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Structurele tijdreeksanalyse

*The author of this computation has been verified*

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

Title produced by software: Structural Time Series Models

Date of computation: Tue, 01 Dec 2009 12:54:37 -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/01/t125969733043fhia1j8lhq69j.htm/, Retrieved Tue, 01 Dec 2009 20:55:37 +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/01/t125969733043fhia1j8lhq69j.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 «

8.4 8.4 8.4 8.6 8.9 8.8 8.3 7.5 7.2 7.4 8.8 9.3 9.3 8.7 8.2 8.3 8.5 8.6 8.5 8.2 8.1 7.9 8.6 8.7 8.7 8.5 8.4 8.5 8.7 8.7 8.6 8.5 8.3 8 8.2 8.1 8.1 8 7.9 7.9 8 8 7.9 8 7.7 7.2 7.5 7.3 7 7 7 7.2 7.3 7.1 6.8 6.4 6.1 6.5 7.7 7.9 7.5 6.9 6.6 6.9 7.7 8 8 7.7 7.3 7.4 8.1 8.3 8.2

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

Structural Time Series Model
t	Observed	Level	Slope	Seasonal	Stand. Residuals
1	8.4	8.4	0	0	0
2	8.4	8.4	0	0	0
3	8.4	8.4	0	0	0
4	8.6	8.59450288184909	0.186336498471207	0.00549711815091083	0.542139808952547
5	8.9	8.89818757487719	0.298820878666345	0.00181242512281285	0.327604743032395
6	8.8	8.81218879419231	-0.0701036903600278	-0.0121887941923082	-1.0744078602474
7	8.3	8.30982820712854	-0.484497368791315	-0.00982820712853705	-1.20682693844648
8	7.5	7.50619437027392	-0.790443779007934	-0.00619437027391512	-0.890998953192124
9	7.2	7.1841981799698	-0.341356654372683	0.0158018200302004	1.30786355065082
10	7.4	7.38783991849545	0.181117624453733	0.0121600815045500	1.52158685435875
11	8.8	8.76940313327973	1.33195018155721	0.0305968667202731	3.35153664288356
12	9.3	9.33141796858167	0.593835381079452	-0.0314179685816659	-2.14959055962222
13	9.3	9.31206024620503	0.00614229913265951	-0.0120602462050237	-1.71219001997661
14	8.7	8.71417121390012	-0.573184008276645	-0.0141712139001166	-1.69423863951449
15	8.2	8.19291377757677	-0.524590012218204	0.00708622242323257	0.142937770280577
16	8.3	8.27870651858461	0.0460847758066103	0.0212934814153921	1.66000271945657
17	8.5	8.49082657038934	0.201233413953079	0.00917342961065437	0.451892590507891
18	8.6	8.61026011305591	0.124775165732414	-0.0102601130559145	-0.222666517111757
19	8.5	8.49906162872325	-0.0957893751730662	0.0009383712767499	-0.642344401582395
20	8.2	8.22059950369853	-0.266530263226196	-0.0205995036985341	-0.497243774358148
21	8.1	8.0671242661379	-0.160859902044925	0.0328757338620984	0.307740764488693
22	7.9	7.9519178929486	-0.118188339236399	-0.0519178929486033	0.124271169664202
23	8.6	8.51974095973871	0.523020367497446	0.080259040261289	1.86737445325227
24	8.7	8.7424253381197	0.242306274407083	-0.0424253381196999	-0.8175171896451
25	8.7	8.69797842724487	-0.0256417206056453	0.00202157275512926	-0.780709100960954
26	8.5	8.50132251246483	-0.185569562503805	-0.00132251246482670	-0.466614956822868
27	8.4	8.41811469353935	-0.0911547063213842	-0.0181146935393472	0.276352870301122
28	8.5	8.4836511593258	0.0535992036847195	0.0163488406741942	0.421298118657457
29	8.7	8.68738617815933	0.192128800651334	0.0126138218406734	0.403448763895573
30	8.7	8.71184394878778	0.0373675017945224	-0.0118439487877778	-0.450712780267858
31	8.6	8.59304152670997	-0.106783563881493	0.00695847329002688	-0.419806817830184
32	8.5	8.52145780871233	-0.0742934064926835	-0.0214578087123312	0.0946201826656657
33	8.3	8.25530817586004	-0.251378877726735	0.0446918241399638	-0.515720954263528
34	8	8.09985758883582	-0.162835388833424	-0.0998575888358217	0.257862671210054
35	8.2	8.10177341255155	-0.0107652022417415	0.0982265874484516	0.442870737944124
36	8.1	8.12955809375315	0.0248150432402895	-0.0295580937531451	0.103619748756330
37	8.1	8.08810141986608	-0.0363394533713269	0.0118985801339163	-0.178202264373939
38	8	8.00409711361563	-0.0803437981701123	-0.00409711361562836	-0.128225093271845
39	7.9	7.9241028133476	-0.080023604231224	-0.0241028133476076	0.000934916941762015
40	7.9	7.89861201034747	-0.0299689247701155	0.00138798965252551	0.145753330538329
41	8	7.97379475851115	0.0664268345841462	0.0262052414888480	0.280701723197123
42	8	8.00277087301101	0.0320854763466518	-0.00277087301101287	-0.100014939197683
43	7.9	7.91018055910298	-0.0822476986425896	-0.0101805591029809	-0.332968048500968
44	8	7.99301301521662	0.0691358795899048	0.00698698478337603	0.440870367703134
45	7.7	7.67726132357204	-0.283812957491661	0.0227386764279626	-1.02788278615441
46	7.2	7.3141959239161	-0.356489541200404	-0.114195923916094	-0.211653986485931
47	7.5	7.38278412037487	0.0333207469073576	0.117215879625126	1.13523790722274
48	7.3	7.33239981069436	-0.0434311786450603	-0.032399810694358	-0.223525681529458
49	7	7.00145157623927	-0.307046239928848	-0.00145157623927222	-0.768172856093289
50	7	6.98466972743133	-0.0409007030036084	0.0153302725686740	0.77512340161062
51	7	7.02053972426448	0.0291743944877572	-0.0205397242644795	0.204371834297510
52	7.2	7.20162942663088	0.168063168540456	-0.00162942663088353	0.404538839841147
53	7.3	7.27829417803854	0.0845877803907532	0.0217058219614643	-0.243056860738686
54	7.1	7.10447766873797	-0.151449040374118	-0.00447766873797478	-0.687434972770834
55	6.8	6.84491095858611	-0.250218854362103	-0.044910958586107	-0.287643356539499
56	6.4	6.37048585356149	-0.455039514945662	0.0295141464385114	-0.596493447011743
57	6.1	6.04662841713655	-0.335201367206379	0.0533715828634461	0.349001215668825
58	6.5	6.60630158823162	0.482295506431774	-0.106301588231619	2.38077547677439
59	7.7	7.56100590096338	0.913854115088924	0.138994099036624	1.25681899661878
60	7.9	7.93198033091878	0.417984603653717	-0.0319803309187814	-1.44415926819459
61	7.5	7.56674274440778	-0.297445557429344	-0.06674274440778	-2.08462594732407
62	6.9	6.90371487086696	-0.631307123974338	-0.00371487086695706	-0.972161148286413
63	6.6	6.61464816362608	-0.319647774563612	-0.0146481636260801	0.908425293627625
64	6.9	6.86899601328774	0.203719304920818	0.0310039867122609	1.52458534731289
65	7.7	7.63511448773715	0.716126230078677	0.0648855122628517	1.49193598767762
66	8	8.0266609508785	0.420394582704619	-0.0266609508785065	-0.861286443508214
67	8	8.03730886785195	0.0470167974500614	-0.0373088678519463	-1.08737666402777
68	7.7	7.66381937553885	-0.336168985306571	0.0361806244611525	-1.11593967357225
69	7.3	7.30718183205484	-0.354820772858166	-0.00718183205484464	-0.0543190866513019
70	7.4	7.53910678983084	0.17984804454146	-0.139106789830835	1.55710462546365
71	8.1	7.93571205300436	0.377362761122093	0.164287946995636	0.575216160067762
72	8.3	8.28099711948655	0.348136768855624	0.0190028805134527	-0.0851193254992055
73	8.2	8.24143453405873	-0.00513654574708017	-0.0414345340587352	-1.02927863813055

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/01/t125969733043fhia1j8lhq69j/146uw1259697275.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t125969733043fhia1j8lhq69j/146uw1259697275.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t125969733043fhia1j8lhq69j/24tpx1259697275.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t125969733043fhia1j8lhq69j/24tpx1259697275.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t125969733043fhia1j8lhq69j/344i21259697275.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t125969733043fhia1j8lhq69j/344i21259697275.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t125969733043fhia1j8lhq69j/4erhh1259697275.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t125969733043fhia1j8lhq69j/4erhh1259697275.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t125969733043fhia1j8lhq69j/5k4v41259697275.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/01/t125969733043fhia1j8lhq69j/5k4v41259697275.ps (open in new window)

Parameters (Session):

par1 = 12 ;

Parameters (R input):

par1 = 12 ;

R code (references can be found in the software module):

par1 <- as.numeric(par1)

nx <- length(x)

x <- ts(x,frequency=par1)

m <- StructTS(x,type='BSM')

m$coef

m$fitted

m$resid

mylevel <- as.numeric(m$fitted[,'level'])

myslope <- as.numeric(m$fitted[,'slope'])

myseas <- as.numeric(m$fitted[,'sea'])

myresid <- as.numeric(m$resid)

myfit <- mylevel+myseas

mylagmax <- nx/2

bitmap(file='test2.png')

op <- par(mfrow = c(2,2))

acf(as.numeric(x),lag.max = mylagmax,main='Observed')

acf(mylevel,na.action=na.pass,lag.max = mylagmax,main='Level')

acf(myseas,na.action=na.pass,lag.max = mylagmax,main='Seasonal')

acf(myresid,na.action=na.pass,lag.max = mylagmax,main='Standardized Residals')

par(op)

dev.off()

bitmap(file='test3.png')

op <- par(mfrow = c(2,2))

spectrum(as.numeric(x),main='Observed')

spectrum(mylevel,main='Level')

spectrum(myseas,main='Seasonal')

spectrum(myresid,main='Standardized Residals')

par(op)

dev.off()

bitmap(file='test4.png')

op <- par(mfrow = c(2,2))

cpgram(as.numeric(x),main='Observed')

cpgram(mylevel,main='Level')

cpgram(myseas,main='Seasonal')

cpgram(myresid,main='Standardized Residals')

par(op)

dev.off()

bitmap(file='test1.png')

plot(as.numeric(m$resid),main='Standardized Residuals',ylab='Residuals',xlab='time',type='b')

grid()

dev.off()

bitmap(file='test5.png')

op <- par(mfrow = c(2,2))

hist(m$resid,main='Residual Histogram')

plot(density(m$resid),main='Residual Kernel Density')

qqnorm(m$resid,main='Residual Normal QQ Plot')

qqline(m$resid)

plot(m$resid^2, myfit^2,main='Sq.Resid vs. Sq.Fit',xlab='Squared residuals',ylab='Squared Fit')

par(op)

dev.off()

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Structural Time Series Model',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,'Level',header=TRUE)

a<-table.element(a,'Slope',header=TRUE)

a<-table.element(a,'Seasonal',header=TRUE)

a<-table.element(a,'Stand. Residuals',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,mylevel[i])

a<-table.element(a,myslope[i])

a<-table.element(a,myseas[i])

a<-table.element(a,myresid[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable.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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