Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
1418 210907 56 396 81 3 79 30 112285 24188 146283 1 869 120982 56 297 55 4 58 28 84786 18273 98364 1 1530 176508 54 559 50 12 60 38 83123 14130 86146 1 2172 179321 89 967 125 2 108 30 101193 32287 96933 1 901 123185 40 270 40 1 49 22 38361 8654 79234 1 463 52746 25 143 37 3 0 26 68504 9245 42551 1 3201 385534 92 1562 63 0 121 25 119182 33251 195663 1 371 33170 18 109 44 0 1 18 22807 1271 6853 1 1192 101645 63 371 88 0 20 11 17140 5279 21529 0 1583 149061 44 656 66 5 43 26 116174 27101 95757 1 1439 165446 33 511 57 0 69 25 57635 16373 85584 1 1764 237213 84 655 74 0 78 38 66198 19716 143983 1 1495 173326 88 465 49 7 86 44 71701 17753 75851 1 1373 133131 55 525 52 7 44 30 57793 9028 59238 1 2187 258873 60 885 88 3 104 40 80444 18653 93163 1 1491 180083 66 497 36 9 63 34 53855 8828 96037 1 4041 324799 154 1436 108 0 158 47 97668 29498 151511 1 1706 230964 53 612 43 4 102 30 133824 27563 136368 1 2152 236785 119 865 75 3 77 31 101481 18293 112642 1 1036 135473 41 385 32 0 82 23 99645 22530 94728 1 1882 202925 61 567 44 7 115 36 114789 15977 105499 1 1929 215147 58 639 85 0 101 36 99052 35082 121527 1 2242 344297 75 963 86 1 80 30 67654 16116 127766 1 1220 153935 33 398 56 5 50 25 65553 15849 98958 1 1289 132943 40 410 50 7 83 39 97500 16026 77900 1 2515 174724 92 966 135 0 123 34 69112 26569 85646 1 2147 174415 100 801 63 0 73 31 82753 24785 98579 1 2352 225548 112 892 81 5 81 31 85323 17569 130767 1 1638 223632 73 513 52 0 105 33 72654 23825 131741 1 1222 124817 40 469 44 0 47 25 30727 7869 53907 1 1812 221698 45 683 113 0 105 33 77873 14975 178812 1 1677 210767 60 643 39 3 94 35 117478 37791 146761 1 1579 170266 62 535 73 4 44 42 74007 9605 82036 1 1731 260561 75 625 48 1 114 43 90183 27295 163253 1 807 84853 31 264 33 4 38 30 61542 2746 27032 1 2452 294424 77 992 59 2 107 33 101494 34461 171975 1 829 101011 34 238 41 0 30 13 27570 8098 65990 0 1940 215641 46 818 69 0 71 32 55813 4787 86572 1 2662 325107 99 937 64 0 84 36 79215 24919 159676 1 186 7176 17 70 1 0 0 0 1423 603 1929 0 1499 167542 66 507 59 2 59 28 55461 16329 85371 1 865 106408 30 260 32 1 33 14 31081 12558 58391 1 1793 96560 76 503 129 0 42 17 22996 7784 31580 0 2527 265769 146 927 37 2 96 32 83122 28522 136815 1 2747 269651 67 1269 31 10 106 30 70106 22265 120642 1 1324 149112 56 537 65 6 56 35 60578 14459 69107 1 2702 175824 107 910 107 0 57 20 39992 14526 50495 0 1383 152871 58 532 74 5 59 28 79892 22240 108016 1 1179 111665 34 345 54 4 39 28 49810 11802 46341 1 2099 116408 61 918 76 1 34 39 71570 7623 78348 1 4308 362301 119 1635 715 2 76 34 100708 11912 79336 1 918 78800 42 330 57 2 20 26 33032 7935 56968 1 1831 183167 66 557 66 0 91 39 82875 18220 93176 1 3373 277965 89 1178 106 8 115 39 139077 19199 161632 1 1713 150629 44 740 54 3 85 33 71595 19918 87850 1 1438 168809 66 452 32 0 76 28 72260 21884 127969 1 496 24188 24 218 20 0 8 4 5950 2694 15049 1 2253 329267 259 764 71 8 79 39 115762 15808 155135 1 744 65029 17 255 21 5 21 18 32551 3597 25109 1 1161 101097 64 454 70 3 30 14 31701 5296 45824 1 2352 218946 41 866 112 1 76 29 80670 25239 102996 1 2144 244052 68 574 66 5 101 44 143558 29801 160604 1 4691 341570 168 1276 190 1 94 21 117105 18450 158051 0 1112 103597 43 379 66 1 27 16 23789 7132 44547 0 2694 233328 132 825 165 5 92 28 120733 34861 162647 1 1973 256462 105 798 56 0 123 35 105195 35940 174141 1 1769 206161 71 663 61 12 75 28 73107 16688 60622 1 3148 311473 112 1069 53 8 128 38 132068 24683 179566 1 2474 235800 94 921 127 8 105 23 149193 46230 184301 1 2084 177939 82 858 63 8 55 36 46821 10387 75661 1 1954 207176 70 711 38 8 56 32 87011 21436 96144 1 1226 196553 57 503 50 2 41 29 95260 30546 129847 1 1389 174184 53 382 52 0 72 25 55183 19746 117286 1 1496 143246 103 464 42 5 67 27 106671 15977 71180 1 2269 187559 121 717 76 8 75 36 73511 22583 109377 1 1833 187681 62 690 67 2 114 28 92945 17274 85298 1 1268 119016 52 462 50 5 118 23 78664 16469 73631 1 1943 182192 52 657 53 12 77 40 70054 14251 86767 1 893 73566 32 385 39 6 22 23 22618 3007 23824 1 1762 194979 62 577 50 7 66 40 74011 16851 93487 1 1403 167488 45 619 77 2 69 28 83737 21113 82981 1 1425 143756 46 479 57 0 105 34 69094 17401 73815 1 1857 275541 63 817 73 4 116 33 93133 23958 94552 1 1840 243199 75 752 34 3 88 28 95536 23567 132190 1 1502 182999 88 430 39 6 73 34 225920 13065 128754 1 1441 135649 46 451 46 2 99 30 62133 15358 66363 1 1420 152299 53 537 63 0 62 33 61370 14587 67808 1 1416 120221 37 519 35 1 53 22 43836 12770 61724 1 2970 346485 90 1000 106 0 118 38 106117 24021 131722 1 1317 145790 63 637 43 5 30 26 38692 9648 68580 1 1644 193339 78 465 47 2 100 35 84651 20537 106175 1 870 80953 25 437 31 0 49 8 56622 7905 55792 1 1654 122774 45 711 162 0 24 24 15986 4527 25157 1 1054 130585 46 299 57 5 67 29 95364 30495 76669 1 937 112611 41 248 36 0 46 20 26706 7117 57283 0 3004 286468 144 1162 263 1 57 29 89691 17719 105805 1 2008 241066 82 714 78 0 75 45 67267 27056 129484 1 2547 148446 91 905 63 1 135 37 126846 33473 72413 1 1885 204713 71 649 54 1 68 33 41140 9758 87831 1 1626 182079 63 512 63 2 124 33 102860 21115 96971 1 1468 140344 53 472 77 6 33 25 51715 7236 71299 1 2445 220516 62 905 79 1 98 32 55801 13790 77494 1 1964 243060 63 786 110 4 58 29 111813 32902 120336 1 1381 162765 32 489 56 2 68 28 120293 25131 93913 1 1369 182613 39 479 56 3 81 28 138599 30910 136048 1 1659 232138 62 617 43 0 131 31 161647 35947 181248 1 2888 265318 117 925 111 10 110 52 115929 29848 146123 1 1290 85574 34 351 71 0 37 21 24266 6943 32036 0 2845 310839 92 1144 62 9 130 24 162901 42705 186646 1 1982 225060 93 669 56 7 93 41 109825 31808 102255 1 1904 232317 54 707 74 0 118 33 129838 26675 168237 1 1391 144966 144 458 60 0 39 32 37510 8435 64219 1 602 43287 14 214 43 4 13 19 43750 7409 19630 1 1743 155754 61 599 68 4 74 20 40652 14993 76825 1 1559 164709 109 572 53 0 81 31 87771 36867 115338 1 2014 201940 38 897 87 0 109 31 85872 33835 109427 1 2143 235454 73 819 46 0 151 32 89275 24164 118168 1 2146 220801 75 720 105 1 51 18 44418 12607 84845 0 874 99466 50 273 32 0 28 23 192565 22609 153197 1 1590 92661 61 508 133 1 40 17 35232 5892 29877 0 1590 133328 55 506 79 0 56 20 40909 17014 63506 0 1210 61361 77 451 51 0 27 12 13294 5394 22445 0 2072 125930 75 699 207 4 37 17 32387 9178 47695 0 1281 100750 72 407 67 0 83 30 140867 6440 68370 1 1401 224549 50 465 47 4 54 31 120662 21916 146304 1 834 82316 32 245 34 4 27 10 21233 4011 38233 0 1105 102010 53 370 66 3 28 13 44332 5818 42071 0 1272 101523 42 316 76 0 59 22 61056 18647 50517 0 1944 243511 71 603 65 0 133 42 101338 20556 103950 1 391 22938 10 154 9 0 12 1 1168 238 5841 1 761 41566 35 229 42 5 0 9 13497 70 2341 0 1605 152474 65 577 45 0 106 32 65567 22392 84396 1 530 61857 25 192 25 4 23 11 25162 3913 24610 1 1988 99923 66 617 115 0 44 25 32334 12237 35753 0 1386 132487 41 411 97 0 71 36 40735 8388 55515 1 2395 317394 86 975 53 1 116 31 91413 22120 209056 1 387 21054 16 146 2 0 4 0 855 338 6622 1 1742 209641 42 705 52 5 62 24 97068 11727 115814 1 620 22648 19 184 44 0 12 13 44339 3704 11609 0 449 31414 19 200 22 0 18 8 14116 3988 13155 1 800 46698 45 274 35 0 14 13 10288 3030 18274 0 1684 131698 65 502 74 0 60 19 65622 13520 72875 0 1050 91735 35 382 103 0 7 18 16563 1421 10112 0 2699 244749 95 964 144 2 98 33 76643 20923 142775 1 1606 184510 49 537 60 7 64 40 110681 20237 68847 1 1502 79863 37 438 134 1 29 22 29011 3219 17659 0 1204 128423 64 369 89 8 32 38 92696 3769 20112 1 1138 97839 38 417 42 2 25 24 94785 12252 61023 1 568 38214 34 276 52 0 16 8 8773 1888 13983 1 1459 151101 32 514 98 2 48 35 83209 14497 65176 1 2158 272458 65 822 99 0 100 43 93815 28864 132432 1 1111 172494 52 389 52 0 46 43 86687 21721 112494 1 1421 108043 62 466 29 1 45 14 34553 4821 45109 0 2833 328107 65 1255 125 3 129 41 105547 33644 170875 1 1955 250579 83 694 106 0 130 38 103487 15923 180759 1 2922 351067 95 1024 95 3 136 45 213688 42935 214921 1 1002 158015 29 400 40 0 59 31 71220 18864 100226 1 1060 98866 18 397 140 0 25 13 23517 4977 32043 0 956 85439 33 350 43 0 32 28 56926 7785 54454 1 2186 229242 247 719 128 4 63 31 91721 17939 78876 1 3604 351619 139 1277 142 4 95 40 115168 23436 170745 1 1035 84207 29 356 73 11 14 30 111194 325 6940 1 1417 120445 118 457 72 0 36 16 51009 13539 49025 0 3261 324598 110 1402 128 0 113 37 135777 34538 122037 1 1587 131069 67 600 61 4 47 30 51513 12198 53782 1 1424 204271 42 480 73 0 92 35 74163 26924 127748 1 1701 165543 65 595 148 1 70 32 51633 12716 86839 1 1249 141722 94 436 64 0 19 27 75345 8172 44830 1 946 116048 64 230 45 0 50 20 33416 10855 77395 0 1926 250047 81 651 58 0 41 18 83305 11932 89324 0 3352 299775 95 1367 97 9 91 31 98952 14300 103300 1 1641 195838 67 564 50 1 111 31 102372 25515 112283 1 2035 173260 63 716 37 3 41 21 37238 2805 10901 1 2312 254488 83 747 50 10 120 39 103772 29402 120691 1 1369 104389 45 467 105 5 135 41 123969 16440 58106 1 1577 136084 30 671 69 0 27 13 27142 11221 57140 0 2201 199476 70 861 46 2 87 32 135400 28732 122422 1 961 92499 32 319 57 0 25 18 21399 5250 25899 0 1900 224330 83 612 52 1 131 39 130115 28608 139296 1 1254 135781 31 433 98 2 45 14 24874 8092 52678 0 1335 74408 67 434 61 4 29 7 34988 4473 23853 0 1597 81240 66 503 89 0 58 17 45549 1572 17306 0 207 14688 10 85 0 0 4 0 6023 2065 7953 1 1645 181633 70 564 48 2 47 30 64466 14817 89455 1 2429 271856 103 824 91 1 109 37 54990 16714 147866 1 151 7199 5 74 0 0 7 0 1644 556 4245 1 474 46660 20 259 7 0 12 5 6179 2089 21509 1 141 17547 5 69 3 0 0 1 3926 2658 7670 1 1639 133368 36 535 54 1 37 16 32755 10695 66675 0 872 95227 34 239 70 0 37 32 34777 1669 14336 1 1318 152601 48 438 36 2 46 24 73224 16267 53608 1 1018 98146 40 459 37 0 15 17 27114 7768 30059 0 1383 79619 43 426 123 3 42 11 20760 7252 29668 0 1314 59194 31 288 247 6 7 24 37636 6387 22097 0 1335 139942 42 498 46 0 54 22 65461 18715 96841 0 1403 118612 46 454 72 2 54 12 30080 7936 41907 0 910 72880 33 376 41 0 14 19 24094 8643 27080 0 616 65475 18 225 24 2 16 13 69008 7294 35885 0 1407 99643 55 555 45 1 33 17 54968 4570 41247 0 771 71965 35 252 33 1 32 15 46090 7185 28313 0 766 77272 59 208 27 2 21 16 27507 10058 36845 0 473 49289 19 130 36 1 15 24 10672 2342 16548 0 1376 135131 66 481 87 0 38 15 34029 8509 36134 0 1232 108446 60 389 90 1 22 17 46300 13275 55764 0 1521 89746 36 565 114 3 28 18 24760 6816 28910 0 572 44296 25 173 31 0 10 20 18779 1930 13339 0 1059 77648 47 278 45 0 31 16 21280 8086 25319 0 1544 181528 54 609 69 0 32 16 40662 10737 66956 0 1230 134019 53 422 51 0 32 18 28987 8033 47487 0 1206 124064 40 445 34 1 43 22 22827 7058 52785 0 1205 92630 40 387 60 4 27 8 18513 6782 44683 0 1255 121848 39 339 45 0 37 17 30594 5401 35619 0 613 52915 14 181 54 0 20 18 24006 6521 21920 0 721 81872 45 245 25 0 32 16 27913 10856 45608 0 1109 58981 36 384 38 7 0 23 42744 2154 7721 0 740 53515 28 212 52 2 5 22 12934 6117 20634 0 1126 60812 44 399 67 0 26 13 22574 5238 29788 0 728 56375 30 229 74 7 10 13 41385 4820 31931 0 689 65490 22 224 38 3 27 16 18653 5615 37754 0 592 80949 17 203 30 0 11 16 18472 4272 32505 0 995 76302 31 333 26 0 29 20 30976 8702 40557 0 1613 104011 55 384 67 6 25 22 63339 15340 94238 0 2048 98104 54 636 132 2 55 17 25568 8030 44197 0 705 67989 21 185 42 0 23 18 33747 9526 43228 0 301 30989 14 93 35 0 5 17 4154 1278 4103 0 1803 135458 81 581 118 3 43 12 19474 4236 44144 0 799 73504 35 248 68 0 23 7 35130 3023 32868 0 861 63123 43 304 43 1 34 17 39067 7196 27640 0 1186 61254 46 344 76 1 36 14 13310 3394 14063 0 1451 74914 30 407 64 0 35 23 65892 6371 28990 0 628 31774 23 170 48 1 0 17 4143 1574 4694 0 1161 81437 38 312 64 0 37 14 28579 9620 42648 0 1463 87186 54 507 56 0 28 15 51776 6978 64329 0 742 50090 20 224 71 0 16 17 21152 4911 21928 0 979 65745 53 340 75 0 26 21 38084 8645 25836 0 675 56653 45 168 39 0 38 18 27717 8987 22779 0 1241 158399 39 443 42 0 23 18 32928 5544 40820 0 676 46455 20 204 39 0 22 17 11342 3083 27530 0 1049 73624 24 367 93 0 30 17 19499 6909 32378 0 620 38395 31 210 38 0 16 16 16380 3189 10824 0 1081 91899 35 335 60 0 18 15 36874 6745 39613 0 1688 139526 151 364 71 0 28 21 48259 16724 60865 0 736 52164 52 178 52 0 32 16 16734 4850 19787 0 617 51567 30 206 27 2 21 14 28207 7025 20107 0 812 70551 31 279 59 0 23 15 30143 6047 36605 0 1051 84856 29 387 40 1 29 17 41369 7377 40961 0 1656 102538 57 490 79 1 50 15 45833 9078 48231 0 705 86678 40 238 44 0 12 15 29156 4605 39725 0 945 85709 44 343 65 0 21 10 35944 3238 21455 0 554 34662 25 232 10 0 18 6 36278 8100 23430 0 1597 150580 77 530 124 0 27 22 45588 9653 62991 0 982 99611 35 291 81 0 41 21 45097 8914 49363 0 222 19349 11 67 15 0 13 1 3895 786 9604 0 1212 99373 63 397 92 1 12 18 28394 6700 24552 0 1143 86230 44 467 42 0 21 17 18632 5788 31493 0 435 30837 19 178 10 0 8 4 2325 593 3439 0 532 31706 13 175 24 0 26 10 25139 4506 19555 0 882 89806 42 299 64 0 27 16 27975 6382 21228 0 608 62088 38 154 45 1 13 16 14483 5621 23177 0 459 40151 29 106 22 0 16 9 13127 3997 22094 0 578 27634 20 189 56 0 2 16 5839 520 2342 0 826 76990 27 194 94 0 42 17 24069 8891 38798 0 509 37460 20 135 19 0 5 7 3738 999 3255 0 717 54157 19 201 35 0 37 15 18625 7067 24261 0 637 49862 37 207 32 0 17 14 36341 4639 18511 0 857 84337 26 280 35 0 38 14 24548 5654 40798 0 830 64175 42 260 48 0 37 18 21792 6928 28893 0 652 59382 49 227 49 0 29 12 26263 1514 21425 0 707 119308 30 239 48 0 32 16 23686 9238 50276 0 954 76702 49 333 62 0 35 21 49303 8204 37643 0 1461 103425 67 428 96 1 17 19 25659 5926 30377 0 672 70344 28 230 45 0 20 16 28904 5785 27126 0 778 43410 19 292 63 0 7 1 2781 4 13 0 1141 104838 49 350 71 1 46 16 29236 5930 42097 0 680 62215 27 186 26 0 24 10 19546 3710 24451 0 1090 69304 30 326 48 6 40 19 22818 705 14335 0 616 53117 22 155 29 3 3 12 32689 443 5084 0 285 19764 12 75 19 1 10 2 5752 2416 9927 0 1145 86680 31 361 45 2 37 14 22197 7747 43527 0 733 84105 20 261 45 0 17 17 20055 5432 27184 0 888 77945 20 299 67 0 28 19 25272 4913 21610 0 849 89113 39 300 30 0 19 14 82206 2650 20484 0 1182 91005 29 450 36 3 29 11 32073 2370 20156 0 528 40248 16 183 34 1 8 4 5444 775 6012 0 642 64187 27 238 36 0 10 16 20154 5576 18475 0 947 50857 21 165 34 0 15 20 36944 1352 12645 0 819 56613 19 234 37 1 15 12 8019 3080 11017 0 757 62792 35 176 46 0 28 15 30884 10205 37623 0 894 72535 14 329 44 0 17 16 19540 6095 35873 0
Names of X columns:
pageviews timerfc logins compendiumviewsinfo compendiumviewspr sharedcompendiums bloggedcomputations compendiumsreviewed totalsize totalsizerevisions totalseconds course
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
library(lattice) library(lmtest) 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 k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) 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)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z 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() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$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$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.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='mytable5.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='mytable6.tab') }
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation