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*The author of this computation has been verified*

R Software Module: /rwasp_decomposeloess.wasp (opens new window with default values)

Title produced by software: Decomposition by Loess

Date of computation: Wed, 09 Dec 2009 12:09:07 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/09/t1260385785pg130sdd0o3gi60.htm/, Retrieved Wed, 09 Dec 2009 20:09:50 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Dec/09/t1260385785pg130sdd0o3gi60.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

2360 2214 2825 2355 2333 3016 2155 2172 2150 2533 2058 2160 2260 2498 2695 2799 2947 2930 2318 2540 2570 2669 2450 2842 3440 2678 2981 2260 2844 2546 2456 2295 2379 2479 2057 2280 2351 2276 2548 2311 2201 2725 2408 2139 1898 2537 2069 2063 2524 2437 2189 2793 2074 2622 2278 2144 2427 2139 1828 2072 1800 1758 2246 1987 1868 2514 2121

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Seasonal Decomposition by Loess - Parameters Component Window Degree Jump Seasonal 671 0 68 Trend 19 1 2 Low-pass 13 1 2 Seasonal Decomposition by Loess - Time Series Components t Observed Fitted Seasonal Trend Remainder 1 2360 2281.55140812987 75.6497893581889 2362.79880251194 -78.448591870132 2 2214 2131.27966452141 -66.4949848066158 2363.21532028521 -82.7203354785925 3 2825 3078.841318848 207.526843093529 2363.63183805847 253.841318847997 4 2355 2294.41635475571 50.4955778680583 2365.08806737624 -60.5836452442941 5 2333 2282.49129209430 16.9644112116970 2366.544296694 -50.5087079056957 6 3016 3289.79323565683 372.422450244446 2369.78431409873 273.793235656828 7 2155 1992.92793146126 -55.9522629647083 2373.02433150345 -162.072068538745 8 2172 2121.98289482621 -155.322515441816 2377.33962061561 -50.017105173792 9 2150 2038.73657830859 -120.391488036351 2381.65490972776 -111.263421691414 10 2533 2598.45612181898 74.1453843980334 2393.39849378298 65.4561218189838 11 2058 2007.77573183094 -296.917809669138 2405.1420778382 -50.2242681690636 12 2160 1991.78839596133 -102.125335732421 2430.33693977109 -168.211604038674 13 2260 1988.81840893782 75.6497893581889 2455.53180170399 -271.181591062177 14 2498 2579.71684285455 -66.4949848066158 2482.77814195206 81.7168428545524 15 2695 2672.44867470633 207.526843093529 2510.02448220014 -22.551325293668 16 2799 3005.60104653696 50.4955778680583 2541.90337559498 206.601046536962 17 2947 3303.25331979848 16.9644112116970 2573.78226898982 356.253319798482 18 2930 2869.29183725825 372.422450244446 2618.2857124973 -60.708162741746 19 2318 2029.16310695993 -55.9522629647083 2662.78915600478 -288.836893040071 20 2540 2540.39166535727 -155.322515441816 2694.93085008455 0.391665357265538 21 2570 2533.31894387203 -120.391488036351 2727.07254416432 -36.6810561279726 22 2669 2533.73034772284 74.1453843980334 2730.12426787913 -135.269652277159 23 2450 2463.74181807521 -296.917809669138 2733.17599159393 13.7418180752088 24 2842 3065.3671341064 -102.125335732421 2720.75820162602 223.367134106401 25 3440 4096.0097989837 75.6497893581889 2708.34041165811 656.0097989837 26 2678 2732.80335096437 -66.4949848066158 2689.69163384225 54.8033509643687 27 2981 3083.43030088009 207.526843093529 2671.04285602638 102.430300880087 28 2260 1833.68488693638 50.4955778680583 2635.81953519556 -426.315113063617 29 2844 3070.43937442357 16.9644112116970 2600.59621436474 226.439374423568 30 2546 2167.10350686122 372.422450244446 2552.47404289434 -378.896493138783 31 2456 2463.60039154077 -55.9522629647083 2504.35187142394 7.60039154077049 32 2295 2281.24172551380 -155.322515441816 2464.08078992801 -13.7582744861970 33 2379 2454.58177960426 -120.391488036351 2423.80970843209 75.5817796042606 34 2479 2481.95721147317 74.1453843980334 2401.8974041288 2.9572114731659 35 2057 2030.93270984363 -296.917809669138 2379.98509982551 -26.0672901563739 36 2280 2295.93346036070 -102.125335732421 2366.19187537172 15.9334603607017 37 2351 2273.95155972388 75.6497893581889 2352.39865091793 -77.0484402761163 38 2276 2279.10943257569 -66.4949848066158 2339.38555223092 3.10943257569079 39 2548 2562.10070336255 207.526843093529 2326.37245354392 14.1007033625478 40 2311 2254.42726786238 50.4955778680583 2317.07715426956 -56.5727321376189 41 2201 2077.25373379310 16.9644112116970 2307.7818549952 -123.746266206896 42 2725 2771.62561130557 372.422450244446 2305.95193844998 46.6256113055701 43 2408 2567.83024105994 -55.9522629647083 2304.12202190477 159.830241059939 44 2139 2125.993776949 -155.322515441816 2307.32873849282 -13.0062230510016 45 1898 1605.85603295548 -120.391488036351 2310.53545508087 -292.143967044517 46 2537 2686.45941530884 74.1453843980334 2313.39520029312 149.459415308842 47 2069 2118.66286416376 -296.917809669138 2316.25494550538 49.6628641637562 48 2063 1912.17715692547 -102.125335732421 2315.94817880695 -150.822843074529 49 2524 2656.70879853329 75.6497893581889 2315.64141210852 132.708798533293 50 2437 2623.65330465633 -66.4949848066158 2316.84168015028 186.653304656334 51 2189 1852.43120871443 207.526843093529 2318.04194819205 -336.568791285575 52 2793 3222.25453654015 50.4955778680583 2313.24988559179 429.254536540152 53 2074 1822.57776579677 16.9644112116970 2308.45782299153 -251.422234203230 54 2622 2586.68177749546 372.422450244446 2284.89577226010 -35.3182225045416 55 2278 2350.61854143605 -55.9522629647083 2261.33372152866 72.6185414360502 56 2144 2218.12428174248 -155.322515441816 2225.19823369934 74.1242817424759 57 2427 2785.32874216633 -120.391488036351 2189.06274587002 358.328742166327 58 2139 2048.71141423345 74.1453843980334 2155.14320136852 -90.288585766552 59 1828 1831.69415280212 -296.917809669138 2121.22365686701 3.69415280212388 60 2072 2147.57876318363 -102.125335732421 2098.54657254879 75.578763183627 61 1800 1448.48072241124 75.6497893581889 2075.86948823057 -351.519277588762 62 1758 1529.83615640349 -66.4949848066158 2052.65882840313 -228.163843596513 63 2246 2255.02498833079 207.526843093529 2029.44816857568 9.02498833078653 64 1987 1916.87666665030 50.4955778680583 2006.62775548164 -70.1233333496953 65 1868 1735.22824640071 16.9644112116970 1983.80734238759 -132.771753599287 66 2514 2692.07489318326 372.422450244446 1963.50265657230 178.074893183255 67 2121 2354.7542922077 -55.9522629647083 1943.19797075701 233.754292207702

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/09/t1260385785pg130sdd0o3gi60/16gk01260385744.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260385785pg130sdd0o3gi60/16gk01260385744.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260385785pg130sdd0o3gi60/2rv5m1260385744.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260385785pg130sdd0o3gi60/2rv5m1260385744.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260385785pg130sdd0o3gi60/3r9081260385744.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260385785pg130sdd0o3gi60/3r9081260385744.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260385785pg130sdd0o3gi60/4y9d91260385744.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260385785pg130sdd0o3gi60/4y9d91260385744.ps (open in new window)

Parameters (Session):

par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;

Parameters (R input):

par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;

R code (references can be found in the software module):

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$time.series m$win m$deg m$jump m$inner m$outer bitmap(file='test1.png') plot(m,main=main) dev.off() mylagmax <- nx/2 bitmap(file='test2.png') 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$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder') par(op) dev.off() bitmap(file='test3.png') op <- par(mfrow = c(2,2)) spectrum(as.numeric(x),main='Observed') spectrum(as.numeric(m$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$time.series[,'remainder']),'remainder']),main='Remainder') par(op) dev.off() bitmap(file='test4.png') op <- par(mfrow = c(2,2)) cpgram(as.numeric(x),main='Observed') cpgram(as.numeric(m$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$time.series[,'remainder']),'remainder']),main='Remainder') par(op) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Seasonal Decomposition by Loess - Parameters',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Component',header=TRUE) a<-table.element(a,'Window',header=TRUE) a<-table.element(a,'Degree',header=TRUE) a<-table.element(a,'Jump',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Seasonal',header=TRUE) a<-table.element(a,m$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='mytable.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='mytable1.tab')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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