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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: Tue, 01 Dec 2009 13:14:03 -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/01/t125969849905k7a2aq4vwsliz.htm/, Retrieved Tue, 01 Dec 2009 21:15:04 +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/01/t125969849905k7a2aq4vwsliz.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 «

105.7 105.7 111.1 82.4 60 107.3 99.3 113.5 108.9 100.2 103.9 138.7 120.2 100.2 143.2 70.9 85.2 133 136.6 117.9 106.3 122.3 125.5 148.4 126.3 99.6 140.4 80.3 92.6 138.5 110.9 119.6 105 109 129.4 148.6 101.4 134.8 143.7 81.6 90.3 141.5 140.7 140.2 100.2 125.7 119.6 134.7 109 116.3 146.9 97.4 89.4 132.1 139.8 129 112.5 121.9 121.7 123.1

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	2 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	105.7	113.438502657381	-1.65475088289069	99.6162482255097	7.73850265738102
2	105.7	114.539396380605	-3.33546746797459	100.196071087370	8.83939638060477
3	111.1	99.500274929356	21.923831121414	100.77589394923	-11.5997250706440
4	82.4	96.1831124324614	-32.8580649204433	101.474952487982	13.7831124324614
5	60	49.9459229325979	-32.1199339593318	102.174011026734	-10.0540770674021
6	107.3	96.9115654916517	14.7768550793945	102.911579428954	-10.3884345083483
7	99.3	85.2772162326346	9.67363593619163	103.649147831174	-14.0227837673654
8	113.5	114.483242716394	8.14175906753394	104.374998216072	0.983242716394017
9	108.9	122.129273121205	-9.4301217221754	105.100848600970	13.2292731212050
10	100.2	94.4827473511068	-0.450283493536637	106.367536142430	-5.71725264889322
11	103.9	96.676223829073	3.48955248703777	107.634223683889	-7.2237761709271
12	138.7	146.273301894470	21.8429880104924	109.283710095037	7.57330189447029
13	120.2	131.121554376705	-1.65475088289069	110.933196506185	10.9215543767054
14	100.2	91.5100880688712	-3.33546746797459	112.225379399103	-8.68991193112879
15	143.2	150.958606586565	21.923831121414	113.517562292021	7.75860658656454
16	70.9	60.1294039101292	-32.8580649204433	114.528661010314	-10.7705960898708
17	85.2	86.980174230725	-32.1199339593318	115.539759728607	1.78017423072492
18	133	134.753933060070	14.7768550793945	116.469211860536	1.75393306006974
19	136.6	146.127700071344	9.67363593619163	117.398663992465	9.52770007134363
20	117.9	109.781223425602	8.14175906753394	117.877017506865	-8.11877657439848
21	106.3	103.674750700911	-9.4301217221754	118.355371021264	-2.62524929908895
22	122.3	126.446253685369	-0.450283493536637	118.604029808168	4.14625368536892
23	125.5	128.657758917891	3.48955248703777	118.852688595071	3.15775891789116
24	148.4	156.247733322763	21.8429880104924	118.709278666744	7.84773332276347
25	126.3	135.688882144473	-1.65475088289069	118.565868738417	9.38888214447344
26	99.6	84.5048637114989	-3.33546746797459	118.030603756476	-15.0951362885011
27	140.4	141.380830104052	21.923831121414	117.495338774534	0.980830104051861
28	80.3	76.522063369575	-32.8580649204433	116.936001550868	-3.77793663042502
29	92.6	100.943269632129	-32.1199339593318	116.376664327203	8.34326963212922
30	138.5	146.042762648497	14.7768550793945	116.180382272109	7.54276264849668
31	110.9	96.1422638467932	9.67363593619163	115.984100217015	-14.7577361532068
32	119.6	114.675430233267	8.14175906753394	116.382810699199	-4.92456976673306
33	105	102.648600540792	-9.4301217221754	116.781521181383	-2.35139945920767
34	109	101.129997978085	-0.450283493536637	117.320285515452	-7.87000202191547
35	129.4	137.451397663441	3.48955248703777	117.859049849521	8.0513976634411
36	148.6	156.572435030982	21.8429880104924	118.784576958526	7.97243503098191
37	101.4	84.7446468153604	-1.65475088289069	119.710104067530	-16.6553531846396
38	134.8	152.143786920774	-3.33546746797459	120.791680547201	17.3437869207736
39	143.7	143.602911851714	21.923831121414	121.873257026872	-0.097088148285792
40	81.6	73.813929741468	-32.8580649204433	122.244135178975	-7.78607025853192
41	90.3	90.104920628253	-32.1199339593318	122.615013331079	-0.195079371746985
42	141.5	145.988827433312	14.7768550793945	122.234317487294	4.48882743331151
43	140.7	149.872742420299	9.67363593619163	121.853621643509	9.17274242029909
44	140.2	150.763088176584	8.14175906753394	121.495152755882	10.5630881765843
45	100.2	88.6934378539211	-9.4301217221754	121.136683868254	-11.5065621460789
46	125.7	130.861258975473	-0.450283493536637	120.989024518063	5.16125897547327
47	119.6	114.869082345090	3.48955248703777	120.841365167872	-4.73091765491023
48	134.7	126.842970034712	21.8429880104924	120.714041954795	-7.85702996528761
49	109	99.0680321411727	-1.65475088289069	120.586718741718	-9.93196785882732
50	116.3	115.322767719634	-3.33546746797459	120.61269974834	-0.977232280365527
51	146.9	151.237488123624	21.923831121414	120.638680754962	4.33748812362379
52	97.4	107.133541442349	-32.8580649204433	120.524523478094	9.7335414423494
53	89.4	90.509567758106	-32.1199339593318	120.410366201226	1.10956775810608
54	132.1	129.411921848330	14.7768550793945	120.011223072275	-2.68807815166954
55	139.8	150.314284120484	9.67363593619163	119.612079943324	10.5142841204839
56	129	130.699720159123	8.14175906753394	119.158520773343	1.69972015912262
57	112.5	115.725160118813	-9.4301217221754	118.704961603362	3.22516011881294
58	121.9	126.089445527798	-0.450283493536637	118.160837965739	4.18944552779811
59	121.7	122.293733184848	3.48955248703777	117.616714328115	0.593733184847636
60	123.1	107.392180424565	21.8429880104924	116.964831564943	-15.707819575435

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/01/t125969849905k7a2aq4vwsliz/13dld1259698441.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t125969849905k7a2aq4vwsliz/13dld1259698441.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t125969849905k7a2aq4vwsliz/2p1l91259698441.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t125969849905k7a2aq4vwsliz/2p1l91259698441.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t125969849905k7a2aq4vwsliz/3njpe1259698441.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t125969849905k7a2aq4vwsliz/3njpe1259698441.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t125969849905k7a2aq4vwsliz/4640e1259698441.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t125969849905k7a2aq4vwsliz/4640e1259698441.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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