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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: Tue, 30 Nov 2010 14:18:58 +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/Nov/30/t1291126626efvgdnjs26atqs9.htm/, Retrieved Tue, 30 Nov 2010 15:17:15 +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/Nov/30/t1291126626efvgdnjs26atqs9.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 «
1483509 8036554 4623093 5528662 4221032 8061847 7640066 2935533 8161548 2543967 13163450 3348436 3997440 2322911 2019457 3047748 5728767 2605173 5646743 13121544 3453409 1878333 4247362 23022552 7646203 9016602 3606568 3173510 17568772 10805045 31056269 15623385 6663443 35435745 2823250 5197089 4120632 8832767 3695374 8385805 3777904 5199532 5297275 14847382 5900158 4416718 3926429 4876884 2795297 3385527 3877941 3556729 4982836 2976325 2295026 2218752 4146062 3302091 3864505 5454794 1749836 6684048 2809918 4092664 5070470 9814477 6665318 3912554 6188129 3627991 3308767 3820332 4932979 5567917 5020814 3803273 3999984 4883104 13731747 47531824 8415570 22178158 61211654 18223748 17678085 49299580 25899948 34121754 9859231 29740892 21085212 43003866 59549247 18026465 4680597 5564728 11792347 10371624 3728446 5732978 4067638 2395508 5018801 22068888 7678580 15510095 6471239 14349204 35151574 8210488 50226 etc...
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ 72.249.76.132


Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal35210353
Trend1912
Low-pass1312


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
11483509-2555079.91942401-281874.8971736585803972.81659767-4038588.91942401
280365548463606.786363841826059.264938625783441.94869754427052.786363838
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981615488669336.48902922165306.57624545488452.9347254507788.489029197
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30072974452864024.67208966190715.64459685011540149.6833135-4433420.32791034
30187397365655871.13307562-281874.89717365812105475.7640980-3083864.86692438
3021245553710947576.48887711826059.2649386212137438.2461842-1507960.51112285
3032429118137976432.2796543-1563471.0079247812169400.728270413685251.2796543
3044215150-1444042.13394379-2268240.4745458412142582.6084896-5659192.13394379
3052865217647003539.0530722-1814951.5417809912115764.488708818351363.0530722
30668511723647769.41265714-2126389.0677361912180963.6550790-3203402.58734286
3073746871-5240131.69580312487710.87435387112246162.8214492-8987002.69580312
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3091682971019554372.24247812165306.576245411939741.18127652724662.24247808
3101377859414981821.8003241654822.67537387311920543.5243021203227.80032414
31164637171839213.50147033-813125.36879778111901345.8673275-4624503.49852967
31289568675083221.76462423190715.64459685012639796.5907789-3873645.23537577
3132120491529313457.5829433-281874.89717365813378247.31423048108542.58294327
3141611585515684595.48073081826059.2649386214721055.2543306-431259.519269181
3152536113-9428166.18650596-1563471.0079247816063863.1944307-11964279.1865060
3161664571717937290.3284689-2268240.4745458417622384.14607691291573.32846890
3171700373016641506.4440578-1814951.5417809919180905.0977232-362223.555942159
3181596900613613558.6302999-2126389.0677361920450842.4374363-2355447.36970008
3193102042739832363.3484967487710.87435387121720779.77714948811936.34849675
3202379889721967746.02375313543437.3579247922086610.6183221-1831150.97624692
3212077032116922893.96425972165306.576245422452441.4594949-3847427.03574029
3224441040266035261.5881692654822.67537387322130719.736457021624859.5881692
3232703749133079109.3553787-813125.36879778121808998.01341906041618.35537873
3242962777137919055.5444315190715.64459685021145770.81097178291284.54443147
3251818979216178915.2886493-281874.89717365820482543.6085243-2010876.71135065
3264654610-12503680.25257161826059.2649386219986840.987633-17158290.2525716
327123072016686734.64118307-1563471.0079247819491138.3667417-5620466.35881693
3281530057813745259.6076479-2268240.4745458419124136.8668980-1555318.39235211
329106238644305544.1747268-1814951.5417809918757135.3670542-6318319.8252732
3306880178-2913246.47063729-2126389.0677361918799991.5383735-9793424.47063729
3312994735740564155.4159534487710.87435387118842847.709692810616798.4159534
3321861139914509034.90911363543437.3579247919170325.7329617-4102364.09088645
3334243260463202097.6675242165306.576245419497803.756230620769493.6675240
3342020827820416110.3561052654822.67537387319345622.9685209207832.356105205
335140043929628467.18798649-813125.36879778119193442.1808113-4375924.81201351
3362573776533040016.9155529190715.64459685018244797.43985027302251.91555293
3371673573816457198.1982845-281874.89717365817296152.6988892-278539.801715504
3382245082527433084.49725841826059.2649386215642506.2378034982259.49725837
33968808401336291.23120792-1563471.0079247813988859.7767169-5544548.76879208
34085103796856165.83010574-2268240.4745458412432832.6444401-1654213.16989426
34181824817303108.02961764-1814951.5417809910876805.5121634-879372.970382363
3421094868314028984.6476435-2126389.067736199994770.420092693080301.64764350
343480527710107.7976241019487710.8743538719112735.32802203-4795169.2023759
3442589229-7920031.963684723543437.357924799555052.60575993-10509260.9636847
3455658407-845862.4597432282165306.57624549997369.88349783-6504269.45974323
3461286261114700831.0160187654822.67537387310369568.30860741838220.01601874
34756661881403734.63508084-813125.36879778110741766.7337169-4262453.36491916
34868755562359841.29239279190715.64459685011200555.0630104-4515714.70760721
34970987662820063.50486989-281874.89717365811659343.3923038-4278702.49513011
3503608330958071655.14848881826059.2649386212268903.586572521988346.1484889
351102003309085667.22708349-1563471.0079247812878463.7808413-1114662.77291651
35277849764244749.08764907-2268240.4745458413593443.3868968-3540226.91235093
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Nov/30/t1291126626efvgdnjs26atqs9/1k6o01291126728.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/30/t1291126626efvgdnjs26atqs9/1k6o01291126728.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/30/t1291126626efvgdnjs26atqs9/2k6o01291126728.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/30/t1291126626efvgdnjs26atqs9/2k6o01291126728.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/30/t1291126626efvgdnjs26atqs9/3k6o01291126728.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/30/t1291126626efvgdnjs26atqs9/3k6o01291126728.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/30/t1291126626efvgdnjs26atqs9/4cx531291126728.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/30/t1291126626efvgdnjs26atqs9/4cx531291126728.ps (open in new window)


 
Parameters (Session):
par1 = 12 ;
 
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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Software written by Ed van Stee & Patrick Wessa


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