R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) 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. 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,90861 + ,0 + ,-276.1 + ,0 + ,2012 + ,0 + ,-10 + ,536 + ,0 + ,-3 + ,0 + ,3 + ,0 + ,118.42 + ,0 + ,90861 + ,0 + ,-139.1 + ,0 + ,2012 + ,0 + ,-10 + ,528 + ,0 + ,-1 + ,0 + ,2 + ,0 + ,118.24 + ,0 + ,90861 + ,0 + ,268 + ,0 + ,2012 + ,0 + ,-13 + ,530 + ,0 + ,-2 + ,0 + ,-4 + ,0 + ,116.47 + ,0 + ,90401 + ,0 + ,570.5 + ,0 + ,2012 + ,0 + ,-16 + ,582 + ,0 + ,-3 + ,0 + ,-3 + ,0 + ,118.96 + ,0 + ,90401 + ,0 + ,-316.5 + ,0 + ,2012 + ,0) + ,dim=c(15 + ,143) + ,dimnames=list(c('I' + ,'W' + ,'W_c' + ,'F' + ,'F_c' + ,'S' + ,'S_c' + ,'C' + ,'C_c' + ,'B' + ,'B_c' + ,'H' + ,'H_c' + ,'T' + ,'c') + ,1:143)) > y <- array(NA,dim=c(15,143),dimnames=list(c('I','W','W_c','F','F_c','S','S_c','C','C_c','B','B_c','H','H_c','T','c'),1:143)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, 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 I W W_c F F_c S S_c C C_c B B_c H H_c T c 1 14 501 501 11 11 20 20 91.81 91.81 77585 77585 1303.2 1303.2 2000 1 2 14 485 485 11 11 19 19 91.98 91.98 77585 77585 -58.7 -58.7 2000 1 3 15 464 464 11 11 18 18 91.72 91.72 77585 77585 -378.9 -378.9 2000 1 4 13 460 0 11 0 13 0 90.27 0.00 78302 0 175.6 0.0 2001 0 5 8 467 0 11 0 17 0 91.89 0.00 78302 0 233.7 0.0 2001 0 6 7 460 0 9 0 17 0 92.07 0.00 78302 0 706.8 0.0 2001 0 7 3 448 0 8 0 13 0 92.92 0.00 78224 0 -23.6 0.0 2001 0 8 3 443 0 6 0 14 0 93.34 0.00 78224 0 420.9 0.0 2001 0 9 4 436 0 7 0 13 0 93.60 0.00 78224 0 722.1 0.0 2001 0 10 4 431 0 8 0 17 0 92.41 0.00 78178 0 1401.3 0.0 2001 0 11 0 484 0 6 0 17 0 93.60 0.00 78178 0 -94.9 0.0 2001 0 12 -4 510 0 5 0 15 0 93.77 0.00 78178 0 1043.6 0.0 2001 0 13 -14 513 0 2 0 9 0 93.60 0.00 77988 0 1300.1 0.0 2001 0 14 -18 503 0 3 0 10 0 93.60 0.00 77988 0 721.1 0.0 2001 0 15 -8 471 0 3 0 9 0 93.51 0.00 77988 0 -45.6 0.0 2001 0 16 -1 471 0 7 0 14 0 92.66 0.00 77876 0 787.5 0.0 2002 0 17 1 476 0 8 0 18 0 94.20 0.00 77876 0 694.3 0.0 2002 0 18 2 475 0 7 0 18 0 94.37 0.00 77876 0 1054.7 0.0 2002 0 19 0 470 0 7 0 12 0 94.45 0.00 78432 0 821.9 0.0 2002 0 20 1 461 0 6 0 16 0 94.62 0.00 78432 0 1100.7 0.0 2002 0 21 0 455 0 6 0 12 0 94.37 0.00 78432 0 862.4 0.0 2002 0 22 -1 456 0 7 0 19 0 93.43 0.00 79025 0 1656.1 0.0 2002 0 23 -3 517 0 5 0 13 0 94.79 0.00 79025 0 -174.0 0.0 2002 0 24 -3 525 0 5 0 12 0 94.88 0.00 79025 0 1337.6 0.0 2002 0 25 -3 523 0 5 0 13 0 94.79 0.00 79407 0 1394.9 0.0 2002 0 26 -4 519 0 4 0 11 0 94.62 0.00 79407 0 915.7 0.0 2002 0 27 -8 509 0 4 0 10 0 94.71 0.00 79407 0 -481.1 0.0 2002 0 28 -9 512 0 4 0 16 0 93.77 0.00 79644 0 167.9 0.0 2003 0 29 -13 519 0 1 0 12 0 95.73 0.00 79644 0 208.2 0.0 2003 0 30 -18 517 0 -1 0 6 0 95.99 0.00 79644 0 382.2 0.0 2003 0 31 -11 510 0 3 0 8 0 95.82 0.00 79381 0 1004.0 0.0 2003 0 32 -9 509 0 4 0 6 0 95.47 0.00 79381 0 864.7 0.0 2003 0 33 -10 501 0 3 0 8 0 95.82 0.00 79381 0 1052.9 0.0 2003 0 34 -13 507 0 2 0 8 0 94.71 0.00 79536 0 1417.6 0.0 2003 0 35 -11 569 0 1 0 9 0 96.33 0.00 79536 0 -197.7 0.0 2003 0 36 -5 580 0 4 0 13 0 96.50 0.00 79536 0 1262.1 0.0 2003 0 37 -15 578 0 3 0 8 0 96.16 0.00 79813 0 1147.2 0.0 2003 0 38 -6 565 0 5 0 11 0 96.33 0.00 79813 0 700.2 0.0 2003 0 39 -6 547 0 6 0 8 0 96.33 0.00 79813 0 45.3 0.0 2003 0 40 -3 555 0 6 0 10 0 95.05 0.00 80332 0 458.5 0.0 2004 0 41 -1 562 0 6 0 15 0 96.84 0.00 80332 0 610.2 0.0 2004 0 42 -3 561 0 6 0 12 0 96.92 0.00 80332 0 786.4 0.0 2004 0 43 -4 555 0 6 0 13 0 97.44 0.00 81434 0 787.2 0.0 2004 0 44 -6 544 0 5 0 12 0 97.78 0.00 81434 0 1040.0 0.0 2004 0 45 0 537 0 6 0 15 0 97.69 0.00 81434 0 324.1 0.0 2004 0 46 -4 543 0 5 0 13 0 96.67 0.00 82167 0 1343.0 0.0 2004 0 47 -2 594 0 6 0 13 0 98.29 0.00 82167 0 -501.2 0.0 2004 0 48 -2 611 0 5 0 16 0 98.20 0.00 82167 0 800.4 0.0 2004 0 49 -6 613 0 7 0 14 0 98.71 0.00 82816 0 916.7 0.0 2004 0 50 -7 611 0 4 0 12 0 98.54 0.00 82816 0 695.8 0.0 2004 0 51 -6 594 0 5 0 15 0 98.20 0.00 82816 0 28.0 0.0 2004 0 52 -6 595 595 6 6 14 14 96.92 96.92 83000 83000 495.6 495.6 2005 1 53 -3 591 591 6 6 19 19 99.06 99.06 83000 83000 366.2 366.2 2005 1 54 -2 589 589 5 5 16 16 99.65 99.65 83000 83000 633.0 633.0 2005 1 55 -5 584 584 3 3 16 16 99.82 99.82 83251 83251 848.3 848.3 2005 1 56 -11 573 573 2 2 11 11 99.99 99.99 83251 83251 472.2 472.2 2005 1 57 -11 567 567 3 3 13 13 100.33 100.33 83251 83251 357.8 357.8 2005 1 58 -11 569 569 3 3 12 12 99.31 99.31 83591 83591 824.3 824.3 2005 1 59 -10 621 621 2 2 11 11 101.10 101.10 83591 83591 -880.1 -880.1 2005 1 60 -14 629 629 0 0 6 6 101.10 101.10 83591 83591 1066.8 1066.8 2005 1 61 -8 628 628 4 4 9 9 100.93 100.93 83910 83910 1052.8 1052.8 2005 1 62 -9 612 612 4 4 6 6 100.85 100.85 83910 83910 -32.1 -32.1 2005 1 63 -5 595 595 5 5 15 15 100.93 100.93 83910 83910 -1331.4 -1331.4 2005 1 64 -1 597 597 6 6 17 17 99.60 99.60 84599 84599 -767.1 -767.1 2006 1 65 -2 593 593 6 6 13 13 101.88 101.88 84599 84599 -236.7 -236.7 2006 1 66 -5 590 590 5 5 12 12 101.81 101.81 84599 84599 -184.9 -184.9 2006 1 67 -4 580 580 5 5 13 13 102.38 102.38 85275 85275 -143.4 -143.4 2006 1 68 -6 574 574 3 3 10 10 102.74 102.74 85275 85275 493.9 493.9 2006 1 69 -2 573 573 5 5 14 14 102.82 102.82 85275 85275 549.7 549.7 2006 1 70 -2 573 573 5 5 13 13 101.72 101.72 85608 85608 982.7 982.7 2006 1 71 -2 620 620 5 5 10 10 103.47 103.47 85608 85608 -856.3 -856.3 2006 1 72 -2 626 626 3 3 11 11 102.98 102.98 85608 85608 967.0 967.0 2006 1 73 2 620 620 6 6 12 12 102.68 102.68 86303 86303 659.4 659.4 2006 1 74 1 588 588 6 6 7 7 102.90 102.90 86303 86303 577.2 577.2 2006 1 75 -8 566 566 4 4 11 11 103.03 103.03 86303 86303 -213.1 -213.1 2006 1 76 -1 557 557 6 6 9 9 101.29 101.29 87115 87115 17.7 17.7 2007 1 77 1 561 561 5 5 13 13 103.69 103.69 87115 87115 390.1 390.1 2007 1 78 -1 549 549 4 4 12 12 103.68 103.68 87115 87115 509.3 509.3 2007 1 79 2 532 532 5 5 5 5 104.20 104.20 87931 87931 410.0 410.0 2007 1 80 2 526 526 5 5 13 13 104.08 104.08 87931 87931 212.5 212.5 2007 1 81 1 511 511 4 4 11 11 104.16 104.16 87931 87931 818.0 818.0 2007 1 82 -1 499 499 3 3 8 8 103.05 103.05 88164 88164 422.7 422.7 2007 1 83 -2 555 555 2 2 8 8 104.66 104.66 88164 88164 -158.0 -158.0 2007 1 84 -2 565 565 3 3 8 8 104.46 104.46 88164 88164 427.2 427.2 2007 1 85 -1 542 542 2 2 8 8 104.95 104.95 88792 88792 243.4 243.4 2007 1 86 -8 527 527 -1 -1 0 0 105.85 105.85 88792 88792 -419.3 -419.3 2007 1 87 -4 510 510 0 0 3 3 106.23 106.23 88792 88792 -1459.8 -1459.8 2007 1 88 -6 514 514 -2 -2 0 0 104.86 104.86 89263 89263 -1389.8 -1389.8 2008 1 89 -3 517 517 1 1 -1 -1 107.44 107.44 89263 89263 -2.1 -2.1 2008 1 90 -3 508 508 -2 -2 -1 -1 108.23 108.23 89263 89263 -938.6 -938.6 2008 1 91 -7 493 493 -2 -2 -4 -4 108.45 108.45 89881 89881 -839.9 -839.9 2008 1 92 -9 490 490 -2 -2 1 1 109.39 109.39 89881 89881 -297.6 -297.6 2008 1 93 -11 469 469 -6 -6 -1 -1 110.15 110.15 89881 89881 -376.3 -376.3 2008 1 94 -13 478 478 -4 -4 0 0 109.13 109.13 90120 90120 -79.4 -79.4 2008 1 95 -11 528 528 -2 -2 -1 -1 110.28 110.28 90120 90120 -2091.3 -2091.3 2008 1 96 -9 534 534 0 0 6 6 110.17 110.17 90120 90120 -1023.0 -1023.0 2008 1 97 -17 518 518 -5 -5 0 0 109.99 109.99 89703 89703 -765.6 -765.6 2008 1 98 -22 506 506 -4 -4 -3 -3 109.26 109.26 89703 89703 -1592.3 -1592.3 2008 1 99 -25 502 502 -5 -5 -3 -3 109.11 109.11 89703 89703 -1588.8 -1588.8 2008 1 100 -20 516 0 -1 0 4 0 107.06 0.00 87818 0 -1318.0 0.0 2009 0 101 -24 528 0 -2 0 1 0 109.53 0.00 87818 0 -402.4 0.0 2009 0 102 -24 533 0 -4 0 0 0 108.92 0.00 87818 0 -814.5 0.0 2009 0 103 -22 536 0 -1 0 -4 0 109.24 0.00 86273 0 -98.4 0.0 2009 0 104 -19 537 0 1 0 -2 0 109.12 0.00 86273 0 -305.9 0.0 2009 0 105 -18 524 0 1 0 3 0 109.00 0.00 86273 0 -18.4 0.0 2009 0 106 -17 536 0 -2 0 2 0 107.23 0.00 86316 0 610.3 0.0 2009 0 107 -11 587 0 1 0 5 0 109.49 0.00 86316 0 -917.3 0.0 2009 0 108 -11 597 0 1 0 6 0 109.04 0.00 86316 0 88.4 0.0 2009 0 109 -12 581 0 3 0 6 0 109.02 0.00 87234 0 -740.2 0.0 2009 0 110 -10 564 0 3 0 3 0 109.23 0.00 87234 0 29.3 0.0 2009 0 111 -15 558 0 1 0 4 0 109.46 0.00 87234 0 -893.2 0.0 2009 0 112 -15 575 0 1 0 7 0 107.90 0.00 87885 0 -1030.2 0.0 2010 0 113 -15 580 0 0 0 5 0 110.42 0.00 87885 0 -403.4 0.0 2010 0 114 -13 575 0 2 0 6 0 110.98 0.00 87885 0 -46.9 0.0 2010 0 115 -8 563 0 2 0 1 0 111.48 0.00 88003 0 -321.2 0.0 2010 0 116 -13 552 0 -1 0 3 0 111.88 0.00 88003 0 -239.9 0.0 2010 0 117 -9 537 0 1 0 6 0 111.89 0.00 88003 0 640.9 0.0 2010 0 118 -7 545 0 0 0 0 0 109.85 0.00 88910 0 511.6 0.0 2010 0 119 -4 601 0 1 0 3 0 112.10 0.00 88910 0 -665.1 0.0 2010 0 120 -4 604 0 1 0 4 0 112.24 0.00 88910 0 657.7 0.0 2010 0 121 -2 586 0 3 0 7 0 112.39 0.00 89397 0 -207.7 0.0 2010 0 122 0 564 0 2 0 6 0 112.52 0.00 89397 0 -885.2 0.0 2010 0 123 -2 549 0 0 0 6 0 113.16 0.00 89397 0 -1595.8 0.0 2010 0 124 -3 551 0 0 0 6 0 111.84 0.00 89813 0 -1374.9 0.0 2011 0 125 1 556 0 3 0 6 0 114.33 0.00 89813 0 -316.6 0.0 2011 0 126 -2 548 0 -2 0 2 0 114.82 0.00 89813 0 -283.4 0.0 2011 0 127 -1 540 0 0 0 2 0 115.20 0.00 90539 0 -175.8 0.0 2011 0 128 1 531 0 1 0 2 0 115.40 0.00 90539 0 -694.2 0.0 2011 0 129 -3 521 0 -1 0 3 0 115.74 0.00 90539 0 -249.9 0.0 2011 0 130 -4 519 0 -2 0 -1 0 114.19 0.00 90688 0 268.2 0.0 2011 0 131 -9 572 0 -1 0 -4 0 115.94 0.00 90688 0 -2105.1 0.0 2011 0 132 -9 581 0 -1 0 4 0 116.03 0.00 90688 0 -762.8 0.0 2011 0 133 -7 563 0 1 0 5 0 116.24 0.00 90691 0 -117.1 0.0 2011 0 134 -14 548 0 -2 0 3 0 116.66 0.00 90691 0 -1094.4 0.0 2011 0 135 -12 539 0 -5 0 -1 0 116.79 0.00 90691 0 -2095.2 0.0 2011 0 136 -16 541 0 -5 0 -4 0 115.48 0.00 90645 0 -1587.6 0.0 2012 0 137 -20 562 0 -6 0 0 0 118.16 0.00 90645 0 -528.0 0.0 2012 0 138 -12 559 0 -4 0 -1 0 118.38 0.00 90645 0 -324.2 0.0 2012 0 139 -12 546 0 -3 0 -1 0 118.51 0.00 90861 0 -276.1 0.0 2012 0 140 -10 536 0 -3 0 3 0 118.42 0.00 90861 0 -139.1 0.0 2012 0 141 -10 528 0 -1 0 2 0 118.24 0.00 90861 0 268.0 0.0 2012 0 142 -13 530 0 -2 0 -4 0 116.47 0.00 90401 0 570.5 0.0 2012 0 143 -16 582 0 -3 0 -3 0 118.96 0.00 90401 0 -316.5 0.0 2012 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) W W_c F F_c S 5.170e+03 -4.186e-02 -1.175e-03 1.972e+00 3.556e-02 4.104e-01 S_c C C_c B B_c H -3.815e-01 8.852e-01 -8.873e-01 1.773e-03 4.108e-04 1.482e-04 H_c T c 6.274e-04 -2.694e+00 5.890e+01 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -9.9520 -1.6001 0.3105 1.9765 9.6244 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.170e+03 1.426e+03 3.625 0.000415 *** W -4.186e-02 1.088e-02 -3.849 0.000187 *** W_c -1.175e-03 1.722e-02 -0.068 0.945691 F 1.972e+00 2.304e-01 8.557 3.07e-14 *** F_c 3.556e-02 4.120e-01 0.086 0.931350 S 4.104e-01 1.527e-01 2.688 0.008145 ** S_c -3.815e-01 2.539e-01 -1.503 0.135395 C 8.852e-01 3.235e-01 2.737 0.007092 ** C_c -8.873e-01 6.465e-01 -1.372 0.172339 B 1.773e-03 6.361e-04 2.787 0.006122 ** B_c 4.108e-04 8.400e-04 0.489 0.625623 H 1.482e-04 6.407e-04 0.231 0.817425 H_c 6.274e-04 1.012e-03 0.620 0.536579 T -2.694e+00 7.314e-01 -3.683 0.000339 *** c 5.890e+01 3.231e+01 1.823 0.070642 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.615 on 128 degrees of freedom Multiple R-squared: 0.7904, Adjusted R-squared: 0.7675 F-statistic: 34.47 on 14 and 128 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,] 7.035762e-01 0.5928475618 0.2964238 [2,] 7.501409e-01 0.4997182957 0.2498591 [3,] 6.366954e-01 0.7266091625 0.3633046 [4,] 5.127086e-01 0.9745827373 0.4872914 [5,] 5.814987e-01 0.8370025485 0.4185013 [6,] 4.981402e-01 0.9962803494 0.5018598 [7,] 4.608990e-01 0.9217979450 0.5391010 [8,] 3.683512e-01 0.7367024798 0.6316488 [9,] 2.984009e-01 0.5968017093 0.7015991 [10,] 2.909525e-01 0.5819050938 0.7090475 [11,] 2.237742e-01 0.4475483247 0.7762258 [12,] 1.715273e-01 0.3430546117 0.8284727 [13,] 1.248172e-01 0.2496343772 0.8751828 [14,] 1.010673e-01 0.2021346839 0.8989327 [15,] 7.772943e-02 0.1554588582 0.9222706 [16,] 5.349353e-02 0.1069870596 0.9465065 [17,] 3.532125e-02 0.0706424919 0.9646788 [18,] 5.075043e-02 0.1015008576 0.9492496 [19,] 4.280590e-02 0.0856117952 0.9571941 [20,] 5.649787e-02 0.1129957326 0.9435021 [21,] 4.319284e-02 0.0863856798 0.9568072 [22,] 4.639670e-02 0.0927934032 0.9536033 [23,] 4.268595e-02 0.0853719027 0.9573140 [24,] 3.610625e-02 0.0722125043 0.9638937 [25,] 3.247693e-02 0.0649538639 0.9675231 [26,] 3.280377e-02 0.0656075482 0.9671962 [27,] 2.681844e-02 0.0536368791 0.9731816 [28,] 2.585803e-02 0.0517160567 0.9741420 [29,] 1.976357e-02 0.0395271481 0.9802364 [30,] 1.863323e-02 0.0372664532 0.9813668 [31,] 2.068765e-02 0.0413752953 0.9793124 [32,] 3.846452e-02 0.0769290345 0.9615355 [33,] 3.104342e-02 0.0620868471 0.9689566 [34,] 3.188667e-02 0.0637733410 0.9681133 [35,] 2.404497e-02 0.0480899337 0.9759550 [36,] 1.681078e-02 0.0336215644 0.9831892 [37,] 1.284594e-02 0.0256918873 0.9871541 [38,] 9.818753e-03 0.0196375058 0.9901812 [39,] 8.742486e-03 0.0174849718 0.9912575 [40,] 8.401554e-03 0.0168031079 0.9915984 [41,] 6.367262e-03 0.0127345245 0.9936327 [42,] 4.448924e-03 0.0088978471 0.9955511 [43,] 3.035143e-03 0.0060702853 0.9969649 [44,] 2.235381e-03 0.0044707613 0.9977646 [45,] 1.594026e-03 0.0031880523 0.9984060 [46,] 1.045158e-03 0.0020903170 0.9989548 [47,] 1.451244e-03 0.0029024885 0.9985488 [48,] 1.051181e-03 0.0021023626 0.9989488 [49,] 6.600521e-04 0.0013201042 0.9993399 [50,] 4.505316e-04 0.0009010632 0.9995495 [51,] 4.177903e-04 0.0008355806 0.9995822 [52,] 2.655356e-04 0.0005310713 0.9997345 [53,] 2.046452e-04 0.0004092904 0.9997954 [54,] 1.913470e-04 0.0003826940 0.9998087 [55,] 6.029315e-04 0.0012058631 0.9993971 [56,] 5.235683e-04 0.0010471367 0.9994764 [57,] 3.546867e-04 0.0007093734 0.9996453 [58,] 2.936689e-04 0.0005873378 0.9997063 [59,] 2.891696e-04 0.0005783391 0.9997108 [60,] 2.307814e-04 0.0004615629 0.9997692 [61,] 1.735487e-04 0.0003470975 0.9998265 [62,] 1.317465e-04 0.0002634931 0.9998683 [63,] 8.185287e-05 0.0001637057 0.9999181 [64,] 5.573714e-05 0.0001114743 0.9999443 [65,] 1.019653e-04 0.0002039305 0.9998980 [66,] 1.330326e-04 0.0002660653 0.9998670 [67,] 9.507960e-05 0.0001901592 0.9999049 [68,] 8.591625e-05 0.0001718325 0.9999141 [69,] 6.362587e-05 0.0001272517 0.9999364 [70,] 5.708662e-05 0.0001141732 0.9999429 [71,] 1.881111e-04 0.0003762221 0.9998119 [72,] 2.178008e-04 0.0004356015 0.9997822 [73,] 1.189742e-03 0.0023794840 0.9988103 [74,] 1.056175e-03 0.0021123491 0.9989438 [75,] 8.808442e-04 0.0017616885 0.9991192 [76,] 5.609500e-04 0.0011219001 0.9994390 [77,] 4.829284e-04 0.0009658568 0.9995171 [78,] 3.550789e-04 0.0007101577 0.9996449 [79,] 2.763095e-04 0.0005526191 0.9997237 [80,] 1.811964e-04 0.0003623928 0.9998188 [81,] 6.653274e-04 0.0013306548 0.9993347 [82,] 1.702583e-03 0.0034051651 0.9982974 [83,] 2.367887e-03 0.0047357730 0.9976321 [84,] 7.012234e-03 0.0140244684 0.9929878 [85,] 1.436448e-02 0.0287289620 0.9856355 [86,] 1.026184e-02 0.0205236886 0.9897382 [87,] 7.927309e-03 0.0158546190 0.9920727 [88,] 7.809773e-03 0.0156195453 0.9921902 [89,] 8.184210e-03 0.0163684207 0.9918158 [90,] 1.251205e-02 0.0250240934 0.9874880 [91,] 2.163130e-02 0.0432626068 0.9783687 [92,] 1.888032e-02 0.0377606325 0.9811197 [93,] 1.446819e-02 0.0289363757 0.9855318 [94,] 1.698315e-02 0.0339662983 0.9830169 [95,] 2.500747e-02 0.0500149465 0.9749925 [96,] 2.433848e-02 0.0486769523 0.9756615 [97,] 4.338880e-02 0.0867775991 0.9566112 [98,] 3.624719e-02 0.0724943824 0.9637528 [99,] 3.531316e-02 0.0706263290 0.9646868 [100,] 8.192683e-02 0.1638536500 0.9180732 [101,] 2.122874e-01 0.4245747974 0.7877126 [102,] 1.935116e-01 0.3870231158 0.8064884 [103,] 1.553538e-01 0.3107076876 0.8446462 [104,] 1.287845e-01 0.2575690583 0.8712155 [105,] 1.047560e-01 0.2095119786 0.8952440 [106,] 1.071874e-01 0.2143748699 0.8928126 [107,] 7.238062e-02 0.1447612404 0.9276194 [108,] 3.968352e-02 0.0793670390 0.9603165 > postscript(file="/var/wessaorg/rcomp/tmp/1ooud1353097932.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2xo451353097932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3vn6w1353097932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4ux441353097932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5hpeb1353097932.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 = 143 Frequency = 1 1 2 3 4 5 6 0.52803230 0.92503331 1.29798817 5.93344556 -1.85787535 0.56267931 7 8 9 10 11 12 -0.83217344 2.05345964 0.92457371 -1.86379588 -0.53369742 -0.97213638 13 14 15 16 17 18 -2.01997826 -8.73472794 0.52947378 1.11226688 -1.64105427 1.08470517 19 20 21 22 23 24 0.31588958 1.07707848 1.72430234 -4.41553963 1.61112839 2.05274083 25 26 27 28 29 30 0.95240203 2.79887056 -1.08191952 -1.40979582 0.69867566 1.76472314 31 32 33 34 35 36 0.28938802 1.42735201 0.90539132 0.78170537 5.74362560 4.28091158 37 38 39 40 41 42 -1.95224810 1.24497039 -0.15163869 5.20744465 3.84132843 2.93387120 43 44 45 46 47 48 -1.14215214 -1.55908585 1.13081449 -0.37349586 0.62929217 1.96784818 49 50 51 52 53 54 -6.69001986 -0.85508098 -3.36962994 -2.93849794 -0.15033041 2.65182743 55 56 57 58 59 60 2.73600844 -1.29357026 -3.52727608 -4.51884465 2.08084812 1.07397038 61 62 63 64 65 66 -1.77039812 -2.53090795 -0.52211419 2.24728862 0.78443047 -0.34898958 67 68 69 70 71 72 -1.31562294 0.03361700 -0.18242494 -1.21896862 2.32063796 5.14888684 73 74 75 76 77 78 1.56053859 -0.60772348 -6.04286425 -2.64906196 1.13078085 0.55788846 79 80 81 82 83 84 -0.68227015 -1.01904182 -1.06908561 -1.69631319 2.17466742 0.14363978 85 86 87 88 89 90 0.93295017 0.05605408 2.03837792 5.91916968 1.98524991 8.34721746 91 92 93 94 95 96 2.36269127 -0.32966303 4.91544987 -1.49477212 0.23471580 -2.55257349 97 98 99 100 101 102 -0.32144216 -7.11855052 -8.28665281 -7.33512754 -9.95200211 -4.78818800 103 104 105 106 107 108 -4.58525582 -6.17034301 -7.70315319 1.52147251 0.73646279 0.99388054 109 110 111 112 113 114 -6.10615994 -3.88640435 -5.67182983 -3.25110092 -2.57289752 -5.68421242 115 116 117 118 119 120 0.25452522 -0.47839643 -2.42008617 4.56562825 4.88979780 4.28494321 121 122 123 124 125 126 -0.51094724 2.93542476 3.78937134 5.96472231 1.89856342 9.62439161 127 128 129 130 131 132 4.70685270 4.25839428 3.00556497 6.56614078 1.84733884 -1.33809398 133 134 135 136 137 138 -4.73198138 -5.85136210 3.36149128 4.53602387 -0.78444132 3.33244258 139 140 141 142 143 0.31153211 0.31050223 -3.45798892 0.39746042 -0.93726879 > postscript(file="/var/wessaorg/rcomp/tmp/695aw1353097932.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 = 143 Frequency = 1 lag(myerror, k = 1) myerror 0 0.52803230 NA 1 0.92503331 0.52803230 2 1.29798817 0.92503331 3 5.93344556 1.29798817 4 -1.85787535 5.93344556 5 0.56267931 -1.85787535 6 -0.83217344 0.56267931 7 2.05345964 -0.83217344 8 0.92457371 2.05345964 9 -1.86379588 0.92457371 10 -0.53369742 -1.86379588 11 -0.97213638 -0.53369742 12 -2.01997826 -0.97213638 13 -8.73472794 -2.01997826 14 0.52947378 -8.73472794 15 1.11226688 0.52947378 16 -1.64105427 1.11226688 17 1.08470517 -1.64105427 18 0.31588958 1.08470517 19 1.07707848 0.31588958 20 1.72430234 1.07707848 21 -4.41553963 1.72430234 22 1.61112839 -4.41553963 23 2.05274083 1.61112839 24 0.95240203 2.05274083 25 2.79887056 0.95240203 26 -1.08191952 2.79887056 27 -1.40979582 -1.08191952 28 0.69867566 -1.40979582 29 1.76472314 0.69867566 30 0.28938802 1.76472314 31 1.42735201 0.28938802 32 0.90539132 1.42735201 33 0.78170537 0.90539132 34 5.74362560 0.78170537 35 4.28091158 5.74362560 36 -1.95224810 4.28091158 37 1.24497039 -1.95224810 38 -0.15163869 1.24497039 39 5.20744465 -0.15163869 40 3.84132843 5.20744465 41 2.93387120 3.84132843 42 -1.14215214 2.93387120 43 -1.55908585 -1.14215214 44 1.13081449 -1.55908585 45 -0.37349586 1.13081449 46 0.62929217 -0.37349586 47 1.96784818 0.62929217 48 -6.69001986 1.96784818 49 -0.85508098 -6.69001986 50 -3.36962994 -0.85508098 51 -2.93849794 -3.36962994 52 -0.15033041 -2.93849794 53 2.65182743 -0.15033041 54 2.73600844 2.65182743 55 -1.29357026 2.73600844 56 -3.52727608 -1.29357026 57 -4.51884465 -3.52727608 58 2.08084812 -4.51884465 59 1.07397038 2.08084812 60 -1.77039812 1.07397038 61 -2.53090795 -1.77039812 62 -0.52211419 -2.53090795 63 2.24728862 -0.52211419 64 0.78443047 2.24728862 65 -0.34898958 0.78443047 66 -1.31562294 -0.34898958 67 0.03361700 -1.31562294 68 -0.18242494 0.03361700 69 -1.21896862 -0.18242494 70 2.32063796 -1.21896862 71 5.14888684 2.32063796 72 1.56053859 5.14888684 73 -0.60772348 1.56053859 74 -6.04286425 -0.60772348 75 -2.64906196 -6.04286425 76 1.13078085 -2.64906196 77 0.55788846 1.13078085 78 -0.68227015 0.55788846 79 -1.01904182 -0.68227015 80 -1.06908561 -1.01904182 81 -1.69631319 -1.06908561 82 2.17466742 -1.69631319 83 0.14363978 2.17466742 84 0.93295017 0.14363978 85 0.05605408 0.93295017 86 2.03837792 0.05605408 87 5.91916968 2.03837792 88 1.98524991 5.91916968 89 8.34721746 1.98524991 90 2.36269127 8.34721746 91 -0.32966303 2.36269127 92 4.91544987 -0.32966303 93 -1.49477212 4.91544987 94 0.23471580 -1.49477212 95 -2.55257349 0.23471580 96 -0.32144216 -2.55257349 97 -7.11855052 -0.32144216 98 -8.28665281 -7.11855052 99 -7.33512754 -8.28665281 100 -9.95200211 -7.33512754 101 -4.78818800 -9.95200211 102 -4.58525582 -4.78818800 103 -6.17034301 -4.58525582 104 -7.70315319 -6.17034301 105 1.52147251 -7.70315319 106 0.73646279 1.52147251 107 0.99388054 0.73646279 108 -6.10615994 0.99388054 109 -3.88640435 -6.10615994 110 -5.67182983 -3.88640435 111 -3.25110092 -5.67182983 112 -2.57289752 -3.25110092 113 -5.68421242 -2.57289752 114 0.25452522 -5.68421242 115 -0.47839643 0.25452522 116 -2.42008617 -0.47839643 117 4.56562825 -2.42008617 118 4.88979780 4.56562825 119 4.28494321 4.88979780 120 -0.51094724 4.28494321 121 2.93542476 -0.51094724 122 3.78937134 2.93542476 123 5.96472231 3.78937134 124 1.89856342 5.96472231 125 9.62439161 1.89856342 126 4.70685270 9.62439161 127 4.25839428 4.70685270 128 3.00556497 4.25839428 129 6.56614078 3.00556497 130 1.84733884 6.56614078 131 -1.33809398 1.84733884 132 -4.73198138 -1.33809398 133 -5.85136210 -4.73198138 134 3.36149128 -5.85136210 135 4.53602387 3.36149128 136 -0.78444132 4.53602387 137 3.33244258 -0.78444132 138 0.31153211 3.33244258 139 0.31050223 0.31153211 140 -3.45798892 0.31050223 141 0.39746042 -3.45798892 142 -0.93726879 0.39746042 143 NA -0.93726879 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.92503331 0.52803230 [2,] 1.29798817 0.92503331 [3,] 5.93344556 1.29798817 [4,] -1.85787535 5.93344556 [5,] 0.56267931 -1.85787535 [6,] -0.83217344 0.56267931 [7,] 2.05345964 -0.83217344 [8,] 0.92457371 2.05345964 [9,] -1.86379588 0.92457371 [10,] -0.53369742 -1.86379588 [11,] -0.97213638 -0.53369742 [12,] -2.01997826 -0.97213638 [13,] -8.73472794 -2.01997826 [14,] 0.52947378 -8.73472794 [15,] 1.11226688 0.52947378 [16,] -1.64105427 1.11226688 [17,] 1.08470517 -1.64105427 [18,] 0.31588958 1.08470517 [19,] 1.07707848 0.31588958 [20,] 1.72430234 1.07707848 [21,] -4.41553963 1.72430234 [22,] 1.61112839 -4.41553963 [23,] 2.05274083 1.61112839 [24,] 0.95240203 2.05274083 [25,] 2.79887056 0.95240203 [26,] -1.08191952 2.79887056 [27,] -1.40979582 -1.08191952 [28,] 0.69867566 -1.40979582 [29,] 1.76472314 0.69867566 [30,] 0.28938802 1.76472314 [31,] 1.42735201 0.28938802 [32,] 0.90539132 1.42735201 [33,] 0.78170537 0.90539132 [34,] 5.74362560 0.78170537 [35,] 4.28091158 5.74362560 [36,] -1.95224810 4.28091158 [37,] 1.24497039 -1.95224810 [38,] -0.15163869 1.24497039 [39,] 5.20744465 -0.15163869 [40,] 3.84132843 5.20744465 [41,] 2.93387120 3.84132843 [42,] -1.14215214 2.93387120 [43,] -1.55908585 -1.14215214 [44,] 1.13081449 -1.55908585 [45,] -0.37349586 1.13081449 [46,] 0.62929217 -0.37349586 [47,] 1.96784818 0.62929217 [48,] -6.69001986 1.96784818 [49,] -0.85508098 -6.69001986 [50,] -3.36962994 -0.85508098 [51,] -2.93849794 -3.36962994 [52,] -0.15033041 -2.93849794 [53,] 2.65182743 -0.15033041 [54,] 2.73600844 2.65182743 [55,] -1.29357026 2.73600844 [56,] -3.52727608 -1.29357026 [57,] -4.51884465 -3.52727608 [58,] 2.08084812 -4.51884465 [59,] 1.07397038 2.08084812 [60,] -1.77039812 1.07397038 [61,] -2.53090795 -1.77039812 [62,] -0.52211419 -2.53090795 [63,] 2.24728862 -0.52211419 [64,] 0.78443047 2.24728862 [65,] -0.34898958 0.78443047 [66,] -1.31562294 -0.34898958 [67,] 0.03361700 -1.31562294 [68,] -0.18242494 0.03361700 [69,] -1.21896862 -0.18242494 [70,] 2.32063796 -1.21896862 [71,] 5.14888684 2.32063796 [72,] 1.56053859 5.14888684 [73,] -0.60772348 1.56053859 [74,] -6.04286425 -0.60772348 [75,] -2.64906196 -6.04286425 [76,] 1.13078085 -2.64906196 [77,] 0.55788846 1.13078085 [78,] -0.68227015 0.55788846 [79,] -1.01904182 -0.68227015 [80,] -1.06908561 -1.01904182 [81,] -1.69631319 -1.06908561 [82,] 2.17466742 -1.69631319 [83,] 0.14363978 2.17466742 [84,] 0.93295017 0.14363978 [85,] 0.05605408 0.93295017 [86,] 2.03837792 0.05605408 [87,] 5.91916968 2.03837792 [88,] 1.98524991 5.91916968 [89,] 8.34721746 1.98524991 [90,] 2.36269127 8.34721746 [91,] -0.32966303 2.36269127 [92,] 4.91544987 -0.32966303 [93,] -1.49477212 4.91544987 [94,] 0.23471580 -1.49477212 [95,] -2.55257349 0.23471580 [96,] -0.32144216 -2.55257349 [97,] -7.11855052 -0.32144216 [98,] -8.28665281 -7.11855052 [99,] -7.33512754 -8.28665281 [100,] -9.95200211 -7.33512754 [101,] -4.78818800 -9.95200211 [102,] -4.58525582 -4.78818800 [103,] -6.17034301 -4.58525582 [104,] -7.70315319 -6.17034301 [105,] 1.52147251 -7.70315319 [106,] 0.73646279 1.52147251 [107,] 0.99388054 0.73646279 [108,] -6.10615994 0.99388054 [109,] -3.88640435 -6.10615994 [110,] -5.67182983 -3.88640435 [111,] -3.25110092 -5.67182983 [112,] -2.57289752 -3.25110092 [113,] -5.68421242 -2.57289752 [114,] 0.25452522 -5.68421242 [115,] -0.47839643 0.25452522 [116,] -2.42008617 -0.47839643 [117,] 4.56562825 -2.42008617 [118,] 4.88979780 4.56562825 [119,] 4.28494321 4.88979780 [120,] -0.51094724 4.28494321 [121,] 2.93542476 -0.51094724 [122,] 3.78937134 2.93542476 [123,] 5.96472231 3.78937134 [124,] 1.89856342 5.96472231 [125,] 9.62439161 1.89856342 [126,] 4.70685270 9.62439161 [127,] 4.25839428 4.70685270 [128,] 3.00556497 4.25839428 [129,] 6.56614078 3.00556497 [130,] 1.84733884 6.56614078 [131,] -1.33809398 1.84733884 [132,] -4.73198138 -1.33809398 [133,] -5.85136210 -4.73198138 [134,] 3.36149128 -5.85136210 [135,] 4.53602387 3.36149128 [136,] -0.78444132 4.53602387 [137,] 3.33244258 -0.78444132 [138,] 0.31153211 3.33244258 [139,] 0.31050223 0.31153211 [140,] -3.45798892 0.31050223 [141,] 0.39746042 -3.45798892 [142,] -0.93726879 0.39746042 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.92503331 0.52803230 2 1.29798817 0.92503331 3 5.93344556 1.29798817 4 -1.85787535 5.93344556 5 0.56267931 -1.85787535 6 -0.83217344 0.56267931 7 2.05345964 -0.83217344 8 0.92457371 2.05345964 9 -1.86379588 0.92457371 10 -0.53369742 -1.86379588 11 -0.97213638 -0.53369742 12 -2.01997826 -0.97213638 13 -8.73472794 -2.01997826 14 0.52947378 -8.73472794 15 1.11226688 0.52947378 16 -1.64105427 1.11226688 17 1.08470517 -1.64105427 18 0.31588958 1.08470517 19 1.07707848 0.31588958 20 1.72430234 1.07707848 21 -4.41553963 1.72430234 22 1.61112839 -4.41553963 23 2.05274083 1.61112839 24 0.95240203 2.05274083 25 2.79887056 0.95240203 26 -1.08191952 2.79887056 27 -1.40979582 -1.08191952 28 0.69867566 -1.40979582 29 1.76472314 0.69867566 30 0.28938802 1.76472314 31 1.42735201 0.28938802 32 0.90539132 1.42735201 33 0.78170537 0.90539132 34 5.74362560 0.78170537 35 4.28091158 5.74362560 36 -1.95224810 4.28091158 37 1.24497039 -1.95224810 38 -0.15163869 1.24497039 39 5.20744465 -0.15163869 40 3.84132843 5.20744465 41 2.93387120 3.84132843 42 -1.14215214 2.93387120 43 -1.55908585 -1.14215214 44 1.13081449 -1.55908585 45 -0.37349586 1.13081449 46 0.62929217 -0.37349586 47 1.96784818 0.62929217 48 -6.69001986 1.96784818 49 -0.85508098 -6.69001986 50 -3.36962994 -0.85508098 51 -2.93849794 -3.36962994 52 -0.15033041 -2.93849794 53 2.65182743 -0.15033041 54 2.73600844 2.65182743 55 -1.29357026 2.73600844 56 -3.52727608 -1.29357026 57 -4.51884465 -3.52727608 58 2.08084812 -4.51884465 59 1.07397038 2.08084812 60 -1.77039812 1.07397038 61 -2.53090795 -1.77039812 62 -0.52211419 -2.53090795 63 2.24728862 -0.52211419 64 0.78443047 2.24728862 65 -0.34898958 0.78443047 66 -1.31562294 -0.34898958 67 0.03361700 -1.31562294 68 -0.18242494 0.03361700 69 -1.21896862 -0.18242494 70 2.32063796 -1.21896862 71 5.14888684 2.32063796 72 1.56053859 5.14888684 73 -0.60772348 1.56053859 74 -6.04286425 -0.60772348 75 -2.64906196 -6.04286425 76 1.13078085 -2.64906196 77 0.55788846 1.13078085 78 -0.68227015 0.55788846 79 -1.01904182 -0.68227015 80 -1.06908561 -1.01904182 81 -1.69631319 -1.06908561 82 2.17466742 -1.69631319 83 0.14363978 2.17466742 84 0.93295017 0.14363978 85 0.05605408 0.93295017 86 2.03837792 0.05605408 87 5.91916968 2.03837792 88 1.98524991 5.91916968 89 8.34721746 1.98524991 90 2.36269127 8.34721746 91 -0.32966303 2.36269127 92 4.91544987 -0.32966303 93 -1.49477212 4.91544987 94 0.23471580 -1.49477212 95 -2.55257349 0.23471580 96 -0.32144216 -2.55257349 97 -7.11855052 -0.32144216 98 -8.28665281 -7.11855052 99 -7.33512754 -8.28665281 100 -9.95200211 -7.33512754 101 -4.78818800 -9.95200211 102 -4.58525582 -4.78818800 103 -6.17034301 -4.58525582 104 -7.70315319 -6.17034301 105 1.52147251 -7.70315319 106 0.73646279 1.52147251 107 0.99388054 0.73646279 108 -6.10615994 0.99388054 109 -3.88640435 -6.10615994 110 -5.67182983 -3.88640435 111 -3.25110092 -5.67182983 112 -2.57289752 -3.25110092 113 -5.68421242 -2.57289752 114 0.25452522 -5.68421242 115 -0.47839643 0.25452522 116 -2.42008617 -0.47839643 117 4.56562825 -2.42008617 118 4.88979780 4.56562825 119 4.28494321 4.88979780 120 -0.51094724 4.28494321 121 2.93542476 -0.51094724 122 3.78937134 2.93542476 123 5.96472231 3.78937134 124 1.89856342 5.96472231 125 9.62439161 1.89856342 126 4.70685270 9.62439161 127 4.25839428 4.70685270 128 3.00556497 4.25839428 129 6.56614078 3.00556497 130 1.84733884 6.56614078 131 -1.33809398 1.84733884 132 -4.73198138 -1.33809398 133 -5.85136210 -4.73198138 134 3.36149128 -5.85136210 135 4.53602387 3.36149128 136 -0.78444132 4.53602387 137 3.33244258 -0.78444132 138 0.31153211 3.33244258 139 0.31050223 0.31153211 140 -3.45798892 0.31050223 141 0.39746042 -3.45798892 142 -0.93726879 0.39746042 > 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/7m60w1353097932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8u2d31353097932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9axni1353097932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/1070ow1353097932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11nl4g1353097932.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12qrxa1353097932.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13rg511353097932.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/14x1go1353097932.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/15uruo1353097932.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/16f9y91353097932.tab") + } > > try(system("convert tmp/1ooud1353097932.ps tmp/1ooud1353097932.png",intern=TRUE)) character(0) > try(system("convert tmp/2xo451353097932.ps tmp/2xo451353097932.png",intern=TRUE)) character(0) > try(system("convert tmp/3vn6w1353097932.ps tmp/3vn6w1353097932.png",intern=TRUE)) character(0) > try(system("convert tmp/4ux441353097932.ps tmp/4ux441353097932.png",intern=TRUE)) character(0) > try(system("convert tmp/5hpeb1353097932.ps tmp/5hpeb1353097932.png",intern=TRUE)) character(0) > try(system("convert tmp/695aw1353097932.ps tmp/695aw1353097932.png",intern=TRUE)) character(0) > try(system("convert tmp/7m60w1353097932.ps tmp/7m60w1353097932.png",intern=TRUE)) character(0) > try(system("convert tmp/8u2d31353097932.ps tmp/8u2d31353097932.png",intern=TRUE)) character(0) > try(system("convert tmp/9axni1353097932.ps tmp/9axni1353097932.png",intern=TRUE)) character(0) > try(system("convert tmp/1070ow1353097932.ps tmp/1070ow1353097932.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 9.126 1.178 10.591