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paper Loess

*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: Sun, 26 Dec 2010 19:01:05 +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/26/t1293389994s972j8qkogs5n2n.htm/, Retrieved Sun, 26 Dec 2010 19:59:54 +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/26/t1293389994s972j8qkogs5n2n.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 «
5,2 7,9 8,7 8,9 15,3 15,4 18,1 19,7 13 12,6 6,2 3,5 3,4 0 9,5 8,9 10,4 13,2 18,9 19 16,3 10,6 5,8 3,6 2,6 5 7,3 9,2 15,7 16,8 18,4 18,1 14,6 7,8 7,6 3,8 5,6 2,2 6,8 11,8 14,9 16,7 16,7 15,9 13,6 9,2 2,8 2,5 4,8 2,8 7,8 9 12,9 16,4 21,8 17,8 13,5 10 10,4 5,5 4 6,8 5,7 9,1 13,6 15 20,9 20,4 14 13,7 7,1 0,8 2,1 1,3 3,9 10,7 11,1 16,4 17,1 17,3 12,9 10,9 5,3 0,7 -0,2 6,5 8,6 8,5 13,3 16,2 17,5 21,2 14,8 10,3 7,3 5,1 4,4 6,2 7,7 9,3 15,6 16,3 16,6 17,4 15,3 9,7 3,7 4,6 5,4 3,1 7,9 10,1 15 15,6 19,7 18,1 17,7 10,7 6,2 4,2 4 5,9 7,1 10,5 15,1 16,8 15,3 18,4 16,1 11,3 7,9 5,6 3,4 4,8 6,5 8,5 15,1 15,7 18,7 19,2 12,9 14,4 6,2 3,3 4,6 7,2 7,8 9,9 13,6 17,1 17,8 18,6 14,7 10,5 8,6 4,4 2,3 2,8 8,8 10,7 13,9 19,3 19,5 20,4 15,3 7,9 8,3 4,5 3,2 5 6,6 11,1 12,8 16,3 17,4 18,9 15,8 11,7 6,4 2,9 4,7 2,4 7,2 10,7 13,4 18,5 18,3 16,8 16,6 14, etc...
 
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'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal24010241
Trend1912
Low-pass1312


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
15.25.03821737221319-7.0457516222851312.4075342500719-0.161782627786810
27.910.0078788709021-6.3832222566592912.17534338575722.10787887090205
38.79.09754119556556-3.6406937170080811.94315252144250.397541195565559
48.96.76871077351061-0.6898405863335311.7211298128229-2.13128922648939
515.315.83488000171463.2660128940820611.49910710420330.534880001714615
615.413.86201724781405.6613450544558211.2766376977302-1.53798275218597
718.117.45415578426327.6916759244798411.0541682912570-0.645844215736824
819.720.97240848399577.6001553882672910.82743612773701.27240848399566
91311.21066157947024.188634456312710.6007039642171-1.78933842052981
1012.614.46766046088240.30943779190250310.42290174721511.86766046088236
116.26.02465952076606-3.8697590509792210.2450995302132-0.175340479233936
123.53.97635329498406-7.08799424592910.11164095094490.476353294984058
133.43.86756925060839-7.045751622285139.978182371676740.467569250608394
140-3.55131239976070-6.383222256659299.93453465641998-3.55131239976070
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168.98.58023583850756-0.689840586333539.90960474782597-0.319764161492436
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1813.210.76810652352185.661345054455829.97054842202237-2.43189347647819
1918.920.09554978596417.6916759244798410.01277428955601.19554978596412
201920.29193029594617.6001553882672910.10791431578661.29193029594611
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235.84.98418657714854-3.8697590509792210.4855724738307-0.815813422851459
243.63.67716820111137-7.08799424592910.61082604481760.0771682011113679
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2655.66120515777008-6.3832222566592910.72201709888920.661205157770077
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347.84.671820321881020.30943779190250310.6187418862165-3.12817967811898
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363.84.07694987649551-7.08799424592910.61104436943350.276949876495511
375.67.64621497717183-7.0457516222851310.59953664511332.04621497717183
382.20.23777656146999-6.3832222566592910.5454456951893-1.96222343853001
396.86.74933897174279-3.6406937170080810.4913547452653-0.0506610282572133
4011.813.9363970106744-0.6898405863335310.35344357565922.13639701067437
4114.916.31845469986493.2660128940820610.21553240605301.41845469986490
4216.717.67829770639805.6613450544558210.06035723914610.978297706398035
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472.8-0.138446154574130-3.869759050979229.60820520555335-2.93844615457413
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502.82.03086283452373-6.383222256659299.95235942213555-0.769137165476268
517.89.10070572841898-3.6406937170080810.13998798858911.30070572841898
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5521.824.93613290980077.6916759244798410.97219116571943.13613290980072
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6720.923.21361126053467.6916759244798410.89471281498552.31361126053461
6820.422.49937135566047.6001553882672910.70047325607232.09937135566043
691413.30513184652834.188634456312710.5062336971590-0.694868153471711
7013.716.73876691132690.30943779190250310.35179529677063.03876691132689
717.17.87240215459702-3.8697590509792210.19735689638220.772402154597017
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741.3-0.685382983217375-6.383222256659299.66860523987666-1.98538298321738
753.91.96252330064528-3.640693717008089.4781704163628-1.93747669935472
7610.712.7199998598348-0.689840586333539.369840726498722.01999985983480
7711.19.67247606928333.266012894082069.26151103663464-1.42752393071671
7816.417.89192935670435.661345054455829.246725588839911.49192935670427
7917.117.27638393447507.691675924479849.231940141045180.176383934474977
8017.317.66524244768187.600155388267299.334602164050950.365242447681760
8112.912.17410135663064.18863445631279.43726418705672-0.725898643369414
8210.911.95206834516090.3094377919025039.538493862936551.05206834516095
835.34.83003551216284-3.869759050979229.63972353881638-0.469964487837156
840.7-1.24525994974193-7.0879942459299.73325419567093-1.94525994974193
85-0.2-3.18103323024036-7.045751622285139.82678485252549-2.98103323024036
866.59.41542393981826-6.383222256659299.967798316841032.91542393981826
878.610.7318819358515-3.6406937170080810.10881178115662.13188193585152
888.57.40133506407535-0.6898405863335310.2885055222582-1.09866493592465
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986.27.63642848409276-6.3832222566592911.14679377256651.43642848409276
997.77.98459564540865-3.6406937170080811.05609807159940.284595645408652
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10216.316.29241106718005.6613450544558210.6462438783642-0.00758893282003825
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17618.919.50335341855887.6001553882672910.69649119317390.603353418558761
17715.816.74645352192514.188634456312710.66491202176220.94645352192514
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1916.15.57022971962473-3.8697590509792210.4995293313545-0.529770280375272
1923.53.59872837782706-7.08799424592910.48926586810200.0987283778270562
1931.7-0.0332507825642765-7.0457516222851310.4790024048494-1.73325078256428
1942.30.418149473470046-6.3832222566592910.5650727831892-1.88185052652995
1954.51.98955055547901-3.6406937170080810.6511431615291-2.51044944452099
1969.38.4575975252842-0.6898405863335310.8322430610493-0.842402474715803
19714.214.12064414534833.2660128940820611.0133429605696-0.0793558546516557
19817.317.59056401733905.6613450544558211.34809092820510.290564017339044
1992326.62548517967957.6916759244798411.68283889584073.62548517967947
20016.312.95155233301787.6001553882672912.0482922787149-3.34844766698222
20118.420.19761988209814.188634456312712.41374566158921.79761988209813
20214.215.45649956053340.30943779190250312.63406264756411.25649956053339
2039.19.21537941744018-3.8697590509792212.85437963353900.115379417440183
2045.96.05982308392201-7.08799424592912.8281711620070.159823083922007
2057.28.64378893181017-7.0457516222851312.80196269047501.44378893181017
2066.87.3923955513986-6.3832222566592912.59082670526070.592395551398598
20787.26100299696166-3.6406937170080812.3796907200464-0.738997003038335
20814.317.1916102068722-0.6898405863335312.09823037946132.89161020687219
20914.614.11721706704173.2660128940820611.8167700388763-0.482782932958333
21017.517.72859517264325.6613450544558211.61005977290100.228595172643152
21117.215.30497456859447.6916759244798411.4033495069258-1.89502543140563
21217.215.53412901872517.6001553882672911.2657155930076-1.66587098127492
21314.112.88328386459784.188634456312711.1280816790895-1.21671613540216
21410.59.632837766624180.30943779190250311.0577244414733-0.867162233375815
2156.86.48239184712206-3.8697590509792210.9873672038572-0.317608152877943
2164.14.28895751725443-7.08799424592910.99903672867460.188957517254435
2176.59.03504536879315-7.0457516222851311.01070625349202.53504536879315
2186.17.54743850985838-6.3832222566592911.03578374680091.44743850985838
2196.35.17983247689826-3.6406937170080811.0608612401098-1.12016752310174
2209.38.28879447428069-0.6898405863335311.0010461120528-1.01120552571931
22116.418.59275612192213.2660128940820610.94123098399592.19275612192207
22216.115.76587072430705.6613450544558210.7727842212371-0.33412927569297
2231817.70398661704177.6916759244798410.6043374584784-0.296013382958273
22417.617.15116522347537.6001553882672910.4486793882574-0.448834776524658
2251413.5183442256514.188634456312710.2930213180363-0.481655774349003
22610.510.41629035117830.30943779190250310.2742718569192-0.0837096488217473
2276.97.41423665517704-3.8697590509792210.25552239580220.514236655177038
2282.82.37678730757095-7.08799424592910.3112069383581-0.423212692429054
2290.7-1.92113985862881-7.0457516222851310.3668914809139-2.62113985862881
2303.63.09767602161974-6.3832222566592910.4855462350395-0.502323978380256
2316.76.43649272784294-3.6406937170080810.6042009891651-0.263507272157064
23212.514.9306536609882-0.6898405863335310.75918692534532.43065366098824
23314.414.61981424439253.2660128940820610.91417286152540.219814244392499
23416.516.28280265035015.6613450544558211.0558522951941-0.217197349649883
23518.718.51079234665757.6916759244798411.1975317288627-0.189207653342534
23619.419.86447993153387.6001553882672911.33536468019890.464479931533766
23715.815.93816791215214.188634456312711.47319763153520.138167912152111
23811.310.68501710665550.30943779190250311.6055451014420-0.614982893344465
2399.711.5318664796305-3.8697590509792211.73789257134871.83186647963049
2402.91.02360266308950-7.08799424592911.8643915828395-1.8763973369105
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/26/t1293389994s972j8qkogs5n2n/1gv951293390059.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/26/t1293389994s972j8qkogs5n2n/1gv951293390059.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/26/t1293389994s972j8qkogs5n2n/2gv951293390059.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/26/t1293389994s972j8qkogs5n2n/2gv951293390059.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/26/t1293389994s972j8qkogs5n2n/3rm8q1293390059.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/26/t1293389994s972j8qkogs5n2n/3rm8q1293390059.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/26/t1293389994s972j8qkogs5n2n/41d7s1293390059.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/26/t1293389994s972j8qkogs5n2n/41d7s1293390059.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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Software written by Ed van Stee & Patrick Wessa


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