WS 9 adh

*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: Thu, 10 Dec 2009 09:56:36 -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/10/t1260464232k93um4bo4ndxkx4.htm/, Retrieved Thu, 10 Dec 2009 17:57:17 +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/10/t1260464232k93um4bo4ndxkx4.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:

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1901 1395 1639 1643 1751 1797 1373 1558 1555 2061 2010 2119 1985 1963 2017 1975 1589 1679 1392 1511 1449 1767 1899 2179 2217 2049 2343 2175 1607 1702 1764 1766 1615 1953 2091 2411 2550 2351 2786 2525 2474 2332 1978 1789 1904 1997 2207 2453 1948 1384 1989 2140 2100 2045 2083 2022 1950 1422 1859 2147

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 601 0 61 Trend 19 1 2 Low-pass 13 1 2 Seasonal Decomposition by Loess - Time Series Components t Observed Fitted Seasonal Trend Remainder 1 1901 2064.41089920635 218.213904537347 1519.37519625631 163.410899206348 2 1395 1318.97132895013 -85.6487898234341 1556.67746087331 -76.0286710498722 3 1639 1455.33204676043 228.688227749267 1593.97972549031 -183.667953239574 4 1643 1496.43158022568 160.274866291010 1629.29355348331 -146.568419774322 5 1751 1869.73121302426 -32.3385945005743 1664.60738147632 118.731213024258 6 1797 1923.91682559587 -27.6404702830841 1697.72364468722 126.916825595869 7 1373 1237.90242522798 -222.742333126094 1730.83990789811 -135.097574772020 8 1558 1567.52465330036 -214.790326887499 1763.26567358714 9.52465330036284 9 1555 1566.94685327191 -252.638292548066 1795.69143927616 11.9468532719086 10 2061 2417.46546724876 -114.77101756095 1819.30555031219 356.465467248765 11 2010 2126.18424917732 50.8960894744706 1842.91966134821 116.184249177317 12 2119 2105.43550890742 292.496748918216 1840.06774217436 -13.5644910925760 13 1985 1914.57027246215 218.213904537347 1837.21582300051 -70.429727537854 14 1963 2187.76201217304 -85.6487898234341 1823.88677765039 224.762012173040 15 2017 1994.75403995045 228.688227749267 1810.55773230028 -22.2459600495474 16 1975 1993.26819115769 160.274866291010 1796.45694255130 18.2681911576869 17 1589 1427.98244169825 -32.3385945005743 1782.35615280233 -161.017558301751 18 1679 1603.67592750730 -27.6404702830841 1781.96454277578 -75.3240724926954 19 1392 1225.16940037686 -222.742333126094 1781.57293274923 -166.830599623140 20 1511 1438.63577105709 -214.790326887499 1798.15455583041 -72.364228942907 21 1449 1335.90211363649 -252.638292548066 1814.73617891158 -113.097886363511 22 1767 1813.05374455668 -114.77101756095 1835.71727300427 46.0537445566822 23 1899 1890.40554342857 50.8960894744706 1856.69836709696 -8.59445657142896 24 2179 2190.81118739763 292.496748918216 1874.69206368416 11.811187397627 25 2217 2323.10033519130 218.213904537347 1892.68576027136 106.100335191297 26 2049 2276.82477741351 -85.6487898234341 1906.82401240993 227.824777413509 27 2343 2536.34950770224 228.688227749267 1920.96226454850 193.349507702238 28 2175 2257.08099473867 160.274866291010 1932.64413897032 82.080994738667 29 1607 1302.01258110842 -32.3385945005743 1944.32601339215 -304.987418891576 30 1702 1472.43427313654 -27.6404702830841 1959.20619714654 -229.565726863457 31 1764 1776.65595222516 -222.742333126094 1974.08638090093 12.6559522251612 32 1766 1742.33570518474 -214.790326887499 2004.45462170276 -23.6642948152632 33 1615 1447.81543004348 -252.638292548066 2034.82286250459 -167.184569956525 34 1953 1937.46344745476 -114.77101756095 2083.30757010619 -15.5365525452366 35 2091 1999.31163281775 50.8960894744706 2131.79227770778 -91.6883671822532 36 2411 2350.94570929048 292.496748918216 2178.55754179130 -60.0542907095196 37 2550 2656.46328958783 218.213904537347 2225.32280587482 106.463289587829 38 2351 2538.51480898551 -85.6487898234341 2249.13398083792 187.514808985514 39 2786 3070.36661644972 228.688227749267 2272.94515580102 284.366616449717 40 2525 2611.73542231799 160.274866291010 2277.989711391 86.7354223179877 41 2474 2697.30432751959 -32.3385945005743 2283.03426698099 223.304327519587 42 2332 2431.74492886365 -27.6404702830841 2259.89554141944 99.7449288636453 43 1978 1941.98551726820 -222.742333126094 2236.75681585789 -36.0144827317968 44 1789 1609.54354022380 -214.790326887499 2183.24678666370 -179.456459776205 45 1904 1930.90153507855 -252.638292548066 2129.73675746952 26.9015350785498 46 1997 2028.49697897619 -114.77101756095 2080.27403858476 31.4969789761894 47 2207 2332.29259082552 50.8960894744706 2030.81131970000 125.292590825525 48 2453 2602.47319399733 292.496748918216 2011.03005708445 149.473193997335 49 1948 1686.53730099376 218.213904537347 1991.24879446889 -261.46269900624 50 1384 863.170973067734 -85.6487898234341 1990.4778167557 -520.829026932266 51 1989 1759.60493320823 228.688227749267 1989.70683904251 -229.395066791774 52 2140 2137.01848590348 160.274866291010 1982.70664780551 -2.98151409651564 53 2100 2256.63213793207 -32.3385945005743 1975.70645656850 156.632137932071 54 2045 2148.95550552802 -27.6404702830841 1968.68496475506 103.955505528023 55 2083 2427.07886018448 -222.742333126094 1961.66347294162 344.078860184476 56 2022 2300.77756012426 -214.790326887499 1958.01276676324 278.77756012426 57 1950 2198.27623196321 -252.638292548066 1954.36206058486 248.276231963207 58 1422 1008.60408040375 -114.77101756095 1950.1669371572 -413.395919596250 59 1859 1721.13209679599 50.8960894744706 1945.97181372954 -137.867903204011 60 2147 2062.08536691704 292.496748918216 1939.41788416474 -84.9146330829583

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/10/t1260464232k93um4bo4ndxkx4/1xm0b1260464194.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/10/t1260464232k93um4bo4ndxkx4/1xm0b1260464194.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/10/t1260464232k93um4bo4ndxkx4/2skrn1260464194.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/10/t1260464232k93um4bo4ndxkx4/2skrn1260464194.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/10/t1260464232k93um4bo4ndxkx4/3sfe71260464194.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/10/t1260464232k93um4bo4ndxkx4/3sfe71260464194.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/10/t1260464232k93um4bo4ndxkx4/4ct051260464194.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/10/t1260464232k93um4bo4ndxkx4/4ct051260464194.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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