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*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: Thu, 09 Dec 2010 16:46:56 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/09/t1291913114ldsvldhq1rj95uy.htm/, Retrieved Thu, 09 Dec 2010 17:45:15 +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/2010/Dec/09/t1291913114ldsvldhq1rj95uy.htm/},
    year = {2010},
}
@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 = {2010},
    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 «
9700 9081 9084 9743 8587 9731 9563 9998 9437 10038 9918 9252 9737 9035 9133 9487 8700 9627 8947 9283 8829 9947 9628 9318 9605 8640 9214 9567 8547 9185 9470 9123 9278 10170 9434 9655 9429 8739 9552 9687 9019 9672 9206 9069 9788 10312 10105 9863 9656 9295 9946 9701 9049 10190 9706 9765 9893 9994 10433 10073 10112 9266 9820 10097 9115 10411 9678 10408 10153 10368 10581 10597 10680 9738 9556
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk


Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal751076
Trend1912
Low-pass1312


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
197009665.38145341098216.6597249533499517.95882163567-34.618546589023
290819165.77198053844-524.326278586299520.5542980478584.771980538444
390848819.30488240033-174.4546568603539523.14977446002-264.695117599666
497439815.13667288808148.1342581676289522.7290689442972.1366728880803
585878390.3144979431-738.6228613716639522.30836342857-196.685502056904
697319725.13807465835217.6548137857959519.20711155585-5.86192534164547
795639776.79463081457-166.9004904976979516.10585968313213.794630814566
8999810477.39159839494.899093226310549513.70930837883479.391598394863
994379409.98788403842-47.30064111293489511.31275707452-27.0121159615846
101003810051.5200681786519.3361529156139505.1437789057613.52006817863
1199189947.88590851771389.1392907452939498.97480073729.8859085177137
1292528874.86319978722155.7816679040929473.3551323087-377.136800212782
1397379809.60481116626216.6597249533499447.7354638803972.6048111662622
1490359183.97766975103-524.326278586299410.34860883526148.977669751028
1591339067.49290307022-174.4546568603539372.96175379014-65.5070969297849
1694879479.52410990384148.1342581676289346.34163192853-7.47589009615876
1787008818.90135130474-738.6228613716639319.72151006693118.901351304736
1896279732.83020877481217.6548137857959303.51497743939105.830208774814
1989478773.59204568585-166.9004904976979287.30844481185-173.407954314154
2092839285.245003453764.899093226310549275.855903319932.245003453756
2188298440.89727928492-47.30064111293489264.40336182801-388.102720715078
22994710115.1181643594519.3361529156139259.54568272501168.118164359375
2396289612.1727056327389.1392907452939254.688003622-15.8272943673019
2493189223.30241590875155.7816679040929256.91591618715-94.6975840912455
2596059734.19644629435216.6597249533499259.1438287523129.196446294349
2686408534.35586053479-524.326278586299269.9704180515-105.644139465214
2792149321.65764950965-174.4546568603539280.7970073507107.657649509649
2895679693.8923956418148.1342581676289291.97334619057126.892395641802
2985478529.47317634122-738.6228613716639303.14968503044-17.5268236587763
3091858843.67047554606217.6548137857959308.67471066814-341.329524453937
3194709792.70075419185-166.9004904976979314.19973630584322.700754191854
3291238918.073575426254.899093226310549323.02733134744-204.92642457375
3392789271.4457147239-47.30064111293489331.85492638903-6.55428527609911
341017010468.1371071333519.3361529156139352.5267399511298.137107133294
3594349105.66215574156389.1392907452939373.19855351315-328.337844258442
3696559760.51674400655155.7816679040929393.70158808936105.516744006552
3794299227.13565238109216.6597249533499414.20462266556-201.864347618912
3887398573.70419384917-524.326278586299428.62208473712-165.295806150829
3995529835.41511005168-174.4546568603539443.03954680868283.415110051677
4096879758.56511370315148.1342581676289467.3006281292271.5651137031509
4190199285.0611519219-738.6228613716639491.56170944977266.061151921895
4296729605.97213280895217.6548137857959520.37305340526-66.0278671910528
4392069029.71609313695-166.9004904976979549.18439736075-176.283906863049
4490698558.394271843344.899093226310549574.70663493034-510.605728156655
45978810023.071768613-47.30064111293489600.22887249994235.071768612994
461031210480.8282002468519.3361529156139623.8356468376168.828200246788
471010510173.4182880795389.1392907452939647.4424211752668.4182880794506
4898639890.35914461412155.7816679040929679.8591874817927.3591446141199
4996569383.06432125833216.6597249533499712.27595378832-272.935678741671
5092959374.16734837944-524.326278586299740.1589302068579.167348379442
51994610298.412750235-174.4546568603539768.04190662537352.412750234978
5297019472.4536957728148.1342581676289781.41204605957-228.5463042272
5390499041.8406758779-738.6228613716639794.78218549377-7.15932412210896
541019010354.4579532181217.6548137857959807.88723299606164.457953218143
5597069757.90820999935-166.9004904976979820.9922804983551.9082099993466
5697659692.770733741144.899093226310549832.33017303255-72.2292662588607
5798939989.63257554619-47.30064111293489843.6680655667596.6325755461876
5899949615.54519313481519.3361529156139853.11865394957-378.454806865186
591043310614.2914669223389.1392907452939862.5692423324181.29146692231
601007310111.7158882826155.7816679040929878.5024438133138.7158882825952
611011210112.9046297524216.6597249533499894.435645294230.904629752420078
6292669136.50938088752-524.326278586299919.81689769877-129.490619112479
6398209869.25650675705-174.4546568603539945.198150103349.2565067570467
641009710071.7159847449148.1342581676289974.14975708747-25.2840152550998
6591158965.52149730002-738.62286137166310003.1013640716-149.478502699976
661041110567.8772587978217.65481378579510036.4679274164156.877258797789
6796789453.0659997365-166.90049049769710069.8344907612-224.934000263493
681040810720.29864439034.8990932263105410090.8022623834312.298644390328
691015310241.5306071074-47.300641112934810111.770034005588.5306071074065
701036810087.1032512669519.33615291561310129.5605958175-280.89674873307
711058110625.5095516253389.13929074529310147.351157629444.5095516253241
721059710873.4076819219155.78166790409210164.810650174276.407681921864
731068010961.0701323279216.65972495334910182.2701427187281.070132327946
7497389801.4948913326-524.3262785862910198.831387253763.4948913325898
7595569071.06202507166-174.45465686035310215.3926317887-484.937974928343
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291913114ldsvldhq1rj95uy/1k9uz1291913211.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291913114ldsvldhq1rj95uy/1k9uz1291913211.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/09/t1291913114ldsvldhq1rj95uy/2k9uz1291913211.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291913114ldsvldhq1rj95uy/2k9uz1291913211.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/09/t1291913114ldsvldhq1rj95uy/3k9uz1291913211.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291913114ldsvldhq1rj95uy/3k9uz1291913211.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/09/t1291913114ldsvldhq1rj95uy/4dic21291913211.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t1291913114ldsvldhq1rj95uy/4dic21291913211.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')
 





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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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