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: Fri, 04 Dec 2009 06:19:50 -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/t1259932838ypmdygf9d2ofzce.htm/, Retrieved Sun, 19 May 2013 06:50:10 +0000

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68] - RMP   [ARIMA Backward Selection] [] [2009-11-27 14:53:14] [b98453cac15ba1066b407e146608df68] - RMPD      [Decomposition by Loess] [ws9] [2009-12-04 13:19:50] [b243db81ea3e1f02fb3382887fb0f701] [Current]

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

System-generated keywords (parent):

t1259333643wfudtum7qa0jfpl (pk = 60855)

Estimated Impact

33

Dataseries X:

» Textfile « » CSV « » Stem and Leaf « » Histogram « » Kernel Density « » Harrell-Davis Quantiles « » Central Tendency « » Variability «

5594 5585 5710 5511 5403 5826 5884 5965 5960 6064 6046 5954 5952 5960 5983 5996 6021 6094 6202 6276 6306 6342 6345 6328 6191 6261 6253 6198 6247 6293 6381 6448 6470 6516 6532 6526 6533 6498 6507 6464 6453 6468 6497 6808 6793 6907 6792 6757 6734 6654 6589 6469 6521 6448 6410 6528 6445 6458 6215 6167

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 5594 5601.38378994445 -23.9565576536318 5610.57276770919 7.3837899444452 2 5585 5574.86465328893 -48.4117417897432 5643.54708850081 -10.1353467110703 3 5710 5790.3455682606 -46.8669775530362 5676.52140929244 80.3455682605954 4 5511 5449.40352261095 -136.533559930839 5709.13003731989 -61.59647738905 5 5403 5208.26151277356 -144.000178120895 5741.73866534734 -194.738487226443 6 5826 5932.75280867462 -54.4925271669023 5773.73971849229 106.752808674616 7 5884 5975.04400092923 -12.7847725664654 5805.74077163724 91.0440009292297 8 5965 5980.77641159491 111.279287387950 5837.94430101714 15.7764115949149 9 5960 5954.90880736059 94.9433622423753 5870.14783039704 -5.09119263941011 10 6064 6072.12644321901 152.135281868172 5903.73827491282 8.12644321900825 11 6046 6079.34402235288 75.3272582185163 5937.3287194286 33.3440223528796 12 5954 5905.55449050247 33.3610693698076 5969.08444012772 -48.4455094975265 13 5952 5927.1163968268 -23.9565576536318 6000.84016082684 -24.8836031732035 14 5960 5939.66427654394 -48.4117417897432 6028.7474652458 -20.3357234560572 15 5983 5956.21220788827 -46.8669775530362 6056.65476966477 -26.7877921117297 16 5996 6044.63718987045 -136.533559930839 6083.89637006039 48.637189870452 17 6021 6074.86220766489 -144.000178120895 6111.137970456 53.8622076648871 18 6094 6105.98318829451 -54.4925271669023 6136.5093388724 11.9831882945064 19 6202 6254.90406527768 -12.7847725664654 6161.88070728878 52.9040652776803 20 6276 6256.85828143089 111.279287387950 6183.86243118116 -19.1417185691089 21 6306 6311.21248268409 94.9433622423753 6205.84415507353 5.21248268409181 22 6342 6307.51604468284 152.135281868172 6224.34867344898 -34.4839553171551 23 6345 6371.81954995705 75.3272582185163 6242.85319182443 26.819549957052 24 6328 6362.82990390336 33.3610693698076 6259.80902672684 34.8299039033564 25 6191 6129.19169602439 -23.9565576536318 6276.76486162924 -61.8083039756093 26 6261 6278.19470355271 -48.4117417897432 6292.21703823704 17.1947035527082 27 6253 6245.19776270821 -46.8669775530362 6307.66921484483 -7.80223729179306 28 6198 6209.93307870649 -136.533559930839 6322.60048122435 11.9330787064855 29 6247 6300.46843051702 -144.000178120895 6337.53174760388 53.4684305170176 30 6293 6285.16834039823 -54.4925271669023 6355.32418676867 -7.83165960176939 31 6381 6401.668146633 -12.7847725664654 6373.11662593347 20.6681466329974 32 6448 6390.74939607617 111.279287387950 6393.97131653588 -57.2506039238306 33 6470 6430.23063061933 94.9433622423753 6414.8260071383 -39.7693693806686 34 6516 6444.34779171294 152.135281868172 6435.51692641889 -71.6522082870588 35 6532 6532.46489608200 75.3272582185163 6456.20784569948 0.464896082004998 36 6526 6543.01815998688 33.3610693698076 6475.62077064331 17.0181599868783 37 6533 6594.92286206648 -23.9565576536318 6495.03369558715 61.9228620664817 38 6498 6527.036258691 -48.4117417897432 6517.37548309875 29.036258690996 39 6507 6521.14970694269 -46.8669775530362 6539.71727061035 14.1497069426905 40 6464 6501.0134331099 -136.533559930839 6563.52012682094 37.0134331098989 41 6453 6462.67719508936 -144.000178120895 6587.32298303153 9.67719508935988 42 6468 6382.93229487866 -54.4925271669023 6607.56023228824 -85.0677051213379 43 6497 6378.98729102152 -12.7847725664654 6627.79748154495 -118.012708978481 44 6808 6862.90792414608 111.279287387950 6641.81278846597 54.9079241460786 45 6793 6835.22854237063 94.9433622423753 6655.828095387 42.2285423706271 46 6907 6998.48901206657 152.135281868172 6663.37570606526 91.4890120665723 47 6792 6837.74942503797 75.3272582185163 6670.92331674351 45.7494250379705 48 6757 6813.55762960394 33.3610693698076 6667.08130102626 56.5576296039371 49 6734 6828.71727234463 -23.9565576536318 6663.239285309 94.7172723446329 50 6654 6714.06088845976 -48.4117417897432 6642.35085332998 60.0608884597641 51 6589 6603.40455620208 -46.8669775530362 6621.46242135096 14.4045562020774 52 6469 6498.91647962698 -136.533559930839 6575.61708030386 29.9164796269824 53 6521 6656.22843886414 -144.000178120895 6529.77173925676 135.228438864139 54 6448 6468.73433272768 -54.4925271669023 6481.75819443922 20.7343327276785 55 6410 6399.04012294477 -12.7847725664654 6433.74464962169 -10.9598770552266 56 6528 6560.5007108128 111.279287387950 6384.22000179924 32.5007108128075 57 6445 6460.36128378083 94.9433622423753 6334.69535397679 15.3612837808314 58 6458 6480.41163467373 152.135281868172 6283.4530834581 22.4116346737328 59 6215 6122.46192884209 75.3272582185163 6232.2108129394 -92.538071157911 60 6167 6121.0410323977 33.3610693698076 6179.59789823249 -45.9589676023006

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259932838ypmdygf9d2ofzce/1o1gs1259932787.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259932838ypmdygf9d2ofzce/1o1gs1259932787.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2009/Dec/04/t1259932838ypmdygf9d2ofzce/2oeih1259932787.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259932838ypmdygf9d2ofzce/2oeih1259932787.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2009/Dec/04/t1259932838ypmdygf9d2ofzce/3iqu81259932787.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259932838ypmdygf9d2ofzce/3iqu81259932787.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2009/Dec/04/t1259932838ypmdygf9d2ofzce/4pzu81259932787.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259932838ypmdygf9d2ofzce/4pzu81259932787.ps (opens in new window) Click here to open pdf file.

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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