WS9 Populaire technieken

*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: Fri, 04 Dec 2009 07:14:20 -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/t1259936098ezr7tksugeey0l5.htm/, Retrieved Fri, 04 Dec 2009 15:15:03 +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/t1259936098ezr7tksugeey0l5.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 «

7.1 6.9 6.8 7.5 7.6 7.8 8.0 8.1 8.2 8.3 8.2 8.0 7.9 7.6 7.6 8.3 8.4 8.4 8.4 8.4 8.6 8.9 8.8 8.3 7.5 7.2 7.4 8.8 9.3 9.3 8.7 8.2 8.3 8.5 8.6 8.5 8.2 8.1 7.9 8.6 8.7 8.7 8.5 8.4 8.5 8.7 8.7 8.6 8.5 8.3 8.0 8.2 8.1 8.1 8.0 7.9 7.9 8.0 8.0 7.9 8.0 7.7 7.2 7.5 7.3 7.0 7.0 7.0 7.2 7.3 7.1 6.8 6.4 6.1 6.5 7.7 7.9 7.5 6.9 6.6 6.9 7.7 8.0 8.0 7.7 7.3 7.4 8.1 8.3 8.2

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Seasonal Decomposition by Loess - Parameters Component Window Degree Jump Seasonal 901 0 91 Trend 19 1 2 Low-pass 13 1 2 Seasonal Decomposition by Loess - Time Series Components t Observed Fitted Seasonal Trend Remainder 1 7.1 7.1406906362288 -0.202719257246648 7.26202862101785 0.0406906362287929 2 6.9 6.93227750320111 -0.476253889833759 7.34397638663265 0.0322775032011071 3 6.8 6.71136459466571 -0.537288746913158 7.42592415224745 -0.0886354053342933 4 7.5 7.30877060536837 0.187365508106366 7.50386388652527 -0.191229394631632 5 7.6 7.33117659065669 0.287019788540229 7.58180362080308 -0.268823409343312 6 7.8 7.74841080894844 0.196664736837789 7.65492445421377 -0.0515891910515576 7 8 8.24811910347119 0.0238356089043564 7.72804528762445 0.248119103471189 8 8.1 8.51047646020337 -0.111071671522154 7.80059521131878 0.410476460203373 9 8.2 8.50140524965623 0.0254496153306581 7.8731451350131 0.301405249656235 10 8.3 8.38770411766504 0.272693134123426 7.93960274821153 0.0877041176650426 11 8.2 8.13114575568222 0.262793882907816 8.00606036140996 -0.0688542443177758 12 8 7.88248025228554 0.0715113398358174 8.04600840787865 -0.117519747714462 13 7.9 7.91676280289932 -0.202719257246648 8.08595645434733 0.0167628028993185 14 7.6 7.5567065643309 -0.476253889833759 8.11954732550286 -0.043293435669101 15 7.6 7.58415055025477 -0.537288746913158 8.15313819665839 -0.0158494497452342 16 8.3 8.21704724356475 0.187365508106366 8.19558724832889 -0.0829527564352528 17 8.4 8.27494391146038 0.287019788540229 8.23803629999939 -0.125056088539615 18 8.4 8.34177789112212 0.196664736837789 8.26155737204009 -0.058222108877878 19 8.4 8.49108594701485 0.0238356089043564 8.2850784440808 0.091085947014852 20 8.4 8.63049885261309 -0.111071671522154 8.28057281890906 0.23049885261309 21 8.6 8.898483190932 0.0254496153306581 8.27606719373734 0.298483190932005 22 8.9 9.23942168608929 0.272693134123426 8.28788517978729 0.339421686089286 23 8.8 9.03750295125494 0.262793882907816 8.29970316583724 0.237502951254944 24 8.3 8.19857359581538 0.0715113398358174 8.3299150643488 -0.101426404184622 25 7.5 6.84259229438628 -0.202719257246648 8.36012696286037 -0.657407705613722 26 7.2 6.50484271577571 -0.476253889833759 8.37141117405805 -0.695157284224289 27 7.4 6.95459336165743 -0.537288746913158 8.38269538525573 -0.445406638342568 28 8.8 9.02765599428972 0.187365508106366 8.38497849760392 0.227655994289719 29 9.3 9.92571860150767 0.287019788540229 8.3872616099521 0.625718601507666 30 9.3 9.99221454722414 0.196664736837789 8.41112071593808 0.692214547224134 31 8.7 8.9411845691716 0.0238356089043564 8.43497982192405 0.241184569171594 32 8.2 8.05082570065131 -0.111071671522154 8.46024597087084 -0.149174299348687 33 8.3 8.08903826485171 0.0254496153306581 8.48551211981763 -0.210961735148286 34 8.5 8.253209604493 0.272693134123426 8.47409726138358 -0.246790395507006 35 8.6 8.47452371414265 0.262793882907816 8.46268240294953 -0.125476285857347 36 8.5 8.48641025325551 0.0715113398358174 8.44207840690867 -0.0135897467444881 37 8.2 8.18124484637883 -0.202719257246648 8.42147441086781 -0.0187551536211643 38 8.1 8.25040438057273 -0.476253889833759 8.42584950926103 0.150404380572731 39 7.9 7.90706413925891 -0.537288746913158 8.43022460765425 0.00706413925890992 40 8.6 8.56994682569246 0.187365508106366 8.44268766620118 -0.0300531743075432 41 8.7 8.65782948671166 0.287019788540229 8.4551507247481 -0.042170513288335 42 8.7 8.73695009148959 0.196664736837789 8.46638517167262 0.0369500914895919 43 8.5 8.49854477249851 0.0238356089043564 8.47761961859713 -0.00145522750148785 44 8.4 8.42406351517681 -0.111071671522154 8.48700815634534 0.0240635151768114 45 8.5 8.47815369057579 0.0254496153306581 8.49639669409355 -0.0218463094242107 46 8.7 8.64497928122497 0.272693134123426 8.48232758465161 -0.0550207187750331 47 8.7 8.66894764188252 0.262793882907816 8.46825847520966 -0.0310523581174795 48 8.6 8.70072977314956 0.0715113398358174 8.42775888701462 0.100729773149558 49 8.5 8.81545995842706 -0.202719257246648 8.38725929881959 0.315459958427061 50 8.3 8.74180483074841 -0.476253889833759 8.33444905908535 0.441804830748413 51 8 8.25564992756205 -0.537288746913158 8.28163881935111 0.255649927562047 52 8.2 7.99359178375118 0.187365508106366 8.21904270814245 -0.206408216248819 53 8.1 7.75653361452598 0.287019788540229 8.15644659693379 -0.343466385474024 54 8.1 7.90910283786285 0.196664736837789 8.09423242529936 -0.190897162137153 55 8 7.94414613743071 0.0238356089043564 8.03201825366493 -0.0558538625692879 56 7.9 7.92780588605632 -0.111071671522154 7.98326578546584 0.0278058860563188 57 7.9 7.8400370674026 0.0254496153306581 7.93451331726674 -0.059962932597399 58 8 7.8428314349939 0.272693134123426 7.88447543088267 -0.157168565006095 59 8 7.90276857259359 0.262793882907816 7.8344375444986 -0.0972314274064114 60 7.9 7.96562202959583 0.0715113398358174 7.76286663056835 0.0656220295958292 61 8 8.51142354060853 -0.202719257246648 7.69129571663811 0.511423540608535 62 7.7 8.26560484848164 -0.476253889833759 7.61064904135212 0.565604848481636 63 7.2 7.40728638084702 -0.537288746913158 7.53000236606614 0.207286380847021 64 7.5 7.36612168289285 0.187365508106366 7.44651280900078 -0.133878317107149 65 7.3 6.94995695952434 0.287019788540229 7.36302325193543 -0.35004304047566 66 7 6.54079739900195 0.196664736837789 7.26253786416026 -0.459202600998051 67 7 6.81411191471055 0.0238356089043564 7.1620524763851 -0.185888085289450 68 7 7.03353250426728 -0.111071671522154 7.07753916725487 0.0335325042672849 69 7.2 7.3815245265447 0.0254496153306581 6.99302585812464 0.181524526544697 70 7.3 7.34927735213005 0.272693134123426 6.97802951374653 0.0492773521300478 71 7.1 6.97417294772378 0.262793882907816 6.96303316936841 -0.125827052276223 72 6.8 6.54802730829004 0.0715113398358174 6.98046135187414 -0.251972691709958 73 6.4 6.00482972286678 -0.202719257246648 6.99788953437987 -0.395170277133225 74 6.1 5.67191387957526 -0.476253889833759 7.0043400102585 -0.428086120424736 75 6.5 6.52649826077604 -0.537288746913158 7.01079048613712 0.02649826077604 76 7.7 8.17037966034222 0.187365508106366 7.04225483155141 0.470379660342224 77 7.9 8.43926103449407 0.287019788540229 7.0737191769657 0.539261034494066 78 7.5 7.65375392344492 0.196664736837789 7.1495813397173 0.153753923444918 79 6.9 6.55072088862676 0.0238356089043564 7.22544350246888 -0.349279111373237 80 6.6 6.00546426594354 -0.111071671522154 7.30560740557861 -0.59453573405646 81 6.9 6.38877907598099 0.0254496153306581 7.38577130868835 -0.511220924019007 82 7.7 7.66441559230786 0.272693134123426 7.46289127356871 -0.035584407692137 83 8 8.19719487864311 0.262793882907816 7.54001123844907 0.197194878643112 84 8 8.30891468882169 0.0715113398358174 7.6195739713425 0.308914688821687 85 7.7 7.90358255301073 -0.202719257246648 7.69913670423592 0.203582553010731 86 7.3 7.29280609047642 -0.476253889833759 7.78344779935734 -0.00719390952358268 87 7.4 7.46952985243439 -0.537288746913158 7.86775889447877 0.0695298524343899 88 8.1 8.05880882954149 0.187365508106366 7.95382566235215 -0.0411911704585144 89 8.3 8.27308778123424 0.287019788540229 8.03989243022553 -0.0269122187657569 90 8.2 8.07758291121078 0.196664736837789 8.12575235195143 -0.122417088789218

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259936098ezr7tksugeey0l5/11ocv1259936054.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259936098ezr7tksugeey0l5/11ocv1259936054.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259936098ezr7tksugeey0l5/2agz91259936054.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259936098ezr7tksugeey0l5/2agz91259936054.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259936098ezr7tksugeey0l5/3r11o1259936054.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259936098ezr7tksugeey0l5/3r11o1259936054.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259936098ezr7tksugeey0l5/41v901259936054.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259936098ezr7tksugeey0l5/41v901259936054.ps (open in new window)

Parameters (Session):

par1 = 0.01 ; par2 = 0.99 ; par3 = 0.005 ;

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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