*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 12:50:19 -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/t1259956261nkcrs08xl5p1uon.htm/, Retrieved Fri, 04 Dec 2009 20:51:06 +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/t1259956261nkcrs08xl5p1uon.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 «

5560 3922 3759 4138 4634 3996 4308 4143 4429 5219 4929 5755 5592 4163 4962 5208 4755 4491 5732 5731 5040 6102 4904 5369 5578 4619 4731 5011 5299 4146 4625 4736 4219 5116 4205 4121 5103 4300 4578 3809 5526 4247 3830 4394 4826 4409 4569 4106 4794 3914 3793 4405 4022 4100 4788 3163 3585 3903 4178 3863 4187

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 611 0 62 Trend 19 1 2 Low-pass 13 1 2 Seasonal Decomposition by Loess - Time Series Components t Observed Fitted Seasonal Trend Remainder 1 5560 6202.81396913073 664.80466520474 4252.38136566453 642.81396913073 2 3922 3984.28860795912 -439.477153762163 4299.18854580305 62.2886079591171 3 3759 3430.52384125353 -258.519567195095 4345.99572594156 -328.476158746468 4 4138 3982.33967714975 -102.977779024254 4396.63810187451 -155.660322850251 5 4634 4584.75507023861 235.964451953943 4447.28047780745 -49.2449297613912 6 3996 3897.41543512960 -404.324843131018 4498.90940800142 -98.5845648704035 7 4308 3998.27548760426 67.1861742003452 4550.53833819540 -309.724512395741 8 4143 3825.70311312112 -142.944320533419 4603.2412074123 -317.296886878884 9 4429 4345.53161877847 -143.475695407679 4655.94407662921 -83.4683812215308 10 5219 5318.44792143048 396.543327377063 4723.00875119245 99.4479214304829 11 4929 5054.16460393401 13.7619703102879 4790.0734257557 125.164603934014 12 5755 6536.92448260413 113.458441025347 4859.61707637052 781.924482604135 13 5592 5590.03460780992 664.80466520474 4929.16072698534 -1.96539219007718 14 4163 3762.24350253943 -439.477153762163 5003.23365122274 -400.756497460575 15 4962 5105.21299173496 -258.519567195095 5077.30657546014 143.212991734956 16 5208 5394.54437561607 -102.977779024254 5124.43340340818 186.544375616074 17 4755 4102.47531668984 235.964451953943 5171.56023135622 -652.524683310165 18 4491 4199.73387056091 -404.324843131018 5186.59097257011 -291.266129439089 19 5732 6195.19211201566 67.1861742003452 5201.62171378399 463.19211201566 20 5731 6396.68667589974 -142.944320533419 5208.25764463367 665.686675899745 21 5040 5008.58211992433 -143.475695407679 5214.89357548335 -31.4178800756727 22 6102 6599.03253069364 396.543327377063 5208.4241419293 497.03253069364 23 4904 4592.28332131447 13.7619703102879 5201.95470837524 -311.716678685530 24 5369 5461.90645471925 113.458441025347 5162.6351042554 92.9064547192493 25 5578 5367.8798346597 664.80466520474 5123.31550013556 -210.120165340304 26 4619 4625.20971954571 -439.477153762163 5052.26743421646 6.20971954570814 27 4731 4739.30019889775 -258.519567195095 4981.21936829735 8.30019889774849 28 5011 5216.06855561586 -102.977779024254 4908.9092234084 205.068555615858 29 5299 5525.43646952661 235.964451953943 4836.59907851945 226.436469526609 30 4146 3926.95403622279 -404.324843131018 4769.37080690823 -219.045963777211 31 4625 4480.67129050264 67.1861742003452 4702.14253529701 -144.328709497358 32 4736 4965.77245826953 -142.944320533419 4649.17186226389 229.772458269527 33 4219 3985.27450617691 -143.475695407679 4596.20118923077 -233.72549382309 34 5116 5270.51307364757 396.543327377063 4564.94359897537 154.513073647568 35 4205 3862.55202096974 13.7619703102879 4533.68600871997 -342.447979030258 36 4121 3612.45048618198 113.458441025347 4516.09107279267 -508.549513818018 37 5103 5042.69919792989 664.80466520474 4498.49613686537 -60.3008020701136 38 4300 4547.7315522841 -439.477153762163 4491.74560147806 247.731552284103 39 4578 4929.52450110435 -258.519567195095 4484.99506609075 351.524501104347 40 3809 3237.03043285023 -102.977779024254 4483.94734617402 -571.969567149769 41 5526 6333.13592178876 235.964451953943 4482.8996262573 807.135921788758 42 4247 4426.66645839021 -404.324843131018 4471.6583847408 179.666458390215 43 3830 3132.39668257534 67.1861742003452 4460.41714322431 -697.603317424655 44 4394 4502.55228618428 -142.944320533419 4428.39203434913 108.552286184285 45 4826 5399.10876993372 -143.475695407679 4396.36692547396 573.108769933721 46 4409 4059.34797975417 396.543327377063 4362.10869286877 -349.652020245828 47 4569 4796.38756942614 13.7619703102879 4327.85046026357 227.387569426138 48 4106 3796.33813084277 113.458441025347 4302.20342813188 -309.661869157226 49 4794 4646.63893879507 664.80466520474 4276.55639600019 -147.361061204925 50 3914 4028.39126154531 -439.477153762163 4239.08589221686 114.391261545305 51 3793 3642.90417876156 -258.519567195095 4201.61538843353 -150.095821238436 52 4405 4763.60867592194 -102.977779024254 4149.36910310231 358.608675921942 53 4022 3710.91273027496 235.964451953943 4097.12281777109 -311.087269725038 54 4100 4558.94866747113 -404.324843131018 4045.37617565989 458.948667471129 55 4788 5515.18429225097 67.1861742003452 3993.62953354868 727.18429225097 56 3163 2528.52453533060 -142.944320533419 3940.41978520282 -634.475464669403 57 3585 3426.26565855072 -143.475695407679 3887.21003685696 -158.734341449280 58 3903 3579.10167211883 396.543327377063 3830.35500050411 -323.898327881172 59 4178 4568.73806553845 13.7619703102879 3773.49996415126 390.738065538451 60 3863 3898.08067622674 113.458441025347 3714.46088274791 35.0806762267393 61 4187 4053.77353345069 664.80466520474 3655.42180134457 -133.226466549306

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956261nkcrs08xl5p1uon/1y0fi1259956216.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956261nkcrs08xl5p1uon/1y0fi1259956216.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956261nkcrs08xl5p1uon/2n6zd1259956216.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956261nkcrs08xl5p1uon/2n6zd1259956216.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956261nkcrs08xl5p1uon/35qzd1259956216.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956261nkcrs08xl5p1uon/35qzd1259956216.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956261nkcrs08xl5p1uon/4dxea1259956216.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956261nkcrs08xl5p1uon/4dxea1259956216.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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