R version 2.8.0 (2008-10-20) Copyright (C) 2008 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(727 + ,0 + ,817 + ,0 + ,918 + ,0 + ,786 + ,0 + ,803 + ,0 + ,756 + ,0 + ,725 + ,0 + ,523 + ,0 + ,538 + ,0 + ,587 + ,0 + ,505 + ,0 + ,521 + ,0 + ,498 + ,0 + ,550 + ,0 + ,637 + ,0 + ,622 + ,0 + ,668 + ,0 + ,669 + ,0 + ,670 + ,0 + ,499 + ,0 + ,539 + ,0 + ,593 + ,0 + ,429 + ,0 + ,622 + ,0 + ,533 + ,0 + ,655 + ,0 + ,835 + ,0 + ,686 + ,0 + ,706 + ,0 + ,869 + ,0 + ,777 + ,0 + ,739 + ,0 + ,637 + ,0 + ,597 + ,0 + ,629 + ,0 + ,940 + ,0 + ,444 + ,0 + ,496 + ,0 + ,801 + ,0 + ,659 + ,0 + ,767 + ,0 + ,876 + ,0 + ,601 + ,0 + ,697 + ,0 + ,745 + ,0 + ,655 + ,0 + ,572 + ,0 + ,628 + ,0 + ,650 + ,0 + ,677 + ,0 + ,900 + ,0 + ,780 + ,0 + ,896 + ,0 + ,1092 + ,0 + ,823 + ,0 + ,735 + ,0 + ,770 + ,0 + ,915 + ,0 + ,645 + ,0 + ,566 + ,0 + ,707 + ,0 + ,785 + ,0 + ,762 + ,0 + ,712 + ,0 + ,714 + ,0 + ,823 + ,0 + ,609 + ,0 + ,620 + ,0 + ,619 + ,0 + ,638 + ,0 + ,483 + ,0 + ,535 + ,0 + ,617 + ,0 + ,698 + ,0 + ,804 + ,0 + ,824 + ,0 + ,878 + ,0 + ,1019 + ,0 + ,974 + ,0 + ,773 + ,0 + ,734 + ,0 + ,827 + ,0 + ,804 + ,0 + ,721 + ,0 + ,659 + ,0 + ,732 + ,0 + ,839 + ,0 + ,994 + ,0 + ,828 + ,0 + ,1039 + ,0 + ,1072 + ,0 + ,803 + ,0 + ,1035 + ,0 + ,922 + ,0 + ,834 + ,0 + ,1739 + ,0 + ,359 + ,1 + ,513 + ,1 + ,699 + ,1 + ,741 + ,1 + ,793 + ,1 + ,877 + ,1 + ,750 + ,1 + ,752 + ,1 + ,675 + ,1 + ,682 + ,1 + ,583 + ,1 + ,632 + ,1 + ,606 + ,1 + ,645 + ,1 + ,980 + ,1 + ,847 + ,1 + ,941 + ,1 + ,1066 + ,1 + ,936 + ,1 + ,880 + ,1 + ,808 + ,1 + ,741 + ,1 + ,780 + ,1 + ,675 + ,1 + ,782 + ,1 + ,795 + ,1 + ,873 + ,1 + ,727 + ,1 + ,998 + ,1 + ,768 + ,1 + ,714 + ,1 + ,782 + ,1 + ,578 + ,1 + ,664 + ,1 + ,560 + ,1 + ,516 + ,1 + ,752 + ,1 + ,597 + ,1 + ,716 + ,1 + ,691 + ,1 + ,752 + ,1 + ,718 + ,1 + ,737 + ,1 + ,621 + ,1 + ,472 + ,1 + ,719 + ,1 + ,497 + ,1 + ,536 + ,1 + ,653 + ,1 + ,605 + ,1 + ,637 + ,1 + ,743 + ,1 + ,719 + ,1 + ,653 + ,1 + ,675 + ,1 + ,590 + ,1 + ,527 + ,1 + ,534 + ,1 + ,463 + ,1 + ,542 + ,1 + ,568 + ,1 + ,501 + ,1 + ,678 + ,1 + ,774 + ,1 + ,665 + ,1 + ,742 + ,1 + ,715 + ,1 + ,638 + ,1 + ,656 + ,1 + ,606 + ,1 + ,498 + ,1 + ,587 + ,1 + ,677 + ,1 + ,547 + ,1 + ,871 + ,1 + ,731 + ,1 + ,752 + ,1 + ,862 + ,1 + ,619 + ,1 + ,700 + ,1 + ,667 + ,1 + ,667 + ,1 + ,650 + ,1 + ,547 + ,1 + ,637 + ,1 + ,655 + ,1 + ,703 + ,1 + ,886 + ,1 + ,896 + ,1 + ,831 + ,1 + ,741 + ,1 + ,833 + ,1 + ,750 + ,1 + ,779 + ,1 + ,655 + ,1 + ,739 + ,1 + ,845 + ,1 + ,795 + ,1 + ,1021 + ,1 + ,726 + ,1 + ,1045 + ,1 + ,915 + ,1 + ,852 + ,1 + ,772 + ,1 + ,729 + ,1 + ,755 + ,1 + ,691 + ,1 + ,729 + ,1 + ,702 + ,1 + ,702 + ,1 + ,894 + ,1 + ,765 + ,1 + ,753 + ,1 + ,876 + ,1 + ,781 + ,1 + ,776 + ,1 + ,606 + ,1 + ,775 + ,1 + ,663 + ,1 + ,649 + ,1 + ,821 + ,1 + ,771 + ,1 + ,635 + ,1 + ,1070 + ,1 + ,693 + ,1 + ,779 + ,1) + ,dim=c(2 + ,222) + ,dimnames=list(c('y' + ,'x') + ,1:222)) > y <- array(NA,dim=c(2,222),dimnames=list(c('y','x'),1:222)) > 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) > 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t 1 727 0 1 0 0 0 0 0 0 0 0 0 0 1 2 817 0 0 1 0 0 0 0 0 0 0 0 0 2 3 918 0 0 0 1 0 0 0 0 0 0 0 0 3 4 786 0 0 0 0 1 0 0 0 0 0 0 0 4 5 803 0 0 0 0 0 1 0 0 0 0 0 0 5 6 756 0 0 0 0 0 0 1 0 0 0 0 0 6 7 725 0 0 0 0 0 0 0 1 0 0 0 0 7 8 523 0 0 0 0 0 0 0 0 1 0 0 0 8 9 538 0 0 0 0 0 0 0 0 0 1 0 0 9 10 587 0 0 0 0 0 0 0 0 0 0 1 0 10 11 505 0 0 0 0 0 0 0 0 0 0 0 1 11 12 521 0 0 0 0 0 0 0 0 0 0 0 0 12 13 498 0 1 0 0 0 0 0 0 0 0 0 0 13 14 550 0 0 1 0 0 0 0 0 0 0 0 0 14 15 637 0 0 0 1 0 0 0 0 0 0 0 0 15 16 622 0 0 0 0 1 0 0 0 0 0 0 0 16 17 668 0 0 0 0 0 1 0 0 0 0 0 0 17 18 669 0 0 0 0 0 0 1 0 0 0 0 0 18 19 670 0 0 0 0 0 0 0 1 0 0 0 0 19 20 499 0 0 0 0 0 0 0 0 1 0 0 0 20 21 539 0 0 0 0 0 0 0 0 0 1 0 0 21 22 593 0 0 0 0 0 0 0 0 0 0 1 0 22 23 429 0 0 0 0 0 0 0 0 0 0 0 1 23 24 622 0 0 0 0 0 0 0 0 0 0 0 0 24 25 533 0 1 0 0 0 0 0 0 0 0 0 0 25 26 655 0 0 1 0 0 0 0 0 0 0 0 0 26 27 835 0 0 0 1 0 0 0 0 0 0 0 0 27 28 686 0 0 0 0 1 0 0 0 0 0 0 0 28 29 706 0 0 0 0 0 1 0 0 0 0 0 0 29 30 869 0 0 0 0 0 0 1 0 0 0 0 0 30 31 777 0 0 0 0 0 0 0 1 0 0 0 0 31 32 739 0 0 0 0 0 0 0 0 1 0 0 0 32 33 637 0 0 0 0 0 0 0 0 0 1 0 0 33 34 597 0 0 0 0 0 0 0 0 0 0 1 0 34 35 629 0 0 0 0 0 0 0 0 0 0 0 1 35 36 940 0 0 0 0 0 0 0 0 0 0 0 0 36 37 444 0 1 0 0 0 0 0 0 0 0 0 0 37 38 496 0 0 1 0 0 0 0 0 0 0 0 0 38 39 801 0 0 0 1 0 0 0 0 0 0 0 0 39 40 659 0 0 0 0 1 0 0 0 0 0 0 0 40 41 767 0 0 0 0 0 1 0 0 0 0 0 0 41 42 876 0 0 0 0 0 0 1 0 0 0 0 0 42 43 601 0 0 0 0 0 0 0 1 0 0 0 0 43 44 697 0 0 0 0 0 0 0 0 1 0 0 0 44 45 745 0 0 0 0 0 0 0 0 0 1 0 0 45 46 655 0 0 0 0 0 0 0 0 0 0 1 0 46 47 572 0 0 0 0 0 0 0 0 0 0 0 1 47 48 628 0 0 0 0 0 0 0 0 0 0 0 0 48 49 650 0 1 0 0 0 0 0 0 0 0 0 0 49 50 677 0 0 1 0 0 0 0 0 0 0 0 0 50 51 900 0 0 0 1 0 0 0 0 0 0 0 0 51 52 780 0 0 0 0 1 0 0 0 0 0 0 0 52 53 896 0 0 0 0 0 1 0 0 0 0 0 0 53 54 1092 0 0 0 0 0 0 1 0 0 0 0 0 54 55 823 0 0 0 0 0 0 0 1 0 0 0 0 55 56 735 0 0 0 0 0 0 0 0 1 0 0 0 56 57 770 0 0 0 0 0 0 0 0 0 1 0 0 57 58 915 0 0 0 0 0 0 0 0 0 0 1 0 58 59 645 0 0 0 0 0 0 0 0 0 0 0 1 59 60 566 0 0 0 0 0 0 0 0 0 0 0 0 60 61 707 0 1 0 0 0 0 0 0 0 0 0 0 61 62 785 0 0 1 0 0 0 0 0 0 0 0 0 62 63 762 0 0 0 1 0 0 0 0 0 0 0 0 63 64 712 0 0 0 0 1 0 0 0 0 0 0 0 64 65 714 0 0 0 0 0 1 0 0 0 0 0 0 65 66 823 0 0 0 0 0 0 1 0 0 0 0 0 66 67 609 0 0 0 0 0 0 0 1 0 0 0 0 67 68 620 0 0 0 0 0 0 0 0 1 0 0 0 68 69 619 0 0 0 0 0 0 0 0 0 1 0 0 69 70 638 0 0 0 0 0 0 0 0 0 0 1 0 70 71 483 0 0 0 0 0 0 0 0 0 0 0 1 71 72 535 0 0 0 0 0 0 0 0 0 0 0 0 72 73 617 0 1 0 0 0 0 0 0 0 0 0 0 73 74 698 0 0 1 0 0 0 0 0 0 0 0 0 74 75 804 0 0 0 1 0 0 0 0 0 0 0 0 75 76 824 0 0 0 0 1 0 0 0 0 0 0 0 76 77 878 0 0 0 0 0 1 0 0 0 0 0 0 77 78 1019 0 0 0 0 0 0 1 0 0 0 0 0 78 79 974 0 0 0 0 0 0 0 1 0 0 0 0 79 80 773 0 0 0 0 0 0 0 0 1 0 0 0 80 81 734 0 0 0 0 0 0 0 0 0 1 0 0 81 82 827 0 0 0 0 0 0 0 0 0 0 1 0 82 83 804 0 0 0 0 0 0 0 0 0 0 0 1 83 84 721 0 0 0 0 0 0 0 0 0 0 0 0 84 85 659 0 1 0 0 0 0 0 0 0 0 0 0 85 86 732 0 0 1 0 0 0 0 0 0 0 0 0 86 87 839 0 0 0 1 0 0 0 0 0 0 0 0 87 88 994 0 0 0 0 1 0 0 0 0 0 0 0 88 89 828 0 0 0 0 0 1 0 0 0 0 0 0 89 90 1039 0 0 0 0 0 0 1 0 0 0 0 0 90 91 1072 0 0 0 0 0 0 0 1 0 0 0 0 91 92 803 0 0 0 0 0 0 0 0 1 0 0 0 92 93 1035 0 0 0 0 0 0 0 0 0 1 0 0 93 94 922 0 0 0 0 0 0 0 0 0 0 1 0 94 95 834 0 0 0 0 0 0 0 0 0 0 0 1 95 96 1739 0 0 0 0 0 0 0 0 0 0 0 0 96 97 359 1 1 0 0 0 0 0 0 0 0 0 0 97 98 513 1 0 1 0 0 0 0 0 0 0 0 0 98 99 699 1 0 0 1 0 0 0 0 0 0 0 0 99 100 741 1 0 0 0 1 0 0 0 0 0 0 0 100 101 793 1 0 0 0 0 1 0 0 0 0 0 0 101 102 877 1 0 0 0 0 0 1 0 0 0 0 0 102 103 750 1 0 0 0 0 0 0 1 0 0 0 0 103 104 752 1 0 0 0 0 0 0 0 1 0 0 0 104 105 675 1 0 0 0 0 0 0 0 0 1 0 0 105 106 682 1 0 0 0 0 0 0 0 0 0 1 0 106 107 583 1 0 0 0 0 0 0 0 0 0 0 1 107 108 632 1 0 0 0 0 0 0 0 0 0 0 0 108 109 606 1 1 0 0 0 0 0 0 0 0 0 0 109 110 645 1 0 1 0 0 0 0 0 0 0 0 0 110 111 980 1 0 0 1 0 0 0 0 0 0 0 0 111 112 847 1 0 0 0 1 0 0 0 0 0 0 0 112 113 941 1 0 0 0 0 1 0 0 0 0 0 0 113 114 1066 1 0 0 0 0 0 1 0 0 0 0 0 114 115 936 1 0 0 0 0 0 0 1 0 0 0 0 115 116 880 1 0 0 0 0 0 0 0 1 0 0 0 116 117 808 1 0 0 0 0 0 0 0 0 1 0 0 117 118 741 1 0 0 0 0 0 0 0 0 0 1 0 118 119 780 1 0 0 0 0 0 0 0 0 0 0 1 119 120 675 1 0 0 0 0 0 0 0 0 0 0 0 120 121 782 1 1 0 0 0 0 0 0 0 0 0 0 121 122 795 1 0 1 0 0 0 0 0 0 0 0 0 122 123 873 1 0 0 1 0 0 0 0 0 0 0 0 123 124 727 1 0 0 0 1 0 0 0 0 0 0 0 124 125 998 1 0 0 0 0 1 0 0 0 0 0 0 125 126 768 1 0 0 0 0 0 1 0 0 0 0 0 126 127 714 1 0 0 0 0 0 0 1 0 0 0 0 127 128 782 1 0 0 0 0 0 0 0 1 0 0 0 128 129 578 1 0 0 0 0 0 0 0 0 1 0 0 129 130 664 1 0 0 0 0 0 0 0 0 0 1 0 130 131 560 1 0 0 0 0 0 0 0 0 0 0 1 131 132 516 1 0 0 0 0 0 0 0 0 0 0 0 132 133 752 1 1 0 0 0 0 0 0 0 0 0 0 133 134 597 1 0 1 0 0 0 0 0 0 0 0 0 134 135 716 1 0 0 1 0 0 0 0 0 0 0 0 135 136 691 1 0 0 0 1 0 0 0 0 0 0 0 136 137 752 1 0 0 0 0 1 0 0 0 0 0 0 137 138 718 1 0 0 0 0 0 1 0 0 0 0 0 138 139 737 1 0 0 0 0 0 0 1 0 0 0 0 139 140 621 1 0 0 0 0 0 0 0 1 0 0 0 140 141 472 1 0 0 0 0 0 0 0 0 1 0 0 141 142 719 1 0 0 0 0 0 0 0 0 0 1 0 142 143 497 1 0 0 0 0 0 0 0 0 0 0 1 143 144 536 1 0 0 0 0 0 0 0 0 0 0 0 144 145 653 1 1 0 0 0 0 0 0 0 0 0 0 145 146 605 1 0 1 0 0 0 0 0 0 0 0 0 146 147 637 1 0 0 1 0 0 0 0 0 0 0 0 147 148 743 1 0 0 0 1 0 0 0 0 0 0 0 148 149 719 1 0 0 0 0 1 0 0 0 0 0 0 149 150 653 1 0 0 0 0 0 1 0 0 0 0 0 150 151 675 1 0 0 0 0 0 0 1 0 0 0 0 151 152 590 1 0 0 0 0 0 0 0 1 0 0 0 152 153 527 1 0 0 0 0 0 0 0 0 1 0 0 153 154 534 1 0 0 0 0 0 0 0 0 0 1 0 154 155 463 1 0 0 0 0 0 0 0 0 0 0 1 155 156 542 1 0 0 0 0 0 0 0 0 0 0 0 156 157 568 1 1 0 0 0 0 0 0 0 0 0 0 157 158 501 1 0 1 0 0 0 0 0 0 0 0 0 158 159 678 1 0 0 1 0 0 0 0 0 0 0 0 159 160 774 1 0 0 0 1 0 0 0 0 0 0 0 160 161 665 1 0 0 0 0 1 0 0 0 0 0 0 161 162 742 1 0 0 0 0 0 1 0 0 0 0 0 162 163 715 1 0 0 0 0 0 0 1 0 0 0 0 163 164 638 1 0 0 0 0 0 0 0 1 0 0 0 164 165 656 1 0 0 0 0 0 0 0 0 1 0 0 165 166 606 1 0 0 0 0 0 0 0 0 0 1 0 166 167 498 1 0 0 0 0 0 0 0 0 0 0 1 167 168 587 1 0 0 0 0 0 0 0 0 0 0 0 168 169 677 1 1 0 0 0 0 0 0 0 0 0 0 169 170 547 1 0 1 0 0 0 0 0 0 0 0 0 170 171 871 1 0 0 1 0 0 0 0 0 0 0 0 171 172 731 1 0 0 0 1 0 0 0 0 0 0 0 172 173 752 1 0 0 0 0 1 0 0 0 0 0 0 173 174 862 1 0 0 0 0 0 1 0 0 0 0 0 174 175 619 1 0 0 0 0 0 0 1 0 0 0 0 175 176 700 1 0 0 0 0 0 0 0 1 0 0 0 176 177 667 1 0 0 0 0 0 0 0 0 1 0 0 177 178 667 1 0 0 0 0 0 0 0 0 0 1 0 178 179 650 1 0 0 0 0 0 0 0 0 0 0 1 179 180 547 1 0 0 0 0 0 0 0 0 0 0 0 180 181 637 1 1 0 0 0 0 0 0 0 0 0 0 181 182 655 1 0 1 0 0 0 0 0 0 0 0 0 182 183 703 1 0 0 1 0 0 0 0 0 0 0 0 183 184 886 1 0 0 0 1 0 0 0 0 0 0 0 184 185 896 1 0 0 0 0 1 0 0 0 0 0 0 185 186 831 1 0 0 0 0 0 1 0 0 0 0 0 186 187 741 1 0 0 0 0 0 0 1 0 0 0 0 187 188 833 1 0 0 0 0 0 0 0 1 0 0 0 188 189 750 1 0 0 0 0 0 0 0 0 1 0 0 189 190 779 1 0 0 0 0 0 0 0 0 0 1 0 190 191 655 1 0 0 0 0 0 0 0 0 0 0 1 191 192 739 1 0 0 0 0 0 0 0 0 0 0 0 192 193 845 1 1 0 0 0 0 0 0 0 0 0 0 193 194 795 1 0 1 0 0 0 0 0 0 0 0 0 194 195 1021 1 0 0 1 0 0 0 0 0 0 0 0 195 196 726 1 0 0 0 1 0 0 0 0 0 0 0 196 197 1045 1 0 0 0 0 1 0 0 0 0 0 0 197 198 915 1 0 0 0 0 0 1 0 0 0 0 0 198 199 852 1 0 0 0 0 0 0 1 0 0 0 0 199 200 772 1 0 0 0 0 0 0 0 1 0 0 0 200 201 729 1 0 0 0 0 0 0 0 0 1 0 0 201 202 755 1 0 0 0 0 0 0 0 0 0 1 0 202 203 691 1 0 0 0 0 0 0 0 0 0 0 1 203 204 729 1 0 0 0 0 0 0 0 0 0 0 0 204 205 702 1 1 0 0 0 0 0 0 0 0 0 0 205 206 702 1 0 1 0 0 0 0 0 0 0 0 0 206 207 894 1 0 0 1 0 0 0 0 0 0 0 0 207 208 765 1 0 0 0 1 0 0 0 0 0 0 0 208 209 753 1 0 0 0 0 1 0 0 0 0 0 0 209 210 876 1 0 0 0 0 0 1 0 0 0 0 0 210 211 781 1 0 0 0 0 0 0 1 0 0 0 0 211 212 776 1 0 0 0 0 0 0 0 1 0 0 0 212 213 606 1 0 0 0 0 0 0 0 0 1 0 0 213 214 775 1 0 0 0 0 0 0 0 0 0 1 0 214 215 663 1 0 0 0 0 0 0 0 0 0 0 1 215 216 649 1 0 0 0 0 0 0 0 0 0 0 0 216 217 821 1 1 0 0 0 0 0 0 0 0 0 0 217 218 771 1 0 1 0 0 0 0 0 0 0 0 0 218 219 635 1 0 0 1 0 0 0 0 0 0 0 0 219 220 1070 1 0 0 0 1 0 0 0 0 0 0 0 220 221 693 1 0 0 0 0 1 0 0 0 0 0 0 221 222 779 1 0 0 0 0 0 1 0 0 0 0 0 222 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) x M1 M2 M3 M4 633.392 -156.736 -36.192 -21.717 117.389 93.021 M5 M6 M7 M8 M9 M10 118.233 167.655 81.145 22.216 -15.046 15.414 M11 t -81.127 1.262 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -235.50 -85.73 -10.55 50.94 984.42 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 633.3924 35.6740 17.755 < 2e-16 *** x -156.7362 34.9900 -4.479 1.23e-05 *** M1 -36.1916 43.5841 -0.830 0.407273 M2 -21.7171 43.5722 -0.498 0.618717 M3 117.3890 43.5620 2.695 0.007620 ** M4 93.0214 43.5534 2.136 0.033864 * M5 118.2328 43.5465 2.715 0.007182 ** M6 167.6546 43.5413 3.850 0.000157 *** M7 81.1450 44.1387 1.838 0.067428 . M8 22.2160 44.1312 0.503 0.615211 M9 -15.0463 44.1254 -0.341 0.733455 M10 15.4135 44.1213 0.349 0.727184 M11 -81.1266 44.1188 -1.839 0.067367 . t 1.2623 0.2705 4.666 5.48e-06 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 132.4 on 208 degrees of freedom Multiple R-squared: 0.2996, Adjusted R-squared: 0.2558 F-statistic: 6.844 on 13 and 208 DF, p-value: 6.322e-11 > postscript(file="/var/www/html/rcomp/tmp/1wuhb1229178400.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/21cun1229178400.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/30rie1229178400.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4rzl01229178400.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5rpxt1229178400.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 222 Frequency = 1 1 2 3 4 5 6 128.5368557 202.8000136 163.4315926 54.5368557 45.0631715 -52.6210390 7 8 9 10 11 12 1.6263037 -142.7070297 -91.7070297 -74.4292519 -61.1514741 -127.5403630 13 14 15 16 17 18 -115.6110873 -79.3479294 -132.7163504 -124.6110873 -105.0847715 -154.7689820 19 20 21 22 23 24 -68.5216393 -181.8549727 -105.8549727 -83.5771949 -152.2994171 -41.6883060 25 26 27 28 29 30 -95.7590303 10.5041276 50.1357066 -75.7590303 -82.2327145 30.0830750 31 32 33 34 35 36 23.3304177 42.9970844 -23.0029156 -94.7251379 32.5526399 261.1637510 37 38 39 40 41 42 -199.9069732 -163.6438153 0.9877636 -117.9069732 -36.3806575 21.9351320 43 44 45 46 47 48 -167.8175253 -14.1508586 69.8491414 -51.8730808 -39.5953031 -65.9841920 49 50 51 52 53 54 -9.0549162 2.2082417 84.8398206 -12.0549162 77.4713996 222.7871890 55 56 57 58 59 60 39.0345317 8.7011984 79.7011984 192.9789762 18.2567539 -143.1321349 61 62 63 64 65 66 32.7971408 95.0602987 -68.3081224 -95.2028592 -119.6765434 -61.3607539 67 68 69 70 71 72 -190.1134113 -121.4467446 -86.4467446 -99.1689668 -158.8911890 -189.2800779 73 74 75 76 77 78 -72.3508022 -7.0876443 -41.4560654 1.6491978 29.1755136 119.4913031 79 80 81 82 83 84 159.7386458 16.4053124 13.4053124 74.6830902 146.9608680 -18.4280209 85 86 87 88 89 90 -45.4987452 11.7644127 -21.6040083 156.5012548 -35.9724294 124.3433601 91 92 93 94 95 96 242.5907028 31.2573694 299.2573694 154.5351472 161.8129250 984.4240361 97 98 99 100 101 102 -203.9105071 -65.6473492 -20.0157702 45.0894929 70.6158087 103.9315982 103 104 105 106 107 108 62.1789409 121.8456075 80.8456075 56.1233853 52.4011631 19.0122742 109 110 111 112 113 114 27.9415499 51.2047078 245.8362868 135.9415499 203.4678657 277.7836552 115 116 117 118 119 120 233.0309979 234.6976645 198.6976645 99.9754423 234.2532201 46.8643312 121 122 123 124 125 126 188.7936069 186.0567648 123.6883438 0.7936069 245.3199227 -35.3642878 127 128 129 130 131 132 -4.1169451 121.5497216 -46.4502784 7.8274993 -0.8947229 -127.2836118 133 134 135 136 137 138 143.6456640 -27.0911781 -48.4595992 -50.3543360 -15.8280203 -100.5122308 139 140 141 142 143 144 3.7351119 -54.5982214 -167.5982214 47.6795563 -79.0426659 -122.4315548 145 146 147 148 149 150 29.4977210 -34.2391211 -142.6075422 -13.5022790 -63.9759632 -180.6601738 151 152 153 154 155 156 -73.4128311 -100.7461644 -127.7461644 -152.4683866 -128.1906089 -131.5794977 157 158 159 160 161 162 -70.6502220 -153.3870641 -116.7554852 2.3497780 -133.1239062 -106.8081167 163 164 165 166 167 168 -48.5607741 -67.8941074 -13.8941074 -95.6163296 -108.3385518 -101.7274407 169 170 171 172 173 174 23.2018350 -122.5350071 61.0965718 -55.7981650 -61.2718492 -1.9560597 175 176 177 178 179 180 -159.7087170 -21.0420504 -18.0420504 -49.7642726 28.5135052 -156.8753837 181 182 183 184 185 186 -31.9461080 -29.6829501 -122.0513711 84.0538920 67.5802078 -48.1040027 187 188 189 190 191 192 -52.8566600 96.8100066 49.8100066 47.0877844 18.3655622 19.9766733 193 194 195 196 197 198 160.9059490 95.1691069 180.8006859 -91.0940510 201.4322648 20.7480543 199 200 201 202 203 204 42.9953970 20.6620637 13.6620637 7.9398414 39.2176192 -5.1712697 205 206 207 208 209 210 2.7580061 -12.9788361 38.6527429 -67.2419939 -105.7156782 -33.3998887 211 212 213 214 215 216 -43.1525460 9.5141207 -124.4858793 12.7918984 -3.9303238 -100.3192127 217 218 219 220 221 222 106.6100631 40.8732210 -235.4952001 222.6100631 -180.8636211 -145.5478317 > postscript(file="/var/www/html/rcomp/tmp/62fij1229178400.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 = 222 Frequency = 1 lag(myerror, k = 1) myerror 0 128.5368557 NA 1 202.8000136 128.5368557 2 163.4315926 202.8000136 3 54.5368557 163.4315926 4 45.0631715 54.5368557 5 -52.6210390 45.0631715 6 1.6263037 -52.6210390 7 -142.7070297 1.6263037 8 -91.7070297 -142.7070297 9 -74.4292519 -91.7070297 10 -61.1514741 -74.4292519 11 -127.5403630 -61.1514741 12 -115.6110873 -127.5403630 13 -79.3479294 -115.6110873 14 -132.7163504 -79.3479294 15 -124.6110873 -132.7163504 16 -105.0847715 -124.6110873 17 -154.7689820 -105.0847715 18 -68.5216393 -154.7689820 19 -181.8549727 -68.5216393 20 -105.8549727 -181.8549727 21 -83.5771949 -105.8549727 22 -152.2994171 -83.5771949 23 -41.6883060 -152.2994171 24 -95.7590303 -41.6883060 25 10.5041276 -95.7590303 26 50.1357066 10.5041276 27 -75.7590303 50.1357066 28 -82.2327145 -75.7590303 29 30.0830750 -82.2327145 30 23.3304177 30.0830750 31 42.9970844 23.3304177 32 -23.0029156 42.9970844 33 -94.7251379 -23.0029156 34 32.5526399 -94.7251379 35 261.1637510 32.5526399 36 -199.9069732 261.1637510 37 -163.6438153 -199.9069732 38 0.9877636 -163.6438153 39 -117.9069732 0.9877636 40 -36.3806575 -117.9069732 41 21.9351320 -36.3806575 42 -167.8175253 21.9351320 43 -14.1508586 -167.8175253 44 69.8491414 -14.1508586 45 -51.8730808 69.8491414 46 -39.5953031 -51.8730808 47 -65.9841920 -39.5953031 48 -9.0549162 -65.9841920 49 2.2082417 -9.0549162 50 84.8398206 2.2082417 51 -12.0549162 84.8398206 52 77.4713996 -12.0549162 53 222.7871890 77.4713996 54 39.0345317 222.7871890 55 8.7011984 39.0345317 56 79.7011984 8.7011984 57 192.9789762 79.7011984 58 18.2567539 192.9789762 59 -143.1321349 18.2567539 60 32.7971408 -143.1321349 61 95.0602987 32.7971408 62 -68.3081224 95.0602987 63 -95.2028592 -68.3081224 64 -119.6765434 -95.2028592 65 -61.3607539 -119.6765434 66 -190.1134113 -61.3607539 67 -121.4467446 -190.1134113 68 -86.4467446 -121.4467446 69 -99.1689668 -86.4467446 70 -158.8911890 -99.1689668 71 -189.2800779 -158.8911890 72 -72.3508022 -189.2800779 73 -7.0876443 -72.3508022 74 -41.4560654 -7.0876443 75 1.6491978 -41.4560654 76 29.1755136 1.6491978 77 119.4913031 29.1755136 78 159.7386458 119.4913031 79 16.4053124 159.7386458 80 13.4053124 16.4053124 81 74.6830902 13.4053124 82 146.9608680 74.6830902 83 -18.4280209 146.9608680 84 -45.4987452 -18.4280209 85 11.7644127 -45.4987452 86 -21.6040083 11.7644127 87 156.5012548 -21.6040083 88 -35.9724294 156.5012548 89 124.3433601 -35.9724294 90 242.5907028 124.3433601 91 31.2573694 242.5907028 92 299.2573694 31.2573694 93 154.5351472 299.2573694 94 161.8129250 154.5351472 95 984.4240361 161.8129250 96 -203.9105071 984.4240361 97 -65.6473492 -203.9105071 98 -20.0157702 -65.6473492 99 45.0894929 -20.0157702 100 70.6158087 45.0894929 101 103.9315982 70.6158087 102 62.1789409 103.9315982 103 121.8456075 62.1789409 104 80.8456075 121.8456075 105 56.1233853 80.8456075 106 52.4011631 56.1233853 107 19.0122742 52.4011631 108 27.9415499 19.0122742 109 51.2047078 27.9415499 110 245.8362868 51.2047078 111 135.9415499 245.8362868 112 203.4678657 135.9415499 113 277.7836552 203.4678657 114 233.0309979 277.7836552 115 234.6976645 233.0309979 116 198.6976645 234.6976645 117 99.9754423 198.6976645 118 234.2532201 99.9754423 119 46.8643312 234.2532201 120 188.7936069 46.8643312 121 186.0567648 188.7936069 122 123.6883438 186.0567648 123 0.7936069 123.6883438 124 245.3199227 0.7936069 125 -35.3642878 245.3199227 126 -4.1169451 -35.3642878 127 121.5497216 -4.1169451 128 -46.4502784 121.5497216 129 7.8274993 -46.4502784 130 -0.8947229 7.8274993 131 -127.2836118 -0.8947229 132 143.6456640 -127.2836118 133 -27.0911781 143.6456640 134 -48.4595992 -27.0911781 135 -50.3543360 -48.4595992 136 -15.8280203 -50.3543360 137 -100.5122308 -15.8280203 138 3.7351119 -100.5122308 139 -54.5982214 3.7351119 140 -167.5982214 -54.5982214 141 47.6795563 -167.5982214 142 -79.0426659 47.6795563 143 -122.4315548 -79.0426659 144 29.4977210 -122.4315548 145 -34.2391211 29.4977210 146 -142.6075422 -34.2391211 147 -13.5022790 -142.6075422 148 -63.9759632 -13.5022790 149 -180.6601738 -63.9759632 150 -73.4128311 -180.6601738 151 -100.7461644 -73.4128311 152 -127.7461644 -100.7461644 153 -152.4683866 -127.7461644 154 -128.1906089 -152.4683866 155 -131.5794977 -128.1906089 156 -70.6502220 -131.5794977 157 -153.3870641 -70.6502220 158 -116.7554852 -153.3870641 159 2.3497780 -116.7554852 160 -133.1239062 2.3497780 161 -106.8081167 -133.1239062 162 -48.5607741 -106.8081167 163 -67.8941074 -48.5607741 164 -13.8941074 -67.8941074 165 -95.6163296 -13.8941074 166 -108.3385518 -95.6163296 167 -101.7274407 -108.3385518 168 23.2018350 -101.7274407 169 -122.5350071 23.2018350 170 61.0965718 -122.5350071 171 -55.7981650 61.0965718 172 -61.2718492 -55.7981650 173 -1.9560597 -61.2718492 174 -159.7087170 -1.9560597 175 -21.0420504 -159.7087170 176 -18.0420504 -21.0420504 177 -49.7642726 -18.0420504 178 28.5135052 -49.7642726 179 -156.8753837 28.5135052 180 -31.9461080 -156.8753837 181 -29.6829501 -31.9461080 182 -122.0513711 -29.6829501 183 84.0538920 -122.0513711 184 67.5802078 84.0538920 185 -48.1040027 67.5802078 186 -52.8566600 -48.1040027 187 96.8100066 -52.8566600 188 49.8100066 96.8100066 189 47.0877844 49.8100066 190 18.3655622 47.0877844 191 19.9766733 18.3655622 192 160.9059490 19.9766733 193 95.1691069 160.9059490 194 180.8006859 95.1691069 195 -91.0940510 180.8006859 196 201.4322648 -91.0940510 197 20.7480543 201.4322648 198 42.9953970 20.7480543 199 20.6620637 42.9953970 200 13.6620637 20.6620637 201 7.9398414 13.6620637 202 39.2176192 7.9398414 203 -5.1712697 39.2176192 204 2.7580061 -5.1712697 205 -12.9788361 2.7580061 206 38.6527429 -12.9788361 207 -67.2419939 38.6527429 208 -105.7156782 -67.2419939 209 -33.3998887 -105.7156782 210 -43.1525460 -33.3998887 211 9.5141207 -43.1525460 212 -124.4858793 9.5141207 213 12.7918984 -124.4858793 214 -3.9303238 12.7918984 215 -100.3192127 -3.9303238 216 106.6100631 -100.3192127 217 40.8732210 106.6100631 218 -235.4952001 40.8732210 219 222.6100631 -235.4952001 220 -180.8636211 222.6100631 221 -145.5478317 -180.8636211 222 NA -145.5478317 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 202.8000136 128.5368557 [2,] 163.4315926 202.8000136 [3,] 54.5368557 163.4315926 [4,] 45.0631715 54.5368557 [5,] -52.6210390 45.0631715 [6,] 1.6263037 -52.6210390 [7,] -142.7070297 1.6263037 [8,] -91.7070297 -142.7070297 [9,] -74.4292519 -91.7070297 [10,] -61.1514741 -74.4292519 [11,] -127.5403630 -61.1514741 [12,] -115.6110873 -127.5403630 [13,] -79.3479294 -115.6110873 [14,] -132.7163504 -79.3479294 [15,] -124.6110873 -132.7163504 [16,] -105.0847715 -124.6110873 [17,] -154.7689820 -105.0847715 [18,] -68.5216393 -154.7689820 [19,] -181.8549727 -68.5216393 [20,] -105.8549727 -181.8549727 [21,] -83.5771949 -105.8549727 [22,] -152.2994171 -83.5771949 [23,] -41.6883060 -152.2994171 [24,] -95.7590303 -41.6883060 [25,] 10.5041276 -95.7590303 [26,] 50.1357066 10.5041276 [27,] -75.7590303 50.1357066 [28,] -82.2327145 -75.7590303 [29,] 30.0830750 -82.2327145 [30,] 23.3304177 30.0830750 [31,] 42.9970844 23.3304177 [32,] -23.0029156 42.9970844 [33,] -94.7251379 -23.0029156 [34,] 32.5526399 -94.7251379 [35,] 261.1637510 32.5526399 [36,] -199.9069732 261.1637510 [37,] -163.6438153 -199.9069732 [38,] 0.9877636 -163.6438153 [39,] -117.9069732 0.9877636 [40,] -36.3806575 -117.9069732 [41,] 21.9351320 -36.3806575 [42,] -167.8175253 21.9351320 [43,] -14.1508586 -167.8175253 [44,] 69.8491414 -14.1508586 [45,] -51.8730808 69.8491414 [46,] -39.5953031 -51.8730808 [47,] -65.9841920 -39.5953031 [48,] -9.0549162 -65.9841920 [49,] 2.2082417 -9.0549162 [50,] 84.8398206 2.2082417 [51,] -12.0549162 84.8398206 [52,] 77.4713996 -12.0549162 [53,] 222.7871890 77.4713996 [54,] 39.0345317 222.7871890 [55,] 8.7011984 39.0345317 [56,] 79.7011984 8.7011984 [57,] 192.9789762 79.7011984 [58,] 18.2567539 192.9789762 [59,] -143.1321349 18.2567539 [60,] 32.7971408 -143.1321349 [61,] 95.0602987 32.7971408 [62,] -68.3081224 95.0602987 [63,] -95.2028592 -68.3081224 [64,] -119.6765434 -95.2028592 [65,] -61.3607539 -119.6765434 [66,] -190.1134113 -61.3607539 [67,] -121.4467446 -190.1134113 [68,] -86.4467446 -121.4467446 [69,] -99.1689668 -86.4467446 [70,] -158.8911890 -99.1689668 [71,] -189.2800779 -158.8911890 [72,] -72.3508022 -189.2800779 [73,] -7.0876443 -72.3508022 [74,] -41.4560654 -7.0876443 [75,] 1.6491978 -41.4560654 [76,] 29.1755136 1.6491978 [77,] 119.4913031 29.1755136 [78,] 159.7386458 119.4913031 [79,] 16.4053124 159.7386458 [80,] 13.4053124 16.4053124 [81,] 74.6830902 13.4053124 [82,] 146.9608680 74.6830902 [83,] -18.4280209 146.9608680 [84,] -45.4987452 -18.4280209 [85,] 11.7644127 -45.4987452 [86,] -21.6040083 11.7644127 [87,] 156.5012548 -21.6040083 [88,] -35.9724294 156.5012548 [89,] 124.3433601 -35.9724294 [90,] 242.5907028 124.3433601 [91,] 31.2573694 242.5907028 [92,] 299.2573694 31.2573694 [93,] 154.5351472 299.2573694 [94,] 161.8129250 154.5351472 [95,] 984.4240361 161.8129250 [96,] -203.9105071 984.4240361 [97,] -65.6473492 -203.9105071 [98,] -20.0157702 -65.6473492 [99,] 45.0894929 -20.0157702 [100,] 70.6158087 45.0894929 [101,] 103.9315982 70.6158087 [102,] 62.1789409 103.9315982 [103,] 121.8456075 62.1789409 [104,] 80.8456075 121.8456075 [105,] 56.1233853 80.8456075 [106,] 52.4011631 56.1233853 [107,] 19.0122742 52.4011631 [108,] 27.9415499 19.0122742 [109,] 51.2047078 27.9415499 [110,] 245.8362868 51.2047078 [111,] 135.9415499 245.8362868 [112,] 203.4678657 135.9415499 [113,] 277.7836552 203.4678657 [114,] 233.0309979 277.7836552 [115,] 234.6976645 233.0309979 [116,] 198.6976645 234.6976645 [117,] 99.9754423 198.6976645 [118,] 234.2532201 99.9754423 [119,] 46.8643312 234.2532201 [120,] 188.7936069 46.8643312 [121,] 186.0567648 188.7936069 [122,] 123.6883438 186.0567648 [123,] 0.7936069 123.6883438 [124,] 245.3199227 0.7936069 [125,] -35.3642878 245.3199227 [126,] -4.1169451 -35.3642878 [127,] 121.5497216 -4.1169451 [128,] -46.4502784 121.5497216 [129,] 7.8274993 -46.4502784 [130,] -0.8947229 7.8274993 [131,] -127.2836118 -0.8947229 [132,] 143.6456640 -127.2836118 [133,] -27.0911781 143.6456640 [134,] -48.4595992 -27.0911781 [135,] -50.3543360 -48.4595992 [136,] -15.8280203 -50.3543360 [137,] -100.5122308 -15.8280203 [138,] 3.7351119 -100.5122308 [139,] -54.5982214 3.7351119 [140,] -167.5982214 -54.5982214 [141,] 47.6795563 -167.5982214 [142,] -79.0426659 47.6795563 [143,] -122.4315548 -79.0426659 [144,] 29.4977210 -122.4315548 [145,] -34.2391211 29.4977210 [146,] -142.6075422 -34.2391211 [147,] -13.5022790 -142.6075422 [148,] -63.9759632 -13.5022790 [149,] -180.6601738 -63.9759632 [150,] -73.4128311 -180.6601738 [151,] -100.7461644 -73.4128311 [152,] -127.7461644 -100.7461644 [153,] -152.4683866 -127.7461644 [154,] -128.1906089 -152.4683866 [155,] -131.5794977 -128.1906089 [156,] -70.6502220 -131.5794977 [157,] -153.3870641 -70.6502220 [158,] -116.7554852 -153.3870641 [159,] 2.3497780 -116.7554852 [160,] -133.1239062 2.3497780 [161,] -106.8081167 -133.1239062 [162,] -48.5607741 -106.8081167 [163,] -67.8941074 -48.5607741 [164,] -13.8941074 -67.8941074 [165,] -95.6163296 -13.8941074 [166,] -108.3385518 -95.6163296 [167,] -101.7274407 -108.3385518 [168,] 23.2018350 -101.7274407 [169,] -122.5350071 23.2018350 [170,] 61.0965718 -122.5350071 [171,] -55.7981650 61.0965718 [172,] -61.2718492 -55.7981650 [173,] -1.9560597 -61.2718492 [174,] -159.7087170 -1.9560597 [175,] -21.0420504 -159.7087170 [176,] -18.0420504 -21.0420504 [177,] -49.7642726 -18.0420504 [178,] 28.5135052 -49.7642726 [179,] -156.8753837 28.5135052 [180,] -31.9461080 -156.8753837 [181,] -29.6829501 -31.9461080 [182,] -122.0513711 -29.6829501 [183,] 84.0538920 -122.0513711 [184,] 67.5802078 84.0538920 [185,] -48.1040027 67.5802078 [186,] -52.8566600 -48.1040027 [187,] 96.8100066 -52.8566600 [188,] 49.8100066 96.8100066 [189,] 47.0877844 49.8100066 [190,] 18.3655622 47.0877844 [191,] 19.9766733 18.3655622 [192,] 160.9059490 19.9766733 [193,] 95.1691069 160.9059490 [194,] 180.8006859 95.1691069 [195,] -91.0940510 180.8006859 [196,] 201.4322648 -91.0940510 [197,] 20.7480543 201.4322648 [198,] 42.9953970 20.7480543 [199,] 20.6620637 42.9953970 [200,] 13.6620637 20.6620637 [201,] 7.9398414 13.6620637 [202,] 39.2176192 7.9398414 [203,] -5.1712697 39.2176192 [204,] 2.7580061 -5.1712697 [205,] -12.9788361 2.7580061 [206,] 38.6527429 -12.9788361 [207,] -67.2419939 38.6527429 [208,] -105.7156782 -67.2419939 [209,] -33.3998887 -105.7156782 [210,] -43.1525460 -33.3998887 [211,] 9.5141207 -43.1525460 [212,] -124.4858793 9.5141207 [213,] 12.7918984 -124.4858793 [214,] -3.9303238 12.7918984 [215,] -100.3192127 -3.9303238 [216,] 106.6100631 -100.3192127 [217,] 40.8732210 106.6100631 [218,] -235.4952001 40.8732210 [219,] 222.6100631 -235.4952001 [220,] -180.8636211 222.6100631 [221,] -145.5478317 -180.8636211 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 202.8000136 128.5368557 2 163.4315926 202.8000136 3 54.5368557 163.4315926 4 45.0631715 54.5368557 5 -52.6210390 45.0631715 6 1.6263037 -52.6210390 7 -142.7070297 1.6263037 8 -91.7070297 -142.7070297 9 -74.4292519 -91.7070297 10 -61.1514741 -74.4292519 11 -127.5403630 -61.1514741 12 -115.6110873 -127.5403630 13 -79.3479294 -115.6110873 14 -132.7163504 -79.3479294 15 -124.6110873 -132.7163504 16 -105.0847715 -124.6110873 17 -154.7689820 -105.0847715 18 -68.5216393 -154.7689820 19 -181.8549727 -68.5216393 20 -105.8549727 -181.8549727 21 -83.5771949 -105.8549727 22 -152.2994171 -83.5771949 23 -41.6883060 -152.2994171 24 -95.7590303 -41.6883060 25 10.5041276 -95.7590303 26 50.1357066 10.5041276 27 -75.7590303 50.1357066 28 -82.2327145 -75.7590303 29 30.0830750 -82.2327145 30 23.3304177 30.0830750 31 42.9970844 23.3304177 32 -23.0029156 42.9970844 33 -94.7251379 -23.0029156 34 32.5526399 -94.7251379 35 261.1637510 32.5526399 36 -199.9069732 261.1637510 37 -163.6438153 -199.9069732 38 0.9877636 -163.6438153 39 -117.9069732 0.9877636 40 -36.3806575 -117.9069732 41 21.9351320 -36.3806575 42 -167.8175253 21.9351320 43 -14.1508586 -167.8175253 44 69.8491414 -14.1508586 45 -51.8730808 69.8491414 46 -39.5953031 -51.8730808 47 -65.9841920 -39.5953031 48 -9.0549162 -65.9841920 49 2.2082417 -9.0549162 50 84.8398206 2.2082417 51 -12.0549162 84.8398206 52 77.4713996 -12.0549162 53 222.7871890 77.4713996 54 39.0345317 222.7871890 55 8.7011984 39.0345317 56 79.7011984 8.7011984 57 192.9789762 79.7011984 58 18.2567539 192.9789762 59 -143.1321349 18.2567539 60 32.7971408 -143.1321349 61 95.0602987 32.7971408 62 -68.3081224 95.0602987 63 -95.2028592 -68.3081224 64 -119.6765434 -95.2028592 65 -61.3607539 -119.6765434 66 -190.1134113 -61.3607539 67 -121.4467446 -190.1134113 68 -86.4467446 -121.4467446 69 -99.1689668 -86.4467446 70 -158.8911890 -99.1689668 71 -189.2800779 -158.8911890 72 -72.3508022 -189.2800779 73 -7.0876443 -72.3508022 74 -41.4560654 -7.0876443 75 1.6491978 -41.4560654 76 29.1755136 1.6491978 77 119.4913031 29.1755136 78 159.7386458 119.4913031 79 16.4053124 159.7386458 80 13.4053124 16.4053124 81 74.6830902 13.4053124 82 146.9608680 74.6830902 83 -18.4280209 146.9608680 84 -45.4987452 -18.4280209 85 11.7644127 -45.4987452 86 -21.6040083 11.7644127 87 156.5012548 -21.6040083 88 -35.9724294 156.5012548 89 124.3433601 -35.9724294 90 242.5907028 124.3433601 91 31.2573694 242.5907028 92 299.2573694 31.2573694 93 154.5351472 299.2573694 94 161.8129250 154.5351472 95 984.4240361 161.8129250 96 -203.9105071 984.4240361 97 -65.6473492 -203.9105071 98 -20.0157702 -65.6473492 99 45.0894929 -20.0157702 100 70.6158087 45.0894929 101 103.9315982 70.6158087 102 62.1789409 103.9315982 103 121.8456075 62.1789409 104 80.8456075 121.8456075 105 56.1233853 80.8456075 106 52.4011631 56.1233853 107 19.0122742 52.4011631 108 27.9415499 19.0122742 109 51.2047078 27.9415499 110 245.8362868 51.2047078 111 135.9415499 245.8362868 112 203.4678657 135.9415499 113 277.7836552 203.4678657 114 233.0309979 277.7836552 115 234.6976645 233.0309979 116 198.6976645 234.6976645 117 99.9754423 198.6976645 118 234.2532201 99.9754423 119 46.8643312 234.2532201 120 188.7936069 46.8643312 121 186.0567648 188.7936069 122 123.6883438 186.0567648 123 0.7936069 123.6883438 124 245.3199227 0.7936069 125 -35.3642878 245.3199227 126 -4.1169451 -35.3642878 127 121.5497216 -4.1169451 128 -46.4502784 121.5497216 129 7.8274993 -46.4502784 130 -0.8947229 7.8274993 131 -127.2836118 -0.8947229 132 143.6456640 -127.2836118 133 -27.0911781 143.6456640 134 -48.4595992 -27.0911781 135 -50.3543360 -48.4595992 136 -15.8280203 -50.3543360 137 -100.5122308 -15.8280203 138 3.7351119 -100.5122308 139 -54.5982214 3.7351119 140 -167.5982214 -54.5982214 141 47.6795563 -167.5982214 142 -79.0426659 47.6795563 143 -122.4315548 -79.0426659 144 29.4977210 -122.4315548 145 -34.2391211 29.4977210 146 -142.6075422 -34.2391211 147 -13.5022790 -142.6075422 148 -63.9759632 -13.5022790 149 -180.6601738 -63.9759632 150 -73.4128311 -180.6601738 151 -100.7461644 -73.4128311 152 -127.7461644 -100.7461644 153 -152.4683866 -127.7461644 154 -128.1906089 -152.4683866 155 -131.5794977 -128.1906089 156 -70.6502220 -131.5794977 157 -153.3870641 -70.6502220 158 -116.7554852 -153.3870641 159 2.3497780 -116.7554852 160 -133.1239062 2.3497780 161 -106.8081167 -133.1239062 162 -48.5607741 -106.8081167 163 -67.8941074 -48.5607741 164 -13.8941074 -67.8941074 165 -95.6163296 -13.8941074 166 -108.3385518 -95.6163296 167 -101.7274407 -108.3385518 168 23.2018350 -101.7274407 169 -122.5350071 23.2018350 170 61.0965718 -122.5350071 171 -55.7981650 61.0965718 172 -61.2718492 -55.7981650 173 -1.9560597 -61.2718492 174 -159.7087170 -1.9560597 175 -21.0420504 -159.7087170 176 -18.0420504 -21.0420504 177 -49.7642726 -18.0420504 178 28.5135052 -49.7642726 179 -156.8753837 28.5135052 180 -31.9461080 -156.8753837 181 -29.6829501 -31.9461080 182 -122.0513711 -29.6829501 183 84.0538920 -122.0513711 184 67.5802078 84.0538920 185 -48.1040027 67.5802078 186 -52.8566600 -48.1040027 187 96.8100066 -52.8566600 188 49.8100066 96.8100066 189 47.0877844 49.8100066 190 18.3655622 47.0877844 191 19.9766733 18.3655622 192 160.9059490 19.9766733 193 95.1691069 160.9059490 194 180.8006859 95.1691069 195 -91.0940510 180.8006859 196 201.4322648 -91.0940510 197 20.7480543 201.4322648 198 42.9953970 20.7480543 199 20.6620637 42.9953970 200 13.6620637 20.6620637 201 7.9398414 13.6620637 202 39.2176192 7.9398414 203 -5.1712697 39.2176192 204 2.7580061 -5.1712697 205 -12.9788361 2.7580061 206 38.6527429 -12.9788361 207 -67.2419939 38.6527429 208 -105.7156782 -67.2419939 209 -33.3998887 -105.7156782 210 -43.1525460 -33.3998887 211 9.5141207 -43.1525460 212 -124.4858793 9.5141207 213 12.7918984 -124.4858793 214 -3.9303238 12.7918984 215 -100.3192127 -3.9303238 216 106.6100631 -100.3192127 217 40.8732210 106.6100631 218 -235.4952001 40.8732210 219 222.6100631 -235.4952001 220 -180.8636211 222.6100631 221 -145.5478317 -180.8636211 > 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/7gqpq1229178400.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/83qb41229178400.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/9mtnz1229178400.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/101v8k1229178400.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/11bymd1229178400.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/12u6c61229178400.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/13vpz01229178400.tab") > > system("convert tmp/1wuhb1229178400.ps tmp/1wuhb1229178400.png") > system("convert tmp/21cun1229178400.ps tmp/21cun1229178400.png") > system("convert tmp/30rie1229178400.ps tmp/30rie1229178400.png") > system("convert tmp/4rzl01229178400.ps tmp/4rzl01229178400.png") > system("convert tmp/5rpxt1229178400.ps tmp/5rpxt1229178400.png") > system("convert tmp/62fij1229178400.ps tmp/62fij1229178400.png") > system("convert tmp/7gqpq1229178400.ps tmp/7gqpq1229178400.png") > system("convert tmp/83qb41229178400.ps tmp/83qb41229178400.png") > system("convert tmp/9mtnz1229178400.ps tmp/9mtnz1229178400.png") > > > proc.time() user system elapsed 2.850 1.684 3.350