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ws 9 decom l

*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: Sun, 29 Nov 2009 06:42:55 -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/Nov/29/t1259502214di00gt9zldyotf5.htm/, Retrieved Sun, 29 Nov 2009 14:43:39 +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/Nov/29/t1259502214di00gt9zldyotf5.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 «

103,63 103,64 103,66 103,77 103,88 103,91 103,91 103,92 104,05 104,23 104,30 104,31 104,31 104,34 104,55 104,65 104,73 104,75 104,75 104,76 104,94 105,29 105,38 105,43 105,43 105,42 105,52 105,69 105,72 105,74 105,74 105,74 105,95 106,17 106,34 106,37 106,37 106,36 106,44 106,29 106,23 106,23 106,23 106,23 106,34 106,44 106,44 106,48 106,50 106,57 106,40 106,37 106,25 106,21 106,21 106,24 106,19 106,08 106,13 106,09

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	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	103.63	103.632160225284	0.0177643461137006	103.610075428603	0.00216022528373117
2	103.64	103.622559745778	-0.0125354309497719	103.669975685172	-0.0174402542218246
3	103.66	103.602959142106	-0.0128350838466383	103.729875941741	-0.0570408578939805
4	103.77	103.758352511154	-0.00793685581647832	103.789584344663	-0.0116474888460516
5	103.88	103.945745884123	-0.0350386317073986	103.849292747584	0.0657458841229612
6	103.91	103.970260843801	-0.0597790060289853	103.909518162228	0.06026084380089
7	103.91	103.940775813935	-0.0905193908067089	103.969743576872	0.0307758139349374
8	103.92	103.920000547305	-0.111193203075876	104.031192655771	5.47305234022133e-07
9	104.05	104.033225199640	-0.0258669343099870	104.092641734670	-0.0167748003595278
10	104.23	104.213259141643	0.0889501808931466	104.157790677464	-0.0167408583574797
11	104.3	104.245293156838	0.131767222903334	104.222939620259	-0.0547068431624922
12	104.31	104.208212585652	0.117222685884742	104.294564728463	-0.101787414347996
13	104.31	104.236045817219	0.0177643461137006	104.366189836667	-0.0739541827810513
14	104.34	104.249831917181	-0.0125354309497719	104.442703513769	-0.0901680828187637
15	104.55	104.593617892977	-0.0128350838466383	104.519217190870	0.0436178929769255
16	104.65	104.703938035553	-0.00793685581647832	104.603998820263	0.053938035553287
17	104.73	104.806258182051	-0.0350386317073986	104.688780449657	0.0762581820506938
18	104.75	104.780858357207	-0.0597790060289853	104.778920648822	0.0308583572071939
19	104.75	104.721458542820	-0.0905193908067089	104.869060847987	-0.0285414571801823
20	104.76	104.674520732285	-0.111193203075876	104.956672470791	-0.0854792677150158
21	104.94	104.861582840715	-0.0258669343099870	105.044284093595	-0.0784171592848963
22	105.29	105.361912133660	0.0889501808931466	105.129137685447	0.0719121336596373
23	105.38	105.414241499797	0.131767222903334	105.213991277300	0.0342414997971048
24	105.43	105.444073366373	0.117222685884742	105.298703947742	0.0140733663733528
25	105.43	105.458819035702	0.0177643461137006	105.383416618184	0.0288190357020426
26	105.42	105.387375420168	-0.0125354309497719	105.465160010782	-0.0326245798322589
27	105.52	105.505931680467	-0.0128350838466383	105.546903403380	-0.0140683195331377
28	105.69	105.763234957268	-0.00793685581647832	105.624701898548	0.0732349572679851
29	105.72	105.772538237990	-0.0350386317073986	105.702500393717	0.0525382379901771
30	105.74	105.759915786828	-0.0597790060289853	105.779863219201	0.0199157868279656
31	105.74	105.713293346122	-0.0905193908067089	105.857226044685	-0.0267066538781222
32	105.74	105.659661136730	-0.111193203075876	105.931532066346	-0.080338863270427
33	105.95	105.920028846302	-0.0258669343099870	106.005838088008	-0.0299711536977725
34	106.17	106.184286466077	0.0889501808931466	106.066763353030	0.0142864660770101
35	106.34	106.420544159045	0.131767222903334	106.127688618052	0.0805441590447487
36	106.37	106.448862404344	0.117222685884742	106.173914909771	0.0788624043440649
37	106.37	106.502094452396	0.0177643461137006	106.220141201490	0.132094452395833
38	106.36	106.478607968218	-0.0125354309497719	106.253927462731	0.118607968218456
39	106.44	106.605121359874	-0.0128350838466383	106.287713723972	0.165121359874490
40	106.29	106.281700611579	-0.00793685581647832	106.306236244238	-0.00829938842142042
41	106.23	106.170279867204	-0.0350386317073986	106.324758764504	-0.0597201327962722
42	106.23	106.186207545071	-0.0597790060289853	106.333571460958	-0.0437924549294166
43	106.23	106.208135233394	-0.0905193908067089	106.342384157413	-0.0218647666064271
44	106.23	106.220250756307	-0.111193203075876	106.350942446769	-0.00974924369339192
45	106.34	106.346366198185	-0.0258669343099870	106.359500736125	0.00636619818459394
46	106.44	106.424223178953	0.0889501808931466	106.366826640154	-0.0157768210472824
47	106.44	106.374080232914	0.131767222903334	106.374152544183	-0.0659197670862142
48	106.48	106.466641891341	0.117222685884742	106.376135422774	-0.0133581086591477
49	106.5	106.604117352520	0.0177643461137006	106.378118301366	0.104117352520376
50	106.57	106.781119110153	-0.0125354309497719	106.371416320797	0.211119110153035
51	106.4	106.448120743619	-0.0128350838466383	106.364714340228	0.0481207436191085
52	106.37	106.415080847972	-0.00793685581647832	106.332856007845	0.0450808479717608
53	106.25	106.234040956245	-0.0350386317073986	106.300997675462	-0.0159590437545347
54	106.21	106.214203450072	-0.0597790060289853	106.265575555957	0.00420345007157152
55	106.21	106.280365954354	-0.0905193908067089	106.230153436453	0.0703659543538038
56	106.24	106.398149351440	-0.111193203075876	106.193043851636	0.158149351439846
57	106.19	106.249932667491	-0.0258669343099870	106.155934266819	0.0599326674908411
58	106.08	105.954443533058	0.0889501808931466	106.116606286049	-0.125556466942172
59	106.13	106.050954471818	0.131767222903334	106.077278305279	-0.0790455281822346
60	106.09	106.026779125081	0.117222685884742	106.035998189035	-0.0632208749192813

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502214di00gt9zldyotf5/1t8gi1259502173.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502214di00gt9zldyotf5/1t8gi1259502173.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502214di00gt9zldyotf5/2dgne1259502173.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502214di00gt9zldyotf5/2dgne1259502173.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502214di00gt9zldyotf5/3g1js1259502173.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502214di00gt9zldyotf5/3g1js1259502173.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502214di00gt9zldyotf5/4lmxk1259502173.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502214di00gt9zldyotf5/4lmxk1259502173.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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