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Type 'q()' to quit R. > x <- c(348542 + ,335658 + ,330664 + ,326814 + ,322900 + ,322310 + ,385164 + ,404861 + ,412136 + ,411057 + ,410040 + ,414980 + ,413626 + ,411062 + ,408352 + ,409780 + ,411318 + ,415555 + ,479481 + ,497826 + ,501638 + ,497990 + ,499287 + ,506247 + ,510401 + ,508642 + ,501805 + ,495476 + ,490336 + ,490042 + ,553155 + ,569999 + ,573170 + ,571687 + ,575453 + ,580177 + ,579849 + ,574346 + ,563325 + ,555604 + ,545544 + ,545109 + ,605181 + ,627856 + ,631421 + ,625671 + ,613577 + ,606463 + ,601676 + ,589121 + ,573559 + ,558487 + ,552148 + ,545720 + ,606569 + ,636067 + ,630704 + ,623275 + ,617771 + ,605401 + ,619393 + ,596019 + ,569977 + ,546213 + ,528492 + ,505944 + ,554910 + ,567831 + ,564021 + ,552800 + ,541102 + ,542378 + ,540380 + ,521219 + ,504652 + ,490626 + ,481686 + ,477930 + ,522605 + ,531432 + ,532355 + ,539954 + ,524987 + ,533307 + ,530541 + ,508392 + ,495208 + ,482223 + ,470495 + ,466106 + ,515037 + ,517752 + ,515565 + ,510727 + ,499725 + ,498369 + ,493756 + ,476141 + ,458458 + ,443182 + ,429597 + ,424476 + ,476257 + ,480555 + ,469762 + ,459820 + ,451028 + ,450065 + ,444385 + ,428846 + ,421020 + ,399778 + ,389005 + ,384018 + ,431933 + ,445844 + ,431464 + ,423263 + ,415881 + ,416208 + ,413491 + ,399153 + ,385939 + ,373917 + ,364635 + ,364696 + ,418358 + ,428212 + ,423730 + ,420677 + ,417428 + ,423245 + ,423113 + ,418873 + ,405733 + ,397812 + ,389918 + ,391116 + ,443814 + ,460373 + ,455422 + ,456288 + ,452233 + ,459256 + ,461146 + ,451391 + ,443101 + ,438810 + ,430457 + ,435721 + ,488280 + ,505814 + ,502338 + ,500910 + ,501434 + ,515476 + ,520862 + ,519517 + ,511805 + ,508607 + ,505327 + ,511435 + ,570158 + ,591665 + ,593572 + ,586346 + ,586063 + ,591504 + ,594033 + ,585597 + ,572450 + ,562917 + ,554675 + ,553997 + ,601310 + ,622255 + ,616735 + ,606480 + ,595079 + ,598588 + ,599917 + ,591573 + ,575489 + ,567223 + ,555338 + ,555252 + ,608249 + ,630859 + ,628632 + ,624435 + ,609670 + ,615830 + ,621170 + ,604212 + ,584348 + ,573717 + ,555234 + ,544897 + ,598866 + ,620081 + ,607699 + ,589960 + ,578665 + ,580166 + ,579457 + ,571560 + ,560460 + ,551397 + ,536763 + ,540562 + ,588184 + ,607049 + ,598968 + ,577644 + ,562640 + ,565867 + ,561274 + ,554144 + ,539900 + ,526271 + ,511841 + ,505282 + ,554083 + ,584225 + ,568858 + ,539516 + ,521612 + ,525562 + ,526519 + ,515713 + ,503454 + ,489301 + ,479020 + ,475102 + ,523682 + ,551528 + ,531626 + ,511037 + ,492417 + ,492188 + ,492865 + ,480961 + ,461935 + ,456608 + ,441977 + ,439148 + ,488180 + ,520564 + ,501492 + ,485025 + ,464196 + ,460170 + ,467037 + ,460070 + ,447988 + ,442867 + ,436087 + ,431328 + ,484015 + ,509673 + ,512927 + ,502831 + ,470984 + ,471067 + ,476049 + ,474605 + ,470439 + ,461251 + ,454724 + ,455626 + ,516847 + ,525192 + ,522975 + ,518585 + ,509239 + ,512238 + ,519164 + ,517009 + ,509933 + ,509127 + ,500857 + ,506971 + ,569323 + ,579714 + ,577992 + ,565464 + ,547344 + ,554788 + ,562325 + ,560854 + ,555332 + ,543599 + ,536662 + ,542722 + ,593530 + ,610763 + ,612613 + ,611324 + ,594167 + ,595454 + ,590865 + ,589379 + ,584428 + ,573100 + ,567456 + ,569028 + ,620735 + ,628884 + ,628232 + ,612117 + ,595404 + ,597141) > par8 = 'TRUE' > 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 8282.031 332211.8 8048.186832 Feb 1 -1916.262 338703.4 -1129.115752 Mar 1 -13991.882 345195.0 -539.092220 Apr 1 -24601.763 351716.7 -300.935016 May 1 -34767.526 358238.4 -570.896094 Jun 1 -36616.122 364760.8 -5834.655040 Jul 1 16067.354 371283.1 -2186.485932 Aug 1 29922.228 377817.4 -2878.671408 Sep 1 26127.816 384351.8 1656.427833 Oct 1 15793.026 391234.0 4029.979698 Nov 1 5861.948 398116.2 6061.817813 Dec 1 9839.153 405564.6 -423.796529 Jan 2 8282.031 413013.1 -7669.084545 Feb 2 -1916.262 420583.3 -7605.086121 Mar 2 -13991.882 428153.6 -5809.761580 Apr 2 -24601.763 435731.3 -1349.539431 May 2 -34767.526 443309.0 2776.564436 Jun 2 -36616.122 451133.9 1037.262991 Jul 2 16067.354 458958.8 4454.889600 Aug 2 29922.228 466724.3 1179.443987 Sep 2 26127.816 474489.9 1020.283091 Oct 2 15793.026 481589.3 607.706530 Nov 2 5861.948 488688.6 4736.416221 Dec 2 9839.153 495110.2 1297.680222 Jan 3 8282.031 501531.7 587.270551 Feb 3 -1916.262 507542.9 3015.319823 Mar 3 -13991.882 513554.2 2242.695213 Apr 3 -24601.763 519445.1 632.674949 May 3 -34767.526 525336.0 -232.463596 Jun 3 -36616.122 531183.7 -4525.566726 Jul 3 16067.354 537031.4 56.258198 Aug 3 29922.228 542598.7 -2521.884783 Sep 3 26127.816 548165.9 -1123.743047 Oct 3 15793.026 553214.2 2679.728533 Nov 3 5861.948 558262.6 11328.486363 Dec 3 9839.153 562927.7 7410.161229 Jan 4 8282.031 567592.8 3974.162424 Feb 4 -1916.262 571801.6 4460.616026 Mar 4 -13991.882 576010.5 1306.395746 Apr 4 -24601.763 579168.0 1037.751744 May 4 -34767.526 582325.5 -2014.010539 Jun 4 -36616.122 584017.2 -2292.103186 Jul 4 16067.354 585708.9 3404.732221 Aug 4 29922.228 586627.7 11306.098401 Sep 4 26127.816 587546.4 17746.749297 Oct 4 15793.026 588185.8 21692.180971 Nov 4 5861.948 588825.2 18889.898895 Dec 4 9839.153 589419.5 7204.377193 Jan 5 8282.031 590013.8 3380.181819 Feb 5 -1916.262 589639.5 1397.723995 Mar 5 -13991.882 589265.3 -1714.407711 Apr 5 -24601.763 588037.5 -4948.729194 May 5 -34767.526 586809.7 105.831041 Jun 5 -36616.122 586214.2 -3878.077498 Jul 5 16067.354 585618.7 4882.942017 Aug 5 29922.228 584820.1 21324.717854 Sep 5 26127.816 584021.4 20554.778408 Oct 5 15793.026 582286.5 25195.467748 Nov 5 5861.948 580551.6 31357.443339 Dec 5 9839.153 579300.0 16261.874058 Jan 6 8282.031 578048.3 33062.631104 Feb 6 -1916.262 578134.6 19800.692625 Mar 6 -13991.882 578220.8 5748.080263 Apr 6 -24601.763 571989.4 -1174.602379 May 6 -34767.526 565757.9 -2498.403301 Jun 6 -36616.122 560194.5 -17634.351920 Jul 6 16067.354 554631.0 -15788.372485 Aug 6 29922.228 549617.3 -11708.494664 Sep 6 26127.816 544603.5 -6710.332126 Oct 6 15793.026 539859.3 -2852.375506 Nov 6 5861.948 535115.2 124.867365 Dec 6 9839.153 531415.8 1123.017761 Jan 7 8282.031 527716.5 4381.494484 Feb 7 -1916.262 524835.3 -1700.035928 Mar 7 -13991.882 521954.1 -3310.240222 Apr 7 -24601.763 520084.7 -4856.922943 May 7 -34767.526 518215.3 -1761.723945 Jun 7 -36616.122 517352.8 -2806.707283 Jul 7 16067.354 516490.4 -9952.762566 Aug 7 29922.228 515690.4 -14180.626447 Sep 7 26127.816 514890.4 -8663.205611 Oct 7 15793.026 514081.8 10079.196700 Nov 7 5861.948 513273.2 5851.885261 Dec 7 9839.153 512588.6 10879.222419 Jan 8 8282.031 511904.1 10354.885905 Feb 8 -1916.262 510479.7 -171.447568 Mar 8 -13991.882 509055.3 144.545076 Apr 8 -24601.763 506659.5 165.223123 May 8 -34767.526 504263.7 998.782888 Jun 8 -36616.122 501686.2 1035.964544 Jul 8 16067.354 499108.6 -138.925746 Aug 8 29922.228 496349.8 -8519.996039 Sep 8 26127.816 493591.0 -4153.781615 Oct 8 15793.026 490482.5 4451.468047 Nov 8 5861.948 487374.0 6489.003961 Dec 8 9839.153 484078.0 4451.870162 Jan 9 8282.031 480781.9 4692.062691 Feb 9 -1916.262 477159.2 898.091764 Mar 9 -13991.882 473536.4 -1086.553045 Apr 9 -24601.763 469475.2 -1691.387521 May 9 -34767.526 465413.9 -1049.340278 Jun 9 -36616.122 461353.6 -261.449985 Jul 9 16067.354 457293.3 2896.368362 Aug 9 29922.228 453571.3 -2938.494109 Sep 9 26127.816 449849.3 -6215.071862 Oct 9 15793.026 446365.5 -2338.523794 Nov 9 5861.948 442881.7 2284.310525 Dec 9 9839.153 439547.1 678.789759 Jan 10 8282.031 436212.4 -109.404679 Feb 10 -1916.262 433012.2 -2249.982071 Mar 10 -13991.882 429812.1 5199.766654 Apr 10 -24601.763 426760.2 -2380.445952 May 10 -34767.526 423708.3 64.223161 Jun 10 -36616.122 420858.9 -224.817914 Jul 10 16067.354 418009.6 -2143.930935 Aug 10 29922.228 415429.2 492.548327 Sep 10 26127.816 412848.9 -7512.687694 Oct 10 15793.026 410611.1 -3141.175102 Nov 10 5861.948 408373.4 1645.623741 Dec 10 9839.153 406694.9 -326.006644 Jan 11 8282.031 405016.3 192.689298 Feb 11 -1916.262 403904.6 -2835.288573 Mar 11 -13991.882 402792.8 -2861.940326 Apr 11 -24601.763 402503.7 -3984.925785 May 11 -34767.526 402214.6 -2812.029526 Jun 11 -36616.122 402812.7 -1500.550003 Jul 11 16067.354 403410.8 -1120.142425 Aug 11 29922.228 404822.8 -6533.017846 Sep 11 26127.816 406234.8 -8632.608551 Oct 11 15793.026 408201.8 -3317.846252 Nov 11 5861.948 410168.8 1397.202297 Dec 11 9839.153 412465.6 940.262024 Jan 12 8282.031 414762.3 68.648078 Feb 12 -1916.262 417265.5 3523.799198 Mar 12 -13991.882 419768.6 -43.723566 Apr 12 -24601.763 422445.4 -31.595050 May 12 -34767.526 425122.1 -436.584816 Jun 12 -36616.122 428024.0 -291.834478 Jul 12 16067.354 430925.8 -3179.156086 Aug 12 29922.228 434029.7 -3578.887518 Sep 12 26127.816 437133.5 -7839.334232 Oct 12 15793.026 440494.7 0.279005 Nov 12 5861.948 443855.9 2515.178493 Dec 12 9839.153 447517.8 1899.093111 Jan 13 8282.031 451179.6 1684.334057 Feb 13 -1916.262 454949.8 -1642.510463 Mar 13 -13991.882 458719.9 -1627.028866 Apr 13 -24601.763 462650.1 761.630516 May 13 -34767.526 466580.4 -1355.828382 Jun 13 -36616.122 471254.3 1082.836605 Jul 13 16067.354 475928.2 -3715.570354 Aug 13 29922.228 481365.5 -5473.692712 Sep 13 26127.816 486802.7 -10592.530352 Oct 13 15793.026 492760.7 -7643.706986 Nov 13 5861.948 498718.6 -3146.597368 Dec 13 9839.153 505216.4 420.439370 Jan 14 8282.031 511714.2 865.802434 Feb 14 -1916.262 518778.9 2654.385502 Mar 14 -13991.882 525843.6 -46.705313 Apr 14 -24601.763 532857.2 351.564958 May 14 -34767.526 539870.8 223.716947 Jun 14 -36616.122 546309.8 1741.321886 Jul 14 16067.354 552748.8 1341.854879 Aug 14 29922.228 558299.2 3443.606617 Sep 14 26127.816 563849.5 3594.643073 Oct 14 15793.026 568374.3 2178.643214 Nov 14 5861.948 572899.1 7301.929605 Dec 14 9839.153 576366.4 5298.425504 Jan 15 8282.031 579833.7 5917.247731 Feb 15 -1916.262 582270.2 5243.015886 Mar 15 -13991.882 584706.8 1735.110158 Apr 15 -24601.763 586158.2 1360.520048 May 15 -34767.526 587609.7 1832.811656 Jun 15 -36616.122 588304.6 2308.571009 Jul 15 16067.354 588999.4 -3756.741585 Aug 15 29922.228 589386.0 2946.795384 Sep 15 26127.816 589772.6 834.617070 Oct 15 15793.026 590046.5 640.498164 Nov 15 5861.948 590320.4 -1103.334492 Dec 15 9839.153 590598.5 -1849.644080 Jan 16 8282.031 590876.6 758.372658 Feb 16 -1916.262 591519.7 1969.586090 Mar 16 -13991.882 592162.8 -2681.874361 Apr 16 -24601.763 593268.5 -1443.756267 May 16 -34767.526 594374.3 -4268.756455 Jun 16 -36616.122 595777.1 -3908.956626 Jul 16 16067.354 597179.9 -4998.228743 Aug 16 29922.228 598296.1 2640.699124 Sep 16 26127.816 599412.3 3091.911709 Oct 16 15793.026 599730.2 8911.753854 Nov 16 5861.948 600048.2 3759.882249 Dec 16 9839.153 599539.7 6451.177293 Jan 17 8282.031 599031.2 13856.798664 Feb 17 -1916.262 597685.9 8442.318434 Mar 17 -13991.882 596340.7 1999.164321 Apr 17 -24601.763 594065.7 4253.070507 May 17 -34767.526 591790.7 -1789.141588 Jun 17 -36616.122 588955.9 -7442.742368 Jul 17 16067.354 586121.1 -3322.415094 Aug 17 29922.228 583601.7 6557.047665 Sep 17 26127.816 581082.4 488.795141 Oct 17 15793.026 579295.3 -5128.295450 Nov 17 5861.948 577508.2 -4705.099790 Dec 17 9839.153 576364.9 -6038.063190 Jan 18 8282.031 575221.7 -4046.700262 Feb 18 -1916.262 574457.2 -980.969885 Mar 18 -13991.882 573692.8 759.086610 Apr 18 -24601.763 572789.1 3209.643873 May 18 -34767.526 571885.4 -354.917145 Jun 18 -36616.122 570511.8 6666.284553 Jul 18 16067.354 569138.2 2978.414305 Aug 18 29922.228 567400.0 9726.723215 Sep 18 26127.816 565661.9 7178.316842 Oct 18 15793.026 563551.3 -1700.309388 Nov 18 5861.948 561440.7 -4662.649368 Dec 18 9839.153 558852.9 -2825.095649 Jan 19 8282.031 556265.2 -3273.215603 Feb 19 -1916.262 553487.5 2572.728893 Mar 19 -13991.882 550709.9 3181.999505 Apr 19 -24601.763 547673.6 3199.140631 May 19 -34767.526 544637.4 1971.163476 Jun 19 -36616.122 541302.3 595.854701 Jul 19 16067.354 537967.2 48.473981 Aug 19 29922.228 534795.1 19507.687350 Sep 19 26127.816 531623.0 11107.185436 Oct 19 15793.026 528609.0 -4885.999141 Nov 19 5861.948 525594.9 -9844.897467 Dec 19 9839.153 522769.2 -7046.393193 Jan 20 8282.031 519943.5 -1706.562591 Feb 20 -1916.262 517659.8 -30.501679 Mar 20 -13991.882 515376.0 2069.885351 Apr 20 -24601.763 513051.0 851.743785 May 20 -34767.526 510726.0 3061.483938 Jun 20 -36616.122 507859.0 3859.099746 Jul 20 16067.354 504992.0 2622.643607 Aug 20 29922.228 501865.8 19739.979285 Sep 20 26127.816 498739.6 6758.599680 Oct 20 15793.026 495605.9 -361.929995 Nov 20 5861.948 492472.2 -5917.173420 Dec 20 9839.153 489354.4 -7005.598301 Jan 21 8282.031 486236.7 -1653.696855 Feb 21 -1916.262 483654.6 -777.321845 Mar 21 -13991.882 481072.5 -5145.620718 Apr 21 -24601.763 479091.5 2118.304332 May 21 -34767.526 477110.4 -365.888899 Jun 21 -36616.122 475285.7 478.415424 Jul 21 16067.354 473461.0 -1348.352199 Aug 21 29922.228 471860.5 18781.284050 Sep 21 26127.816 470260.0 5104.205015 Oct 21 15793.026 469276.7 -44.694190 Nov 21 5861.948 468293.4 -9959.307144 Dec 21 9839.153 467636.4 -17305.509868 Jan 22 8282.031 466979.4 -8224.386264 Feb 22 -1916.262 466978.7 -4992.444370 Mar 22 -13991.882 466978.1 -4998.176359 Apr 22 -24601.763 468132.4 -663.625249 May 22 -34767.526 469286.7 1567.807579 Jun 22 -36616.122 471319.3 -3375.197485 Jul 22 16067.354 473351.9 -5404.274496 Aug 22 29922.228 474631.8 5118.993855 Sep 22 26127.816 475911.6 10887.546923 Oct 22 15793.026 477523.7 9514.305779 Nov 22 5861.948 479135.7 -14013.649114 Dec 22 9839.153 480991.6 -19763.757349 Jan 23 8282.031 482847.5 -15080.539256 Feb 23 -1916.262 484329.2 -7807.960954 Mar 23 -13991.882 485810.9 -1380.056535 Apr 23 -24601.763 487415.8 -1563.028611 May 23 -34767.526 489020.6 470.881032 Jun 23 -36616.122 491607.3 634.831180 Jul 23 16067.354 494193.9 6585.709383 Aug 23 29922.228 497281.9 -2012.099036 Sep 23 26127.816 500369.8 -3522.622739 Oct 23 15793.026 503977.5 -1185.504314 Nov 23 5861.948 507585.2 -4208.099639 Dec 23 9839.153 511693.7 -9294.828752 Jan 24 8282.031 515802.2 -4920.231538 Feb 24 -1916.262 520318.3 -1393.074855 Mar 24 -13991.882 524834.5 -909.592055 Apr 24 -24601.763 529282.2 4446.601595 May 24 -34767.526 533729.8 1894.676964 Jun 24 -36616.122 537504.2 6082.938073 Jul 24 16067.354 541278.5 11977.127236 Aug 24 29922.228 544610.8 5180.968220 Sep 24 26127.816 547943.1 3921.093921 Oct 24 15793.026 551056.1 -1385.109021 Nov 24 5861.948 554169.1 -12687.025713 Dec 24 9839.153 556941.9 -11993.087936 Jan 25 8282.031 559714.8 -5671.823832 Feb 25 -1916.262 562557.3 212.985694 Mar 25 -13991.882 565399.8 3924.121337 Apr 25 -24601.763 568621.2 -420.451800 May 25 -34767.526 571842.7 -413.143220 Jun 25 -36616.122 574796.6 4541.501805 Jul 25 16067.354 577750.6 -287.925116 Aug 25 29922.228 580163.7 677.062852 Sep 25 26127.816 582576.8 3908.335537 Oct 25 15793.026 584881.2 10649.740339 Nov 25 5861.948 587185.6 1119.431392 Dec 25 9839.153 589345.8 -3730.909962 Jan 26 8282.031 591505.9 -8922.924989 Feb 26 -1916.262 593171.7 -1876.471598 Mar 26 -13991.882 594837.6 3582.307911 Apr 26 -24601.763 595776.9 1924.839662 May 26 -34767.526 596716.3 5507.253132 Jun 26 -36616.122 597541.9 8102.258390 Jul 26 16067.354 598367.5 6300.191702 Aug 26 29922.228 599130.2 -168.385211 Sep 26 26127.816 599892.9 2211.322592 Oct 26 15793.026 600526.6 -4202.668543 Nov 26 5861.948 601160.4 -11618.373428 Dec 26 9839.153 601667.2 -14365.303873 > m$win s t l 3121 19 13 > m$deg s t l 0 1 1 > m$jump s t l 313 2 2 > m$inner [1] 1 > m$outer [1] 15 > postscript(file="/var/wessaorg/rcomp/tmp/14gp31322560059.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(m,main=main) > dev.off() null device 1 > mylagmax <- nx/2 > postscript(file="/var/wessaorg/rcomp/tmp/2y4ae1322560059.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow = c(2,2)) > acf(as.numeric(x),lag.max = mylagmax,main='Observed') > acf(as.numeric(m$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/3tnj91322560059.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/4d8ea1322560059.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$jump['l']) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/528tp1322560059.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'t',header=TRUE) > a<-table.element(a,'Observed',header=TRUE) > a<-table.element(a,'Fitted',header=TRUE) > a<-table.element(a,'Seasonal',header=TRUE) > a<-table.element(a,'Trend',header=TRUE) > a<-table.element(a,'Remainder',header=TRUE) > a<-table.row.end(a) > for (i in 1:nx) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]+m$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/69qqr1322560059.tab") > > try(system("convert tmp/14gp31322560059.ps tmp/14gp31322560059.png",intern=TRUE)) character(0) > try(system("convert tmp/2y4ae1322560059.ps tmp/2y4ae1322560059.png",intern=TRUE)) character(0) > try(system("convert tmp/3tnj91322560059.ps tmp/3tnj91322560059.png",intern=TRUE)) character(0) > try(system("convert tmp/4d8ea1322560059.ps tmp/4d8ea1322560059.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.880 0.226 4.121