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Type 'q()' to quit R. > x <- c(117 + ,116 + ,166 + ,180 + ,202 + ,290 + ,298 + ,441 + ,388 + ,260 + ,175 + ,105 + ,137 + ,142 + ,176 + ,231 + ,240 + ,316 + ,363 + ,537 + ,487 + ,324 + ,185 + ,133 + ,169 + ,157 + ,206 + ,244 + ,243 + ,393 + ,405 + ,579 + ,525 + ,373 + ,198 + ,148 + ,201 + ,177 + ,222 + ,275 + ,290 + ,402 + ,534 + ,614 + ,578 + ,419 + ,203 + ,173 + ,229 + ,192 + ,294 + ,310 + ,365 + ,509 + ,537 + ,655 + ,643 + ,444 + ,259 + ,229 + ,276 + ,245 + ,324 + ,323 + ,349 + ,480 + ,530 + ,676 + ,670 + ,476 + ,281 + ,240 + ,259 + ,237 + ,400 + ,367 + ,497 + ,593 + ,696 + ,969 + ,878 + ,581 + ,373 + ,232 + ,358 + ,318 + ,410 + ,480 + ,604 + ,713 + ,844 + ,1134 + ,1013 + ,755 + ,371 + ,280 + ,417 + ,417 + ,514 + ,548 + ,583 + ,839 + ,924 + ,1179 + ,1109 + ,896 + ,452 + ,337 + ,484 + ,524 + ,575 + ,622 + ,664 + ,926 + ,1028 + ,1361 + ,1304 + ,937 + ,505 + ,427 + ,580 + ,483 + ,625 + ,695 + ,729 + ,1099 + ,1090 + ,1393 + ,1261 + ,988 + ,525 + ,416 + ,516 + ,454 + ,629 + ,755 + ,706 + ,951 + ,1099 + ,1444 + ,1316 + ,1066 + ,585 + ,430 + ,669 + ,598 + ,714 + ,835 + ,912 + ,1031 + ,1210 + ,1581 + ,1416 + ,1120 + ,652 + ,505 + ,741 + ,675 + ,782 + ,956 + ,996 + ,1259 + ,1389 + ,1868 + ,1609 + ,1385 + ,735 + ,577 + ,815 + ,798 + ,940 + ,1007 + ,1094 + ,1413 + ,1552 + ,2038 + ,1762 + ,1411 + ,805 + ,729 + ,912 + ,753 + ,989 + ,1137 + ,1256 + ,1554 + ,1629 + ,2024 + ,1900 + ,1563 + ,905 + ,766 + ,952 + ,915 + ,1197 + ,1242 + ,1197 + ,1522 + ,1591 + ,2128 + ,1962 + ,1653 + ,987 + ,877 + ,990 + ,880 + ,1258 + ,1240 + ,1312 + ,1713 + ,1683 + ,2220 + ,1996 + ,1628 + ,1119 + ,890 + ,1118 + ,1164 + ,1364 + ,1412 + ,1721 + ,1752 + ,1794 + ,2434 + ,2390 + ,1929 + ,1352 + ,1060 + ,1435 + ,1196 + ,1478 + ,1648 + ,1812 + ,2118 + ,2211 + ,2826 + ,2534 + ,2290 + ,1367 + ,1105 + ,1463 + ,1299 + ,1576 + ,1850 + ,1929 + ,2367 + ,2508 + ,3073 + ,2922 + ,2377 + ,1627 + ,1259 + ,1547 + ,1436 + ,1905 + ,2079 + ,1994 + ,2501 + ,2569 + ,3467 + ,2885 + ,2211 + ,1597 + ,1141 + ,1533 + ,1546 + ,1967 + ,2171 + ,2021 + ,2753 + ,2626 + ,3532 + ,3096 + ,2639 + ,1653 + ,1425 + ,1802 + ,1674 + ,1970 + ,2092 + ,2280 + ,2715 + ,2971 + ,3937 + ,3110 + ,2662 + ,1728 + ,1609 + ,1922 + ,1863 + ,1945 + ,2365 + ,2275 + ,2962 + ,2930 + ,4062 + ,3445 + ,2943 + ,1879 + ,1694 + ,2147 + ,1999 + ,2266 + ,2562 + ,2583 + ,2965 + ,3142 + ,4115 + ,3654 + ,2992 + ,2031 + ,1699 + ,2313 + ,1970 + ,2382 + ,2830 + ,2614 + ,3321 + ,3418 + ,4468 + ,3657 + ,3250 + ,2174 + ,2014 + ,2118 + ,2227 + ,2563 + ,2817 + ,2680 + ,3337 + ,3559 + ,4608 + ,3930 + ,3133 + ,2042 + ,1999 + ,2679 + ,2425 + ,2693 + ,2760 + ,2941 + ,3611 + ,3779 + ,4945 + ,4034 + ,2906 + ,2132 + ,1932 + ,2268 + ,2178 + ,2317 + ,2552 + ,2582 + ,2886 + ,3283 + ,4125 + ,3536 + ,2568 + ,1802 + ,1598 + ,2013 + ,1872 + ,2227 + ,2497 + ,2530 + ,3119 + ,3411 + ,4511 + ,3528 + ,2833 + ,1760 + ,1517 + ,1968 + ,1809 + ,2104 + ,2391 + ,2691 + ,3023 + ,3188 + ,4057 + ,3476) > par8 = 'FALSE' > par7 = '1' > par6 = '' > par5 = '1' > par4 = '' > par3 = '0' > par2 = 'periodic' > par1 = '12' > main = 'Seasonal Decomposition by Loess' > #'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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > par1 <- as.numeric(par1) #seasonal period > if (par2 != 'periodic') par2 <- as.numeric(par2) #s.window > par3 <- as.numeric(par3) #s.degree > if (par4 == '') par4 <- NULL else par4 <- as.numeric(par4)#t.window > par5 <- as.numeric(par5)#t.degree > if (par6 != '') par6 <- as.numeric(par6)#l.window > par7 <- as.numeric(par7)#l.degree > if (par8 == 'FALSE') par8 <- FALSE else par9 <- TRUE #robust > nx <- length(x) > x <- ts(x,frequency=par1) > if (par6 != '') { + m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.window=par6, l.degree=par7, robust=par8) + } else { + m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.degree=par7, robust=par8) + } > m$time.series seasonal trend remainder Jan 1 -356.75046 306.7825 166.9679974 Feb 1 -439.89123 287.4558 268.4354774 Mar 1 -246.53197 268.1291 144.4029193 Apr 1 -118.30748 257.2896 41.0178374 May 1 -79.95798 246.4502 35.5077375 Jun 1 244.49658 241.7095 -196.2060483 Jul 1 344.48234 236.9687 -283.4510324 Aug 1 907.82502 231.8051 -698.6300975 Sep 1 590.01159 226.6415 -428.6530433 Oct 1 177.16881 235.8945 -153.0633202 Nov 1 -427.75025 245.1476 357.6026911 Dec 1 -594.79491 264.6460 435.1489050 Jan 2 -356.75046 284.1445 209.6059982 Feb 2 -439.89123 285.8470 296.0442354 Mar 2 -246.53197 287.5495 134.9824344 Apr 2 -118.30748 272.6666 76.6408890 May 2 -79.95798 257.7837 62.1743256 Jun 2 244.49658 247.9376 -176.4342011 Jul 2 344.48234 238.0916 -219.5739260 Aug 2 907.82502 246.0340 -616.8590596 Sep 2 590.01159 253.9765 -356.9880740 Oct 2 177.16881 271.4744 -124.6432621 Nov 2 -427.75025 288.9724 323.7778379 Dec 2 -594.79491 306.1212 421.6737524 Jan 3 -356.75046 323.2699 202.4805462 Feb 3 -439.89123 322.5290 274.3622094 Mar 3 -246.53197 321.7881 130.7438345 Apr 3 -118.30748 305.7877 56.5198253 May 3 -79.95798 289.7872 33.1707982 Jun 3 244.49658 280.0393 -131.5358796 Jul 3 344.48234 270.2914 -209.7737557 Aug 3 907.82502 278.1815 -607.0065600 Sep 3 590.01159 286.0717 -351.0832450 Oct 3 177.16881 303.3723 -107.5411492 Nov 3 -427.75025 320.6730 305.0772347 Dec 3 -594.79491 338.1776 404.6173024 Jan 4 -356.75046 355.6822 202.0682493 Feb 4 -439.89123 356.3172 260.5740459 Mar 4 -246.53197 356.9522 111.5798043 Apr 4 -118.30748 342.3062 51.0013115 May 4 -79.95798 327.6602 42.2978008 Jun 4 244.49658 318.5373 -161.0338674 Jul 4 344.48234 309.4144 -119.8967338 Aug 4 907.82502 317.8303 -611.6552893 Sep 4 590.01159 326.2461 -338.2577255 Oct 4 177.16881 344.5746 -102.7433951 Nov 4 -427.75025 362.9030 267.8472233 Dec 4 -594.79491 380.8464 386.9485175 Jan 5 -356.75046 398.7898 186.9606910 Feb 5 -439.89123 399.4638 232.4274128 Mar 5 -246.53197 400.1379 140.3940964 Apr 5 -118.30748 386.8014 41.5061185 May 5 -79.95798 373.4649 71.4931227 Jun 5 244.49658 366.1924 -101.6889598 Jul 5 344.48234 358.9199 -166.4022406 Aug 5 907.82502 367.3570 -620.1820564 Sep 5 590.01159 375.7942 -322.8057529 Oct 5 177.16881 390.7327 -123.9015157 Nov 5 -427.75025 405.6712 281.0790096 Dec 5 -594.79491 418.4094 405.3854685 Jan 6 -356.75046 431.1477 201.6028066 Feb 6 -439.89123 427.9686 256.9226566 Mar 6 -246.53197 424.7895 145.7424685 Apr 6 -118.30748 408.5565 32.7510244 May 6 -79.95798 392.3234 36.6345624 Jun 6 244.49658 381.9968 -146.4933441 Jul 6 344.48234 371.6701 -186.1524487 Aug 6 907.82502 379.1329 -610.9579255 Sep 6 590.01159 386.5957 -306.6072830 Oct 6 177.16881 406.5458 -107.7146565 Nov 6 -427.75025 426.4960 282.2542580 Dec 6 -594.79491 450.7797 384.0152361 Jan 7 -356.75046 475.0634 140.6870935 Feb 7 -439.89123 487.3588 189.5324453 Mar 7 -246.53197 499.6542 146.8777590 Apr 7 -118.30748 496.4235 -11.1160531 May 7 -79.95798 493.1929 83.7651168 Jun 7 244.49658 490.4877 -141.9842526 Jul 7 344.48234 487.7825 -136.2648202 Aug 7 907.82502 497.4032 -436.2282377 Sep 7 590.01159 507.0239 -219.0355359 Oct 7 177.16881 524.4095 -120.5783600 Nov 7 -427.75025 541.7951 258.9551039 Dec 7 -594.79491 561.1042 265.6906644 Jan 8 -356.75046 580.4134 134.3371040 Feb 8 -439.89123 589.5510 168.3402415 Mar 8 -246.53197 598.6886 57.8433409 Apr 8 -118.30748 596.0961 2.2113647 May 8 -79.95798 593.5036 90.4543706 Jun 8 244.49658 592.2717 -123.7682702 Jul 8 344.48234 591.0398 -91.5221092 Aug 8 907.82502 600.6100 -374.4350106 Sep 8 590.01159 610.1802 -187.1917926 Oct 8 177.16881 624.4064 -46.5752257 Nov 8 -427.75025 638.6326 160.1176293 Dec 8 -594.79491 651.8431 222.9518215 Jan 9 -356.75046 665.0536 108.6968930 Feb 9 -439.89123 669.2303 187.6609269 Mar 9 -246.53197 673.4070 87.1249226 Apr 9 -118.30748 670.4746 -4.1670692 May 9 -79.95798 667.5421 -4.5840789 Jun 9 244.49658 668.0391 -73.5356590 Jul 9 344.48234 668.5361 -89.0184374 Aug 9 907.82502 678.8080 -407.6330206 Sep 9 590.01159 689.0799 -170.0914846 Oct 9 177.16881 704.2695 14.5617201 Nov 9 -427.75025 719.4590 160.2912128 Dec 9 -594.79491 734.6742 197.1206990 Jan 10 -356.75046 749.8894 90.8610645 Feb 10 -439.89123 757.4887 206.4025709 Mar 10 -246.53197 765.0879 56.4440392 Apr 10 -118.30748 764.3089 -24.0014009 May 10 -79.95798 763.5298 -19.5718590 Jun 10 244.49658 764.1763 -82.6728742 Jul 10 344.48234 764.8227 -81.3050876 Aug 10 907.82502 772.5129 -319.3379432 Sep 10 590.01159 780.2031 -66.2146794 Oct 10 177.16881 792.5384 -32.7072531 Nov 10 -427.75025 804.8738 127.8764613 Dec 10 -594.79491 816.8457 204.9491898 Jan 11 -356.75046 828.8177 107.9327975 Feb 11 -439.89123 830.1801 92.7111417 Mar 11 -246.53197 831.5425 39.9894478 Apr 11 -118.30748 824.6167 -11.3092649 May 11 -79.95798 817.6910 -8.7329955 Jun 11 244.49658 812.5456 41.9578170 Jul 11 344.48234 807.4002 -61.8825688 Aug 11 907.82502 808.8281 -323.6531171 Sep 11 590.01159 810.2560 -139.2675462 Oct 11 177.16881 814.6066 -3.7754025 Nov 11 -427.75025 818.9572 133.7930293 Dec 11 -594.79491 822.1392 188.6556836 Jan 12 -356.75046 825.3212 47.4292171 Feb 12 -439.89123 824.2302 69.6609920 Mar 12 -246.53197 823.1392 52.3927289 Apr 12 -118.30748 820.0928 53.2146627 May 12 -79.95798 817.0464 -31.0884214 Jun 12 244.49658 819.9124 -113.4089701 Jul 12 344.48234 822.7784 -68.2607171 Aug 12 907.82502 835.1254 -298.9504681 Sep 12 590.01159 847.4725 -121.4840998 Oct 12 177.16881 864.2878 24.5433522 Nov 12 -427.75025 881.1032 131.6470923 Dec 12 -594.79491 896.7346 128.0603321 Jan 13 -356.75046 912.3660 113.3844512 Feb 13 -439.89123 918.7865 119.1047555 Mar 13 -246.53197 925.2069 35.3250216 Apr 13 -118.30748 924.2682 29.0392603 May 13 -79.95798 923.3295 68.6284811 Jun 13 244.49658 924.8085 -138.3050583 Jul 13 344.48234 926.2875 -60.7697960 Aug 13 907.82502 934.8909 -261.7159277 Sep 13 590.01159 943.4944 -117.5059401 Oct 13 177.16881 958.2380 -15.4068314 Nov 13 -427.75025 972.9817 106.7685653 Dec 13 -594.79491 991.8141 107.9807829 Jan 14 -356.75046 1010.6466 87.1038797 Feb 14 -439.89123 1027.1811 87.7101549 Mar 14 -246.53197 1043.7156 -15.1836081 Apr 14 -118.30748 1053.8864 20.4210432 May 14 -79.95798 1064.0573 11.9006767 Jun 14 244.49658 1071.5113 -57.0079289 Jul 14 344.48234 1078.9654 -34.4477327 Aug 14 907.82502 1089.2455 -129.0705015 Sep 14 590.01159 1099.5256 -80.5371511 Oct 14 177.16881 1111.5243 96.3068880 Nov 14 -427.75025 1123.5230 39.2272152 Dec 14 -594.79491 1136.2050 35.5899195 Jan 15 -356.75046 1148.8870 22.8635031 Feb 15 -439.89123 1159.0598 78.8314492 Mar 15 -246.53197 1169.2326 17.2993572 Apr 15 -118.30748 1175.4677 -50.1602250 May 15 -79.95798 1181.7028 -7.7448251 Jun 15 244.49658 1187.6471 -19.1436393 Jul 15 344.48234 1193.5913 13.9263483 Aug 15 907.82502 1200.0495 -69.8744952 Sep 15 590.01159 1206.5076 -34.5192194 Oct 15 177.16881 1215.3523 18.4788645 Nov 15 -427.75025 1224.1970 8.5532364 Dec 15 -594.79491 1233.8121 89.9828543 Jan 16 -356.75046 1243.4271 25.3233513 Feb 16 -439.89123 1250.5171 -57.6258659 Mar 16 -246.53197 1257.6071 -22.0751212 Apr 16 -118.30748 1264.1140 -8.8065545 May 16 -79.95798 1270.6210 65.3369941 Jun 16 244.49658 1277.9908 31.5126474 Jul 16 344.48234 1285.3606 -0.8428976 Aug 16 907.82502 1295.1184 -178.9434490 Sep 16 590.01159 1304.8763 5.1121190 Oct 16 177.16881 1312.5243 73.3068949 Nov 16 -427.75025 1320.1723 12.5779587 Dec 16 -594.79491 1322.8621 37.9328359 Jan 17 -356.75046 1325.5519 -16.8014077 Feb 17 -439.89123 1327.0012 27.8900474 Mar 17 -246.53197 1328.4505 115.0814644 Apr 17 -118.30748 1331.8193 28.4882170 May 17 -79.95798 1335.1880 -58.2300484 Jun 17 244.49658 1339.9367 -62.4333288 Jul 17 344.48234 1344.6855 -98.1678075 Aug 17 907.82502 1349.4460 -129.2710143 Sep 17 590.01159 1354.2065 17.7818982 Oct 17 177.16881 1361.6747 114.1564549 Nov 17 -427.75025 1369.1429 45.6072996 Dec 17 -594.79491 1379.0242 92.7707290 Jan 18 -356.75046 1388.9054 -42.1549624 Feb 18 -439.89123 1393.4887 -73.5974484 Mar 18 -246.53197 1398.0719 106.4600274 Apr 18 -118.30748 1400.2638 -41.9563486 May 18 -79.95798 1402.4557 -10.4977425 Jun 18 244.49658 1409.5437 58.9597481 Jul 18 344.48234 1416.6316 -78.1139595 Aug 18 907.82502 1430.2955 -118.1205207 Sep 18 590.01159 1443.9594 -37.9709626 Oct 18 177.16881 1462.3327 -11.5015196 Nov 18 -427.75025 1480.7060 66.0442114 Dec 18 -594.79491 1499.1543 -14.3593577 Jan 19 -356.75046 1517.6025 -42.8520475 Feb 19 -439.89123 1536.2871 67.6041239 Mar 19 -246.53197 1554.9717 55.5602571 Apr 19 -118.30748 1575.0456 -44.7381287 May 19 -79.95798 1595.1195 205.8384675 Jun 19 244.49658 1613.7008 -106.1973780 Jul 19 344.48234 1632.2821 -182.7644217 Aug 19 907.82502 1646.2866 -120.1116137 Sep 19 590.01159 1660.2911 139.6973136 Oct 19 177.16881 1676.3418 75.4894088 Nov 19 -427.75025 1692.3925 87.3577920 Dec 19 -594.79491 1715.1279 -60.3330211 Jan 20 -356.75046 1737.8634 53.8870450 Feb 20 -439.89123 1762.4994 -126.6081705 Mar 20 -246.53197 1787.1354 -62.6034241 Apr 20 -118.30748 1805.5656 -39.2581401 May 20 -79.95798 1823.9959 67.9621259 Jun 20 244.49658 1835.8471 37.6563566 Jul 20 344.48234 1847.6983 18.8193890 Aug 20 907.82502 1853.9504 64.2245700 Sep 20 590.01159 1860.2025 83.7858704 Oct 20 177.16881 1867.3716 245.4596142 Nov 20 -427.75025 1874.5406 -79.7903540 Dec 20 -594.79491 1887.9404 -188.1454439 Jan 21 -356.75046 1901.3401 -81.5896545 Feb 21 -439.89123 1924.0058 -185.1145927 Mar 21 -246.53197 1946.6715 -124.1395689 Apr 21 -118.30748 1973.1875 -4.8800250 May 21 -79.95798 1999.7035 9.2545010 Jun 21 244.49658 2021.1879 101.3155135 Jul 21 344.48234 2042.6723 120.8453279 Aug 21 907.82502 2055.3888 109.7861778 Sep 21 590.01159 2068.1053 263.8831472 Oct 21 177.16881 2075.2169 124.6142555 Nov 21 -427.75025 2082.3286 -27.5783481 Dec 21 -594.79491 2088.2677 -234.4727631 Jan 22 -356.75046 2094.2068 -190.4562989 Feb 22 -439.89123 2104.6187 -228.7274942 Mar 22 -246.53197 2115.0307 36.5012725 Apr 22 -118.30748 2124.2060 73.1015169 May 22 -79.95798 2133.3812 -59.4232566 Jun 22 244.49658 2135.0761 121.4273470 Jul 22 344.48234 2136.7709 87.7467523 Aug 22 907.82502 2133.1174 426.0576075 Sep 22 590.01159 2129.4638 165.5245820 Oct 22 177.16881 2126.2243 -92.3930882 Nov 22 -427.75025 2122.9847 -98.2344705 Dec 22 -594.79491 2126.8717 -391.0767464 Jan 23 -356.75046 2130.7586 -241.0081431 Feb 23 -439.89123 2149.0208 -163.1295768 Mar 23 -246.53197 2167.2830 46.2489513 Apr 23 -118.30748 2198.3179 90.9895569 May 23 -79.95798 2229.3528 -128.3948554 Jun 23 244.49658 2253.6621 254.8413038 Jul 23 344.48234 2277.9714 3.5462647 Aug 23 907.82502 2284.0572 340.1178076 Sep 23 590.01159 2290.1429 215.8454697 Oct 23 177.16881 2286.4566 175.3745576 Nov 23 -427.75025 2282.7703 -202.0200665 Dec 23 -594.79491 2286.4032 -266.6082417 Jan 24 -356.75046 2290.0360 -131.2855376 Feb 24 -439.89123 2308.1322 -194.2409755 Mar 24 -246.53197 2326.2284 -109.6964515 Apr 24 -118.30748 2349.3113 -139.0038664 May 24 -79.95798 2372.3943 -12.4362991 Jun 24 244.49658 2390.8512 79.6522486 Jul 24 344.48234 2409.3081 217.2095982 Aug 24 907.82502 2416.2659 612.9090722 Sep 24 590.01159 2423.2237 96.7646656 Oct 24 177.16881 2421.6593 63.1718617 Nov 24 -427.75025 2420.0949 -264.3446542 Dec 24 -594.79491 2418.2607 -214.4657535 Jan 25 -356.75046 2416.4264 -137.6759735 Feb 25 -439.89123 2432.6698 -129.7785985 Mar 25 -246.53197 2448.9132 -257.3812617 Apr 25 -118.30748 2479.8024 3.5051108 May 25 -79.95798 2510.6915 -155.7335347 Jun 25 244.49658 2535.5208 181.9825662 Jul 25 344.48234 2560.3502 25.1674688 Aug 25 907.82502 2574.0527 580.1222327 Sep 25 590.01159 2587.7553 267.2331160 Oct 25 177.16881 2595.4717 170.3594921 Nov 25 -427.75025 2603.1881 -296.4378437 Dec 25 -594.79491 2605.7735 -316.9785996 Jan 26 -356.75046 2608.3589 -104.6084763 Feb 26 -439.89123 2621.3257 -182.4344779 Mar 26 -246.53197 2634.2925 -121.7605176 Apr 26 -118.30748 2656.9149 23.3925508 May 26 -79.95798 2679.5374 -16.5793988 Jun 26 244.49658 2695.7226 24.7807995 Jul 26 344.48234 2711.9079 85.6097995 Aug 26 907.82502 2714.3661 492.8088406 Sep 26 590.01159 2716.8244 347.1640010 Oct 26 177.16881 2718.0523 96.7788822 Nov 26 -427.75025 2719.2802 -260.5299485 Dec 26 -594.79491 2727.6874 -433.8924626 Jan 27 -356.75046 2736.0946 -66.3440975 Feb 27 -439.89123 2759.6014 -349.7102076 Mar 27 -246.53197 2783.1083 -154.5763558 Apr 27 -118.30748 2815.6286 132.6788353 May 27 -79.95798 2848.1490 -154.1909916 Jun 27 244.49658 2870.3568 206.1466020 Jul 27 344.48234 2892.5647 180.9529973 Aug 27 907.82502 2895.5991 664.5759003 Sep 27 590.01159 2898.6335 168.3549226 Oct 27 177.16881 2890.4426 182.3886136 Nov 27 -427.75025 2882.2517 -280.5014073 Dec 27 -594.79491 2876.1970 -267.4020740 Jan 28 -356.75046 2870.1423 -395.3918614 Feb 28 -439.89123 2882.8778 -215.9865414 Mar 28 -246.53197 2895.6132 -86.0812595 Apr 28 -118.30748 2918.0465 17.2609609 May 28 -79.95798 2940.4798 -180.5218366 Jun 28 244.49658 2959.4403 133.0631566 Jul 28 344.48234 2978.4007 236.1169516 Aug 28 907.82502 2987.7964 712.3786235 Sep 28 590.01159 2997.1920 342.7964148 Oct 28 177.16881 2996.8935 -41.0623358 Nov 28 -427.75025 2996.5950 -526.8447984 Dec 28 -594.79491 3001.0335 -407.2386316 Jan 29 -356.75046 3005.4720 30.2784145 Feb 29 -439.89123 3027.9999 -163.1086643 Mar 29 -246.53197 3050.5278 -110.9957812 Apr 29 -118.30748 3073.0372 -194.7297412 May 29 -79.95798 3095.5467 -74.5887192 Jun 29 244.49658 3093.1190 273.3843979 Jul 29 344.48234 3090.6913 343.8263167 Aug 29 907.82502 3059.7677 977.4072719 Sep 29 590.01159 3028.8441 415.1443464 Oct 29 177.16881 2981.0244 -252.1932541 Nov 29 -427.75025 2933.2048 -373.4545667 Dec 29 -594.79491 2873.9668 -347.1719107 Jan 30 -356.75046 2814.7288 -189.9783754 Feb 30 -439.89123 2772.9857 -155.0944483 Mar 30 -246.53197 2731.2425 -167.7105593 Apr 30 -118.30748 2712.9210 -42.6135547 May 30 -79.95798 2694.5995 -32.6415680 Jun 30 244.49658 2676.3620 -34.8586089 Jul 30 344.48234 2658.1245 280.3931520 Aug 30 907.82502 2633.5034 583.6715804 Sep 30 590.01159 2608.8823 337.1061281 Oct 30 177.16881 2590.1626 -199.3314365 Nov 30 -427.75025 2571.4430 -341.6927131 Dec 30 -594.79491 2568.7814 -375.9865150 Jan 31 -356.75046 2566.1199 -196.3694377 Feb 31 -439.89123 2589.3596 -277.4684040 Mar 31 -246.53197 2612.5994 -139.0674085 Apr 31 -118.30748 2643.4425 -28.1349682 May 31 -79.95798 2674.2855 -64.3275460 Jun 31 244.49658 2684.3618 190.1416580 Jul 31 344.48234 2694.4380 372.0796637 Aug 31 907.82502 2679.5360 923.6389625 Sep 31 590.01159 2664.6340 273.3543806 Oct 31 177.16881 2640.2056 15.6255515 Nov 31 -427.75025 2615.7772 -428.0269895 Dec 31 -594.79491 2609.0111 -497.2162150 Jan 32 -356.75046 2602.2450 -277.4945612 Feb 32 -439.89123 2620.0273 -371.1360177 Mar 32 -246.53197 2637.8095 -287.2775123 Apr 32 -118.30748 2659.8285 -150.5209721 May 32 -79.95798 2681.8474 89.1105501 Jun 32 244.49658 2713.1749 65.3285473 Jul 32 344.48234 2744.5023 99.0153463 Aug 32 907.82502 2785.6456 363.5293367 Sep 32 590.01159 2826.7890 59.1994465 > m$win s t l 3811 19 13 > m$deg s t l 0 1 1 > m$jump s t l 382 2 2 > m$inner [1] 2 > m$outer [1] 0 > postscript(file="/var/wessaorg/rcomp/tmp/1ubpw1324225649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(m,main=main) > dev.off() null device 1 > mylagmax <- nx/2 > postscript(file="/var/wessaorg/rcomp/tmp/2k8o11324225649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow = c(2,2)) > acf(as.numeric(x),lag.max = mylagmax,main='Observed') > acf(as.numeric(m$time.series[,'trend']),na.action=na.pass,lag.max = mylagmax,main='Trend') > acf(as.numeric(m$time.series[,'seasonal']),na.action=na.pass,lag.max = mylagmax,main='Seasonal') > acf(as.numeric(m$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder') > par(op) > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/35c7a1324225649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow = c(2,2)) > spectrum(as.numeric(x),main='Observed') > spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend') > spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal') > spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder') > par(op) > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/400l31324225649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow = c(2,2)) > cpgram(as.numeric(x),main='Observed') > cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend') > cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal') > cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder') > par(op) > 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,'Seasonal Decomposition by Loess - Parameters',4,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Component',header=TRUE) > a<-table.element(a,'Window',header=TRUE) > a<-table.element(a,'Degree',header=TRUE) > a<-table.element(a,'Jump',header=TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Seasonal',header=TRUE) > a<-table.element(a,m$win['s']) > a<-table.element(a,m$deg['s']) > a<-table.element(a,m$jump['s']) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Trend',header=TRUE) > a<-table.element(a,m$win['t']) > a<-table.element(a,m$deg['t']) > a<-table.element(a,m$jump['t']) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Low-pass',header=TRUE) > a<-table.element(a,m$win['l']) > a<-table.element(a,m$deg['l']) > a<-table.element(a,m$jump['l']) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/5q3pg1324225649.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'t',header=TRUE) > a<-table.element(a,'Observed',header=TRUE) > a<-table.element(a,'Fitted',header=TRUE) > a<-table.element(a,'Seasonal',header=TRUE) > a<-table.element(a,'Trend',header=TRUE) > a<-table.element(a,'Remainder',header=TRUE) > a<-table.row.end(a) > for (i in 1:nx) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]+m$time.series[i,'remainder']) + a<-table.element(a,m$time.series[i,'seasonal']) + a<-table.element(a,m$time.series[i,'trend']) + a<-table.element(a,m$time.series[i,'remainder']) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/690zv1324225649.tab") > > try(system("convert tmp/1ubpw1324225649.ps tmp/1ubpw1324225649.png",intern=TRUE)) character(0) > try(system("convert tmp/2k8o11324225649.ps tmp/2k8o11324225649.png",intern=TRUE)) character(0) > try(system("convert tmp/35c7a1324225649.ps tmp/35c7a1324225649.png",intern=TRUE)) character(0) > try(system("convert tmp/400l31324225649.ps tmp/400l31324225649.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.142 0.421 5.565