R version 2.9.0 (2009-04-17) Copyright (C) 2009 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 <- c(25.2609 + ,16.8622 + ,13.3181 + ,12.5621 + ,14.2754 + ,12.2961 + ,10.0871 + ,13.5117 + ,13.9921 + ,13.6932 + ,14.4211 + ,15.3397 + ,16.5182 + ,15.2809 + ,15.6204 + ,15.5698 + ,15.9458 + ,16.4063 + ,17.55 + ,17.0353 + ,16.0591 + ,16.3643 + ,14.6527 + ,13.4664 + ,13.3266 + ,13.1823 + ,12.113 + ,13.354 + ,13.4537 + ,13.2715 + ,13.1959 + ,13.5542 + ,12.124 + ,10.967 + ,10.9201 + ,12.5971 + ,14.3177 + ,14.2471 + ,16.0926 + ,17.1334 + ,16.5866 + ,16.361 + ,15.8494 + ,15.5932 + ,16.6387 + ,16.8312 + ,16.5044 + ,16.5556 + ,16.7469 + ,15.9543 + ,15.5888 + ,14.3945 + ,13.8889 + ,12.9999 + ,14.1022 + ,19.6245 + ,24.7658 + ,25.9843 + ,22.9635 + ,19.6288 + ,17.3363 + ,13.311 + ,14.6359 + ,15.834 + ,16.2415 + ,15.9808 + ,16.9726 + ,16.8708 + ,16.923 + ,18.1138 + ,16.7716 + ,14.0299 + ,13.822 + ,14.2537 + ,14.3985 + ,15.2454 + ,15.6683 + ,16.1721 + ,14.8679 + ,14.1948 + ,14.7056 + ,15.3819 + ,15.5001 + ,14.7886 + ,14.563 + ,15.5528 + ,15.9781 + ,15.5139 + ,15.3603 + ,15.0512 + ,14.7874 + ,14.9624 + ,13.9188 + ,14.5146 + ,13.7115 + ,12.0738 + ,12.5688 + ,12.2547 + ,11.8741 + ,13.0261 + ,13.8681 + ,14.2137 + ,14.4743 + ,13.9764 + ,13.1558 + ,13.0991 + ,13.7831 + ,13.2546 + ,13.3426 + ,13.5011 + ,12.8245 + ,13.6596 + ,13.8754 + ,12.9011 + ,11.871 + ,12.3954 + ,12.8179 + ,12.1219 + ,12.6176 + ,13.6362 + ,13.5422 + ,13.362 + ,14.5735 + ,15.8357 + ,14.9927 + ,14.5078 + ,15.2648 + ,15.7163 + ,17.7969 + ,19.0408 + ,17.8571 + ,18.815 + ,19.0961 + ,17.6215 + ,17.0163 + ,15.8286 + ,16.7818 + ,15.8726 + ,16.6621 + ,17.5709 + ,16.9914 + ,18.0412 + ,16.9764 + ,15.7649 + ,14.3928 + ,13.5061 + ,12.7433 + ,13.017 + ,13.0171 + ,12.2412 + ,11.8878 + ,11.2511 + ,11.8583 + ,11.1202 + ,10.185 + ,8.7563 + ,9.5267 + ,9.4106 + ,11.878 + ,14.4228 + ,14.896 + ,15.6664 + ,18.147 + ,19.3069 + ,21.6807 + ,20.7934 + ,23.4241 + ,24.8273 + ,24.9276 + ,27.4256 + ,28.1746 + ,24.5615 + ,30.2532 + ,31.2514 + ,30.4733 + ,33.3047 + ,37.2103 + ,36.7711 + ,37.7163 + ,28.8488 + ,27.4682 + ,29.8793 + ,28.0598 + ,29.7733 + ,32.6926 + ,32.4803 + ,29.4168 + ,28.7054 + ,28.7614 + ,23.8075 + ,21.6987 + ,21.4691 + ,22.5709 + ,23.4546 + ,27.8976 + ,29.2965 + ,28.1191 + ,25.812 + ,25.931 + ,26.9925 + ,28.9213 + ,27.8898 + ,24.2473 + ,27.1056 + ,28.2833 + ,29.8076 + ,27.1826 + ,22.8764 + ,21.938 + ,23.3076 + ,24.9572 + ,26.4694 + ,23.9297 + ,24.7033 + ,24.646 + ,24.0496 + ,24.2096 + ,24.0717 + ,26.6673 + ,27.6457 + ,30.8791 + ,29.3278 + ,30.7268 + ,34.1204 + ,35.0205 + ,39.3565 + ,34.4724 + ,29.9762 + ,33.6008 + ,35.2464 + ,40.4137 + ,41.3922 + ,39.4243 + ,45.7259 + ,48.2549 + ,52.0461 + ,52.1871 + ,49.3474 + ,47.8653 + ,48.5179 + ,52.4815 + ,51.8171 + ,52.5811 + ,57.5617 + ,55.7091 + ,55.4378 + ,58.7493 + ,57.794 + ,50.282 + ,47.6976 + ,46.7381 + ,47.4282 + ,42.2269 + ,44.9066 + ,47.2648 + ,50.2325 + ,50.2504 + ,52.5685 + ,55.2325 + ,52.3674 + ,55.1692 + ,57.7252 + ,62.8232 + ,62.7599 + ,62.4387 + ,64.0862 + ,66.1209 + ,69.8474 + ,80.1039 + ,85.9319 + ,85.2843 + ,77.0383 + ,69.9981 + ,55.2039 + ,43.1188 + ,32.077 + ,34.2974 + ,34.5613 + ,36.5235 + ,39.0474 + ,42.8033 + ,49.5164 + ,46.459 + ,51.1313 + ,46.9331 + ,49.7654 + ,52.0729 + ,51.6425 + ,53.9784 + ,54.4891 + ,59.0665 + ,63.9929 + ,61.6167 + ,62.1816 + ,58.9178 + ,59.9151) > 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 -1.28780868 17.05477 9.493942648 Feb 1 -1.52721085 16.57067 1.818740520 Mar 1 -0.83221442 16.08657 -1.936260206 Apr 1 -0.05430754 15.69912 -3.082708076 May 1 0.64360019 15.31166 -1.679856793 Jun 1 1.07437789 14.97522 -3.753496229 Jul 1 1.01707340 14.63878 -5.568753469 Aug 1 1.45608952 14.34840 -2.292789041 Sep 1 1.31695297 14.05802 -1.382871940 Oct 1 0.58564873 14.23421 -1.126661476 Nov 1 -0.52653503 14.41041 0.537228505 Dec 1 -1.86566573 14.79900 2.406362050 Jan 2 -1.28780868 15.18760 2.618407840 Feb 2 -1.52721085 15.48513 1.322980301 Mar 2 -0.83221442 15.78266 0.669954164 Apr 2 -0.05430754 15.85446 -0.230355805 May 2 0.64360019 15.92627 -0.624066620 Jun 2 1.07437789 15.81186 -0.479939768 Jul 2 1.01707340 15.69746 0.835469281 Aug 2 1.45608952 15.50758 0.071634304 Sep 2 1.31695297 15.31770 -0.575548000 Oct 2 0.58564873 15.10079 0.677859200 Nov 2 -0.52653503 14.88389 0.295345919 Dec 2 -1.86566573 14.60874 0.723323102 Jan 3 -1.28780868 14.33360 0.280812529 Feb 3 -1.52721085 13.99256 0.716951107 Mar 3 -0.83221442 13.65152 -0.706308913 Apr 3 -0.05430754 13.30901 0.099294110 May 3 0.64360019 12.96650 -0.156403712 Jun 3 1.07437789 12.79963 -0.602506722 Jul 3 1.01707340 12.63275 -0.453927536 Aug 3 1.45608952 12.74825 -0.650143212 Sep 3 1.31695297 12.86375 -2.056706215 Oct 3 0.58564873 13.17451 -2.793158865 Nov 3 -0.52653503 13.48527 -2.038631996 Dec 3 -1.86566573 13.83485 0.627917569 Jan 4 -1.28780868 14.18443 1.421079379 Feb 4 -1.52721085 14.52457 1.249739397 Mar 4 -0.83221442 14.86471 2.060100816 Apr 4 -0.05430754 15.19141 1.996302531 May 4 0.64360019 15.51810 0.424903400 Jun 4 1.07437789 15.76014 -0.473520060 Jul 4 1.01707340 16.00219 -1.169861324 Aug 4 1.45608952 16.10889 -1.971779182 Sep 4 1.31695297 16.21559 -0.893844368 Oct 4 0.58564873 16.17227 0.073283442 Nov 4 -0.52653503 16.12894 0.901990769 Dec 4 -1.86566573 16.02442 2.396844495 Jan 5 -1.28780868 15.91990 2.114810465 Feb 5 -1.52721085 16.04476 1.436752039 Mar 5 -0.83221442 16.16962 0.251395014 Apr 5 -0.05430754 16.57850 -2.129694705 May 5 0.64360019 16.98739 -3.742085271 Jun 5 1.07437789 17.38338 -5.457859796 Jul 5 1.01707340 17.77938 -4.694252125 Aug 5 1.45608952 18.00484 0.163571887 Sep 5 1.31695297 18.23030 5.218548573 Oct 5 0.58564873 18.38669 7.011959550 Nov 5 -0.52653503 18.54308 4.946950046 Dec 5 -1.86566573 18.52541 2.969054386 Jan 6 -1.28780868 18.50774 0.116370972 Feb 6 -1.52721085 18.11362 -3.275406406 Mar 6 -0.83221442 17.71950 -2.251382381 Apr 6 -0.05430754 17.17575 -1.287438101 May 6 0.64360019 16.63199 -1.034094667 Jun 6 1.07437789 16.32581 -1.419385046 Jul 6 1.01707340 16.01962 -0.064093228 Aug 6 1.45608952 15.95377 -0.539057660 Sep 6 1.31695297 15.88792 -0.281869419 Oct 6 0.58564873 15.86792 1.660229316 Nov 6 -0.52653503 15.84793 1.450207569 Dec 6 -1.86566573 15.76374 0.131822422 Jan 7 -1.28780868 15.67956 -0.569750480 Feb 7 -1.52721085 15.48770 0.293208526 Mar 7 -0.83221442 15.29585 -0.065131067 Apr 7 -0.05430754 15.11343 0.186272549 May 7 0.64360019 14.93102 0.093675319 Jun 7 1.07437789 14.89211 0.205608541 Jul 7 1.01707340 14.85320 -1.002376040 Aug 7 1.45608952 14.94123 -2.202516124 Sep 7 1.31695297 15.02925 -1.640603534 Oct 7 0.58564873 15.12170 -0.325446492 Nov 7 -0.52653503 15.21414 0.812490068 Dec 7 -1.86566573 15.26237 1.391899530 Jan 8 -1.28780868 15.31059 0.540221237 Feb 8 -1.52721085 15.27524 1.804774616 Mar 8 -0.83221442 15.23989 1.570429398 Apr 8 -0.05430754 15.08816 0.480049087 May 8 0.64360019 14.93643 -0.219732069 Jun 8 1.07437789 14.70540 -0.728577659 Jul 8 1.01707340 14.47437 -0.704041052 Aug 8 1.45608952 14.23975 -0.733438101 Sep 8 1.31695297 14.00513 -1.403282476 Oct 8 0.58564873 13.83126 0.097687570 Nov 8 -0.52653503 13.65740 0.580637135 Dec 8 -1.86566573 13.57534 0.364126180 Jan 9 -1.28780868 13.49328 0.363327471 Feb 9 -1.52721085 13.42096 0.360948312 Mar 9 -0.83221442 13.34864 -0.642329446 Apr 9 -0.05430754 13.28334 -0.202931853 May 9 0.64360019 13.21803 0.006464893 Jun 9 1.07437789 13.23521 -0.095883654 Jul 9 1.01707340 13.25238 0.204849996 Aug 9 1.45608952 13.34646 -0.826152543 Sep 9 1.31695297 13.44055 -1.601702409 Oct 9 0.58564873 13.51009 -0.996637631 Nov 9 -0.52653503 13.57963 0.730006666 Dec 9 -1.86566573 13.54495 1.575318417 Jan 10 -1.28780868 13.51027 1.120142414 Feb 10 -1.52721085 13.38723 1.641081474 Mar 10 -0.83221442 13.26419 0.392521935 Apr 10 -0.05430754 13.11294 0.600963542 May 10 0.64360019 12.96170 0.270104303 Jun 10 1.07437789 12.87625 -1.049526189 Jul 10 1.01707340 12.79080 -1.936874484 Aug 10 1.45608952 12.85517 -1.915863638 Sep 10 1.31695297 12.91955 -1.418600119 Oct 10 0.58564873 13.11772 -1.581469629 Nov 10 -0.52653503 13.31589 -0.171759621 Dec 10 -1.86566573 13.57571 1.926155610 Jan 11 -1.28780868 13.83553 0.994483086 Feb 11 -1.52721085 14.14184 0.747369188 Mar 11 -0.83221442 14.44816 0.957556692 Apr 11 -0.05430754 14.80745 1.082554770 May 11 0.64360019 15.16675 -0.817647998 Jun 11 1.07437789 15.57357 -2.140151601 Jul 11 1.01707340 15.98040 -1.732673006 Aug 11 1.45608952 16.36089 -2.100680974 Sep 11 1.31695297 16.74138 -0.261436268 Oct 11 0.58564873 17.00697 1.448179043 Nov 11 -0.52653503 17.27256 1.111073873 Dec 11 -1.86566573 17.40485 3.275817317 Jan 12 -1.28780868 17.53714 2.846773006 Feb 12 -1.52721085 17.50577 1.642941796 Mar 12 -0.83221442 17.47440 0.374111987 Apr 12 -0.05430754 17.29984 -1.416932427 May 12 0.64360019 17.12528 -0.987077687 Jun 12 1.07437789 16.88935 -2.091130732 Jul 12 1.01707340 16.65343 -1.008401580 Aug 12 1.45608952 16.41456 -0.299749874 Sep 12 1.31695297 16.17569 -0.501245495 Oct 12 0.58564873 15.92921 1.526338884 Nov 12 -0.52653503 15.68273 1.820202781 Dec 12 -1.86566573 15.32769 2.302876701 Jan 13 -1.28780868 14.97265 0.707962867 Feb 13 -1.52721085 14.45924 0.574071022 Mar 13 -0.83221442 13.94583 -0.370319420 Apr 13 -0.05430754 13.35589 -0.284581263 May 13 0.64360019 12.76594 -0.392443951 Jun 13 1.07437789 12.25403 -1.087206142 Jul 13 1.01707340 11.74211 -0.871386135 Aug 13 1.45608952 11.47414 -1.679126846 Sep 13 1.31695297 11.20616 -0.664814884 Oct 13 0.58564873 11.25055 -0.715998027 Nov 13 -0.52653503 11.29494 -0.583401652 Dec 13 -1.86566573 11.64364 -1.021677718 Jan 14 -1.28780868 11.99235 -1.177841539 Feb 14 -1.52721085 12.63125 -1.693442021 Mar 14 -0.83221442 13.27016 -0.559941101 Apr 14 -0.05430754 14.19898 0.278127545 May 14 0.64360019 15.12780 -0.875404655 Jun 14 1.07437789 16.34743 -1.755410788 Jul 14 1.01707340 17.56706 -0.437134723 Aug 14 1.45608952 18.92172 -1.070910572 Sep 14 1.31695297 20.27638 0.087366252 Oct 14 0.58564873 21.55755 -1.349798878 Nov 14 -0.52653503 22.83872 1.111915509 Dec 14 -1.86566573 24.02597 2.666992668 Jan 15 -1.28780868 25.21323 1.002182071 Feb 15 -1.52721085 26.35520 2.597610202 Mar 15 -0.83221442 27.49717 1.509639735 Apr 15 -0.05430754 28.55752 -3.941714089 May 15 0.64360019 29.61787 -0.008268758 Jun 15 1.07437789 30.31798 -0.140961036 Jul 15 1.01707340 31.01810 -1.561871116 Aug 15 1.45608952 31.35769 0.490923350 Sep 15 1.31695297 31.69728 4.196070490 Oct 15 0.58564873 31.86579 4.319663115 Nov 15 -0.52653503 32.03430 6.208535259 Dec 15 -1.86566573 31.96835 -1.253882650 Jan 16 -1.28780868 31.90240 -3.146388314 Feb 16 -1.52721085 31.43133 -0.024817469 Mar 16 -0.83221442 30.96026 -2.068245223 Apr 16 -0.05430754 30.15413 -0.326522717 May 16 0.64360019 29.34800 2.700998943 Jun 16 1.07437789 28.59870 2.807226930 Jul 16 1.01707340 27.84939 0.550337114 Aug 16 1.45608952 27.35551 -0.106199649 Sep 16 1.31695297 26.86163 0.582816261 Oct 16 0.58564873 26.53937 -3.317517670 Nov 16 -0.52653503 26.21711 -3.991872083 Dec 16 -1.86566573 25.97860 -2.643837973 Jan 17 -1.28780868 25.74010 -1.881391618 Feb 17 -1.52721085 25.71837 -0.736556835 Mar 17 -0.83221442 25.69664 3.033179350 Apr 17 -0.05430754 25.89916 3.451650852 May 17 0.64360019 26.10168 1.373821508 Jun 17 1.07437789 26.40762 -1.669997635 Jul 17 1.01707340 26.71356 -1.799634581 Aug 17 1.45608952 26.90250 -1.366093598 Sep 17 1.31695297 27.09145 0.512900058 Oct 17 0.58564873 26.99988 0.304266775 Nov 17 -0.52653503 26.90832 -2.134486989 Dec 17 -1.86566573 26.68408 2.287185777 Jan 18 -1.28780868 26.45984 3.111270788 Feb 18 -1.52721085 26.18812 5.146691893 Mar 18 -0.83221442 25.91640 2.098414400 Apr 18 -0.05430754 25.60192 -2.671213405 May 18 0.64360019 25.28744 -3.993042057 Jun 18 1.07437789 24.98603 -2.752812528 Jul 18 1.01707340 24.68463 -0.744500802 Aug 18 1.45608952 24.57757 0.435742192 Sep 18 1.31695297 24.47051 -1.857762141 Oct 18 0.58564873 24.77804 -0.660386584 Nov 18 -0.52653503 25.08557 0.086968492 Dec 18 -1.86566573 25.63637 0.278894050 Jan 19 -1.28780868 26.18718 -0.689768147 Feb 19 -1.52721085 26.89462 -1.295704508 Mar 19 -0.83221442 27.60205 -0.102539466 Apr 19 -0.05430754 28.48454 -0.784527592 May 19 0.64360019 29.36702 0.868483435 Jun 19 1.07437789 30.22872 -1.975296244 Jul 19 1.01707340 31.09042 -1.380693727 Aug 19 1.45608952 32.00334 0.660967147 Sep 19 1.31695297 32.91627 0.787280695 Oct 19 0.58564873 33.93082 4.840027230 Nov 19 -0.52653503 34.94538 0.053553283 Dec 19 -1.86566573 36.11281 -4.270947056 Jan 20 -1.28780868 37.28024 -2.391635150 Feb 20 -1.52721085 38.59187 -1.818258955 Mar 20 -0.83221442 39.90350 1.342418642 Apr 20 -0.05430754 41.25224 0.194266232 May 20 0.64360019 42.60099 -3.820287024 Jun 20 1.07437789 44.00165 0.649874267 Jul 20 1.01707340 45.40231 1.835517754 Aug 20 1.45608952 46.74033 3.849682694 Sep 20 1.31695297 48.07835 2.791800307 Oct 20 0.58564873 49.25678 -0.495033050 Nov 20 -0.52653503 50.43522 -2.043386888 Dec 20 -1.86566573 51.36030 -0.976732718 Jan 21 -1.28780868 52.28537 1.483933697 Feb 21 -1.52721085 52.78074 0.563569925 Mar 21 -0.83221442 53.27611 0.137207555 Apr 21 -0.05430754 53.32435 4.291661685 May 21 0.64360019 53.37258 1.692914968 Jun 21 1.07437789 52.98321 1.380212740 Jul 21 1.01707340 52.59383 5.138392709 Aug 21 1.45608952 51.91530 4.422609606 Sep 21 1.31695297 51.23677 -2.271720824 Oct 21 0.58564873 50.57692 -3.464968550 Nov 21 -0.52653503 49.91707 -2.652436757 Dec 21 -1.86566573 49.52655 -0.232689106 Jan 22 -1.28780868 49.13604 -5.621329209 Feb 22 -1.52721085 49.20402 -2.770204442 Mar 22 -0.83221442 49.27199 -1.174978273 Apr 22 -0.05430754 50.05287 0.233940673 May 22 0.64360019 50.83374 -1.226941228 Jun 22 1.07437789 52.17778 -0.683659214 Jul 22 1.01707340 53.52182 0.693604996 Aug 22 1.45608952 55.15498 -4.243668576 Sep 22 1.31695297 56.78814 -2.935889474 Oct 22 0.58564873 58.73381 -1.594261502 Nov 22 -0.52653503 60.67949 2.670245989 Dec 22 -1.86566573 63.06174 1.563826808 Jan 23 -1.28780868 65.44399 -1.717480127 Feb 23 -1.52721085 67.40344 -1.790032940 Mar 23 -0.83221442 69.36290 -2.409784351 Apr 23 -0.05430754 69.60542 0.296287682 May 23 0.64360019 69.84794 9.612358870 Jun 23 1.07437789 68.11446 16.743065567 Jul 23 1.01707340 66.38097 17.886254462 Aug 23 1.45608952 63.45861 12.123597707 Sep 23 1.31695297 60.53625 8.144893626 Oct 23 0.58564873 57.06492 -2.446666868 Nov 23 -0.52653503 53.59358 -9.948247843 Dec 23 -1.86566573 50.34622 -16.403550615 Jan 24 -1.28780868 47.09885 -11.513641142 Feb 24 -1.52721085 45.14930 -9.060793688 Mar 24 -0.83221442 43.19976 -5.844044831 Apr 24 -0.05430754 43.09817 -3.996463413 May 24 0.64360019 42.99658 -0.836882841 Jun 24 1.07437789 44.24908 4.192939892 Jul 24 1.01707340 45.50158 -0.059655179 Aug 24 1.45608952 47.20178 2.473425516 Sep 24 1.31695297 48.90199 -3.285841116 Oct 24 0.58564873 50.57281 -1.393061725 Nov 24 -0.52653503 52.24364 0.355797183 Dec 24 -1.86566573 53.49412 0.014042893 Jan 25 -1.28780868 54.74461 0.521600849 Feb 25 -1.52721085 55.87847 0.137839199 Mar 25 -0.83221442 57.01234 2.886378950 Apr 25 -0.05430754 58.15134 5.895863555 May 25 0.64360019 59.29035 1.682747314 Jun 25 1.07437789 60.40619 0.701027997 Jul 25 1.01707340 61.52204 -3.621309122 Aug 25 1.45608952 62.59343 -4.134415010 > m$win s t l 2961 19 13 > m$deg s t l 0 1 1 > m$jump s t l 297 2 2 > m$inner [1] 2 > m$outer [1] 0 > postscript(file="/var/www/html/rcomp/tmp/1oc6f1292176043.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(m,main=main) > dev.off() null device 1 > mylagmax <- nx/2 > postscript(file="/var/www/html/rcomp/tmp/2zl501292176043.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow = c(2,2)) > acf(as.numeric(x),lag.max = mylagmax,main='Observed') > acf(as.numeric(m$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/www/html/rcomp/tmp/3zl501292176043.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/www/html/rcomp/tmp/4ad431292176043.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/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,'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$jump['l']) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/5gwje1292176043.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'t',header=TRUE) > a<-table.element(a,'Observed',header=TRUE) > a<-table.element(a,'Fitted',header=TRUE) > a<-table.element(a,'Seasonal',header=TRUE) > a<-table.element(a,'Trend',header=TRUE) > a<-table.element(a,'Remainder',header=TRUE) > a<-table.row.end(a) > for (i in 1:nx) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]+m$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/www/html/rcomp/tmp/69niz1292176043.tab") > > try(system("convert tmp/1oc6f1292176043.ps tmp/1oc6f1292176043.png",intern=TRUE)) character(0) > try(system("convert tmp/2zl501292176043.ps tmp/2zl501292176043.png",intern=TRUE)) character(0) > try(system("convert tmp/3zl501292176043.ps tmp/3zl501292176043.png",intern=TRUE)) character(0) > try(system("convert tmp/4ad431292176043.ps tmp/4ad431292176043.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.175 0.796 7.096