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Seizoenale decompositie met de Loess techniek

*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 08:57:17 -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/t12605470928jk1aqiorx36sp1.htm/, Retrieved Fri, 11 Dec 2009 16:58:17 +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/t12605470928jk1aqiorx36sp1.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 «

8.3 8.2 8 7.9 7.6 7.6 8.3 8.4 8.4 8.4 8.4 8.6 8.9 8.8 8.3 7.5 7.2 7.4 8.8 9.3 9.3 8.7 8.2 8.3 8.5 8.6 8.5 8.2 8.1 7.9 8.6 8.7 8.7 8.5 8.4 8.5 8.7 8.7 8.6 8.5 8.3 8 8.2 8.1 8.1 8 7.9 7.9 8 8 7.9 8 7.7 7.2 7.5 7.3 7 7 7 7.2 7.3 7.1 6.8 6.4 6.1 6.5 7.7 7.9 7.5 6.9 6.6 6.9

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	721	0	73
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	8.3	8.38169615990256	0.277639293792519	7.94066454630492	0.081696159902564
2	8.2	8.18503074300507	0.234155553774526	7.9808137032204	-0.0149692569949327
3	8	7.95503202966448	0.0240051101996253	8.0209628601359	-0.0449679703355192
4	7.9	7.97169041532253	-0.232841275216927	8.0611508598944	0.0716904153225286
5	7.6	7.57168208161353	-0.473020941266434	8.1013388596529	-0.0283179183864704
6	7.6	7.58760053614236	-0.527791762265371	8.14019122612301	-0.0123994638576423
7	8.3	8.18685231873455	0.234104088672330	8.17904359259312	-0.113147681265453
8	8.4	8.23862836146345	0.346806919815766	8.21456471872079	-0.161371638536552
9	8.4	8.30707091802786	0.242843237123693	8.25008584484845	-0.0929290819721427
10	8.4	8.52759194014847	0.00603602267169722	8.26637203717983	0.127591940148468
11	8.4	8.66477968163613	-0.147437911147342	8.28265822951122	0.264779681636126
12	8.6	8.90161391023905	0.0155015625210638	8.28288452723988	0.301613910239054
13	8.9	9.23924988123893	0.277639293792519	8.28311082496855	0.339249881238933
14	8.8	9.05755799665698	0.234155553774526	8.3082864495685	0.257557996656976
15	8.3	8.24253281563193	0.0240051101996253	8.33346207416845	-0.0574671843680754
16	7.5	6.87741119703519	-0.232841275216927	8.35543007818174	-0.622588802964814
17	7.2	6.49562285907141	-0.473020941266434	8.37739808219503	-0.704377140928594
18	7.4	6.94945509767655	-0.527791762265371	8.37833666458882	-0.450544902323447
19	8.8	8.98662066434506	0.234104088672330	8.3792752469826	0.186620664345062
20	9.3	9.86243575244455	0.346806919815766	8.39075732773968	0.562435752444554
21	9.3	9.95491735437955	0.242843237123693	8.40223940849676	0.654917354379553
22	8.7	8.95951958251291	0.00603602267169722	8.43444439481539	0.259519582512914
23	8.2	8.08078853001332	-0.147437911147342	8.46664938113402	-0.119211469986679
24	8.3	8.10798406189246	0.0155015625210638	8.47651437558648	-0.192015938107545
25	8.5	8.23598133616854	0.277639293792519	8.48637937003894	-0.264018663831461
26	8.6	8.50249417226413	0.234155553774526	8.46335027396135	-0.0975058277358727
27	8.5	8.53567371191662	0.0240051101996253	8.44032117788375	0.0356737119166244
28	8.2	8.2028342323488	-0.232841275216927	8.43000704286812	0.00283423234880686
29	8.1	8.25332803341394	-0.473020941266434	8.41969290785249	0.153328033413944
30	7.9	7.89764343790296	-0.527791762265371	8.43014832436242	-0.00235656209704516
31	8.6	8.52529217045532	0.234104088672330	8.44060374087235	-0.0747078295446766
32	8.7	8.60140842351039	0.346806919815766	8.45178465667384	-0.0985915764896106
33	8.7	8.69419119040097	0.242843237123693	8.46296557247534	-0.00580880959903318
34	8.5	8.5175166649428	0.00603602267169722	8.4764473123855	0.0175166649427965
35	8.4	8.45750885885167	-0.147437911147342	8.48992905229567	0.0575088588516692
36	8.5	8.49277864497527	0.0155015625210638	8.49171979250366	-0.00722135502472732
37	8.7	8.62885017349583	0.277639293792519	8.49351053271165	-0.0711498265041737
38	8.7	8.70149845027473	0.234155553774526	8.46434599595074	0.00149845027472928
39	8.6	8.74081343061054	0.0240051101996253	8.43518145918983	0.140813430610542
40	8.5	8.84718098574828	-0.232841275216927	8.38566028946865	0.347180985748281
41	8.3	8.73688182151897	-0.473020941266434	8.33613911974746	0.436881821518973
42	8	8.25047209161098	-0.527791762265371	8.2773196706544	0.250472091610977
43	8.2	7.94739568976634	0.234104088672330	8.21850022156133	-0.252604310233659
44	8.1	7.69963112989341	0.346806919815766	8.15356195029082	-0.400368870106585
45	8.1	7.868533083856	0.242843237123693	8.08862367902031	-0.231466916144003
46	8	7.95941641054035	0.00603602267169722	8.03454756678795	-0.0405835894596454
47	7.9	7.96696645659176	-0.147437911147342	7.98047145455558	0.066966456591758
48	7.9	7.84836033073857	0.0155015625210638	7.93613810674036	-0.0516396692614256
49	8	7.83055594728234	0.277639293792519	7.89180475892514	-0.169444052717657
50	8	7.9344433202293	0.234155553774526	7.83140112599617	-0.0655566797706921
51	7.9	8.00499739673318	0.0240051101996253	7.7709974930672	0.104997396733181
52	8	8.54246374306716	-0.232841275216927	7.69037753214976	0.542463743067163
53	7.7	8.2632633700341	-0.473020941266434	7.60975757123233	0.563263370034102
54	7.2	7.39969993335546	-0.527791762265371	7.52809182890992	0.199699933355455
55	7.5	7.31946982474017	0.234104088672330	7.4464260865875	-0.180530175259831
56	7.3	6.89817354098995	0.346806919815766	7.35501953919429	-0.401826459010055
57	7	6.49354377107523	0.242843237123693	7.26361299180108	-0.506456228924768
58	7	6.83000651049536	0.00603602267169722	7.16395746683294	-0.169993489504642
59	7	7.08313596928253	-0.147437911147342	7.06430194186481	0.0831359692825284
60	7.2	7.37016762469271	0.0155015625210638	7.01433081278623	0.170167624692708
61	7.3	7.35800102249984	0.277639293792519	6.96435968370764	0.0580010224998393
62	7.1	6.99162072223912	0.234155553774526	6.97422372398636	-0.108379277760884
63	6.8	6.5919071255353	0.0240051101996253	6.98408776426507	-0.208092874464698
64	6.4	6.03966181499422	-0.232841275216927	6.9931794602227	-0.360338185005777
65	6.1	5.6707497850861	-0.473020941266434	7.00227115618033	-0.429250214913901
66	6.5	6.52451746952528	-0.527791762265371	7.00327429274009	0.0245174695252803
67	7.7	8.16161848202782	0.234104088672330	7.00427742929985	0.461618482027822
68	7.9	8.44359314117838	0.346806919815766	7.00959993900586	0.543593141178374
69	7.5	7.74223431416444	0.242843237123693	7.01492244871187	0.242234314164437
70	6.9	6.77170283402314	0.00603602267169722	7.02226114330516	-0.128297165976859
71	6.6	6.31783807324889	-0.147437911147342	7.02959983789845	-0.282161926751111
72	6.9	6.74863785093774	0.0155015625210638	7.0358605865412	-0.151362149062257

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/11/t12605470928jk1aqiorx36sp1/1t6ei1260547035.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t12605470928jk1aqiorx36sp1/1t6ei1260547035.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t12605470928jk1aqiorx36sp1/2cdba1260547035.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t12605470928jk1aqiorx36sp1/2cdba1260547035.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t12605470928jk1aqiorx36sp1/3ouc21260547035.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t12605470928jk1aqiorx36sp1/3ouc21260547035.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t12605470928jk1aqiorx36sp1/4umsi1260547035.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t12605470928jk1aqiorx36sp1/4umsi1260547035.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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