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Paper

*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, 28 Dec 2010 17:57:06 +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/28/t12935589439p5hhy49w9tqoum.htm/, Retrieved Tue, 28 Dec 2010 18:55:48 +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/28/t12935589439p5hhy49w9tqoum.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 «
26548 26752 26967 27034 27056 27476 28497 29085 28720 29067 29249 29672 29761 30066 30315 30571 30757 30742 31310 31381 31470 31226 31081 31061 31114 30828 30418 30195 29877 29192 29876 29409 28458 28340 28164 28438 28053 27599 27226 27119 26625 26541 27023 26631 26154 26029 26008 26632 27010 27041 27244 26976 26715 27017 27714 27655 27103 27088 26968 27770 27616 27481 27279 26918 26503 26547 27467 27305 26259 26048 25743
 
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'Gwilym Jenkins' @ 72.249.127.135
R Framework
error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.


Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal711072
Trend1912
Low-pass1312


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
12654826574.2192792634200.18915984199126321.591560894626.2192792633614
22675226757.6845332928121.32518766936426624.99027903795.68453329278054
32696726960.31644168145.294561137918226928.3889971811-6.68355831897861
42703426909.2815591475-69.97203176902727228.6904726216-124.718440852532
52705626875.5800921463-292.5720402083227528.9919480621-180.419907853739
62747627424.6959500742-297.63463830803627824.9386882339-51.3040499258168
72849728444.1450680925428.96950350182228120.8854284056-52.8549319074664
82908529389.6934923612365.72245115986528414.5840564790304.693492361181
92872028909.408229775-177.69091432728528708.2826845523189.408229775021
102906729357.9314367822-221.71383881894228997.7824020367290.931436782223
112924929512.9545628542-302.23668237538229287.2821195212263.954562854207
122967229596.3491461681200.31897002075129547.3318838111-75.6508538318594
132976129514.4291920570200.18915984199129807.3816481010-246.570807943030
143006629982.0936298337121.32518766936430028.5811824970-83.9063701663217
153031530334.924721969245.294561137918230249.780716892919.9247219692043
163057130776.1387975620-69.97203176902730435.833234207205.138797562035
173075731184.6862886872-292.5720402083230621.8857515211427.686288687219
183074231023.9894098673-297.63463830803630757.6452284407281.989409867299
193131031297.6257911378428.96950350182230893.4047053604-12.3742088621912
203138131445.8363623075365.72245115986530950.441186532664.8363623075384
213147032110.2132466225-177.69091432728531007.4776677048640.213246622458
223122631698.1329622633-221.71383881894230975.5808765557472.132962263262
233108131520.5525969689-302.23668237538230943.6840854065439.552596968853
243106131090.3622357454200.31897002075130831.318794233929.3622357453933
253111431308.8573370968200.18915984199130718.9535030612194.857337096826
263082831000.9508171649121.32518766936430533.7239951657172.950817164947
273041830442.210951591945.294561137918230348.494487270224.2109515918855
283019530345.9897876008-69.97203176902730113.9822441682150.989787600807
292987730167.1020391421-292.5720402083229879.4700010662290.102039142083
302919229049.8097108155-297.63463830803629631.8249274925-142.190289184484
312987629938.8506425794428.96950350182229384.179853918862.8506425793785
322940929326.9214828894365.72245115986529125.3560659507-82.0785171105708
332845828227.1586363447-177.69091432728528866.5322779826-230.841363655330
342834028294.1405231177-221.71383881894228607.5733157012-45.8594768822804
352816428281.6223289555-302.23668237538228348.6143534198117.622328955549
362843828569.0645924284200.31897002075128106.6164375508131.0645924284
372805328041.1923184761200.18915984199127864.6185216819-11.8076815238564
382759927433.044847423121.32518766936427643.6299649076-165.955152576989
392722626984.064030728745.294561137918227422.6414081334-241.935969271301
402711927078.8109687477-69.97203176902727229.1610630214-40.1890312523356
412662526506.891322299-292.5720402083227035.6807179093-118.108677701024
422654126482.5999304067-297.63463830803626897.0347079014-58.4000695933464
432702326858.6417986048428.96950350182226758.3886978934-164.35820139524
442663126198.0919112588365.72245115986526698.1856375814-432.90808874122
452615425847.708337058-177.69091432728526637.9825772693-306.291662942007
462602925639.4245178292-221.71383881894226640.2893209897-389.5754821708
472600825675.6406176652-302.23668237538226642.5960647102-332.359382334809
482663226371.8938357903200.31897002075126691.7871941889-260.106164209683
492701027078.8325164903200.18915984199126740.978323667768.8325164903326
502704127138.6410208951121.32518766936426822.033791435697.6410208950583
512724427539.616179658645.294561137918226903.0892592035295.616179658602
522697627033.7794697871-69.97203176902726988.192561981957.7794697870886
532671526649.2761754479-292.5720402083227073.2958647604-65.7238245520784
542701727191.6313223076-297.63463830803627140.0033160004174.631322307607
552771427792.3197292577428.96950350182227206.710767240578.3197292577206
562765527702.5271435863365.72245115986527241.750405253847.5271435863142
572710327106.9008710601-177.69091432728527276.79004326723.90087106009742
582708827120.7375918957-221.71383881894227276.976246923232.7375918957187
592696826961.0742317961-302.23668237538227277.1624505793-6.92576820387694
602777028087.5901246201200.31897002075127252.0909053592317.590124620092
612761627804.7914800190200.18915984199127227.0193601391188.791480018954
622748127673.3696985076121.32518766936427167.3051138230192.369698507599
632727927405.114571355145.294561137918227107.590867507126.114571355065
642691826890.5829047022-69.97203176902727015.3891270669-27.4170952978275
652650326375.3846535816-292.5720402083226923.1873866267-127.61534641837
662654726565.5727312636-297.63463830803626826.061907044518.5727312635609
672746727776.0940690359428.96950350182226728.9364274623309.094069035920
682730527615.3760171577365.72245115986526628.9015316824310.376017157705
692625926166.8242784247-177.69091432728526528.8666359026-92.1757215753205
702604825892.8846842854-221.71383881894226424.8291545335-155.115315714567
712574325467.4450092110-302.23668237538226320.7916731644-275.554990789027
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935589439p5hhy49w9tqoum/11aw31293559023.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935589439p5hhy49w9tqoum/11aw31293559023.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12935589439p5hhy49w9tqoum/2b1do1293559023.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935589439p5hhy49w9tqoum/2b1do1293559023.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12935589439p5hhy49w9tqoum/3b1do1293559023.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935589439p5hhy49w9tqoum/3b1do1293559023.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12935589439p5hhy49w9tqoum/4msur1293559023.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935589439p5hhy49w9tqoum/4msur1293559023.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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