0.2954285 -0.6260510 -1.5527240 1.3821715 0.4007503 0.4255745 -0.3161414
218 219 220 221 222 223 224
1.5815963 0.3821715 -0.5930044 -0.5816594 -0.6829331 -0.6870443 -0.3574104
225 226 227 228 229 230 231
-1.3501765 0.5991866 -0.5816594 -0.5703143 -0.5599578 0.6539347 0.3780603
232 233 234 235 236 237 238
-0.6115833 0.5702512 1.4906790 0.4906790 -0.4690406 1.6105316 0.5454271
239 240 241 242 243 244 245
0.3708264 -1.5278999 -0.5785367 0.5485497 -0.4772630 -0.6188171 0.4472760
246 247 248 249 250 251 252
-2.4328714 1.4658549 -0.5908703 0.7510972 0.4689775 -0.3192641 1.4586210
253 254 255 256 257 258 259
-0.6188171 -0.6291736 0.3583359 1.4689775 -1.4576956 0.4762113 -0.3378429
260 261 262 263 264 265 266
0.4369195 -0.1921776 0.5010355 -1.4825197 0.6001752 0.4224518 1.3645811
267 268 269 270 271 272 273
1.2519624 -0.5020871 -1.4731518 1.3594814 -0.2768496 0.5174803 -0.5093210
274 275 276 277 278 279 280
1.3852941 -0.6539977 0.3418911 -1.0516121 0.4762113 -0.6188171 1.4586210
281 282 283 284 285 286 287
-1.3429427 0.4699661 0.5010355 -0.5196775 1.4120954 -1.4193923 0.4586210
288 289 290 291 292 293 294
1.3563587 -0.6074721 0.4969243 -1.5093210 -0.2303239 -1.3791119 1.6756362
295 296 297 298 299 300 301
0.6394670 1.4586210 1.4400422 0.4803225 0.3346572 0.4865678 -0.4174152
302 303 304 305 306 307 308
-1.6653428 0.5289823 -0.4070587 0.4142294 0.3718150 0.5227370 -0.5816594
309 310 311 312 313 314 315
-0.3677669 -0.5341451 1.4793340 -0.4762744 1.3852941 -1.6942781 0.3821715
316 317 318 319 320 321 322
-0.5454902 1.7665534 1.5526609 0.5960640 -1.5341451 -0.4649294 1.5444385
323 324 325 326 327 328 329
-2.5310225 0.4296857 0.3780603 -0.4825197 -0.5785367 -1.6477525 1.4069956
330 331 332 333 334 335 336
1.6394670 0.5330935 1.3667152 -0.6901669 0.3274234 1.2767865 -0.2664931
337 338 339 340 341 342 343
0.5196144 -0.6147059 -2.2293354 0.3635926 2.4337969 0.5857075 -1.3470539
344 345 346 347 348 349 350
-0.6612316 -1.6436413 2.3377799 -0.4184037 0.5164917 1.5495383 0.4214633
351 352 353 354 355 356 357
-0.6508751 -0.3894684 0.5268482 -1.5486128 1.3935165 -0.4938647 -0.6105947
358 359 360 361 362 363 364
-0.5496014 0.4503986 0.3718150 0.5526609 -0.3832231 1.3626040 0.5372047
365 366 367 368 369 370 371
0.6435782 -0.4627953 0.3635926 -0.4907421 0.6467008 -1.4080473 1.3667152
372 373 374 375 376 377 378
1.4834452 -1.5486128 1.5661400 -2.6436413 1.4183406 -1.6209512 -0.3781233
379 380 381 382 383 384 385
0.4214633 -1.4359940 0.4865678 -0.5879046 1.4482646 -1.6178285 -0.6147059
386 387 388 389 390 391 392
-0.5671917 -2.5744255 1.3821715 -0.5920158 1.4328083 0.5423044 1.6032978
393 394 395 396 397 398 399
0.4689775 0.3708264 1.4462874 -0.4649294 -1.5496014 1.4938017 -0.6147059
400 401 402 403 404 405 406
-0.5103095 -0.4876195 -1.6043494 0.4503986 -0.4545729 -0.4979759 -0.5496014
407 408 409 410 411 412 413
-0.5454902 -0.4989645 -0.2923058 0.4627322 2.6001752 0.7118054 -0.4576956
414 415 416 417 418 419 420
0.3356458 -1.5568352 1.4069956 0.7014489 -0.5744255 -1.4948533 -0.5165548
421 422 423 424 425 426 427
-1.6229283 -0.5382563 0.4038730 1.5010355 0.6105316 -0.6115833 0.4762113
428 429 430 431 432 433 434
-1.7222249 1.5092579 -0.2375578 0.5640060 0.1912158 0.4048615 0.3966391
435 436 437 438 439 440 441
-0.6364074 0.5712398 -0.4741404 -0.5517354 0.5815963 -0.6663313 -1.4793971
442 443 444 445 446 447 448
-0.4359940 -0.5124436 1.4296857 0.6404555 0.5309594 1.5051467 0.2912542
449 450 451 452 453 454 455
-0.6033609 1.6435782 1.4255745 -0.6395301 -1.3161414 0.4255745 1.6467008
456 457 458 459 460 461 462
0.7696761 0.4111068 -0.4731518 -0.4142925 -0.5496014 -1.4039360 0.5123805
463 464 465 466 467 468 469
-0.4225149 -0.5083324 -0.3967022 -0.5527240 -0.2872061 -1.3708895 1.4369195
470 471 472 473 474 475 476
0.3852941 -1.4762744 -3.5961270 0.3946620 0.3718150 0.4762113 0.4410307
477 478 479 480 481 482 483
-0.5888932 -0.4039360 0.3501135 -1.3388315 0.6353558 -0.2872061 1.5164917
484 485 486 487 488 489 490
0.6032978 0.5888301 0.4152180 0.5784736 0.5082693 -1.3750007 -1.4576956
491 492 493 494 495 496 497
0.2984880 -3.2944399 1.4824566 1.5743624 0.3211781 1.0455505 1.4142294
498 499 500 501 502 503 504
0.4668434 -0.4772630 -2.3822345 -0.5992497 0.6136543 -1.4287602 -0.5052098
505 506 507 508 509 510 511
-1.2913173 1.4545098 0.8574707 1.5413159 -1.3274865 -0.3057850 -3.5992497
512 513 514 515 516 517 518
-0.6147059 0.4224518 -0.5310225 -0.6539977 0.6684023 2.7417293 -1.4287602
519 520 521 522 523 524 525
0.3862827 -0.4907421 -0.5527240 0.5557836 -0.5527240 -0.6002382 0.5702512
526 527 528 529 530 531 532
-1.4142925 -0.5454902 -0.3998248 0.5299708 0.4576325 -0.4576956 -1.4762744
533 534 535 536 537 538 539
-1.4504617 -1.6539977 -0.5464787 -0.7294588 1.4689775 -0.6209512 -0.5310225
540 541 542 543 544 545 546
0.7851323 1.3418911 1.4255745 -0.5124436 -1.3677669 1.3790488 1.3645811
547 548 549 550 551 552 553
-0.5196775 0.6218767 -1.2872061 0.3614585 -0.5816594 0.4286971 1.5557836
554 555 556 557 558 559 560
-0.6240738 0.4410307 0.6208881 2.2664300 0.5237256 -0.7159796 0.4152180
561 562 563 564 565 566 567
-1.4101813 -0.5165548 -0.6270395 0.3604699 3.5020241 0.5237256 0.5671286
568 569 570 571 572 573 574
0.3522475 -0.5589693 0.3894053 1.4482646 0.5020241 1.4142294 0.5299708
575 576 577 578 579 580 581
-0.6260510 -0.3615216 0.7272616 0.7241390 -1.4111699 -0.4256376 -0.5971156
582 583 584 585 586 587 588
0.5268482 0.5010355 0.3098331 -0.4256376 -2.3202526 1.6281219 -1.5992497
589 590 591 592 593 594 595
-0.2582707 0.7675420 0.4658549 0.4979129 -1.4835083 1.2509738 -1.6405186
596 597 598 599 600 601 602
0.4576325 -0.4111699 0.4793340 -0.5722915 0.7624422 -0.5310225 -1.5093210
603 604 605 606 607 608 609
0.7303842 1.3491249 1.2994766 0.3563587 -0.5930044 0.3894053 0.3450137
610 611 612 613 614 615 616
0.4007503 0.3418911 -0.5847820 -0.5237887 -0.4576956 -1.3460653 0.5557836
617 618 619 620 621 622 623
-1.4514503 -1.3925910 0.3935165 0.6187540 -0.5816594 0.4545098 0.6652797
624 625 626 627 628 629 630
-0.6137173 1.4431648 -0.6281850 0.5434500 -0.3708895 1.2736639 -1.2664931
631 632 633 634 635 636 637
0.5061353 0.4359310 0.4865678 0.4328083 -0.2447916 -0.5020871 0.3604699
638 639 640 641 642 643 644
-0.4545729 -0.6798105 0.5516724 0.3057219 -1.3089076 -0.4225149 0.7127939
645 646 647 648 649 650 651
-0.4876195 0.7086827 -0.4979759 0.5857075 1.2871430 0.5640060 0.4658549
652 653
1.5960640 -0.5558466
> postscript(file="/var/wessaorg/rcomp/tmp/6neqn1321619915.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 = 653
Frequency = 1
lag(myerror, k = 1) myerror
0 0.4762113 NA
1 0.4576325 0.4762113
2 -0.4721632 0.4576325
3 0.4865678 -0.4721632
4 0.5413159 0.4865678
5 0.6105316 0.5413159
6 1.4617437 0.6105316
7 0.5082693 1.4617437
8 0.6322331 0.5082693
9 0.4576325 0.6322331
10 -1.4772630 0.4576325
11 -1.5496014 -1.4772630
12 0.6363443 -1.5496014
13 0.4482646 0.6363443
14 -0.3149959 0.4482646
15 -0.2737270 -0.3149959
16 1.7665534 -0.2737270
17 0.7448519 1.7665534
18 -1.5681802 0.7448519
19 -0.3781233 -1.5681802
20 1.3346572 -0.3781233
21 -0.4948533 1.3346572
22 1.3192010 -0.4948533
23 -2.6549863 1.3192010
24 -1.3967022 -2.6549863
25 -0.4039360 -1.3967022
26 -0.5527240 -0.4039360
27 -0.4473391 -0.5527240
28 -0.3677669 -0.4473391
29 -1.4762744 -0.3677669
30 1.5784736 -1.4762744
31 1.5237256 1.5784736
32 -1.3708895 1.5237256
33 0.4462874 -1.3708895
34 1.4265630 0.4462874
35 -0.6188171 1.4265630
36 -0.3120302 -0.6188171
37 -1.4711747 -0.3120302
38 -1.3460653 -1.4711747
39 0.6404555 -1.3460653
40 -0.3523106 0.6404555
41 1.6146428 -0.3523106
42 -0.4369826 1.6146428
43 1.3821715 -0.4369826
44 -1.3894684 1.3821715
45 1.4762113 -1.3894684
46 -0.4731518 1.4762113
47 1.6983262 -0.4731518
48 -0.4256376 1.6983262
49 2.4545098 -0.4256376
50 0.5092579 2.4545098
51 -0.6105947 0.5092579
52 -0.3967022 -0.6105947
53 -1.5341451 -0.3967022
54 0.4111068 -1.5341451
55 -0.3532992 0.4111068
56 -0.4152811 -0.3532992
57 -2.5228001 -0.4152811
58 0.5526609 -2.5228001
59 0.4586210 0.5526609
60 1.5051467 0.4586210
61 -0.6023723 1.5051467
62 -0.3853572 -0.6023723
63 -0.5785367 -0.3853572
64 0.4142294 -0.5785367
65 -0.5413790 0.4142294
66 -0.5454902 -0.5413790
67 0.4865678 -0.5454902
68 0.6476894 0.4865678
69 -0.1601196 0.6476894
70 -0.5206660 -0.1601196
71 -0.3636557 -0.5206660
72 -1.6219397 -0.3636557
73 -2.5269113 -1.6219397
74 0.4007503 -2.5269113
75 -0.4121585 0.4007503
76 1.4906790 -0.4121585
77 -0.6178285 1.4906790
78 1.3966391 -0.6178285
79 0.3790488 1.3966391
80 -0.6332848 0.3790488
81 -1.5589693 -0.6332848
82 0.3201895 -1.5589693
83 0.6684023 0.3201895
84 1.6218767 0.6684023
85 0.5268482 1.6218767
86 1.5464157 0.5268482
87 -0.5392449 1.5464157
88 0.5278368 -0.5392449
89 0.3780603 0.5278368
90 -0.5713029 0.3780603
91 -0.6105947 -0.5713029
92 0.4545098 -0.6105947
93 -0.5206660 0.4545098
94 -0.6064835 -0.5206660
95 0.4513872 -0.6064835
96 0.5309594 0.4513872
97 -1.5030757 0.5309594
98 -1.5247772 -1.5030757
99 -0.4514503 -1.5247772
100 0.5620288 -0.4514503
101 0.6394670 0.5620288
102 -0.4080473 0.6394670
103 2.4730887 -0.4080473
104 -1.5423675 2.4730887
105 -1.4256376 -1.5423675
106 -0.3646442 -1.4256376
107 -0.5496014 -0.3646442
108 -1.6395301 -0.5496014
109 1.3098331 -1.6395301
110 -1.2944399 1.3098331
111 0.4834452 -1.2944399
112 -1.5898818 0.4834452
113 0.4803225 -1.5898818
114 -0.2985511 0.4803225
115 0.6869812 -0.2985511
116 0.2840204 0.6869812
117 -0.6539977 0.2840204
118 0.7118054 -0.6539977
119 0.3780603 0.7118054
120 -0.3957136 0.3780603
121 -0.6291736 -0.3957136
122 0.3925279 -0.6291736
123 -0.4524388 0.3925279
124 1.4400422 -0.4524388
125 1.3645811 1.4400422
126 -0.6508751 1.3645811
127 0.4369195 -0.6508751
128 1.3274234 0.4369195
129 1.5381932 1.3274234
130 -0.5165548 1.5381932
131 0.7086827 -0.5165548
132 0.3346572 0.7086827
133 -0.5630805 0.3346572
134 -1.5196775 -0.5630805
135 -0.4586841 -1.5196775
136 1.4865678 -0.4586841
137 0.4152180 1.4865678
138 0.5784736 0.4152180
139 -0.5486128 0.5784736
140 -1.4215264 -0.5486128
141 -0.4938647 -1.4215264
142 -1.4215264 -0.4938647
143 -0.4803856 -1.4215264
144 0.4824566 -0.4803856
145 -0.4700292 0.4824566
146 -0.5847820 -0.4700292
147 -1.5630805 -0.5847820
148 0.5020241 -1.5630805
149 -0.6467639 0.5020241
150 -0.6178285 -0.6467639
151 0.4255745 -0.6178285
152 0.2695527 0.4255745
153 -0.5341451 0.2695527
154 1.3635926 -0.5341451
155 -0.5196775 1.3635926
156 1.6580459 -0.5196775
157 -0.4690406 1.6580459
158 1.6312446 -0.4690406
159 1.4503986 1.6312446
160 -0.4659180 1.4503986
161 -1.6860557 -0.4659180
162 -1.5486128 -1.6860557
163 0.5712398 -1.5486128
164 1.3501135 0.5712398
165 -0.5247772 1.3501135
166 0.6498235 -0.5247772
167 -2.2985511 0.6498235
168 -1.5237887 -2.2985511
169 1.4689775 -1.5237887
170 0.3170669 1.4689775
171 0.4896905 0.3170669
172 -0.1890550 0.4896905
173 -0.3791119 -0.1890550
174 -0.1828097 -0.3791119
175 -0.5754141 -0.1828097
176 -1.5888932 -0.5754141
177 0.6240107 -1.5888932
178 -0.4514503 0.6240107
179 -0.4576956 -0.4514503
180 -0.4844968 -0.4576956
181 0.4938017 -0.4844968
182 -1.6219397 0.4938017
183 -1.1765644 -1.6219397
184 -1.4627953 -1.1765644
185 -2.3140074 -1.4627953
186 -0.5093210 -2.3140074
187 -0.5888932 -0.5093210
188 0.4875564 -0.5888932
189 -0.3098962 0.4875564
190 -0.4803856 -0.3098962
191 -1.4483276 -0.4803856
192 -0.5237887 -1.4483276
193 0.6476894 -0.5237887
194 0.4142294 0.6476894
195 -0.4690406 0.4142294
196 0.3966391 -0.4690406
197 0.6580459 0.3966391
198 0.6570573 0.6580459
199 2.4617437 0.6570573
200 0.4617437 2.4617437
201 -0.3450768 0.4617437
202 -0.6074721 -0.3450768
203 1.6353558 -0.6074721
204 1.6901038 1.6353558
205 0.7892435 1.6901038
206 0.4296857 0.7892435
207 -0.4762744 0.4296857
208 1.8027226 -0.4762744
209 -0.4690406 1.8027226
210 -0.2954285 -0.4690406
211 -0.6260510 -0.2954285
212 -1.5527240 -0.6260510
213 1.3821715 -1.5527240
214 0.4007503 1.3821715
215 0.4255745 0.4007503
216 -0.3161414 0.4255745
217 1.5815963 -0.3161414
218 0.3821715 1.5815963
219 -0.5930044 0.3821715
220 -0.5816594 -0.5930044
221 -0.6829331 -0.5816594
222 -0.6870443 -0.6829331
223 -0.3574104 -0.6870443
224 -1.3501765 -0.3574104
225 0.5991866 -1.3501765
226 -0.5816594 0.5991866
227 -0.5703143 -0.5816594
228 -0.5599578 -0.5703143
229 0.6539347 -0.5599578
230 0.3780603 0.6539347
231 -0.6115833 0.3780603
232 0.5702512 -0.6115833
233 1.4906790 0.5702512
234 0.4906790 1.4906790
235 -0.4690406 0.4906790
236 1.6105316 -0.4690406
237 0.5454271 1.6105316
238 0.3708264 0.5454271
239 -1.5278999 0.3708264
240 -0.5785367 -1.5278999
241 0.5485497 -0.5785367
242 -0.4772630 0.5485497
243 -0.6188171 -0.4772630
244 0.4472760 -0.6188171
245 -2.4328714 0.4472760
246 1.4658549 -2.4328714
247 -0.5908703 1.4658549
248 0.7510972 -0.5908703
249 0.4689775 0.7510972
250 -0.3192641 0.4689775
251 1.4586210 -0.3192641
252 -0.6188171 1.4586210
253 -0.6291736 -0.6188171
254 0.3583359 -0.6291736
255 1.4689775 0.3583359
256 -1.4576956 1.4689775
257 0.4762113 -1.4576956
258 -0.3378429 0.4762113
259 0.4369195 -0.3378429
260 -0.1921776 0.4369195
261 0.5010355 -0.1921776
262 -1.4825197 0.5010355
263 0.6001752 -1.4825197
264 0.4224518 0.6001752
265 1.3645811 0.4224518
266 1.2519624 1.3645811
267 -0.5020871 1.2519624
268 -1.4731518 -0.5020871
269 1.3594814 -1.4731518
270 -0.2768496 1.3594814
271 0.5174803 -0.2768496
272 -0.5093210 0.5174803
273 1.3852941 -0.5093210
274 -0.6539977 1.3852941
275 0.3418911 -0.6539977
276 -1.0516121 0.3418911
277 0.4762113 -1.0516121
278 -0.6188171 0.4762113
279 1.4586210 -0.6188171
280 -1.3429427 1.4586210
281 0.4699661 -1.3429427
282 0.5010355 0.4699661
283 -0.5196775 0.5010355
284 1.4120954 -0.5196775
285 -1.4193923 1.4120954
286 0.4586210 -1.4193923
287 1.3563587 0.4586210
288 -0.6074721 1.3563587
289 0.4969243 -0.6074721
290 -1.5093210 0.4969243
291 -0.2303239 -1.5093210
292 -1.3791119 -0.2303239
293 1.6756362 -1.3791119
294 0.6394670 1.6756362
295 1.4586210 0.6394670
296 1.4400422 1.4586210
297 0.4803225 1.4400422
298 0.3346572 0.4803225
299 0.4865678 0.3346572
300 -0.4174152 0.4865678
301 -1.6653428 -0.4174152
302 0.5289823 -1.6653428
303 -0.4070587 0.5289823
304 0.4142294 -0.4070587
305 0.3718150 0.4142294
306 0.5227370 0.3718150
307 -0.5816594 0.5227370
308 -0.3677669 -0.5816594
309 -0.5341451 -0.3677669
310 1.4793340 -0.5341451
311 -0.4762744 1.4793340
312 1.3852941 -0.4762744
313 -1.6942781 1.3852941
314 0.3821715 -1.6942781
315 -0.5454902 0.3821715
316 1.7665534 -0.5454902
317 1.5526609 1.7665534
318 0.5960640 1.5526609
319 -1.5341451 0.5960640
320 -0.4649294 -1.5341451
321 1.5444385 -0.4649294
322 -2.5310225 1.5444385
323 0.4296857 -2.5310225
324 0.3780603 0.4296857
325 -0.4825197 0.3780603
326 -0.5785367 -0.4825197
327 -1.6477525 -0.5785367
328 1.4069956 -1.6477525
329 1.6394670 1.4069956
330 0.5330935 1.6394670
331 1.3667152 0.5330935
332 -0.6901669 1.3667152
333 0.3274234 -0.6901669
334 1.2767865 0.3274234
335 -0.2664931 1.2767865
336 0.5196144 -0.2664931
337 -0.6147059 0.5196144
338 -2.2293354 -0.6147059
339 0.3635926 -2.2293354
340 2.4337969 0.3635926
341 0.5857075 2.4337969
342 -1.3470539 0.5857075
343 -0.6612316 -1.3470539
344 -1.6436413 -0.6612316
345 2.3377799 -1.6436413
346 -0.4184037 2.3377799
347 0.5164917 -0.4184037
348 1.5495383 0.5164917
349 0.4214633 1.5495383
350 -0.6508751 0.4214633
351 -0.3894684 -0.6508751
352 0.5268482 -0.3894684
353 -1.5486128 0.5268482
354 1.3935165 -1.5486128
355 -0.4938647 1.3935165
356 -0.6105947 -0.4938647
357 -0.5496014 -0.6105947
358 0.4503986 -0.5496014
359 0.3718150 0.4503986
360 0.5526609 0.3718150
361 -0.3832231 0.5526609
362 1.3626040 -0.3832231
363 0.5372047 1.3626040
364 0.6435782 0.5372047
365 -0.4627953 0.6435782
366 0.3635926 -0.4627953
367 -0.4907421 0.3635926
368 0.6467008 -0.4907421
369 -1.4080473 0.6467008
370 1.3667152 -1.4080473
371 1.4834452 1.3667152
372 -1.5486128 1.4834452
373 1.5661400 -1.5486128
374 -2.6436413 1.5661400
375 1.4183406 -2.6436413
376 -1.6209512 1.4183406
377 -0.3781233 -1.6209512
378 0.4214633 -0.3781233
379 -1.4359940 0.4214633
380 0.4865678 -1.4359940
381 -0.5879046 0.4865678
382 1.4482646 -0.5879046
383 -1.6178285 1.4482646
384 -0.6147059 -1.6178285
385 -0.5671917 -0.6147059
386 -2.5744255 -0.5671917
387 1.3821715 -2.5744255
388 -0.5920158 1.3821715
389 1.4328083 -0.5920158
390 0.5423044 1.4328083
391 1.6032978 0.5423044
392 0.4689775 1.6032978
393 0.3708264 0.4689775
394 1.4462874 0.3708264
395 -0.4649294 1.4462874
396 -1.5496014 -0.4649294
397 1.4938017 -1.5496014
398 -0.6147059 1.4938017
399 -0.5103095 -0.6147059
400 -0.4876195 -0.5103095
401 -1.6043494 -0.4876195
402 0.4503986 -1.6043494
403 -0.4545729 0.4503986
404 -0.4979759 -0.4545729
405 -0.5496014 -0.4979759
406 -0.5454902 -0.5496014
407 -0.4989645 -0.5454902
408 -0.2923058 -0.4989645
409 0.4627322 -0.2923058
410 2.6001752 0.4627322
411 0.7118054 2.6001752
412 -0.4576956 0.7118054
413 0.3356458 -0.4576956
414 -1.5568352 0.3356458
415 1.4069956 -1.5568352
416 0.7014489 1.4069956
417 -0.5744255 0.7014489
418 -1.4948533 -0.5744255
419 -0.5165548 -1.4948533
420 -1.6229283 -0.5165548
421 -0.5382563 -1.6229283
422 0.4038730 -0.5382563
423 1.5010355 0.4038730
424 0.6105316 1.5010355
425 -0.6115833 0.6105316
426 0.4762113 -0.6115833
427 -1.7222249 0.4762113
428 1.5092579 -1.7222249
429 -0.2375578 1.5092579
430 0.5640060 -0.2375578
431 0.1912158 0.5640060
432 0.4048615 0.1912158
433 0.3966391 0.4048615
434 -0.6364074 0.3966391
435 0.5712398 -0.6364074
436 -0.4741404 0.5712398
437 -0.5517354 -0.4741404
438 0.5815963 -0.5517354
439 -0.6663313 0.5815963
440 -1.4793971 -0.6663313
441 -0.4359940 -1.4793971
442 -0.5124436 -0.4359940
443 1.4296857 -0.5124436
444 0.6404555 1.4296857
445 0.5309594 0.6404555
446 1.5051467 0.5309594
447 0.2912542 1.5051467
448 -0.6033609 0.2912542
449 1.6435782 -0.6033609
450 1.4255745 1.6435782
451 -0.6395301 1.4255745
452 -1.3161414 -0.6395301
453 0.4255745 -1.3161414
454 1.6467008 0.4255745
455 0.7696761 1.6467008
456 0.4111068 0.7696761
457 -0.4731518 0.4111068
458 -0.4142925 -0.4731518
459 -0.5496014 -0.4142925
460 -1.4039360 -0.5496014
461 0.5123805 -1.4039360
462 -0.4225149 0.5123805
463 -0.5083324 -0.4225149
464 -0.3967022 -0.5083324
465 -0.5527240 -0.3967022
466 -0.2872061 -0.5527240
467 -1.3708895 -0.2872061
468 1.4369195 -1.3708895
469 0.3852941 1.4369195
470 -1.4762744 0.3852941
471 -3.5961270 -1.4762744
472 0.3946620 -3.5961270
473 0.3718150 0.3946620
474 0.4762113 0.3718150
475 0.4410307 0.4762113
476 -0.5888932 0.4410307
477 -0.4039360 -0.5888932
478 0.3501135 -0.4039360
479 -1.3388315 0.3501135
480 0.6353558 -1.3388315
481 -0.2872061 0.6353558
482 1.5164917 -0.2872061
483 0.6032978 1.5164917
484 0.5888301 0.6032978
485 0.4152180 0.5888301
486 0.5784736 0.4152180
487 0.5082693 0.5784736
488 -1.3750007 0.5082693
489 -1.4576956 -1.3750007
490 0.2984880 -1.4576956
491 -3.2944399 0.2984880
492 1.4824566 -3.2944399
493 1.5743624 1.4824566
494 0.3211781 1.5743624
495 1.0455505 0.3211781
496 1.4142294 1.0455505
497 0.4668434 1.4142294
498 -0.4772630 0.4668434
499 -2.3822345 -0.4772630
500 -0.5992497 -2.3822345
501 0.6136543 -0.5992497
502 -1.4287602 0.6136543
503 -0.5052098 -1.4287602
504 -1.2913173 -0.5052098
505 1.4545098 -1.2913173
506 0.8574707 1.4545098
507 1.5413159 0.8574707
508 -1.3274865 1.5413159
509 -0.3057850 -1.3274865
510 -3.5992497 -0.3057850
511 -0.6147059 -3.5992497
512 0.4224518 -0.6147059
513 -0.5310225 0.4224518
514 -0.6539977 -0.5310225
515 0.6684023 -0.6539977
516 2.7417293 0.6684023
517 -1.4287602 2.7417293
518 0.3862827 -1.4287602
519 -0.4907421 0.3862827
520 -0.5527240 -0.4907421
521 0.5557836 -0.5527240
522 -0.5527240 0.5557836
523 -0.6002382 -0.5527240
524 0.5702512 -0.6002382
525 -1.4142925 0.5702512
526 -0.5454902 -1.4142925
527 -0.3998248 -0.5454902
528 0.5299708 -0.3998248
529 0.4576325 0.5299708
530 -0.4576956 0.4576325
531 -1.4762744 -0.4576956
532 -1.4504617 -1.4762744
533 -1.6539977 -1.4504617
534 -0.5464787 -1.6539977
535 -0.7294588 -0.5464787
536 1.4689775 -0.7294588
537 -0.6209512 1.4689775
538 -0.5310225 -0.6209512
539 0.7851323 -0.5310225
540 1.3418911 0.7851323
541 1.4255745 1.3418911
542 -0.5124436 1.4255745
543 -1.3677669 -0.5124436
544 1.3790488 -1.3677669
545 1.3645811 1.3790488
546 -0.5196775 1.3645811
547 0.6218767 -0.5196775
548 -1.2872061 0.6218767
549 0.3614585 -1.2872061
550 -0.5816594 0.3614585
551 0.4286971 -0.5816594
552 1.5557836 0.4286971
553 -0.6240738 1.5557836
554 0.4410307 -0.6240738
555 0.6208881 0.4410307
556 2.2664300 0.6208881
557 0.5237256 2.2664300
558 -0.7159796 0.5237256
559 0.4152180 -0.7159796
560 -1.4101813 0.4152180
561 -0.5165548 -1.4101813
562 -0.6270395 -0.5165548
563 0.3604699 -0.6270395
564 3.5020241 0.3604699
565 0.5237256 3.5020241
566 0.5671286 0.5237256
567 0.3522475 0.5671286
568 -0.5589693 0.3522475
569 0.3894053 -0.5589693
570 1.4482646 0.3894053
571 0.5020241 1.4482646
572 1.4142294 0.5020241
573 0.5299708 1.4142294
574 -0.6260510 0.5299708
575 -0.3615216 -0.6260510
576 0.7272616 -0.3615216
577 0.7241390 0.7272616
578 -1.4111699 0.7241390
579 -0.4256376 -1.4111699
580 -0.5971156 -0.4256376
581 0.5268482 -0.5971156
582 0.5010355 0.5268482
583 0.3098331 0.5010355
584 -0.4256376 0.3098331
585 -2.3202526 -0.4256376
586 1.6281219 -2.3202526
587 -1.5992497 1.6281219
588 -0.2582707 -1.5992497
589 0.7675420 -0.2582707
590 0.4658549 0.7675420
591 0.4979129 0.4658549
592 -1.4835083 0.4979129
593 1.2509738 -1.4835083
594 -1.6405186 1.2509738
595 0.4576325 -1.6405186
596 -0.4111699 0.4576325
597 0.4793340 -0.4111699
598 -0.5722915 0.4793340
599 0.7624422 -0.5722915
600 -0.5310225 0.7624422
601 -1.5093210 -0.5310225
602 0.7303842 -1.5093210
603 1.3491249 0.7303842
604 1.2994766 1.3491249
605 0.3563587 1.2994766
606 -0.5930044 0.3563587
607 0.3894053 -0.5930044
608 0.3450137 0.3894053
609 0.4007503 0.3450137
610 0.3418911 0.4007503
611 -0.5847820 0.3418911
612 -0.5237887 -0.5847820
613 -0.4576956 -0.5237887
614 -1.3460653 -0.4576956
615 0.5557836 -1.3460653
616 -1.4514503 0.5557836
617 -1.3925910 -1.4514503
618 0.3935165 -1.3925910
619 0.6187540 0.3935165
620 -0.5816594 0.6187540
621 0.4545098 -0.5816594
622 0.6652797 0.4545098
623 -0.6137173 0.6652797
624 1.4431648 -0.6137173
625 -0.6281850 1.4431648
626 0.5434500 -0.6281850
627 -0.3708895 0.5434500
628 1.2736639 -0.3708895
629 -1.2664931 1.2736639
630 0.5061353 -1.2664931
631 0.4359310 0.5061353
632 0.4865678 0.4359310
633 0.4328083 0.4865678
634 -0.2447916 0.4328083
635 -0.5020871 -0.2447916
636 0.3604699 -0.5020871
637 -0.4545729 0.3604699
638 -0.6798105 -0.4545729
639 0.5516724 -0.6798105
640 0.3057219 0.5516724
641 -1.3089076 0.3057219
642 -0.4225149 -1.3089076
643 0.7127939 -0.4225149
644 -0.4876195 0.7127939
645 0.7086827 -0.4876195
646 -0.4979759 0.7086827
647 0.5857075 -0.4979759
648 1.2871430 0.5857075
649 0.5640060 1.2871430
650 0.4658549 0.5640060
651 1.5960640 0.4658549
652 -0.5558466 1.5960640
653 NA -0.5558466
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.4576325 0.4762113
[2,] -0.4721632 0.4576325
[3,] 0.4865678 -0.4721632
[4,] 0.5413159 0.4865678
[5,] 0.6105316 0.5413159
[6,] 1.4617437 0.6105316
[7,] 0.5082693 1.4617437
[8,] 0.6322331 0.5082693
[9,] 0.4576325 0.6322331
[10,] -1.4772630 0.4576325
[11,] -1.5496014 -1.4772630
[12,] 0.6363443 -1.5496014
[13,] 0.4482646 0.6363443
[14,] -0.3149959 0.4482646
[15,] -0.2737270 -0.3149959
[16,] 1.7665534 -0.2737270
[17,] 0.7448519 1.7665534
[18,] -1.5681802 0.7448519
[19,] -0.3781233 -1.5681802
[20,] 1.3346572 -0.3781233
[21,] -0.4948533 1.3346572
[22,] 1.3192010 -0.4948533
[23,] -2.6549863 1.3192010
[24,] -1.3967022 -2.6549863
[25,] -0.4039360 -1.3967022
[26,] -0.5527240 -0.4039360
[27,] -0.4473391 -0.5527240
[28,] -0.3677669 -0.4473391
[29,] -1.4762744 -0.3677669
[30,] 1.5784736 -1.4762744
[31,] 1.5237256 1.5784736
[32,] -1.3708895 1.5237256
[33,] 0.4462874 -1.3708895
[34,] 1.4265630 0.4462874
[35,] -0.6188171 1.4265630
[36,] -0.3120302 -0.6188171
[37,] -1.4711747 -0.3120302
[38,] -1.3460653 -1.4711747
[39,] 0.6404555 -1.3460653
[40,] -0.3523106 0.6404555
[41,] 1.6146428 -0.3523106
[42,] -0.4369826 1.6146428
[43,] 1.3821715 -0.4369826
[44,] -1.3894684 1.3821715
[45,] 1.4762113 -1.3894684
[46,] -0.4731518 1.4762113
[47,] 1.6983262 -0.4731518
[48,] -0.4256376 1.6983262
[49,] 2.4545098 -0.4256376
[50,] 0.5092579 2.4545098
[51,] -0.6105947 0.5092579
[52,] -0.3967022 -0.6105947
[53,] -1.5341451 -0.3967022
[54,] 0.4111068 -1.5341451
[55,] -0.3532992 0.4111068
[56,] -0.4152811 -0.3532992
[57,] -2.5228001 -0.4152811
[58,] 0.5526609 -2.5228001
[59,] 0.4586210 0.5526609
[60,] 1.5051467 0.4586210
[61,] -0.6023723 1.5051467
[62,] -0.3853572 -0.6023723
[63,] -0.5785367 -0.3853572
[64,] 0.4142294 -0.5785367
[65,] -0.5413790 0.4142294
[66,] -0.5454902 -0.5413790
[67,] 0.4865678 -0.5454902
[68,] 0.6476894 0.4865678
[69,] -0.1601196 0.6476894
[70,] -0.5206660 -0.1601196
[71,] -0.3636557 -0.5206660
[72,] -1.6219397 -0.3636557
[73,] -2.5269113 -1.6219397
[74,] 0.4007503 -2.5269113
[75,] -0.4121585 0.4007503
[76,] 1.4906790 -0.4121585
[77,] -0.6178285 1.4906790
[78,] 1.3966391 -0.6178285
[79,] 0.3790488 1.3966391
[80,] -0.6332848 0.3790488
[81,] -1.5589693 -0.6332848
[82,] 0.3201895 -1.5589693
[83,] 0.6684023 0.3201895
[84,] 1.6218767 0.6684023
[85,] 0.5268482 1.6218767
[86,] 1.5464157 0.5268482
[87,] -0.5392449 1.5464157
[88,] 0.5278368 -0.5392449
[89,] 0.3780603 0.5278368
[90,] -0.5713029 0.3780603
[91,] -0.6105947 -0.5713029
[92,] 0.4545098 -0.6105947
[93,] -0.5206660 0.4545098
[94,] -0.6064835 -0.5206660
[95,] 0.4513872 -0.6064835
[96,] 0.5309594 0.4513872
[97,] -1.5030757 0.5309594
[98,] -1.5247772 -1.5030757
[99,] -0.4514503 -1.5247772
[100,] 0.5620288 -0.4514503
[101,] 0.6394670 0.5620288
[102,] -0.4080473 0.6394670
[103,] 2.4730887 -0.4080473
[104,] -1.5423675 2.4730887
[105,] -1.4256376 -1.5423675
[106,] -0.3646442 -1.4256376
[107,] -0.5496014 -0.3646442
[108,] -1.6395301 -0.5496014
[109,] 1.3098331 -1.6395301
[110,] -1.2944399 1.3098331
[111,] 0.4834452 -1.2944399
[112,] -1.5898818 0.4834452
[113,] 0.4803225 -1.5898818
[114,] -0.2985511 0.4803225
[115,] 0.6869812 -0.2985511
[116,] 0.2840204 0.6869812
[117,] -0.6539977 0.2840204
[118,] 0.7118054 -0.6539977
[119,] 0.3780603 0.7118054
[120,] -0.3957136 0.3780603
[121,] -0.6291736 -0.3957136
[122,] 0.3925279 -0.6291736
[123,] -0.4524388 0.3925279
[124,] 1.4400422 -0.4524388
[125,] 1.3645811 1.4400422
[126,] -0.6508751 1.3645811
[127,] 0.4369195 -0.6508751
[128,] 1.3274234 0.4369195
[129,] 1.5381932 1.3274234
[130,] -0.5165548 1.5381932
[131,] 0.7086827 -0.5165548
[132,] 0.3346572 0.7086827
[133,] -0.5630805 0.3346572
[134,] -1.5196775 -0.5630805
[135,] -0.4586841 -1.5196775
[136,] 1.4865678 -0.4586841
[137,] 0.4152180 1.4865678
[138,] 0.5784736 0.4152180
[139,] -0.5486128 0.5784736
[140,] -1.4215264 -0.5486128
[141,] -0.4938647 -1.4215264
[142,] -1.4215264 -0.4938647
[143,] -0.4803856 -1.4215264
[144,] 0.4824566 -0.4803856
[145,] -0.4700292 0.4824566
[146,] -0.5847820 -0.4700292
[147,] -1.5630805 -0.5847820
[148,] 0.5020241 -1.5630805
[149,] -0.6467639 0.5020241
[150,] -0.6178285 -0.6467639
[151,] 0.4255745 -0.6178285
[152,] 0.2695527 0.4255745
[153,] -0.5341451 0.2695527
[154,] 1.3635926 -0.5341451
[155,] -0.5196775 1.3635926
[156,] 1.6580459 -0.5196775
[157,] -0.4690406 1.6580459
[158,] 1.6312446 -0.4690406
[159,] 1.4503986 1.6312446
[160,] -0.4659180 1.4503986
[161,] -1.6860557 -0.4659180
[162,] -1.5486128 -1.6860557
[163,] 0.5712398 -1.5486128
[164,] 1.3501135 0.5712398
[165,] -0.5247772 1.3501135
[166,] 0.6498235 -0.5247772
[167,] -2.2985511 0.6498235
[168,] -1.5237887 -2.2985511
[169,] 1.4689775 -1.5237887
[170,] 0.3170669 1.4689775
[171,] 0.4896905 0.3170669
[172,] -0.1890550 0.4896905
[173,] -0.3791119 -0.1890550
[174,] -0.1828097 -0.3791119
[175,] -0.5754141 -0.1828097
[176,] -1.5888932 -0.5754141
[177,] 0.6240107 -1.5888932
[178,] -0.4514503 0.6240107
[179,] -0.4576956 -0.4514503
[180,] -0.4844968 -0.4576956
[181,] 0.4938017 -0.4844968
[182,] -1.6219397 0.4938017
[183,] -1.1765644 -1.6219397
[184,] -1.4627953 -1.1765644
[185,] -2.3140074 -1.4627953
[186,] -0.5093210 -2.3140074
[187,] -0.5888932 -0.5093210
[188,] 0.4875564 -0.5888932
[189,] -0.3098962 0.4875564
[190,] -0.4803856 -0.3098962
[191,] -1.4483276 -0.4803856
[192,] -0.5237887 -1.4483276
[193,] 0.6476894 -0.5237887
[194,] 0.4142294 0.6476894
[195,] -0.4690406 0.4142294
[196,] 0.3966391 -0.4690406
[197,] 0.6580459 0.3966391
[198,] 0.6570573 0.6580459
[199,] 2.4617437 0.6570573
[200,] 0.4617437 2.4617437
[201,] -0.3450768 0.4617437
[202,] -0.6074721 -0.3450768
[203,] 1.6353558 -0.6074721
[204,] 1.6901038 1.6353558
[205,] 0.7892435 1.6901038
[206,] 0.4296857 0.7892435
[207,] -0.4762744 0.4296857
[208,] 1.8027226 -0.4762744
[209,] -0.4690406 1.8027226
[210,] -0.2954285 -0.4690406
[211,] -0.6260510 -0.2954285
[212,] -1.5527240 -0.6260510
[213,] 1.3821715 -1.5527240
[214,] 0.4007503 1.3821715
[215,] 0.4255745 0.4007503
[216,] -0.3161414 0.4255745
[217,] 1.5815963 -0.3161414
[218,] 0.3821715 1.5815963
[219,] -0.5930044 0.3821715
[220,] -0.5816594 -0.5930044
[221,] -0.6829331 -0.5816594
[222,] -0.6870443 -0.6829331
[223,] -0.3574104 -0.6870443
[224,] -1.3501765 -0.3574104
[225,] 0.5991866 -1.3501765
[226,] -0.5816594 0.5991866
[227,] -0.5703143 -0.5816594
[228,] -0.5599578 -0.5703143
[229,] 0.6539347 -0.5599578
[230,] 0.3780603 0.6539347
[231,] -0.6115833 0.3780603
[232,] 0.5702512 -0.6115833
[233,] 1.4906790 0.5702512
[234,] 0.4906790 1.4906790
[235,] -0.4690406 0.4906790
[236,] 1.6105316 -0.4690406
[237,] 0.5454271 1.6105316
[238,] 0.3708264 0.5454271
[239,] -1.5278999 0.3708264
[240,] -0.5785367 -1.5278999
[241,] 0.5485497 -0.5785367
[242,] -0.4772630 0.5485497
[243,] -0.6188171 -0.4772630
[244,] 0.4472760 -0.6188171
[245,] -2.4328714 0.4472760
[246,] 1.4658549 -2.4328714
[247,] -0.5908703 1.4658549
[248,] 0.7510972 -0.5908703
[249,] 0.4689775 0.7510972
[250,] -0.3192641 0.4689775
[251,] 1.4586210 -0.3192641
[252,] -0.6188171 1.4586210
[253,] -0.6291736 -0.6188171
[254,] 0.3583359 -0.6291736
[255,] 1.4689775 0.3583359
[256,] -1.4576956 1.4689775
[257,] 0.4762113 -1.4576956
[258,] -0.3378429 0.4762113
[259,] 0.4369195 -0.3378429
[260,] -0.1921776 0.4369195
[261,] 0.5010355 -0.1921776
[262,] -1.4825197 0.5010355
[263,] 0.6001752 -1.4825197
[264,] 0.4224518 0.6001752
[265,] 1.3645811 0.4224518
[266,] 1.2519624 1.3645811
[267,] -0.5020871 1.2519624
[268,] -1.4731518 -0.5020871
[269,] 1.3594814 -1.4731518
[270,] -0.2768496 1.3594814
[271,] 0.5174803 -0.2768496
[272,] -0.5093210 0.5174803
[273,] 1.3852941 -0.5093210
[274,] -0.6539977 1.3852941
[275,] 0.3418911 -0.6539977
[276,] -1.0516121 0.3418911
[277,] 0.4762113 -1.0516121
[278,] -0.6188171 0.4762113
[279,] 1.4586210 -0.6188171
[280,] -1.3429427 1.4586210
[281,] 0.4699661 -1.3429427
[282,] 0.5010355 0.4699661
[283,] -0.5196775 0.5010355
[284,] 1.4120954 -0.5196775
[285,] -1.4193923 1.4120954
[286,] 0.4586210 -1.4193923
[287,] 1.3563587 0.4586210
[288,] -0.6074721 1.3563587
[289,] 0.4969243 -0.6074721
[290,] -1.5093210 0.4969243
[291,] -0.2303239 -1.5093210
[292,] -1.3791119 -0.2303239
[293,] 1.6756362 -1.3791119
[294,] 0.6394670 1.6756362
[295,] 1.4586210 0.6394670
[296,] 1.4400422 1.4586210
[297,] 0.4803225 1.4400422
[298,] 0.3346572 0.4803225
[299,] 0.4865678 0.3346572
[300,] -0.4174152 0.4865678
[301,] -1.6653428 -0.4174152
[302,] 0.5289823 -1.6653428
[303,] -0.4070587 0.5289823
[304,] 0.4142294 -0.4070587
[305,] 0.3718150 0.4142294
[306,] 0.5227370 0.3718150
[307,] -0.5816594 0.5227370
[308,] -0.3677669 -0.5816594
[309,] -0.5341451 -0.3677669
[310,] 1.4793340 -0.5341451
[311,] -0.4762744 1.4793340
[312,] 1.3852941 -0.4762744
[313,] -1.6942781 1.3852941
[314,] 0.3821715 -1.6942781
[315,] -0.5454902 0.3821715
[316,] 1.7665534 -0.5454902
[317,] 1.5526609 1.7665534
[318,] 0.5960640 1.5526609
[319,] -1.5341451 0.5960640
[320,] -0.4649294 -1.5341451
[321,] 1.5444385 -0.4649294
[322,] -2.5310225 1.5444385
[323,] 0.4296857 -2.5310225
[324,] 0.3780603 0.4296857
[325,] -0.4825197 0.3780603
[326,] -0.5785367 -0.4825197
[327,] -1.6477525 -0.5785367
[328,] 1.4069956 -1.6477525
[329,] 1.6394670 1.4069956
[330,] 0.5330935 1.6394670
[331,] 1.3667152 0.5330935
[332,] -0.6901669 1.3667152
[333,] 0.3274234 -0.6901669
[334,] 1.2767865 0.3274234
[335,] -0.2664931 1.2767865
[336,] 0.5196144 -0.2664931
[337,] -0.6147059 0.5196144
[338,] -2.2293354 -0.6147059
[339,] 0.3635926 -2.2293354
[340,] 2.4337969 0.3635926
[341,] 0.5857075 2.4337969
[342,] -1.3470539 0.5857075
[343,] -0.6612316 -1.3470539
[344,] -1.6436413 -0.6612316
[345,] 2.3377799 -1.6436413
[346,] -0.4184037 2.3377799
[347,] 0.5164917 -0.4184037
[348,] 1.5495383 0.5164917
[349,] 0.4214633 1.5495383
[350,] -0.6508751 0.4214633
[351,] -0.3894684 -0.6508751
[352,] 0.5268482 -0.3894684
[353,] -1.5486128 0.5268482
[354,] 1.3935165 -1.5486128
[355,] -0.4938647 1.3935165
[356,] -0.6105947 -0.4938647
[357,] -0.5496014 -0.6105947
[358,] 0.4503986 -0.5496014
[359,] 0.3718150 0.4503986
[360,] 0.5526609 0.3718150
[361,] -0.3832231 0.5526609
[362,] 1.3626040 -0.3832231
[363,] 0.5372047 1.3626040
[364,] 0.6435782 0.5372047
[365,] -0.4627953 0.6435782
[366,] 0.3635926 -0.4627953
[367,] -0.4907421 0.3635926
[368,] 0.6467008 -0.4907421
[369,] -1.4080473 0.6467008
[370,] 1.3667152 -1.4080473
[371,] 1.4834452 1.3667152
[372,] -1.5486128 1.4834452
[373,] 1.5661400 -1.5486128
[374,] -2.6436413 1.5661400
[375,] 1.4183406 -2.6436413
[376,] -1.6209512 1.4183406
[377,] -0.3781233 -1.6209512
[378,] 0.4214633 -0.3781233
[379,] -1.4359940 0.4214633
[380,] 0.4865678 -1.4359940
[381,] -0.5879046 0.4865678
[382,] 1.4482646 -0.5879046
[383,] -1.6178285 1.4482646
[384,] -0.6147059 -1.6178285
[385,] -0.5671917 -0.6147059
[386,] -2.5744255 -0.5671917
[387,] 1.3821715 -2.5744255
[388,] -0.5920158 1.3821715
[389,] 1.4328083 -0.5920158
[390,] 0.5423044 1.4328083
[391,] 1.6032978 0.5423044
[392,] 0.4689775 1.6032978
[393,] 0.3708264 0.4689775
[394,] 1.4462874 0.3708264
[395,] -0.4649294 1.4462874
[396,] -1.5496014 -0.4649294
[397,] 1.4938017 -1.5496014
[398,] -0.6147059 1.4938017
[399,] -0.5103095 -0.6147059
[400,] -0.4876195 -0.5103095
[401,] -1.6043494 -0.4876195
[402,] 0.4503986 -1.6043494
[403,] -0.4545729 0.4503986
[404,] -0.4979759 -0.4545729
[405,] -0.5496014 -0.4979759
[406,] -0.5454902 -0.5496014
[407,] -0.4989645 -0.5454902
[408,] -0.2923058 -0.4989645
[409,] 0.4627322 -0.2923058
[410,] 2.6001752 0.4627322
[411,] 0.7118054 2.6001752
[412,] -0.4576956 0.7118054
[413,] 0.3356458 -0.4576956
[414,] -1.5568352 0.3356458
[415,] 1.4069956 -1.5568352
[416,] 0.7014489 1.4069956
[417,] -0.5744255 0.7014489
[418,] -1.4948533 -0.5744255
[419,] -0.5165548 -1.4948533
[420,] -1.6229283 -0.5165548
[421,] -0.5382563 -1.6229283
[422,] 0.4038730 -0.5382563
[423,] 1.5010355 0.4038730
[424,] 0.6105316 1.5010355
[425,] -0.6115833 0.6105316
[426,] 0.4762113 -0.6115833
[427,] -1.7222249 0.4762113
[428,] 1.5092579 -1.7222249
[429,] -0.2375578 1.5092579
[430,] 0.5640060 -0.2375578
[431,] 0.1912158 0.5640060
[432,] 0.4048615 0.1912158
[433,] 0.3966391 0.4048615
[434,] -0.6364074 0.3966391
[435,] 0.5712398 -0.6364074
[436,] -0.4741404 0.5712398
[437,] -0.5517354 -0.4741404
[438,] 0.5815963 -0.5517354
[439,] -0.6663313 0.5815963
[440,] -1.4793971 -0.6663313
[441,] -0.4359940 -1.4793971
[442,] -0.5124436 -0.4359940
[443,] 1.4296857 -0.5124436
[444,] 0.6404555 1.4296857
[445,] 0.5309594 0.6404555
[446,] 1.5051467 0.5309594
[447,] 0.2912542 1.5051467
[448,] -0.6033609 0.2912542
[449,] 1.6435782 -0.6033609
[450,] 1.4255745 1.6435782
[451,] -0.6395301 1.4255745
[452,] -1.3161414 -0.6395301
[453,] 0.4255745 -1.3161414
[454,] 1.6467008 0.4255745
[455,] 0.7696761 1.6467008
[456,] 0.4111068 0.7696761
[457,] -0.4731518 0.4111068
[458,] -0.4142925 -0.4731518
[459,] -0.5496014 -0.4142925
[460,] -1.4039360 -0.5496014
[461,] 0.5123805 -1.4039360
[462,] -0.4225149 0.5123805
[463,] -0.5083324 -0.4225149
[464,] -0.3967022 -0.5083324
[465,] -0.5527240 -0.3967022
[466,] -0.2872061 -0.5527240
[467,] -1.3708895 -0.2872061
[468,] 1.4369195 -1.3708895
[469,] 0.3852941 1.4369195
[470,] -1.4762744 0.3852941
[471,] -3.5961270 -1.4762744
[472,] 0.3946620 -3.5961270
[473,] 0.3718150 0.3946620
[474,] 0.4762113 0.3718150
[475,] 0.4410307 0.4762113
[476,] -0.5888932 0.4410307
[477,] -0.4039360 -0.5888932
[478,] 0.3501135 -0.4039360
[479,] -1.3388315 0.3501135
[480,] 0.6353558 -1.3388315
[481,] -0.2872061 0.6353558
[482,] 1.5164917 -0.2872061
[483,] 0.6032978 1.5164917
[484,] 0.5888301 0.6032978
[485,] 0.4152180 0.5888301
[486,] 0.5784736 0.4152180
[487,] 0.5082693 0.5784736
[488,] -1.3750007 0.5082693
[489,] -1.4576956 -1.3750007
[490,] 0.2984880 -1.4576956
[491,] -3.2944399 0.2984880
[492,] 1.4824566 -3.2944399
[493,] 1.5743624 1.4824566
[494,] 0.3211781 1.5743624
[495,] 1.0455505 0.3211781
[496,] 1.4142294 1.0455505
[497,] 0.4668434 1.4142294
[498,] -0.4772630 0.4668434
[499,] -2.3822345 -0.4772630
[500,] -0.5992497 -2.3822345
[501,] 0.6136543 -0.5992497
[502,] -1.4287602 0.6136543
[503,] -0.5052098 -1.4287602
[504,] -1.2913173 -0.5052098
[505,] 1.4545098 -1.2913173
[506,] 0.8574707 1.4545098
[507,] 1.5413159 0.8574707
[508,] -1.3274865 1.5413159
[509,] -0.3057850 -1.3274865
[510,] -3.5992497 -0.3057850
[511,] -0.6147059 -3.5992497
[512,] 0.4224518 -0.6147059
[513,] -0.5310225 0.4224518
[514,] -0.6539977 -0.5310225
[515,] 0.6684023 -0.6539977
[516,] 2.7417293 0.6684023
[517,] -1.4287602 2.7417293
[518,] 0.3862827 -1.4287602
[519,] -0.4907421 0.3862827
[520,] -0.5527240 -0.4907421
[521,] 0.5557836 -0.5527240
[522,] -0.5527240 0.5557836
[523,] -0.6002382 -0.5527240
[524,] 0.5702512 -0.6002382
[525,] -1.4142925 0.5702512
[526,] -0.5454902 -1.4142925
[527,] -0.3998248 -0.5454902
[528,] 0.5299708 -0.3998248
[529,] 0.4576325 0.5299708
[530,] -0.4576956 0.4576325
[531,] -1.4762744 -0.4576956
[532,] -1.4504617 -1.4762744
[533,] -1.6539977 -1.4504617
[534,] -0.5464787 -1.6539977
[535,] -0.7294588 -0.5464787
[536,] 1.4689775 -0.7294588
[537,] -0.6209512 1.4689775
[538,] -0.5310225 -0.6209512
[539,] 0.7851323 -0.5310225
[540,] 1.3418911 0.7851323
[541,] 1.4255745 1.3418911
[542,] -0.5124436 1.4255745
[543,] -1.3677669 -0.5124436
[544,] 1.3790488 -1.3677669
[545,] 1.3645811 1.3790488
[546,] -0.5196775 1.3645811
[547,] 0.6218767 -0.5196775
[548,] -1.2872061 0.6218767
[549,] 0.3614585 -1.2872061
[550,] -0.5816594 0.3614585
[551,] 0.4286971 -0.5816594
[552,] 1.5557836 0.4286971
[553,] -0.6240738 1.5557836
[554,] 0.4410307 -0.6240738
[555,] 0.6208881 0.4410307
[556,] 2.2664300 0.6208881
[557,] 0.5237256 2.2664300
[558,] -0.7159796 0.5237256
[559,] 0.4152180 -0.7159796
[560,] -1.4101813 0.4152180
[561,] -0.5165548 -1.4101813
[562,] -0.6270395 -0.5165548
[563,] 0.3604699 -0.6270395
[564,] 3.5020241 0.3604699
[565,] 0.5237256 3.5020241
[566,] 0.5671286 0.5237256
[567,] 0.3522475 0.5671286
[568,] -0.5589693 0.3522475
[569,] 0.3894053 -0.5589693
[570,] 1.4482646 0.3894053
[571,] 0.5020241 1.4482646
[572,] 1.4142294 0.5020241
[573,] 0.5299708 1.4142294
[574,] -0.6260510 0.5299708
[575,] -0.3615216 -0.6260510
[576,] 0.7272616 -0.3615216
[577,] 0.7241390 0.7272616
[578,] -1.4111699 0.7241390
[579,] -0.4256376 -1.4111699
[580,] -0.5971156 -0.4256376
[581,] 0.5268482 -0.5971156
[582,] 0.5010355 0.5268482
[583,] 0.3098331 0.5010355
[584,] -0.4256376 0.3098331
[585,] -2.3202526 -0.4256376
[586,] 1.6281219 -2.3202526
[587,] -1.5992497 1.6281219
[588,] -0.2582707 -1.5992497
[589,] 0.7675420 -0.2582707
[590,] 0.4658549 0.7675420
[591,] 0.4979129 0.4658549
[592,] -1.4835083 0.4979129
[593,] 1.2509738 -1.4835083
[594,] -1.6405186 1.2509738
[595,] 0.4576325 -1.6405186
[596,] -0.4111699 0.4576325
[597,] 0.4793340 -0.4111699
[598,] -0.5722915 0.4793340
[599,] 0.7624422 -0.5722915
[600,] -0.5310225 0.7624422
[601,] -1.5093210 -0.5310225
[602,] 0.7303842 -1.5093210
[603,] 1.3491249 0.7303842
[604,] 1.2994766 1.3491249
[605,] 0.3563587 1.2994766
[606,] -0.5930044 0.3563587
[607,] 0.3894053 -0.5930044
[608,] 0.3450137 0.3894053
[609,] 0.4007503 0.3450137
[610,] 0.3418911 0.4007503
[611,] -0.5847820 0.3418911
[612,] -0.5237887 -0.5847820
[613,] -0.4576956 -0.5237887
[614,] -1.3460653 -0.4576956
[615,] 0.5557836 -1.3460653
[616,] -1.4514503 0.5557836
[617,] -1.3925910 -1.4514503
[618,] 0.3935165 -1.3925910
[619,] 0.6187540 0.3935165
[620,] -0.5816594 0.6187540
[621,] 0.4545098 -0.5816594
[622,] 0.6652797 0.4545098
[623,] -0.6137173 0.6652797
[624,] 1.4431648 -0.6137173
[625,] -0.6281850 1.4431648
[626,] 0.5434500 -0.6281850
[627,] -0.3708895 0.5434500
[628,] 1.2736639 -0.3708895
[629,] -1.2664931 1.2736639
[630,] 0.5061353 -1.2664931
[631,] 0.4359310 0.5061353
[632,] 0.4865678 0.4359310
[633,] 0.4328083 0.4865678
[634,] -0.2447916 0.4328083
[635,] -0.5020871 -0.2447916
[636,] 0.3604699 -0.5020871
[637,] -0.4545729 0.3604699
[638,] -0.6798105 -0.4545729
[639,] 0.5516724 -0.6798105
[640,] 0.3057219 0.5516724
[641,] -1.3089076 0.3057219
[642,] -0.4225149 -1.3089076
[643,] 0.7127939 -0.4225149
[644,] -0.4876195 0.7127939
[645,] 0.7086827 -0.4876195
[646,] -0.4979759 0.7086827
[647,] 0.5857075 -0.4979759
[648,] 1.2871430 0.5857075
[649,] 0.5640060 1.2871430
[650,] 0.4658549 0.5640060
[651,] 1.5960640 0.4658549
[652,] -0.5558466 1.5960640
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.4576325 0.4762113
2 -0.4721632 0.4576325
3 0.4865678 -0.4721632
4 0.5413159 0.4865678
5 0.6105316 0.5413159
6 1.4617437 0.6105316
7 0.5082693 1.4617437
8 0.6322331 0.5082693
9 0.4576325 0.6322331
10 -1.4772630 0.4576325
11 -1.5496014 -1.4772630
12 0.6363443 -1.5496014
13 0.4482646 0.6363443
14 -0.3149959 0.4482646
15 -0.2737270 -0.3149959
16 1.7665534 -0.2737270
17 0.7448519 1.7665534
18 -1.5681802 0.7448519
19 -0.3781233 -1.5681802
20 1.3346572 -0.3781233
21 -0.4948533 1.3346572
22 1.3192010 -0.4948533
23 -2.6549863 1.3192010
24 -1.3967022 -2.6549863
25 -0.4039360 -1.3967022
26 -0.5527240 -0.4039360
27 -0.4473391 -0.5527240
28 -0.3677669 -0.4473391
29 -1.4762744 -0.3677669
30 1.5784736 -1.4762744
31 1.5237256 1.5784736
32 -1.3708895 1.5237256
33 0.4462874 -1.3708895
34 1.4265630 0.4462874
35 -0.6188171 1.4265630
36 -0.3120302 -0.6188171
37 -1.4711747 -0.3120302
38 -1.3460653 -1.4711747
39 0.6404555 -1.3460653
40 -0.3523106 0.6404555
41 1.6146428 -0.3523106
42 -0.4369826 1.6146428
43 1.3821715 -0.4369826
44 -1.3894684 1.3821715
45 1.4762113 -1.3894684
46 -0.4731518 1.4762113
47 1.6983262 -0.4731518
48 -0.4256376 1.6983262
49 2.4545098 -0.4256376
50 0.5092579 2.4545098
51 -0.6105947 0.5092579
52 -0.3967022 -0.6105947
53 -1.5341451 -0.3967022
54 0.4111068 -1.5341451
55 -0.3532992 0.4111068
56 -0.4152811 -0.3532992
57 -2.5228001 -0.4152811
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639 0.5516724 -0.6798105
640 0.3057219 0.5516724
641 -1.3089076 0.3057219
642 -0.4225149 -1.3089076
643 0.7127939 -0.4225149
644 -0.4876195 0.7127939
645 0.7086827 -0.4876195
646 -0.4979759 0.7086827
647 0.5857075 -0.4979759
648 1.2871430 0.5857075
649 0.5640060 1.2871430
650 0.4658549 0.5640060
651 1.5960640 0.4658549
652 -0.5558466 1.5960640
> 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/7n4f41321619915.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8w7jo1321619915.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/943531321619915.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10jkcv1321619915.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11rg311321619915.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/12wi1j1321619915.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/13c2yz1321619916.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/14gpn31321619916.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/15u38q1321619916.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/16arff1321619916.tab")
+ }
>
> try(system("convert tmp/1rxjg1321619915.ps tmp/1rxjg1321619915.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ygf91321619915.ps tmp/2ygf91321619915.png",intern=TRUE))
character(0)
> try(system("convert tmp/3b7471321619915.ps tmp/3b7471321619915.png",intern=TRUE))
character(0)
> try(system("convert tmp/49ffz1321619915.ps tmp/49ffz1321619915.png",intern=TRUE))
character(0)
> try(system("convert tmp/5nt0f1321619915.ps tmp/5nt0f1321619915.png",intern=TRUE))
character(0)
> try(system("convert tmp/6neqn1321619915.ps tmp/6neqn1321619915.png",intern=TRUE))
character(0)
> try(system("convert tmp/7n4f41321619915.ps tmp/7n4f41321619915.png",intern=TRUE))
character(0)
> try(system("convert tmp/8w7jo1321619915.ps tmp/8w7jo1321619915.png",intern=TRUE))
character(0)
> try(system("convert tmp/943531321619915.ps tmp/943531321619915.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jkcv1321619915.ps tmp/10jkcv1321619915.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
21.402 0.704 22.534