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ws9

*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, 04 Dec 2009 12:53:56 -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/04/t12599565848ej9h0kt4hnomo8.htm/, Retrieved Fri, 04 Dec 2009 20:56:29 +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/04/t12599565848ej9h0kt4hnomo8.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:

ws9.9

Dataseries X:

» Textbox « » Textfile « » CSV «

126.51 131.02 136.51 138.04 132.92 129.61 122.96 124.04 121.29 124.56 118.53 113.14 114.15 122.17 129.23 131.19 129.12 128.28 126.83 138.13 140.52 146.83 135.14 131.84 125.7 128.98 133.25 136.76 133.24 128.54 121.08 120.23 119.08 125.75 126.89 126.6 121.89 123.44 126.46 129.49 127.78 125.29 119.02 119.96 122.86 131.89 132.73 135.01 136.71 142.73 144.43 144.93 138.75 130.22 122.19 128.4 140.43 153.5 149.33 142.97

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	126.51	123.838897343835	-4.5918005989784	133.772903255143	-2.67110265616452
2	131.02	129.308688077918	0.138482229739662	132.592829692342	-1.71131192208190
3	136.51	137.106477613466	4.50076625699235	131.412756129542	0.596477613466021
4	138.04	139.349404832538	6.49900336878182	130.231591798680	1.30940483253787
5	132.92	134.118331551022	2.67124098115847	129.050427467819	1.19833155102251
6	129.61	132.826406712296	-1.52509748305060	127.918690770755	3.21640671229549
7	122.96	126.852482426437	-7.71943650012788	126.786954073691	3.89248242643669
8	124.04	126.555044457224	-4.20978257221519	125.734738114991	2.51504445722391
9	121.29	119.649607995001	-1.75213015129263	124.682522156291	-1.64039200499874
10	124.56	119.536471808628	5.72707326460173	123.856454926770	-5.02352819137209
11	118.53	112.475333553246	1.55427874950482	123.030387697249	-6.05466644675418
12	113.14	104.515928357071	-1.29259843532114	123.056670078250	-8.6240716429291
13	114.15	109.808848139727	-4.5918005989784	123.082952459251	-4.34115186027273
14	122.17	120.027299803603	0.138482229739662	124.174217966657	-2.14270019639709
15	129.23	128.693750268944	4.50076625699235	125.265483474064	-0.53624973105606
16	131.19	128.836560171072	6.49900336878182	127.044436460146	-2.35343982892822
17	129.12	126.745369572612	2.67124098115847	128.823389446229	-2.37463042738753
18	128.28	127.735098199716	-1.52509748305060	130.349999283335	-0.544901800283981
19	126.83	129.502827379688	-7.71943650012788	131.87660912044	2.67282737968785
20	138.13	147.790655660336	-4.20978257221519	132.679126911879	9.66065566033603
21	140.52	149.310485447974	-1.75213015129263	133.481644703318	8.7904854479743
22	146.83	154.249919723817	5.72707326460173	133.683007011582	7.41991972381663
23	135.14	134.841351930650	1.55427874950482	133.884369319845	-0.298648069349781
24	131.84	131.520839315356	-1.29259843532114	133.451759119965	-0.319160684643975
25	125.7	122.972651678893	-4.5918005989784	133.019148920085	-2.72734832110692
26	128.98	125.930956946743	0.138482229739662	131.890560823517	-3.04904305325715
27	133.25	131.237261016058	4.50076625699235	130.761972726950	-2.01273898394203
28	136.76	137.452193261748	6.49900336878182	129.56880336947	0.692193261748059
29	133.24	135.433125006851	2.67124098115847	128.375634011991	2.19312500685101
30	128.54	130.943318536748	-1.52509748305060	127.661778946303	2.40331853674766
31	121.08	122.931512619513	-7.71943650012788	126.947923880615	1.85151261951258
32	120.23	118.241497062424	-4.20978257221519	126.428285509791	-1.98850293757611
33	119.08	114.003483012325	-1.75213015129263	125.908647138967	-5.07651698767468
34	125.75	120.360273353769	5.72707326460173	125.412653381629	-5.38972664623063
35	126.89	127.309061626205	1.55427874950482	124.916659624291	0.41906162620468
36	126.6	129.815414265316	-1.29259843532114	124.677184170005	3.21541426531593
37	121.89	123.934091883258	-4.5918005989784	124.43770871572	2.04409188325849
38	123.44	122.212452207483	0.138482229739662	124.529065562777	-1.22754779251665
39	126.46	123.798811333174	4.50076625699235	124.620422409834	-2.66118866682640
40	129.49	127.523697040720	6.49900336878182	124.957299590498	-1.96630295928028
41	127.78	127.594582247679	2.67124098115847	125.294176771163	-0.185417752321342
42	125.29	126.037330163208	-1.52509748305060	126.067767319843	0.747330163207522
43	119.02	118.918078631605	-7.71943650012788	126.841357868523	-0.101921368395423
44	119.96	115.959921852565	-4.20978257221519	128.16986071965	-4.00007814743475
45	122.86	117.973766580516	-1.75213015129263	129.498363570777	-4.88623341948391
46	131.89	127.206573948357	5.72707326460173	130.846352787041	-4.68342605164267
47	132.73	131.711379247190	1.55427874950482	132.194342003305	-1.01862075281016
48	135.01	138.194081434020	-1.29259843532114	133.118517001301	3.1840814340203
49	136.71	143.969108599682	-4.5918005989784	134.042691999296	7.2591085996821
50	142.73	150.442934668644	0.138482229739662	134.878583101616	7.71293466864418
51	144.43	148.644759539072	4.50076625699235	135.714474203936	4.21475953907168
52	144.93	146.713340993749	6.49900336878182	136.647655637469	1.78334099374891
53	138.75	137.247921947839	2.67124098115847	137.580837071003	-1.50207805216104
54	130.22	123.495875217497	-1.52509748305060	138.469222265553	-6.7241247825028
55	122.19	112.741829040024	-7.71943650012788	139.357607460104	-9.44817095997632
56	128.4	120.846209507489	-4.20978257221519	140.163573064726	-7.55379049251053
57	140.43	141.642591481945	-1.75213015129263	140.969538669347	1.21259148194540
58	153.5	159.469716500382	5.72707326460173	141.803210235016	5.96971650038182
59	149.33	154.468839449810	1.55427874950482	142.636881800686	5.13883944980952
60	142.97	143.699372329758	-1.29259843532114	143.533226105563	0.72937232975849

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599565848ej9h0kt4hnomo8/16kgq1259956434.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599565848ej9h0kt4hnomo8/16kgq1259956434.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599565848ej9h0kt4hnomo8/237dn1259956434.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599565848ej9h0kt4hnomo8/237dn1259956434.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599565848ej9h0kt4hnomo8/3t7z01259956434.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599565848ej9h0kt4hnomo8/3t7z01259956434.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599565848ej9h0kt4hnomo8/4v3pw1259956434.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599565848ej9h0kt4hnomo8/4v3pw1259956434.ps (open in new window)

Parameters (Session):

par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ; par9 = 1 ;

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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