Workshop 9

*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 05:08:33 -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/t1259928568ssqsbtoafks21cv.htm/, Retrieved Fri, 04 Dec 2009 13:09:33 +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/t1259928568ssqsbtoafks21cv.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 «

130 136.7 138.1 139.5 140.4 144.6 151.4 147.9 141.5 143.8 143.6 150.5 150.1 154.9 162.1 176.7 186.6 194.8 196.3 228.8 267.2 237.2 254.7 258.2 257.9 269.6 266.9 269.6 253.9 258.6 274.2 301.5 304.5 285.1 287.7 265.5 264.1 276.1 258.9 239.1 250.1 276.8 297.6 295.4 283 275.8 279.7 254.6 234.6 176.9 148.1 122.7 124.9 121.6 128.4 144.5 151.8 167.1 173.8 203.7 199.8

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 4 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 1 130 109.414427356952 6.02685985459014 144.558712788458 -20.5855726430478 2 136.7 135.140139694063 -6.27184026099176 144.531700566929 -1.55986030593678 3 138.1 145.843332110333 -14.1480204557324 144.504688345399 7.74333211033297 4 139.5 154.571973445527 -20.1942005000934 144.622227054566 15.0719734455271 5 140.4 155.340622855522 -19.2803886192554 144.739765763733 14.9406228555222 6 144.6 156.252202857982 -12.2718611693765 145.219658311395 11.6522028579816 7 151.4 160.163784876028 -3.06333573508466 145.699550859057 8.76378487602798 8 147.9 138.455123262175 10.9157211484992 146.429155589325 -9.44487673782459 9 141.5 119.006465318688 16.8347743617181 147.158760319594 -22.4935346813123 10 143.8 129.228508039739 9.6627377275261 148.708754232735 -14.5714919602609 11 143.6 120.550535233292 16.3907166208327 150.258748145875 -23.0494647667081 12 150.5 130.55488935607 15.3988661361994 155.046244507731 -19.94511064393 13 150.1 134.339399275824 6.02685985459014 159.833740869586 -15.7606007241760 14 154.9 148.769235997266 -6.27184026099176 167.302604263726 -6.13076400273422 15 162.1 163.576552797866 -14.1480204557324 174.771467657866 1.47655279786622 16 176.7 189.760895894151 -20.1942005000934 183.833304605942 13.0608958941509 17 186.6 199.585247065237 -19.2803886192554 192.895141554019 12.9852470652366 18 194.8 199.942149651617 -12.2718611693765 201.929711517760 5.14214965161690 19 196.3 184.699054253584 -3.06333573508466 210.964281481501 -11.6009457464158 20 228.8 227.289619621462 10.9157211484992 219.394659230039 -1.51038037853812 21 267.2 289.740188659704 16.8347743617181 227.825036978577 22.5401886597045 22 237.2 229.406181555481 9.6627377275261 235.331080716992 -7.79381844451856 23 254.7 250.172158923760 16.3907166208327 242.837124455408 -4.52784107624026 24 258.2 251.822697263066 15.3988661361994 249.178436600735 -6.37730273693415 25 257.9 254.253391399348 6.02685985459014 255.519748746062 -3.64660860065214 26 269.6 284.718236218106 -6.27184026099176 260.753604042886 15.1182362181058 27 266.9 281.960561116022 -14.1480204557324 265.98745933971 15.0605611160223 28 269.6 289.878788713588 -20.1942005000934 269.515411786506 20.2787887135877 29 253.9 254.037024385954 -19.2803886192554 273.043364233301 0.137024385954021 30 258.6 255.103212150484 -12.2718611693765 274.368649018892 -3.49678784951584 31 274.2 275.769401930601 -3.06333573508466 275.693933804483 1.56940193060126 32 301.5 316.825428627849 10.9157211484992 275.258850223652 15.3254286278493 33 304.5 317.341458995462 16.8347743617181 274.823766642820 12.8414589954623 34 285.1 286.594724057033 9.6627377275261 273.942538215441 1.49472405703278 35 287.7 285.947973591105 16.3907166208327 273.061309788063 -1.75202640889529 36 265.5 242.579317814562 15.3988661361994 273.021816049238 -22.9206821854376 37 264.1 249.190817834996 6.02685985459014 272.982322310414 -14.9091821650038 38 276.1 285.312486673218 -6.27184026099176 273.159353587774 9.21248667321805 39 258.9 258.611635590598 -14.1480204557324 273.336384865134 -0.288364409401481 40 239.1 225.162789496661 -20.1942005000934 273.231411003432 -13.9372105033385 41 250.1 246.353951477525 -19.2803886192554 273.12643714173 -3.74604852247478 42 276.8 294.487511162009 -12.2718611693765 271.384350007367 17.6875111620092 43 297.6 328.62107286208 -3.06333573508466 269.642262873004 31.0210728620803 44 295.4 316.067953560044 10.9157211484992 263.816325291457 20.6679535600435 45 283 291.174837928372 16.8347743617181 257.99038770991 8.17483792837169 46 275.8 294.216479765338 9.6627377275261 247.720782507136 18.416479765338 47 279.7 305.558106074806 16.3907166208327 237.451177304362 25.8581060748058 48 254.6 269.477250872174 15.3988661361994 224.323882991627 14.8772508721738 49 234.6 251.976551466518 6.02685985459014 211.196588678892 17.3765514665179 50 176.9 162.047529894875 -6.27184026099176 198.024310366117 -14.8524701051248 51 148.1 125.495988402391 -14.1480204557324 184.852032053341 -22.6040115976089 52 122.7 88.6917949880213 -20.1942005000934 176.902405512072 -34.0082050119787 53 124.9 100.127609648452 -19.2803886192554 168.952778970803 -24.7723903515476 54 121.6 89.5536760072704 -12.2718611693765 165.918185162106 -32.0463239927296 55 128.4 96.9797443816753 -3.06333573508466 162.883591353409 -31.4202556183247 56 144.5 117.679895382132 10.9157211484992 160.404383469369 -26.8201046178681 57 151.8 128.840050052953 16.8347743617181 157.925175585328 -22.9599499470465 58 167.1 168.240019310439 9.6627377275261 156.297242962035 1.14001931043933 59 173.8 176.539973040427 16.3907166208327 154.669310338741 2.73997304042658 60 203.7 237.88222816065 15.3988661361994 154.118905703151 34.1822281606499 61 199.8 240.004639077849 6.02685985459014 153.568501067561 40.2046390778491

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259928568ssqsbtoafks21cv/10swe1259928509.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259928568ssqsbtoafks21cv/10swe1259928509.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259928568ssqsbtoafks21cv/2jwe51259928509.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259928568ssqsbtoafks21cv/2jwe51259928509.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259928568ssqsbtoafks21cv/3sj5r1259928509.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259928568ssqsbtoafks21cv/3sj5r1259928509.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259928568ssqsbtoafks21cv/4r7ur1259928509.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259928568ssqsbtoafks21cv/4r7ur1259928509.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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