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workshop 9 - ad hoc link 2

*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, 04 Dec 2009 03:27:29 -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/04/t1259922511z12es6e9mkt74pp.htm/, Retrieved Fri, 04 Dec 2009 11:28:36 +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/04/t1259922511z12es6e9mkt74pp.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 «

8,6 8,5 8,3 7,8 7,8 8 8,6 8,9 8,9 8,6 8,3 8,3 8,3 8,4 8,5 8,4 8,6 8,5 8,5 8,4 8,5 8,5 8,5 8,5 8,5 8,5 8,5 8,5 8,6 8,4 8,1 8 8 8 8 7,9 7,8 7,8 7,9 8,1 8 7,6 7,3 7 6,8 7 7,1 7,2 7,1 6,9 6,7 6,7 6,6 6,9 7,3 7,5 7,3 7,1 6,9 7,1

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	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Seasonal Decomposition by Loess - Parameters
Component	Window	Degree	Jump
Seasonal	601	0	61
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	8.6	8.8917487954401	0.0572591558852893	8.25099204867462	0.291748795440094
2	8.5	8.69293096860266	0.0343845388726763	8.27268449252466	0.19293096860266
3	8.3	8.29411310255472	0.0115099610705650	8.29437693637471	-0.00588689744527926
4	7.8	7.33852292359227	-0.0527696968651813	8.3142467732729	-0.461477076407725
5	7.8	7.28293282305083	-0.0170494332219303	8.3341166101711	-0.517067176949167
6	8	7.69397509708618	-0.0428444964328317	8.34886939934665	-0.306024902913816
7	8.6	8.78501750182325	0.0513603096545494	8.3636221885222	0.185017501823252
8	8.9	9.3533122621975	0.0679929936840678	8.37869474411843	0.453312262197498
9	8.9	9.38160677423854	0.0246259260467945	8.39376729971467	0.481606774238536
10	8.6	8.79343434441067	-0.0198118695098658	8.4263775250992	0.193434344410671
11	8.3	8.22526192765297	-0.0842496781366893	8.45898775048372	-0.0747380723470314
12	8.3	8.1578136582686	-0.0304077235495284	8.47259406528094	-0.142186341731406
13	8.3	8.05654046403656	0.0572591558852893	8.48620038007815	-0.243459535963437
14	8.4	8.29255863298956	0.0343845388726763	8.47305682813777	-0.107441367010443
15	8.5	8.52857676273205	0.0115099610705650	8.45991327619739	0.0285767627320492
16	8.4	8.39670194117588	-0.0527696968651813	8.4560677556893	-0.00329805882411804
17	8.6	8.76482719804071	-0.0170494332219303	8.45222223518122	0.164827198040713
18	8.5	8.57913037608005	-0.0428444964328317	8.46371412035278	0.0791303760800517
19	8.5	8.4734336848211	0.0513603096545494	8.47520600552435	-0.0265663151788953
20	8.4	8.24919710195767	0.0679929936840678	8.48280990435826	-0.150802898042333
21	8.5	8.48496027076102	0.0246259260467945	8.49041380319218	-0.0150397292389783
22	8.5	8.52773216250104	-0.0198118695098658	8.49207970700882	0.0277321625010423
23	8.5	8.59050406731123	-0.0842496781366893	8.49374561082546	0.090504067311226
24	8.5	8.54543280136111	-0.0304077235495284	8.48497492218842	0.0454328013611107
25	8.5	8.46653661056334	0.0572591558852893	8.47620423355137	-0.0334633894366583
26	8.5	8.5183027916317	0.0343845388726763	8.44731266949563	0.0183027916316956
27	8.5	8.57006893348955	0.0115099610705650	8.41842110543989	0.0700689334895461
28	8.5	8.67941164323185	-0.0527696968651813	8.37335805363333	0.17941164323185
29	8.6	8.88875443139515	-0.0170494332219303	8.32829500182677	0.288754431395155
30	8.4	8.56888171093395	-0.0428444964328317	8.27396278549888	0.168881710933949
31	8.1	7.92900912117446	0.0513603096545494	8.21963056917099	-0.170990878825542
32	8	7.77040328531527	0.0679929936840678	8.16160372100066	-0.229596714684727
33	8	7.87179720112288	0.0246259260467945	8.10357687283033	-0.12820279887712
34	8	7.96677192393867	-0.0198118695098658	8.0530399455712	-0.0332280760613308
35	8	8.08174665982462	-0.0842496781366893	8.00250301831207	0.0817466598246241
36	7.9	7.87877386390164	-0.0304077235495284	7.95163385964789	-0.0212261360983597
37	7.8	7.641976143131	0.0572591558852893	7.90076470098371	-0.158023856869000
38	7.8	7.73621244023879	0.0343845388726763	7.82940302088854	-0.0637875597612139
39	7.9	8.03044869813607	0.0115099610705650	7.75804134079336	0.130448698136072
40	8.1	8.58010261060897	-0.0527696968651813	7.67266708625621	0.480102610608968
41	8	8.42975660150287	-0.0170494332219303	7.58729283171906	0.429756601502866
42	7.6	7.73662298309183	-0.0428444964328317	7.506221513341	0.136622983091835
43	7.3	7.12348949538252	0.0513603096545494	7.42515019496293	-0.176510504617479
44	7	6.5947472919709	0.0679929936840678	7.33725971434503	-0.405252708029095
45	6.8	6.32600484022608	0.0246259260467945	7.24936923372712	-0.473995159773919
46	7	6.86307422029934	-0.0198118695098658	7.15673764921053	-0.136925779700659
47	7.1	7.22014361344276	-0.0842496781366893	7.06410606469393	0.120143613442764
48	7.2	7.4153139941659	-0.0304077235495284	7.01509372938363	0.215313994165901
49	7.1	7.17665945004138	0.0572591558852893	6.96608139407333	0.0766594500413822
50	6.9	6.79452375962856	0.0343845388726763	6.97109170149877	-0.105476240371444
51	6.7	6.41238803000523	0.0115099610705650	6.97610200892421	-0.287611969994771
52	6.7	6.45580891673688	-0.0527696968651813	6.9969607801283	-0.244191083263125
53	6.6	6.19922988188952	-0.0170494332219303	7.0178195513324	-0.400770118110476
54	6.9	6.8087482226009	-0.0428444964328317	7.03409627383193	-0.0912517773990995
55	7.3	7.498266694014	0.0513603096545494	7.05037299633146	0.198266694013994
56	7.5	7.86110927404903	0.0679929936840678	7.0708977322669	0.361109274049025
57	7.3	7.48395160575085	0.0246259260467945	7.09142246820236	0.183951605750848
58	7.1	7.10401487542067	-0.0198118695098658	7.11579699408919	0.00401487542067436
59	6.9	6.74407815816067	-0.0842496781366893	7.14017151997602	-0.155921841839334
60	7.1	7.06458363025804	-0.0304077235495284	7.16582409329149	-0.0354163697419576

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922511z12es6e9mkt74pp/1fznq1259922447.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922511z12es6e9mkt74pp/1fznq1259922447.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922511z12es6e9mkt74pp/2pjyr1259922447.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922511z12es6e9mkt74pp/2pjyr1259922447.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922511z12es6e9mkt74pp/3d3ug1259922447.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922511z12es6e9mkt74pp/3d3ug1259922447.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922511z12es6e9mkt74pp/4hvrv1259922447.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922511z12es6e9mkt74pp/4hvrv1259922447.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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