WS9

*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, 03 Dec 2009 17:54:40 -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/04/t1259888135f4k2vok5y0ph9ou.htm/, Retrieved Fri, 04 Dec 2009 01:55:40 +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/04/t1259888135f4k2vok5y0ph9ou.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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10284,5 12792 12823,61538 13845,66667 15335,63636 11188,5 13633,25 12298,46667 15353,63636 12696,15385 12213,93333 13683,72727 11214,14286 13950,23077 11179,13333 11801,875 11188,82353 16456,27273 11110,0625 16530,69231 10038,41176 11681,25 11148,88235 8631 9386,444444 9764,736842 12043,75 12948,06667 10987,125 11648,3125 10633,35294 10219,3 9037,6 10296,31579 11705,41176 10681,94444 9362,947368 11306,35294 10984,45 10062,61905 8118,583333 8867,48 8346,72 8529,307692 10697,18182 8591,84 8695,607143 8125,571429 7009,758621 7883,466667 7527,645161 6763,758621 6682,333333 7855,681818 6738,88 7895,434783 6361,884615 6935,956522 8344,454545 9107,944444

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 1 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 10284.5 9291.177005915 -1468.57422071846 12746.3972148035 -993.322994085001 2 12792 12477.8313814501 312.435926112883 12793.7326924371 -314.168618549935 3 12823.61538 12628.1535221999 178.009067729489 12841.0681700706 -195.46185780013 4 13845.66667 14405.0719554158 408.826095056046 12877.4352895282 559.405285415773 5 15335.63636 17912.4038524574 -154.933541443125 12913.8024089857 2576.76749245741 6 11188.5 8792.89496897249 641.638186768704 12942.4668442588 -2395.60503102751 7 13633.25 14708.7073412955 -413.338620827355 12971.1312795319 1075.45734129547 8 12298.46667 10960.1815201275 652.242166369445 12984.509653503 -1338.28514987245 9 15353.63636 17790.6482645279 -81.2635720020682 12997.8880274741 2437.01190452794 10 12696.15385 12700.3044399575 -247.816022400768 12939.8192824433 4.15058995745858 11 12213.93333 11321.6910665885 224.425055998965 12881.7505374125 -892.242263411452 12 13683.72727 14575.2620912332 -51.6503651039884 12843.8428138708 891.534821233232 13 11214.14286 11090.9248503894 -1468.57422071846 12805.9350903290 -123.218009610562 14 13950.23077 14806.4343281709 312.435926112883 12781.5912857162 856.203558170944 15 11179.13333 9423.01011116719 178.009067729489 12757.2474811033 -1756.12321883281 16 11801.875 10556.8722023113 408.826095056046 12638.0517026326 -1245.00279768867 17 11188.82353 10013.7246772812 -154.933541443125 12518.8559241619 -1175.09885271881 18 16456.27273 19948.5789530333 641.638186768704 12322.3283201980 3492.30622303331 19 11110.0625 10507.6629045933 -413.338620827355 12125.8007162340 -602.39959540668 20 16530.69231 20460.6850828685 652.242166369445 11948.4573707621 3929.9927728685 21 10038.41176 8386.973066712 -81.2635720020682 11771.1140252901 -1651.43869328801 22 11681.25 11964.440625471 -247.816022400768 11645.8753969298 283.190625470999 23 11148.88235 10552.7028754316 224.425055998965 11520.6367685695 -596.179474568427 24 8631 5964.39184004069 -51.6503651039884 11349.2585250633 -2666.60815995931 25 9386.444444 9063.58282716132 -1468.57422071846 11177.8802815571 -322.861616838682 26 9764.736842 8214.34934247213 312.435926112883 11002.688415415 -1550.38749952787 27 12043.75 13081.9943829977 178.009067729489 10827.4965492728 1038.24438299767 28 12948.06667 14717.6082298387 408.826095056046 10769.6990151053 1769.54155983868 29 10987.125 11417.2820605054 -154.933541443125 10711.9014809377 430.157060505422 30 11648.3125 11921.9154636438 641.638186768704 10733.0713495875 273.602963643794 31 10633.35294 10925.8032825901 -413.338620827355 10754.2412182373 292.450342590055 32 10219.3 9048.5949750748 652.242166369445 10737.7628585558 -1170.70502492520 33 9037.6 7435.17907312786 -81.2635720020682 10721.2844988742 -1602.42092687214 34 10296.31579 10243.8378484522 -247.816022400768 10596.6097539485 -52.4779415477788 35 11705.41176 12714.4634549782 224.425055998965 10471.9350090229 1009.05169497815 36 10681.94444 11106.3806929396 -51.6503651039884 10309.1585521644 424.436252939571 37 9362.947368 10048.0868614125 -1468.57422071846 10146.3820953059 685.139493412513 38 11306.35294 12291.5791273580 312.435926112883 10008.6908265292 985.22618735795 39 10984.45 11919.8913745181 178.009067729489 9870.99955775239 935.441374518126 40 10062.61905 9999.64734246416 408.826095056046 9716.7646624798 -62.9717075358403 41 8118.583333 6829.57044023593 -154.933541443125 9562.5297672072 -1289.01289276407 42 8867.48 7740.59247353761 641.638186768704 9352.72933969368 -1126.88752646239 43 8346.72 7963.84970864719 -413.338620827355 9142.92891218016 -382.87029135281 44 8529.307692 7484.14848837522 652.242166369445 8922.22472925533 -1045.15920362478 45 10697.18182 12774.1066656716 -81.2635720020682 8701.5205463305 2076.92484567156 46 8591.84 8922.86080265207 -247.816022400768 8508.6352197487 331.020802652065 47 8695.607143 8851.03933683413 224.425055998965 8315.7498931669 155.432193834133 48 8125.571429 8149.65485169739 -51.6503651039884 8153.1383714066 24.0834226973884 49 7009.758621 7497.56461307217 -1468.57422071846 7990.5268496463 487.805992072165 50 7883.466667 7644.10615210004 312.435926112883 7810.39125578708 -239.360514899964 51 7527.645161 7247.02559234265 178.009067729489 7630.25566192786 -280.619568657352 52 6763.758621 5546.85037730292 408.826095056046 7571.84076964103 -1216.90824369708 53 6682.333333 6006.17433008892 -154.933541443125 7513.4258773542 -676.159002911081 54 7855.681818 7573.82597733885 641.638186768704 7495.89947189245 -281.855840661148 55 6738.88 6412.72555439667 -413.338620827355 7478.37306643068 -326.15444560333 56 7895.434783 7663.32609397059 652.242166369445 7475.30130565996 -232.108689029409 57 6361.884615 5332.80325711283 -81.2635720020682 7472.22954488924 -1029.08135788717 58 6935.956522 6632.86406850106 -247.816022400768 7486.86499789971 -303.092453498944 59 8344.454545 8962.98358309085 224.425055998965 7501.50045091018 618.529038090854 60 9107.944444 10733.7896776422 -51.6503651039884 7533.7495754618 1625.84523364219

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259888135f4k2vok5y0ph9ou/1ezqj1259888078.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259888135f4k2vok5y0ph9ou/1ezqj1259888078.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259888135f4k2vok5y0ph9ou/22dmn1259888078.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259888135f4k2vok5y0ph9ou/22dmn1259888078.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259888135f4k2vok5y0ph9ou/31q731259888078.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259888135f4k2vok5y0ph9ou/31q731259888078.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259888135f4k2vok5y0ph9ou/4ptnn1259888078.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259888135f4k2vok5y0ph9ou/4ptnn1259888078.ps (open in new window)

Parameters (Session):

par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ; par9 = 1 ;

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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