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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, 28 Dec 2010 10:40:34 +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/t1293532982qlqsmyilvxq0vfp.htm/, Retrieved Tue, 28 Dec 2010 11:43:07 +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/t1293532982qlqsmyilvxq0vfp.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 «
655362 873127 1107897 1555964 1671159 1493308 2957796 2638691 1305669 1280496 921900 867888 652586 913831 1108544 1555827 1699283 1509458 3268975 2425016 1312703 1365498 934453 775019 651142 843192 1146766 1652601 1465906 1652734 2922334 2702805 1458956 1410363 1019279 936574 708917 885295 1099663 1576220 1487870 1488635 2882530 2677026 1404398 1344370 936865 872705 628151 953712 1160384 1400618 1661511 1495347 2918786 2775677 1407026 1370199 964526 850851 683118 847224 1073256 1514326 1503734 1507712 2865698 2788128 1391596 1366378 946295 859626
 
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'Gwilym Jenkins' @ 72.249.127.135


Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal721073
Trend1912
Low-pass1312


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
1655362643683.108619118-794327.4521456221461368.34352650-11678.8913808824
2873127859178.451900511-571497.8044093771458573.35250887-13948.5480994887
311078971101513.12756765-341497.4890588751455778.36149123-6383.87243235088
415559641574289.1396054684223.89527983251453414.9651147018325.1396054649
516711591768844.39157654122422.0396852801451051.5687381897685.3915765414
614933081471862.6464543165235.36616556971449517.98738012-21445.353545686
729577962957693.315781011509914.278196941447984.40602205-102.684218993178
826386912621832.72159371208627.850833361446921.42757294-16858.2784062976
913056691244507.86563022-79028.31475403191445858.44912382-61161.1343697847
1012804961217324.41975379-102317.7721752601445985.35242147-63171.5802462101
11921900901785.305688588-504097.5614077111446112.25571912-20114.6943114123
12867888881253.437832332-597657.0032393191452179.5654069913365.4378323315
13652586641252.57705077-794327.4521456221458246.87509485-11333.4229492294
14913831936069.847081493-571497.8044093771463089.9573278822238.8470814929
1511085441090652.44949796-341497.4890588751467933.03956092-17891.5505020409
1615558271558796.8662548684223.89527983251468633.238465312969.86625485658
1716992831806810.52294501122422.0396852801469333.43736971107527.522945015
1815094581487558.8657962665235.36616556971466121.76803817-21899.1342037374
1932689753565125.623096431509914.278196941462910.09870663296150.623096431
2024250162182122.672734261208627.850833361459281.47643238-242893.327265739
2113127031248781.46059591-79028.31475403191455652.85415812-63921.5394040917
2213654981381168.98229354-102317.7721752601452144.7898817215670.982293542
23934453924366.835802399-504097.5614077111448636.72560531-10086.1641976011
24775019700437.39587553-597657.0032393191447257.60736379-74581.6041244697
25651142650732.963023357-794327.4521456221445878.48912226-409.036976642674
26843192807042.807161906-571497.8044093771450838.99724747-36149.1928380942
2711467661179229.98368620-341497.4890588751455799.5053726832463.9836861982
2816526011756148.1408626884223.89527983251464829.96385748103547.140862683
2914659061335529.53797243122422.0396852801473860.42234229-130376.462027572
3016527341758828.5517024465235.36616556971481404.08213199106094.551702438
3129223342845805.979881371509914.278196941488947.74192169-76528.0201186324
3227028052705103.713510671208627.850833361491878.435655972298.71351066511
3314589561502131.18536378-79028.31475403191494809.1293902543175.1853637795
3414103631430730.45585989-102317.7721752601492313.3163153720367.4558598865
3510192791052838.05816722-504097.5614077111489817.5032404933559.0581672161
36936574986803.030900418-597657.0032393191484001.972338950229.0309004178
37708917733975.010708315-794327.4521456221478186.4414373125058.0107083146
38885295870738.592909515-571497.8044093771471349.21149986-14556.4070904849
3910996631076311.50749646-341497.4890588751464511.98156242-23351.4925035401
4015762201610095.2882011184223.89527983251458120.8165190633875.2882011107
4114878701401588.30883902122422.0396852801451729.65147570-86281.6911609776
4214886351464178.011870165235.36616556971447856.62196433-24456.9881299015
4328825302811162.129350101509914.278196941443983.59245297-71367.870649905
4426770262701274.946498661208627.850833361444149.2026679824248.9464986601
4514043981443509.50187104-79028.31475403191444314.8128829939111.5018710424
4613443701344365.56271665-102317.7721752601446692.20945861-4.43728335341439
47936865928757.955373474-504097.5614077111449069.60603424-8107.04462652607
48872705891139.228241781-597657.0032393191451927.7749975418434.228241781
49628151595843.508184783-794327.4521456221454785.94396084-32307.4918152166
509537121021293.18106075-571497.8044093771457628.6233486267581.1810607526
5111603841201794.18632247-341497.4890588751460471.3027364141410.1863224655
5214006181254411.7380917184223.89527983251462600.36662846-146206.261908289
5316615111735870.52979422122422.0396852801464729.4305205074359.5297942169
5414953471459925.9823963165235.36616556971465532.65143812-35421.0176036891
5529187862861321.849447331509914.278196941466335.87235574-57464.1505526747
5627756772877821.083663351208627.850833361464905.06550329102144.083663347
5714070261429606.05610319-79028.31475403191463474.2586508522580.0561031858
5813701991382040.85085889-102317.7721752601460674.9213163711841.8508588891
59964526975273.977425816-504097.5614077111457875.5839819010747.9774258155
60850851845872.928284845-597657.0032393191453486.07495447-4978.07171515469
61683118711466.88621857-794327.4521456221449096.5659270528348.8862185702
62847224819578.254722053-571497.8044093771446367.54968732-27645.7452779473
6310732561044370.95561128-341497.4890588751443638.53344760-28885.0443887208
6415143261498930.9893158484223.89527983251445497.11540433-15395.0106841638
6515037341437690.26295365122422.0396852801447355.69736107-66043.7370463456
6615077121501539.2912868365235.36616556971448649.34254760-6172.7087131741
6728656982771538.734068921509914.278196941449942.98773414-94159.2659310822
6827881282915716.961107421208627.850833361451911.18805922127588.961107421
6913915961408340.92636974-79028.31475403191453879.3883842916744.9263697413
7013663781378370.01277823-102317.7721752601456703.7593970311992.0127782272
71946295937159.430997936-504097.5614077111459528.13040978-9135.56900206418
72859626854130.861506892-597657.0032393191462778.14173243-5495.1384931081
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293532982qlqsmyilvxq0vfp/1raym1293532831.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293532982qlqsmyilvxq0vfp/1raym1293532831.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293532982qlqsmyilvxq0vfp/211xp1293532831.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293532982qlqsmyilvxq0vfp/211xp1293532831.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293532982qlqsmyilvxq0vfp/311xp1293532831.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293532982qlqsmyilvxq0vfp/311xp1293532831.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293532982qlqsmyilvxq0vfp/4cse91293532831.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293532982qlqsmyilvxq0vfp/4cse91293532831.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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