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verbetering

*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: Thu, 10 Dec 2009 10:07:42 -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/10/t12604648980gp7tdl2axw63rx.htm/, Retrieved Thu, 10 Dec 2009 18:08:23 +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/10/t12604648980gp7tdl2axw63rx.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 «

102.86 102.55 102.28 102.26 102.57 103.08 102.76 102.51 102.87 103.14 103.12 103.16 102.48 102.57 102.88 102.63 102.38 101.69 101.96 102.19 101.87 101.6 101.63 101.22 101.21 101.49 101.64 101.66 101.77 101.82 101.78 101.28 101.29 101.37 101.12 101.51 102.24 102.94 103.09 103.46 103.64 104.39 104.15 105.21 105.8 105.91 105.39 105.46 104.72 103.14 102.63 102.32 101.93 100.62 100.6 99.63 98.9 98.32 99.22 98.81

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	3 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	102.86	103.08547145967	0.0731892146297798	102.561339325700	0.225471459669947
2	102.55	102.551112589115	-0.04377717818096	102.592664589066	0.00111258911509537
3	102.28	101.966754085832	-0.0307439382634395	102.623989852431	-0.313245914167993
4	102.26	101.889815981638	-0.0209319035555342	102.651115921917	-0.37018401836157
5	102.57	102.442877944103	0.0188800644945374	102.678241991403	-0.127122055897331
6	103.08	103.528165216000	-0.0689499181799595	102.700784702180	0.448165215999566
7	102.76	102.885452635589	-0.0887800485473834	102.723327412958	0.125452635589411
8	102.51	102.374200378563	-0.0999361373969808	102.745735758834	-0.135799621436931
9	102.87	103.014948343729	-0.0430924484393207	102.768144104710	0.144948343729482
10	103.14	103.517590574046	-0.0138632058109881	102.776272631765	0.377590574046323
11	103.12	103.334233244828	0.121365596352626	102.784401158820	0.214233244827867
12	103.16	103.387806442080	0.196640115483002	102.735553442437	0.227806442080208
13	102.48	102.200105059316	0.0731892146297798	102.686705726054	-0.279894940683832
14	102.57	102.576139417176	-0.04377717818096	102.607637761005	0.00613941717639932
15	102.88	103.262174142308	-0.0307439382634395	102.528569795955	0.382174142308372
16	102.63	102.859174479712	-0.0209319035555342	102.421757423843	0.229174479712071
17	102.38	102.426174883774	0.0188800644945374	102.314945051732	0.0461748837736025
18	101.69	101.260580368539	-0.0689499181799595	102.188369549641	-0.429419631461244
19	101.96	101.946986000997	-0.0887800485473834	102.061794047551	-0.0130139990031637
20	102.19	102.531792863875	-0.0999361373969808	101.948143273522	0.341792863874502
21	101.87	101.948599948945	-0.0430924484393207	101.834492499494	0.0785999489448983
22	101.6	101.455850286774	-0.0138632058109881	101.758012919037	-0.144149713226426
23	101.63	101.457101065067	0.121365596352626	101.681533338580	-0.172898934933031
24	101.22	100.596619951157	0.196640115483002	101.64673993336	-0.623380048843075
25	101.21	100.734864257230	0.0731892146297798	101.611946528140	-0.475135742769538
26	101.49	101.435740464715	-0.04377717818096	101.588036713466	-0.0542595352851691
27	101.64	101.746617039471	-0.0307439382634395	101.564126898792	0.106617039470947
28	101.66	101.791104664448	-0.0209319035555342	101.549827239107	0.131104664448500
29	101.77	101.985592356084	0.0188800644945374	101.535527579422	0.215592356083889
30	101.82	102.155838830403	-0.0689499181799595	101.553111087777	0.33583883040302
31	101.78	102.078085452415	-0.0887800485473834	101.570694596132	0.29808545241508
32	101.28	101.015917993642	-0.0999361373969808	101.644018143755	-0.264082006357768
33	101.29	100.905750757062	-0.0430924484393207	101.717341691377	-0.384249242937884
34	101.37	100.899097992791	-0.0138632058109881	101.854765213020	-0.470902007208849
35	101.12	100.126445668985	0.121365596352626	101.992188734662	-0.993554331015119
36	101.51	100.614840442413	0.196640115483002	102.208519442104	-0.895159557587135
37	102.24	101.981960635824	0.0731892146297798	102.424850149546	-0.258039364175559
38	102.94	103.181383832347	-0.04377717818096	102.742393345834	0.241383832347367
39	103.09	103.150807396142	-0.0307439382634395	103.059936542121	0.0608073961420388
40	103.46	103.510837120658	-0.0209319035555342	103.430094782898	0.0508371206579739
41	103.64	103.460866911832	0.0188800644945374	103.800253023674	-0.179133088168257
42	104.39	104.753929478628	-0.0689499181799595	104.095020439552	0.363929478627526
43	104.15	103.998992193116	-0.0887800485473834	104.389787855431	-0.151007806883740
44	105.21	106.025943876246	-0.0999361373969808	104.493992261151	0.815943876245598
45	105.8	107.044895781568	-0.0430924484393207	104.598196666872	1.24489578156769
46	105.91	107.331425829351	-0.0138632058109881	104.502437376460	1.42142582935124
47	105.39	106.251956317599	0.121365596352626	104.406678086048	0.861956317599493
48	105.46	106.60856178864	0.196640115483002	104.114798095877	1.14856178864007
49	104.72	105.543892679664	0.0731892146297798	103.822918105706	0.82389267966424
50	103.14	102.978936228574	-0.04377717818096	103.344840949607	-0.161063771426356
51	102.63	102.423980144755	-0.0307439382634395	102.866763793509	-0.206019855245202
52	102.32	102.362638044278	-0.0209319035555342	102.298293859278	0.0426380442775667
53	101.93	102.111296010458	0.0188800644945374	101.729823925047	0.181296010458183
54	100.62	100.129552701452	-0.0689499181799595	101.179397216728	-0.490447298548105
55	100.6	100.659809540139	-0.0887800485473834	100.628970508409	0.0598095401385308
56	99.63	99.2821965412	-0.0999361373969808	100.077739596197	-0.347803458799959
57	98.9	98.3165837644543	-0.0430924484393207	99.526508683985	-0.583416235545698
58	98.32	97.676922855797	-0.0138632058109881	98.976940350014	-0.643077144202948
59	99.22	99.8912623876045	0.121365596352626	98.4273720160429	0.671262387604514
60	98.81	99.5402529831572	0.196640115483002	97.8831069013598	0.730252983157214

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/10/t12604648980gp7tdl2axw63rx/1uznt1260464859.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/10/t12604648980gp7tdl2axw63rx/1uznt1260464859.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/10/t12604648980gp7tdl2axw63rx/2dz3a1260464859.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/10/t12604648980gp7tdl2axw63rx/2dz3a1260464859.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/10/t12604648980gp7tdl2axw63rx/359pj1260464859.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/10/t12604648980gp7tdl2axw63rx/359pj1260464859.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/10/t12604648980gp7tdl2axw63rx/4yrqw1260464859.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/10/t12604648980gp7tdl2axw63rx/4yrqw1260464859.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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