R version 3.2.2 (2015-08-14) -- "Fire Safety"
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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+ ,'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 = '41'
> par4 = '11'
> par3 = 'Seasonal Differences (s=12)'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> par5 <- '41'
> par4 <- '11'
> par3 <- 'Seasonal Differences (s=12)'
> par2 <- 'Include Monthly Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 (Sun, 06 Dec 2015 16:18:54 +0000)
> #Author: root
> #To cite this work: Wessa P., (2015), Multiple Regression (v1.0.38) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> 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] 65
> 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)
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)
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)
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-11)
504 0 0 0
505 0 0 0
506 0 0 0
507 -1 0 0
508 0 -1 0
509 2 0 -1
510 0 2 0
511 3 0 2
512 -1 3 0
513 0 -1 3
514 0 0 -1
515 0 0 0
516 0 0 0
517 0 0 0
518 0 0 0
519 1 0 0
520 -3 1 0
521 -4 -3 1
522 1 -4 -3
523 -1 1 -4
524 0 -1 1
525 0 0 -1
526 0 0 0
527 0 0 0
528 0 0 0
529 1 0 0
530 0 1 0
531 -1 0 1
532 0 -1 0
533 0 0 -1
534 1 0 0
535 -2 1 0
536 0 -2 1
537 0 0 -2
538 0 0 0
539 0 0 0
540 0 0 0
541 -1 0 0
542 0 -1 0
543 1 0 -1
544 0 1 0
545 3 0 1
546 -1 3 0
547 -1 -1 3
548 0 -1 -1
549 0 0 -1
550 0 0 0
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 -2
560 2 0 -1
561 0 2 0
562 0 0 2
563 0 0 0
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 5
571 4 1 5
572 -2 4 1
573 0 -2 4
574 0 0 -2
575 0 0 0
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 -3
583 -1 -1 -3
584 1 -1 -1
585 0 1 -1
586 0 0 1
587 0 0 0
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 1
595 -1 -1 -4
596 -1 -1 -1
597 0 -1 -1
598 0 0 -1
599 0 0 0
600 0 0 0
601 0 0 0
602 0 0 0
603 -1 0 0
604 -2 -1 0
605 4 -2 -1
606 5 4 -2
607 -1 5 4
608 1 -1 5
609 0 1 -1
610 0 0 1
611 0 0 0
612 0 0 0
613 0 0 0
614 0 0 0
615 1 0 0
616 -1 1 0
617 0 -1 1
618 -3 0 -1
619 2 -3 0
620 0 2 -3
621 0 0 2
622 0 0 0
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 -1
630 4 0 0
631 1 4 0
632 -1 1 4
633 0 -1 1
634 0 0 -1
635 0 0 0
636 0 0 0
637 0 0 0
638 0 0 0
639 1 0 0
640 1 1 0
641 -1 1 1
642 -2 -1 1
643 1 -2 -1
644 2 1 -2
645 0 2 1
646 0 0 2
647 0 0 0
648 0 0 0
(1-B12)Orkanen(t-1s) (1-B12)Orkanen(t-2s) (1-B12)Orkanen(t-3s)
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 1 0
511 0 3 0
512 2 -1 1
513 0 -4 3
514 3 0 0
515 -1 0 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 1
523 -3 0 3
524 -4 2 -1
525 1 0 -4
526 -1 3 0
527 0 -1 0
528 0 0 0
529 0 0 0
530 0 0 0
531 0 0 0
532 1 0 0
533 0 0 0
534 -1 1 -1
535 0 -3 0
536 0 -4 2
537 1 1 0
538 -2 -1 3
539 0 0 -1
540 0 0 0
541 0 0 0
542 0 0 0
543 0 0 0
544 -1 1 0
545 0 0 0
546 1 -1 1
547 0 0 -3
548 3 0 -4
549 -1 1 1
550 -1 -2 -1
551 0 0 0
552 0 0 0
553 0 0 0
554 0 0 0
555 0 0 0
556 0 -1 1
557 0 0 0
558 0 1 -1
559 0 0 0
560 -2 3 0
561 -1 -1 1
562 0 -1 -2
563 2 0 0
564 0 0 0
565 0 0 0
566 0 0 0
567 0 0 0
568 0 0 -1
569 0 0 0
570 0 0 1
571 5 0 0
572 5 -2 3
573 1 -1 -1
574 4 0 -1
575 -2 2 0
576 0 0 0
577 0 0 0
578 0 0 0
579 0 0 0
580 0 0 0
581 0 0 0
582 0 0 0
583 -3 5 0
584 -3 5 -2
585 -1 1 -1
586 -1 4 0
587 1 -2 2
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 1 -3 5
596 -4 -3 5
597 -1 -1 1
598 -1 -1 4
599 -1 1 -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 -1 0 0
607 -2 1 -3
608 4 -4 -3
609 5 -1 -1
610 -1 -1 -1
611 1 -1 1
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 -1 0
619 -1 -2 1
620 0 4 -4
621 -3 5 -1
622 2 -1 -1
623 0 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 -1
631 0 -1 -2
632 0 0 4
633 4 -3 5
634 1 2 -1
635 -1 0 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 1 0 -1
644 -1 0 0
645 -2 4 -3
646 1 1 2
647 2 -1 0
648 0 0 0
(1-B12)Orkanen(t-4s) (1-B12)Orkanen(t-5s) (1-B12)Orkanen(t-6s)
504 0 -1 1
505 0 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 0 0 0
510 -2 2 0
511 0 -2 2
512 2 -2 -1
513 1 -1 -3
514 1 -2 1
515 -1 0 0
516 0 0 -1
517 0 0 0
518 0 0 0
519 0 0 0
520 0 0 0
521 0 0 0
522 0 -2 2
523 0 0 -2
524 1 2 -2
525 3 1 -1
526 0 1 -2
527 1 -1 0
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 0 -2
535 3 0 0
536 -1 1 2
537 -4 3 1
538 0 0 1
539 0 1 -1
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 1 0
547 0 3 0
548 2 -1 1
549 0 -4 3
550 3 0 0
551 -1 0 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 1
559 -3 0 3
560 -4 2 -1
561 1 0 -4
562 -1 3 0
563 0 -1 0
564 0 0 0
565 0 0 0
566 0 0 0
567 0 0 0
568 1 0 0
569 0 0 0
570 -1 1 -1
571 0 -3 0
572 0 -4 2
573 1 1 0
574 -2 -1 3
575 0 0 -1
576 0 0 0
577 0 0 0
578 0 0 0
579 0 0 0
580 -1 1 0
581 0 0 0
582 1 -1 1
583 0 0 -3
584 3 0 -4
585 -1 1 1
586 -1 -2 -1
587 0 0 0
588 0 0 0
589 0 0 0
590 0 0 0
591 0 0 0
592 0 -1 1
593 0 0 0
594 0 1 -1
595 0 0 0
596 -2 3 0
597 -1 -1 1
598 0 -1 -2
599 2 0 0
600 0 0 0
601 0 0 0
602 0 0 0
603 0 0 0
604 0 0 -1
605 0 0 0
606 0 0 1
607 5 0 0
608 5 -2 3
609 1 -1 -1
610 4 0 -1
611 -2 2 0
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 0 0 0
619 -3 5 0
620 -3 5 -2
621 -1 1 -1
622 -1 4 0
623 1 -2 2
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 1 -3 5
632 -4 -3 5
633 -1 -1 1
634 -1 -1 4
635 -1 1 -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 -1 0 0
643 -2 1 -3
644 4 -4 -3
645 5 -1 -1
646 -1 -1 -1
647 1 -1 1
648 0 0 0
(1-B12)Orkanen(t-7s) (1-B12)Orkanen(t-8s) (1-B12)Orkanen(t-9s)
504 0 0 0
505 0 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 0 0 -1
510 0 -2 1
511 0 0 0
512 2 1 -2
513 4 -1 -1
514 1 0 -1
515 1 0 -2
516 1 0 0
517 0 0 0
518 0 0 0
519 0 0 0
520 0 0 0
521 0 0 0
522 0 0 -2
523 2 0 0
524 -1 2 1
525 -3 4 -1
526 1 1 0
527 0 1 0
528 -1 1 0
529 0 0 0
530 0 0 0
531 0 0 0
532 0 0 0
533 0 0 0
534 2 0 0
535 -2 2 0
536 -2 -1 2
537 -1 -3 4
538 -2 1 1
539 0 0 1
540 0 -1 1
541 0 0 0
542 0 0 0
543 0 0 0
544 0 0 0
545 0 0 0
546 -2 2 0
547 0 -2 2
548 2 -2 -1
549 1 -1 -3
550 1 -2 1
551 -1 0 0
552 0 0 -1
553 0 0 0
554 0 0 0
555 0 0 0
556 0 0 0
557 0 0 0
558 0 -2 2
559 0 0 -2
560 1 2 -2
561 3 1 -1
562 0 1 -2
563 1 -1 0
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 0 -2
571 3 0 0
572 -1 1 2
573 -4 3 1
574 0 0 1
575 0 1 -1
576 0 0 0
577 0 0 0
578 0 0 0
579 0 0 0
580 0 0 0
581 0 0 0
582 -1 1 0
583 0 3 0
584 2 -1 1
585 0 -4 3
586 3 0 0
587 -1 0 1
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 1 -1 1
595 -3 0 3
596 -4 2 -1
597 1 0 -4
598 -1 3 0
599 0 -1 0
600 0 0 0
601 0 0 0
602 0 0 0
603 0 0 0
604 1 0 0
605 0 0 0
606 -1 1 -1
607 0 -3 0
608 0 -4 2
609 1 1 0
610 -2 -1 3
611 0 0 -1
612 0 0 0
613 0 0 0
614 0 0 0
615 0 0 0
616 -1 1 0
617 0 0 0
618 1 -1 1
619 0 0 -3
620 3 0 -4
621 -1 1 1
622 -1 -2 -1
623 0 0 0
624 0 0 0
625 0 0 0
626 0 0 0
627 0 0 0
628 0 -1 1
629 0 0 0
630 0 1 -1
631 0 0 0
632 -2 3 0
633 -1 -1 1
634 0 -1 -2
635 2 0 0
636 0 0 0
637 0 0 0
638 0 0 0
639 0 0 0
640 0 0 -1
641 0 0 0
642 0 0 1
643 5 0 0
644 5 -2 3
645 1 -1 -1
646 4 0 -1
647 -2 2 0
648 0 0 0
(1-B12)Orkanen(t-10s) (1-B12)Orkanen(t-11s) (1-B12)Orkanen(t-12s)
504 0 0 0
505 0 0 -1
506 0 0 0
507 0 0 0
508 0 0 0
509 1 0 0
510 1 -1 1
511 -1 -1 1
512 1 -1 -1
513 -1 3 -1
514 0 0 -2
515 0 2 0
516 0 0 0
517 0 0 0
518 0 0 0
519 0 0 0
520 0 0 0
521 -1 1 0
522 1 1 -1
523 0 -1 -1
524 -2 1 -1
525 -1 -1 3
526 -1 0 0
527 -2 0 2
528 0 0 0
529 0 0 0
530 0 0 0
531 0 0 0
532 0 0 0
533 0 -1 1
534 -2 1 1
535 0 0 -1
536 1 -2 1
537 -1 -1 -1
538 0 -1 0
539 0 -2 0
540 0 0 0
541 0 0 0
542 0 0 0
543 0 0 0
544 0 0 0
545 0 0 -1
546 0 -2 1
547 0 0 0
548 2 1 -2
549 4 -1 -1
550 1 0 -1
551 1 0 -2
552 1 0 0
553 0 0 0
554 0 0 0
555 0 0 0
556 0 0 0
557 0 0 0
558 0 0 -2
559 2 0 0
560 -1 2 1
561 -3 4 -1
562 1 1 0
563 0 1 0
564 -1 1 0
565 0 0 0
566 0 0 0
567 0 0 0
568 0 0 0
569 0 0 0
570 2 0 0
571 -2 2 0
572 -2 -1 2
573 -1 -3 4
574 -2 1 1
575 0 0 1
576 0 -1 1
577 0 0 0
578 0 0 0
579 0 0 0
580 0 0 0
581 0 0 0
582 -2 2 0
583 0 -2 2
584 2 -2 -1
585 1 -1 -3
586 1 -2 1
587 -1 0 0
588 0 0 -1
589 0 0 0
590 0 0 0
591 0 0 0
592 0 0 0
593 0 0 0
594 0 -2 2
595 0 0 -2
596 1 2 -2
597 3 1 -1
598 0 1 -2
599 1 -1 0
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 1 0 -2
607 3 0 0
608 -1 1 2
609 -4 3 1
610 0 0 1
611 0 1 -1
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 1 0
619 0 3 0
620 2 -1 1
621 0 -4 3
622 3 0 0
623 -1 0 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 1
631 -3 0 3
632 -4 2 -1
633 1 0 -4
634 -1 3 0
635 0 -1 0
636 0 0 0
637 0 0 0
638 0 0 0
639 0 0 0
640 1 0 0
641 0 0 0
642 -1 1 -1
643 0 -3 0
644 0 -4 2
645 1 1 0
646 -2 -1 3
647 0 0 -1
648 0 0 0
(1-B12)Orkanen(t-13s) (1-B12)Orkanen(t-14s) (1-B12)Orkanen(t-15s)
504 0 -1 1
505 1 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 0 -1 1
510 0 0 -1
511 1 -1 0
512 3 -5 4
513 0 1 -1
514 1 1 0
515 0 0 0
516 0 0 -1
517 -1 1 0
518 0 0 0
519 0 0 0
520 0 0 0
521 0 0 -1
522 1 0 0
523 1 1 -1
524 -1 3 -5
525 -1 0 1
526 -2 1 1
527 0 0 0
528 0 0 0
529 0 -1 1
530 0 0 0
531 0 0 0
532 0 0 0
533 0 0 0
534 -1 1 0
535 -1 1 1
536 -1 -1 3
537 3 -1 0
538 0 -2 1
539 2 0 0
540 0 0 0
541 0 0 -1
542 0 0 0
543 0 0 0
544 0 0 0
545 1 0 0
546 1 -1 1
547 -1 -1 1
548 1 -1 -1
549 -1 3 -1
550 0 0 -2
551 0 2 0
552 0 0 0
553 0 0 0
554 0 0 0
555 0 0 0
556 0 0 0
557 -1 1 0
558 1 1 -1
559 0 -1 -1
560 -2 1 -1
561 -1 -1 3
562 -1 0 0
563 -2 0 2
564 0 0 0
565 0 0 0
566 0 0 0
567 0 0 0
568 0 0 0
569 0 -1 1
570 -2 1 1
571 0 0 -1
572 1 -2 1
573 -1 -1 -1
574 0 -1 0
575 0 -2 0
576 0 0 0
577 0 0 0
578 0 0 0
579 0 0 0
580 0 0 0
581 0 0 -1
582 0 -2 1
583 0 0 0
584 2 1 -2
585 4 -1 -1
586 1 0 -1
587 1 0 -2
588 1 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 -2
595 2 0 0
596 -1 2 1
597 -3 4 -1
598 1 1 0
599 0 1 0
600 -1 1 0
601 0 0 0
602 0 0 0
603 0 0 0
604 0 0 0
605 0 0 0
606 2 0 0
607 -2 2 0
608 -2 -1 2
609 -1 -3 4
610 -2 1 1
611 0 0 1
612 0 -1 1
613 0 0 0
614 0 0 0
615 0 0 0
616 0 0 0
617 0 0 0
618 -2 2 0
619 0 -2 2
620 2 -2 -1
621 1 -1 -3
622 1 -2 1
623 -1 0 0
624 0 0 -1
625 0 0 0
626 0 0 0
627 0 0 0
628 0 0 0
629 0 0 0
630 0 -2 2
631 0 0 -2
632 1 2 -2
633 3 1 -1
634 0 1 -2
635 1 -1 0
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 0 -2
643 3 0 0
644 -1 1 2
645 -4 3 1
646 0 0 1
647 0 1 -1
648 0 0 0
(1-B12)Orkanen(t-16s) (1-B12)Orkanen(t-17s) (1-B12)Orkanen(t-18s)
504 0 0 0
505 0 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 0 0 -1
510 0 1 -1
511 0 -1 2
512 -2 2 0
513 -1 2 0
514 0 -1 2
515 0 0 -1
516 1 0 0
517 0 0 0
518 0 0 0
519 0 0 0
520 0 0 0
521 1 0 0
522 -1 0 1
523 0 0 -1
524 4 -2 2
525 -1 -1 2
526 0 0 -1
527 0 0 0
528 -1 1 0
529 0 0 0
530 0 0 0
531 0 0 0
532 0 0 0
533 -1 1 0
534 0 -1 0
535 -1 0 0
536 -5 4 -2
537 1 -1 -1
538 1 0 0
539 0 0 0
540 0 -1 1
541 1 0 0
542 0 0 0
543 0 0 0
544 0 0 0
545 0 -1 1
546 0 0 -1
547 1 -1 0
548 3 -5 4
549 0 1 -1
550 1 1 0
551 0 0 0
552 0 0 -1
553 -1 1 0
554 0 0 0
555 0 0 0
556 0 0 0
557 0 0 -1
558 1 0 0
559 1 1 -1
560 -1 3 -5
561 -1 0 1
562 -2 1 1
563 0 0 0
564 0 0 0
565 0 -1 1
566 0 0 0
567 0 0 0
568 0 0 0
569 0 0 0
570 -1 1 0
571 -1 1 1
572 -1 -1 3
573 3 -1 0
574 0 -2 1
575 2 0 0
576 0 0 0
577 0 0 -1
578 0 0 0
579 0 0 0
580 0 0 0
581 1 0 0
582 1 -1 1
583 -1 -1 1
584 1 -1 -1
585 -1 3 -1
586 0 0 -2
587 0 2 0
588 0 0 0
589 0 0 0
590 0 0 0
591 0 0 0
592 0 0 0
593 -1 1 0
594 1 1 -1
595 0 -1 -1
596 -2 1 -1
597 -1 -1 3
598 -1 0 0
599 -2 0 2
600 0 0 0
601 0 0 0
602 0 0 0
603 0 0 0
604 0 0 0
605 0 -1 1
606 -2 1 1
607 0 0 -1
608 1 -2 1
609 -1 -1 -1
610 0 -1 0
611 0 -2 0
612 0 0 0
613 0 0 0
614 0 0 0
615 0 0 0
616 0 0 0
617 0 0 -1
618 0 -2 1
619 0 0 0
620 2 1 -2
621 4 -1 -1
622 1 0 -1
623 1 0 -2
624 1 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 -2
631 2 0 0
632 -1 2 1
633 -3 4 -1
634 1 1 0
635 0 1 0
636 -1 1 0
637 0 0 0
638 0 0 0
639 0 0 0
640 0 0 0
641 0 0 0
642 2 0 0
643 -2 2 0
644 -2 -1 2
645 -1 -3 4
646 -2 1 1
647 0 0 1
648 0 -1 1
(1-B12)Orkanen(t-19s) (1-B12)Orkanen(t-20s) (1-B12)Orkanen(t-21s)
504 0 0 0
505 0 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 1 -1 1
510 1 0 0
511 -1 -1 1
512 -2 2 -3
513 -4 3 -3
514 -1 -1 -3
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 -1 1 -1
522 -1 1 0
523 2 -1 -1
524 0 -2 2
525 0 -4 3
526 2 -1 -1
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 -1 1
534 1 -1 1
535 -1 2 -1
536 2 0 -2
537 2 0 -4
538 -1 2 -1
539 0 -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 -1
546 0 1 -1
547 0 -1 2
548 -2 2 0
549 -1 2 0
550 0 -1 2
551 0 0 -1
552 1 0 0
553 0 0 0
554 0 0 0
555 0 0 0
556 0 0 0
557 1 0 0
558 -1 0 1
559 0 0 -1
560 4 -2 2
561 -1 -1 2
562 0 0 -1
563 0 0 0
564 -1 1 0
565 0 0 0
566 0 0 0
567 0 0 0
568 0 0 0
569 -1 1 0
570 0 -1 0
571 -1 0 0
572 -5 4 -2
573 1 -1 -1
574 1 0 0
575 0 0 0
576 0 -1 1
577 1 0 0
578 0 0 0
579 0 0 0
580 0 0 0
581 0 -1 1
582 0 0 -1
583 1 -1 0
584 3 -5 4
585 0 1 -1
586 1 1 0
587 0 0 0
588 0 0 -1
589 -1 1 0
590 0 0 0
591 0 0 0
592 0 0 0
593 0 0 -1
594 1 0 0
595 1 1 -1
596 -1 3 -5
597 -1 0 1
598 -2 1 1
599 0 0 0
600 0 0 0
601 0 -1 1
602 0 0 0
603 0 0 0
604 0 0 0
605 0 0 0
606 -1 1 0
607 -1 1 1
608 -1 -1 3
609 3 -1 0
610 0 -2 1
611 2 0 0
612 0 0 0
613 0 0 -1
614 0 0 0
615 0 0 0
616 0 0 0
617 1 0 0
618 1 -1 1
619 -1 -1 1
620 1 -1 -1
621 -1 3 -1
622 0 0 -2
623 0 2 0
624 0 0 0
625 0 0 0
626 0 0 0
627 0 0 0
628 0 0 0
629 -1 1 0
630 1 1 -1
631 0 -1 -1
632 -2 1 -1
633 -1 -1 3
634 -1 0 0
635 -2 0 2
636 0 0 0
637 0 0 0
638 0 0 0
639 0 0 0
640 0 0 0
641 0 -1 1
642 -2 1 1
643 0 0 -1
644 1 -2 1
645 -1 -1 -1
646 0 -1 0
647 0 -2 0
648 0 0 0
(1-B12)Orkanen(t-22s) (1-B12)Orkanen(t-23s) (1-B12)Orkanen(t-24s)
504 -2 1 -1
505 0 0 -2
506 0 0 0
507 0 0 0
508 0 0 0
509 0 0 0
510 0 0 0
511 1 0 0
512 4 1 0
513 5 0 1
514 4 0 -1
515 1 -5 3
516 0 -2 1
517 0 0 0
518 0 0 0
519 0 0 0
520 0 0 0
521 1 0 0
522 0 0 0
523 1 1 0
524 -3 4 1
525 -3 5 0
526 -3 4 0
527 -1 1 -5
528 0 0 -2
529 0 0 0
530 0 0 0
531 0 0 0
532 0 0 0
533 -1 1 0
534 0 0 0
535 -1 1 1
536 2 -3 4
537 3 -3 5
538 -1 -3 4
539 1 -1 1
540 0 0 0
541 0 0 0
542 0 0 0
543 0 0 0
544 0 0 0
545 1 -1 1
546 1 0 0
547 -1 -1 1
548 -2 2 -3
549 -4 3 -3
550 -1 -1 -3
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 -1 1 -1
558 -1 1 0
559 2 -1 -1
560 0 -2 2
561 0 -4 3
562 2 -1 -1
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 -1 1
570 1 -1 1
571 -1 2 -1
572 2 0 -2
573 2 0 -4
574 -1 2 -1
575 0 -1 0
576 0 0 0
577 0 0 0
578 0 0 0
579 0 0 0
580 0 0 0
581 0 0 -1
582 0 1 -1
583 0 -1 2
584 -2 2 0
585 -1 2 0
586 0 -1 2
587 0 0 -1
588 1 0 0
589 0 0 0
590 0 0 0
591 0 0 0
592 0 0 0
593 1 0 0
594 -1 0 1
595 0 0 -1
596 4 -2 2
597 -1 -1 2
598 0 0 -1
599 0 0 0
600 -1 1 0
601 0 0 0
602 0 0 0
603 0 0 0
604 0 0 0
605 -1 1 0
606 0 -1 0
607 -1 0 0
608 -5 4 -2
609 1 -1 -1
610 1 0 0
611 0 0 0
612 0 -1 1
613 1 0 0
614 0 0 0
615 0 0 0
616 0 0 0
617 0 -1 1
618 0 0 -1
619 1 -1 0
620 3 -5 4
621 0 1 -1
622 1 1 0
623 0 0 0
624 0 0 -1
625 -1 1 0
626 0 0 0
627 0 0 0
628 0 0 0
629 0 0 -1
630 1 0 0
631 1 1 -1
632 -1 3 -5
633 -1 0 1
634 -2 1 1
635 0 0 0
636 0 0 0
637 0 -1 1
638 0 0 0
639 0 0 0
640 0 0 0
641 0 0 0
642 -1 1 0
643 -1 1 1
644 -1 -1 3
645 3 -1 0
646 0 -2 1
647 2 0 0
648 0 0 0
(1-B12)Orkanen(t-25s) (1-B12)Orkanen(t-26s) (1-B12)Orkanen(t-27s)
504 2 -2 1
505 2 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 0 0 0
510 0 0 0
511 0 0 -1
512 0 -1 -1
513 -1 -1 1
514 2 -2 1
515 -4 4 -1
516 -1 2 -2
517 -2 2 0
518 0 0 0
519 0 0 0
520 0 0 0
521 0 0 0
522 0 0 0
523 0 0 0
524 0 0 -1
525 1 -1 -1
526 -1 2 -2
527 3 -4 4
528 1 -1 2
529 0 -2 2
530 0 0 0
531 0 0 0
532 0 0 0
533 0 0 0
534 0 0 0
535 0 0 0
536 1 0 0
537 0 1 -1
538 0 -1 2
539 -5 3 -4
540 -2 1 -1
541 0 0 -2
542 0 0 0
543 0 0 0
544 0 0 0
545 0 0 0
546 0 0 0
547 1 0 0
548 4 1 0
549 5 0 1
550 4 0 -1
551 1 -5 3
552 0 -2 1
553 0 0 0
554 0 0 0
555 0 0 0
556 0 0 0
557 1 0 0
558 0 0 0
559 1 1 0
560 -3 4 1
561 -3 5 0
562 -3 4 0
563 -1 1 -5
564 0 0 -2
565 0 0 0
566 0 0 0
567 0 0 0
568 0 0 0
569 -1 1 0
570 0 0 0
571 -1 1 1
572 2 -3 4
573 3 -3 5
574 -1 -3 4
575 1 -1 1
576 0 0 0
577 0 0 0
578 0 0 0
579 0 0 0
580 0 0 0
581 1 -1 1
582 1 0 0
583 -1 -1 1
584 -2 2 -3
585 -4 3 -3
586 -1 -1 -3
587 0 1 -1
588 0 0 0
589 0 0 0
590 0 0 0
591 0 0 0
592 0 0 0
593 -1 1 -1
594 -1 1 0
595 2 -1 -1
596 0 -2 2
597 0 -4 3
598 2 -1 -1
599 -1 0 1
600 0 0 0
601 0 0 0
602 0 0 0
603 0 0 0
604 0 0 0
605 0 -1 1
606 1 -1 1
607 -1 2 -1
608 2 0 -2
609 2 0 -4
610 -1 2 -1
611 0 -1 0
612 0 0 0
613 0 0 0
614 0 0 0
615 0 0 0
616 0 0 0
617 0 0 -1
618 0 1 -1
619 0 -1 2
620 -2 2 0
621 -1 2 0
622 0 -1 2
623 0 0 -1
624 1 0 0
625 0 0 0
626 0 0 0
627 0 0 0
628 0 0 0
629 1 0 0
630 -1 0 1
631 0 0 -1
632 4 -2 2
633 -1 -1 2
634 0 0 -1
635 0 0 0
636 -1 1 0
637 0 0 0
638 0 0 0
639 0 0 0
640 0 0 0
641 -1 1 0
642 0 -1 0
643 -1 0 0
644 -5 4 -2
645 1 -1 -1
646 1 0 0
647 0 0 0
648 0 -1 1
(1-B12)Orkanen(t-28s) (1-B12)Orkanen(t-29s) (1-B12)Orkanen(t-30s)
504 0 1 0
505 0 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 0 0 0
510 0 0 -1
511 1 -1 -1
512 1 1 -2
513 1 -6 4
514 -3 2 2
515 -1 1 3
516 1 0 1
517 0 0 0
518 0 0 0
519 0 0 0
520 0 0 0
521 0 0 0
522 0 0 0
523 -1 1 -1
524 -1 1 1
525 1 1 -6
526 1 -3 2
527 -1 -1 1
528 -2 1 0
529 0 0 0
530 0 0 0
531 0 0 0
532 0 0 0
533 0 0 0
534 0 0 0
535 0 -1 1
536 -1 -1 1
537 -1 1 1
538 -2 1 -3
539 4 -1 -1
540 2 -2 1
541 2 0 0
542 0 0 0
543 0 0 0
544 0 0 0
545 0 0 0
546 0 0 0
547 0 0 -1
548 0 -1 -1
549 -1 -1 1
550 2 -2 1
551 -4 4 -1
552 -1 2 -2
553 -2 2 0
554 0 0 0
555 0 0 0
556 0 0 0
557 0 0 0
558 0 0 0
559 0 0 0
560 0 0 -1
561 1 -1 -1
562 -1 2 -2
563 3 -4 4
564 1 -1 2
565 0 -2 2
566 0 0 0
567 0 0 0
568 0 0 0
569 0 0 0
570 0 0 0
571 0 0 0
572 1 0 0
573 0 1 -1
574 0 -1 2
575 -5 3 -4
576 -2 1 -1
577 0 0 -2
578 0 0 0
579 0 0 0
580 0 0 0
581 0 0 0
582 0 0 0
583 1 0 0
584 4 1 0
585 5 0 1
586 4 0 -1
587 1 -5 3
588 0 -2 1
589 0 0 0
590 0 0 0
591 0 0 0
592 0 0 0
593 1 0 0
594 0 0 0
595 1 1 0
596 -3 4 1
597 -3 5 0
598 -3 4 0
599 -1 1 -5
600 0 0 -2
601 0 0 0
602 0 0 0
603 0 0 0
604 0 0 0
605 -1 1 0
606 0 0 0
607 -1 1 1
608 2 -3 4
609 3 -3 5
610 -1 -3 4
611 1 -1 1
612 0 0 0
613 0 0 0
614 0 0 0
615 0 0 0
616 0 0 0
617 1 -1 1
618 1 0 0
619 -1 -1 1
620 -2 2 -3
621 -4 3 -3
622 -1 -1 -3
623 0 1 -1
624 0 0 0
625 0 0 0
626 0 0 0
627 0 0 0
628 0 0 0
629 -1 1 -1
630 -1 1 0
631 2 -1 -1
632 0 -2 2
633 0 -4 3
634 2 -1 -1
635 -1 0 1
636 0 0 0
637 0 0 0
638 0 0 0
639 0 0 0
640 0 0 0
641 0 -1 1
642 1 -1 1
643 -1 2 -1
644 2 0 -2
645 2 0 -4
646 -1 2 -1
647 0 -1 0
648 0 0 0
(1-B12)Orkanen(t-31s) (1-B12)Orkanen(t-32s) (1-B12)Orkanen(t-33s)
504 0 0 0
505 0 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 -1 1 0
510 -1 1 -1
511 1 1 0
512 1 -3 3
513 -1 -1 1
514 -2 1 0
515 0 0 0
516 0 0 0
517 0 0 0
518 0 0 0
519 0 0 0
520 0 0 0
521 0 -1 1
522 -1 -1 1
523 -1 1 1
524 -2 1 -3
525 4 -1 -1
526 2 -2 1
527 3 0 0
528 1 0 0
529 0 0 0
530 0 0 0
531 0 0 0
532 0 0 0
533 0 0 -1
534 0 -1 -1
535 -1 -1 1
536 1 -2 1
537 -6 4 -1
538 2 2 -2
539 1 3 0
540 0 1 0
541 0 0 0
542 0 0 0
543 0 0 0
544 0 0 0
545 0 0 0
546 0 0 -1
547 1 -1 -1
548 1 1 -2
549 1 -6 4
550 -3 2 2
551 -1 1 3
552 1 0 1
553 0 0 0
554 0 0 0
555 0 0 0
556 0 0 0
557 0 0 0
558 0 0 0
559 -1 1 -1
560 -1 1 1
561 1 1 -6
562 1 -3 2
563 -1 -1 1
564 -2 1 0
565 0 0 0
566 0 0 0
567 0 0 0
568 0 0 0
569 0 0 0
570 0 0 0
571 0 -1 1
572 -1 -1 1
573 -1 1 1
574 -2 1 -3
575 4 -1 -1
576 2 -2 1
577 2 0 0
578 0 0 0
579 0 0 0
580 0 0 0
581 0 0 0
582 0 0 0
583 0 0 -1
584 0 -1 -1
585 -1 -1 1
586 2 -2 1
587 -4 4 -1
588 -1 2 -2
589 -2 2 0
590 0 0 0
591 0 0 0
592 0 0 0
593 0 0 0
594 0 0 0
595 0 0 0
596 0 0 -1
597 1 -1 -1
598 -1 2 -2
599 3 -4 4
600 1 -1 2
601 0 -2 2
602 0 0 0
603 0 0 0
604 0 0 0
605 0 0 0
606 0 0 0
607 0 0 0
608 1 0 0
609 0 1 -1
610 0 -1 2
611 -5 3 -4
612 -2 1 -1
613 0 0 -2
614 0 0 0
615 0 0 0
616 0 0 0
617 0 0 0
618 0 0 0
619 1 0 0
620 4 1 0
621 5 0 1
622 4 0 -1
623 1 -5 3
624 0 -2 1
625 0 0 0
626 0 0 0
627 0 0 0
628 0 0 0
629 1 0 0
630 0 0 0
631 1 1 0
632 -3 4 1
633 -3 5 0
634 -3 4 0
635 -1 1 -5
636 0 0 -2
637 0 0 0
638 0 0 0
639 0 0 0
640 0 0 0
641 -1 1 0
642 0 0 0
643 -1 1 1
644 2 -3 4
645 3 -3 5
646 -1 -3 4
647 1 -1 1
648 0 0 0
(1-B12)Orkanen(t-34s) (1-B12)Orkanen(t-35s) (1-B12)Orkanen(t-36s)
504 0 -1 0
505 0 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 0 0 0
510 1 1 -1
511 -1 0 0
512 0 -3 2
513 0 -2 1
514 0 -1 1
515 0 0 -1
516 0 0 -1
517 0 0 0
518 0 0 0
519 0 0 0
520 0 0 0
521 0 0 0
522 -1 1 1
523 0 -1 0
524 3 0 -3
525 1 0 -2
526 0 0 -1
527 0 0 0
528 0 0 0
529 0 0 0
530 0 0 0
531 0 0 0
532 0 0 0
533 1 0 0
534 1 -1 1
535 1 0 -1
536 -3 3 0
537 -1 1 0
538 1 0 0
539 0 0 0
540 0 0 0
541 0 0 0
542 0 0 0
543 0 0 0
544 0 0 0
545 -1 1 0
546 -1 1 -1
547 1 1 0
548 1 -3 3
549 -1 -1 1
550 -2 1 0
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 0 -1 1
558 -1 -1 1
559 -1 1 1
560 -2 1 -3
561 4 -1 -1
562 2 -2 1
563 3 0 0
564 1 0 0
565 0 0 0
566 0 0 0
567 0 0 0
568 0 0 0
569 0 0 -1
570 0 -1 -1
571 -1 -1 1
572 1 -2 1
573 -6 4 -1
574 2 2 -2
575 1 3 0
576 0 1 0
577 0 0 0
578 0 0 0
579 0 0 0
580 0 0 0
581 0 0 0
582 0 0 -1
583 1 -1 -1
584 1 1 -2
585 1 -6 4
586 -3 2 2
587 -1 1 3
588 1 0 1
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 -1 1 -1
596 -1 1 1
597 1 1 -6
598 1 -3 2
599 -1 -1 1
600 -2 1 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 0 -1 1
608 -1 -1 1
609 -1 1 1
610 -2 1 -3
611 4 -1 -1
612 2 -2 1
613 2 0 0
614 0 0 0
615 0 0 0
616 0 0 0
617 0 0 0
618 0 0 0
619 0 0 -1
620 0 -1 -1
621 -1 -1 1
622 2 -2 1
623 -4 4 -1
624 -1 2 -2
625 -2 2 0
626 0 0 0
627 0 0 0
628 0 0 0
629 0 0 0
630 0 0 0
631 0 0 0
632 0 0 -1
633 1 -1 -1
634 -1 2 -2
635 3 -4 4
636 1 -1 2
637 0 -2 2
638 0 0 0
639 0 0 0
640 0 0 0
641 0 0 0
642 0 0 0
643 0 0 0
644 1 0 0
645 0 1 -1
646 0 -1 2
647 -5 3 -4
648 -2 1 -1
(1-B12)Orkanen(t-37s) (1-B12)Orkanen(t-38s) (1-B12)Orkanen(t-39s)
504 1 0 0
505 0 0 0
506 0 -1 1
507 0 0 0
508 0 0 0
509 -1 1 -1
510 1 0 0
511 1 0 0
512 -1 1 -1
513 1 1 -2
514 -3 2 0
515 0 1 0
516 0 1 0
517 0 0 0
518 0 0 -1
519 0 0 0
520 0 0 0
521 0 -1 1
522 -1 1 0
523 0 1 0
524 2 -1 1
525 1 1 1
526 1 -3 2
527 -1 0 1
528 -1 0 1
529 0 0 0
530 0 0 0
531 0 0 0
532 0 0 0
533 0 0 -1
534 1 -1 1
535 0 0 1
536 -3 2 -1
537 -2 1 1
538 -1 1 -3
539 0 -1 0
540 0 -1 0
541 0 0 0
542 0 0 0
543 0 0 0
544 0 0 0
545 0 0 0
546 1 1 -1
547 -1 0 0
548 0 -3 2
549 0 -2 1
550 0 -1 1
551 0 0 -1
552 0 0 -1
553 0 0 0
554 0 0 0
555 0 0 0
556 0 0 0
557 0 0 0
558 -1 1 1
559 0 -1 0
560 3 0 -3
561 1 0 -2
562 0 0 -1
563 0 0 0
564 0 0 0
565 0 0 0
566 0 0 0
567 0 0 0
568 0 0 0
569 1 0 0
570 1 -1 1
571 1 0 -1
572 -3 3 0
573 -1 1 0
574 1 0 0
575 0 0 0
576 0 0 0
577 0 0 0
578 0 0 0
579 0 0 0
580 0 0 0
581 -1 1 0
582 -1 1 -1
583 1 1 0
584 1 -3 3
585 -1 -1 1
586 -2 1 0
587 0 0 0
588 0 0 0
589 0 0 0
590 0 0 0
591 0 0 0
592 0 0 0
593 0 -1 1
594 -1 -1 1
595 -1 1 1
596 -2 1 -3
597 4 -1 -1
598 2 -2 1
599 3 0 0
600 1 0 0
601 0 0 0
602 0 0 0
603 0 0 0
604 0 0 0
605 0 0 -1
606 0 -1 -1
607 -1 -1 1
608 1 -2 1
609 -6 4 -1
610 2 2 -2
611 1 3 0
612 0 1 0
613 0 0 0
614 0 0 0
615 0 0 0
616 0 0 0
617 0 0 0
618 0 0 -1
619 1 -1 -1
620 1 1 -2
621 1 -6 4
622 -3 2 2
623 -1 1 3
624 1 0 1
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 -1 1 -1
632 -1 1 1
633 1 1 -6
634 1 -3 2
635 -1 -1 1
636 -2 1 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 0 -1 1
644 -1 -1 1
645 -1 1 1
646 -2 1 -3
647 4 -1 -1
648 2 -2 1
(1-B12)Orkanen(t-40s) (1-B12)Orkanen(t-41s) M1 M2 M3 M4 M5 M6 M7 M8 M9 M10
504 0 0 1 0 0 0 0 0 0 0 0 0
505 0 0 0 1 0 0 0 0 0 0 0 0
506 0 0 0 0 1 0 0 0 0 0 0 0
507 0 0 0 0 0 1 0 0 0 0 0 0
508 0 0 0 0 0 0 1 0 0 0 0 0
509 1 0 0 0 0 0 0 1 0 0 0 0
510 0 0 0 0 0 0 0 0 1 0 0 0
511 0 0 0 0 0 0 0 0 0 1 0 0
512 -1 1 0 0 0 0 0 0 0 0 1 0
513 1 -4 0 0 0 0 0 0 0 0 0 1
514 -4 4 0 0 0 0 0 0 0 0 0 0
515 0 -1 0 0 0 0 0 0 0 0 0 0
516 0 0 1 0 0 0 0 0 0 0 0 0
517 0 0 0 1 0 0 0 0 0 0 0 0
518 1 0 0 0 1 0 0 0 0 0 0 0
519 0 0 0 0 0 1 0 0 0 0 0 0
520 0 0 0 0 0 0 1 0 0 0 0 0
521 -1 1 0 0 0 0 0 1 0 0 0 0
522 0 0 0 0 0 0 0 0 1 0 0 0
523 0 0 0 0 0 0 0 0 0 1 0 0
524 -1 -1 0 0 0 0 0 0 0 0 1 0
525 -2 1 0 0 0 0 0 0 0 0 0 1
526 0 -4 0 0 0 0 0 0 0 0 0 0
527 0 0 0 0 0 0 0 0 0 0 0 0
528 0 0 1 0 0 0 0 0 0 0 0 0
529 0 0 0 1 0 0 0 0 0 0 0 0
530 -1 1 0 0 1 0 0 0 0 0 0 0
531 0 0 0 0 0 1 0 0 0 0 0 0
532 0 0 0 0 0 0 1 0 0 0 0 0
533 1 -1 0 0 0 0 0 1 0 0 0 0
534 0 0 0 0 0 0 0 0 1 0 0 0
535 0 0 0 0 0 0 0 0 0 1 0 0
536 1 -1 0 0 0 0 0 0 0 0 1 0
537 1 -2 0 0 0 0 0 0 0 0 0 1
538 2 0 0 0 0 0 0 0 0 0 0 0
539 1 0 0 0 0 0 0 0 0 0 0 0
540 1 0 1 0 0 0 0 0 0 0 0 0
541 0 0 0 1 0 0 0 0 0 0 0 0
542 0 -1 0 0 1 0 0 0 0 0 0 0
543 0 0 0 0 0 1 0 0 0 0 0 0
544 0 0 0 0 0 0 1 0 0 0 0 0
545 -1 1 0 0 0 0 0 1 0 0 0 0
546 1 0 0 0 0 0 0 0 1 0 0 0
547 1 0 0 0 0 0 0 0 0 1 0 0
548 -1 1 0 0 0 0 0 0 0 0 1 0
549 1 1 0 0 0 0 0 0 0 0 0 1
550 -3 2 0 0 0 0 0 0 0 0 0 0
551 0 1 0 0 0 0 0 0 0 0 0 0
552 0 1 1 0 0 0 0 0 0 0 0 0
553 0 0 0 1 0 0 0 0 0 0 0 0
554 0 0 0 0 1 0 0 0 0 0 0 0
555 0 0 0 0 0 1 0 0 0 0 0 0
556 0 0 0 0 0 0 1 0 0 0 0 0
557 0 -1 0 0 0 0 0 1 0 0 0 0
558 -1 1 0 0 0 0 0 0 1 0 0 0
559 0 1 0 0 0 0 0 0 0 1 0 0
560 2 -1 0 0 0 0 0 0 0 0 1 0
561 1 1 0 0 0 0 0 0 0 0 0 1
562 1 -3 0 0 0 0 0 0 0 0 0 0
563 -1 0 0 0 0 0 0 0 0 0 0 0
564 -1 0 1 0 0 0 0 0 0 0 0 0
565 0 0 0 1 0 0 0 0 0 0 0 0
566 0 0 0 0 1 0 0 0 0 0 0 0
567 0 0 0 0 0 1 0 0 0 0 0 0
568 0 0 0 0 0 0 1 0 0 0 0 0
569 0 0 0 0 0 0 0 1 0 0 0 0
570 1 -1 0 0 0 0 0 0 1 0 0 0
571 0 0 0 0 0 0 0 0 0 1 0 0
572 -3 2 0 0 0 0 0 0 0 0 1 0
573 -2 1 0 0 0 0 0 0 0 0 0 1
574 -1 1 0 0 0 0 0 0 0 0 0 0
575 0 -1 0 0 0 0 0 0 0 0 0 0
576 0 -1 1 0 0 0 0 0 0 0 0 0
577 0 0 0 1 0 0 0 0 0 0 0 0
578 0 0 0 0 1 0 0 0 0 0 0 0
579 0 0 0 0 0 1 0 0 0 0 0 0
580 0 0 0 0 0 0 1 0 0 0 0 0
581 0 0 0 0 0 0 0 1 0 0 0 0
582 1 1 0 0 0 0 0 0 1 0 0 0
583 -1 0 0 0 0 0 0 0 0 1 0 0
584 0 -3 0 0 0 0 0 0 0 0 1 0
585 0 -2 0 0 0 0 0 0 0 0 0 1
586 0 -1 0 0 0 0 0 0 0 0 0 0
587 0 0 0 0 0 0 0 0 0 0 0 0
588 0 0 1 0 0 0 0 0 0 0 0 0
589 0 0 0 1 0 0 0 0 0 0 0 0
590 0 0 0 0 1 0 0 0 0 0 0 0
591 0 0 0 0 0 1 0 0 0 0 0 0
592 0 0 0 0 0 0 1 0 0 0 0 0
593 0 0 0 0 0 0 0 1 0 0 0 0
594 -1 1 0 0 0 0 0 0 1 0 0 0
595 0 -1 0 0 0 0 0 0 0 1 0 0
596 3 0 0 0 0 0 0 0 0 0 1 0
597 1 0 0 0 0 0 0 0 0 0 0 1
598 0 0 0 0 0 0 0 0 0 0 0 0
599 0 0 0 0 0 0 0 0 0 0 0 0
600 0 0 1 0 0 0 0 0 0 0 0 0
601 0 0 0 1 0 0 0 0 0 0 0 0
602 0 0 0 0 1 0 0 0 0 0 0 0
603 0 0 0 0 0 1 0 0 0 0 0 0
604 0 0 0 0 0 0 1 0 0 0 0 0
605 1 0 0 0 0 0 0 1 0 0 0 0
606 1 -1 0 0 0 0 0 0 1 0 0 0
607 1 0 0 0 0 0 0 0 0 1 0 0
608 -3 3 0 0 0 0 0 0 0 0 1 0
609 -1 1 0 0 0 0 0 0 0 0 0 1
610 1 0 0 0 0 0 0 0 0 0 0 0
611 0 0 0 0 0 0 0 0 0 0 0 0
612 0 0 1 0 0 0 0 0 0 0 0 0
613 0 0 0 1 0 0 0 0 0 0 0 0
614 0 0 0 0 1 0 0 0 0 0 0 0
615 0 0 0 0 0 1 0 0 0 0 0 0
616 0 0 0 0 0 0 1 0 0 0 0 0
617 -1 1 0 0 0 0 0 1 0 0 0 0
618 -1 1 0 0 0 0 0 0 1 0 0 0
619 1 1 0 0 0 0 0 0 0 1 0 0
620 1 -3 0 0 0 0 0 0 0 0 1 0
621 -1 -1 0 0 0 0 0 0 0 0 0 1
622 -2 1 0 0 0 0 0 0 0 0 0 0
623 0 0 0 0 0 0 0 0 0 0 0 0
624 0 0 1 0 0 0 0 0 0 0 0 0
625 0 0 0 1 0 0 0 0 0 0 0 0
626 0 0 0 0 1 0 0 0 0 0 0 0
627 0 0 0 0 0 1 0 0 0 0 0 0
628 0 0 0 0 0 0 1 0 0 0 0 0
629 0 -1 0 0 0 0 0 1 0 0 0 0
630 -1 -1 0 0 0 0 0 0 1 0 0 0
631 -1 1 0 0 0 0 0 0 0 1 0 0
632 -2 1 0 0 0 0 0 0 0 0 1 0
633 4 -1 0 0 0 0 0 0 0 0 0 1
634 2 -2 0 0 0 0 0 0 0 0 0 0
635 3 0 0 0 0 0 0 0 0 0 0 0
636 1 0 1 0 0 0 0 0 0 0 0 0
637 0 0 0 1 0 0 0 0 0 0 0 0
638 0 0 0 0 1 0 0 0 0 0 0 0
639 0 0 0 0 0 1 0 0 0 0 0 0
640 0 0 0 0 0 0 1 0 0 0 0 0
641 0 0 0 0 0 0 0 1 0 0 0 0
642 0 -1 0 0 0 0 0 0 1 0 0 0
643 -1 -1 0 0 0 0 0 0 0 1 0 0
644 1 -2 0 0 0 0 0 0 0 0 1 0
645 -6 4 0 0 0 0 0 0 0 0 0 1
646 2 2 0 0 0 0 0 0 0 0 0 0
647 1 3 0 0 0 0 0 0 0 0 0 0
648 0 1 1 0 0 0 0 0 0 0 0 0
M11
504 0
505 0
506 0
507 0
508 0
509 0
510 0
511 0
512 0
513 0
514 1
515 0
516 0
517 0
518 0
519 0
520 0
521 0
522 0
523 0
524 0
525 0
526 1
527 0
528 0
529 0
530 0
531 0
532 0
533 0
534 0
535 0
536 0
537 0
538 1
539 0
540 0
541 0
542 0
543 0
544 0
545 0
546 0
547 0
548 0
549 0
550 1
551 0
552 0
553 0
554 0
555 0
556 0
557 0
558 0
559 0
560 0
561 0
562 1
563 0
564 0
565 0
566 0
567 0
568 0
569 0
570 0
571 0
572 0
573 0
574 1
575 0
576 0
577 0
578 0
579 0
580 0
581 0
582 0
583 0
584 0
585 0
586 1
587 0
588 0
589 0
590 0
591 0
592 0
593 0
594 0
595 0
596 0
597 0
598 1
599 0
600 0
601 0
602 0
603 0
604 0
605 0
606 0
607 0
608 0
609 0
610 1
611 0
612 0
613 0
614 0
615 0
616 0
617 0
618 0
619 0
620 0
621 0
622 1
623 0
624 0
625 0
626 0
627 0
628 0
629 0
630 0
631 0
632 0
633 0
634 1
635 0
636 0
637 0
638 0
639 0
640 0
641 0
642 0
643 0
644 0
645 0
646 1
647 0
648 0
> (k <- length(x[n,]))
[1] 65
> head(x)
(1-B12)Orkanen (1-B12)Temperatuur (1-B12)Orkanen(t-1) (1-B12)Orkanen(t-2)
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
(1-B12)Orkanen(t-3) (1-B12)Orkanen(t-4) (1-B12)Orkanen(t-5)
504 0 2 0
505 3 0 2
506 -1 3 0
507 0 -1 3
508 0 0 -1
509 0 0 0
(1-B12)Orkanen(t-6) (1-B12)Orkanen(t-7) (1-B12)Orkanen(t-8)
504 -1 0 0
505 0 -1 0
506 2 0 -1
507 0 2 0
508 3 0 2
509 -1 3 0
(1-B12)Orkanen(t-9) (1-B12)Orkanen(t-10) (1-B12)Orkanen(t-11)
504 0 0 0
505 0 0 0
506 0 0 0
507 -1 0 0
508 0 -1 0
509 2 0 -1
(1-B12)Orkanen(t-1s) (1-B12)Orkanen(t-2s) (1-B12)Orkanen(t-3s)
504 0 0 0
505 0 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 0 0 0
(1-B12)Orkanen(t-4s) (1-B12)Orkanen(t-5s) (1-B12)Orkanen(t-6s)
504 0 -1 1
505 0 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 0 0 0
(1-B12)Orkanen(t-7s) (1-B12)Orkanen(t-8s) (1-B12)Orkanen(t-9s)
504 0 0 0
505 0 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 0 0 -1
(1-B12)Orkanen(t-10s) (1-B12)Orkanen(t-11s) (1-B12)Orkanen(t-12s)
504 0 0 0
505 0 0 -1
506 0 0 0
507 0 0 0
508 0 0 0
509 1 0 0
(1-B12)Orkanen(t-13s) (1-B12)Orkanen(t-14s) (1-B12)Orkanen(t-15s)
504 0 -1 1
505 1 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 0 -1 1
(1-B12)Orkanen(t-16s) (1-B12)Orkanen(t-17s) (1-B12)Orkanen(t-18s)
504 0 0 0
505 0 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 0 0 -1
(1-B12)Orkanen(t-19s) (1-B12)Orkanen(t-20s) (1-B12)Orkanen(t-21s)
504 0 0 0
505 0 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 1 -1 1
(1-B12)Orkanen(t-22s) (1-B12)Orkanen(t-23s) (1-B12)Orkanen(t-24s)
504 -2 1 -1
505 0 0 -2
506 0 0 0
507 0 0 0
508 0 0 0
509 0 0 0
(1-B12)Orkanen(t-25s) (1-B12)Orkanen(t-26s) (1-B12)Orkanen(t-27s)
504 2 -2 1
505 2 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 0 0 0
(1-B12)Orkanen(t-28s) (1-B12)Orkanen(t-29s) (1-B12)Orkanen(t-30s)
504 0 1 0
505 0 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 0 0 0
(1-B12)Orkanen(t-31s) (1-B12)Orkanen(t-32s) (1-B12)Orkanen(t-33s)
504 0 0 0
505 0 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 -1 1 0
(1-B12)Orkanen(t-34s) (1-B12)Orkanen(t-35s) (1-B12)Orkanen(t-36s)
504 0 -1 0
505 0 0 0
506 0 0 0
507 0 0 0
508 0 0 0
509 0 0 0
(1-B12)Orkanen(t-37s) (1-B12)Orkanen(t-38s) (1-B12)Orkanen(t-39s)
504 1 0 0
505 0 0 0
506 0 -1 1
507 0 0 0
508 0 0 0
509 -1 1 -1
(1-B12)Orkanen(t-40s) (1-B12)Orkanen(t-41s) M1 M2 M3 M4 M5 M6 M7 M8 M9 M10
504 0 0 1 0 0 0 0 0 0 0 0 0
505 0 0 0 1 0 0 0 0 0 0 0 0
506 0 0 0 0 1 0 0 0 0 0 0 0
507 0 0 0 0 0 1 0 0 0 0 0 0
508 0 0 0 0 0 0 1 0 0 0 0 0
509 1 0 0 0 0 0 0 1 0 0 0 0
M11
504 0
505 0
506 0
507 0
508 0
509 0
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `(1-B12)Temperatuur` `(1-B12)Orkanen(t-1)`
-2.919e-02 4.991e-01 -6.837e-05
`(1-B12)Orkanen(t-2)` `(1-B12)Orkanen(t-3)` `(1-B12)Orkanen(t-4)`
-7.325e-02 5.969e-02 -6.043e-03
`(1-B12)Orkanen(t-5)` `(1-B12)Orkanen(t-6)` `(1-B12)Orkanen(t-7)`
8.540e-02 3.074e-02 1.972e-02
`(1-B12)Orkanen(t-8)` `(1-B12)Orkanen(t-9)` `(1-B12)Orkanen(t-10)`
-5.525e-02 8.930e-02 -7.627e-02
`(1-B12)Orkanen(t-11)` `(1-B12)Orkanen(t-1s)` `(1-B12)Orkanen(t-2s)`
-2.160e-01 -6.944e-01 -7.135e-01
`(1-B12)Orkanen(t-3s)` `(1-B12)Orkanen(t-4s)` `(1-B12)Orkanen(t-5s)`
-1.026e+00 -1.067e+00 -9.339e-01
`(1-B12)Orkanen(t-6s)` `(1-B12)Orkanen(t-7s)` `(1-B12)Orkanen(t-8s)`
-7.943e-01 -8.579e-01 -8.361e-01
`(1-B12)Orkanen(t-9s)` `(1-B12)Orkanen(t-10s)` `(1-B12)Orkanen(t-11s)`
-5.916e-01 -6.316e-01 -7.155e-01
`(1-B12)Orkanen(t-12s)` `(1-B12)Orkanen(t-13s)` `(1-B12)Orkanen(t-14s)`
-7.457e-01 -1.606e+00 -9.948e-01
`(1-B12)Orkanen(t-15s)` `(1-B12)Orkanen(t-16s)` `(1-B12)Orkanen(t-17s)`
-1.089e+00 -1.152e+00 -5.481e-01
`(1-B12)Orkanen(t-18s)` `(1-B12)Orkanen(t-19s)` `(1-B12)Orkanen(t-20s)`
-7.668e-01 -6.651e-01 -2.337e-01
`(1-B12)Orkanen(t-21s)` `(1-B12)Orkanen(t-22s)` `(1-B12)Orkanen(t-23s)`
-1.111e-01 3.509e-01 2.485e-01
`(1-B12)Orkanen(t-24s)` `(1-B12)Orkanen(t-25s)` `(1-B12)Orkanen(t-26s)`
2.348e-01 1.843e-01 3.037e-01
`(1-B12)Orkanen(t-27s)` `(1-B12)Orkanen(t-28s)` `(1-B12)Orkanen(t-29s)`
3.182e-01 1.650e-01 3.452e-01
`(1-B12)Orkanen(t-30s)` `(1-B12)Orkanen(t-31s)` `(1-B12)Orkanen(t-32s)`
2.615e-01 5.061e-01 6.516e-01
`(1-B12)Orkanen(t-33s)` `(1-B12)Orkanen(t-34s)` `(1-B12)Orkanen(t-35s)`
3.954e-01 4.772e-01 3.506e-01
`(1-B12)Orkanen(t-36s)` `(1-B12)Orkanen(t-37s)` `(1-B12)Orkanen(t-38s)`
3.562e-01 5.226e-01 3.477e-01
`(1-B12)Orkanen(t-39s)` `(1-B12)Orkanen(t-40s)` `(1-B12)Orkanen(t-41s)`
4.509e-01 2.080e-01 2.605e-01
M1 M2 M3
-1.235e-01 -9.696e-02 2.008e-02
M4 M5 M6
-2.232e-02 6.984e-02 -1.712e-01
M7 M8 M9
3.725e-01 1.534e-01 2.735e-01
M10 M11
9.159e-01 7.495e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.78595 -0.37581 -0.03585 0.34278 1.93310
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.919e-02 1.060e+00 -0.028 0.97811
`(1-B12)Temperatuur` 4.991e-01 4.554e-01 1.096 0.27643
`(1-B12)Orkanen(t-1)` -6.837e-05 7.664e-02 -0.001 0.99929
`(1-B12)Orkanen(t-2)` -7.325e-02 7.121e-02 -1.029 0.30672
`(1-B12)Orkanen(t-3)` 5.969e-02 6.255e-02 0.954 0.34283
`(1-B12)Orkanen(t-4)` -6.043e-03 6.052e-02 -0.100 0.92072
`(1-B12)Orkanen(t-5)` 8.540e-02 5.498e-02 1.553 0.12432
`(1-B12)Orkanen(t-6)` 3.074e-02 5.472e-02 0.562 0.57593
`(1-B12)Orkanen(t-7)` 1.972e-02 5.420e-02 0.364 0.71696
`(1-B12)Orkanen(t-8)` -5.525e-02 5.786e-02 -0.955 0.34249
`(1-B12)Orkanen(t-9)` 8.930e-02 6.358e-02 1.404 0.16405
`(1-B12)Orkanen(t-10)` -7.627e-02 6.884e-02 -1.108 0.27121
`(1-B12)Orkanen(t-11)` -2.160e-01 7.645e-02 -2.825 0.00597 **
`(1-B12)Orkanen(t-1s)` -6.944e-01 1.197e-01 -5.801 1.27e-07 ***
`(1-B12)Orkanen(t-2s)` -7.135e-01 1.619e-01 -4.408 3.21e-05 ***
`(1-B12)Orkanen(t-3s)` -1.026e+00 2.358e-01 -4.351 3.97e-05 ***
`(1-B12)Orkanen(t-4s)` -1.067e+00 3.293e-01 -3.240 0.00174 **
`(1-B12)Orkanen(t-5s)` -9.339e-01 4.093e-01 -2.282 0.02516 *
`(1-B12)Orkanen(t-6s)` -7.943e-01 5.003e-01 -1.587 0.11634
`(1-B12)Orkanen(t-7s)` -8.579e-01 5.361e-01 -1.600 0.11348
`(1-B12)Orkanen(t-8s)` -8.361e-01 5.797e-01 -1.442 0.15310
`(1-B12)Orkanen(t-9s)` -5.916e-01 5.904e-01 -1.002 0.31937
`(1-B12)Orkanen(t-10s)` -6.316e-01 5.932e-01 -1.065 0.29017
`(1-B12)Orkanen(t-11s)` -7.155e-01 5.904e-01 -1.212 0.22916
`(1-B12)Orkanen(t-12s)` -7.457e-01 6.068e-01 -1.229 0.22274
`(1-B12)Orkanen(t-13s)` -1.606e+00 6.779e-01 -2.369 0.02025 *
`(1-B12)Orkanen(t-14s)` -9.948e-01 7.869e-01 -1.264 0.20980
`(1-B12)Orkanen(t-15s)` -1.089e+00 8.565e-01 -1.271 0.20747
`(1-B12)Orkanen(t-16s)` -1.152e+00 9.199e-01 -1.252 0.21425
`(1-B12)Orkanen(t-17s)` -5.481e-01 9.721e-01 -0.564 0.57443
`(1-B12)Orkanen(t-18s)` -7.668e-01 9.846e-01 -0.779 0.43838
`(1-B12)Orkanen(t-19s)` -6.651e-01 1.017e+00 -0.654 0.51517
`(1-B12)Orkanen(t-20s)` -2.337e-01 1.023e+00 -0.228 0.81984
`(1-B12)Orkanen(t-21s)` -1.111e-01 1.031e+00 -0.108 0.91443
`(1-B12)Orkanen(t-22s)` 3.509e-01 1.042e+00 0.337 0.73729
`(1-B12)Orkanen(t-23s)` 2.485e-01 1.021e+00 0.243 0.80828
`(1-B12)Orkanen(t-24s)` 2.348e-01 9.946e-01 0.236 0.81398
`(1-B12)Orkanen(t-25s)` 1.843e-01 9.557e-01 0.193 0.84760
`(1-B12)Orkanen(t-26s)` 3.037e-01 9.118e-01 0.333 0.73996
`(1-B12)Orkanen(t-27s)` 3.182e-01 8.602e-01 0.370 0.71244
`(1-B12)Orkanen(t-28s)` 1.650e-01 7.967e-01 0.207 0.83642
`(1-B12)Orkanen(t-29s)` 3.452e-01 7.370e-01 0.468 0.64080
`(1-B12)Orkanen(t-30s)` 2.615e-01 6.817e-01 0.384 0.70233
`(1-B12)Orkanen(t-31s)` 5.061e-01 6.250e-01 0.810 0.42049
`(1-B12)Orkanen(t-32s)` 6.516e-01 5.603e-01 1.163 0.24829
`(1-B12)Orkanen(t-33s)` 3.954e-01 5.006e-01 0.790 0.43196
`(1-B12)Orkanen(t-34s)` 4.772e-01 4.678e-01 1.020 0.31073
`(1-B12)Orkanen(t-35s)` 3.506e-01 4.200e-01 0.835 0.40637
`(1-B12)Orkanen(t-36s)` 3.562e-01 3.842e-01 0.927 0.35667
`(1-B12)Orkanen(t-37s)` 5.226e-01 3.452e-01 1.514 0.13398
`(1-B12)Orkanen(t-38s)` 3.477e-01 2.782e-01 1.250 0.21504
`(1-B12)Orkanen(t-39s)` 4.509e-01 2.409e-01 1.872 0.06491 .
`(1-B12)Orkanen(t-40s)` 2.080e-01 1.919e-01 1.083 0.28186
`(1-B12)Orkanen(t-41s)` 2.605e-01 1.335e-01 1.951 0.05450 .
M1 -1.235e-01 6.595e-01 -0.187 0.85189
M2 -9.696e-02 1.023e+00 -0.095 0.92471
M3 2.008e-02 1.079e+00 0.019 0.98519
M4 -2.232e-02 1.089e+00 -0.020 0.98370
M5 6.984e-02 1.116e+00 0.063 0.95024
M6 -1.712e-01 1.046e+00 -0.164 0.87039
M7 3.725e-01 1.250e+00 0.298 0.76652
M8 1.534e-01 1.301e+00 0.118 0.90645
M9 2.735e-01 2.154e+00 0.127 0.89928
M10 9.159e-01 1.756e+00 0.522 0.60337
M11 7.495e-01 8.328e-01 0.900 0.37083
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8399 on 80 degrees of freedom
Multiple R-squared: 0.8131, Adjusted R-squared: 0.6636
F-statistic: 5.439 on 64 and 80 DF, p-value: 1.828e-12
> 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,] 0.07776452 0.15552905 0.9222355
[2,] 0.02584964 0.05169928 0.9741504
[3,] 0.08105319 0.16210638 0.9189468
[4,] 0.03465512 0.06931024 0.9653449
[5,] 0.08663947 0.17327893 0.9133605
[6,] 0.25047747 0.50095493 0.7495225
[7,] 0.25623752 0.51247503 0.7437625
[8,] 0.19216751 0.38433503 0.8078325
[9,] 0.42012721 0.84025442 0.5798728
[10,] 0.61455076 0.77089849 0.3854492
> postscript(file="/var/wessaorg/rcomp/tmp/1vkp71450035906.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/27ely1450035906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3t6hm1450035906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/48tnv1450035906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ba801450035906.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 = 145
Frequency = 1
504 505 506 507 508 509
0.410975766 0.673946650 -0.177941221 0.018801066 -0.003252398 -0.035849509
510 511 512 513 514 515
-0.851512408 -1.785948565 -0.198603876 -0.340411991 0.302596537 -0.559444621
516 517 518 519 520 521
0.500185334 -0.258832418 0.427872195 -0.094960614 1.203322408 0.018662547
522 523 524 525 526 527
-0.589641802 -0.332099549 -0.005534612 -0.804651208 -0.171455106 -0.319515340
528 529 530 531 532 533
-1.054807283 0.296614215 -0.064966144 0.516790101 -0.375814549 -0.072967453
534 535 536 537 538 539
0.115918289 -0.423873284 0.107009327 -0.541724550 -1.132021201 -0.500691757
540 541 542 543 544 545
0.010370405 0.362591737 0.395397776 -0.171826849 0.230998000 0.797595895
546 547 548 549 550 551
0.924675567 -0.733959347 -0.558697178 -0.897559573 -0.736267999 0.544107251
552 553 554 555 556 557
-0.360395164 -0.606313879 -0.342698048 0.038945606 -0.029633593 0.012228700
558 559 560 561 562 563
0.523954992 1.749406718 0.558271442 0.477785276 1.213411003 -1.042066183
564 565 566 567 568 569
0.041436957 -0.342745084 -0.050267368 -0.060415508 -0.071691613 -0.486668247
570 571 572 573 574 575
0.231114154 -0.227563507 -0.047419759 -0.460801290 -1.099848346 0.348783270
576 577 578 579 580 581
0.311833432 -0.009624473 0.240016144 -0.037716310 -0.171594074 0.274814217
582 583 584 585 586 587
0.416496968 1.221175369 -0.697725415 -0.694320171 0.495422914 -0.090361315
588 589 590 591 592 593
-0.092962150 -0.444773485 -0.008143820 0.245818007 -0.479506650 -0.645840812
594 595 596 597 598 599
-0.860556557 1.297930768 -0.070255685 0.142060679 -1.320226077 0.115187061
600 601 602 603 604 605
-0.532951096 0.468701921 -0.527543534 0.033919902 0.342779869 0.288948109
606 607 608 609 610 611
1.482512728 -0.859330314 1.933099406 0.047247719 0.782849995 0.811219424
612 613 614 615 616 617
0.509204966 -0.506234331 0.427205662 -0.266450400 -0.009000131 0.196268871
618 619 620 621 622 623
-1.044000614 -0.926213701 -0.889272310 1.368653796 0.527104110 0.016780747
624 625 626 627 628 629
0.644156961 0.307905418 -0.277084393 -0.123478336 -0.345787502 -0.413671452
630 631 632 633 634 635
-0.551059248 0.187201371 -0.370508874 0.739690322 1.383282396 1.366553370
636 637 638 639 640 641
-0.198003368 0.058763730 -0.041847249 -0.099426666 -0.290819767 0.066479133
642 643 644 645 646 647
0.202097929 0.833274042 0.239637533 0.964030991 -0.244848227 -0.690551906
648
-0.189044759
> postscript(file="/var/wessaorg/rcomp/tmp/6t7h91450035906.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 = 145
Frequency = 1
lag(myerror, k = 1) myerror
0 0.410975766 NA
1 0.673946650 0.410975766
2 -0.177941221 0.673946650
3 0.018801066 -0.177941221
4 -0.003252398 0.018801066
5 -0.035849509 -0.003252398
6 -0.851512408 -0.035849509
7 -1.785948565 -0.851512408
8 -0.198603876 -1.785948565
9 -0.340411991 -0.198603876
10 0.302596537 -0.340411991
11 -0.559444621 0.302596537
12 0.500185334 -0.559444621
13 -0.258832418 0.500185334
14 0.427872195 -0.258832418
15 -0.094960614 0.427872195
16 1.203322408 -0.094960614
17 0.018662547 1.203322408
18 -0.589641802 0.018662547
19 -0.332099549 -0.589641802
20 -0.005534612 -0.332099549
21 -0.804651208 -0.005534612
22 -0.171455106 -0.804651208
23 -0.319515340 -0.171455106
24 -1.054807283 -0.319515340
25 0.296614215 -1.054807283
26 -0.064966144 0.296614215
27 0.516790101 -0.064966144
28 -0.375814549 0.516790101
29 -0.072967453 -0.375814549
30 0.115918289 -0.072967453
31 -0.423873284 0.115918289
32 0.107009327 -0.423873284
33 -0.541724550 0.107009327
34 -1.132021201 -0.541724550
35 -0.500691757 -1.132021201
36 0.010370405 -0.500691757
37 0.362591737 0.010370405
38 0.395397776 0.362591737
39 -0.171826849 0.395397776
40 0.230998000 -0.171826849
41 0.797595895 0.230998000
42 0.924675567 0.797595895
43 -0.733959347 0.924675567
44 -0.558697178 -0.733959347
45 -0.897559573 -0.558697178
46 -0.736267999 -0.897559573
47 0.544107251 -0.736267999
48 -0.360395164 0.544107251
49 -0.606313879 -0.360395164
50 -0.342698048 -0.606313879
51 0.038945606 -0.342698048
52 -0.029633593 0.038945606
53 0.012228700 -0.029633593
54 0.523954992 0.012228700
55 1.749406718 0.523954992
56 0.558271442 1.749406718
57 0.477785276 0.558271442
58 1.213411003 0.477785276
59 -1.042066183 1.213411003
60 0.041436957 -1.042066183
61 -0.342745084 0.041436957
62 -0.050267368 -0.342745084
63 -0.060415508 -0.050267368
64 -0.071691613 -0.060415508
65 -0.486668247 -0.071691613
66 0.231114154 -0.486668247
67 -0.227563507 0.231114154
68 -0.047419759 -0.227563507
69 -0.460801290 -0.047419759
70 -1.099848346 -0.460801290
71 0.348783270 -1.099848346
72 0.311833432 0.348783270
73 -0.009624473 0.311833432
74 0.240016144 -0.009624473
75 -0.037716310 0.240016144
76 -0.171594074 -0.037716310
77 0.274814217 -0.171594074
78 0.416496968 0.274814217
79 1.221175369 0.416496968
80 -0.697725415 1.221175369
81 -0.694320171 -0.697725415
82 0.495422914 -0.694320171
83 -0.090361315 0.495422914
84 -0.092962150 -0.090361315
85 -0.444773485 -0.092962150
86 -0.008143820 -0.444773485
87 0.245818007 -0.008143820
88 -0.479506650 0.245818007
89 -0.645840812 -0.479506650
90 -0.860556557 -0.645840812
91 1.297930768 -0.860556557
92 -0.070255685 1.297930768
93 0.142060679 -0.070255685
94 -1.320226077 0.142060679
95 0.115187061 -1.320226077
96 -0.532951096 0.115187061
97 0.468701921 -0.532951096
98 -0.527543534 0.468701921
99 0.033919902 -0.527543534
100 0.342779869 0.033919902
101 0.288948109 0.342779869
102 1.482512728 0.288948109
103 -0.859330314 1.482512728
104 1.933099406 -0.859330314
105 0.047247719 1.933099406
106 0.782849995 0.047247719
107 0.811219424 0.782849995
108 0.509204966 0.811219424
109 -0.506234331 0.509204966
110 0.427205662 -0.506234331
111 -0.266450400 0.427205662
112 -0.009000131 -0.266450400
113 0.196268871 -0.009000131
114 -1.044000614 0.196268871
115 -0.926213701 -1.044000614
116 -0.889272310 -0.926213701
117 1.368653796 -0.889272310
118 0.527104110 1.368653796
119 0.016780747 0.527104110
120 0.644156961 0.016780747
121 0.307905418 0.644156961
122 -0.277084393 0.307905418
123 -0.123478336 -0.277084393
124 -0.345787502 -0.123478336
125 -0.413671452 -0.345787502
126 -0.551059248 -0.413671452
127 0.187201371 -0.551059248
128 -0.370508874 0.187201371
129 0.739690322 -0.370508874
130 1.383282396 0.739690322
131 1.366553370 1.383282396
132 -0.198003368 1.366553370
133 0.058763730 -0.198003368
134 -0.041847249 0.058763730
135 -0.099426666 -0.041847249
136 -0.290819767 -0.099426666
137 0.066479133 -0.290819767
138 0.202097929 0.066479133
139 0.833274042 0.202097929
140 0.239637533 0.833274042
141 0.964030991 0.239637533
142 -0.244848227 0.964030991
143 -0.690551906 -0.244848227
144 -0.189044759 -0.690551906
145 NA -0.189044759
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.673946650 0.410975766
[2,] -0.177941221 0.673946650
[3,] 0.018801066 -0.177941221
[4,] -0.003252398 0.018801066
[5,] -0.035849509 -0.003252398
[6,] -0.851512408 -0.035849509
[7,] -1.785948565 -0.851512408
[8,] -0.198603876 -1.785948565
[9,] -0.340411991 -0.198603876
[10,] 0.302596537 -0.340411991
[11,] -0.559444621 0.302596537
[12,] 0.500185334 -0.559444621
[13,] -0.258832418 0.500185334
[14,] 0.427872195 -0.258832418
[15,] -0.094960614 0.427872195
[16,] 1.203322408 -0.094960614
[17,] 0.018662547 1.203322408
[18,] -0.589641802 0.018662547
[19,] -0.332099549 -0.589641802
[20,] -0.005534612 -0.332099549
[21,] -0.804651208 -0.005534612
[22,] -0.171455106 -0.804651208
[23,] -0.319515340 -0.171455106
[24,] -1.054807283 -0.319515340
[25,] 0.296614215 -1.054807283
[26,] -0.064966144 0.296614215
[27,] 0.516790101 -0.064966144
[28,] -0.375814549 0.516790101
[29,] -0.072967453 -0.375814549
[30,] 0.115918289 -0.072967453
[31,] -0.423873284 0.115918289
[32,] 0.107009327 -0.423873284
[33,] -0.541724550 0.107009327
[34,] -1.132021201 -0.541724550
[35,] -0.500691757 -1.132021201
[36,] 0.010370405 -0.500691757
[37,] 0.362591737 0.010370405
[38,] 0.395397776 0.362591737
[39,] -0.171826849 0.395397776
[40,] 0.230998000 -0.171826849
[41,] 0.797595895 0.230998000
[42,] 0.924675567 0.797595895
[43,] -0.733959347 0.924675567
[44,] -0.558697178 -0.733959347
[45,] -0.897559573 -0.558697178
[46,] -0.736267999 -0.897559573
[47,] 0.544107251 -0.736267999
[48,] -0.360395164 0.544107251
[49,] -0.606313879 -0.360395164
[50,] -0.342698048 -0.606313879
[51,] 0.038945606 -0.342698048
[52,] -0.029633593 0.038945606
[53,] 0.012228700 -0.029633593
[54,] 0.523954992 0.012228700
[55,] 1.749406718 0.523954992
[56,] 0.558271442 1.749406718
[57,] 0.477785276 0.558271442
[58,] 1.213411003 0.477785276
[59,] -1.042066183 1.213411003
[60,] 0.041436957 -1.042066183
[61,] -0.342745084 0.041436957
[62,] -0.050267368 -0.342745084
[63,] -0.060415508 -0.050267368
[64,] -0.071691613 -0.060415508
[65,] -0.486668247 -0.071691613
[66,] 0.231114154 -0.486668247
[67,] -0.227563507 0.231114154
[68,] -0.047419759 -0.227563507
[69,] -0.460801290 -0.047419759
[70,] -1.099848346 -0.460801290
[71,] 0.348783270 -1.099848346
[72,] 0.311833432 0.348783270
[73,] -0.009624473 0.311833432
[74,] 0.240016144 -0.009624473
[75,] -0.037716310 0.240016144
[76,] -0.171594074 -0.037716310
[77,] 0.274814217 -0.171594074
[78,] 0.416496968 0.274814217
[79,] 1.221175369 0.416496968
[80,] -0.697725415 1.221175369
[81,] -0.694320171 -0.697725415
[82,] 0.495422914 -0.694320171
[83,] -0.090361315 0.495422914
[84,] -0.092962150 -0.090361315
[85,] -0.444773485 -0.092962150
[86,] -0.008143820 -0.444773485
[87,] 0.245818007 -0.008143820
[88,] -0.479506650 0.245818007
[89,] -0.645840812 -0.479506650
[90,] -0.860556557 -0.645840812
[91,] 1.297930768 -0.860556557
[92,] -0.070255685 1.297930768
[93,] 0.142060679 -0.070255685
[94,] -1.320226077 0.142060679
[95,] 0.115187061 -1.320226077
[96,] -0.532951096 0.115187061
[97,] 0.468701921 -0.532951096
[98,] -0.527543534 0.468701921
[99,] 0.033919902 -0.527543534
[100,] 0.342779869 0.033919902
[101,] 0.288948109 0.342779869
[102,] 1.482512728 0.288948109
[103,] -0.859330314 1.482512728
[104,] 1.933099406 -0.859330314
[105,] 0.047247719 1.933099406
[106,] 0.782849995 0.047247719
[107,] 0.811219424 0.782849995
[108,] 0.509204966 0.811219424
[109,] -0.506234331 0.509204966
[110,] 0.427205662 -0.506234331
[111,] -0.266450400 0.427205662
[112,] -0.009000131 -0.266450400
[113,] 0.196268871 -0.009000131
[114,] -1.044000614 0.196268871
[115,] -0.926213701 -1.044000614
[116,] -0.889272310 -0.926213701
[117,] 1.368653796 -0.889272310
[118,] 0.527104110 1.368653796
[119,] 0.016780747 0.527104110
[120,] 0.644156961 0.016780747
[121,] 0.307905418 0.644156961
[122,] -0.277084393 0.307905418
[123,] -0.123478336 -0.277084393
[124,] -0.345787502 -0.123478336
[125,] -0.413671452 -0.345787502
[126,] -0.551059248 -0.413671452
[127,] 0.187201371 -0.551059248
[128,] -0.370508874 0.187201371
[129,] 0.739690322 -0.370508874
[130,] 1.383282396 0.739690322
[131,] 1.366553370 1.383282396
[132,] -0.198003368 1.366553370
[133,] 0.058763730 -0.198003368
[134,] -0.041847249 0.058763730
[135,] -0.099426666 -0.041847249
[136,] -0.290819767 -0.099426666
[137,] 0.066479133 -0.290819767
[138,] 0.202097929 0.066479133
[139,] 0.833274042 0.202097929
[140,] 0.239637533 0.833274042
[141,] 0.964030991 0.239637533
[142,] -0.244848227 0.964030991
[143,] -0.690551906 -0.244848227
[144,] -0.189044759 -0.690551906
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.673946650 0.410975766
2 -0.177941221 0.673946650
3 0.018801066 -0.177941221
4 -0.003252398 0.018801066
5 -0.035849509 -0.003252398
6 -0.851512408 -0.035849509
7 -1.785948565 -0.851512408
8 -0.198603876 -1.785948565
9 -0.340411991 -0.198603876
10 0.302596537 -0.340411991
11 -0.559444621 0.302596537
12 0.500185334 -0.559444621
13 -0.258832418 0.500185334
14 0.427872195 -0.258832418
15 -0.094960614 0.427872195
16 1.203322408 -0.094960614
17 0.018662547 1.203322408
18 -0.589641802 0.018662547
19 -0.332099549 -0.589641802
20 -0.005534612 -0.332099549
21 -0.804651208 -0.005534612
22 -0.171455106 -0.804651208
23 -0.319515340 -0.171455106
24 -1.054807283 -0.319515340
25 0.296614215 -1.054807283
26 -0.064966144 0.296614215
27 0.516790101 -0.064966144
28 -0.375814549 0.516790101
29 -0.072967453 -0.375814549
30 0.115918289 -0.072967453
31 -0.423873284 0.115918289
32 0.107009327 -0.423873284
33 -0.541724550 0.107009327
34 -1.132021201 -0.541724550
35 -0.500691757 -1.132021201
36 0.010370405 -0.500691757
37 0.362591737 0.010370405
38 0.395397776 0.362591737
39 -0.171826849 0.395397776
40 0.230998000 -0.171826849
41 0.797595895 0.230998000
42 0.924675567 0.797595895
43 -0.733959347 0.924675567
44 -0.558697178 -0.733959347
45 -0.897559573 -0.558697178
46 -0.736267999 -0.897559573
47 0.544107251 -0.736267999
48 -0.360395164 0.544107251
49 -0.606313879 -0.360395164
50 -0.342698048 -0.606313879
51 0.038945606 -0.342698048
52 -0.029633593 0.038945606
53 0.012228700 -0.029633593
54 0.523954992 0.012228700
55 1.749406718 0.523954992
56 0.558271442 1.749406718
57 0.477785276 0.558271442
58 1.213411003 0.477785276
59 -1.042066183 1.213411003
60 0.041436957 -1.042066183
61 -0.342745084 0.041436957
62 -0.050267368 -0.342745084
63 -0.060415508 -0.050267368
64 -0.071691613 -0.060415508
65 -0.486668247 -0.071691613
66 0.231114154 -0.486668247
67 -0.227563507 0.231114154
68 -0.047419759 -0.227563507
69 -0.460801290 -0.047419759
70 -1.099848346 -0.460801290
71 0.348783270 -1.099848346
72 0.311833432 0.348783270
73 -0.009624473 0.311833432
74 0.240016144 -0.009624473
75 -0.037716310 0.240016144
76 -0.171594074 -0.037716310
77 0.274814217 -0.171594074
78 0.416496968 0.274814217
79 1.221175369 0.416496968
80 -0.697725415 1.221175369
81 -0.694320171 -0.697725415
82 0.495422914 -0.694320171
83 -0.090361315 0.495422914
84 -0.092962150 -0.090361315
85 -0.444773485 -0.092962150
86 -0.008143820 -0.444773485
87 0.245818007 -0.008143820
88 -0.479506650 0.245818007
89 -0.645840812 -0.479506650
90 -0.860556557 -0.645840812
91 1.297930768 -0.860556557
92 -0.070255685 1.297930768
93 0.142060679 -0.070255685
94 -1.320226077 0.142060679
95 0.115187061 -1.320226077
96 -0.532951096 0.115187061
97 0.468701921 -0.532951096
98 -0.527543534 0.468701921
99 0.033919902 -0.527543534
100 0.342779869 0.033919902
101 0.288948109 0.342779869
102 1.482512728 0.288948109
103 -0.859330314 1.482512728
104 1.933099406 -0.859330314
105 0.047247719 1.933099406
106 0.782849995 0.047247719
107 0.811219424 0.782849995
108 0.509204966 0.811219424
109 -0.506234331 0.509204966
110 0.427205662 -0.506234331
111 -0.266450400 0.427205662
112 -0.009000131 -0.266450400
113 0.196268871 -0.009000131
114 -1.044000614 0.196268871
115 -0.926213701 -1.044000614
116 -0.889272310 -0.926213701
117 1.368653796 -0.889272310
118 0.527104110 1.368653796
119 0.016780747 0.527104110
120 0.644156961 0.016780747
121 0.307905418 0.644156961
122 -0.277084393 0.307905418
123 -0.123478336 -0.277084393
124 -0.345787502 -0.123478336
125 -0.413671452 -0.345787502
126 -0.551059248 -0.413671452
127 0.187201371 -0.551059248
128 -0.370508874 0.187201371
129 0.739690322 -0.370508874
130 1.383282396 0.739690322
131 1.366553370 1.383282396
132 -0.198003368 1.366553370
133 0.058763730 -0.198003368
134 -0.041847249 0.058763730
135 -0.099426666 -0.041847249
136 -0.290819767 -0.099426666
137 0.066479133 -0.290819767
138 0.202097929 0.066479133
139 0.833274042 0.202097929
140 0.239637533 0.833274042
141 0.964030991 0.239637533
142 -0.244848227 0.964030991
143 -0.690551906 -0.244848227
144 -0.189044759 -0.690551906
> 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/7adbz1450035906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8nkv01450035906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/92wsa1450035906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/103ihi1450035906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11xkiz1450035906.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/12jcsg1450035906.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/13amj31450035906.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/14yarc1450035906.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/1527n51450035906.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/16bxgb1450035906.tab")
+ }
+ }
>
> try(system("convert tmp/1vkp71450035906.ps tmp/1vkp71450035906.png",intern=TRUE))
character(0)
> try(system("convert tmp/27ely1450035906.ps tmp/27ely1450035906.png",intern=TRUE))
character(0)
> try(system("convert tmp/3t6hm1450035906.ps tmp/3t6hm1450035906.png",intern=TRUE))
character(0)
> try(system("convert tmp/48tnv1450035906.ps tmp/48tnv1450035906.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ba801450035906.ps tmp/5ba801450035906.png",intern=TRUE))
character(0)
> try(system("convert tmp/6t7h91450035906.ps tmp/6t7h91450035906.png",intern=TRUE))
character(0)
> try(system("convert tmp/7adbz1450035906.ps tmp/7adbz1450035906.png",intern=TRUE))
character(0)
> try(system("convert tmp/8nkv01450035906.ps tmp/8nkv01450035906.png",intern=TRUE))
character(0)
> try(system("convert tmp/92wsa1450035906.ps tmp/92wsa1450035906.png",intern=TRUE))
character(0)
> try(system("convert tmp/103ihi1450035906.ps tmp/103ihi1450035906.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.726 0.759 5.518