Home » date » 2009 » Jun » 01 »

Kim Van Assche Loess

*Unverified author*

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

Title produced by software: Decomposition by Loess

Date of computation: Mon, 01 Jun 2009 11:07:01 -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/Jun/01/t12438760470da11v1p6un7txz.htm/, Retrieved Mon, 01 Jun 2009 19:07:32 +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/Jun/01/t12438760470da11v1p6un7txz.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 «

41086 39690 43129 37863 35953 29133 24693 22205 21725 27192 21790 13253 37702 30364 32609 30212 29965 28352 25814 22414 20506 28806 22228 13971 36845 35338 35022 34777 26887 23970 22780 17351 21382 24561 17409 11514 31514 27071 29462 26105 22397 23843 21705 18089 20764 25316 17704 15548 28029 29383 36438 32034 22679 24319 18004 17537 20366 22782 19169 13807 29743 25591 29096 26482 22405 27044 17970 18730 19684 19785 18479 10698

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	721	0	73
Trend	19	1	2
Low-pass	13	1	2

Seasonal Decomposition by Loess - Time Series Components
t	Observed	Fitted	Seasonal	Trend	Remainder
1	41086	39623.4323723412	8106.6521068729	34441.9155207859	-1462.56762765881
2	39690	40646.5183917583	5114.15325087309	33619.3283573686	956.51839175832
3	43129	45230.3819301418	8230.8768759069	32796.7411939513	2101.38193014182
4	37863	38190.3071907934	5525.94474272954	32009.7480664771	327.30719079341
5	35953	39596.0866974192	1087.15836357793	31222.7549390028	3643.08669741924
6	29133	27153.5111085948	638.988436628145	30473.5004547770	-1979.48889140519
7	24693	22944.3255211360	-3282.57149168731	29724.2459705513	-1748.67447886395
8	22205	20994.5988280935	-5559.28789629083	28974.6890681973	-1210.40117190651
9	21725	19098.6140205484	-3873.74618639183	28225.1321658434	-2626.38597945158
10	27192	26568.7227985254	216.823247461416	27598.4539540132	-623.27720147459
11	21790	21574.0479714897	-4965.82371367262	26971.7757421829	-215.952028510314
12	13253	10942.2318130608	-11239.1657998506	26802.9339867897	-2310.76818693915
13	37702	40663.2556617306	8106.6521068729	26634.0922313965	2961.25566173058
14	30364	28938.4759123215	5114.15325087309	26675.3708368054	-1425.52408767848
15	32609	30270.4736818788	8230.8768759069	26716.6494422143	-2338.52631812119
16	30212	28111.2324306314	5525.94474272954	26786.822826639	-2100.76756936855
17	29965	31985.8454253583	1087.15836357793	26856.9962110637	2020.84542535832
18	28352	29108.4983249874	638.988436628145	26956.5132383844	756.498324987424
19	25814	27854.5412259822	-3282.57149168731	27056.0302657051	2040.54122598220
20	22414	23173.0367266159	-5559.28789629083	27214.2511696749	759.036726615934
21	20506	17513.2741127471	-3873.74618639183	27372.4720736447	-2992.72588725285
22	28806	29961.2486064573	216.823247461416	27433.9281460813	1155.24860645725
23	22228	21926.4394951546	-4965.82371367262	27495.384218518	-301.560504845365
24	13971	11854.1766844923	-11239.1657998506	27326.9891153583	-2116.82331550774
25	36845	38424.7538809285	8106.6521068729	27158.5940121986	1579.75388092845
26	35338	38652.3252843406	5114.15325087309	26909.5214647863	3314.3252843406
27	35022	35152.6742067191	8230.8768759069	26660.448917374	130.674206719104
28	34777	37665.3131581136	5525.94474272954	26362.7420991569	2888.31315811357
29	26887	26621.8063554823	1087.15836357793	26065.0352809398	-265.193644517738
30	23970	21643.9058242101	638.988436628145	25657.1057391618	-2326.09417578992
31	22780	23593.3952943036	-3282.57149168731	25249.1761973837	813.395294303573
32	17351	15519.4583436319	-5559.28789629083	24741.8295526589	-1831.54165636807
33	21382	22403.2632784578	-3873.74618639183	24234.4829079340	1021.26327845778
34	24561	25117.9969308069	216.823247461416	23787.1798217317	556.996930806916
35	17409	16443.9469781433	-4965.82371367262	23339.8767355293	-965.053021856675
36	11514	11163.5229612626	-11239.1657998506	23103.6428385879	-350.477038737365
37	31514	32053.9389514805	8106.6521068729	22867.4089416466	539.938951480515
38	27071	26204.1905065275	5114.15325087309	22823.6562425994	-866.809493472538
39	29462	27913.2195805408	8230.8768759069	22779.9035435523	-1548.78041945923
40	26105	23828.2127190660	5525.94474272954	22855.8425382045	-2276.78728093404
41	22397	20775.0601035654	1087.15836357793	22931.7815328567	-1621.93989643461
42	23843	23959.4450608898	638.988436628145	23087.5665024821	116.445060889801
43	21705	23449.2200195799	-3282.57149168731	23243.3514721074	1744.22001957988
44	18089	18243.4875699334	-5559.28789629083	23493.8003263574	154.487569933401
45	20764	21657.4970057844	-3873.74618639183	23744.2491806074	893.497005784408
46	25316	26447.8356317311	216.823247461416	23967.3411208075	1131.83563173107
47	17704	16183.3906526650	-4965.82371367262	24190.4330610076	-1520.60934733498
48	15548	18138.9839290493	-11239.1657998506	24196.1818708013	2590.98392904925
49	28029	23749.4172125321	8106.6521068729	24201.9306805950	-4279.58278746795
50	29383	29562.0159529435	5114.15325087309	24089.8307961834	179.015952943533
51	36438	40667.3922123214	8230.8768759069	23977.7309117717	4229.39221232137
52	32034	34648.5536559515	5525.94474272954	23893.5016013190	2614.5536559515
53	22679	20461.5693455559	1087.15836357793	23809.2722908662	-2217.43065444413
54	24319	24291.6885124726	638.988436628145	23707.3230508993	-27.3114875274441
55	18004	15685.1976807549	-3282.57149168731	23605.3738109324	-2318.80231924509
56	17537	17255.6820062560	-5559.28789629083	23377.6058900348	-281.317993743956
57	20366	21455.9082172547	-3873.74618639183	23149.8379691372	1089.90821725466
58	22782	22442.8369838476	216.823247461416	22904.3397686910	-339.163016152444
59	19169	20644.9821454277	-4965.82371367262	22658.8415682449	1475.98214542773
60	13807	16280.5330032728	-11239.1657998506	22572.6327965777	2473.53300327283
61	29743	28892.9238682165	8106.6521068729	22486.4240249106	-850.076131783488
62	25591	23616.8855506895	5114.15325087309	22450.9611984374	-1974.11444931049
63	29096	27545.6247521289	8230.8768759069	22415.4983719642	-1550.37524787113
64	26482	25045.0106047409	5525.94474272954	22393.0446525296	-1436.98939525914
65	22405	21352.2507033271	1087.15836357793	22370.590933095	-1052.74929667292
66	27044	31098.8661906805	638.988436628145	22350.1453726914	4054.86619068048
67	17970	16892.8716793996	-3282.57149168731	22329.6998122878	-1077.12832060044
68	18730	20686.3497060211	-5559.28789629083	22332.9381902697	1956.34970602109
69	19684	20905.5696181401	-3873.74618639183	22336.1765682517	1221.5696181401
70	19785	16999.594883085	216.823247461416	22353.5818694536	-2785.40511691499
71	18479	19552.8365430172	-4965.82371367262	22370.9871706554	1073.83654301720
72	10698	10243.0555312244	-11239.1657998506	22392.1102686262	-454.944468775611

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438760470da11v1p6un7txz/1avqa1243876019.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438760470da11v1p6un7txz/1avqa1243876019.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438760470da11v1p6un7txz/2xqwk1243876019.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438760470da11v1p6un7txz/2xqwk1243876019.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438760470da11v1p6un7txz/38vhg1243876019.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438760470da11v1p6un7txz/38vhg1243876019.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438760470da11v1p6un7txz/47jub1243876019.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438760470da11v1p6un7txz/47jub1243876019.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

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

We may request personal information to be submitted to our servers in order to be able to:

personalize online software applications according to your needs
enforce strict security rules with respect to the data that you upload (e.g. statistical data)
manage user sessions of online applications
alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by