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LOESS - OPJV

*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: Wed, 29 Dec 2010 21:52:41 +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/29/t12936594508le9lnhvqa83bom.htm/, Retrieved Wed, 29 Dec 2010 22:50:54 +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/29/t12936594508le9lnhvqa83bom.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 «
20503 22885 26217 26583 27751 28158 27373 28367 26851 26733 26849 26733 27951 29781 32914 33488 35652 36488 35387 35676 34844 32447 31068 29010 29812 30951 32974 32936 34012 32946 31948 30599 27691 25073 23406 22248 22896 25317 26558 26471 27543 26198 24725 25005 23462 20780 19815 19761 21454 23899 24939 23580 24562 24696 23785 23812 21917 19713 19282 18788 21453 24482 27474 27264 27349 30632 29429 30084 26290 24379 23335 21346 21106 24514 28353 30805 31348 34556 33855 34787 32529 29998 29257 28155 30466 35704 39327 39351 42234 43630 43722 43121 37985 37135 34646 33026 35087 38846 42013 43908 42868 44423 44167 43636 44382 42142 43452 36912 42413 45344 44873 47510 49554 47369 45998 48140 48441 44928 40454 38661 37246 36843 36424 37594 38144 38737 34560 36080 33508 35462 33374 32110 35533 35532 37903 36763 40399 44164 44496 43110 43880 43930 44327
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ www.yougetit.org


Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal14310144
Trend1912
Low-pass1312


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
12050321040.6323242025-3122.8661046980623088.2337804956537.632324202455
22288523073.9284537408-947.8727762804823643.9443225396188.928453740849
32621727199.47574841741034.8693869989624199.6548645837982.475748417379
42658326982.7684083011405.4381793373324777.7934123617399.768408300959
52775127630.1453867432515.9226531172525355.9319601398-120.854613257012
62815827100.40248827423267.3837900821525948.2137216436-1057.59751172577
72737326114.40949307142091.0950237811226540.4954831475-1258.59050692859
82836727402.23663263432204.0588386656427127.7045287-964.763367365656
92685125637.4815834614349.6048422860427714.9135742526-1213.5184165386
102673326529.9725415441-1406.3512946446528342.3787531005-203.027458455876
112684927423.3793187264-2695.2232506748928969.8439319485574.379318726402
122673328512.6358703723-4696.0587627963529649.42289242411779.63587037229
132795128695.8642517984-3122.8661046980630329.0018528996744.864251798419
142978129562.8163621983-947.8727762804830947.0564140822-218.183637801718
153291433228.01963773631034.8693869989631565.1109752648314.019637736277
163348833548.52834667021405.4381793373332022.033473992560.5283466701585
173565236309.12137416252515.9226531172532478.9559727203657.121374162496
183648836962.68363260663267.3837900821532745.9325773113474.68363260655
193538735669.99579431652091.0950237811233012.9091819023282.995794316543
203567636037.49518918952204.0588386656433110.4459721448361.495189189533
213484436130.4123953267349.6048422860433207.98276238731286.41239532665
223244733166.2201301907-1406.3512946446533134.131164454719.220130190675
233106831770.9436841543-2695.2232506748933060.2795665206702.943684154256
242901029912.1948191988-4696.0587627963532803.8639435975902.194819198805
252981230199.4177840236-3122.8661046980632547.4483206745387.417784023593
263095130745.2770933585-947.8727762804832104.595682922-205.722906641542
273297433251.38756783151034.8693869989631661.7430451696277.387567831462
283293633390.84532667761405.4381793373331075.7164939851454.845326677558
293401235018.38740408212515.9226531172530489.68994280061006.38740408211
303294632739.6759785913267.3837900821529884.9402313268-206.324021408986
313194832524.71445636592091.0950237811229280.190519853576.714456365855
323059930276.48397574912204.0588386656428717.4571855852-322.516024250868
332769126877.6713063965349.6048422860428154.7238513174-813.328693603475
342507323930.2977659678-1406.3512946446527622.0535286769-1142.70223403221
352340622417.8400446386-2695.2232506748927089.3832060363-988.159955361403
362224822615.6734181031-4696.0587627963526576.3853446932367.673418103139
372289622851.4786213479-3122.8661046980626063.3874833501-44.5213786520762
382531725969.2623019885-947.8727762804825612.610474292652.262301988474
392655826919.29714776721034.8693869989625161.8334652339361.29714776716
402647126745.32553547761405.4381793373324791.236285185274.325535477648
412754328149.43824174662515.9226531172524420.6391051362606.43824174659
422619824969.40901877413267.3837900821524159.2071911437-1228.59098122586
432472523461.12969906762091.0950237811223897.7752771513-1263.87030093238
442500524064.83710008972204.0588386656423741.1040612446-940.162899910261
452346222989.962312376349.6048422860423584.432845338-472.037687624019
462078019499.596949623-1406.3512946446523466.7543450217-1280.40305037702
471981518976.1474059695-2695.2232506748923349.0758447054-838.852594030475
481976120977.5218524528-4696.0587627963523240.53691034351216.52185245281
492145422898.8681287163-3122.8661046980623131.99797598171444.86812871634
502389925727.9395025972-947.8727762804823017.93327368331828.93950259723
512493925939.26204161631034.8693869989622903.86857138481000.26204161625
522358022994.01551184361405.4381793373322760.5463088191-585.98448815645
532456223990.85330062932515.9226531172522617.2240462535-571.146699370696
542469623603.74136569763267.3837900821522520.8748442203-1092.2586343024
552378523054.37933403182091.0950237811222424.5256421871-730.620665968185
562381222910.71724899462204.0588386656422509.2239123397-901.282751005387
572191720890.4729752215349.6048422860422593.9221824924-1026.52702477846
581971317940.6732240863-1406.3512946446522891.6780705584-1772.32677591373
591928218069.7892920505-2695.2232506748923189.4339586243-1212.21070794946
601878818635.6408455071-4696.0587627963523636.4179172893-152.359154492915
612145321945.4642287439-3122.8661046980624083.4018759542492.464228743869
622448225345.9851826461-947.8727762804824565.8875936343863.985182646138
632747428864.75730168651034.8693869989625048.37331131451390.75730168654
642726427694.99314046971405.4381793373325427.5686801929430.993140469731
652734926375.31329781142515.9226531172525806.7640490714-973.686702188625
663063232022.07008101683267.3837900821525974.5461289011390.07008101684
672942930624.57676748822091.0950237811226142.32820873061195.57676748823
683008431766.45039129922204.0588386656426197.49077003521682.45039129916
692629025977.7418263742349.6048422860426252.6533313397-312.258173625785
702437923773.0585590008-1406.3512946446526391.2927356439-605.941440999231
712333522835.2911107269-2695.2232506748926529.932139948-499.708889273126
722134620554.1074393442-4696.0587627963526833.9513234521-791.89256065576
732110618196.8955977418-3122.8661046980627137.9705069562-2909.10440225815
742451422380.4349004135-947.8727762804827595.437875867-2133.56509958655
752835327618.22536822321034.8693869989628052.9052447778-734.774631776814
763080531572.41277666141405.4381793373328632.1490440013767.412776661404
773134830968.68450365812515.9226531172529211.3928432247-379.315496341922
783455635972.18392865943267.3837900821529872.43228125851416.18392865939
793385535085.43325692662091.0950237811230533.47171929231230.43325692663
803478736081.27701791132204.0588386656431288.66414342311294.2770179113
813252932664.5385901601349.6048422860432043.8565675539135.538590160104
822999828576.8953815257-1406.3512946446532825.455913119-1421.10461847434
832925727602.1679919908-2695.2232506748933607.0552586841-1654.83200800923
842815526592.9462726164-4696.0587627963534413.1124901799-1562.05372738357
853046628835.6963830223-3122.8661046980635219.1697216757-1630.30361697765
863570436379.3281624328-947.8727762804835976.5446138477675.328162432808
873932740885.21110698141034.8693869989636733.91950601961558.21110698141
883935139955.44083071391405.4381793373337341.1209899487604.440830713924
894223444003.75487300492515.9226531172537948.32247387791769.75487300489
904363045663.82788618943267.3837900821538328.78832372852033.82788618936
914372246643.65080263982091.0950237811238709.25417357912921.65080263977
924312145115.7251306622204.0588386656438922.21603067241994.725130662
933798536485.2172699484349.6048422860439135.1778877656-1499.78273005164
943713536409.0064607871-1406.3512946446539267.3448338576-725.993539212912
953464632587.7114707254-2695.2232506748939399.5117799495-2058.28852927462
963302631223.5132254266-4696.0587627963539524.5455373697-1802.48677457339
973508733647.2868099081-3122.8661046980639649.57929479-1439.71319009193
983884638725.6295936767-947.8727762804839914.2431826038-120.370406323316
994201342812.22354258341034.8693869989640178.9070704176799.223542583444
1004390845755.06618683061405.4381793373340655.4956338321847.06618683065
1014286842087.99314963632515.9226531172541132.0841972464-780.006850363687
1024442343935.45574743273267.3837900821541643.1604624851-487.544252567284
1034416744088.6682484952091.0950237811242154.2367277238-78.3317515049494
1044363642474.75837144352204.0588386656442593.1827898908-1161.24162855649
1054438245382.2663056561349.6048422860443032.12885205791000.2663056561
1064214242266.47105256-1406.3512946446543423.8802420846124.471052560002
1074345245783.5916185635-2695.2232506748943815.63163211142331.59161856346
1083691234375.3359197502-4696.0587627963544144.7228430461-2536.66408024979
1094241343475.0520507172-3122.8661046980644473.81405398091062.0520507172
1104534446903.0889235179-947.8727762804844732.78385276261559.08892351785
1114487343719.37696145671034.8693869989644991.7536515444-1153.62303854334
1124751048485.12285647431405.4381793373345129.4389641884975.122856474278
1134955451324.95307005032515.9226531172545267.12427683241770.95307005035
1144736946319.0961206543267.3837900821545151.5200892638-1049.90387934598
1154599844868.98907452362091.0950237811245035.9159016952-1129.01092547636
1164814049552.88487808542204.0588386656444523.05628324891412.88487808542
1174844152522.1984929113349.6048422860444010.19666480264081.19849291133
1184492848056.5838325196-1406.3512946446543205.76746212513128.58383251957
1194045441201.8849912274-2695.2232506748942401.3382594475747.884991227358
1203866140605.4005239209-4696.0587627963541412.65823887541944.40052392091
1213724637190.8878863947-3122.8661046980640423.9782183034-55.1121136052898
1223684335280.3951818328-947.8727762804839353.4775944476-1562.60481816716
1233642433530.15364240911034.8693869989638282.9769705919-2893.84635759088
1243759436382.84461181881405.4381793373337399.7172088438-1211.15538818117
1253814437255.6198997872515.9226531172536516.4574470958-888.380100213013
1263873738114.0688229543267.3837900821536092.5473869639-622.931177046004
1273456031360.26764938692091.0950237811235668.6373268319-3199.73235061306
1283608034345.16219744242204.0588386656435610.7789638919-1734.83780255758
1293350831113.474556762349.6048422860435552.9206009519-2394.52544323797
1303546236609.8325521036-1406.3512946446535720.5187425411147.8325521036
1313337433555.1063665447-2695.2232506748935888.1168841302181.106366544722
1323211032600.6926556934-4696.0587627963536315.3661071029490.692655693434
1333553337446.2507746224-3122.8661046980636742.61533007571913.25077462239
1343553234564.9323300217-947.8727762804837446.9404462588-967.067669978307
1353790336619.86505055911034.8693869989638151.2655624419-1283.13494944087
1363676333124.09865747681405.4381793373338996.4631631859-3638.90134252318
1374039938440.4165829532515.9226531172539841.6607639298-1958.58341704703
1384416444363.95584008983267.3837900821540696.660369828199.955840089839
1394449645349.24500049272091.0950237811241551.6599757262853.24500049265
1404311041580.00654857572204.0588386656442435.9346127586-1529.99345142426
1414388044090.185907923349.6048422860443320.209249791210.185907922954
1424393045022.6654713196-1406.3512946446544243.6858233251092.6654713196
1434432746182.0608538158-2695.2232506748945167.16239685911855.0608538158
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936594508le9lnhvqa83bom/1v1k51293659559.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936594508le9lnhvqa83bom/1v1k51293659559.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t12936594508le9lnhvqa83bom/2v1k51293659559.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936594508le9lnhvqa83bom/2v1k51293659559.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t12936594508le9lnhvqa83bom/3v1k51293659559.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936594508le9lnhvqa83bom/3v1k51293659559.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t12936594508le9lnhvqa83bom/46s1q1293659559.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t12936594508le9lnhvqa83bom/46s1q1293659559.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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