R version 2.9.0 (2009-04-17) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. 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(543 + ,0 + ,537 + ,544 + ,555 + ,561 + ,562 + ,555 + ,594 + ,0 + ,543 + ,537 + ,544 + ,555 + ,561 + ,562 + ,611 + ,0 + ,594 + ,543 + ,537 + ,544 + ,555 + ,561 + ,613 + ,0 + ,611 + ,594 + ,543 + ,537 + ,544 + ,555 + ,611 + ,0 + ,613 + ,611 + ,594 + ,543 + ,537 + ,544 + ,594 + ,0 + ,611 + ,613 + ,611 + ,594 + ,543 + ,537 + ,595 + ,0 + ,594 + ,611 + ,613 + ,611 + ,594 + ,543 + ,591 + ,0 + ,595 + ,594 + ,611 + ,613 + ,611 + ,594 + ,589 + ,0 + ,591 + ,595 + ,594 + ,611 + ,613 + ,611 + ,584 + ,0 + ,589 + ,591 + ,595 + ,594 + ,611 + ,613 + ,573 + ,0 + ,584 + ,589 + ,591 + ,595 + ,594 + ,611 + ,567 + ,0 + ,573 + ,584 + ,589 + ,591 + ,595 + ,594 + ,569 + ,0 + ,567 + ,573 + ,584 + ,589 + ,591 + ,595 + ,621 + ,0 + ,569 + ,567 + ,573 + ,584 + ,589 + ,591 + ,629 + ,0 + ,621 + ,569 + ,567 + ,573 + ,584 + ,589 + ,628 + ,0 + ,629 + ,621 + ,569 + ,567 + ,573 + ,584 + ,612 + ,0 + ,628 + ,629 + ,621 + ,569 + ,567 + ,573 + ,595 + ,0 + ,612 + ,628 + ,629 + ,621 + ,569 + ,567 + ,597 + ,0 + ,595 + ,612 + ,628 + ,629 + ,621 + ,569 + ,593 + ,0 + ,597 + ,595 + ,612 + ,628 + ,629 + ,621 + ,590 + ,0 + ,593 + ,597 + ,595 + ,612 + ,628 + ,629 + ,580 + ,0 + ,590 + ,593 + ,597 + ,595 + ,612 + ,628 + ,574 + ,0 + ,580 + ,590 + ,593 + ,597 + ,595 + ,612 + ,573 + ,0 + ,574 + ,580 + ,590 + ,593 + ,597 + ,595 + ,573 + ,0 + ,573 + ,574 + ,580 + ,590 + ,593 + ,597 + ,620 + ,0 + ,573 + ,573 + ,574 + ,580 + ,590 + ,593 + ,626 + ,0 + ,620 + ,573 + ,573 + ,574 + ,580 + ,590 + ,620 + ,0 + ,626 + ,620 + ,573 + ,573 + ,574 + ,580 + ,588 + ,0 + ,620 + ,626 + ,620 + ,573 + ,573 + ,574 + ,566 + ,0 + ,588 + ,620 + ,626 + ,620 + ,573 + ,573 + ,557 + ,0 + ,566 + ,588 + ,620 + ,626 + ,620 + ,573 + ,561 + ,0 + ,557 + ,566 + ,588 + ,620 + ,626 + ,620 + ,549 + ,0 + ,561 + ,557 + ,566 + ,588 + ,620 + ,626 + ,532 + ,0 + ,549 + ,561 + ,557 + ,566 + ,588 + ,620 + ,526 + ,0 + ,532 + ,549 + ,561 + ,557 + ,566 + ,588 + ,511 + ,0 + ,526 + ,532 + ,549 + ,561 + ,557 + ,566 + ,499 + ,0 + ,511 + ,526 + ,532 + ,549 + ,561 + ,557 + ,555 + ,0 + ,499 + ,511 + ,526 + ,532 + ,549 + ,561 + ,565 + ,0 + ,555 + ,499 + ,511 + ,526 + ,532 + ,549 + ,542 + ,0 + ,565 + ,555 + ,499 + ,511 + ,526 + ,532 + ,527 + ,0 + ,542 + ,565 + ,555 + ,499 + ,511 + ,526 + ,510 + ,0 + ,527 + ,542 + ,565 + ,555 + ,499 + ,511 + ,514 + ,0 + ,510 + ,527 + ,542 + ,565 + ,555 + ,499 + ,517 + ,0 + ,514 + ,510 + ,527 + ,542 + ,565 + ,555 + ,508 + ,0 + ,517 + ,514 + ,510 + ,527 + ,542 + ,565 + ,493 + ,0 + ,508 + ,517 + ,514 + ,510 + ,527 + ,542 + ,490 + ,1 + ,493 + ,508 + ,517 + ,514 + ,510 + ,527 + ,469 + ,1 + ,490 + ,493 + ,508 + ,517 + ,514 + ,510 + ,478 + ,1 + ,469 + ,490 + ,493 + ,508 + ,517 + ,514 + ,528 + ,1 + ,478 + ,469 + ,490 + ,493 + ,508 + ,517 + ,534 + ,1 + ,528 + ,478 + ,469 + ,490 + ,493 + ,508 + ,518 + ,1 + ,534 + ,528 + ,478 + ,469 + ,490 + ,493 + ,506 + ,1 + ,518 + ,534 + ,528 + ,478 + ,469 + ,490 + ,502 + ,1 + ,506 + ,518 + ,534 + ,528 + ,478 + ,469 + ,516 + ,1 + ,502 + ,506 + ,518 + ,534 + ,528 + ,478 + ,528 + ,1 + ,516 + ,502 + ,506 + ,518 + ,534 + ,528 + ,533 + ,1 + ,528 + ,516 + ,502 + ,506 + ,518 + ,534 + ,536 + ,1 + ,533 + ,528 + ,516 + ,502 + ,506 + ,518 + ,537 + ,1 + ,536 + ,533 + ,528 + ,516 + ,502 + ,506 + ,524 + ,1 + ,537 + ,536 + ,533 + ,528 + ,516 + ,502 + ,536 + ,1 + ,524 + ,537 + ,536 + ,533 + ,528 + ,516 + ,587 + ,1 + ,536 + ,524 + ,537 + ,536 + ,533 + ,528 + ,597 + ,1 + ,587 + ,536 + ,524 + ,537 + ,536 + ,533 + ,581 + ,1 + ,597 + ,587 + ,536 + ,524 + ,537 + ,536) + ,dim=c(8 + ,64) + ,dimnames=list(c('Y' + ,'X' + ,'Y1' + ,'Y2' + ,'Y3' + ,'Y4' + ,'Y5' + ,'Y6') + ,1:64)) > y <- array(NA,dim=c(8,64),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4','Y5','Y6'),1:64)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'Linear Trend' > par2 = 'Include Monthly Dummies' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x Y X Y1 Y2 Y3 Y4 Y5 Y6 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 543 0 537 544 555 561 562 555 1 0 0 0 0 0 0 0 0 0 0 1 2 594 0 543 537 544 555 561 562 0 1 0 0 0 0 0 0 0 0 0 2 3 611 0 594 543 537 544 555 561 0 0 1 0 0 0 0 0 0 0 0 3 4 613 0 611 594 543 537 544 555 0 0 0 1 0 0 0 0 0 0 0 4 5 611 0 613 611 594 543 537 544 0 0 0 0 1 0 0 0 0 0 0 5 6 594 0 611 613 611 594 543 537 0 0 0 0 0 1 0 0 0 0 0 6 7 595 0 594 611 613 611 594 543 0 0 0 0 0 0 1 0 0 0 0 7 8 591 0 595 594 611 613 611 594 0 0 0 0 0 0 0 1 0 0 0 8 9 589 0 591 595 594 611 613 611 0 0 0 0 0 0 0 0 1 0 0 9 10 584 0 589 591 595 594 611 613 0 0 0 0 0 0 0 0 0 1 0 10 11 573 0 584 589 591 595 594 611 0 0 0 0 0 0 0 0 0 0 1 11 12 567 0 573 584 589 591 595 594 0 0 0 0 0 0 0 0 0 0 0 12 13 569 0 567 573 584 589 591 595 1 0 0 0 0 0 0 0 0 0 0 13 14 621 0 569 567 573 584 589 591 0 1 0 0 0 0 0 0 0 0 0 14 15 629 0 621 569 567 573 584 589 0 0 1 0 0 0 0 0 0 0 0 15 16 628 0 629 621 569 567 573 584 0 0 0 1 0 0 0 0 0 0 0 16 17 612 0 628 629 621 569 567 573 0 0 0 0 1 0 0 0 0 0 0 17 18 595 0 612 628 629 621 569 567 0 0 0 0 0 1 0 0 0 0 0 18 19 597 0 595 612 628 629 621 569 0 0 0 0 0 0 1 0 0 0 0 19 20 593 0 597 595 612 628 629 621 0 0 0 0 0 0 0 1 0 0 0 20 21 590 0 593 597 595 612 628 629 0 0 0 0 0 0 0 0 1 0 0 21 22 580 0 590 593 597 595 612 628 0 0 0 0 0 0 0 0 0 1 0 22 23 574 0 580 590 593 597 595 612 0 0 0 0 0 0 0 0 0 0 1 23 24 573 0 574 580 590 593 597 595 0 0 0 0 0 0 0 0 0 0 0 24 25 573 0 573 574 580 590 593 597 1 0 0 0 0 0 0 0 0 0 0 25 26 620 0 573 573 574 580 590 593 0 1 0 0 0 0 0 0 0 0 0 26 27 626 0 620 573 573 574 580 590 0 0 1 0 0 0 0 0 0 0 0 27 28 620 0 626 620 573 573 574 580 0 0 0 1 0 0 0 0 0 0 0 28 29 588 0 620 626 620 573 573 574 0 0 0 0 1 0 0 0 0 0 0 29 30 566 0 588 620 626 620 573 573 0 0 0 0 0 1 0 0 0 0 0 30 31 557 0 566 588 620 626 620 573 0 0 0 0 0 0 1 0 0 0 0 31 32 561 0 557 566 588 620 626 620 0 0 0 0 0 0 0 1 0 0 0 32 33 549 0 561 557 566 588 620 626 0 0 0 0 0 0 0 0 1 0 0 33 34 532 0 549 561 557 566 588 620 0 0 0 0 0 0 0 0 0 1 0 34 35 526 0 532 549 561 557 566 588 0 0 0 0 0 0 0 0 0 0 1 35 36 511 0 526 532 549 561 557 566 0 0 0 0 0 0 0 0 0 0 0 36 37 499 0 511 526 532 549 561 557 1 0 0 0 0 0 0 0 0 0 0 37 38 555 0 499 511 526 532 549 561 0 1 0 0 0 0 0 0 0 0 0 38 39 565 0 555 499 511 526 532 549 0 0 1 0 0 0 0 0 0 0 0 39 40 542 0 565 555 499 511 526 532 0 0 0 1 0 0 0 0 0 0 0 40 41 527 0 542 565 555 499 511 526 0 0 0 0 1 0 0 0 0 0 0 41 42 510 0 527 542 565 555 499 511 0 0 0 0 0 1 0 0 0 0 0 42 43 514 0 510 527 542 565 555 499 0 0 0 0 0 0 1 0 0 0 0 43 44 517 0 514 510 527 542 565 555 0 0 0 0 0 0 0 1 0 0 0 44 45 508 0 517 514 510 527 542 565 0 0 0 0 0 0 0 0 1 0 0 45 46 493 0 508 517 514 510 527 542 0 0 0 0 0 0 0 0 0 1 0 46 47 490 1 493 508 517 514 510 527 0 0 0 0 0 0 0 0 0 0 1 47 48 469 1 490 493 508 517 514 510 0 0 0 0 0 0 0 0 0 0 0 48 49 478 1 469 490 493 508 517 514 1 0 0 0 0 0 0 0 0 0 0 49 50 528 1 478 469 490 493 508 517 0 1 0 0 0 0 0 0 0 0 0 50 51 534 1 528 478 469 490 493 508 0 0 1 0 0 0 0 0 0 0 0 51 52 518 1 534 528 478 469 490 493 0 0 0 1 0 0 0 0 0 0 0 52 53 506 1 518 534 528 478 469 490 0 0 0 0 1 0 0 0 0 0 0 53 54 502 1 506 518 534 528 478 469 0 0 0 0 0 1 0 0 0 0 0 54 55 516 1 502 506 518 534 528 478 0 0 0 0 0 0 1 0 0 0 0 55 56 528 1 516 502 506 518 534 528 0 0 0 0 0 0 0 1 0 0 0 56 57 533 1 528 516 502 506 518 534 0 0 0 0 0 0 0 0 1 0 0 57 58 536 1 533 528 516 502 506 518 0 0 0 0 0 0 0 0 0 1 0 58 59 537 1 536 533 528 516 502 506 0 0 0 0 0 0 0 0 0 0 1 59 60 524 1 537 536 533 528 516 502 0 0 0 0 0 0 0 0 0 0 0 60 61 536 1 524 537 536 533 528 516 1 0 0 0 0 0 0 0 0 0 0 61 62 587 1 536 524 537 536 533 528 0 1 0 0 0 0 0 0 0 0 0 62 63 597 1 587 536 524 537 536 533 0 0 1 0 0 0 0 0 0 0 0 63 64 581 1 597 587 536 524 537 536 0 0 0 1 0 0 0 0 0 0 0 64 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) X Y1 Y2 Y3 Y4 2.879e+01 6.155e+00 1.040e+00 5.291e-02 1.733e-04 -3.125e-02 Y5 Y6 M1 M2 M3 M4 -1.984e-01 7.572e-02 1.436e+01 6.212e+01 1.688e+01 -6.391e+00 M5 M6 M7 M8 M9 M10 -1.345e+01 -9.609e+00 2.021e+01 1.865e+01 9.298e+00 2.083e+00 M11 t 3.618e+00 -2.036e-01 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -12.5650 -4.4988 -0.1568 3.9528 13.1096 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.879e+01 2.685e+01 1.072 0.28941 X 6.155e+00 4.210e+00 1.462 0.15088 Y1 1.040e+00 1.489e-01 6.988 1.19e-08 *** Y2 5.291e-02 2.135e-01 0.248 0.80545 Y3 1.733e-04 2.115e-01 0.001 0.99935 Y4 -3.125e-02 2.139e-01 -0.146 0.88451 Y5 -1.984e-01 2.185e-01 -0.908 0.36869 Y6 7.572e-02 1.570e-01 0.482 0.63209 M1 1.436e+01 4.401e+00 3.262 0.00214 ** M2 6.212e+01 4.713e+00 13.182 < 2e-16 *** M3 1.688e+01 9.936e+00 1.699 0.09634 . M4 -6.391e+00 1.023e+01 -0.625 0.53526 M5 -1.345e+01 9.742e+00 -1.381 0.17428 M6 -9.609e+00 9.057e+00 -1.061 0.29451 M7 2.021e+01 9.165e+00 2.205 0.03272 * M8 1.865e+01 5.357e+00 3.480 0.00114 ** M9 9.298e+00 6.194e+00 1.501 0.14045 M10 2.083e+00 6.226e+00 0.335 0.73953 M11 3.618e+00 5.390e+00 0.671 0.50560 t -2.036e-01 9.105e-02 -2.236 0.03049 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 6.889 on 44 degrees of freedom Multiple R-squared: 0.9811, Adjusted R-squared: 0.9729 F-statistic: 120.1 on 19 and 44 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 0.30075819 0.601516382 0.699241809 [2,] 0.25895955 0.517919101 0.741040449 [3,] 0.17751461 0.355029225 0.822485387 [4,] 0.09728540 0.194570805 0.902714597 [5,] 0.05114262 0.102285250 0.948857375 [6,] 0.09285437 0.185708732 0.907145634 [7,] 0.42919919 0.858398374 0.570800813 [8,] 0.33038120 0.660762397 0.669618801 [9,] 0.47484953 0.949699060 0.525150470 [10,] 0.39670589 0.793411777 0.603294111 [11,] 0.30632625 0.612652499 0.693673750 [12,] 0.38270239 0.765404779 0.617297610 [13,] 0.37330416 0.746608321 0.626695840 [14,] 0.42338153 0.846763064 0.576618468 [15,] 0.77670658 0.446586849 0.223293424 [16,] 0.98554603 0.028907941 0.014453970 [17,] 0.99515206 0.009695888 0.004847944 [18,] 0.98536973 0.029260536 0.014630268 [19,] 0.95405057 0.091898855 0.045949428 > postscript(file="/var/www/html/rcomp/tmp/1rhdx1258730363.ps",horizontal=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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/295b61258730363.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/3dwd41258730363.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4t7eb1258730363.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5s5qp1258730363.ps",horizontal=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 = 64 Frequency = 1 1 2 3 4 5 6 -0.44244181 -3.78855771 3.82147406 6.96812724 8.87618999 -6.47785191 7 8 9 10 11 12 -7.10506612 -9.90571062 0.80463754 4.43529633 -5.77798413 5.11196958 13 14 15 16 17 18 -1.14770098 1.28021612 -0.66443520 8.74818421 0.32658719 -1.14117128 19 20 21 22 23 24 0.19296166 -5.60078068 3.70541217 0.82550530 1.95795771 13.10958428 25 26 27 28 29 30 -0.72222385 -1.83380149 -1.23214526 7.05290770 -11.50953689 -1.99935164 31 32 33 34 35 36 -6.51943532 7.22292739 -1.55097304 -5.44193346 3.32311817 -0.70731734 37 38 39 40 41 42 -9.83474003 8.66771641 3.83664519 -9.42104005 3.33549443 0.01978278 43 44 45 46 47 48 5.22108159 3.75305734 -4.81402816 -4.95860738 -1.47691183 -12.56499734 49 50 51 52 53 54 4.30122281 -3.99209918 -7.42808427 -8.95443795 -1.02873472 9.59859205 55 56 57 58 59 60 8.21045820 4.53050657 1.85495149 5.13973922 1.97382008 -4.94923917 61 62 63 64 7.84588387 -0.33347414 1.66654547 -4.39374115 > postscript(file="/var/www/html/rcomp/tmp/635cr1258730363.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 64 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.44244181 NA 1 -3.78855771 -0.44244181 2 3.82147406 -3.78855771 3 6.96812724 3.82147406 4 8.87618999 6.96812724 5 -6.47785191 8.87618999 6 -7.10506612 -6.47785191 7 -9.90571062 -7.10506612 8 0.80463754 -9.90571062 9 4.43529633 0.80463754 10 -5.77798413 4.43529633 11 5.11196958 -5.77798413 12 -1.14770098 5.11196958 13 1.28021612 -1.14770098 14 -0.66443520 1.28021612 15 8.74818421 -0.66443520 16 0.32658719 8.74818421 17 -1.14117128 0.32658719 18 0.19296166 -1.14117128 19 -5.60078068 0.19296166 20 3.70541217 -5.60078068 21 0.82550530 3.70541217 22 1.95795771 0.82550530 23 13.10958428 1.95795771 24 -0.72222385 13.10958428 25 -1.83380149 -0.72222385 26 -1.23214526 -1.83380149 27 7.05290770 -1.23214526 28 -11.50953689 7.05290770 29 -1.99935164 -11.50953689 30 -6.51943532 -1.99935164 31 7.22292739 -6.51943532 32 -1.55097304 7.22292739 33 -5.44193346 -1.55097304 34 3.32311817 -5.44193346 35 -0.70731734 3.32311817 36 -9.83474003 -0.70731734 37 8.66771641 -9.83474003 38 3.83664519 8.66771641 39 -9.42104005 3.83664519 40 3.33549443 -9.42104005 41 0.01978278 3.33549443 42 5.22108159 0.01978278 43 3.75305734 5.22108159 44 -4.81402816 3.75305734 45 -4.95860738 -4.81402816 46 -1.47691183 -4.95860738 47 -12.56499734 -1.47691183 48 4.30122281 -12.56499734 49 -3.99209918 4.30122281 50 -7.42808427 -3.99209918 51 -8.95443795 -7.42808427 52 -1.02873472 -8.95443795 53 9.59859205 -1.02873472 54 8.21045820 9.59859205 55 4.53050657 8.21045820 56 1.85495149 4.53050657 57 5.13973922 1.85495149 58 1.97382008 5.13973922 59 -4.94923917 1.97382008 60 7.84588387 -4.94923917 61 -0.33347414 7.84588387 62 1.66654547 -0.33347414 63 -4.39374115 1.66654547 64 NA -4.39374115 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -3.78855771 -0.44244181 [2,] 3.82147406 -3.78855771 [3,] 6.96812724 3.82147406 [4,] 8.87618999 6.96812724 [5,] -6.47785191 8.87618999 [6,] -7.10506612 -6.47785191 [7,] -9.90571062 -7.10506612 [8,] 0.80463754 -9.90571062 [9,] 4.43529633 0.80463754 [10,] -5.77798413 4.43529633 [11,] 5.11196958 -5.77798413 [12,] -1.14770098 5.11196958 [13,] 1.28021612 -1.14770098 [14,] -0.66443520 1.28021612 [15,] 8.74818421 -0.66443520 [16,] 0.32658719 8.74818421 [17,] -1.14117128 0.32658719 [18,] 0.19296166 -1.14117128 [19,] -5.60078068 0.19296166 [20,] 3.70541217 -5.60078068 [21,] 0.82550530 3.70541217 [22,] 1.95795771 0.82550530 [23,] 13.10958428 1.95795771 [24,] -0.72222385 13.10958428 [25,] -1.83380149 -0.72222385 [26,] -1.23214526 -1.83380149 [27,] 7.05290770 -1.23214526 [28,] -11.50953689 7.05290770 [29,] -1.99935164 -11.50953689 [30,] -6.51943532 -1.99935164 [31,] 7.22292739 -6.51943532 [32,] -1.55097304 7.22292739 [33,] -5.44193346 -1.55097304 [34,] 3.32311817 -5.44193346 [35,] -0.70731734 3.32311817 [36,] -9.83474003 -0.70731734 [37,] 8.66771641 -9.83474003 [38,] 3.83664519 8.66771641 [39,] -9.42104005 3.83664519 [40,] 3.33549443 -9.42104005 [41,] 0.01978278 3.33549443 [42,] 5.22108159 0.01978278 [43,] 3.75305734 5.22108159 [44,] -4.81402816 3.75305734 [45,] -4.95860738 -4.81402816 [46,] -1.47691183 -4.95860738 [47,] -12.56499734 -1.47691183 [48,] 4.30122281 -12.56499734 [49,] -3.99209918 4.30122281 [50,] -7.42808427 -3.99209918 [51,] -8.95443795 -7.42808427 [52,] -1.02873472 -8.95443795 [53,] 9.59859205 -1.02873472 [54,] 8.21045820 9.59859205 [55,] 4.53050657 8.21045820 [56,] 1.85495149 4.53050657 [57,] 5.13973922 1.85495149 [58,] 1.97382008 5.13973922 [59,] -4.94923917 1.97382008 [60,] 7.84588387 -4.94923917 [61,] -0.33347414 7.84588387 [62,] 1.66654547 -0.33347414 [63,] -4.39374115 1.66654547 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -3.78855771 -0.44244181 2 3.82147406 -3.78855771 3 6.96812724 3.82147406 4 8.87618999 6.96812724 5 -6.47785191 8.87618999 6 -7.10506612 -6.47785191 7 -9.90571062 -7.10506612 8 0.80463754 -9.90571062 9 4.43529633 0.80463754 10 -5.77798413 4.43529633 11 5.11196958 -5.77798413 12 -1.14770098 5.11196958 13 1.28021612 -1.14770098 14 -0.66443520 1.28021612 15 8.74818421 -0.66443520 16 0.32658719 8.74818421 17 -1.14117128 0.32658719 18 0.19296166 -1.14117128 19 -5.60078068 0.19296166 20 3.70541217 -5.60078068 21 0.82550530 3.70541217 22 1.95795771 0.82550530 23 13.10958428 1.95795771 24 -0.72222385 13.10958428 25 -1.83380149 -0.72222385 26 -1.23214526 -1.83380149 27 7.05290770 -1.23214526 28 -11.50953689 7.05290770 29 -1.99935164 -11.50953689 30 -6.51943532 -1.99935164 31 7.22292739 -6.51943532 32 -1.55097304 7.22292739 33 -5.44193346 -1.55097304 34 3.32311817 -5.44193346 35 -0.70731734 3.32311817 36 -9.83474003 -0.70731734 37 8.66771641 -9.83474003 38 3.83664519 8.66771641 39 -9.42104005 3.83664519 40 3.33549443 -9.42104005 41 0.01978278 3.33549443 42 5.22108159 0.01978278 43 3.75305734 5.22108159 44 -4.81402816 3.75305734 45 -4.95860738 -4.81402816 46 -1.47691183 -4.95860738 47 -12.56499734 -1.47691183 48 4.30122281 -12.56499734 49 -3.99209918 4.30122281 50 -7.42808427 -3.99209918 51 -8.95443795 -7.42808427 52 -1.02873472 -8.95443795 53 9.59859205 -1.02873472 54 8.21045820 9.59859205 55 4.53050657 8.21045820 56 1.85495149 4.53050657 57 5.13973922 1.85495149 58 1.97382008 5.13973922 59 -4.94923917 1.97382008 60 7.84588387 -4.94923917 61 -0.33347414 7.84588387 62 1.66654547 -0.33347414 63 -4.39374115 1.66654547 > 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/www/html/rcomp/tmp/77yk51258730363.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/8swka1258730363.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9r6lx1258730363.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/www/html/rcomp/tmp/10p6gj1258730363.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/11ujkx1258730363.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/12if3r1258730363.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/13jp741258730363.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/14uomg1258730363.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/html/rcomp/tmp/152aek1258730363.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/html/rcomp/tmp/16c9ag1258730363.tab") + } > > system("convert tmp/1rhdx1258730363.ps tmp/1rhdx1258730363.png") > system("convert tmp/295b61258730363.ps tmp/295b61258730363.png") > system("convert tmp/3dwd41258730363.ps tmp/3dwd41258730363.png") > system("convert tmp/4t7eb1258730363.ps tmp/4t7eb1258730363.png") > system("convert tmp/5s5qp1258730363.ps tmp/5s5qp1258730363.png") > system("convert tmp/635cr1258730363.ps tmp/635cr1258730363.png") > system("convert tmp/77yk51258730363.ps tmp/77yk51258730363.png") > system("convert tmp/8swka1258730363.ps tmp/8swka1258730363.png") > system("convert tmp/9r6lx1258730363.ps tmp/9r6lx1258730363.png") > system("convert tmp/10p6gj1258730363.ps tmp/10p6gj1258730363.png") > > > proc.time() user system elapsed 2.424 1.567 2.801