WS8

*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: Tue, 01 Dec 2009 10:46:51 -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/01/t1259689673u3azjgpmmvg8ti9.htm/, Retrieved Tue, 01 Dec 2009 18:47:58 +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/01/t1259689673u3azjgpmmvg8ti9.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 «

325412 326011 328282 317480 317539 313737 312276 309391 302950 300316 304035 333476 337698 335932 323931 313927 314485 313218 309664 302963 298989 298423 310631 329765 335083 327616 309119 295916 291413 291542 284678 276475 272566 264981 263290 296806 303598 286994 276427 266424 267153 268381 262522 255542 253158 243803 250741 280445 285257 270976 261076 255603 260376 263903 264291 263276 262572 256167 264221 293860 300713 287224

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Seasonal Decomposition by Loess - Parameters Component Window Degree Jump Seasonal 621 0 63 Trend 19 1 2 Low-pass 13 1 2 Seasonal Decomposition by Loess - Time Series Components t Observed Fitted Seasonal Trend Remainder 1 325412 314472.338582408 22528.7036520167 313822.957765575 -10939.6614175918 2 326011 323745.175202946 14011.088210348 314265.736586706 -2265.82479705353 3 328282 335640.752360360 6214.73223180358 314708.515407836 7358.75236036046 4 317480 322898.355960872 -3058.85249673189 315120.496535860 5418.35596087226 5 317539 321657.759978620 -2112.23764250279 315532.477663883 4118.75997861952 6 313737 313208.331526176 -1686.63032315432 315952.298796979 -528.668473824451 7 312276 312873.905653478 -4694.02558355252 316372.119930074 597.905653478345 8 309391 311413.905328532 -9363.8726170059 316731.967288474 2022.90532853157 9 302950 301167.502348333 -12359.3169952074 317091.814646874 -1782.49765166698 10 300316 300684.564439968 -17076.6166014324 317024.052161464 368.564439968206 11 304035 301753.026622756 -10639.3162988098 316956.289676054 -2281.97337724408 12 333476 331911.435937268 18236.3487384872 316804.215324245 -1564.56406273192 13 337698 336215.155375548 22528.7036520167 316652.140972436 -1482.84462445223 14 335932 341440.445700041 14011.088210348 316412.466089611 5508.44570004137 15 323931 325474.476561411 6214.73223180358 316172.791206786 1543.47656141070 16 313927 314898.608778239 -3058.85249673189 316014.243718493 971.60877823917 17 314485 315226.541412303 -2112.23764250279 315855.6962302 741.54141230305 18 313218 312487.203944343 -1686.63032315432 315635.426378812 -730.796055657382 19 309664 308606.869056129 -4694.02558355252 315415.156527424 -1057.13094387116 20 302963 300516.583771211 -9363.8726170059 314773.288845794 -2446.41622878856 21 298989 296205.895831042 -12359.3169952074 314131.421164165 -2783.10416895774 22 298423 301062.481365667 -17076.6166014324 312860.135235766 2639.48136566661 23 310631 320312.466991443 -10639.3162988098 311588.849307366 9681.46699144348 24 329765 331552.615572765 18236.3487384872 309741.035688748 1787.61557276506 25 335083 339744.074277854 22528.7036520167 307893.222070129 4661.07427785418 26 327616 335739.928218372 14011.088210348 305480.983571280 8123.92821837164 27 309119 308954.522695765 6214.73223180358 303068.745072432 -164.477304235159 28 295916 294821.812193233 -3058.85249673189 300069.040303499 -1094.18780676735 29 291413 287868.902107936 -2112.23764250279 297069.335534567 -3544.09789206414 30 291542 290846.746667485 -1686.63032315432 293923.883655669 -695.253332515073 31 284678 283271.593806781 -4694.02558355252 290778.431776772 -1406.40619321936 32 276475 274381.573073312 -9363.8726170059 287932.299543694 -2093.42692668777 33 272566 272405.149684592 -12359.3169952074 285086.167310615 -160.850315407908 34 264981 264346.243146358 -17076.6166014324 282692.373455074 -634.756853641884 35 263290 256920.736699277 -10639.3162988098 280298.579599533 -6369.2633007234 36 296806 297073.180370414 18236.3487384872 278302.470891099 267.180370414222 37 303598 308360.934165319 22528.7036520167 276306.362182664 4762.93416531943 38 286994 285377.306687556 14011.088210348 274599.605102097 -1616.69331244449 39 276427 273746.419746667 6214.73223180358 272892.848021529 -2680.58025333268 40 266424 264540.526635960 -3058.85249673189 271366.325860772 -1883.47336403979 41 267153 266578.433942489 -2112.23764250279 269839.803700014 -574.566057511431 42 268381 269978.349616801 -1686.63032315432 268470.280706353 1597.3496168009 43 262522 262637.26787086 -4694.02558355252 267100.757712693 115.267870859941 44 255542 254619.645420988 -9363.8726170059 265828.227196018 -922.354579012084 45 253158 254119.620315864 -12359.3169952074 264555.696679343 961.6203158641 46 243803 241085.257404177 -17076.6166014324 263597.359197255 -2717.7425958229 47 250741 249482.294583643 -10639.3162988098 262639.021715167 -1258.70541635738 48 280445 280395.059236098 18236.3487384872 262258.592025415 -49.9407639022102 49 285257 286107.134012320 22528.7036520167 261878.162335663 850.134012320457 50 270976 265634.744844007 14011.088210348 262306.166945645 -5341.25515599258 51 261076 253203.09621257 6214.73223180358 262734.171555626 -7872.90378742991 52 255603 250499.841959281 -3058.85249673189 263765.010537451 -5103.1580407189 53 260376 258068.388123228 -2112.23764250279 264795.849519275 -2307.61187677237 54 263903 263185.486049595 -1686.63032315432 266307.144273559 -717.513950405002 55 264291 265457.586555709 -4694.02558355252 267818.439027843 1166.58655570907 56 263276 266611.612476806 -9363.8726170059 269304.2601402 3335.61247680604 57 262572 266713.235742651 -12359.3169952074 270790.081252556 4141.23574265122 58 256167 257083.587092163 -17076.6166014324 272327.029509269 916.587092163216 59 264221 265217.338532828 -10639.3162988098 273863.977765982 996.338532827795 60 293860 294072.924398415 18236.3487384872 275410.726863098 212.924398415023 61 300713 301939.82038777 22528.7036520167 276957.475960214 1226.82038776978 62 287224 281951.377068058 14011.088210348 278485.534721594 -5272.62293194205

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259689673u3azjgpmmvg8ti9/1t91y1259689607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259689673u3azjgpmmvg8ti9/1t91y1259689607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259689673u3azjgpmmvg8ti9/2ics11259689607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259689673u3azjgpmmvg8ti9/2ics11259689607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259689673u3azjgpmmvg8ti9/3fbh81259689607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259689673u3azjgpmmvg8ti9/3fbh81259689607.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259689673u3azjgpmmvg8ti9/4ls731259689607.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259689673u3azjgpmmvg8ti9/4ls731259689607.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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