Home » date » 2009 » May » 31 »

Decomposition of Loess - Prijsevolutie slagroom - Van Hal Elien

*Unverified author*

R Software Module: rwasp_decomposeloess.wasp (opens new window with default values)

Title produced by software: Decomposition by Loess

Date of computation: Sun, 31 May 2009 05:45:41 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/May/31/t12437703808oryilmtnp5blof.htm/, Retrieved Sun, 31 May 2009 13:46:24 +0200

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/May/31/t12437703808oryilmtnp5blof.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 «

4.2 4.19 4.19 4.19 4.19 4.18 4.2 4.19 4.17 4.21 4.22 4.23 4.21 4.23 4.23 4.22 4.25 4.28 4.3 4.32 4.33 4.32 4.34 4.33 4.31 4.31 4.3 4.3 4.29 4.33 4.32 4.32 4.35 4.37 4.39 4.4 4.41 4.44 4.47 4.47 4.47 4.48 4.47 4.48 4.46 4.44 4.43 4.41 4.41 4.38 4.35 4.37 4.4 4.39 4.36 4.34 4.33 4.33 4.34 4.34 4.35 4.37 4.39 4.4 4.38 4.37 4.36 4.33 4.33 4.33 4.32 4.33 4.34

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	731	0	74
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	4.2	4.21637813521329	-0.000430755143020124	4.18405261992973	0.0163781352132872
2	4.19	4.19305762240418	0.000723516271687587	4.18621886132414	0.00305762240417717
3	4.19	4.19383863649028	-0.00222373920882139	4.18838510271854	0.00383863649028271
4	4.19	4.19563337397418	-0.00645658738504976	4.19082321341088	0.00563337397417474
5	4.19	4.17949261331817	0.00724606257862169	4.19326132410321	-0.0105073866818337
6	4.18	4.15304106193278	0.0111127957010559	4.19584614236617	-0.0269589380672226
7	4.2	4.19585444002308	0.00571459934779752	4.19843096062912	-0.00414555997691757
8	4.19	4.18606171747	-0.00710801991975789	4.20104630244976	-0.00393828253000184
9	4.17	4.14491832989752	-0.00857997416792511	4.2036616442704	-0.0250816701024750
10	4.21	4.21437689102759	-0.00194026744215082	4.20756337641456	0.00437689102758654
11	4.22	4.22697381015659	0.00156108128468570	4.21146510855873	0.0069738101565866
12	4.23	4.24146904184242	0.000381268082867018	4.21814969007472	0.0114690418424184
13	4.21	4.19559648355232	-0.000430755143020124	4.2248342715907	-0.0144035164476826
14	4.23	4.22542985434861	0.000723516271687587	4.23384662937970	-0.00457014565139069
15	4.23	4.21936475204012	-0.00222373920882139	4.24285898716871	-0.0106352479598844
16	4.22	4.19321296710922	-0.00645658738504976	4.25324362027582	-0.0267870328907751
17	4.25	4.22912568403843	0.00724606257862169	4.26362825338294	-0.0208743159615654
18	4.28	4.27599304520967	0.0111127957010559	4.27289415908928	-0.00400695479033342
19	4.3	4.31212533585659	0.00571459934779752	4.28216006479561	0.0121253358565907
20	4.32	4.35732918994034	-0.00710801991975789	4.28977882997942	0.0373291899403432
21	4.33	4.37118237900471	-0.00857997416792511	4.29739759516322	0.0411823790047059
22	4.32	4.33880759951884	-0.00194026744215082	4.30313266792331	0.0188075995188397
23	4.34	4.36957117803191	0.00156108128468570	4.3088677406834	0.0295711780319126
24	4.33	4.34784434491073	0.000381268082867018	4.31177438700641	0.0178443449107277
25	4.31	4.30574972181361	-0.000430755143020124	4.31468103332941	-0.00425027818638934
26	4.31	4.30320762353795	0.000723516271687587	4.31606886019037	-0.00679237646205433
27	4.3	4.28476705215750	-0.00222373920882139	4.31745668705133	-0.0152329478425042
28	4.3	4.285478372769	-0.00645658738504976	4.32097821461605	-0.0145216272310016
29	4.29	4.2482541952406	0.00724606257862169	4.32449974218078	-0.0417458047594002
30	4.33	4.31754662966055	0.0111127957010559	4.33134057463839	-0.0124533703394469
31	4.32	4.2961039935562	0.00571459934779752	4.33818140709600	-0.0238960064438016
32	4.32	4.2984380676256	-0.00710801991975789	4.34866995229416	-0.0215619323743992
33	4.35	4.34942147667561	-0.00857997416792511	4.35915849749231	-0.00057852332438646
34	4.37	4.36915177659266	-0.00194026744215082	4.37278849084949	-0.000848223407343696
35	4.39	4.39202043450864	0.00156108128468570	4.38641848420668	0.00202043450863698
36	4.4	4.39949539364693	0.000381268082867018	4.4001233382702	-0.000504606353068482
37	4.41	4.40660256280929	-0.000430755143020124	4.41382819233373	-0.00339743719070551
38	4.44	4.45451600752694	0.000723516271687587	4.42476047620137	0.0145160075269413
39	4.47	4.5065309791398	-0.00222373920882139	4.43569276006902	0.036530979139803
40	4.47	4.50480569646698	-0.00645658738504976	4.44165089091807	0.0348056964669841
41	4.47	4.48514491565427	0.00724606257862169	4.44760902176711	0.0151449156542656
42	4.48	4.4999365674551	0.0111127957010559	4.44895063684384	0.0199365674551029
43	4.47	4.48399314873163	0.00571459934779752	4.45029225192057	0.0139931487316307
44	4.48	4.52060640547879	-0.00710801991975789	4.44650161444097	0.0406064054787896
45	4.46	4.48586899720656	-0.00857997416792511	4.44271097696137	0.0258689972065582
46	4.44	4.44715273769133	-0.00194026744215082	4.43478752975082	0.00715273769132807
47	4.43	4.43157483617503	0.00156108128468570	4.42686408254028	0.00157483617503473
48	4.41	4.40161567843739	0.000381268082867018	4.41800305347974	-0.00838432156260538
49	4.41	4.41128873072382	-0.000430755143020124	4.4091420244192	0.00128873072382163
50	4.38	4.35954638993194	0.000723516271687587	4.39973009379638	-0.0204536100680635
51	4.35	4.31190557603527	-0.00222373920882139	4.39031816317355	-0.0380944239647327
52	4.37	4.36468194233013	-0.00645658738504976	4.38177464505492	-0.00531805766987148
53	4.4	4.41952281048509	0.00724606257862169	4.37323112693629	0.0195228104850900
54	4.39	4.40161532965715	0.0111127957010559	4.3672718746418	0.0116153296571486
55	4.36	4.3529727783049	0.00571459934779752	4.3613126223473	-0.00702722169509862
56	4.34	4.32873617722858	-0.00710801991975789	4.35837184269118	-0.0112638227714212
57	4.33	4.31314891113287	-0.00857997416792511	4.35543106303506	-0.0168510888671323
58	4.33	4.30693981742511	-0.00194026744215082	4.35500045001704	-0.0230601825748940
59	4.34	4.32386908171628	0.00156108128468570	4.35456983699903	-0.0161309182837162
60	4.34	4.32490229044795	0.000381268082867018	4.35471644146918	-0.0150977095520508
61	4.35	4.34556770920368	-0.000430755143020124	4.35486304593934	-0.00443229079631724
62	4.37	4.38441827946648	0.000723516271687587	4.35485820426183	0.0144182794664847
63	4.39	4.4273703766245	-0.00222373920882139	4.35485336258432	0.0373703766245015
64	4.4	4.45305045231911	-0.00645658738504976	4.35340613506594	0.0530504523191118
65	4.38	4.40079502987382	0.00724606257862169	4.35195890754756	0.0207950298738213
66	4.37	4.37926783987372	0.0111127957010559	4.34961936442523	0.00926783987371582
67	4.36	4.3670055793493	0.00571459934779752	4.3472798213029	0.00700557934930224
68	4.33	4.32241385544245	-0.00710801991975789	4.34469416447731	-0.00758614455755335
69	4.33	4.3264714665162	-0.00857997416792511	4.34210850765172	-0.00352853348379689
70	4.33	4.32285187237440	-0.00194026744215082	4.33908839506775	-0.00714812762559536
71	4.32	4.30237063623155	0.00156108128468570	4.33606828248377	-0.0176293637684539
72	4.33	4.32689875783801	0.000381268082867018	4.33271997407913	-0.00310124216199448
73	4.34	4.35105908946853	-0.000430755143020124	4.32937166567449	0.0110590894685334

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t12437703808oryilmtnp5blof/1w5z01243770339.png (open in new window)

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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t12437703808oryilmtnp5blof/34grd1243770339.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t12437703808oryilmtnp5blof/34grd1243770339.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t12437703808oryilmtnp5blof/4oub71243770339.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t12437703808oryilmtnp5blof/4oub71243770339.ps (open in new window)

Parameters (Session):

par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = TRUE ;

Parameters (R input):

par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = TRUE ;

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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