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Shwws9_v2

*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: Fri, 11 Dec 2009 09:20:26 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/11/t1260548485vqqdxulsnoxaqk7.htm/, Retrieved Fri, 11 Dec 2009 17:21:30 +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/2009/Dec/11/t1260548485vqqdxulsnoxaqk7.htm/},
    year = {2009},
}
@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 = {2009},
    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 «

102.1 102.86 102.99 103.73 105.02 104.43 104.63 104.93 105.87 105.66 106.76 106 107.22 107.33 107.11 108.86 107.72 107.88 108.38 107.72 108.41 109.9 111.45 112.18 113.34 113.46 114.06 115.54 116.39 115.94 116.97 115.94 115.91 116.43 116.26 116.35 117.9 117.7 117.53 117.86 117.65 116.51 115.93 115.31 115

Output produced by software:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	451	0	46
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	102.1	101.71789587861	0.306364698881669	102.175739422508	-0.38210412139
2	102.86	102.899312875405	0.207556324706251	102.613130799889	0.0393128754047325
3	102.99	102.933229652914	-0.00375183018394935	103.050522177270	-0.0567703470857737
4	103.73	103.176943718673	0.806249542265543	103.476806739061	-0.55305628132669
5	105.02	105.398158816535	0.738749882612615	103.903091300853	0.378158816534807
6	104.43	104.559711418388	-0.0180685164206585	104.318357098032	0.129711418388482
7	104.63	104.508763882823	0.0176132219647960	104.733622895212	-0.121236117176593
8	104.93	105.460856641852	-0.74262547011397	105.141768828262	0.530856641851742
9	105.87	106.867948991548	-0.677863752860336	105.549914761313	0.99794899154766
10	105.66	105.788524369454	-0.378163541538795	105.909639172085	0.128524369453999
11	106.76	107.211050625906	0.0395857912375619	106.269363582857	0.451050625905538
12	106	105.751291797757	-0.295647430392697	106.544355632635	-0.248708202242668
13	107.22	107.314287618705	0.306364698881669	106.819347682414	0.0942876187045414
14	107.33	107.380688136070	0.207556324706251	107.071755539224	0.0506881360697093
15	107.11	106.899588434150	-0.00375183018394935	107.324163396034	-0.210411565850350
16	108.86	109.271178365475	0.806249542265543	107.642572092259	0.411178365475124
17	107.72	106.740269328903	0.738749882612615	107.960980788484	-0.979730671096974
18	107.88	107.372144245166	-0.0180685164206585	108.405924271255	-0.507855754834068
19	108.38	107.89151902401	0.0176132219647960	108.850867754025	-0.488480975989887
20	107.72	106.783333443193	-0.74262547011397	109.399292026921	-0.936666556806841
21	108.41	107.550147453044	-0.677863752860336	109.947716299817	-0.85985254695619
22	109.9	109.594816271259	-0.378163541538795	110.583347270280	-0.305183728741454
23	111.45	111.641435968018	0.0395857912375619	111.218978240744	0.191435968018439
24	112.18	112.738054313861	-0.295647430392697	111.917593116532	0.558054313860865
25	113.34	113.757427308799	0.306364698881669	112.616207992320	0.417427308798679
26	113.46	113.429353165914	0.207556324706251	113.283090509379	-0.0306468340855446
27	114.06	114.173778803745	-0.00375183018394935	113.949973026439	0.113778803745021
28	115.54	115.789120473626	0.806249542265543	114.484629984108	0.249120473625993
29	116.39	117.021963175609	0.738749882612615	115.019286941778	0.631963175609371
30	115.94	116.481839384768	-0.0180685164206585	115.416229131653	0.541839384768124
31	116.97	118.109215456508	0.0176132219647960	115.813171321527	1.13921545650818
32	115.94	116.506054121162	-0.74262547011397	116.116571348952	0.56605412116177
33	115.91	116.077892376483	-0.677863752860336	116.419971376377	0.167892376482968
34	116.43	116.633709187523	-0.378163541538795	116.604454354016	0.203709187523017
35	116.26	115.691476877108	0.0395857912375619	116.788937331654	-0.568523122891762
36	116.35	116.189462522522	-0.295647430392697	116.806184907870	-0.16053747747776
37	117.9	118.670202817032	0.306364698881669	116.823432484087	0.77020281703166
38	117.7	118.429877537452	0.207556324706251	116.762566137841	0.729877537452467
39	117.53	118.362052038588	-0.00375183018394935	116.701699791596	0.832052038588046
40	117.86	118.274970930082	0.806249542265543	116.638779527652	0.414970930082063
41	117.65	117.985390853679	0.738749882612615	116.575859263709	0.335390853678533
42	116.51	116.535812073296	-0.0180685164206585	116.502256443125	0.0258120732955263
43	115.93	115.413733155494	0.0176132219647960	116.428653622541	-0.516266844506177
44	115.31	115.023630217753	-0.74262547011397	116.338995252361	-0.286369782247007
45	115	114.428526870680	-0.677863752860336	116.249336882181	-0.571473129320225

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260548485vqqdxulsnoxaqk7/1k0ph1260548422.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260548485vqqdxulsnoxaqk7/1k0ph1260548422.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260548485vqqdxulsnoxaqk7/2xfgr1260548422.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260548485vqqdxulsnoxaqk7/2xfgr1260548422.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260548485vqqdxulsnoxaqk7/33hvh1260548422.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260548485vqqdxulsnoxaqk7/33hvh1260548422.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260548485vqqdxulsnoxaqk7/44dyk1260548423.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260548485vqqdxulsnoxaqk7/44dyk1260548423.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')

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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