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,14.47 + ,1 + ,14.45 + ,1 + ,14.46 + ,3 + ,14.45 + ,5 + ,14.45 + ,5 + ,14.43 + ,2 + ,14.68 + ,0 + ,14.47 + ,0 + ,14.74 + ,0 + ,14.75 + ,0 + ,14.81 + ,0 + ,14.54 + ,0 + ,14.51 + ,0 + ,14.43 + ,1 + ,14.53 + ,3 + ,14.43 + ,8 + ,14.46 + ,0 + ,14.48 + ,0 + ,14.51 + ,0 + ,14.33) + ,dim=c(2 + ,660) + ,dimnames=list(c('Orkanen' + ,'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 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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 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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