R version 3.2.3 (2015-12-10) -- "Wooden Christmas-Tree"
Copyright (C) 2015 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(0
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+ ,dim=c(2
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+ ,'Temperatuur')
+ ,1:660))
> y <- array(NA,dim=c(2,660),dimnames=list(c('Orkanen','Temperatuur'),1:660))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par5 = '10'
> par4 = '10'
> par3 = 'Seasonal Differences (s=12)'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> mywarning <- ''
> par1 <- as.numeric(par1)
> if(is.na(par1)) {
+ par1 <- 1
+ mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
+ }
> if (par4=='') par4 <- 0
> par4 <- as.numeric(par4)
> if (par5=='') par5 <- 0
> par5 <- as.numeric(par5)
> x <- na.omit(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'){
+ (n <- n -1)
+ x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par3 == 'Seasonal Differences (s=12)'){
+ (n <- n - 12)
+ x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
+ for (i in 1:n) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+12,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par3 == 'First and Seasonal Differences (s=12)'){
+ (n <- n -1)
+ x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ (n <- n - 12)
+ x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
+ for (i in 1:n) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+12,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if(par4 > 0) {
+ x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
+ for (i in 1:(n-par4)) {
+ for (j in 1:par4) {
+ x2[i,j] <- x[i+par4-j,par1]
+ }
+ }
+ x <- cbind(x[(par4+1):n,], x2)
+ n <- n - par4
+ }
> if(par5 > 0) {
+ x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
+ for (i in 1:(n-par5*12)) {
+ for (j in 1:par5) {
+ x2[i,j] <- x[i+par5*12-j*12,par1]
+ }
+ }
+ x <- cbind(x[(par5*12+1):n,], x2)
+ n <- n - par5*12
+ }
> 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[n,]))
[1] 33
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
(1-B12)Orkanen (1-B12)Temperatuur (1-B12)Orkanen(t-1) (1-B12)Orkanen(t-2)
131 0 -0.14 1 -1
132 0 -0.07 0 1
133 0 -0.10 0 0
134 0 0.07 0 0
135 0 -0.51 0 0
136 0 -0.25 0 0
137 -1 -0.14 0 0
138 -1 -0.04 -1 0
139 1 -0.05 -1 -1
140 1 0.01 1 -1
141 -1 0.06 1 1
142 -2 0.06 -1 1
143 0 0.01 -2 -1
144 0 0.14 0 -2
145 0 0.05 0 0
146 0 0.01 0 0
147 0 0.43 0 0
148 0 0.19 0 0
149 0 0.22 0 0
150 -1 0.10 0 0
151 -1 -0.03 -1 0
152 -2 0.02 -1 -1
153 4 -0.03 -2 -1
154 2 0.06 4 -2
155 3 0.12 2 4
156 1 -0.15 3 2
157 0 0.05 1 3
158 0 0.01 0 1
159 0 0.22 0 0
160 0 0.12 0 0
161 0 -0.06 0 0
162 0 -0.09 0 0
163 -1 0.11 0 0
164 1 -0.05 -1 0
165 -6 0.07 1 -1
166 2 -0.07 -6 1
167 1 -0.02 2 -6
168 0 0.02 1 2
169 0 -0.05 0 1
170 0 -0.11 0 0
171 0 -0.27 0 0
172 0 -0.11 0 0
173 0 0.14 0 0
174 0 0.13 0 0
175 1 -0.04 0 0
176 1 0.07 1 0
177 1 -0.02 1 1
178 -3 -0.01 1 1
179 -1 -0.02 -3 1
180 1 -0.06 -1 -3
181 0 -0.14 1 -1
182 0 -0.07 0 1
183 0 -0.10 0 0
184 0 0.07 0 0
185 0 -0.51 0 0
186 0 -0.25 0 0
187 -1 -0.14 0 0
188 -1 -0.04 -1 0
189 1 -0.05 -1 -1
190 1 0.01 1 -1
191 -1 0.06 1 1
192 -2 0.06 -1 1
193 0 0.01 -2 -1
194 0 0.14 0 -2
195 0 0.05 0 0
196 0 0.01 0 0
197 0 0.43 0 0
198 0 0.19 0 0
199 0 0.22 0 0
200 -1 0.10 0 0
201 -1 -0.03 -1 0
202 -2 0.02 -1 -1
203 4 -0.03 -2 -1
204 2 -0.01 4 -2
205 2 0.11 2 4
206 0 -0.27 2 2
207 0 0.03 0 2
208 0 -0.04 0 0
209 0 -0.07 0 0
210 0 -0.03 0 0
211 0 -0.21 0 0
212 0 -0.12 0 0
213 -1 -0.01 0 0
214 2 -0.06 -1 0
215 -4 -0.04 2 -1
216 -1 -0.01 -4 2
217 -2 0.02 -1 -4
218 0 0.12 -2 -1
219 0 -0.12 0 -2
220 0 0.03 0 0
221 0 -0.12 0 0
222 0 -0.10 0 0
223 0 0.08 0 0
224 0 0.03 0 0
225 1 0.13 0 0
226 -1 0.23 1 0
227 3 0.21 -1 1
228 1 0.13 3 -1
229 0 0.11 1 3
230 0 0.01 0 1
231 0 0.05 0 0
232 0 -0.28 0 0
233 0 -0.16 0 0
234 0 -0.20 0 0
235 0 -0.20 0 0
236 1 -0.08 0 0
237 0 -0.23 1 0
238 0 -0.12 0 1
239 -5 -0.22 0 0
240 -2 -0.25 -5 0
241 0 -0.29 -2 -5
242 0 -0.14 0 -2
243 0 0.01 0 0
244 0 0.28 0 0
245 0 0.35 0 0
246 0 0.32 0 0
247 1 0.20 0 0
248 4 0.10 1 0
249 5 0.17 4 1
250 4 0.01 5 4
251 1 0.18 4 5
252 0 0.33 1 4
253 0 0.23 0 1
254 0 0.36 0 0
255 0 0.00 0 0
256 0 -0.12 0 0
257 1 -0.17 0 0
258 0 -0.09 1 0
259 1 -0.05 0 1
260 -3 -0.13 1 0
261 -3 -0.01 -3 1
262 -3 -0.13 -3 -3
263 -1 -0.17 -3 -3
264 0 -0.37 -1 -3
265 0 -0.13 0 -1
266 0 -0.45 0 0
267 0 -0.23 0 0
268 0 -0.18 0 0
269 -1 -0.11 0 0
270 0 -0.23 -1 0
271 -1 -0.09 0 -1
272 2 0.00 -1 0
273 3 -0.11 2 -1
274 -1 -0.06 3 2
275 1 -0.04 -1 3
276 0 0.05 1 -1
277 0 -0.22 0 1
278 0 -0.01 0 0
279 0 0.19 0 0
280 0 0.10 0 0
281 1 0.11 0 0
282 1 0.26 1 0
283 -1 0.12 1 1
284 -2 0.20 -1 1
285 -4 0.06 -2 -1
286 -1 0.15 -4 -2
287 0 0.08 -1 -4
288 0 0.27 0 -1
289 0 0.50 0 0
290 0 0.52 0 0
291 0 0.27 0 0
292 0 0.21 0 0
293 -1 0.19 0 0
294 -1 0.10 -1 0
295 2 0.07 -1 -1
296 0 -0.13 2 -1
297 0 0.03 0 2
298 2 0.01 0 0
299 -1 -0.04 2 0
300 0 -0.24 -1 2
301 0 -0.43 0 -1
302 0 -0.56 0 0
303 0 -0.34 0 0
304 0 -0.29 0 0
305 0 -0.27 0 0
306 1 -0.24 0 0
307 -1 -0.13 1 0
308 2 0.02 -1 1
309 2 -0.15 2 -1
310 -1 -0.19 2 2
311 0 -0.11 -1 2
312 0 -0.07 0 -1
313 0 0.20 0 0
314 0 0.21 0 0
315 0 0.14 0 0
316 0 0.07 0 0
317 0 0.17 0 0
318 0 0.05 0 0
319 0 0.00 0 0
320 -2 -0.18 0 0
321 -1 0.05 -2 0
322 0 -0.01 -1 -2
323 0 -0.06 0 -1
324 1 -0.07 0 0
325 0 -0.11 1 0
326 0 -0.06 0 1
327 0 -0.33 0 0
328 0 -0.11 0 0
329 1 -0.32 0 0
330 -1 -0.09 1 0
331 0 -0.05 -1 1
332 4 0.01 0 -1
333 -1 -0.04 4 0
334 0 -0.13 -1 4
335 0 0.04 0 -1
336 -1 0.17 0 0
337 0 0.05 -1 0
338 0 0.24 0 -1
339 0 0.45 0 0
340 0 0.30 0 0
341 -1 0.44 0 0
342 0 0.31 -1 0
343 -1 0.25 0 -1
344 -5 0.25 -1 0
345 1 0.14 -5 -1
346 1 0.26 1 -5
347 0 0.28 1 1
348 0 0.06 0 1
349 1 0.07 0 0
350 0 -0.09 1 0
351 0 -0.06 0 1
352 0 -0.08 0 0
353 0 -0.22 0 0
354 0 -0.23 0 0
355 1 -0.11 0 0
356 3 -0.24 1 0
357 0 -0.01 3 1
358 1 -0.02 0 3
359 0 -0.07 1 0
360 0 -0.02 0 1
361 -1 0.00 0 0
362 0 -0.15 -1 0
363 0 -0.03 0 -1
364 0 -0.05 0 0
365 0 -0.01 0 0
366 1 0.14 0 0
367 1 -0.05 1 0
368 -1 0.25 1 1
369 -1 0.13 -1 1
370 -2 0.19 -1 -1
371 0 0.10 -2 -1
372 0 0.37 0 -2
373 0 0.14 0 0
374 0 0.32 0 0
375 0 0.04 0 0
376 0 0.16 0 0
377 0 0.28 0 0
378 -1 0.06 0 0
379 -1 0.16 -1 0
380 -1 0.02 -1 -1
381 3 -0.06 -1 -1
382 0 -0.09 3 -1
383 2 0.05 0 3
384 0 -0.27 2 0
385 0 0.22 0 2
386 0 0.08 0 0
387 0 0.24 0 0
388 0 0.04 0 0
389 1 -0.11 0 0
390 1 0.03 1 0
391 -1 0.04 1 1
392 1 0.11 -1 1
393 -1 0.01 1 -1
394 0 -0.02 -1 1
395 0 -0.08 0 -1
396 0 0.17 0 0
397 0 -0.37 0 0
398 0 -0.24 0 0
399 0 -0.43 0 0
400 0 -0.19 0 0
401 -1 -0.02 0 0
402 1 -0.17 -1 0
403 0 -0.07 1 -1
404 -2 -0.25 0 1
405 -1 -0.07 -2 0
406 -1 -0.02 -1 -2
407 -2 -0.09 -1 -1
408 0 0.05 -2 -1
409 0 0.42 0 -2
410 0 0.33 0 0
411 0 0.45 0 0
412 0 0.18 0 0
413 0 0.13 0 0
414 -2 0.15 0 0
415 0 0.00 -2 0
416 1 0.27 0 -2
417 -1 0.24 1 0
418 0 0.07 -1 1
419 0 0.23 0 -1
420 0 -0.21 0 0
421 0 -0.23 0 0
422 0 -0.30 0 0
423 0 -0.22 0 0
424 0 -0.22 0 0
425 0 -0.03 0 0
426 0 -0.17 0 0
427 0 0.00 0 0
428 2 -0.17 0 0
429 4 -0.17 2 0
430 1 -0.07 4 2
431 1 -0.33 1 4
432 1 -0.31 1 1
433 0 -0.13 1 1
434 0 -0.22 0 1
435 0 -0.07 0 0
436 0 0.07 0 0
437 0 -0.13 0 0
438 0 0.10 0 0
439 2 -0.17 0 0
440 -1 -0.01 2 0
441 -3 -0.11 -1 2
442 1 0.00 -3 -1
443 0 0.02 1 -3
444 -1 0.23 0 1
445 0 0.15 -1 0
446 0 0.41 0 -1
447 0 0.13 0 0
448 0 0.08 0 0
449 0 0.02 0 0
450 2 -0.01 0 0
451 -2 0.12 2 0
452 -2 0.00 -2 2
453 -1 0.02 -2 -2
454 -2 0.03 -1 -2
455 0 0.03 -2 -1
456 0 0.04 0 -2
457 0 -0.01 0 0
458 0 0.11 0 0
459 0 -0.10 0 0
460 0 0.05 0 0
461 0 0.10 0 0
462 -2 0.19 0 0
463 0 0.29 -2 0
464 2 0.10 0 -2
465 1 0.32 2 0
466 1 0.18 1 2
467 -1 0.21 1 1
468 0 0.38 -1 1
469 0 0.28 0 -1
470 0 -0.07 0 0
471 0 0.31 0 0
472 0 0.14 0 0
473 0 0.09 0 0
474 0 0.06 0 0
475 0 -0.12 0 0
476 1 0.09 0 0
477 3 -0.08 1 0
478 0 0.00 3 1
479 1 -0.17 0 3
480 0 -0.24 1 0
481 0 -0.40 0 1
482 0 -0.07 0 0
483 0 -0.18 0 0
484 0 -0.19 0 0
485 0 -0.23 0 0
486 1 -0.26 0 0
487 3 -0.02 1 0
488 -1 -0.03 3 1
489 -4 -0.01 -1 3
490 0 -0.03 -4 -1
491 0 0.09 0 -4
492 0 0.07 0 0
493 0 0.22 0 0
494 0 0.09 0 0
495 0 0.39 0 0
496 0 0.27 0 0
497 0 0.25 0 0
498 -1 0.25 0 0
499 0 0.12 -1 0
500 2 0.04 0 -1
501 0 -0.02 2 0
502 3 0.17 0 2
503 -1 0.31 3 0
504 0 0.10 -1 3
505 0 0.02 0 -1
506 0 0.09 0 0
507 0 -0.36 0 0
508 0 -0.02 0 0
509 0 -0.01 0 0
510 1 0.11 0 0
511 -3 0.09 1 0
512 -4 0.02 -3 1
513 1 0.13 -4 -3
514 -1 -0.18 1 -4
515 0 -0.29 -1 1
516 0 -0.19 0 -1
517 0 0.03 0 0
518 0 -0.06 0 0
519 0 0.05 0 0
520 1 -0.22 0 0
521 0 -0.09 1 0
522 -1 -0.27 0 1
523 0 -0.43 -1 0
524 0 -0.29 0 -1
525 1 -0.42 0 0
526 -2 -0.22 1 0
527 0 -0.23 -2 1
528 0 -0.07 0 -2
529 0 -0.10 0 0
530 0 -0.11 0 0
531 0 -0.04 0 0
532 -1 -0.01 0 0
533 0 -0.04 -1 0
534 1 -0.05 0 -1
535 0 0.09 1 0
536 3 0.02 0 1
537 -1 0.08 3 0
538 -1 0.16 -1 3
539 0 0.07 -1 -1
540 0 0.01 0 -1
541 0 0.00 0 0
542 0 -0.32 0 0
543 0 -0.05 0 0
544 0 0.08 0 0
545 0 0.04 0 0
546 0 0.19 0 0
547 0 0.11 0 0
548 -2 0.16 0 0
549 -1 0.23 -2 0
550 0 0.22 -1 -2
551 2 0.37 0 -1
552 0 0.15 2 0
553 0 0.16 0 2
554 0 0.76 0 0
555 0 0.19 0 0
556 0 0.08 0 0
557 0 -0.11 0 0
558 0 0.04 0 0
559 5 0.26 0 0
560 5 0.18 5 0
561 1 -0.01 5 5
562 4 0.04 1 5
563 -2 0.00 4 1
564 0 -0.03 -2 4
565 0 -0.21 0 -2
566 0 -0.27 0 0
567 0 -0.19 0 0
568 0 -0.14 0 0
569 0 0.10 0 0
570 0 -0.15 0 0
571 -3 -0.16 0 0
572 -3 -0.05 -3 0
573 -1 -0.08 -3 -3
574 -1 -0.27 -1 -3
575 1 -0.09 -1 -1
576 0 0.05 1 -1
577 0 0.04 0 1
578 0 -0.12 0 0
579 0 0.21 0 0
580 0 0.13 0 0
581 0 0.10 0 0
582 0 0.25 0 0
583 1 -0.02 0 0
584 -4 0.04 1 0
585 -1 0.29 -4 1
586 -1 0.35 -1 -4
587 -1 0.27 -1 -1
588 0 0.22 -1 -1
589 0 0.28 0 -1
590 0 0.52 0 0
591 0 0.10 0 0
592 0 0.25 0 0
593 0 0.28 0 0
594 -1 0.16 0 0
595 -2 0.39 -1 0
596 4 0.24 -2 -1
597 5 -0.02 4 -2
598 -1 -0.08 5 4
599 1 -0.15 -1 5
600 0 -0.02 1 -1
601 0 -0.10 0 1
602 0 -0.19 0 0
603 0 -0.29 0 0
604 0 -0.30 0 0
605 0 -0.36 0 0
606 1 -0.31 0 0
607 -1 -0.41 1 0
608 0 -0.33 -1 1
609 -3 -0.18 0 -1
610 2 -0.12 -3 0
611 0 -0.08 2 -3
612 0 -0.14 0 2
613 0 -0.24 0 0
614 0 -0.15 0 0
615 0 0.19 0 0
616 0 0.22 0 0
617 0 0.07 0 0
618 -1 0.03 0 0
619 0 0.02 -1 0
620 0 0.10 0 -1
621 4 0.04 0 0
622 1 -0.09 4 0
623 -1 -0.11 1 4
624 0 -0.18 -1 1
625 0 0.17 0 -1
626 0 -0.09 0 0
627 0 0.10 0 0
628 0 -0.08 0 0
629 0 0.17 0 0
630 1 0.14 0 0
631 1 0.18 1 0
632 -1 0.08 1 1
633 -2 0.16 -1 1
634 1 0.22 -2 -1
635 2 0.47 1 -2
636 0 0.28 2 1
637 0 0.36 0 2
638 0 0.35 0 0
639 0 0.25 0 0
640 0 0.12 0 0
641 0 0.04 0 0
642 -1 -0.02 0 0
643 0 0.07 -1 0
644 0 -0.02 0 -1
645 3 0.01 0 0
646 -5 0.05 3 0
647 -2 -0.17 -5 3
648 0 -0.14 -2 -5
(1-B12)Orkanen(t-3) (1-B12)Orkanen(t-4) (1-B12)Orkanen(t-5)
131 -3 1 1
132 -1 -3 1
133 1 -1 -3
134 0 1 -1
135 0 0 1
136 0 0 0
137 0 0 0
138 0 0 0
139 0 0 0
140 -1 0 0
141 -1 -1 0
142 1 -1 -1
143 1 1 -1
144 -1 1 1
145 -2 -1 1
146 0 -2 -1
147 0 0 -2
148 0 0 0
149 0 0 0
150 0 0 0
151 0 0 0
152 0 0 0
153 -1 0 0
154 -1 -1 0
155 -2 -1 -1
156 4 -2 -1
157 2 4 -2
158 3 2 4
159 1 3 2
160 0 1 3
161 0 0 1
162 0 0 0
163 0 0 0
164 0 0 0
165 0 0 0
166 -1 0 0
167 1 -1 0
168 -6 1 -1
169 2 -6 1
170 1 2 -6
171 0 1 2
172 0 0 1
173 0 0 0
174 0 0 0
175 0 0 0
176 0 0 0
177 0 0 0
178 1 0 0
179 1 1 0
180 1 1 1
181 -3 1 1
182 -1 -3 1
183 1 -1 -3
184 0 1 -1
185 0 0 1
186 0 0 0
187 0 0 0
188 0 0 0
189 0 0 0
190 -1 0 0
191 -1 -1 0
192 1 -1 -1
193 1 1 -1
194 -1 1 1
195 -2 -1 1
196 0 -2 -1
197 0 0 -2
198 0 0 0
199 0 0 0
200 0 0 0
201 0 0 0
202 0 0 0
203 -1 0 0
204 -1 -1 0
205 -2 -1 -1
206 4 -2 -1
207 2 4 -2
208 2 2 4
209 0 2 2
210 0 0 2
211 0 0 0
212 0 0 0
213 0 0 0
214 0 0 0
215 0 0 0
216 -1 0 0
217 2 -1 0
218 -4 2 -1
219 -1 -4 2
220 -2 -1 -4
221 0 -2 -1
222 0 0 -2
223 0 0 0
224 0 0 0
225 0 0 0
226 0 0 0
227 0 0 0
228 1 0 0
229 -1 1 0
230 3 -1 1
231 1 3 -1
232 0 1 3
233 0 0 1
234 0 0 0
235 0 0 0
236 0 0 0
237 0 0 0
238 0 0 0
239 1 0 0
240 0 1 0
241 0 0 1
242 -5 0 0
243 -2 -5 0
244 0 -2 -5
245 0 0 -2
246 0 0 0
247 0 0 0
248 0 0 0
249 0 0 0
250 1 0 0
251 4 1 0
252 5 4 1
253 4 5 4
254 1 4 5
255 0 1 4
256 0 0 1
257 0 0 0
258 0 0 0
259 0 0 0
260 1 0 0
261 0 1 0
262 1 0 1
263 -3 1 0
264 -3 -3 1
265 -3 -3 -3
266 -1 -3 -3
267 0 -1 -3
268 0 0 -1
269 0 0 0
270 0 0 0
271 0 0 0
272 -1 0 0
273 0 -1 0
274 -1 0 -1
275 2 -1 0
276 3 2 -1
277 -1 3 2
278 1 -1 3
279 0 1 -1
280 0 0 1
281 0 0 0
282 0 0 0
283 0 0 0
284 1 0 0
285 1 1 0
286 -1 1 1
287 -2 -1 1
288 -4 -2 -1
289 -1 -4 -2
290 0 -1 -4
291 0 0 -1
292 0 0 0
293 0 0 0
294 0 0 0
295 0 0 0
296 -1 0 0
297 -1 -1 0
298 2 -1 -1
299 0 2 -1
300 0 0 2
301 2 0 0
302 -1 2 0
303 0 -1 2
304 0 0 -1
305 0 0 0
306 0 0 0
307 0 0 0
308 0 0 0
309 1 0 0
310 -1 1 0
311 2 -1 1
312 2 2 -1
313 -1 2 2
314 0 -1 2
315 0 0 -1
316 0 0 0
317 0 0 0
318 0 0 0
319 0 0 0
320 0 0 0
321 0 0 0
322 0 0 0
323 -2 0 0
324 -1 -2 0
325 0 -1 -2
326 0 0 -1
327 1 0 0
328 0 1 0
329 0 0 1
330 0 0 0
331 0 0 0
332 1 0 0
333 -1 1 0
334 0 -1 1
335 4 0 -1
336 -1 4 0
337 0 -1 4
338 0 0 -1
339 -1 0 0
340 0 -1 0
341 0 0 -1
342 0 0 0
343 0 0 0
344 -1 0 0
345 0 -1 0
346 -1 0 -1
347 -5 -1 0
348 1 -5 -1
349 1 1 -5
350 0 1 1
351 0 0 1
352 1 0 0
353 0 1 0
354 0 0 1
355 0 0 0
356 0 0 0
357 0 0 0
358 1 0 0
359 3 1 0
360 0 3 1
361 1 0 3
362 0 1 0
363 0 0 1
364 -1 0 0
365 0 -1 0
366 0 0 -1
367 0 0 0
368 0 0 0
369 1 0 0
370 1 1 0
371 -1 1 1
372 -1 -1 1
373 -2 -1 -1
374 0 -2 -1
375 0 0 -2
376 0 0 0
377 0 0 0
378 0 0 0
379 0 0 0
380 0 0 0
381 -1 0 0
382 -1 -1 0
383 -1 -1 -1
384 3 -1 -1
385 0 3 -1
386 2 0 3
387 0 2 0
388 0 0 2
389 0 0 0
390 0 0 0
391 0 0 0
392 1 0 0
393 1 1 0
394 -1 1 1
395 1 -1 1
396 -1 1 -1
397 0 -1 1
398 0 0 -1
399 0 0 0
400 0 0 0
401 0 0 0
402 0 0 0
403 0 0 0
404 -1 0 0
405 1 -1 0
406 0 1 -1
407 -2 0 1
408 -1 -2 0
409 -1 -1 -2
410 -2 -1 -1
411 0 -2 -1
412 0 0 -2
413 0 0 0
414 0 0 0
415 0 0 0
416 0 0 0
417 -2 0 0
418 0 -2 0
419 1 0 -2
420 -1 1 0
421 0 -1 1
422 0 0 -1
423 0 0 0
424 0 0 0
425 0 0 0
426 0 0 0
427 0 0 0
428 0 0 0
429 0 0 0
430 0 0 0
431 2 0 0
432 4 2 0
433 1 4 2
434 1 1 4
435 1 1 1
436 0 1 1
437 0 0 1
438 0 0 0
439 0 0 0
440 0 0 0
441 0 0 0
442 2 0 0
443 -1 2 0
444 -3 -1 2
445 1 -3 -1
446 0 1 -3
447 -1 0 1
448 0 -1 0
449 0 0 -1
450 0 0 0
451 0 0 0
452 0 0 0
453 2 0 0
454 -2 2 0
455 -2 -2 2
456 -1 -2 -2
457 -2 -1 -2
458 0 -2 -1
459 0 0 -2
460 0 0 0
461 0 0 0
462 0 0 0
463 0 0 0
464 0 0 0
465 -2 0 0
466 0 -2 0
467 2 0 -2
468 1 2 0
469 1 1 2
470 -1 1 1
471 0 -1 1
472 0 0 -1
473 0 0 0
474 0 0 0
475 0 0 0
476 0 0 0
477 0 0 0
478 0 0 0
479 1 0 0
480 3 1 0
481 0 3 1
482 1 0 3
483 0 1 0
484 0 0 1
485 0 0 0
486 0 0 0
487 0 0 0
488 0 0 0
489 1 0 0
490 3 1 0
491 -1 3 1
492 -4 -1 3
493 0 -4 -1
494 0 0 -4
495 0 0 0
496 0 0 0
497 0 0 0
498 0 0 0
499 0 0 0
500 0 0 0
501 -1 0 0
502 0 -1 0
503 2 0 -1
504 0 2 0
505 3 0 2
506 -1 3 0
507 0 -1 3
508 0 0 -1
509 0 0 0
510 0 0 0
511 0 0 0
512 0 0 0
513 1 0 0
514 -3 1 0
515 -4 -3 1
516 1 -4 -3
517 -1 1 -4
518 0 -1 1
519 0 0 -1
520 0 0 0
521 0 0 0
522 0 0 0
523 1 0 0
524 0 1 0
525 -1 0 1
526 0 -1 0
527 0 0 -1
528 1 0 0
529 -2 1 0
530 0 -2 1
531 0 0 -2
532 0 0 0
533 0 0 0
534 0 0 0
535 -1 0 0
536 0 -1 0
537 1 0 -1
538 0 1 0
539 3 0 1
540 -1 3 0
541 -1 -1 3
542 0 -1 -1
543 0 0 -1
544 0 0 0
545 0 0 0
546 0 0 0
547 0 0 0
548 0 0 0
549 0 0 0
550 0 0 0
551 -2 0 0
552 -1 -2 0
553 0 -1 -2
554 2 0 -1
555 0 2 0
556 0 0 2
557 0 0 0
558 0 0 0
559 0 0 0
560 0 0 0
561 0 0 0
562 5 0 0
563 5 5 0
564 1 5 5
565 4 1 5
566 -2 4 1
567 0 -2 4
568 0 0 -2
569 0 0 0
570 0 0 0
571 0 0 0
572 0 0 0
573 0 0 0
574 -3 0 0
575 -3 -3 0
576 -1 -3 -3
577 -1 -1 -3
578 1 -1 -1
579 0 1 -1
580 0 0 1
581 0 0 0
582 0 0 0
583 0 0 0
584 0 0 0
585 0 0 0
586 1 0 0
587 -4 1 0
588 -1 -4 1
589 -1 -1 -4
590 -1 -1 -1
591 0 -1 -1
592 0 0 -1
593 0 0 0
594 0 0 0
595 0 0 0
596 0 0 0
597 -1 0 0
598 -2 -1 0
599 4 -2 -1
600 5 4 -2
601 -1 5 4
602 1 -1 5
603 0 1 -1
604 0 0 1
605 0 0 0
606 0 0 0
607 0 0 0
608 0 0 0
609 1 0 0
610 -1 1 0
611 0 -1 1
612 -3 0 -1
613 2 -3 0
614 0 2 -3
615 0 0 2
616 0 0 0
617 0 0 0
618 0 0 0
619 0 0 0
620 0 0 0
621 -1 0 0
622 0 -1 0
623 0 0 -1
624 4 0 0
625 1 4 0
626 -1 1 4
627 0 -1 1
628 0 0 -1
629 0 0 0
630 0 0 0
631 0 0 0
632 0 0 0
633 1 0 0
634 1 1 0
635 -1 1 1
636 -2 -1 1
637 1 -2 -1
638 2 1 -2
639 0 2 1
640 0 0 2
641 0 0 0
642 0 0 0
643 0 0 0
644 0 0 0
645 -1 0 0
646 0 -1 0
647 0 0 -1
648 3 0 0
(1-B12)Orkanen(t-6) (1-B12)Orkanen(t-7) (1-B12)Orkanen(t-8)
131 1 0 0
132 1 1 0
133 1 1 1
134 -3 1 1
135 -1 -3 1
136 1 -1 -3
137 0 1 -1
138 0 0 1
139 0 0 0
140 0 0 0
141 0 0 0
142 0 0 0
143 -1 0 0
144 -1 -1 0
145 1 -1 -1
146 1 1 -1
147 -1 1 1
148 -2 -1 1
149 0 -2 -1
150 0 0 -2
151 0 0 0
152 0 0 0
153 0 0 0
154 0 0 0
155 0 0 0
156 -1 0 0
157 -1 -1 0
158 -2 -1 -1
159 4 -2 -1
160 2 4 -2
161 3 2 4
162 1 3 2
163 0 1 3
164 0 0 1
165 0 0 0
166 0 0 0
167 0 0 0
168 0 0 0
169 -1 0 0
170 1 -1 0
171 -6 1 -1
172 2 -6 1
173 1 2 -6
174 0 1 2
175 0 0 1
176 0 0 0
177 0 0 0
178 0 0 0
179 0 0 0
180 0 0 0
181 1 0 0
182 1 1 0
183 1 1 1
184 -3 1 1
185 -1 -3 1
186 1 -1 -3
187 0 1 -1
188 0 0 1
189 0 0 0
190 0 0 0
191 0 0 0
192 0 0 0
193 -1 0 0
194 -1 -1 0
195 1 -1 -1
196 1 1 -1
197 -1 1 1
198 -2 -1 1
199 0 -2 -1
200 0 0 -2
201 0 0 0
202 0 0 0
203 0 0 0
204 0 0 0
205 0 0 0
206 -1 0 0
207 -1 -1 0
208 -2 -1 -1
209 4 -2 -1
210 2 4 -2
211 2 2 4
212 0 2 2
213 0 0 2
214 0 0 0
215 0 0 0
216 0 0 0
217 0 0 0
218 0 0 0
219 -1 0 0
220 2 -1 0
221 -4 2 -1
222 -1 -4 2
223 -2 -1 -4
224 0 -2 -1
225 0 0 -2
226 0 0 0
227 0 0 0
228 0 0 0
229 0 0 0
230 0 0 0
231 1 0 0
232 -1 1 0
233 3 -1 1
234 1 3 -1
235 0 1 3
236 0 0 1
237 0 0 0
238 0 0 0
239 0 0 0
240 0 0 0
241 0 0 0
242 1 0 0
243 0 1 0
244 0 0 1
245 -5 0 0
246 -2 -5 0
247 0 -2 -5
248 0 0 -2
249 0 0 0
250 0 0 0
251 0 0 0
252 0 0 0
253 1 0 0
254 4 1 0
255 5 4 1
256 4 5 4
257 1 4 5
258 0 1 4
259 0 0 1
260 0 0 0
261 0 0 0
262 0 0 0
263 1 0 0
264 0 1 0
265 1 0 1
266 -3 1 0
267 -3 -3 1
268 -3 -3 -3
269 -1 -3 -3
270 0 -1 -3
271 0 0 -1
272 0 0 0
273 0 0 0
274 0 0 0
275 -1 0 0
276 0 -1 0
277 -1 0 -1
278 2 -1 0
279 3 2 -1
280 -1 3 2
281 1 -1 3
282 0 1 -1
283 0 0 1
284 0 0 0
285 0 0 0
286 0 0 0
287 1 0 0
288 1 1 0
289 -1 1 1
290 -2 -1 1
291 -4 -2 -1
292 -1 -4 -2
293 0 -1 -4
294 0 0 -1
295 0 0 0
296 0 0 0
297 0 0 0
298 0 0 0
299 -1 0 0
300 -1 -1 0
301 2 -1 -1
302 0 2 -1
303 0 0 2
304 2 0 0
305 -1 2 0
306 0 -1 2
307 0 0 -1
308 0 0 0
309 0 0 0
310 0 0 0
311 0 0 0
312 1 0 0
313 -1 1 0
314 2 -1 1
315 2 2 -1
316 -1 2 2
317 0 -1 2
318 0 0 -1
319 0 0 0
320 0 0 0
321 0 0 0
322 0 0 0
323 0 0 0
324 0 0 0
325 0 0 0
326 -2 0 0
327 -1 -2 0
328 0 -1 -2
329 0 0 -1
330 1 0 0
331 0 1 0
332 0 0 1
333 0 0 0
334 0 0 0
335 1 0 0
336 -1 1 0
337 0 -1 1
338 4 0 -1
339 -1 4 0
340 0 -1 4
341 0 0 -1
342 -1 0 0
343 0 -1 0
344 0 0 -1
345 0 0 0
346 0 0 0
347 -1 0 0
348 0 -1 0
349 -1 0 -1
350 -5 -1 0
351 1 -5 -1
352 1 1 -5
353 0 1 1
354 0 0 1
355 1 0 0
356 0 1 0
357 0 0 1
358 0 0 0
359 0 0 0
360 0 0 0
361 1 0 0
362 3 1 0
363 0 3 1
364 1 0 3
365 0 1 0
366 0 0 1
367 -1 0 0
368 0 -1 0
369 0 0 -1
370 0 0 0
371 0 0 0
372 1 0 0
373 1 1 0
374 -1 1 1
375 -1 -1 1
376 -2 -1 -1
377 0 -2 -1
378 0 0 -2
379 0 0 0
380 0 0 0
381 0 0 0
382 0 0 0
383 0 0 0
384 -1 0 0
385 -1 -1 0
386 -1 -1 -1
387 3 -1 -1
388 0 3 -1
389 2 0 3
390 0 2 0
391 0 0 2
392 0 0 0
393 0 0 0
394 0 0 0
395 1 0 0
396 1 1 0
397 -1 1 1
398 1 -1 1
399 -1 1 -1
400 0 -1 1
401 0 0 -1
402 0 0 0
403 0 0 0
404 0 0 0
405 0 0 0
406 0 0 0
407 -1 0 0
408 1 -1 0
409 0 1 -1
410 -2 0 1
411 -1 -2 0
412 -1 -1 -2
413 -2 -1 -1
414 0 -2 -1
415 0 0 -2
416 0 0 0
417 0 0 0
418 0 0 0
419 0 0 0
420 -2 0 0
421 0 -2 0
422 1 0 -2
423 -1 1 0
424 0 -1 1
425 0 0 -1
426 0 0 0
427 0 0 0
428 0 0 0
429 0 0 0
430 0 0 0
431 0 0 0
432 0 0 0
433 0 0 0
434 2 0 0
435 4 2 0
436 1 4 2
437 1 1 4
438 1 1 1
439 0 1 1
440 0 0 1
441 0 0 0
442 0 0 0
443 0 0 0
444 0 0 0
445 2 0 0
446 -1 2 0
447 -3 -1 2
448 1 -3 -1
449 0 1 -3
450 -1 0 1
451 0 -1 0
452 0 0 -1
453 0 0 0
454 0 0 0
455 0 0 0
456 2 0 0
457 -2 2 0
458 -2 -2 2
459 -1 -2 -2
460 -2 -1 -2
461 0 -2 -1
462 0 0 -2
463 0 0 0
464 0 0 0
465 0 0 0
466 0 0 0
467 0 0 0
468 -2 0 0
469 0 -2 0
470 2 0 -2
471 1 2 0
472 1 1 2
473 -1 1 1
474 0 -1 1
475 0 0 -1
476 0 0 0
477 0 0 0
478 0 0 0
479 0 0 0
480 0 0 0
481 0 0 0
482 1 0 0
483 3 1 0
484 0 3 1
485 1 0 3
486 0 1 0
487 0 0 1
488 0 0 0
489 0 0 0
490 0 0 0
491 0 0 0
492 1 0 0
493 3 1 0
494 -1 3 1
495 -4 -1 3
496 0 -4 -1
497 0 0 -4
498 0 0 0
499 0 0 0
500 0 0 0
501 0 0 0
502 0 0 0
503 0 0 0
504 -1 0 0
505 0 -1 0
506 2 0 -1
507 0 2 0
508 3 0 2
509 -1 3 0
510 0 -1 3
511 0 0 -1
512 0 0 0
513 0 0 0
514 0 0 0
515 0 0 0
516 1 0 0
517 -3 1 0
518 -4 -3 1
519 1 -4 -3
520 -1 1 -4
521 0 -1 1
522 0 0 -1
523 0 0 0
524 0 0 0
525 0 0 0
526 1 0 0
527 0 1 0
528 -1 0 1
529 0 -1 0
530 0 0 -1
531 1 0 0
532 -2 1 0
533 0 -2 1
534 0 0 -2
535 0 0 0
536 0 0 0
537 0 0 0
538 -1 0 0
539 0 -1 0
540 1 0 -1
541 0 1 0
542 3 0 1
543 -1 3 0
544 -1 -1 3
545 0 -1 -1
546 0 0 -1
547 0 0 0
548 0 0 0
549 0 0 0
550 0 0 0
551 0 0 0
552 0 0 0
553 0 0 0
554 -2 0 0
555 -1 -2 0
556 0 -1 -2
557 2 0 -1
558 0 2 0
559 0 0 2
560 0 0 0
561 0 0 0
562 0 0 0
563 0 0 0
564 0 0 0
565 5 0 0
566 5 5 0
567 1 5 5
568 4 1 5
569 -2 4 1
570 0 -2 4
571 0 0 -2
572 0 0 0
573 0 0 0
574 0 0 0
575 0 0 0
576 0 0 0
577 -3 0 0
578 -3 -3 0
579 -1 -3 -3
580 -1 -1 -3
581 1 -1 -1
582 0 1 -1
583 0 0 1
584 0 0 0
585 0 0 0
586 0 0 0
587 0 0 0
588 0 0 0
589 1 0 0
590 -4 1 0
591 -1 -4 1
592 -1 -1 -4
593 -1 -1 -1
594 0 -1 -1
595 0 0 -1
596 0 0 0
597 0 0 0
598 0 0 0
599 0 0 0
600 -1 0 0
601 -2 -1 0
602 4 -2 -1
603 5 4 -2
604 -1 5 4
605 1 -1 5
606 0 1 -1
607 0 0 1
608 0 0 0
609 0 0 0
610 0 0 0
611 0 0 0
612 1 0 0
613 -1 1 0
614 0 -1 1
615 -3 0 -1
616 2 -3 0
617 0 2 -3
618 0 0 2
619 0 0 0
620 0 0 0
621 0 0 0
622 0 0 0
623 0 0 0
624 -1 0 0
625 0 -1 0
626 0 0 -1
627 4 0 0
628 1 4 0
629 -1 1 4
630 0 -1 1
631 0 0 -1
632 0 0 0
633 0 0 0
634 0 0 0
635 0 0 0
636 1 0 0
637 1 1 0
638 -1 1 1
639 -2 -1 1
640 1 -2 -1
641 2 1 -2
642 0 2 1
643 0 0 2
644 0 0 0
645 0 0 0
646 0 0 0
647 0 0 0
648 -1 0 0
(1-B12)Orkanen(t-9) (1-B12)Orkanen(t-10) (1-B12)Orkanen(t-1s)
131 0 0 0
132 0 0 0
133 0 0 0
134 1 0 0
135 1 1 0
136 1 1 0
137 -3 1 1
138 -1 -3 1
139 1 -1 1
140 0 1 -3
141 0 0 -1
142 0 0 1
143 0 0 0
144 0 0 0
145 0 0 0
146 -1 0 0
147 -1 -1 0
148 1 -1 0
149 1 1 -1
150 -1 1 -1
151 -2 -1 1
152 0 -2 1
153 0 0 -1
154 0 0 -2
155 0 0 0
156 0 0 0
157 0 0 0
158 0 0 0
159 -1 0 0
160 -1 -1 0
161 -2 -1 0
162 4 -2 -1
163 2 4 -1
164 3 2 -2
165 1 3 4
166 0 1 2
167 0 0 3
168 0 0 1
169 0 0 0
170 0 0 0
171 0 0 0
172 -1 0 0
173 1 -1 0
174 -6 1 0
175 2 -6 -1
176 1 2 1
177 0 1 -6
178 0 0 2
179 0 0 1
180 0 0 0
181 0 0 0
182 0 0 0
183 0 0 0
184 1 0 0
185 1 1 0
186 1 1 0
187 -3 1 1
188 -1 -3 1
189 1 -1 1
190 0 1 -3
191 0 0 -1
192 0 0 1
193 0 0 0
194 0 0 0
195 0 0 0
196 -1 0 0
197 -1 -1 0
198 1 -1 0
199 1 1 -1
200 -1 1 -1
201 -2 -1 1
202 0 -2 1
203 0 0 -1
204 0 0 -2
205 0 0 0
206 0 0 0
207 0 0 0
208 0 0 0
209 -1 0 0
210 -1 -1 0
211 -2 -1 0
212 4 -2 -1
213 2 4 -1
214 2 2 -2
215 0 2 4
216 0 0 2
217 0 0 2
218 0 0 0
219 0 0 0
220 0 0 0
221 0 0 0
222 -1 0 0
223 2 -1 0
224 -4 2 0
225 -1 -4 -1
226 -2 -1 2
227 0 -2 -4
228 0 0 -1
229 0 0 -2
230 0 0 0
231 0 0 0
232 0 0 0
233 0 0 0
234 1 0 0
235 -1 1 0
236 3 -1 0
237 1 3 1
238 0 1 -1
239 0 0 3
240 0 0 1
241 0 0 0
242 0 0 0
243 0 0 0
244 0 0 0
245 1 0 0
246 0 1 0
247 0 0 0
248 -5 0 1
249 -2 -5 0
250 0 -2 0
251 0 0 -5
252 0 0 -2
253 0 0 0
254 0 0 0
255 0 0 0
256 1 0 0
257 4 1 0
258 5 4 0
259 4 5 1
260 1 4 4
261 0 1 5
262 0 0 4
263 0 0 1
264 0 0 0
265 0 0 0
266 1 0 0
267 0 1 0
268 1 0 0
269 -3 1 1
270 -3 -3 0
271 -3 -3 1
272 -1 -3 -3
273 0 -1 -3
274 0 0 -3
275 0 0 -1
276 0 0 0
277 0 0 0
278 -1 0 0
279 0 -1 0
280 -1 0 0
281 2 -1 -1
282 3 2 0
283 -1 3 -1
284 1 -1 2
285 0 1 3
286 0 0 -1
287 0 0 1
288 0 0 0
289 0 0 0
290 1 0 0
291 1 1 0
292 -1 1 0
293 -2 -1 1
294 -4 -2 1
295 -1 -4 -1
296 0 -1 -2
297 0 0 -4
298 0 0 -1
299 0 0 0
300 0 0 0
301 0 0 0
302 -1 0 0
303 -1 -1 0
304 2 -1 0
305 0 2 -1
306 0 0 -1
307 2 0 2
308 -1 2 0
309 0 -1 0
310 0 0 2
311 0 0 -1
312 0 0 0
313 0 0 0
314 0 0 0
315 1 0 0
316 -1 1 0
317 2 -1 0
318 2 2 1
319 -1 2 -1
320 0 -1 2
321 0 0 2
322 0 0 -1
323 0 0 0
324 0 0 0
325 0 0 0
326 0 0 0
327 0 0 0
328 0 0 0
329 -2 0 0
330 -1 -2 0
331 0 -1 0
332 0 0 -2
333 1 0 -1
334 0 1 0
335 0 0 0
336 0 0 1
337 0 0 0
338 1 0 0
339 -1 1 0
340 0 -1 0
341 4 0 1
342 -1 4 -1
343 0 -1 0
344 0 0 4
345 -1 0 -1
346 0 -1 0
347 0 0 0
348 0 0 -1
349 0 0 0
350 -1 0 0
351 0 -1 0
352 -1 0 0
353 -5 -1 -1
354 1 -5 0
355 1 1 -1
356 0 1 -5
357 0 0 1
358 1 0 1
359 0 1 0
360 0 0 0
361 0 0 1
362 0 0 0
363 0 0 0
364 1 0 0
365 3 1 0
366 0 3 0
367 1 0 1
368 0 1 3
369 0 0 0
370 -1 0 1
371 0 -1 0
372 0 0 0
373 0 0 -1
374 0 0 0
375 1 0 0
376 1 1 0
377 -1 1 0
378 -1 -1 1
379 -2 -1 1
380 0 -2 -1
381 0 0 -1
382 0 0 -2
383 0 0 0
384 0 0 0
385 0 0 0
386 0 0 0
387 -1 0 0
388 -1 -1 0
389 -1 -1 0
390 3 -1 -1
391 0 3 -1
392 2 0 -1
393 0 2 3
394 0 0 0
395 0 0 2
396 0 0 0
397 0 0 0
398 1 0 0
399 1 1 0
400 -1 1 0
401 1 -1 1
402 -1 1 1
403 0 -1 -1
404 0 0 1
405 0 0 -1
406 0 0 0
407 0 0 0
408 0 0 0
409 0 0 0
410 -1 0 0
411 1 -1 0
412 0 1 0
413 -2 0 -1
414 -1 -2 1
415 -1 -1 0
416 -2 -1 -2
417 0 -2 -1
418 0 0 -1
419 0 0 -2
420 0 0 0
421 0 0 0
422 0 0 0
423 -2 0 0
424 0 -2 0
425 1 0 0
426 -1 1 -2
427 0 -1 0
428 0 0 1
429 0 0 -1
430 0 0 0
431 0 0 0
432 0 0 0
433 0 0 0
434 0 0 0
435 0 0 0
436 0 0 0
437 2 0 0
438 4 2 0
439 1 4 0
440 1 1 2
441 1 1 4
442 0 1 1
443 0 0 1
444 0 0 1
445 0 0 0
446 0 0 0
447 0 0 0
448 2 0 0
449 -1 2 0
450 -3 -1 0
451 1 -3 2
452 0 1 -1
453 -1 0 -3
454 0 -1 1
455 0 0 0
456 0 0 -1
457 0 0 0
458 0 0 0
459 2 0 0
460 -2 2 0
461 -2 -2 0
462 -1 -2 2
463 -2 -1 -2
464 0 -2 -2
465 0 0 -1
466 0 0 -2
467 0 0 0
468 0 0 0
469 0 0 0
470 0 0 0
471 -2 0 0
472 0 -2 0
473 2 0 0
474 1 2 -2
475 1 1 0
476 -1 1 2
477 0 -1 1
478 0 0 1
479 0 0 -1
480 0 0 0
481 0 0 0
482 0 0 0
483 0 0 0
484 0 0 0
485 1 0 0
486 3 1 0
487 0 3 0
488 1 0 1
489 0 1 3
490 0 0 0
491 0 0 1
492 0 0 0
493 0 0 0
494 0 0 0
495 1 0 0
496 3 1 0
497 -1 3 0
498 -4 -1 1
499 0 -4 3
500 0 0 -1
501 0 0 -4
502 0 0 0
503 0 0 0
504 0 0 0
505 0 0 0
506 0 0 0
507 -1 0 0
508 0 -1 0
509 2 0 0
510 0 2 -1
511 3 0 0
512 -1 3 2
513 0 -1 0
514 0 0 3
515 0 0 -1
516 0 0 0
517 0 0 0
518 0 0 0
519 1 0 0
520 -3 1 0
521 -4 -3 0
522 1 -4 1
523 -1 1 -3
524 0 -1 -4
525 0 0 1
526 0 0 -1
527 0 0 0
528 0 0 0
529 1 0 0
530 0 1 0
531 -1 0 0
532 0 -1 1
533 0 0 0
534 1 0 -1
535 -2 1 0
536 0 -2 0
537 0 0 1
538 0 0 -2
539 0 0 0
540 0 0 0
541 -1 0 0
542 0 -1 0
543 1 0 0
544 0 1 -1
545 3 0 0
546 -1 3 1
547 -1 -1 0
548 0 -1 3
549 0 0 -1
550 0 0 -1
551 0 0 0
552 0 0 0
553 0 0 0
554 0 0 0
555 0 0 0
556 0 0 0
557 -2 0 0
558 -1 -2 0
559 0 -1 0
560 2 0 -2
561 0 2 -1
562 0 0 0
563 0 0 2
564 0 0 0
565 0 0 0
566 0 0 0
567 0 0 0
568 5 0 0
569 5 5 0
570 1 5 0
571 4 1 5
572 -2 4 5
573 0 -2 1
574 0 0 4
575 0 0 -2
576 0 0 0
577 0 0 0
578 0 0 0
579 0 0 0
580 -3 0 0
581 -3 -3 0
582 -1 -3 0
583 -1 -1 -3
584 1 -1 -3
585 0 1 -1
586 0 0 -1
587 0 0 1
588 0 0 0
589 0 0 0
590 0 0 0
591 0 0 0
592 1 0 0
593 -4 1 0
594 -1 -4 0
595 -1 -1 1
596 -1 -1 -4
597 0 -1 -1
598 0 0 -1
599 0 0 -1
600 0 0 0
601 0 0 0
602 0 0 0
603 -1 0 0
604 -2 -1 0
605 4 -2 0
606 5 4 -1
607 -1 5 -2
608 1 -1 4
609 0 1 5
610 0 0 -1
611 0 0 1
612 0 0 0
613 0 0 0
614 0 0 0
615 1 0 0
616 -1 1 0
617 0 -1 0
618 -3 0 1
619 2 -3 -1
620 0 2 0
621 0 0 -3
622 0 0 2
623 0 0 0
624 0 0 0
625 0 0 0
626 0 0 0
627 -1 0 0
628 0 -1 0
629 0 0 0
630 4 0 -1
631 1 4 0
632 -1 1 0
633 0 -1 4
634 0 0 1
635 0 0 -1
636 0 0 0
637 0 0 0
638 0 0 0
639 1 0 0
640 1 1 0
641 -1 1 0
642 -2 -1 1
643 1 -2 1
644 2 1 -1
645 0 2 -2
646 0 0 1
647 0 0 2
648 0 0 0
(1-B12)Orkanen(t-2s) (1-B12)Orkanen(t-3s) (1-B12)Orkanen(t-4s)
131 0 0 -1
132 0 0 -1
133 0 0 0
134 0 0 0
135 0 0 0
136 0 0 0
137 0 0 0
138 -1 1 1
139 0 -1 0
140 3 0 -3
141 1 0 -2
142 0 0 -1
143 0 0 0
144 0 0 0
145 0 0 0
146 0 0 0
147 0 0 0
148 0 0 0
149 1 0 0
150 1 -1 1
151 1 0 -1
152 -3 3 0
153 -1 1 0
154 1 0 0
155 0 0 0
156 0 0 0
157 0 0 0
158 0 0 0
159 0 0 0
160 0 0 0
161 -1 1 0
162 -1 1 -1
163 1 1 0
164 1 -3 3
165 -1 -1 1
166 -2 1 0
167 0 0 0
168 0 0 0
169 0 0 0
170 0 0 0
171 0 0 0
172 0 0 0
173 0 -1 1
174 -1 -1 1
175 -1 1 1
176 -2 1 -3
177 4 -1 -1
178 2 -2 1
179 3 0 0
180 1 0 0
181 0 0 0
182 0 0 0
183 0 0 0
184 0 0 0
185 0 0 -1
186 0 -1 -1
187 -1 -1 1
188 1 -2 1
189 -6 4 -1
190 2 2 -2
191 1 3 0
192 0 1 0
193 0 0 0
194 0 0 0
195 0 0 0
196 0 0 0
197 0 0 0
198 0 0 -1
199 1 -1 -1
200 1 1 -2
201 1 -6 4
202 -3 2 2
203 -1 1 3
204 1 0 1
205 0 0 0
206 0 0 0
207 0 0 0
208 0 0 0
209 0 0 0
210 0 0 0
211 -1 1 -1
212 -1 1 1
213 1 1 -6
214 1 -3 2
215 -1 -1 1
216 -2 1 0
217 0 0 0
218 0 0 0
219 0 0 0
220 0 0 0
221 0 0 0
222 0 0 0
223 0 -1 1
224 -1 -1 1
225 -1 1 1
226 -2 1 -3
227 4 -1 -1
228 2 -2 1
229 2 0 0
230 0 0 0
231 0 0 0
232 0 0 0
233 0 0 0
234 0 0 0
235 0 0 -1
236 0 -1 -1
237 -1 -1 1
238 2 -2 1
239 -4 4 -1
240 -1 2 -2
241 -2 2 0
242 0 0 0
243 0 0 0
244 0 0 0
245 0 0 0
246 0 0 0
247 0 0 0
248 0 0 -1
249 1 -1 -1
250 -1 2 -2
251 3 -4 4
252 1 -1 2
253 0 -2 2
254 0 0 0
255 0 0 0
256 0 0 0
257 0 0 0
258 0 0 0
259 0 0 0
260 1 0 0
261 0 1 -1
262 0 -1 2
263 -5 3 -4
264 -2 1 -1
265 0 0 -2
266 0 0 0
267 0 0 0
268 0 0 0
269 0 0 0
270 0 0 0
271 1 0 0
272 4 1 0
273 5 0 1
274 4 0 -1
275 1 -5 3
276 0 -2 1
277 0 0 0
278 0 0 0
279 0 0 0
280 0 0 0
281 1 0 0
282 0 0 0
283 1 1 0
284 -3 4 1
285 -3 5 0
286 -3 4 0
287 -1 1 -5
288 0 0 -2
289 0 0 0
290 0 0 0
291 0 0 0
292 0 0 0
293 -1 1 0
294 0 0 0
295 -1 1 1
296 2 -3 4
297 3 -3 5
298 -1 -3 4
299 1 -1 1
300 0 0 0
301 0 0 0
302 0 0 0
303 0 0 0
304 0 0 0
305 1 -1 1
306 1 0 0
307 -1 -1 1
308 -2 2 -3
309 -4 3 -3
310 -1 -1 -3
311 0 1 -1
312 0 0 0
313 0 0 0
314 0 0 0
315 0 0 0
316 0 0 0
317 -1 1 -1
318 -1 1 0
319 2 -1 -1
320 0 -2 2
321 0 -4 3
322 2 -1 -1
323 -1 0 1
324 0 0 0
325 0 0 0
326 0 0 0
327 0 0 0
328 0 0 0
329 0 -1 1
330 1 -1 1
331 -1 2 -1
332 2 0 -2
333 2 0 -4
334 -1 2 -1
335 0 -1 0
336 0 0 0
337 0 0 0
338 0 0 0
339 0 0 0
340 0 0 0
341 0 0 -1
342 0 1 -1
343 0 -1 2
344 -2 2 0
345 -1 2 0
346 0 -1 2
347 0 0 -1
348 1 0 0
349 0 0 0
350 0 0 0
351 0 0 0
352 0 0 0
353 1 0 0
354 -1 0 1
355 0 0 -1
356 4 -2 2
357 -1 -1 2
358 0 0 -1
359 0 0 0
360 -1 1 0
361 0 0 0
362 0 0 0
363 0 0 0
364 0 0 0
365 -1 1 0
366 0 -1 0
367 -1 0 0
368 -5 4 -2
369 1 -1 -1
370 1 0 0
371 0 0 0
372 0 -1 1
373 1 0 0
374 0 0 0
375 0 0 0
376 0 0 0
377 0 -1 1
378 0 0 -1
379 1 -1 0
380 3 -5 4
381 0 1 -1
382 1 1 0
383 0 0 0
384 0 0 -1
385 -1 1 0
386 0 0 0
387 0 0 0
388 0 0 0
389 0 0 -1
390 1 0 0
391 1 1 -1
392 -1 3 -5
393 -1 0 1
394 -2 1 1
395 0 0 0
396 0 0 0
397 0 -1 1
398 0 0 0
399 0 0 0
400 0 0 0
401 0 0 0
402 -1 1 0
403 -1 1 1
404 -1 -1 3
405 3 -1 0
406 0 -2 1
407 2 0 0
408 0 0 0
409 0 0 -1
410 0 0 0
411 0 0 0
412 0 0 0
413 1 0 0
414 1 -1 1
415 -1 -1 1
416 1 -1 -1
417 -1 3 -1
418 0 0 -2
419 0 2 0
420 0 0 0
421 0 0 0
422 0 0 0
423 0 0 0
424 0 0 0
425 -1 1 0
426 1 1 -1
427 0 -1 -1
428 -2 1 -1
429 -1 -1 3
430 -1 0 0
431 -2 0 2
432 0 0 0
433 0 0 0
434 0 0 0
435 0 0 0
436 0 0 0
437 0 -1 1
438 -2 1 1
439 0 0 -1
440 1 -2 1
441 -1 -1 -1
442 0 -1 0
443 0 -2 0
444 0 0 0
445 0 0 0
446 0 0 0
447 0 0 0
448 0 0 0
449 0 0 -1
450 0 -2 1
451 0 0 0
452 2 1 -2
453 4 -1 -1
454 1 0 -1
455 1 0 -2
456 1 0 0
457 0 0 0
458 0 0 0
459 0 0 0
460 0 0 0
461 0 0 0
462 0 0 -2
463 2 0 0
464 -1 2 1
465 -3 4 -1
466 1 1 0
467 0 1 0
468 -1 1 0
469 0 0 0
470 0 0 0
471 0 0 0
472 0 0 0
473 0 0 0
474 2 0 0
475 -2 2 0
476 -2 -1 2
477 -1 -3 4
478 -2 1 1
479 0 0 1
480 0 -1 1
481 0 0 0
482 0 0 0
483 0 0 0
484 0 0 0
485 0 0 0
486 -2 2 0
487 0 -2 2
488 2 -2 -1
489 1 -1 -3
490 1 -2 1
491 -1 0 0
492 0 0 -1
493 0 0 0
494 0 0 0
495 0 0 0
496 0 0 0
497 0 0 0
498 0 -2 2
499 0 0 -2
500 1 2 -2
501 3 1 -1
502 0 1 -2
503 1 -1 0
504 0 0 0
505 0 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 0 0 0
510 1 0 -2
511 3 0 0
512 -1 1 2
513 -4 3 1
514 0 0 1
515 0 1 -1
516 0 0 0
517 0 0 0
518 0 0 0
519 0 0 0
520 0 0 0
521 0 0 0
522 -1 1 0
523 0 3 0
524 2 -1 1
525 0 -4 3
526 3 0 0
527 -1 0 1
528 0 0 0
529 0 0 0
530 0 0 0
531 0 0 0
532 0 0 0
533 0 0 0
534 1 -1 1
535 -3 0 3
536 -4 2 -1
537 1 0 -4
538 -1 3 0
539 0 -1 0
540 0 0 0
541 0 0 0
542 0 0 0
543 0 0 0
544 1 0 0
545 0 0 0
546 -1 1 -1
547 0 -3 0
548 0 -4 2
549 1 1 0
550 -2 -1 3
551 0 0 -1
552 0 0 0
553 0 0 0
554 0 0 0
555 0 0 0
556 -1 1 0
557 0 0 0
558 1 -1 1
559 0 0 -3
560 3 0 -4
561 -1 1 1
562 -1 -2 -1
563 0 0 0
564 0 0 0
565 0 0 0
566 0 0 0
567 0 0 0
568 0 -1 1
569 0 0 0
570 0 1 -1
571 0 0 0
572 -2 3 0
573 -1 -1 1
574 0 -1 -2
575 2 0 0
576 0 0 0
577 0 0 0
578 0 0 0
579 0 0 0
580 0 0 -1
581 0 0 0
582 0 0 1
583 5 0 0
584 5 -2 3
585 1 -1 -1
586 4 0 -1
587 -2 2 0
588 0 0 0
589 0 0 0
590 0 0 0
591 0 0 0
592 0 0 0
593 0 0 0
594 0 0 0
595 -3 5 0
596 -3 5 -2
597 -1 1 -1
598 -1 4 0
599 1 -2 2
600 0 0 0
601 0 0 0
602 0 0 0
603 0 0 0
604 0 0 0
605 0 0 0
606 0 0 0
607 1 -3 5
608 -4 -3 5
609 -1 -1 1
610 -1 -1 4
611 -1 1 -2
612 0 0 0
613 0 0 0
614 0 0 0
615 0 0 0
616 0 0 0
617 0 0 0
618 -1 0 0
619 -2 1 -3
620 4 -4 -3
621 5 -1 -1
622 -1 -1 -1
623 1 -1 1
624 0 0 0
625 0 0 0
626 0 0 0
627 0 0 0
628 0 0 0
629 0 0 0
630 1 -1 0
631 -1 -2 1
632 0 4 -4
633 -3 5 -1
634 2 -1 -1
635 0 1 -1
636 0 0 0
637 0 0 0
638 0 0 0
639 0 0 0
640 0 0 0
641 0 0 0
642 -1 1 -1
643 0 -1 -2
644 0 0 4
645 4 -3 5
646 1 2 -1
647 -1 0 1
648 0 0 0
(1-B12)Orkanen(t-5s) (1-B12)Orkanen(t-6s) (1-B12)Orkanen(t-7s)
131 0 1 0
132 0 1 0
133 0 0 0
134 0 0 -1
135 0 0 0
136 0 0 0
137 0 -1 1
138 -1 1 0
139 0 1 0
140 2 -1 1
141 1 1 1
142 1 -3 2
143 -1 0 1
144 -1 0 1
145 0 0 0
146 0 0 0
147 0 0 0
148 0 0 0
149 0 0 -1
150 1 -1 1
151 0 0 1
152 -3 2 -1
153 -2 1 1
154 -1 1 -3
155 0 -1 0
156 0 -1 0
157 0 0 0
158 0 0 0
159 0 0 0
160 0 0 0
161 0 0 0
162 1 1 -1
163 -1 0 0
164 0 -3 2
165 0 -2 1
166 0 -1 1
167 0 0 -1
168 0 0 -1
169 0 0 0
170 0 0 0
171 0 0 0
172 0 0 0
173 0 0 0
174 -1 1 1
175 0 -1 0
176 3 0 -3
177 1 0 -2
178 0 0 -1
179 0 0 0
180 0 0 0
181 0 0 0
182 0 0 0
183 0 0 0
184 0 0 0
185 1 0 0
186 1 -1 1
187 1 0 -1
188 -3 3 0
189 -1 1 0
190 1 0 0
191 0 0 0
192 0 0 0
193 0 0 0
194 0 0 0
195 0 0 0
196 0 0 0
197 -1 1 0
198 -1 1 -1
199 1 1 0
200 1 -3 3
201 -1 -1 1
202 -2 1 0
203 0 0 0
204 0 0 0
205 0 0 0
206 0 0 0
207 0 0 0
208 0 0 0
209 0 -1 1
210 -1 -1 1
211 -1 1 1
212 -2 1 -3
213 4 -1 -1
214 2 -2 1
215 3 0 0
216 1 0 0
217 0 0 0
218 0 0 0
219 0 0 0
220 0 0 0
221 0 0 -1
222 0 -1 -1
223 -1 -1 1
224 1 -2 1
225 -6 4 -1
226 2 2 -2
227 1 3 0
228 0 1 0
229 0 0 0
230 0 0 0
231 0 0 0
232 0 0 0
233 0 0 0
234 0 0 -1
235 1 -1 -1
236 1 1 -2
237 1 -6 4
238 -3 2 2
239 -1 1 3
240 1 0 1
241 0 0 0
242 0 0 0
243 0 0 0
244 0 0 0
245 0 0 0
246 0 0 0
247 -1 1 -1
248 -1 1 1
249 1 1 -6
250 1 -3 2
251 -1 -1 1
252 -2 1 0
253 0 0 0
254 0 0 0
255 0 0 0
256 0 0 0
257 0 0 0
258 0 0 0
259 0 -1 1
260 -1 -1 1
261 -1 1 1
262 -2 1 -3
263 4 -1 -1
264 2 -2 1
265 2 0 0
266 0 0 0
267 0 0 0
268 0 0 0
269 0 0 0
270 0 0 0
271 0 0 -1
272 0 -1 -1
273 -1 -1 1
274 2 -2 1
275 -4 4 -1
276 -1 2 -2
277 -2 2 0
278 0 0 0
279 0 0 0
280 0 0 0
281 0 0 0
282 0 0 0
283 0 0 0
284 0 0 -1
285 1 -1 -1
286 -1 2 -2
287 3 -4 4
288 1 -1 2
289 0 -2 2
290 0 0 0
291 0 0 0
292 0 0 0
293 0 0 0
294 0 0 0
295 0 0 0
296 1 0 0
297 0 1 -1
298 0 -1 2
299 -5 3 -4
300 -2 1 -1
301 0 0 -2
302 0 0 0
303 0 0 0
304 0 0 0
305 0 0 0
306 0 0 0
307 1 0 0
308 4 1 0
309 5 0 1
310 4 0 -1
311 1 -5 3
312 0 -2 1
313 0 0 0
314 0 0 0
315 0 0 0
316 0 0 0
317 1 0 0
318 0 0 0
319 1 1 0
320 -3 4 1
321 -3 5 0
322 -3 4 0
323 -1 1 -5
324 0 0 -2
325 0 0 0
326 0 0 0
327 0 0 0
328 0 0 0
329 -1 1 0
330 0 0 0
331 -1 1 1
332 2 -3 4
333 3 -3 5
334 -1 -3 4
335 1 -1 1
336 0 0 0
337 0 0 0
338 0 0 0
339 0 0 0
340 0 0 0
341 1 -1 1
342 1 0 0
343 -1 -1 1
344 -2 2 -3
345 -4 3 -3
346 -1 -1 -3
347 0 1 -1
348 0 0 0
349 0 0 0
350 0 0 0
351 0 0 0
352 0 0 0
353 -1 1 -1
354 -1 1 0
355 2 -1 -1
356 0 -2 2
357 0 -4 3
358 2 -1 -1
359 -1 0 1
360 0 0 0
361 0 0 0
362 0 0 0
363 0 0 0
364 0 0 0
365 0 -1 1
366 1 -1 1
367 -1 2 -1
368 2 0 -2
369 2 0 -4
370 -1 2 -1
371 0 -1 0
372 0 0 0
373 0 0 0
374 0 0 0
375 0 0 0
376 0 0 0
377 0 0 -1
378 0 1 -1
379 0 -1 2
380 -2 2 0
381 -1 2 0
382 0 -1 2
383 0 0 -1
384 1 0 0
385 0 0 0
386 0 0 0
387 0 0 0
388 0 0 0
389 1 0 0
390 -1 0 1
391 0 0 -1
392 4 -2 2
393 -1 -1 2
394 0 0 -1
395 0 0 0
396 -1 1 0
397 0 0 0
398 0 0 0
399 0 0 0
400 0 0 0
401 -1 1 0
402 0 -1 0
403 -1 0 0
404 -5 4 -2
405 1 -1 -1
406 1 0 0
407 0 0 0
408 0 -1 1
409 1 0 0
410 0 0 0
411 0 0 0
412 0 0 0
413 0 -1 1
414 0 0 -1
415 1 -1 0
416 3 -5 4
417 0 1 -1
418 1 1 0
419 0 0 0
420 0 0 -1
421 -1 1 0
422 0 0 0
423 0 0 0
424 0 0 0
425 0 0 -1
426 1 0 0
427 1 1 -1
428 -1 3 -5
429 -1 0 1
430 -2 1 1
431 0 0 0
432 0 0 0
433 0 -1 1
434 0 0 0
435 0 0 0
436 0 0 0
437 0 0 0
438 -1 1 0
439 -1 1 1
440 -1 -1 3
441 3 -1 0
442 0 -2 1
443 2 0 0
444 0 0 0
445 0 0 -1
446 0 0 0
447 0 0 0
448 0 0 0
449 1 0 0
450 1 -1 1
451 -1 -1 1
452 1 -1 -1
453 -1 3 -1
454 0 0 -2
455 0 2 0
456 0 0 0
457 0 0 0
458 0 0 0
459 0 0 0
460 0 0 0
461 -1 1 0
462 1 1 -1
463 0 -1 -1
464 -2 1 -1
465 -1 -1 3
466 -1 0 0
467 -2 0 2
468 0 0 0
469 0 0 0
470 0 0 0
471 0 0 0
472 0 0 0
473 0 -1 1
474 -2 1 1
475 0 0 -1
476 1 -2 1
477 -1 -1 -1
478 0 -1 0
479 0 -2 0
480 0 0 0
481 0 0 0
482 0 0 0
483 0 0 0
484 0 0 0
485 0 0 -1
486 0 -2 1
487 0 0 0
488 2 1 -2
489 4 -1 -1
490 1 0 -1
491 1 0 -2
492 1 0 0
493 0 0 0
494 0 0 0
495 0 0 0
496 0 0 0
497 0 0 0
498 0 0 -2
499 2 0 0
500 -1 2 1
501 -3 4 -1
502 1 1 0
503 0 1 0
504 -1 1 0
505 0 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 0 0 0
510 2 0 0
511 -2 2 0
512 -2 -1 2
513 -1 -3 4
514 -2 1 1
515 0 0 1
516 0 -1 1
517 0 0 0
518 0 0 0
519 0 0 0
520 0 0 0
521 0 0 0
522 -2 2 0
523 0 -2 2
524 2 -2 -1
525 1 -1 -3
526 1 -2 1
527 -1 0 0
528 0 0 -1
529 0 0 0
530 0 0 0
531 0 0 0
532 0 0 0
533 0 0 0
534 0 -2 2
535 0 0 -2
536 1 2 -2
537 3 1 -1
538 0 1 -2
539 1 -1 0
540 0 0 0
541 0 0 0
542 0 0 0
543 0 0 0
544 0 0 0
545 0 0 0
546 1 0 -2
547 3 0 0
548 -1 1 2
549 -4 3 1
550 0 0 1
551 0 1 -1
552 0 0 0
553 0 0 0
554 0 0 0
555 0 0 0
556 0 0 0
557 0 0 0
558 -1 1 0
559 0 3 0
560 2 -1 1
561 0 -4 3
562 3 0 0
563 -1 0 1
564 0 0 0
565 0 0 0
566 0 0 0
567 0 0 0
568 0 0 0
569 0 0 0
570 1 -1 1
571 -3 0 3
572 -4 2 -1
573 1 0 -4
574 -1 3 0
575 0 -1 0
576 0 0 0
577 0 0 0
578 0 0 0
579 0 0 0
580 1 0 0
581 0 0 0
582 -1 1 -1
583 0 -3 0
584 0 -4 2
585 1 1 0
586 -2 -1 3
587 0 0 -1
588 0 0 0
589 0 0 0
590 0 0 0
591 0 0 0
592 -1 1 0
593 0 0 0
594 1 -1 1
595 0 0 -3
596 3 0 -4
597 -1 1 1
598 -1 -2 -1
599 0 0 0
600 0 0 0
601 0 0 0
602 0 0 0
603 0 0 0
604 0 -1 1
605 0 0 0
606 0 1 -1
607 0 0 0
608 -2 3 0
609 -1 -1 1
610 0 -1 -2
611 2 0 0
612 0 0 0
613 0 0 0
614 0 0 0
615 0 0 0
616 0 0 -1
617 0 0 0
618 0 0 1
619 5 0 0
620 5 -2 3
621 1 -1 -1
622 4 0 -1
623 -2 2 0
624 0 0 0
625 0 0 0
626 0 0 0
627 0 0 0
628 0 0 0
629 0 0 0
630 0 0 0
631 -3 5 0
632 -3 5 -2
633 -1 1 -1
634 -1 4 0
635 1 -2 2
636 0 0 0
637 0 0 0
638 0 0 0
639 0 0 0
640 0 0 0
641 0 0 0
642 0 0 0
643 1 -3 5
644 -4 -3 5
645 -1 -1 1
646 -1 -1 4
647 -1 1 -2
648 0 0 0
(1-B12)Orkanen(t-8s) (1-B12)Orkanen(t-9s) (1-B12)Orkanen(t-10s) M1 M2 M3 M4
131 0 -1 0 1 0 0 0
132 0 0 0 0 1 0 0
133 0 0 0 0 0 1 0
134 1 0 0 0 0 0 1
135 0 0 0 0 0 0 0
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402 0 0 0 1 0 0 0
403 0 0 0 0 1 0 0
404 0 0 0 0 0 1 0
405 0 0 0 0 0 0 1
406 0 0 0 0 0 0 0
407 0 0 0 0 0 0 0
408 0 0 0 0 0 0 0
409 0 0 0 0 0 0 0
410 0 0 0 0 0 0 0
411 1 0 0 0 0 0 0
412 0 1 0 0 0 0 0
413 0 0 1 0 0 0 0
414 0 0 0 1 0 0 0
415 0 0 0 0 1 0 0
416 0 0 0 0 0 1 0
417 0 0 0 0 0 0 1
418 0 0 0 0 0 0 0
419 0 0 0 0 0 0 0
420 0 0 0 0 0 0 0
421 0 0 0 0 0 0 0
422 0 0 0 0 0 0 0
423 1 0 0 0 0 0 0
424 0 1 0 0 0 0 0
425 0 0 1 0 0 0 0
426 0 0 0 1 0 0 0
427 0 0 0 0 1 0 0
428 0 0 0 0 0 1 0
429 0 0 0 0 0 0 1
430 0 0 0 0 0 0 0
431 0 0 0 0 0 0 0
432 0 0 0 0 0 0 0
433 0 0 0 0 0 0 0
434 0 0 0 0 0 0 0
435 1 0 0 0 0 0 0
436 0 1 0 0 0 0 0
437 0 0 1 0 0 0 0
438 0 0 0 1 0 0 0
439 0 0 0 0 1 0 0
440 0 0 0 0 0 1 0
441 0 0 0 0 0 0 1
442 0 0 0 0 0 0 0
443 0 0 0 0 0 0 0
444 0 0 0 0 0 0 0
445 0 0 0 0 0 0 0
446 0 0 0 0 0 0 0
447 1 0 0 0 0 0 0
448 0 1 0 0 0 0 0
449 0 0 1 0 0 0 0
450 0 0 0 1 0 0 0
451 0 0 0 0 1 0 0
452 0 0 0 0 0 1 0
453 0 0 0 0 0 0 1
454 0 0 0 0 0 0 0
455 0 0 0 0 0 0 0
456 0 0 0 0 0 0 0
457 0 0 0 0 0 0 0
458 0 0 0 0 0 0 0
459 1 0 0 0 0 0 0
460 0 1 0 0 0 0 0
461 0 0 1 0 0 0 0
462 0 0 0 1 0 0 0
463 0 0 0 0 1 0 0
464 0 0 0 0 0 1 0
465 0 0 0 0 0 0 1
466 0 0 0 0 0 0 0
467 0 0 0 0 0 0 0
468 0 0 0 0 0 0 0
469 0 0 0 0 0 0 0
470 0 0 0 0 0 0 0
471 1 0 0 0 0 0 0
472 0 1 0 0 0 0 0
473 0 0 1 0 0 0 0
474 0 0 0 1 0 0 0
475 0 0 0 0 1 0 0
476 0 0 0 0 0 1 0
477 0 0 0 0 0 0 1
478 0 0 0 0 0 0 0
479 0 0 0 0 0 0 0
480 0 0 0 0 0 0 0
481 0 0 0 0 0 0 0
482 0 0 0 0 0 0 0
483 1 0 0 0 0 0 0
484 0 1 0 0 0 0 0
485 0 0 1 0 0 0 0
486 0 0 0 1 0 0 0
487 0 0 0 0 1 0 0
488 0 0 0 0 0 1 0
489 0 0 0 0 0 0 1
490 0 0 0 0 0 0 0
491 0 0 0 0 0 0 0
492 0 0 0 0 0 0 0
493 0 0 0 0 0 0 0
494 0 0 0 0 0 0 0
495 1 0 0 0 0 0 0
496 0 1 0 0 0 0 0
497 0 0 1 0 0 0 0
498 0 0 0 1 0 0 0
499 0 0 0 0 1 0 0
500 0 0 0 0 0 1 0
501 0 0 0 0 0 0 1
502 0 0 0 0 0 0 0
503 0 0 0 0 0 0 0
504 0 0 0 0 0 0 0
505 0 0 0 0 0 0 0
506 0 0 0 0 0 0 0
507 1 0 0 0 0 0 0
508 0 1 0 0 0 0 0
509 0 0 1 0 0 0 0
510 0 0 0 1 0 0 0
511 0 0 0 0 1 0 0
512 0 0 0 0 0 1 0
513 0 0 0 0 0 0 1
514 0 0 0 0 0 0 0
515 0 0 0 0 0 0 0
516 0 0 0 0 0 0 0
517 0 0 0 0 0 0 0
518 0 0 0 0 0 0 0
519 1 0 0 0 0 0 0
520 0 1 0 0 0 0 0
521 0 0 1 0 0 0 0
522 0 0 0 1 0 0 0
523 0 0 0 0 1 0 0
524 0 0 0 0 0 1 0
525 0 0 0 0 0 0 1
526 0 0 0 0 0 0 0
527 0 0 0 0 0 0 0
528 0 0 0 0 0 0 0
529 0 0 0 0 0 0 0
530 0 0 0 0 0 0 0
531 1 0 0 0 0 0 0
532 0 1 0 0 0 0 0
533 0 0 1 0 0 0 0
534 0 0 0 1 0 0 0
535 0 0 0 0 1 0 0
536 0 0 0 0 0 1 0
537 0 0 0 0 0 0 1
538 0 0 0 0 0 0 0
539 0 0 0 0 0 0 0
540 0 0 0 0 0 0 0
541 0 0 0 0 0 0 0
542 0 0 0 0 0 0 0
543 1 0 0 0 0 0 0
544 0 1 0 0 0 0 0
545 0 0 1 0 0 0 0
546 0 0 0 1 0 0 0
547 0 0 0 0 1 0 0
548 0 0 0 0 0 1 0
549 0 0 0 0 0 0 1
550 0 0 0 0 0 0 0
551 0 0 0 0 0 0 0
552 0 0 0 0 0 0 0
553 0 0 0 0 0 0 0
554 0 0 0 0 0 0 0
555 1 0 0 0 0 0 0
556 0 1 0 0 0 0 0
557 0 0 1 0 0 0 0
558 0 0 0 1 0 0 0
559 0 0 0 0 1 0 0
560 0 0 0 0 0 1 0
561 0 0 0 0 0 0 1
562 0 0 0 0 0 0 0
563 0 0 0 0 0 0 0
564 0 0 0 0 0 0 0
565 0 0 0 0 0 0 0
566 0 0 0 0 0 0 0
567 1 0 0 0 0 0 0
568 0 1 0 0 0 0 0
569 0 0 1 0 0 0 0
570 0 0 0 1 0 0 0
571 0 0 0 0 1 0 0
572 0 0 0 0 0 1 0
573 0 0 0 0 0 0 1
574 0 0 0 0 0 0 0
575 0 0 0 0 0 0 0
576 0 0 0 0 0 0 0
577 0 0 0 0 0 0 0
578 0 0 0 0 0 0 0
579 1 0 0 0 0 0 0
580 0 1 0 0 0 0 0
581 0 0 1 0 0 0 0
582 0 0 0 1 0 0 0
583 0 0 0 0 1 0 0
584 0 0 0 0 0 1 0
585 0 0 0 0 0 0 1
586 0 0 0 0 0 0 0
587 0 0 0 0 0 0 0
588 0 0 0 0 0 0 0
589 0 0 0 0 0 0 0
590 0 0 0 0 0 0 0
591 1 0 0 0 0 0 0
592 0 1 0 0 0 0 0
593 0 0 1 0 0 0 0
594 0 0 0 1 0 0 0
595 0 0 0 0 1 0 0
596 0 0 0 0 0 1 0
597 0 0 0 0 0 0 1
598 0 0 0 0 0 0 0
599 0 0 0 0 0 0 0
600 0 0 0 0 0 0 0
601 0 0 0 0 0 0 0
602 0 0 0 0 0 0 0
603 1 0 0 0 0 0 0
604 0 1 0 0 0 0 0
605 0 0 1 0 0 0 0
606 0 0 0 1 0 0 0
607 0 0 0 0 1 0 0
608 0 0 0 0 0 1 0
609 0 0 0 0 0 0 1
610 0 0 0 0 0 0 0
611 0 0 0 0 0 0 0
612 0 0 0 0 0 0 0
613 0 0 0 0 0 0 0
614 0 0 0 0 0 0 0
615 1 0 0 0 0 0 0
616 0 1 0 0 0 0 0
617 0 0 1 0 0 0 0
618 0 0 0 1 0 0 0
619 0 0 0 0 1 0 0
620 0 0 0 0 0 1 0
621 0 0 0 0 0 0 1
622 0 0 0 0 0 0 0
623 0 0 0 0 0 0 0
624 0 0 0 0 0 0 0
625 0 0 0 0 0 0 0
626 0 0 0 0 0 0 0
627 1 0 0 0 0 0 0
628 0 1 0 0 0 0 0
629 0 0 1 0 0 0 0
630 0 0 0 1 0 0 0
631 0 0 0 0 1 0 0
632 0 0 0 0 0 1 0
633 0 0 0 0 0 0 1
634 0 0 0 0 0 0 0
635 0 0 0 0 0 0 0
636 0 0 0 0 0 0 0
637 0 0 0 0 0 0 0
638 0 0 0 0 0 0 0
639 1 0 0 0 0 0 0
640 0 1 0 0 0 0 0
641 0 0 1 0 0 0 0
642 0 0 0 1 0 0 0
643 0 0 0 0 1 0 0
644 0 0 0 0 0 1 0
645 0 0 0 0 0 0 1
646 0 0 0 0 0 0 0
647 0 0 0 0 0 0 0
648 0 0 0 0 0 0 0
> (k <- length(x[n,]))
[1] 33
> head(x)
(1-B12)Orkanen (1-B12)Temperatuur (1-B12)Orkanen(t-1) (1-B12)Orkanen(t-2)
131 0 -0.14 1 -1
132 0 -0.07 0 1
133 0 -0.10 0 0
134 0 0.07 0 0
135 0 -0.51 0 0
136 0 -0.25 0 0
(1-B12)Orkanen(t-3) (1-B12)Orkanen(t-4) (1-B12)Orkanen(t-5)
131 -3 1 1
132 -1 -3 1
133 1 -1 -3
134 0 1 -1
135 0 0 1
136 0 0 0
(1-B12)Orkanen(t-6) (1-B12)Orkanen(t-7) (1-B12)Orkanen(t-8)
131 1 0 0
132 1 1 0
133 1 1 1
134 -3 1 1
135 -1 -3 1
136 1 -1 -3
(1-B12)Orkanen(t-9) (1-B12)Orkanen(t-10) (1-B12)Orkanen(t-1s)
131 0 0 0
132 0 0 0
133 0 0 0
134 1 0 0
135 1 1 0
136 1 1 0
(1-B12)Orkanen(t-2s) (1-B12)Orkanen(t-3s) (1-B12)Orkanen(t-4s)
131 0 0 -1
132 0 0 -1
133 0 0 0
134 0 0 0
135 0 0 0
136 0 0 0
(1-B12)Orkanen(t-5s) (1-B12)Orkanen(t-6s) (1-B12)Orkanen(t-7s)
131 0 1 0
132 0 1 0
133 0 0 0
134 0 0 -1
135 0 0 0
136 0 0 0
(1-B12)Orkanen(t-8s) (1-B12)Orkanen(t-9s) (1-B12)Orkanen(t-10s) M1 M2 M3 M4
131 0 -1 0 1 0 0 0
132 0 0 0 0 1 0 0
133 0 0 0 0 0 1 0
134 1 0 0 0 0 0 1
135 0 0 0 0 0 0 0
136 0 0 0 0 0 0 0
M5 M6 M7 M8 M9 M10 M11
131 0 0 0 0 0 0 0
132 0 0 0 0 0 0 0
133 0 0 0 0 0 0 0
134 0 0 0 0 0 0 0
135 1 0 0 0 0 0 0
136 0 1 0 0 0 0 0
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `(1-B12)Temperatuur` `(1-B12)Orkanen(t-1)`
0.034770 -0.053644 0.102200
`(1-B12)Orkanen(t-2)` `(1-B12)Orkanen(t-3)` `(1-B12)Orkanen(t-4)`
-0.023705 -0.002138 -0.019445
`(1-B12)Orkanen(t-5)` `(1-B12)Orkanen(t-6)` `(1-B12)Orkanen(t-7)`
0.003126 -0.008202 0.008783
`(1-B12)Orkanen(t-8)` `(1-B12)Orkanen(t-9)` `(1-B12)Orkanen(t-10)`
0.018069 -0.007397 -0.054219
`(1-B12)Orkanen(t-1s)` `(1-B12)Orkanen(t-2s)` `(1-B12)Orkanen(t-3s)`
-0.761711 -0.556840 -0.484030
`(1-B12)Orkanen(t-4s)` `(1-B12)Orkanen(t-5s)` `(1-B12)Orkanen(t-6s)`
-0.371810 -0.215750 -0.191727
`(1-B12)Orkanen(t-7s)` `(1-B12)Orkanen(t-8s)` `(1-B12)Orkanen(t-9s)`
-0.225571 -0.231220 -0.215077
`(1-B12)Orkanen(t-10s)` M1 M2
-0.068239 0.019870 -0.048887
M3 M4 M5
-0.031967 -0.037789 -0.033470
M6 M7 M8
-0.031716 -0.067092 -0.087450
M9 M10 M11
0.022729 0.083495 0.127667
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.8141 -0.3546 -0.0384 0.1209 4.3350
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.034770 0.160229 0.217 0.828299
`(1-B12)Temperatuur` -0.053644 0.255408 -0.210 0.833731
`(1-B12)Orkanen(t-1)` 0.102200 0.036788 2.778 0.005680 **
`(1-B12)Orkanen(t-2)` -0.023705 0.036458 -0.650 0.515874
`(1-B12)Orkanen(t-3)` -0.002138 0.036913 -0.058 0.953833
`(1-B12)Orkanen(t-4)` -0.019445 0.037029 -0.525 0.599732
`(1-B12)Orkanen(t-5)` 0.003126 0.037194 0.084 0.933059
`(1-B12)Orkanen(t-6)` -0.008202 0.037014 -0.222 0.824720
`(1-B12)Orkanen(t-7)` 0.008783 0.036890 0.238 0.811916
`(1-B12)Orkanen(t-8)` 0.018069 0.036958 0.489 0.625136
`(1-B12)Orkanen(t-9)` -0.007397 0.037031 -0.200 0.841752
`(1-B12)Orkanen(t-10)` -0.054219 0.036869 -1.471 0.142046
`(1-B12)Orkanen(t-1s)` -0.761711 0.045980 -16.566 < 2e-16 ***
`(1-B12)Orkanen(t-2s)` -0.556840 0.057296 -9.719 < 2e-16 ***
`(1-B12)Orkanen(t-3s)` -0.484030 0.063293 -7.647 1.11e-13 ***
`(1-B12)Orkanen(t-4s)` -0.371810 0.067092 -5.542 4.92e-08 ***
`(1-B12)Orkanen(t-5s)` -0.215750 0.070393 -3.065 0.002298 **
`(1-B12)Orkanen(t-6s)` -0.191727 0.070462 -2.721 0.006742 **
`(1-B12)Orkanen(t-7s)` -0.225571 0.068640 -3.286 0.001089 **
`(1-B12)Orkanen(t-8s)` -0.231220 0.066964 -3.453 0.000603 ***
`(1-B12)Orkanen(t-9s)` -0.215077 0.062072 -3.465 0.000577 ***
`(1-B12)Orkanen(t-10s)` -0.068239 0.049146 -1.389 0.165622
M1 0.019870 0.224846 0.088 0.929618
M2 -0.048887 0.224966 -0.217 0.828060
M3 -0.031967 0.226172 -0.141 0.887662
M4 -0.037789 0.226192 -0.167 0.867387
M5 -0.033470 0.226200 -0.148 0.882430
M6 -0.031716 0.226213 -0.140 0.888558
M7 -0.067092 0.226143 -0.297 0.766839
M8 -0.087450 0.226449 -0.386 0.699534
M9 0.022729 0.226658 0.100 0.920163
M10 0.083495 0.226252 0.369 0.712261
M11 0.127667 0.225953 0.565 0.572325
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.047 on 485 degrees of freedom
Multiple R-squared: 0.4083, Adjusted R-squared: 0.3692
F-statistic: 10.46 on 32 and 485 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,] 0.429372144 0.8587442888 0.5706278556
[2,] 0.306677108 0.6133542167 0.6933228916
[3,] 0.183955732 0.3679114642 0.8160442679
[4,] 0.104506178 0.2090123567 0.8954938216
[5,] 0.155299100 0.3105982007 0.8447008997
[6,] 0.126746120 0.2534922395 0.8732538803
[7,] 0.087291322 0.1745826443 0.9127086778
[8,] 0.124622808 0.2492456157 0.8753771921
[9,] 0.107318855 0.2146377092 0.8926811454
[10,] 0.630912978 0.7381740430 0.3690870215
[11,] 0.629946972 0.7401060555 0.3700530277
[12,] 0.996761683 0.0064766345 0.0032383172
[13,] 0.998181348 0.0036373042 0.0018186521
[14,] 0.997900726 0.0041985486 0.0020992743
[15,] 0.997449272 0.0051014552 0.0025507276
[16,] 0.995933794 0.0081324111 0.0040662056
[17,] 0.993735435 0.0125291308 0.0062645654
[18,] 0.990559430 0.0188811392 0.0094405696
[19,] 0.986162068 0.0276758637 0.0138379318
[20,] 0.980366755 0.0392664905 0.0196332452
[21,] 0.982989029 0.0340219423 0.0170109712
[22,] 0.984586627 0.0308267455 0.0154133727
[23,] 0.979252607 0.0414947856 0.0207473928
[24,] 0.976794134 0.0464117312 0.0232058656
[25,] 0.972701383 0.0545972348 0.0272986174
[26,] 0.966764650 0.0664707004 0.0332353502
[27,] 0.959365757 0.0812684859 0.0406342429
[28,] 0.948545297 0.1029094063 0.0514547032
[29,] 0.934735492 0.1305290154 0.0652645077
[30,] 0.917379138 0.1652417233 0.0826208617
[31,] 0.896799641 0.2064007188 0.1032003594
[32,] 0.873735503 0.2525289949 0.1262644975
[33,] 0.847016259 0.3059674821 0.1529837411
[34,] 0.825788571 0.3484228582 0.1742114291
[35,] 0.805089169 0.3898216628 0.1949108314
[36,] 0.790931649 0.4181367018 0.2090683509
[37,] 0.864037899 0.2719242020 0.1359621010
[38,] 0.955355844 0.0892883114 0.0446441557
[39,] 0.957478964 0.0850420719 0.0425210359
[40,] 0.981579841 0.0368403176 0.0184201588
[41,] 0.976972783 0.0460544337 0.0230272168
[42,] 0.970878481 0.0582430375 0.0291215187
[43,] 0.963314268 0.0733714636 0.0366857318
[44,] 0.954801340 0.0903973191 0.0451986596
[45,] 0.946935181 0.1061296372 0.0530648186
[46,] 0.935868568 0.1282628640 0.0641314320
[47,] 0.926574844 0.1468503124 0.0734251562
[48,] 0.932769872 0.1344602566 0.0672301283
[49,] 0.919138116 0.1617237688 0.0808618844
[50,] 0.933947778 0.1321044434 0.0660522217
[51,] 0.925519895 0.1489602092 0.0744801046
[52,] 0.912727824 0.1745443529 0.0872721764
[53,] 0.898390982 0.2032180365 0.1016090183
[54,] 0.881050368 0.2378992631 0.1189496315
[55,] 0.861286638 0.2774267241 0.1387133621
[56,] 0.839140009 0.3217199822 0.1608599911
[57,] 0.815171268 0.3696574640 0.1848287320
[58,] 0.791882745 0.4162345094 0.2081172547
[59,] 0.783144296 0.4337114087 0.2168557043
[60,] 0.762670191 0.4746596177 0.2373298088
[61,] 0.737505407 0.5249891865 0.2624945933
[62,] 0.750464193 0.4990716145 0.2495358073
[63,] 0.739806372 0.5203872562 0.2601936281
[64,] 0.715632405 0.5687351909 0.2843675955
[65,] 0.684497580 0.6310048395 0.3155024198
[66,] 0.651528887 0.6969422255 0.3484711127
[67,] 0.616864572 0.7662708552 0.3831354276
[68,] 0.581434503 0.8371309946 0.4185654973
[69,] 0.548927230 0.9021455408 0.4510727704
[70,] 0.515980655 0.9680386900 0.4840193450
[71,] 0.514680159 0.9706396816 0.4853198408
[72,] 0.535447003 0.9291059932 0.4645529966
[73,] 0.503732331 0.9925353373 0.4962676687
[74,] 0.713255337 0.5734893260 0.2867446630
[75,] 0.713262440 0.5734751192 0.2867375596
[76,] 0.693147492 0.6137050153 0.3068525077
[77,] 0.663565610 0.6728687797 0.3364343899
[78,] 0.633377760 0.7332444803 0.3666222402
[79,] 0.601082570 0.7978348606 0.3989174303
[80,] 0.568191449 0.8636171015 0.4318085507
[81,] 0.536845176 0.9263096487 0.4631548244
[82,] 0.506328631 0.9873427371 0.4936713685
[83,] 0.941052230 0.1178955395 0.0589477698
[84,] 0.986773046 0.0264539072 0.0132269536
[85,] 0.998934266 0.0021314680 0.0010657340
[86,] 0.999463058 0.0010738832 0.0005369416
[87,] 0.999307475 0.0013850495 0.0006925247
[88,] 0.999104621 0.0017907576 0.0008953788
[89,] 0.998868767 0.0022624662 0.0011312331
[90,] 0.998578153 0.0028436943 0.0014218471
[91,] 0.998309439 0.0033811225 0.0016905612
[92,] 0.998888591 0.0022228173 0.0011114087
[93,] 0.999017000 0.0019659991 0.0009829995
[94,] 0.999534832 0.0009303361 0.0004651681
[95,] 0.999664007 0.0006719864 0.0003359932
[96,] 0.999589910 0.0008201794 0.0004100897
[97,] 0.999468992 0.0010620156 0.0005310078
[98,] 0.999626066 0.0007478670 0.0003739335
[99,] 0.999534065 0.0009318706 0.0004659353
[100,] 0.999399171 0.0012016581 0.0006008290
[101,] 0.999217807 0.0015643852 0.0007821926
[102,] 0.998990024 0.0020199512 0.0010099756
[103,] 0.998699327 0.0026013470 0.0013006735
[104,] 0.998341619 0.0033167624 0.0016583812
[105,] 0.997919134 0.0041617322 0.0020808661
[106,] 0.997778651 0.0044426989 0.0022213495
[107,] 0.998642694 0.0027146112 0.0013573056
[108,] 0.999495889 0.0010082228 0.0005041114
[109,] 0.999734568 0.0005308631 0.0002654315
[110,] 0.999684378 0.0006312447 0.0003156223
[111,] 0.999617107 0.0007657863 0.0003828931
[112,] 0.999501082 0.0009978368 0.0004989184
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[446,] 0.868247915 0.2635041700 0.1317520850
[447,] 0.786932901 0.4261341977 0.2130670989
> postscript(file="/var/wessaorg/rcomp/tmp/162wj1450341626.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/2ryk21450341626.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/38jwa1450341626.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4m4is1450341626.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/54ob91450341626.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 518
Frequency = 1
131 132 133 134 135
-0.5651075033 -0.2101959806 -0.0347462328 -0.0090670333 0.0299112654
136 137 138 139 140
0.1163428321 -0.1544634510 0.0012978649 1.4408999107 -0.8343102441
141 142 143 144 145
-2.0950567408 -2.0255707978 0.0846800517 -0.0012001695 0.0080856453
146 147 148 149 150
-0.0382608082 -0.0686517601 -0.0653739346 -0.2046854650 -0.9264933070
151 152 153 154 155
0.1463882798 -2.1453188148 3.0350919871 0.0128228218 2.8611153368
156 157 158 159 160
0.7509599176 0.0576843297 -0.0763289644 0.1257706120 -0.0307562923
161 162 163 164 165
-0.2654614735 -0.8784856959 -0.5882866973 -0.4178466104 -3.5848120134
166 167 168 169 170
3.3145698726 2.8549885380 0.7213591775 -0.1055030606 0.0056485052
171 172 173 174 175
-0.0425181011 0.0315560212 -0.1669995262 -0.4590847233 0.1724047558
176 177 178 179 180
0.2018949223 -2.1073856012 -1.5739165303 1.5654235287 1.4542989896
181 182 183 184 185
-0.1181108782 -0.0412109889 -0.0332427167 -0.0207891795 -0.1607660395
186 187 188 189 190
-0.3497877038 -0.8726269785 -0.8878453676 -0.3516111284 -0.0601801770
191 192 193 194 195
-0.1193587869 -0.8402779998 0.1949345316 -0.0221189239 0.0095891613
196 197 198 199 200
-0.0281917674 -0.0590526054 -0.2529855576 -0.8328990601 -1.6858765590
201 202 203 204 205
-1.7730688254 -1.6473809299 4.3349779068 0.8727778401 1.8729231019
206 207 208 209 210
-0.2038677209 0.1366095384 0.0359105285 0.1560959674 -0.1291980967
211 212 213 214 215
-0.6693633534 -1.1475085435 -2.3786969180 0.7177722238 -1.1514175844
216 217 218 219 220
0.5771728691 -0.4860985665 0.2236142503 -0.1495199569 0.0125246321
221 222 223 224 225
-0.0365375406 -0.2959547207 -0.1591241862 -0.6617922049 -0.5756150418
226 227 228 229 230
-0.8202718663 2.0897003807 0.6403718906 -0.4204216173 0.0111043691
231 232 233 234 235
0.0731845455 -0.0249920397 0.0197922516 -0.2606821767 -0.2114562076
236 237 238 239 240
-0.2814520430 -0.4246557538 -0.2675782150 -2.7895310212 -0.5982194635
241 242 243 244 245
-0.0812319366 -0.0543896762 -0.1110484793 -0.0293636640 -0.1231033388
246 247 248 249 250
-0.2264824873 0.8636415131 3.9234195418 3.5723115657 3.3031211438
251 252 253 254 255
-1.3824175997 -0.6057576147 -0.0897210044 0.1106469349 -0.0065469453
256 257 258 259 260
-0.0886001596 0.9214999475 -0.1648571239 1.6636375545 0.2900541326
261 262 263 264 265
0.7680680395 0.3388162481 -1.5425283160 -0.5640720036 -0.4108409500
266 267 268 269 270
-0.0982091295 0.0141876626 0.0537612484 -0.1074882935 -0.0475588407
271 272 273 274 275
-0.2119311465 2.1305177782 2.4325750494 -1.5791836785 -0.5495167070
276 277 278 279 280
-0.6321192234 0.0208672342 -0.0064114438 0.0023539016 -0.0789016227
281 282 283 284 285
0.7567070189 1.1043440061 -0.7975873443 -0.0389593102 -1.0326455134
286 287 288 289 290
-1.7082366184 -0.8271977113 -0.5558201244 -0.0170151354 0.0056794206
291 292 293 294 295
0.0807522746 0.1180999956 -0.2565416899 -0.1980051786 1.1909268640
296 297 298 299 300
-0.9465988947 -1.0861090168 0.4776611084 -1.5470399077 -0.2196384107
301 302 303 304 305
0.0092551087 0.0028361570 -0.1429892213 -0.0385048586 -0.2165816686
306 307 308 309 310
0.8075802140 -0.1319356920 1.6201145399 0.5848940970 -1.0795695839
311 312 313 314 315
-0.7776991740 -0.0542750923 -0.0108440941 -0.0042933726 0.0336409615
316 317 318 319 320
-0.0143827233 -0.2542072382 0.8855646549 -0.0528514006 -0.6061660206
321 322 323 324 325
0.0066691572 -0.3022712126 -1.3164414241 0.6708115484 -0.4177738462
326 327 328 329 330
0.0102270171 -0.0075000125 0.0554104919 0.8790622051 -0.7173612719
331 332 333 334 335
0.2436789352 3.6790954746 -1.3371869337 0.2590867766 -1.0745767932
336 337 338 339 340
-0.4719969868 -0.0756332617 0.0535897392 0.0241894018 -0.1241169231
341 342 343 344 345
-0.2537963514 -0.0609544847 -0.8043253740 -1.6703037028 1.2148399500
346 347 348 349 350
0.7372609675 -1.2015562991 -0.6089249273 1.0480308889 -0.1273148650
351 352 353 354 355
0.0320278356 0.0771574332 -0.3013371020 -0.4536039166 0.3333971296
356 357 358 359 360
1.3090398998 0.2920570534 1.6155602158 -0.4280314933 -0.1173290386
361 362 363 364 365
-0.2401295040 0.1324414910 -0.0741575148 -0.0464805936 0.0248601975
366 367 368 369 370
0.9734694104 1.0359154086 -0.3563934956 -1.3895135209 -1.2747090705
371 372 373 374 375
-0.0159652306 -0.1421713470 -0.2213404090 -0.0506332081 -0.0029925791
376 377 378 379 380
0.0775926215 -0.1236100195 -0.3823044176 -0.2998533613 -1.4562821212
381 382 383 384 385
1.9445506864 -1.2656366468 1.9219676362 -0.3789333316 0.0456408262
386 387 388 389 390
0.0208589606 0.0945264564 -0.0770573110 0.6865619423 0.6913039106
391 392 393 394 395
-1.1464265274 -0.0580282070 0.3303916041 -0.2000440282 1.2655735550
396 397 398 399 400
0.0190659432 -0.1924955657 -0.0004151990 0.0383336729 0.0242899272
401 402 403 404 405
-0.4066543888 1.7741365367 -0.7891429160 -1.7108580796 -0.5663853252
406 407 408 409 410
-1.2082850725 -1.0979756953 0.2072938381 -0.1897891620 -0.0417446361
411 412 413 414 415
-0.0503825969 0.1037906820 -0.2043182278 -0.8918768532 -0.6022150785
416 417 418 419 420
-0.4840145859 -1.4376573880 -1.1204531703 -0.6812179608 0.0094029397
421 422 423 424 425
-0.0441686772 0.0343915429 -0.0448813120 -0.1325799712 -0.0109824595
426 427 428 429 430
-0.7112106855 -0.8538906400 1.4956455427 2.6368971530 -0.0391934380
431 432 433 434 435
0.5544961937 0.9502935616 0.0192396039 0.0404074069 0.0286460816
436 437 438 439 440
-0.0460467572 -0.1641994678 -0.3195162809 1.7293231207 0.0413273645
441 442 443 444 445
-0.9195214094 1.6576369362 0.0350221017 -0.3670790908 0.0764249513
446 447 448 449 450
0.0043624436 -0.0515509885 0.0492069935 -0.1199135500 1.5345668338
451 452 453 454 455
-1.0442814877 -2.0121097821 -1.6404090959 -1.0560813289 0.2749196725
456 457 458 459 460
-0.3226297130 -0.0709225235 -0.0618201861 0.0598835511 0.1217875270
461 462 463 464 465
-0.1421737987 -0.8238995679 -0.3555395644 0.5983324610 -0.1156128899
466 467 468 469 470
0.0037807513 -0.6076693507 0.1122202569 -0.1254285879 0.0659872074
471 472 473 474 475
-0.0313996234 -0.1375747667 0.0507346579 -0.2776370276 -0.3033457614
476 477 478 479 480
1.4190034871 2.7624094702 -0.3674358528 0.6985787415 -0.1873167702
481 482 483 484 485
-0.0135846559 0.0002271966 0.0243136617 -0.0607899744 -0.0129732121
486 487 488 489 490
0.8872384933 2.3813208035 -0.7398614499 -1.8993141242 0.1888659888
491 492 493 494 495
0.3080729651 -0.1673612161 -0.0498324966 -0.0322694042 -0.0512130988
496 497 498 499 500
0.1410419512 0.2571256484 -0.5535302842 1.5239557915 1.9574179676
501 502 503 504 505
-1.9816430042 2.9189815533 -1.3986310978 0.1994618488 -0.0164893925
506 507 508 509 510
0.0985178212 -0.0743974713 -0.0667507795 -0.1348092226 0.4254820382
511 512 513 514 515
-1.5603753761 -1.7401773732 0.3234954768 1.1992479034 -0.8683805404
516 517 518 519 520
-0.0439995015 -0.0047733233 -0.0472990836 0.1094461278 1.0724604357
521 522 523 524 525
-0.3444775587 -0.8505705630 -0.6947620481 -1.8741704212 0.7396642913
526 527 528 529 530
-1.0303586716 -0.1505680042 -0.0555322760 0.0231818227 0.0273901957
531 532 533 534 535
0.0036112891 -0.3212869412 0.1318735963 0.6839461952 -0.6613754049
536 537 538 539 540
1.5627561252 -0.9163529828 -1.2829187452 -0.2052478188 0.0572741503
541 542 543 544 545
-0.0499444194 -0.0781470475 -0.0280098861 -0.2030393860 0.0835118072
546 547 548 549 550
0.7803392236 -0.9503383438 -1.1851868812 -1.3322834904 -1.1657198210
551 552 553 554 555
1.5998121984 -0.3701027733 0.0399966386 0.0347860974 0.0571468927
556 557 558 559 560
-0.0329041721 0.0460997935 0.3097361819 4.0321668500 3.4844339070
561 562 563 564 565
-0.1884886520 2.6142382113 -1.0297939021 0.3272234395 -0.0080982091
566 567 568 569 570
0.0560104043 -0.1889414752 -0.1458617021 0.2761649247 0.3365553160
571 572 573 574 575
0.7554023321 1.3544768319 -0.7930678998 1.2326405392 0.3689498589
576 577 578 579 580
-0.1602018340 -0.0137649707 -0.0158569210 0.1048892561 -0.1226720866
581 582 583 584 585
-0.1121097103 0.1222793076 1.4792859807 -3.8141355224 -1.5247982073
586 587 588 589 590
0.2090852589 -0.4134111072 0.0213683733 -0.0123656436 -0.0291357838
591 592 593 594 595
-0.0033933022 0.0697125759 0.0906214614 -0.9027090086 -0.4980683683
596 597 598 599 600
1.0334199492 2.6635492094 -0.9925095303 0.5385366482 -0.0263395719
601 602 603 604 605
0.0905004906 0.0283346583 0.0403340064 -0.1818354527 -0.1391962210
606 607 608 609 610
0.5880274414 -1.9846240202 0.7502802898 -0.2235884127 1.7385024680
611 612 613 614 615
-0.1953138814 0.0589306858 -0.0867220834 0.0339550902 0.0035001540
616 617 618 619 620
0.1039718332 0.0184976644 -0.6579795614 -2.1753622659 -0.2041801604
621 622 623 624 625
3.7255461546 0.8075292974 -0.7364577585 0.1307163684 0.0713132362
626 627 628 629 630
0.0210635898 0.0069055941 -0.1015114060 -0.0478181160 0.4759749542
631 632 633 634 635
0.0458821969 -0.4158202463 1.1471338694 2.0204058277 1.4690902649
636 637 638 639 640
-0.1702021234 0.0297117979 0.0167138969 0.0295831373 -0.0442532650
641 642 643 644 645
0.1250486524 -0.5891130478 0.2137611778 0.5239664265 3.4953350747
646 647 648
-3.3000811280 -0.1528529097 0.0906929455
> postscript(file="/var/wessaorg/rcomp/tmp/693yp1450341626.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 = 518
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.5651075033 NA
1 -0.2101959806 -0.5651075033
2 -0.0347462328 -0.2101959806
3 -0.0090670333 -0.0347462328
4 0.0299112654 -0.0090670333
5 0.1163428321 0.0299112654
6 -0.1544634510 0.1163428321
7 0.0012978649 -0.1544634510
8 1.4408999107 0.0012978649
9 -0.8343102441 1.4408999107
10 -2.0950567408 -0.8343102441
11 -2.0255707978 -2.0950567408
12 0.0846800517 -2.0255707978
13 -0.0012001695 0.0846800517
14 0.0080856453 -0.0012001695
15 -0.0382608082 0.0080856453
16 -0.0686517601 -0.0382608082
17 -0.0653739346 -0.0686517601
18 -0.2046854650 -0.0653739346
19 -0.9264933070 -0.2046854650
20 0.1463882798 -0.9264933070
21 -2.1453188148 0.1463882798
22 3.0350919871 -2.1453188148
23 0.0128228218 3.0350919871
24 2.8611153368 0.0128228218
25 0.7509599176 2.8611153368
26 0.0576843297 0.7509599176
27 -0.0763289644 0.0576843297
28 0.1257706120 -0.0763289644
29 -0.0307562923 0.1257706120
30 -0.2654614735 -0.0307562923
31 -0.8784856959 -0.2654614735
32 -0.5882866973 -0.8784856959
33 -0.4178466104 -0.5882866973
34 -3.5848120134 -0.4178466104
35 3.3145698726 -3.5848120134
36 2.8549885380 3.3145698726
37 0.7213591775 2.8549885380
38 -0.1055030606 0.7213591775
39 0.0056485052 -0.1055030606
40 -0.0425181011 0.0056485052
41 0.0315560212 -0.0425181011
42 -0.1669995262 0.0315560212
43 -0.4590847233 -0.1669995262
44 0.1724047558 -0.4590847233
45 0.2018949223 0.1724047558
46 -2.1073856012 0.2018949223
47 -1.5739165303 -2.1073856012
48 1.5654235287 -1.5739165303
49 1.4542989896 1.5654235287
50 -0.1181108782 1.4542989896
51 -0.0412109889 -0.1181108782
52 -0.0332427167 -0.0412109889
53 -0.0207891795 -0.0332427167
54 -0.1607660395 -0.0207891795
55 -0.3497877038 -0.1607660395
56 -0.8726269785 -0.3497877038
57 -0.8878453676 -0.8726269785
58 -0.3516111284 -0.8878453676
59 -0.0601801770 -0.3516111284
60 -0.1193587869 -0.0601801770
61 -0.8402779998 -0.1193587869
62 0.1949345316 -0.8402779998
63 -0.0221189239 0.1949345316
64 0.0095891613 -0.0221189239
65 -0.0281917674 0.0095891613
66 -0.0590526054 -0.0281917674
67 -0.2529855576 -0.0590526054
68 -0.8328990601 -0.2529855576
69 -1.6858765590 -0.8328990601
70 -1.7730688254 -1.6858765590
71 -1.6473809299 -1.7730688254
72 4.3349779068 -1.6473809299
73 0.8727778401 4.3349779068
74 1.8729231019 0.8727778401
75 -0.2038677209 1.8729231019
76 0.1366095384 -0.2038677209
77 0.0359105285 0.1366095384
78 0.1560959674 0.0359105285
79 -0.1291980967 0.1560959674
80 -0.6693633534 -0.1291980967
81 -1.1475085435 -0.6693633534
82 -2.3786969180 -1.1475085435
83 0.7177722238 -2.3786969180
84 -1.1514175844 0.7177722238
85 0.5771728691 -1.1514175844
86 -0.4860985665 0.5771728691
87 0.2236142503 -0.4860985665
88 -0.1495199569 0.2236142503
89 0.0125246321 -0.1495199569
90 -0.0365375406 0.0125246321
91 -0.2959547207 -0.0365375406
92 -0.1591241862 -0.2959547207
93 -0.6617922049 -0.1591241862
94 -0.5756150418 -0.6617922049
95 -0.8202718663 -0.5756150418
96 2.0897003807 -0.8202718663
97 0.6403718906 2.0897003807
98 -0.4204216173 0.6403718906
99 0.0111043691 -0.4204216173
100 0.0731845455 0.0111043691
101 -0.0249920397 0.0731845455
102 0.0197922516 -0.0249920397
103 -0.2606821767 0.0197922516
104 -0.2114562076 -0.2606821767
105 -0.2814520430 -0.2114562076
106 -0.4246557538 -0.2814520430
107 -0.2675782150 -0.4246557538
108 -2.7895310212 -0.2675782150
109 -0.5982194635 -2.7895310212
110 -0.0812319366 -0.5982194635
111 -0.0543896762 -0.0812319366
112 -0.1110484793 -0.0543896762
113 -0.0293636640 -0.1110484793
114 -0.1231033388 -0.0293636640
115 -0.2264824873 -0.1231033388
116 0.8636415131 -0.2264824873
117 3.9234195418 0.8636415131
118 3.5723115657 3.9234195418
119 3.3031211438 3.5723115657
120 -1.3824175997 3.3031211438
121 -0.6057576147 -1.3824175997
122 -0.0897210044 -0.6057576147
123 0.1106469349 -0.0897210044
124 -0.0065469453 0.1106469349
125 -0.0886001596 -0.0065469453
126 0.9214999475 -0.0886001596
127 -0.1648571239 0.9214999475
128 1.6636375545 -0.1648571239
129 0.2900541326 1.6636375545
130 0.7680680395 0.2900541326
131 0.3388162481 0.7680680395
132 -1.5425283160 0.3388162481
133 -0.5640720036 -1.5425283160
134 -0.4108409500 -0.5640720036
135 -0.0982091295 -0.4108409500
136 0.0141876626 -0.0982091295
137 0.0537612484 0.0141876626
138 -0.1074882935 0.0537612484
139 -0.0475588407 -0.1074882935
140 -0.2119311465 -0.0475588407
141 2.1305177782 -0.2119311465
142 2.4325750494 2.1305177782
143 -1.5791836785 2.4325750494
144 -0.5495167070 -1.5791836785
145 -0.6321192234 -0.5495167070
146 0.0208672342 -0.6321192234
147 -0.0064114438 0.0208672342
148 0.0023539016 -0.0064114438
149 -0.0789016227 0.0023539016
150 0.7567070189 -0.0789016227
151 1.1043440061 0.7567070189
152 -0.7975873443 1.1043440061
153 -0.0389593102 -0.7975873443
154 -1.0326455134 -0.0389593102
155 -1.7082366184 -1.0326455134
156 -0.8271977113 -1.7082366184
157 -0.5558201244 -0.8271977113
158 -0.0170151354 -0.5558201244
159 0.0056794206 -0.0170151354
160 0.0807522746 0.0056794206
161 0.1180999956 0.0807522746
162 -0.2565416899 0.1180999956
163 -0.1980051786 -0.2565416899
164 1.1909268640 -0.1980051786
165 -0.9465988947 1.1909268640
166 -1.0861090168 -0.9465988947
167 0.4776611084 -1.0861090168
168 -1.5470399077 0.4776611084
169 -0.2196384107 -1.5470399077
170 0.0092551087 -0.2196384107
171 0.0028361570 0.0092551087
172 -0.1429892213 0.0028361570
173 -0.0385048586 -0.1429892213
174 -0.2165816686 -0.0385048586
175 0.8075802140 -0.2165816686
176 -0.1319356920 0.8075802140
177 1.6201145399 -0.1319356920
178 0.5848940970 1.6201145399
179 -1.0795695839 0.5848940970
180 -0.7776991740 -1.0795695839
181 -0.0542750923 -0.7776991740
182 -0.0108440941 -0.0542750923
183 -0.0042933726 -0.0108440941
184 0.0336409615 -0.0042933726
185 -0.0143827233 0.0336409615
186 -0.2542072382 -0.0143827233
187 0.8855646549 -0.2542072382
188 -0.0528514006 0.8855646549
189 -0.6061660206 -0.0528514006
190 0.0066691572 -0.6061660206
191 -0.3022712126 0.0066691572
192 -1.3164414241 -0.3022712126
193 0.6708115484 -1.3164414241
194 -0.4177738462 0.6708115484
195 0.0102270171 -0.4177738462
196 -0.0075000125 0.0102270171
197 0.0554104919 -0.0075000125
198 0.8790622051 0.0554104919
199 -0.7173612719 0.8790622051
200 0.2436789352 -0.7173612719
201 3.6790954746 0.2436789352
202 -1.3371869337 3.6790954746
203 0.2590867766 -1.3371869337
204 -1.0745767932 0.2590867766
205 -0.4719969868 -1.0745767932
206 -0.0756332617 -0.4719969868
207 0.0535897392 -0.0756332617
208 0.0241894018 0.0535897392
209 -0.1241169231 0.0241894018
210 -0.2537963514 -0.1241169231
211 -0.0609544847 -0.2537963514
212 -0.8043253740 -0.0609544847
213 -1.6703037028 -0.8043253740
214 1.2148399500 -1.6703037028
215 0.7372609675 1.2148399500
216 -1.2015562991 0.7372609675
217 -0.6089249273 -1.2015562991
218 1.0480308889 -0.6089249273
219 -0.1273148650 1.0480308889
220 0.0320278356 -0.1273148650
221 0.0771574332 0.0320278356
222 -0.3013371020 0.0771574332
223 -0.4536039166 -0.3013371020
224 0.3333971296 -0.4536039166
225 1.3090398998 0.3333971296
226 0.2920570534 1.3090398998
227 1.6155602158 0.2920570534
228 -0.4280314933 1.6155602158
229 -0.1173290386 -0.4280314933
230 -0.2401295040 -0.1173290386
231 0.1324414910 -0.2401295040
232 -0.0741575148 0.1324414910
233 -0.0464805936 -0.0741575148
234 0.0248601975 -0.0464805936
235 0.9734694104 0.0248601975
236 1.0359154086 0.9734694104
237 -0.3563934956 1.0359154086
238 -1.3895135209 -0.3563934956
239 -1.2747090705 -1.3895135209
240 -0.0159652306 -1.2747090705
241 -0.1421713470 -0.0159652306
242 -0.2213404090 -0.1421713470
243 -0.0506332081 -0.2213404090
244 -0.0029925791 -0.0506332081
245 0.0775926215 -0.0029925791
246 -0.1236100195 0.0775926215
247 -0.3823044176 -0.1236100195
248 -0.2998533613 -0.3823044176
249 -1.4562821212 -0.2998533613
250 1.9445506864 -1.4562821212
251 -1.2656366468 1.9445506864
252 1.9219676362 -1.2656366468
253 -0.3789333316 1.9219676362
254 0.0456408262 -0.3789333316
255 0.0208589606 0.0456408262
256 0.0945264564 0.0208589606
257 -0.0770573110 0.0945264564
258 0.6865619423 -0.0770573110
259 0.6913039106 0.6865619423
260 -1.1464265274 0.6913039106
261 -0.0580282070 -1.1464265274
262 0.3303916041 -0.0580282070
263 -0.2000440282 0.3303916041
264 1.2655735550 -0.2000440282
265 0.0190659432 1.2655735550
266 -0.1924955657 0.0190659432
267 -0.0004151990 -0.1924955657
268 0.0383336729 -0.0004151990
269 0.0242899272 0.0383336729
270 -0.4066543888 0.0242899272
271 1.7741365367 -0.4066543888
272 -0.7891429160 1.7741365367
273 -1.7108580796 -0.7891429160
274 -0.5663853252 -1.7108580796
275 -1.2082850725 -0.5663853252
276 -1.0979756953 -1.2082850725
277 0.2072938381 -1.0979756953
278 -0.1897891620 0.2072938381
279 -0.0417446361 -0.1897891620
280 -0.0503825969 -0.0417446361
281 0.1037906820 -0.0503825969
282 -0.2043182278 0.1037906820
283 -0.8918768532 -0.2043182278
284 -0.6022150785 -0.8918768532
285 -0.4840145859 -0.6022150785
286 -1.4376573880 -0.4840145859
287 -1.1204531703 -1.4376573880
288 -0.6812179608 -1.1204531703
289 0.0094029397 -0.6812179608
290 -0.0441686772 0.0094029397
291 0.0343915429 -0.0441686772
292 -0.0448813120 0.0343915429
293 -0.1325799712 -0.0448813120
294 -0.0109824595 -0.1325799712
295 -0.7112106855 -0.0109824595
296 -0.8538906400 -0.7112106855
297 1.4956455427 -0.8538906400
298 2.6368971530 1.4956455427
299 -0.0391934380 2.6368971530
300 0.5544961937 -0.0391934380
301 0.9502935616 0.5544961937
302 0.0192396039 0.9502935616
303 0.0404074069 0.0192396039
304 0.0286460816 0.0404074069
305 -0.0460467572 0.0286460816
306 -0.1641994678 -0.0460467572
307 -0.3195162809 -0.1641994678
308 1.7293231207 -0.3195162809
309 0.0413273645 1.7293231207
310 -0.9195214094 0.0413273645
311 1.6576369362 -0.9195214094
312 0.0350221017 1.6576369362
313 -0.3670790908 0.0350221017
314 0.0764249513 -0.3670790908
315 0.0043624436 0.0764249513
316 -0.0515509885 0.0043624436
317 0.0492069935 -0.0515509885
318 -0.1199135500 0.0492069935
319 1.5345668338 -0.1199135500
320 -1.0442814877 1.5345668338
321 -2.0121097821 -1.0442814877
322 -1.6404090959 -2.0121097821
323 -1.0560813289 -1.6404090959
324 0.2749196725 -1.0560813289
325 -0.3226297130 0.2749196725
326 -0.0709225235 -0.3226297130
327 -0.0618201861 -0.0709225235
328 0.0598835511 -0.0618201861
329 0.1217875270 0.0598835511
330 -0.1421737987 0.1217875270
331 -0.8238995679 -0.1421737987
332 -0.3555395644 -0.8238995679
333 0.5983324610 -0.3555395644
334 -0.1156128899 0.5983324610
335 0.0037807513 -0.1156128899
336 -0.6076693507 0.0037807513
337 0.1122202569 -0.6076693507
338 -0.1254285879 0.1122202569
339 0.0659872074 -0.1254285879
340 -0.0313996234 0.0659872074
341 -0.1375747667 -0.0313996234
342 0.0507346579 -0.1375747667
343 -0.2776370276 0.0507346579
344 -0.3033457614 -0.2776370276
345 1.4190034871 -0.3033457614
346 2.7624094702 1.4190034871
347 -0.3674358528 2.7624094702
348 0.6985787415 -0.3674358528
349 -0.1873167702 0.6985787415
350 -0.0135846559 -0.1873167702
351 0.0002271966 -0.0135846559
352 0.0243136617 0.0002271966
353 -0.0607899744 0.0243136617
354 -0.0129732121 -0.0607899744
355 0.8872384933 -0.0129732121
356 2.3813208035 0.8872384933
357 -0.7398614499 2.3813208035
358 -1.8993141242 -0.7398614499
359 0.1888659888 -1.8993141242
360 0.3080729651 0.1888659888
361 -0.1673612161 0.3080729651
362 -0.0498324966 -0.1673612161
363 -0.0322694042 -0.0498324966
364 -0.0512130988 -0.0322694042
365 0.1410419512 -0.0512130988
366 0.2571256484 0.1410419512
367 -0.5535302842 0.2571256484
368 1.5239557915 -0.5535302842
369 1.9574179676 1.5239557915
370 -1.9816430042 1.9574179676
371 2.9189815533 -1.9816430042
372 -1.3986310978 2.9189815533
373 0.1994618488 -1.3986310978
374 -0.0164893925 0.1994618488
375 0.0985178212 -0.0164893925
376 -0.0743974713 0.0985178212
377 -0.0667507795 -0.0743974713
378 -0.1348092226 -0.0667507795
379 0.4254820382 -0.1348092226
380 -1.5603753761 0.4254820382
381 -1.7401773732 -1.5603753761
382 0.3234954768 -1.7401773732
383 1.1992479034 0.3234954768
384 -0.8683805404 1.1992479034
385 -0.0439995015 -0.8683805404
386 -0.0047733233 -0.0439995015
387 -0.0472990836 -0.0047733233
388 0.1094461278 -0.0472990836
389 1.0724604357 0.1094461278
390 -0.3444775587 1.0724604357
391 -0.8505705630 -0.3444775587
392 -0.6947620481 -0.8505705630
393 -1.8741704212 -0.6947620481
394 0.7396642913 -1.8741704212
395 -1.0303586716 0.7396642913
396 -0.1505680042 -1.0303586716
397 -0.0555322760 -0.1505680042
398 0.0231818227 -0.0555322760
399 0.0273901957 0.0231818227
400 0.0036112891 0.0273901957
401 -0.3212869412 0.0036112891
402 0.1318735963 -0.3212869412
403 0.6839461952 0.1318735963
404 -0.6613754049 0.6839461952
405 1.5627561252 -0.6613754049
406 -0.9163529828 1.5627561252
407 -1.2829187452 -0.9163529828
408 -0.2052478188 -1.2829187452
409 0.0572741503 -0.2052478188
410 -0.0499444194 0.0572741503
411 -0.0781470475 -0.0499444194
412 -0.0280098861 -0.0781470475
413 -0.2030393860 -0.0280098861
414 0.0835118072 -0.2030393860
415 0.7803392236 0.0835118072
416 -0.9503383438 0.7803392236
417 -1.1851868812 -0.9503383438
418 -1.3322834904 -1.1851868812
419 -1.1657198210 -1.3322834904
420 1.5998121984 -1.1657198210
421 -0.3701027733 1.5998121984
422 0.0399966386 -0.3701027733
423 0.0347860974 0.0399966386
424 0.0571468927 0.0347860974
425 -0.0329041721 0.0571468927
426 0.0460997935 -0.0329041721
427 0.3097361819 0.0460997935
428 4.0321668500 0.3097361819
429 3.4844339070 4.0321668500
430 -0.1884886520 3.4844339070
431 2.6142382113 -0.1884886520
432 -1.0297939021 2.6142382113
433 0.3272234395 -1.0297939021
434 -0.0080982091 0.3272234395
435 0.0560104043 -0.0080982091
436 -0.1889414752 0.0560104043
437 -0.1458617021 -0.1889414752
438 0.2761649247 -0.1458617021
439 0.3365553160 0.2761649247
440 0.7554023321 0.3365553160
441 1.3544768319 0.7554023321
442 -0.7930678998 1.3544768319
443 1.2326405392 -0.7930678998
444 0.3689498589 1.2326405392
445 -0.1602018340 0.3689498589
446 -0.0137649707 -0.1602018340
447 -0.0158569210 -0.0137649707
448 0.1048892561 -0.0158569210
449 -0.1226720866 0.1048892561
450 -0.1121097103 -0.1226720866
451 0.1222793076 -0.1121097103
452 1.4792859807 0.1222793076
453 -3.8141355224 1.4792859807
454 -1.5247982073 -3.8141355224
455 0.2090852589 -1.5247982073
456 -0.4134111072 0.2090852589
457 0.0213683733 -0.4134111072
458 -0.0123656436 0.0213683733
459 -0.0291357838 -0.0123656436
460 -0.0033933022 -0.0291357838
461 0.0697125759 -0.0033933022
462 0.0906214614 0.0697125759
463 -0.9027090086 0.0906214614
464 -0.4980683683 -0.9027090086
465 1.0334199492 -0.4980683683
466 2.6635492094 1.0334199492
467 -0.9925095303 2.6635492094
468 0.5385366482 -0.9925095303
469 -0.0263395719 0.5385366482
470 0.0905004906 -0.0263395719
471 0.0283346583 0.0905004906
472 0.0403340064 0.0283346583
473 -0.1818354527 0.0403340064
474 -0.1391962210 -0.1818354527
475 0.5880274414 -0.1391962210
476 -1.9846240202 0.5880274414
477 0.7502802898 -1.9846240202
478 -0.2235884127 0.7502802898
479 1.7385024680 -0.2235884127
480 -0.1953138814 1.7385024680
481 0.0589306858 -0.1953138814
482 -0.0867220834 0.0589306858
483 0.0339550902 -0.0867220834
484 0.0035001540 0.0339550902
485 0.1039718332 0.0035001540
486 0.0184976644 0.1039718332
487 -0.6579795614 0.0184976644
488 -2.1753622659 -0.6579795614
489 -0.2041801604 -2.1753622659
490 3.7255461546 -0.2041801604
491 0.8075292974 3.7255461546
492 -0.7364577585 0.8075292974
493 0.1307163684 -0.7364577585
494 0.0713132362 0.1307163684
495 0.0210635898 0.0713132362
496 0.0069055941 0.0210635898
497 -0.1015114060 0.0069055941
498 -0.0478181160 -0.1015114060
499 0.4759749542 -0.0478181160
500 0.0458821969 0.4759749542
501 -0.4158202463 0.0458821969
502 1.1471338694 -0.4158202463
503 2.0204058277 1.1471338694
504 1.4690902649 2.0204058277
505 -0.1702021234 1.4690902649
506 0.0297117979 -0.1702021234
507 0.0167138969 0.0297117979
508 0.0295831373 0.0167138969
509 -0.0442532650 0.0295831373
510 0.1250486524 -0.0442532650
511 -0.5891130478 0.1250486524
512 0.2137611778 -0.5891130478
513 0.5239664265 0.2137611778
514 3.4953350747 0.5239664265
515 -3.3000811280 3.4953350747
516 -0.1528529097 -3.3000811280
517 0.0906929455 -0.1528529097
518 NA 0.0906929455
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.2101959806 -0.5651075033
[2,] -0.0347462328 -0.2101959806
[3,] -0.0090670333 -0.0347462328
[4,] 0.0299112654 -0.0090670333
[5,] 0.1163428321 0.0299112654
[6,] -0.1544634510 0.1163428321
[7,] 0.0012978649 -0.1544634510
[8,] 1.4408999107 0.0012978649
[9,] -0.8343102441 1.4408999107
[10,] -2.0950567408 -0.8343102441
[11,] -2.0255707978 -2.0950567408
[12,] 0.0846800517 -2.0255707978
[13,] -0.0012001695 0.0846800517
[14,] 0.0080856453 -0.0012001695
[15,] -0.0382608082 0.0080856453
[16,] -0.0686517601 -0.0382608082
[17,] -0.0653739346 -0.0686517601
[18,] -0.2046854650 -0.0653739346
[19,] -0.9264933070 -0.2046854650
[20,] 0.1463882798 -0.9264933070
[21,] -2.1453188148 0.1463882798
[22,] 3.0350919871 -2.1453188148
[23,] 0.0128228218 3.0350919871
[24,] 2.8611153368 0.0128228218
[25,] 0.7509599176 2.8611153368
[26,] 0.0576843297 0.7509599176
[27,] -0.0763289644 0.0576843297
[28,] 0.1257706120 -0.0763289644
[29,] -0.0307562923 0.1257706120
[30,] -0.2654614735 -0.0307562923
[31,] -0.8784856959 -0.2654614735
[32,] -0.5882866973 -0.8784856959
[33,] -0.4178466104 -0.5882866973
[34,] -3.5848120134 -0.4178466104
[35,] 3.3145698726 -3.5848120134
[36,] 2.8549885380 3.3145698726
[37,] 0.7213591775 2.8549885380
[38,] -0.1055030606 0.7213591775
[39,] 0.0056485052 -0.1055030606
[40,] -0.0425181011 0.0056485052
[41,] 0.0315560212 -0.0425181011
[42,] -0.1669995262 0.0315560212
[43,] -0.4590847233 -0.1669995262
[44,] 0.1724047558 -0.4590847233
[45,] 0.2018949223 0.1724047558
[46,] -2.1073856012 0.2018949223
[47,] -1.5739165303 -2.1073856012
[48,] 1.5654235287 -1.5739165303
[49,] 1.4542989896 1.5654235287
[50,] -0.1181108782 1.4542989896
[51,] -0.0412109889 -0.1181108782
[52,] -0.0332427167 -0.0412109889
[53,] -0.0207891795 -0.0332427167
[54,] -0.1607660395 -0.0207891795
[55,] -0.3497877038 -0.1607660395
[56,] -0.8726269785 -0.3497877038
[57,] -0.8878453676 -0.8726269785
[58,] -0.3516111284 -0.8878453676
[59,] -0.0601801770 -0.3516111284
[60,] -0.1193587869 -0.0601801770
[61,] -0.8402779998 -0.1193587869
[62,] 0.1949345316 -0.8402779998
[63,] -0.0221189239 0.1949345316
[64,] 0.0095891613 -0.0221189239
[65,] -0.0281917674 0.0095891613
[66,] -0.0590526054 -0.0281917674
[67,] -0.2529855576 -0.0590526054
[68,] -0.8328990601 -0.2529855576
[69,] -1.6858765590 -0.8328990601
[70,] -1.7730688254 -1.6858765590
[71,] -1.6473809299 -1.7730688254
[72,] 4.3349779068 -1.6473809299
[73,] 0.8727778401 4.3349779068
[74,] 1.8729231019 0.8727778401
[75,] -0.2038677209 1.8729231019
[76,] 0.1366095384 -0.2038677209
[77,] 0.0359105285 0.1366095384
[78,] 0.1560959674 0.0359105285
[79,] -0.1291980967 0.1560959674
[80,] -0.6693633534 -0.1291980967
[81,] -1.1475085435 -0.6693633534
[82,] -2.3786969180 -1.1475085435
[83,] 0.7177722238 -2.3786969180
[84,] -1.1514175844 0.7177722238
[85,] 0.5771728691 -1.1514175844
[86,] -0.4860985665 0.5771728691
[87,] 0.2236142503 -0.4860985665
[88,] -0.1495199569 0.2236142503
[89,] 0.0125246321 -0.1495199569
[90,] -0.0365375406 0.0125246321
[91,] -0.2959547207 -0.0365375406
[92,] -0.1591241862 -0.2959547207
[93,] -0.6617922049 -0.1591241862
[94,] -0.5756150418 -0.6617922049
[95,] -0.8202718663 -0.5756150418
[96,] 2.0897003807 -0.8202718663
[97,] 0.6403718906 2.0897003807
[98,] -0.4204216173 0.6403718906
[99,] 0.0111043691 -0.4204216173
[100,] 0.0731845455 0.0111043691
[101,] -0.0249920397 0.0731845455
[102,] 0.0197922516 -0.0249920397
[103,] -0.2606821767 0.0197922516
[104,] -0.2114562076 -0.2606821767
[105,] -0.2814520430 -0.2114562076
[106,] -0.4246557538 -0.2814520430
[107,] -0.2675782150 -0.4246557538
[108,] -2.7895310212 -0.2675782150
[109,] -0.5982194635 -2.7895310212
[110,] -0.0812319366 -0.5982194635
[111,] -0.0543896762 -0.0812319366
[112,] -0.1110484793 -0.0543896762
[113,] -0.0293636640 -0.1110484793
[114,] -0.1231033388 -0.0293636640
[115,] -0.2264824873 -0.1231033388
[116,] 0.8636415131 -0.2264824873
[117,] 3.9234195418 0.8636415131
[118,] 3.5723115657 3.9234195418
[119,] 3.3031211438 3.5723115657
[120,] -1.3824175997 3.3031211438
[121,] -0.6057576147 -1.3824175997
[122,] -0.0897210044 -0.6057576147
[123,] 0.1106469349 -0.0897210044
[124,] -0.0065469453 0.1106469349
[125,] -0.0886001596 -0.0065469453
[126,] 0.9214999475 -0.0886001596
[127,] -0.1648571239 0.9214999475
[128,] 1.6636375545 -0.1648571239
[129,] 0.2900541326 1.6636375545
[130,] 0.7680680395 0.2900541326
[131,] 0.3388162481 0.7680680395
[132,] -1.5425283160 0.3388162481
[133,] -0.5640720036 -1.5425283160
[134,] -0.4108409500 -0.5640720036
[135,] -0.0982091295 -0.4108409500
[136,] 0.0141876626 -0.0982091295
[137,] 0.0537612484 0.0141876626
[138,] -0.1074882935 0.0537612484
[139,] -0.0475588407 -0.1074882935
[140,] -0.2119311465 -0.0475588407
[141,] 2.1305177782 -0.2119311465
[142,] 2.4325750494 2.1305177782
[143,] -1.5791836785 2.4325750494
[144,] -0.5495167070 -1.5791836785
[145,] -0.6321192234 -0.5495167070
[146,] 0.0208672342 -0.6321192234
[147,] -0.0064114438 0.0208672342
[148,] 0.0023539016 -0.0064114438
[149,] -0.0789016227 0.0023539016
[150,] 0.7567070189 -0.0789016227
[151,] 1.1043440061 0.7567070189
[152,] -0.7975873443 1.1043440061
[153,] -0.0389593102 -0.7975873443
[154,] -1.0326455134 -0.0389593102
[155,] -1.7082366184 -1.0326455134
[156,] -0.8271977113 -1.7082366184
[157,] -0.5558201244 -0.8271977113
[158,] -0.0170151354 -0.5558201244
[159,] 0.0056794206 -0.0170151354
[160,] 0.0807522746 0.0056794206
[161,] 0.1180999956 0.0807522746
[162,] -0.2565416899 0.1180999956
[163,] -0.1980051786 -0.2565416899
[164,] 1.1909268640 -0.1980051786
[165,] -0.9465988947 1.1909268640
[166,] -1.0861090168 -0.9465988947
[167,] 0.4776611084 -1.0861090168
[168,] -1.5470399077 0.4776611084
[169,] -0.2196384107 -1.5470399077
[170,] 0.0092551087 -0.2196384107
[171,] 0.0028361570 0.0092551087
[172,] -0.1429892213 0.0028361570
[173,] -0.0385048586 -0.1429892213
[174,] -0.2165816686 -0.0385048586
[175,] 0.8075802140 -0.2165816686
[176,] -0.1319356920 0.8075802140
[177,] 1.6201145399 -0.1319356920
[178,] 0.5848940970 1.6201145399
[179,] -1.0795695839 0.5848940970
[180,] -0.7776991740 -1.0795695839
[181,] -0.0542750923 -0.7776991740
[182,] -0.0108440941 -0.0542750923
[183,] -0.0042933726 -0.0108440941
[184,] 0.0336409615 -0.0042933726
[185,] -0.0143827233 0.0336409615
[186,] -0.2542072382 -0.0143827233
[187,] 0.8855646549 -0.2542072382
[188,] -0.0528514006 0.8855646549
[189,] -0.6061660206 -0.0528514006
[190,] 0.0066691572 -0.6061660206
[191,] -0.3022712126 0.0066691572
[192,] -1.3164414241 -0.3022712126
[193,] 0.6708115484 -1.3164414241
[194,] -0.4177738462 0.6708115484
[195,] 0.0102270171 -0.4177738462
[196,] -0.0075000125 0.0102270171
[197,] 0.0554104919 -0.0075000125
[198,] 0.8790622051 0.0554104919
[199,] -0.7173612719 0.8790622051
[200,] 0.2436789352 -0.7173612719
[201,] 3.6790954746 0.2436789352
[202,] -1.3371869337 3.6790954746
[203,] 0.2590867766 -1.3371869337
[204,] -1.0745767932 0.2590867766
[205,] -0.4719969868 -1.0745767932
[206,] -0.0756332617 -0.4719969868
[207,] 0.0535897392 -0.0756332617
[208,] 0.0241894018 0.0535897392
[209,] -0.1241169231 0.0241894018
[210,] -0.2537963514 -0.1241169231
[211,] -0.0609544847 -0.2537963514
[212,] -0.8043253740 -0.0609544847
[213,] -1.6703037028 -0.8043253740
[214,] 1.2148399500 -1.6703037028
[215,] 0.7372609675 1.2148399500
[216,] -1.2015562991 0.7372609675
[217,] -0.6089249273 -1.2015562991
[218,] 1.0480308889 -0.6089249273
[219,] -0.1273148650 1.0480308889
[220,] 0.0320278356 -0.1273148650
[221,] 0.0771574332 0.0320278356
[222,] -0.3013371020 0.0771574332
[223,] -0.4536039166 -0.3013371020
[224,] 0.3333971296 -0.4536039166
[225,] 1.3090398998 0.3333971296
[226,] 0.2920570534 1.3090398998
[227,] 1.6155602158 0.2920570534
[228,] -0.4280314933 1.6155602158
[229,] -0.1173290386 -0.4280314933
[230,] -0.2401295040 -0.1173290386
[231,] 0.1324414910 -0.2401295040
[232,] -0.0741575148 0.1324414910
[233,] -0.0464805936 -0.0741575148
[234,] 0.0248601975 -0.0464805936
[235,] 0.9734694104 0.0248601975
[236,] 1.0359154086 0.9734694104
[237,] -0.3563934956 1.0359154086
[238,] -1.3895135209 -0.3563934956
[239,] -1.2747090705 -1.3895135209
[240,] -0.0159652306 -1.2747090705
[241,] -0.1421713470 -0.0159652306
[242,] -0.2213404090 -0.1421713470
[243,] -0.0506332081 -0.2213404090
[244,] -0.0029925791 -0.0506332081
[245,] 0.0775926215 -0.0029925791
[246,] -0.1236100195 0.0775926215
[247,] -0.3823044176 -0.1236100195
[248,] -0.2998533613 -0.3823044176
[249,] -1.4562821212 -0.2998533613
[250,] 1.9445506864 -1.4562821212
[251,] -1.2656366468 1.9445506864
[252,] 1.9219676362 -1.2656366468
[253,] -0.3789333316 1.9219676362
[254,] 0.0456408262 -0.3789333316
[255,] 0.0208589606 0.0456408262
[256,] 0.0945264564 0.0208589606
[257,] -0.0770573110 0.0945264564
[258,] 0.6865619423 -0.0770573110
[259,] 0.6913039106 0.6865619423
[260,] -1.1464265274 0.6913039106
[261,] -0.0580282070 -1.1464265274
[262,] 0.3303916041 -0.0580282070
[263,] -0.2000440282 0.3303916041
[264,] 1.2655735550 -0.2000440282
[265,] 0.0190659432 1.2655735550
[266,] -0.1924955657 0.0190659432
[267,] -0.0004151990 -0.1924955657
[268,] 0.0383336729 -0.0004151990
[269,] 0.0242899272 0.0383336729
[270,] -0.4066543888 0.0242899272
[271,] 1.7741365367 -0.4066543888
[272,] -0.7891429160 1.7741365367
[273,] -1.7108580796 -0.7891429160
[274,] -0.5663853252 -1.7108580796
[275,] -1.2082850725 -0.5663853252
[276,] -1.0979756953 -1.2082850725
[277,] 0.2072938381 -1.0979756953
[278,] -0.1897891620 0.2072938381
[279,] -0.0417446361 -0.1897891620
[280,] -0.0503825969 -0.0417446361
[281,] 0.1037906820 -0.0503825969
[282,] -0.2043182278 0.1037906820
[283,] -0.8918768532 -0.2043182278
[284,] -0.6022150785 -0.8918768532
[285,] -0.4840145859 -0.6022150785
[286,] -1.4376573880 -0.4840145859
[287,] -1.1204531703 -1.4376573880
[288,] -0.6812179608 -1.1204531703
[289,] 0.0094029397 -0.6812179608
[290,] -0.0441686772 0.0094029397
[291,] 0.0343915429 -0.0441686772
[292,] -0.0448813120 0.0343915429
[293,] -0.1325799712 -0.0448813120
[294,] -0.0109824595 -0.1325799712
[295,] -0.7112106855 -0.0109824595
[296,] -0.8538906400 -0.7112106855
[297,] 1.4956455427 -0.8538906400
[298,] 2.6368971530 1.4956455427
[299,] -0.0391934380 2.6368971530
[300,] 0.5544961937 -0.0391934380
[301,] 0.9502935616 0.5544961937
[302,] 0.0192396039 0.9502935616
[303,] 0.0404074069 0.0192396039
[304,] 0.0286460816 0.0404074069
[305,] -0.0460467572 0.0286460816
[306,] -0.1641994678 -0.0460467572
[307,] -0.3195162809 -0.1641994678
[308,] 1.7293231207 -0.3195162809
[309,] 0.0413273645 1.7293231207
[310,] -0.9195214094 0.0413273645
[311,] 1.6576369362 -0.9195214094
[312,] 0.0350221017 1.6576369362
[313,] -0.3670790908 0.0350221017
[314,] 0.0764249513 -0.3670790908
[315,] 0.0043624436 0.0764249513
[316,] -0.0515509885 0.0043624436
[317,] 0.0492069935 -0.0515509885
[318,] -0.1199135500 0.0492069935
[319,] 1.5345668338 -0.1199135500
[320,] -1.0442814877 1.5345668338
[321,] -2.0121097821 -1.0442814877
[322,] -1.6404090959 -2.0121097821
[323,] -1.0560813289 -1.6404090959
[324,] 0.2749196725 -1.0560813289
[325,] -0.3226297130 0.2749196725
[326,] -0.0709225235 -0.3226297130
[327,] -0.0618201861 -0.0709225235
[328,] 0.0598835511 -0.0618201861
[329,] 0.1217875270 0.0598835511
[330,] -0.1421737987 0.1217875270
[331,] -0.8238995679 -0.1421737987
[332,] -0.3555395644 -0.8238995679
[333,] 0.5983324610 -0.3555395644
[334,] -0.1156128899 0.5983324610
[335,] 0.0037807513 -0.1156128899
[336,] -0.6076693507 0.0037807513
[337,] 0.1122202569 -0.6076693507
[338,] -0.1254285879 0.1122202569
[339,] 0.0659872074 -0.1254285879
[340,] -0.0313996234 0.0659872074
[341,] -0.1375747667 -0.0313996234
[342,] 0.0507346579 -0.1375747667
[343,] -0.2776370276 0.0507346579
[344,] -0.3033457614 -0.2776370276
[345,] 1.4190034871 -0.3033457614
[346,] 2.7624094702 1.4190034871
[347,] -0.3674358528 2.7624094702
[348,] 0.6985787415 -0.3674358528
[349,] -0.1873167702 0.6985787415
[350,] -0.0135846559 -0.1873167702
[351,] 0.0002271966 -0.0135846559
[352,] 0.0243136617 0.0002271966
[353,] -0.0607899744 0.0243136617
[354,] -0.0129732121 -0.0607899744
[355,] 0.8872384933 -0.0129732121
[356,] 2.3813208035 0.8872384933
[357,] -0.7398614499 2.3813208035
[358,] -1.8993141242 -0.7398614499
[359,] 0.1888659888 -1.8993141242
[360,] 0.3080729651 0.1888659888
[361,] -0.1673612161 0.3080729651
[362,] -0.0498324966 -0.1673612161
[363,] -0.0322694042 -0.0498324966
[364,] -0.0512130988 -0.0322694042
[365,] 0.1410419512 -0.0512130988
[366,] 0.2571256484 0.1410419512
[367,] -0.5535302842 0.2571256484
[368,] 1.5239557915 -0.5535302842
[369,] 1.9574179676 1.5239557915
[370,] -1.9816430042 1.9574179676
[371,] 2.9189815533 -1.9816430042
[372,] -1.3986310978 2.9189815533
[373,] 0.1994618488 -1.3986310978
[374,] -0.0164893925 0.1994618488
[375,] 0.0985178212 -0.0164893925
[376,] -0.0743974713 0.0985178212
[377,] -0.0667507795 -0.0743974713
[378,] -0.1348092226 -0.0667507795
[379,] 0.4254820382 -0.1348092226
[380,] -1.5603753761 0.4254820382
[381,] -1.7401773732 -1.5603753761
[382,] 0.3234954768 -1.7401773732
[383,] 1.1992479034 0.3234954768
[384,] -0.8683805404 1.1992479034
[385,] -0.0439995015 -0.8683805404
[386,] -0.0047733233 -0.0439995015
[387,] -0.0472990836 -0.0047733233
[388,] 0.1094461278 -0.0472990836
[389,] 1.0724604357 0.1094461278
[390,] -0.3444775587 1.0724604357
[391,] -0.8505705630 -0.3444775587
[392,] -0.6947620481 -0.8505705630
[393,] -1.8741704212 -0.6947620481
[394,] 0.7396642913 -1.8741704212
[395,] -1.0303586716 0.7396642913
[396,] -0.1505680042 -1.0303586716
[397,] -0.0555322760 -0.1505680042
[398,] 0.0231818227 -0.0555322760
[399,] 0.0273901957 0.0231818227
[400,] 0.0036112891 0.0273901957
[401,] -0.3212869412 0.0036112891
[402,] 0.1318735963 -0.3212869412
[403,] 0.6839461952 0.1318735963
[404,] -0.6613754049 0.6839461952
[405,] 1.5627561252 -0.6613754049
[406,] -0.9163529828 1.5627561252
[407,] -1.2829187452 -0.9163529828
[408,] -0.2052478188 -1.2829187452
[409,] 0.0572741503 -0.2052478188
[410,] -0.0499444194 0.0572741503
[411,] -0.0781470475 -0.0499444194
[412,] -0.0280098861 -0.0781470475
[413,] -0.2030393860 -0.0280098861
[414,] 0.0835118072 -0.2030393860
[415,] 0.7803392236 0.0835118072
[416,] -0.9503383438 0.7803392236
[417,] -1.1851868812 -0.9503383438
[418,] -1.3322834904 -1.1851868812
[419,] -1.1657198210 -1.3322834904
[420,] 1.5998121984 -1.1657198210
[421,] -0.3701027733 1.5998121984
[422,] 0.0399966386 -0.3701027733
[423,] 0.0347860974 0.0399966386
[424,] 0.0571468927 0.0347860974
[425,] -0.0329041721 0.0571468927
[426,] 0.0460997935 -0.0329041721
[427,] 0.3097361819 0.0460997935
[428,] 4.0321668500 0.3097361819
[429,] 3.4844339070 4.0321668500
[430,] -0.1884886520 3.4844339070
[431,] 2.6142382113 -0.1884886520
[432,] -1.0297939021 2.6142382113
[433,] 0.3272234395 -1.0297939021
[434,] -0.0080982091 0.3272234395
[435,] 0.0560104043 -0.0080982091
[436,] -0.1889414752 0.0560104043
[437,] -0.1458617021 -0.1889414752
[438,] 0.2761649247 -0.1458617021
[439,] 0.3365553160 0.2761649247
[440,] 0.7554023321 0.3365553160
[441,] 1.3544768319 0.7554023321
[442,] -0.7930678998 1.3544768319
[443,] 1.2326405392 -0.7930678998
[444,] 0.3689498589 1.2326405392
[445,] -0.1602018340 0.3689498589
[446,] -0.0137649707 -0.1602018340
[447,] -0.0158569210 -0.0137649707
[448,] 0.1048892561 -0.0158569210
[449,] -0.1226720866 0.1048892561
[450,] -0.1121097103 -0.1226720866
[451,] 0.1222793076 -0.1121097103
[452,] 1.4792859807 0.1222793076
[453,] -3.8141355224 1.4792859807
[454,] -1.5247982073 -3.8141355224
[455,] 0.2090852589 -1.5247982073
[456,] -0.4134111072 0.2090852589
[457,] 0.0213683733 -0.4134111072
[458,] -0.0123656436 0.0213683733
[459,] -0.0291357838 -0.0123656436
[460,] -0.0033933022 -0.0291357838
[461,] 0.0697125759 -0.0033933022
[462,] 0.0906214614 0.0697125759
[463,] -0.9027090086 0.0906214614
[464,] -0.4980683683 -0.9027090086
[465,] 1.0334199492 -0.4980683683
[466,] 2.6635492094 1.0334199492
[467,] -0.9925095303 2.6635492094
[468,] 0.5385366482 -0.9925095303
[469,] -0.0263395719 0.5385366482
[470,] 0.0905004906 -0.0263395719
[471,] 0.0283346583 0.0905004906
[472,] 0.0403340064 0.0283346583
[473,] -0.1818354527 0.0403340064
[474,] -0.1391962210 -0.1818354527
[475,] 0.5880274414 -0.1391962210
[476,] -1.9846240202 0.5880274414
[477,] 0.7502802898 -1.9846240202
[478,] -0.2235884127 0.7502802898
[479,] 1.7385024680 -0.2235884127
[480,] -0.1953138814 1.7385024680
[481,] 0.0589306858 -0.1953138814
[482,] -0.0867220834 0.0589306858
[483,] 0.0339550902 -0.0867220834
[484,] 0.0035001540 0.0339550902
[485,] 0.1039718332 0.0035001540
[486,] 0.0184976644 0.1039718332
[487,] -0.6579795614 0.0184976644
[488,] -2.1753622659 -0.6579795614
[489,] -0.2041801604 -2.1753622659
[490,] 3.7255461546 -0.2041801604
[491,] 0.8075292974 3.7255461546
[492,] -0.7364577585 0.8075292974
[493,] 0.1307163684 -0.7364577585
[494,] 0.0713132362 0.1307163684
[495,] 0.0210635898 0.0713132362
[496,] 0.0069055941 0.0210635898
[497,] -0.1015114060 0.0069055941
[498,] -0.0478181160 -0.1015114060
[499,] 0.4759749542 -0.0478181160
[500,] 0.0458821969 0.4759749542
[501,] -0.4158202463 0.0458821969
[502,] 1.1471338694 -0.4158202463
[503,] 2.0204058277 1.1471338694
[504,] 1.4690902649 2.0204058277
[505,] -0.1702021234 1.4690902649
[506,] 0.0297117979 -0.1702021234
[507,] 0.0167138969 0.0297117979
[508,] 0.0295831373 0.0167138969
[509,] -0.0442532650 0.0295831373
[510,] 0.1250486524 -0.0442532650
[511,] -0.5891130478 0.1250486524
[512,] 0.2137611778 -0.5891130478
[513,] 0.5239664265 0.2137611778
[514,] 3.4953350747 0.5239664265
[515,] -3.3000811280 3.4953350747
[516,] -0.1528529097 -3.3000811280
[517,] 0.0906929455 -0.1528529097
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.2101959806 -0.5651075033
2 -0.0347462328 -0.2101959806
3 -0.0090670333 -0.0347462328
4 0.0299112654 -0.0090670333
5 0.1163428321 0.0299112654
6 -0.1544634510 0.1163428321
7 0.0012978649 -0.1544634510
8 1.4408999107 0.0012978649
9 -0.8343102441 1.4408999107
10 -2.0950567408 -0.8343102441
11 -2.0255707978 -2.0950567408
12 0.0846800517 -2.0255707978
13 -0.0012001695 0.0846800517
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502 1.1471338694 -0.4158202463
503 2.0204058277 1.1471338694
504 1.4690902649 2.0204058277
505 -0.1702021234 1.4690902649
506 0.0297117979 -0.1702021234
507 0.0167138969 0.0297117979
508 0.0295831373 0.0167138969
509 -0.0442532650 0.0295831373
510 0.1250486524 -0.0442532650
511 -0.5891130478 0.1250486524
512 0.2137611778 -0.5891130478
513 0.5239664265 0.2137611778
514 3.4953350747 0.5239664265
515 -3.3000811280 3.4953350747
516 -0.1528529097 -3.3000811280
517 0.0906929455 -0.1528529097
> 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/7tzpv1450341626.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/8ie581450341626.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/9ao521450341626.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/10remo1450341626.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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, mywarning)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11go4x1450341627.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
+ a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
+ a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
+ a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
+ a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12hkyk1450341627.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
> 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,formatC(signif(mysum$sigma,6),format='g',flag=' '))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13myl71450341627.tab")
> if(n < 200) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a, 'Time or Index', 1, TRUE)
+ a<-table.element(a, 'Actuals', 1, TRUE)
+ a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
+ a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
+ a<-table.row.end(a)
+ for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
+ a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
+ a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/14ow2w1450341627.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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
+ a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
+ a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15e9ki1450341627.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,signif(numsignificant1,6))
+ a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
+ 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,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ 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,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ 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/161mfp1450341627.tab")
+ }
+ }
>
> try(system("convert tmp/162wj1450341626.ps tmp/162wj1450341626.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ryk21450341626.ps tmp/2ryk21450341626.png",intern=TRUE))
character(0)
> try(system("convert tmp/38jwa1450341626.ps tmp/38jwa1450341626.png",intern=TRUE))
character(0)
> try(system("convert tmp/4m4is1450341626.ps tmp/4m4is1450341626.png",intern=TRUE))
character(0)
> try(system("convert tmp/54ob91450341626.ps tmp/54ob91450341626.png",intern=TRUE))
character(0)
> try(system("convert tmp/693yp1450341626.ps tmp/693yp1450341626.png",intern=TRUE))
character(0)
> try(system("convert tmp/7tzpv1450341626.ps tmp/7tzpv1450341626.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ie581450341626.ps tmp/8ie581450341626.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ao521450341626.ps tmp/9ao521450341626.png",intern=TRUE))
character(0)
> try(system("convert tmp/10remo1450341626.ps tmp/10remo1450341626.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.465 0.846 9.350