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Ad hoc techniek 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 06:42:18 -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/t1259934206il1e5zf4126vcq7.htm/, Retrieved Fri, 04 Dec 2009 14:43:32 +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/t1259934206il1e5zf4126vcq7.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 «

562 561 555 544 537 543 594 611 613 611 594 595 591 589 584 573 567 569 621 629 628 612 595 597 593 590 580 574 573 573 620 626 620 588 566 557 561 549 532 526 511 499 555 565 542 527 510 514 517 508 493 490 469 478 528 534 518 506 502 516 528

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	611	0	62
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	562	561.926941045229	3.6908251759214	558.38223377885	-0.0730589547709997
2	561	564.240035699221	-3.75992434092145	561.5198886417	3.24003569922127
3	555	558.937635370478	-13.5951788750283	564.657543504551	3.93763537047766
4	544	540.461438477241	-20.2371237683152	567.775685291074	-3.53856152275898
5	537	532.585237353755	-29.479064431353	570.893827077598	-4.41476264624475
6	543	540.028749274319	-27.9471389393755	573.918389665056	-2.97125072568087
7	594	587.272256218122	23.7847915293633	576.942952252515	-6.7277437818783
8	611	608.325176066316	33.842152100764	579.83267183292	-2.67482393368437
9	613	617.578094919156	25.6995136675183	582.722391413326	4.57809491915623
10	611	625.401740535918	11.1796328761253	585.418626587957	14.4017405359180
11	594	603.225382295687	-3.34024405827524	588.114861762588	9.22538229568738
12	595	599.640026257207	0.161753634368499	590.198220108424	4.64002625720707
13	591	586.027596369818	3.6908251759214	592.281578454261	-4.97240363018227
14	589	588.215465257235	-3.75992434092145	593.544459083686	-0.784534742764663
15	584	586.787839161917	-13.5951788750283	594.807339713111	2.78783916191696
16	573	570.853662651814	-20.2371237683152	595.383461116501	-2.14633734818619
17	567	567.519481911462	-29.479064431353	595.959582519891	0.519481911461639
18	569	569.705997475435	-27.9471389393755	596.241141463941	0.705997475434742
19	621	621.692508062647	23.7847915293633	596.52270040799	0.692508062646766
20	629	627.57712157037	33.842152100764	596.580726328866	-1.42287842963026
21	628	633.66173408274	25.6995136675183	596.638752249743	5.66173408273926
22	612	616.1997161655	11.1796328761253	596.620650958374	4.19971616550049
23	595	596.73769439127	-3.34024405827524	596.602549667006	1.73769439126954
24	597	597.217587160785	0.161753634368499	596.620659204847	0.217587160784888
25	593	585.670406081391	3.6908251759214	596.638768742687	-7.3295939186089
26	590	587.487299958086	-3.75992434092145	596.272624382835	-2.51270004191394
27	580	577.688698852045	-13.5951788750283	595.906480022983	-2.31130114795485
28	574	573.68222192624	-20.2371237683152	594.554901842075	-0.317778073759541
29	573	582.275740770187	-29.479064431353	593.203323661166	9.27574077018653
30	573	583.270047719643	-27.9471389393755	590.677091219732	10.2700477196431
31	620	628.064349692339	23.7847915293633	588.150858778298	8.06434969233862
32	626	633.695246993867	33.842152100764	584.462600905369	7.69524699386716
33	620	633.526143300042	25.6995136675183	580.77434303244	13.5261433000422
34	588	588.859723590779	11.1796328761253	575.960643533096	0.859723590778913
35	566	564.193300024523	-3.34024405827524	571.146944033752	-1.80669997547670
36	557	548.272245007887	0.161753634368499	565.566001357745	-8.7277549921132
37	561	558.324116142341	3.6908251759214	559.985058681737	-2.67588385765873
38	549	547.340253648099	-3.75992434092145	554.419670692823	-1.65974635190128
39	532	528.74089617112	-13.5951788750283	548.854282703908	-3.2591038288798
40	526	528.370610021579	-20.2371237683152	543.866513746736	2.37061002157884
41	511	512.600319641788	-29.479064431353	538.878744789565	1.60031964178836
42	499	491.200933165474	-27.9471389393755	534.746205773902	-7.79906683452634
43	555	555.601541712398	23.7847915293633	530.613666758239	0.601541712397761
44	565	569.068025240991	33.842152100764	527.089822658245	4.06802524099112
45	542	534.734507774231	25.6995136675183	523.565978558251	-7.26549222576898
46	527	522.283886871987	11.1796328761253	520.536480251888	-4.71611312801349
47	510	505.83326211275	-3.34024405827524	517.506981945525	-4.16673788725018
48	514	512.79977306019	0.161753634368499	515.038473305442	-1.20022693981014
49	517	517.739210158721	3.6908251759214	512.569964665358	0.739210158720766
50	508	509.259552472559	-3.75992434092145	510.500371868362	1.25955247255939
51	493	491.164399803662	-13.5951788750283	508.430779071366	-1.83560019633796
52	490	493.006814649273	-20.2371237683152	507.230309119042	3.00681464927317
53	469	461.449225264635	-29.479064431353	506.029839166718	-7.55077473536466
54	478	477.914293660279	-27.9471389393755	506.032845279096	-0.0857063397208435
55	528	526.179357079162	23.7847915293633	506.035851391475	-1.82064292083828
56	534	528.01048079679	33.842152100764	506.147367102446	-5.98951920320974
57	518	504.041603519065	25.6995136675183	506.258882813417	-13.9583964809348
58	506	494.389438527226	11.1796328761253	506.430928596649	-11.6105614727742
59	502	500.737269678394	-3.34024405827524	506.602974379881	-1.26273032160589
60	516	524.890030316045	0.161753634368499	506.948216049587	8.89003031604477
61	528	545.015717104786	3.6908251759214	507.293457719292	17.0157171047864

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259934206il1e5zf4126vcq7/15j381259934136.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259934206il1e5zf4126vcq7/15j381259934136.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259934206il1e5zf4126vcq7/2jjqr1259934136.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259934206il1e5zf4126vcq7/2jjqr1259934136.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259934206il1e5zf4126vcq7/3jn1f1259934136.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259934206il1e5zf4126vcq7/3jn1f1259934136.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259934206il1e5zf4126vcq7/4n37m1259934136.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259934206il1e5zf4126vcq7/4n37m1259934136.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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