Home » date » 2009 » Jun » 01 »

Ken soltvedt Sigaretten O9

*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 12:02:35 -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/t1243879381wskbbff7s18q7hw.htm/, Retrieved Mon, 01 Jun 2009 20:03:05 +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/t1243879381wskbbff7s18q7hw.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 «

3.42 3.42 3.43 3.47 3.51 3.52 3.52 3.52 3.52 3.52 3.52 3.52 3.52 3.52 3.58 3.6 3.61 3.61 3.61 3.63 3.68 3.69 3.69 3.69 3.69 3.69 3.69 3.69 3.69 3.78 3.79 3.79 3.8 3.8 3.8 3.8 3.81 3.95 3.99 4 4.06 4.16 4.19 4.2 4.2 4.2 4.2 4.2 4.23 4.38 4.43 4.44 4.44 4.44 4.44 4.44 4.45 4.45 4.45 4.45 4.45 4.45 4.45 4.45 4.46 4.46 4.46 4.48 4.58 4.67 4.68 4.68

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	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	3.42	3.42277036083856	-0.0264371028788476	3.44366674204029	0.00277036083856208
2	3.42	3.41136724319596	-0.0236142090737903	3.45224696587783	-0.00863275680403497
3	3.43	3.4162215802799	-0.0170487699952644	3.46082718971536	-0.0137784197201003
4	3.47	3.49049235535188	-0.0199828374925187	3.46949048214064	0.0204923553518763
5	3.51	3.53564115785085	0.0062050675832312	3.47815377456592	0.0256411578508491
6	3.52	3.51166948724288	0.0413094765754451	3.48702103618167	-0.00833051275711716
7	3.52	3.51240065327161	0.0317110489309615	3.49588829779742	-0.00759934672838636
8	3.52	3.51650924559827	0.0185775973908057	3.50491315701093	-0.00349075440173285
9	3.52	3.51693191566135	0.00913006811421942	3.51393801622443	-0.00306808433864836
10	3.52	3.50965939295142	0.00705884749325362	3.52328175955533	-0.0103406070485819
11	3.52	3.51407069225833	-0.00669619514455846	3.53262550288623	-0.00592930774166911
12	3.52	3.51798764208719	-0.0202129792610042	3.54222533717382	-0.00201235791281373
13	3.52	3.51461193141744	-0.0264371028788476	3.55182517146141	-0.00538806858256047
14	3.52	3.50126284691584	-0.0236142090737903	3.56235136215796	-0.0187371530841647
15	3.58	3.60417121714076	-0.0170487699952644	3.5728775528545	0.0241712171407622
16	3.6	3.63427855744771	-0.0199828374925187	3.58570428004481	0.0342785574477129
17	3.61	3.61526392518166	0.0062050675832312	3.59853100723511	0.00526392518165997
18	3.61	3.56623895923089	0.0413094765754451	3.61245156419366	-0.0437610407691085
19	3.61	3.56191682991682	0.0317110489309615	3.62637212115222	-0.0480831700831801
20	3.63	3.60240334130584	0.0185775973908057	3.63901906130335	-0.0275966586941596
21	3.68	3.69920393043129	0.00913006811421942	3.65166600145449	0.0192039304312921
22	3.69	3.71011071723028	0.00705884749325362	3.66283043527647	0.0201107172302786
23	3.69	3.71270132604611	-0.00669619514455846	3.67399486909845	0.0227013260461120
24	3.69	3.71494954045873	-0.0202129792610042	3.68526343880227	0.0249495404587341
25	3.69	3.70990509437275	-0.0264371028788476	3.69653200850609	0.0199050943727537
26	3.69	3.69646996914736	-0.0236142090737903	3.70714423992643	0.00646996914735665
27	3.69	3.67929229864849	-0.0170487699952644	3.71775647134677	-0.0107077013515093
28	3.69	3.67293648607637	-0.0199828374925187	3.72704635141614	-0.0170635139236262
29	3.69	3.63745870093125	0.0062050675832312	3.73633623148552	-0.0525412990687473
30	3.78	3.77078566585380	0.0413094765754451	3.74790485757075	-0.00921433414619566
31	3.79	3.78881546741305	0.0317110489309615	3.75947348365598	-0.00118453258694640
32	3.79	3.78093885290362	0.0185775973908057	3.78048354970558	-0.00906114709638217
33	3.8	3.78937631613061	0.00913006811421942	3.80149361575517	-0.0106236838693881
34	3.8	3.76359468202719	0.00705884749325362	3.82934647047956	-0.0364053179728132
35	3.8	3.74949686994061	-0.00669619514455846	3.85719932520395	-0.0505031300593917
36	3.8	3.73140680392041	-0.0202129792610042	3.88880617534059	-0.0685931960795867
37	3.81	3.72602407740162	-0.0264371028788476	3.92041302547723	-0.0839759225983836
38	3.95	3.96963478110675	-0.0236142090737903	3.95397942796704	0.0196347811067459
39	3.99	4.00950293953841	-0.0170487699952644	3.98754583045686	0.0195029395384059
40	4	3.99796180444714	-0.0199828374925187	4.02202103304538	-0.00203819555286255
41	4.06	4.05729869678286	0.0062050675832312	4.0564962356339	-0.00270130321713591
42	4.16	4.19109645672942	0.0413094765754451	4.08759406669514	0.0310964567294194
43	4.19	4.22959705331267	0.0317110489309615	4.11869189775637	0.0395970533126722
44	4.2	4.23635282839909	0.0185775973908057	4.14506957421011	0.0363528283990870
45	4.2	4.21942268122193	0.00913006811421942	4.17144725066385	0.0194226812219336
46	4.2	4.19707367867651	0.00705884749325362	4.19586747383024	-0.00292632132349446
47	4.2	4.18640849814792	-0.00669619514455846	4.22028769699663	-0.0135915018520762
48	4.2	4.17575548641116	-0.0202129792610042	4.24445749284984	-0.0242445135888385
49	4.23	4.2178098141758	-0.0264371028788476	4.26862728870305	-0.0121901858242044
50	4.38	4.49339368253614	-0.0236142090737903	4.29022052653765	0.113393682536142
51	4.43	4.56523500562302	-0.0170487699952644	4.31181376437224	0.135235005623019
52	4.44	4.56700194062333	-0.0199828374925187	4.33298089686919	0.127001940623327
53	4.44	4.51964690305063	0.0062050675832312	4.35414802936614	0.0796469030506293
54	4.44	4.46168020386545	0.0413094765754451	4.37701031955911	0.0216802038654471
55	4.44	4.44841634131696	0.0317110489309615	4.39987260975208	0.00841634131696356
56	4.44	4.44460725067592	0.0185775973908057	4.41681515193327	0.00460725067592449
57	4.45	4.45711223777132	0.00913006811421942	4.43375769411446	0.00711223777131664
58	4.45	4.4511385851173	0.00705884749325362	4.44180256738945	0.00113858511729781
59	4.45	4.45684875448013	-0.00669619514455846	4.44984744066443	0.00684875448012612
60	4.45	4.46354484788485	-0.0202129792610042	4.45666813137615	0.0135448478848499
61	4.45	4.46294828079097	-0.0264371028788476	4.46348882208788	0.0129482807909689
62	4.45	4.44789407820248	-0.0236142090737903	4.47572013087131	-0.00210592179751856
63	4.45	4.42909733034053	-0.0170487699952644	4.48795143965474	-0.0209026696594750
64	4.45	4.40643934174566	-0.0199828374925187	4.51354349574686	-0.043560658254342
65	4.46	4.37465938057779	0.0062050675832312	4.53913555183898	-0.0853406194222135
66	4.46	4.318912952949	0.0413094765754451	4.55977757047555	-0.141087047050997
67	4.46	4.30786936195692	0.0317110489309615	4.58041958911212	-0.152130638043082
68	4.48	4.34056145940953	0.0185775973908057	4.60086094319967	-0.139438540590472
69	4.58	4.52956763459857	0.00913006811421942	4.62130229728721	-0.0504323654014325
70	4.67	4.6906810868241	0.00705884749325362	4.64226006568265	0.0206810868240970
71	4.68	4.70347836106647	-0.00669619514455846	4.66321783407808	0.0234783610664744
72	4.68	4.69552129136193	-0.0202129792610042	4.68469168789907	0.0155212913619325

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243879381wskbbff7s18q7hw/1pj9a1243879353.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243879381wskbbff7s18q7hw/1pj9a1243879353.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243879381wskbbff7s18q7hw/2ye0o1243879353.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243879381wskbbff7s18q7hw/2ye0o1243879353.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243879381wskbbff7s18q7hw/3sdz61243879353.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243879381wskbbff7s18q7hw/3sdz61243879353.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243879381wskbbff7s18q7hw/46ant1243879353.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243879381wskbbff7s18q7hw/46ant1243879353.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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