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Type 'q()' to quit R. > x <- c(2030 + ,1855 + ,1834 + ,2092 + ,2164 + ,2368 + ,2072 + ,2521 + ,1823 + ,1947 + ,2226 + ,1754 + ,1786 + ,2072 + ,1846 + ,2137 + ,2466 + ,2154 + ,2289 + ,2628 + ,2074 + ,2798 + ,2194 + ,2442 + ,2565 + ,2063 + ,2069 + ,2539 + ,1898 + ,2139 + ,2408 + ,2725 + ,2201 + ,2311 + ,2548 + ,2276 + ,2351 + ,2280 + ,2057 + ,2479 + ,2379 + ,2295 + ,2456 + ,2546 + ,2844 + ,2260 + ,2981 + ,2678 + ,3440 + ,2842 + ,2450 + ,2669 + ,2570 + ,2540 + ,2318 + ,2930 + ,2947 + ,2799 + ,2695 + ,2498 + ,2260 + ,2160 + ,2058 + ,2533 + ,2150 + ,2172 + ,2155 + ,3016 + ,2333 + ,2355 + ,2825 + ,2214 + ,2360 + ,2299 + ,1746 + ,2069 + ,2267 + ,1878 + ,2266 + ,2282 + ,2085 + ,2277 + ,2251 + ,1828 + ,1954 + ,1851 + ,1570 + ,1852 + ,2187 + ,1855 + ,2218 + ,2253 + ,2028 + ,2169 + ,1997 + ,2034 + ,1791 + ,1627 + ,1631 + ,2319 + ,1707 + ,1747 + ,2397 + ,2059 + ,2251 + ,2558 + ,2406 + ,2049 + ,2074 + ,1734 + ,1983 + ,2121 + ,1905 + ,2126 + ,2363 + ,2173 + ,2710 + ,2137 + ,2742 + ,2419 + ,2194 + ,2660 + ,2189 + ,2310 + ,2349 + ,2540 + ,2434 + ,2916 + ,2446 + ,2375 + ,3032 + ,2218 + ,1920 + ,2039 + ,1889 + ,2014 + ,2105 + ,2153 + ,2309 + ,2955 + ,2225 + ,2160 + ,2386 + ,1653 + ,1099 + ,5010 + ,2672 + ,2729 + ,2955 + ,2409 + ,3086 + ,3384 + ,2458 + ,2913 + ,2448 + ,2215 + ,2179 + ,2461 + ,2098 + ,2621 + ,2703 + ,2388 + ,3880 + ,3310 + ,3093 + ,3237 + ,3002 + ,2670 + ,2311 + ,2062 + ,2059 + ,2465 + ,2213 + ,2028 + ,2322 + ,2825 + ,2687 + ,2373 + ,2889 + ,2708 + ,2542 + ,2477 + ,2419 + ,2977 + ,3001 + ,3075 + ,2870 + ,3756 + ,3443 + ,2948 + ,3560 + ,3257 + ,2600 + ,2741 + ,2349 + ,2783 + ,2845 + ,2987 + ,2696 + ,3874 + ,2912 + ,2743 + ,3857 + ,2660 + ,2226 + ,2942 + ,2420 + ,2516 + ,2421 + ,2631 + ,2887 + ,3328 + ,2587 + ,2695 + ,3669 + ,2773 + ,2527 + ,2750 + ,2014 + ,2763 + ,2726 + ,1826 + ,2713 + ,3040 + ,2405 + ,2526 + ,2526 + ,2529 + ,2474 + ,2576 + ,2219 + ,2900 + ,2274 + ,2184 + ,2629 + ,2739 + ,2933 + ,3144 + ,3354 + ,3357 + ,3329) > 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 -187.4402629 2174.267 43.173745 Feb 1 -13.4608923 2156.881 -288.420178 Mar 1 -362.0486604 2139.496 56.553038 Apr 1 -0.6432559 2122.215 -29.571644 May 1 -85.1876880 2104.934 144.253510 Jun 1 -179.6558886 2090.412 457.243998 Jul 1 79.0259927 2075.890 -82.915596 Aug 1 399.1386821 2065.285 56.576260 Sep 1 55.9015921 2054.681 -287.582105 Oct 1 63.4330677 2049.578 -166.011144 Nov 1 302.1146233 2044.476 -120.590263 Dec 1 -71.1772917 2057.032 -231.854703 Jan 2 -187.4402629 2069.588 -96.148086 Feb 2 -13.4608923 2096.937 -11.476046 Mar 2 -362.0486604 2124.286 83.763132 Apr 2 -0.6432559 2159.407 -21.763473 May 2 -85.1876880 2194.528 356.659758 Jun 2 -179.6558886 2229.951 103.705086 Jul 2 79.0259927 2265.374 -55.399668 Aug 2 399.1386821 2289.097 -60.235964 Sep 2 55.9015921 2312.821 -294.722481 Oct 2 63.4330677 2319.831 414.736261 Nov 2 302.1146233 2326.840 -434.955076 Dec 2 -71.1772917 2326.954 186.223531 Jan 3 -187.4402629 2327.067 425.373194 Feb 3 -13.4608923 2327.218 -250.756619 Mar 3 -362.0486604 2327.368 103.680707 Apr 3 -0.6432559 2320.659 218.984034 May 3 -85.1876880 2313.950 -330.762803 Jun 3 -179.6558886 2307.264 11.391641 Jul 3 79.0259927 2300.578 28.396003 Aug 3 399.1386821 2301.398 24.463816 Sep 3 55.9015921 2302.217 -157.118592 Oct 3 63.4330677 2314.439 -66.872157 Nov 3 302.1146233 2326.661 -80.775802 Dec 3 -71.1772917 2342.035 5.142773 Jan 4 -187.4402629 2357.408 181.032404 Feb 4 -13.4608923 2371.663 -78.202440 Mar 4 -362.0486604 2385.919 33.129854 Apr 4 -0.6432559 2404.726 74.917032 May 4 -85.1876880 2423.534 40.654046 Jun 4 -179.6558886 2461.357 13.298763 Jul 4 79.0259927 2499.181 -122.206601 Aug 4 399.1386821 2546.999 -400.138140 Sep 4 55.9015921 2594.818 193.280101 Oct 4 63.4330677 2633.061 -436.493761 Nov 4 302.1146233 2671.303 7.582298 Dec 4 -71.1772917 2692.326 56.850940 Jan 5 -187.4402629 2713.350 914.090639 Feb 5 -13.4608923 2726.230 129.231232 Mar 5 -362.0486604 2739.110 72.938964 Apr 5 -0.6432559 2732.953 -63.309829 May 5 -85.1876880 2726.796 -71.608784 Jun 5 -179.6558886 2692.207 27.448708 Jul 5 79.0259927 2657.618 -418.643881 Aug 5 399.1386821 2612.811 -81.949808 Sep 5 55.9015921 2568.004 323.094045 Oct 5 63.4330677 2539.145 196.422395 Nov 5 302.1146233 2510.285 -117.399335 Dec 5 -71.1772917 2485.413 83.764426 Jan 6 -187.4402629 2460.541 -13.100756 Feb 6 -13.4608923 2435.808 -262.347509 Mar 6 -362.0486604 2411.076 8.972877 Apr 6 -0.6432559 2390.398 143.245447 May 6 -85.1876880 2369.720 -134.532147 Jun 6 -179.6558886 2364.430 -12.774062 Jul 6 79.0259927 2359.140 -283.166059 Aug 6 399.1386821 2354.624 262.236818 Sep 6 55.9015921 2350.109 -73.010525 Oct 6 63.4330677 2338.887 -47.319848 Nov 6 302.1146233 2327.665 195.220749 Dec 6 -71.1772917 2311.232 -26.054660 Jan 7 -187.4402629 2294.799 252.640987 Feb 7 -13.4608923 2269.040 43.420838 Mar 7 -362.0486604 2243.281 -135.232173 Apr 7 -0.6432559 2211.121 -141.477676 May 7 -85.1876880 2178.961 173.226657 Jun 7 -179.6558886 2146.484 -88.827875 Jul 7 79.0259927 2114.006 72.967511 Aug 7 399.1386821 2086.545 -203.683496 Sep 7 55.9015921 2059.083 -29.984724 Oct 7 63.4330677 2042.956 170.610465 Nov 7 302.1146233 2026.830 -77.944426 Dec 7 -71.1772917 2020.173 -120.995779 Jan 8 -187.4402629 2013.516 127.923923 Feb 8 -13.4608923 2011.176 -146.714835 Mar 8 -362.0486604 2008.835 -76.786455 Apr 8 -0.6432559 2003.909 -151.265270 May 8 -85.1876880 1998.982 273.205752 Jun 8 -179.6558886 1994.614 40.041831 Jul 8 79.0259927 1990.246 148.727829 Aug 8 399.1386821 1985.389 -131.527951 Sep 8 55.9015921 1980.532 -8.433952 Oct 8 63.4330677 1976.025 129.542187 Nov 8 302.1146233 1971.517 -276.631754 Dec 8 -71.1772917 1967.773 137.404351 Jan 9 -187.4402629 1964.029 14.411513 Feb 9 -13.4608923 1970.136 -329.675151 Mar 9 -362.0486604 1976.243 16.805324 Apr 9 -0.6432559 1995.610 324.033386 May 9 -85.1876880 2014.976 -222.788717 Jun 9 -179.6558886 2037.309 -110.652909 Jul 9 79.0259927 2059.641 258.332816 Aug 9 399.1386821 2075.879 -416.017833 Sep 9 55.9015921 2092.117 102.981297 Oct 9 63.4330677 2102.881 391.685980 Nov 9 302.1146233 2113.645 -9.759417 Dec 9 -71.1772917 2124.625 -4.447225 Jan 10 -187.4402629 2135.604 125.836022 Feb 10 -13.4608923 2142.782 -395.320766 Mar 10 -362.0486604 2149.959 195.089585 Apr 10 -0.6432559 2159.132 -37.488859 May 10 -85.1876880 2168.305 -178.117467 Jun 10 -179.6558886 2192.214 113.441890 Jul 10 79.0259927 2216.123 67.851164 Aug 10 399.1386821 2251.445 -477.584156 Sep 10 55.9015921 2286.768 367.330303 Oct 10 63.4330677 2319.968 -246.400814 Nov 10 302.1146233 2353.167 86.717988 Dec 10 -71.1772917 2383.194 106.983540 Jan 11 -187.4402629 2413.220 -31.779852 Feb 11 -13.4608923 2434.313 239.148098 Mar 11 -362.0486604 2455.405 95.643188 Apr 11 -0.6432559 2464.044 -153.400410 May 11 -85.1876880 2472.682 -38.494172 Jun 11 -179.6558886 2463.346 256.310132 Jul 11 79.0259927 2454.010 -99.035645 Aug 11 399.1386821 2427.675 89.186417 Sep 11 55.9015921 2401.340 -11.241740 Oct 11 63.4330677 2370.102 -58.535049 Nov 11 302.1146233 2338.864 391.021563 Dec 11 -71.1772917 2314.452 -25.274285 Jan 12 -187.4402629 2290.039 -182.599076 Feb 12 -13.4608923 2273.709 -221.247706 Mar 12 -362.0486604 2257.378 -6.329197 Apr 12 -0.6432559 2236.914 -222.270432 May 12 -85.1876880 2216.450 -26.261831 Jun 12 -179.6558886 2205.010 127.645974 Jul 12 79.0259927 2193.570 36.403698 Aug 12 399.1386821 2245.520 310.340950 Sep 12 55.9015921 2297.470 -128.372019 Oct 12 63.4330677 2373.894 -277.326730 Nov 12 302.1146233 2450.317 -366.431520 Dec 12 -71.1772917 2515.765 -791.587446 Jan 13 -187.4402629 2581.213 -1294.772316 Feb 13 -13.4608923 2637.601 2385.859927 Mar 13 -362.0486604 2693.989 340.059307 Apr 13 -0.6432559 2738.158 -8.514693 May 13 -85.1876880 2782.327 257.861144 Jun 13 -179.6558886 2779.092 -190.436340 Jul 13 79.0259927 2775.858 231.116095 Aug 13 399.1386821 2721.195 263.666659 Sep 13 55.9015921 2666.531 -264.432997 Oct 13 63.4330677 2622.758 226.808795 Nov 13 302.1146233 2578.985 -433.099493 Dec 13 -71.1772917 2580.726 -294.548435 Jan 14 -187.4402629 2582.467 -216.026321 Feb 14 -13.4608923 2616.593 -142.132551 Mar 14 -362.0486604 2650.720 -190.671643 Apr 14 -0.6432559 2702.096 -80.453168 May 14 -85.1876880 2753.473 34.715142 Jun 14 -179.6558886 2788.851 -221.195457 Jul 14 79.0259927 2824.230 976.743862 Aug 14 399.1386821 2822.029 88.831955 Sep 14 55.9015921 2819.829 217.269827 Oct 14 63.4330677 2782.827 390.740076 Nov 14 302.1146233 2745.825 -45.939755 Dec 14 -71.1772917 2680.495 60.682783 Jan 15 -187.4402629 2615.164 -116.723622 Feb 15 -13.4608923 2549.004 -473.543469 Mar 15 -362.0486604 2482.845 -61.796178 Apr 15 -0.6432559 2447.796 17.847578 May 15 -85.1876880 2412.747 -114.558829 Jun 15 -179.6558886 2415.983 -208.327209 Jul 15 79.0259927 2419.220 -176.245672 Aug 15 399.1386821 2449.095 -23.234176 Sep 15 55.9015921 2478.971 152.127099 Oct 15 63.4330677 2528.825 -219.258515 Nov 15 302.1146233 2578.680 8.205791 Dec 15 -71.1772917 2642.828 136.349521 Jan 16 -187.4402629 2706.976 22.464307 Feb 16 -13.4608923 2773.411 -282.949864 Mar 16 -362.0486604 2839.846 -58.796898 Apr 16 -0.6432559 2897.895 79.748561 May 16 -85.1876880 2955.944 130.243856 Jun 16 -179.6558886 2997.421 257.235092 Jul 16 79.0259927 3038.898 -247.923753 Aug 16 399.1386821 3050.809 306.052067 Sep 16 55.9015921 3062.721 324.377665 Oct 16 63.4330677 3050.230 -165.663150 Nov 16 302.1146233 3037.739 220.145955 Dec 16 -71.1772917 3018.028 310.149196 Jan 17 -187.4402629 2998.317 -210.876507 Feb 17 -13.4608923 2981.321 -226.860000 Mar 17 -362.0486604 2964.325 -253.276354 Apr 17 -0.6432559 2952.883 -169.239785 May 17 -85.1876880 2941.441 -11.253379 Jun 17 -179.6558886 2934.415 232.240638 Jul 17 79.0259927 2927.389 -310.415427 Aug 17 399.1386821 2922.159 552.702213 Sep 17 55.9015921 2916.929 -60.830367 Oct 17 63.4330677 2897.974 -218.406807 Nov 17 302.1146233 2879.019 675.866674 Dec 17 -71.1772917 2852.493 -121.315828 Jan 18 -187.4402629 2825.968 -412.527273 Feb 18 -13.4608923 2800.479 154.982109 Mar 18 -362.0486604 2774.990 7.058629 Apr 18 -0.6432559 2763.244 -246.600897 May 18 -85.1876880 2751.498 -245.310586 Jun 18 -179.6558886 2754.524 56.131692 Jul 18 79.0259927 2757.550 50.423888 Aug 18 399.1386821 2763.444 165.416872 Sep 18 55.9015921 2769.339 -238.240364 Oct 18 63.4330677 2764.936 -133.369148 Nov 18 302.1146233 2760.533 606.351989 Dec 18 -71.1772917 2742.176 102.001721 Jan 19 -187.4402629 2723.818 -9.377490 Feb 19 -13.4608923 2693.873 69.588234 Mar 19 -362.0486604 2663.928 -287.878903 Apr 19 -0.6432559 2626.142 137.501346 May 19 -85.1876880 2588.356 222.831432 Jun 19 -179.6558886 2556.781 -551.124941 Jul 19 79.0259927 2525.205 108.768604 Aug 19 399.1386821 2517.777 123.084088 Sep 19 55.9015921 2510.349 -161.250649 Oct 19 63.4330677 2514.223 -51.656050 Nov 19 302.1146233 2518.097 -294.211531 Dec 19 -71.1772917 2521.154 79.023531 Jan 20 -187.4402629 2524.211 137.229649 Feb 20 -13.4608923 2534.995 54.466020 Mar 20 -362.0486604 2545.779 35.269529 Apr 20 -0.6432559 2591.234 309.409326 May 20 -85.1876880 2636.689 -277.501041 Jun 20 -179.6558886 2708.580 -344.924557 Jul 20 79.0259927 2780.472 -230.498155 Aug 20 399.1386821 2852.210 -512.349164 Sep 20 55.9015921 2923.949 -46.850393 Oct 20 63.4330677 2999.319 81.247905 Nov 20 302.1146233 3074.689 -22.803878 Dec 20 -71.1772917 3156.746 271.431495 Jan 21 -187.4402629 3238.802 277.637923 > m$win s t l 2411 19 13 > m$deg s t l 0 1 1 > m$jump s t l 242 2 2 > m$inner [1] 2 > m$outer [1] 0 > postscript(file="/var/www/html/rcomp/tmp/1kbfp1293362837.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(m,main=main) > dev.off() null device 1 > mylagmax <- nx/2 > postscript(file="/var/www/html/rcomp/tmp/2kbfp1293362837.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow = c(2,2)) > acf(as.numeric(x),lag.max = mylagmax,main='Observed') > acf(as.numeric(m$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/3kbfp1293362837.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/48z731293362837.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$jump['l']) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/54r5u1293362837.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'t',header=TRUE) > a<-table.element(a,'Observed',header=TRUE) > a<-table.element(a,'Fitted',header=TRUE) > a<-table.element(a,'Seasonal',header=TRUE) > a<-table.element(a,'Trend',header=TRUE) > a<-table.element(a,'Remainder',header=TRUE) > a<-table.row.end(a) > for (i in 1:nx) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]+m$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/6pr401293362837.tab") > > try(system("convert tmp/1kbfp1293362837.ps tmp/1kbfp1293362837.png",intern=TRUE)) character(0) > try(system("convert tmp/2kbfp1293362837.ps tmp/2kbfp1293362837.png",intern=TRUE)) character(0) > try(system("convert tmp/3kbfp1293362837.ps tmp/3kbfp1293362837.png",intern=TRUE)) character(0) > try(system("convert tmp/48z731293362837.ps tmp/48z731293362837.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.398 0.714 5.014