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ws8dollar2

*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, 07 Dec 2010 21:51:21 +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/07/t12917587808ikhyltjmqmumr8.htm/, Retrieved Tue, 07 Dec 2010 22:53:05 +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/07/t12917587808ikhyltjmqmumr8.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 «
1.2998 1.3146 1.3225 1.3321 1.3339 1.3496 1.3647 1.3674 1.3647 1.3481 1.3612 1.3626 1.3711 1.37 1.377 1.3945 1.3917 1.4084 1.4244 1.4014 1.4018 1.3926 1.3857 1.3857 1.3803 1.3912 1.4031 1.3934 1.4016 1.3861 1.3859 1.3896 1.4089 1.4101 1.3958 1.3833 1.3936 1.3874 1.397 1.3856 1.378 1.3705 1.3726 1.3648 1.3611 1.346 1.3477 1.3412 1.3323 1.3364 1.312 1.3074 1.306 1.3078 1.2989 1.285 1.2801 1.2725 1.2715 1.2697 1.2744 1.2874 1.2834 1.2818 1.28 1.268 1.27 1.2713 1.2693 1.2613 1.2611 1.2704 1.2711 1.2836 1.288 1.286 1.282 1.2799 1.279 1.3016 1.3133 1.3253 1.3176 1.3184 1.3206 1.3221 1.3073 1.3028 1.3069 1.2992 1.3033 1.2931 1.2897 1.285 1.2817 1.2844 1.2957 1.3 1.2828 1.2703 1.2569 1.2572 1.2637 1.266 1.2567 1.2579 1.2531 1.2548 1.2328 1.2271 1.2198 1.2339 1.2294 1.2262 1.2271 1.2258 1.2391 1.2372 1.2363 1.2277 1.2258 1.2249 1.2127 1.2045 1.201 1.1942 1.1959 1.206 1.2268 1.2218 1.2155 1 etc...
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time16 seconds
R Server'George Udny Yule' @ 72.249.76.132


Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal49110492
Trend911
Low-pass511


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
11.29981.29839090449317-0.001133703370409541.30234279887724-0.00140909550683044
21.31461.316386513325940.001089140563050811.311724346111010.00178651332594204
31.32251.323331333991670.0004998966355639531.321168769372760.000831333991672345
41.33211.333273241319590.0002066171914246181.330720141488990.00117324131959018
51.33391.3282664129731-0.0006619510692042131.34019553809610-0.00563358702689998
61.34961.35128908360361-0.001133703370409541.349044619766800.00168908360361164
71.36471.372687170017310.001089140563050811.355623689419640.00798717001730576
81.36741.375070562710740.0004998966355639531.359229540653690.00767056271074451
91.36471.368580640420350.0002066171914246181.360612742388220.00388064042035219
101.34811.33607330085204-0.0006619510692042131.36078865021717-0.0120266991479623
111.36121.36215423303608-0.001133703370409541.361379470334330.00095423303608122
121.36261.360407583182660.001089140563050811.36370327625429-0.00219241681733928
131.37111.373177862866470.0004998966355639531.368522240497960.00207786286647216
141.371.365119042437860.0002066171914246181.37467434037071-0.00488095756213647
151.3771.37348191496213-0.0006619510692042131.38118003610708-0.00351808503787177
161.39451.40104846502706-0.001133703370409541.389085238343350.00654846502706308
171.39171.384514947603590.001089140563050811.39779591183336-0.00718505239640965
181.40841.412690260258020.0004998966355639531.403609843106410.0042902602580237
191.42441.442791255462490.0002066171914246181.405802127346080.0183912554624917
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211.40181.40434106906359-0.001133703370409541.400392634306820.00254106906359075
221.39261.389969414041680.001089140563050811.39414144539527-0.00263058595831844
231.38571.381389102146340.0004998966355639531.38951100121810-0.00431089785366146
241.38571.383418351115690.0002066171914246181.38777503169289-0.00228164888431426
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261.39121.39237549669558-0.001133703370409541.391158206674830.00117549669557993
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301.38611.38035405899741-0.0006619510692042131.3925078920718-0.00574594100259507
311.38591.37928928043931-0.001133703370409541.39364442293110-0.0066107195606877
321.38961.38210302076720.001089140563050811.39600783866975-0.00749697923279968
331.40891.419402905273370.0004998966355639531.397897198091070.0105029052733669
341.41011.421621885593160.0002066171914246181.398371497215410.0115218855931618
351.39581.39508675318481-0.0006619510692042131.39717519788439-0.000713246815190027
361.38331.37360966227191-0.001133703370409541.39412404109850-0.00969033772808858
371.39361.394762942614360.001089140563050811.391347916822590.00116294261435512
381.38741.384582631621040.0004998966355639531.38971747174340-0.00281736837896096
391.3971.406033437969240.0002066171914246181.387759944839330.00903343796924228
401.38561.38757329326644-0.0006619510692042131.384288657802770.00197329326643736
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421.37051.365382621568990.001089140563050811.37452823786796-0.00511737843100701
431.37261.375626967411110.0004998966355639531.369073135953330.00302696741111030
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451.36111.36478330689381-0.0006619510692042131.35807864417540.00368330689380514
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471.34771.348365776588350.001089140563050811.345945082848600.000665776588353539
481.34121.341622027861530.0004998966355639531.340278075502910.000422027861530117
491.33231.330595808067440.0002066171914246181.33379757474113-0.00170419193255822
501.33641.34718556798079-0.0006619510692042131.326276383088420.0107855679807869
511.3121.30592325414665-0.001133703370409541.31921044922376-0.00607674585334728
521.30741.300737841250320.001089140563050811.31297301818663-0.00666215874968468
531.3061.304648617169950.0004998966355639531.30685148619449-0.00135138283005443
541.30781.314132613658930.0002066171914246181.301260769149650.00633261365892923
551.29891.30299125718950-0.0006619510692042131.295470693879710.00409125718949599
561.2851.28245427481308-0.001133703370409541.28867942855733-0.00254572518692253
571.28011.277390435656030.001089140563050811.28172042378092-0.00270956434396785
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591.27151.268572565509480.0002066171914246181.27422081729909-0.00292743449051946
601.26971.26501778645431-0.0006619510692042131.27504416461489-0.00468221354568543
611.27441.27268893582143-0.001133703370409541.27724476754898-0.00171106417857447
621.28741.294169812859570.001089140563050811.279541046577380.00676981285956568
631.28341.285452239572300.0004998966355639531.280847863792140.00205223957229705
641.28181.283623517742000.0002066171914246181.279769865066580.00182351774199496
651.281.28373214820841-0.0006619510692042131.276929802860790.00373214820841494
661.2681.26305841641791-0.001133703370409541.27407528695250-0.00494158358208852
671.271.267600972778590.001089140563050811.27130988665835-0.00239902722140517
681.27131.273504777532970.0004998966355639531.268595325831460.0022047775329741
691.26931.271423930615810.0002066171914246181.266969452192760.00212393061581317
701.26131.25678609577916-0.0006619510692042131.26647585529004-0.00451390422083997
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741.28361.287629827792910.0002066171914246181.279363555015670.00402982779291028
751.2881.29432381566678-0.0006619510692042131.282338135402430.00632381566677642
761.2861.28966224751282-0.001133703370409541.283471455857590.00366224751282451
771.2821.279221840612330.001089140563050811.28368901882461-0.00277815938766546
781.27991.273443832475980.0004998966355639531.28585627088846-0.00645616752402467
791.2791.266344317829930.0002066171914246181.29144906497865-0.0126556821700747
801.30161.30432886632155-0.0006619510692042131.299533084747660.00272886632154701
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821.32531.334835929318230.001089140563050811.314674930118710.00953592931823488
831.31761.316058064773720.0004998966355639531.31864203859072-0.00154193522628443
841.31841.317051233137110.0002066171914246181.31954214967146-0.00134876686288621
851.32061.32427101278657-0.0006619510692042131.317590938282630.00367101278657334
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881.30281.297537266744880.0004998966355639531.30756283661956-0.00526273325512316
891.30691.309529438459910.0002066171914246181.304063944348670.00262943845991015
901.29921.29781516019663-0.0006619510692042131.30124679087257-0.00138483980337001
911.30331.30958164235042-0.001133703370409541.298152061019990.0062816423504164
921.29311.290819978676800.001089140563050811.29429088076015-0.00228002132320149
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951.28171.27667035021866-0.0006619510692042131.28739160085055-0.0050296497813418
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971.29571.301465599862760.001089140563050811.288845259574190.00576559986276148
981.31.313074832442120.0004998966355639531.286425270922310.0130748324421242
991.28281.284508978554880.0002066171914246181.280884404253690.00170897855488272
1001.27031.26792579320913-0.0006619510692042131.27333615786007-0.00237420679086520
1011.25691.24828467198070-0.001133703370409541.26664903138971-0.00861532801930287
1021.25721.250641809486430.001089140563050811.26266904995052-0.0065581905135721
1031.26371.266066410686770.0004998966355639531.260833692677660.00236641068677135
1041.2661.271544551902730.0002066171914246181.260248830905840.00554455190273062
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1091.23281.227093347441340.0002066171914246181.23830003536724-0.00570665255866398
1101.22711.22188601289441-0.0006619510692042131.23297593817480-0.0052139871055914
1111.21981.21151017414912-0.001133703370409541.22922352922128-0.00828982585087501
1121.23391.239084827988270.001089140563050811.227626031448680.00518482798827402
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1191.23631.239707601590590.0002066171914246181.232685781217990.00340760159058684
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1371.2361.236856879386220.001089140563050811.234053980050730.000856879386223808
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1741.34821.351235773238080.0002066171914246181.344957609570490.00303577323808146
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4211.32761.33369411842097-0.001133703370409541.322639584949440.00609411842096619
4221.32731.32813049002930.001089140563050811.325380369407650.000830490029300135
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4241.32551.317452957278810.0002066171914246181.33334042552977-0.00804704272119205
4251.34961.36395319342362-0.0006619510692042131.335908757645590.0143531934236156
4261.34251.34916856445964-0.001133703370409541.336965138910770.00666856445964092
4271.33921.341568486846650.001089140563050811.335742372590290.00236848684665492
4281.32461.316790507715170.0004998966355639531.33190959564927-0.00780949228483041
4291.33081.331478608385850.0002066171914246181.329914774422730.000678608385847879
4301.31931.30662144371354-0.0006619510692042131.33264050735566-0.0126785562864551
4311.32951.32254151046868-0.001133703370409541.33759219290173-0.00695848953131928
4321.36071.377260522597940.001089140563050811.343050336839010.0165605225979368
4331.34941.349324074606280.0004998966355639531.34897602875815-7.59253937163695e-05
4341.35071.347861429127450.0002066171914246181.35333195368112-0.0028385708725458
4351.35581.35853123894674-0.0006619510692042131.353730712122460.00273123894673954
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4371.36711.395156783572630.001089140563050811.337954075864310.0280567835726344
4381.3131.299642537725180.0004998966355639531.32585756563926-0.0133574622748209
4391.29421.276200297733030.0002066171914246181.31199308507555-0.0179997022669718
4401.30421.31063384742287-0.0006619510692042131.298428103646340.0064338474228689
4411.29051.29207637592713-0.001133703370409541.290057327443280.00157637592713011
4421.27821.270980964344890.001089140563050811.28432989509206-0.00721903565510851
4431.27861.279225166830230.0004998966355639531.277474936534200.000625166830231949
4441.27831.284785358146800.0002066171914246181.271608024661770.00648535814680451
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4461.26581.27027053588837-0.001133703370409541.262463167482040.00447053588837121
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4481.25551.251310637364490.0004998966355639531.25918946599995-0.00418936263551384
4491.26151.262494941764860.0002066171914246181.260298441043720.00099494176485826
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4521.27821.283296838418460.001089140563050811.272014021018490.00509683841846376
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4611.28231.28581248978212-0.001133703370409541.279921213588290.00351248978211882
4621.28331.279421092555520.001089140563050811.28608976688143-0.00387890744447872
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4661.27961.27206232280572-0.001133703370409541.28827138056469-0.007537677194281
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4701.2761.26535425109004-0.0006619510692042131.28730769997916-0.0106457489099567
4711.28161.26939212793940-0.001133703370409541.29494157543101-0.0122078720605967
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4731.3261.344723271684390.0004998966355639531.306776831680040.0187232716843921
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4751.2991.29586316105715-0.0006619510692042131.30279879001206-0.00313683894285188
4761.27951.26306097697913-0.001133703370409541.29707272639128-0.0164390230208744
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4801.31821.32839764108820-0.0006619510692042131.308664309981010.0101976410881981
4811.3271.34157998621299-0.001133703370409541.313553717157420.0145799862129894
4821.30851.297207978769270.001089140563050811.31870288066768-0.0112920212307264
4831.31731.310054162159210.0004998966355639531.32404594120523-0.00724583784078914
4841.32621.320055710947470.0002066171914246181.33213767186111-0.00614428905253317
4851.33941.33719343514027-0.0006619510692042131.34226851592893-0.00220656485972581
4861.36841.38784283892511-0.001133703370409541.350090864445300.0194428389251073
4871.36171.368674528352290.001089140563050811.353636331084650.00697452835229484
4881.35951.361873177900290.0004998966355639531.356626925464150.00237317790028979
4891.33321.305285685054430.0002066171914246181.36090769775414-0.0279143149455687
4901.35821.35229328128066-0.0006619510692042131.36476866978854-0.00590671871933801
4911.38661.40540095921827-0.001133703370409541.368932744152140.0188009592182732
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/07/t12917587808ikhyltjmqmumr8/1mgdn1291758664.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/07/t12917587808ikhyltjmqmumr8/1mgdn1291758664.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/07/t12917587808ikhyltjmqmumr8/2mgdn1291758664.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/07/t12917587808ikhyltjmqmumr8/2mgdn1291758664.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/07/t12917587808ikhyltjmqmumr8/3wpuq1291758664.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/07/t12917587808ikhyltjmqmumr8/3wpuq1291758664.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/07/t12917587808ikhyltjmqmumr8/4wpuq1291758664.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/07/t12917587808ikhyltjmqmumr8/4wpuq1291758664.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 <- 5
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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