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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 12:47:57 +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/t129354036396b13064p3efxt2.htm/, Retrieved Tue, 28 Dec 2010 13:46:08 +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/t129354036396b13064p3efxt2.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 «
11100 8962 9173 8738 8459 8078 8411 8291 7810 8616 8312 9692 9911 8915 9452 9112 8472 8230 8384 8625 8221 8649 8625 10443 10357 8586 8892 8329 8101 7922 8120 7838 7735 8406 8209 9451 10041 9411 10405 8467 8464 8102 7627 7513 7510 8291 8064 9383 9706 8579 9474 8318 8213 8059 9111 7708 7680 8014 8007 8718 9486 9113 9025 8476 7952 7759 7835 7600 7651 8319 8812 8630
 
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
11110011816.98608865611447.344494852438935.66941649144716.986088656138
289628731.50941337677284.0715913252898908.41899529794-230.490586623229
391738695.6991313018769.1322945937558881.16857410444-477.300868698199
487388676.3272773875-55.05977849095888854.73250110346-61.672722612504
584598435.2882864028-345.5847145052788828.29642810248-23.7117135972057
680787943.1945433327-589.4726750790178802.27813174632-134.805456667304
784118404.26758587416-358.5274212643198776.25983539016-6.73241412583957
882918497.53331922379-671.3539483359668755.82062911218206.533319223790
978107711.29893320712-826.6803560413088735.38142283419-98.7010667928807
1086168701.19948708108-208.2283350298238739.0288479487585.1994870810759
1183128130.10013961025-248.7764126735618742.6762730633-181.899860389745
1296929828.19067548415803.135108242658752.6742162732136.190675484149
1399119611.983345664481447.344494852438762.6721594831-299.016654335521
1489158764.476579926284.0715913252898781.45182874871-150.523420074001
1594529334.63620739191769.1322945937558800.23149801433-117.363792608086
1691129449.61795267224-55.05977849095888829.44182581871337.617952672244
1784728430.93256088218-345.5847145052788858.6521536231-41.0674391178218
1882308158.86709498972-589.4726750790178890.6055800893-71.1329050102831
1983848203.96841470882-358.5274212643198922.5590065555-180.031585291183
2086259001.39068284571-671.3539483359668919.96326549025376.390682845715
2182218351.31283161631-826.6803560413088917.367524425130.312831616309
2286498627.79869872992-208.2283350298238878.4296362999-21.2013012700809
2386258659.28466449875-248.7764126735618839.491748174834.2846644987530
241044311291.6440143809803.135108242658791.22087737646848.64401438089
251035710523.70549856951447.344494852438742.95000657811166.705498569461
2685868193.88362338514284.0715913252898694.04478528957-392.116376614864
2788928369.72814140521769.1322945937558645.13956400103-522.271858594789
2883298113.27433248527-55.05977849095888599.78544600569-215.725667514729
2981017993.15338649494-345.5847145052788554.43132801034-107.846613505064
3079227899.71119245662-589.4726750790178533.7614826224-22.2888075433839
3181208085.43578402986-358.5274212643198513.09163723446-34.5642159701401
3278387800.29719444345-671.3539483359668547.05675389252-37.7028055565515
3377357715.65848549073-826.6803560413088581.02187055058-19.3415145092677
3484068389.50066307947-208.2283350298238630.72767195035-16.4993369205313
3582097986.34293932343-248.7764126735618680.43347335013-222.657060676571
3694519402.86613187513803.135108242658695.99875988222-48.1338681248653
37100419923.091458733281447.344494852438711.5640464143-117.908541266725
3894119841.00068016598284.0715913252898696.92772850873430.00068016598
391040511358.5762948031769.1322945937558682.29141060316953.57629480308
4084678331.84187237878-55.05977849095888657.21790611218-135.158127621216
4184648641.4403128841-345.5847145052788632.14440162118177.440312884093
4281028199.13349637121-589.4726750790178594.339178707897.1334963712125
4376277055.9934654699-358.5274212643198556.53395579443-571.006534530106
4475137189.33860874268-671.3539483359668508.01533959328-323.661391257317
4575107387.18363264917-826.6803560413088459.49672339214-122.816367350832
4682918356.2841814953-208.2283350298238433.9441535345365.2841814952935
4780647968.38482899664-248.7764126735618408.39158367692-95.6151710033573
4893839530.76071461756803.135108242658432.10417713979147.760714617560
4997069508.83873454491447.344494852438455.81677060266-197.161265455088
5085798383.37531805971284.0715913252898490.553090615-195.624681940288
5194749653.57829477891769.1322945937558525.28941062733179.578294778910
5283188160.13135574045-55.05977849095888530.92842275051-157.868644259554
5382138235.01727963159-345.5847145052788536.5674348736922.0172796315856
5480598188.03101795137-589.4726750790178519.44165712765129.031017951365
55911110078.2115418827-358.5274212643198502.31587938161967.211541882705
5677087601.57755757025-671.3539483359668485.77639076571-106.422442429748
5776807717.4434538915-826.6803560413088469.2369021498137.4434538914957
5880147789.96194172784-208.2283350298238446.26639330198-224.038058272155
5980077839.48052821942-248.7764126735618423.29588445414-167.519471780583
6087188242.8706521394803.135108242658389.99423961795-475.129347860604
6194869167.96291036581447.344494852438356.69259478177-318.037089634194
6291139603.38577912301284.0715913252898338.5426295517490.385779123015
6390258960.47504108462769.1322945937558320.39266432162-64.5249589153773
6484768667.63034271007-55.05977849095888339.42943578089191.630342710068
6579527891.11850726512-345.5847145052788358.46620724016-60.8814927348831
6677597737.3452935961-589.4726750790178370.12738148292-21.6547064039041
6778357646.73886553864-358.5274212643198381.78855572568-188.261134461362
6876007479.96025207962-671.3539483359668391.39369625635-120.039747920380
6976517727.6815192543-826.6803560413088400.9988367870176.6815192543
7083198435.4351887547-208.2283350298238410.79314627512116.435188754698
7188129452.18895691032-248.7764126735618420.58745576324640.18895691032
7286308026.17422294916803.135108242658430.6906688082-603.825777050843
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t129354036396b13064p3efxt2/1vtv11293540474.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t129354036396b13064p3efxt2/1vtv11293540474.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t129354036396b13064p3efxt2/2vtv11293540474.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t129354036396b13064p3efxt2/2vtv11293540474.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t129354036396b13064p3efxt2/362u41293540474.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t129354036396b13064p3efxt2/362u41293540474.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t129354036396b13064p3efxt2/4gbc71293540474.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t129354036396b13064p3efxt2/4gbc71293540474.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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This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa


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