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Shw9

*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: Wed, 09 Dec 2009 08:48:56 -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/09/t1260373781bfyeqpwu51najnm.htm/, Retrieved Wed, 09 Dec 2009 16:49:46 +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/09/t1260373781bfyeqpwu51najnm.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 «

0.7461 0.7775 0.7790 0.7744 0.7905 0.7719 0.7811 0.7557 0.7637 0.7595 0.7471 0.7615 0.7487 0.7389 0.7337 0.7510 0.7382 0.7159 0.7542 0.7636 0.7433 0.7658 0.7627 0.7480 0.7692 0.7850 0.7913 0.7720 0.7880 0.8070 0.8268 0.8244 0.8487 0.8572 0.8214 0.8827 0.9216 0.8865 0.8816 0.8884 0.9466 0.9180 0.9337 0.9559 0.9626 0.9434 0.8639 0.7996 0.6680 0.6572 0.6928 0.6438 0.6454 0.6873 0.7265 0.7912 0.8114 0.8281 0.8393

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	591	0	60
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	0.7461	0.71755662311106	-0.0240351364326094	0.79867851332155	-0.0285433768889406
2	0.7775	0.787391087968745	-0.0258115035255482	0.793420415556803	0.00989108796874472
3	0.779	0.789065555189964	-0.0192278729820206	0.788162317792057	0.0100655551899637
4	0.7744	0.795420405284004	-0.0299035306422491	0.783283125358245	0.0210204052840038
5	0.7905	0.817595262344958	-0.0149991952693911	0.778403932924433	0.0270952623449576
6	0.7719	0.785593250942249	-0.0159158276877833	0.774122576745534	0.0136932509422492
7	0.7811	0.78303123180311	0.00932754763025572	0.769841220566635	0.00193123180310939
8	0.7557	0.720801170029757	0.0248408392284675	0.765757990741776	-0.0348988299702434
9	0.7637	0.731291104078995	0.0344341350040878	0.761674760916917	-0.0324088959210048
10	0.7595	0.720400292073472	0.0408579608078573	0.75774174711867	-0.0390997079265276
11	0.7471	0.721889500281628	0.0185017663979487	0.753808733320423	-0.0252104997183722
12	0.7615	0.768961991146929	0.00193090335229326	0.752107105500778	0.00746199114692914
13	0.7487	0.771029658751478	-0.0240351364326094	0.750405477681132	0.0223296587514777
14	0.7389	0.753711846905933	-0.0258115035255482	0.749899656619615	0.0148118469059332
15	0.7337	0.737234037423922	-0.0192278729820206	0.749393835558098	0.00353403742392222
16	0.751	0.78316981031331	-0.0299035306422491	0.748733720328939	0.0321698103133102
17	0.7382	0.743325590169612	-0.0149991952693911	0.74807360509978	0.00512559016961167
18	0.7159	0.699998904077967	-0.0159158276877833	0.747716923609816	-0.0159010959220329
19	0.7542	0.751712210249891	0.00932754763025572	0.747360242119853	-0.00248778975010888
20	0.7636	0.753016283178996	0.0248408392284675	0.749342877592536	-0.0105837168210039
21	0.7433	0.700840351930693	0.0344341350040878	0.75132551306522	-0.0424596480693074
22	0.7658	0.734812322319464	0.0408579608078573	0.755929716872678	-0.0309876776805356
23	0.7627	0.746364312921914	0.0185017663979487	0.760533920680137	-0.0163356870780859
24	0.748	0.726845844743815	0.00193090335229326	0.767223251903892	-0.0211541552561848
25	0.7692	0.788522553304963	-0.0240351364326094	0.773912583127646	0.0193225533049634
26	0.785	0.814376763371537	-0.0258115035255482	0.781434740154011	0.0293767633715369
27	0.7913	0.812870975801644	-0.0192278729820206	0.788956897180377	0.021570975801644
28	0.772	0.777877753117851	-0.0299035306422491	0.796025777524398	0.0058777531178511
29	0.788	0.787904537400972	-0.0149991952693911	0.803094657868419	-9.5462599028262e-05
30	0.807	0.819084814096165	-0.0159158276877833	0.810831013591618	0.0120848140961650
31	0.8268	0.825705083054927	0.00932754763025572	0.818567369314817	-0.00109491694507313
32	0.8244	0.796324935750798	0.0248408392284675	0.827634225020734	-0.0280750642492017
33	0.8487	0.826264784269261	0.0344341350040878	0.836701080726651	-0.0224352157307388
34	0.8572	0.82612730861274	0.0408579608078573	0.847414730579403	-0.0310726913872607
35	0.8214	0.766169853169895	0.0185017663979487	0.858128380432156	-0.0552301468301045
36	0.8827	0.89390211553294	0.00193090335229326	0.869566981114767	0.0112021155329395
37	0.9216	0.98622955463523	-0.0240351364326094	0.881005581797379	0.0646295546352305
38	0.8865	0.907116222944489	-0.0258115035255482	0.89169528058106	0.0206162229444887
39	0.8816	0.880042893617281	-0.0192278729820206	0.90238497936474	-0.00155710638271922
40	0.8884	0.899028980059324	-0.0299035306422491	0.907674550582925	0.0106289800593239
41	0.9466	0.99523507346828	-0.0149991952693911	0.91296412180111	0.0486350734682807
42	0.918	0.945272170639938	-0.0159158276877833	0.906643657047845	0.0272721706399379
43	0.9337	0.957749260075164	0.00932754763025572	0.90032319229458	0.0240492600751638
44	0.9559	1.00383626115496	0.0248408392284675	0.883122899616571	0.0479362611549611
45	0.9626	1.02484325805735	0.0344341350040878	0.865922606938562	0.0622432580573496
46	0.9434	1.00238984491808	0.0408579608078573	0.843552194274064	0.0589898449180789
47	0.8639	0.888116451992486	0.0185017663979487	0.821181781609565	0.0242164519924861
48	0.7996	0.798457618801739	0.00193090335229326	0.798811477845968	-0.00114238119826116
49	0.668	0.583593962350239	-0.0240351364326094	0.77644117408237	-0.0844060376497613
50	0.6572	0.57564600287312	-0.0258115035255482	0.764565500652428	-0.0815539971268797
51	0.6928	0.652138045759535	-0.0192278729820206	0.752689827222485	-0.0406619542404646
52	0.6438	0.56749854722605	-0.0299035306422491	0.750004983416199	-0.0763014527739497
53	0.6454	0.558479055659479	-0.0149991952693911	0.747320139609912	-0.0869209443405213
54	0.6873	0.644755902597721	-0.0159158276877833	0.745759925090063	-0.0425440974022793
55	0.7265	0.699472741799532	0.00932754763025572	0.744199710570213	-0.0270272582004684
56	0.7912	0.812949756265965	0.0248408392284675	0.744609404505567	0.0217497562659652
57	0.8114	0.84334676655499	0.0344341350040878	0.745019098440922	0.0319467665549902
58	0.8281	0.867641682773543	0.0408579608078573	0.747700356418599	0.0395416827735434
59	0.8393	0.909716619205775	0.0185017663979487	0.750381614396276	0.0704166192057748

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/09/t1260373781bfyeqpwu51najnm/1asz31260373734.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/09/t1260373781bfyeqpwu51najnm/1asz31260373734.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/09/t1260373781bfyeqpwu51najnm/23qhl1260373734.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/09/t1260373781bfyeqpwu51najnm/23qhl1260373734.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/09/t1260373781bfyeqpwu51najnm/3k6901260373734.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/09/t1260373781bfyeqpwu51najnm/3k6901260373734.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/09/t1260373781bfyeqpwu51najnm/4q9d41260373734.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/09/t1260373781bfyeqpwu51najnm/4q9d41260373734.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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