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verbetering

*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: Thu, 10 Dec 2009 10:10:47 -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/10/t1260465098oef22y0o3ce5kev.htm/, Retrieved Thu, 10 Dec 2009 18:11:45 +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/10/t1260465098oef22y0o3ce5kev.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 «

102.86 102.55 102.28 102.26 102.57 103.08 102.76 102.51 102.87 103.14 103.12 103.16 102.48 102.57 102.88 102.63 102.38 101.69 101.96 102.19 101.87 101.6 101.63 101.22 101.21 101.49 101.64 101.66 101.77 101.82 101.78 101.28 101.29 101.37 101.12 101.51 102.24 102.94 103.09 103.46 103.64 104.39 104.15 105.21 105.8 105.91 105.39 105.46 104.72 103.14 102.63 102.32 101.93 100.62 100.6 99.63 98.9 98.32 99.22 98.81

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

Structural Time Series Model
t	Observed	Level	Slope	Seasonal	Stand. Residuals
1	102.86	102.86	0	0	0
2	102.55	102.565682179366	-0.0241017981932413	-0.0156821793657601	-0.418447443081315
3	102.28	102.295654255638	-0.0590197890480614	-0.0156542556376815	-0.547626616947499
4	102.26	102.275657863465	-0.0517706878412844	-0.0156578634650731	0.0846528878937142
5	102.57	102.585684161599	0.0255774064202126	-0.0156841615986339	0.77123147447451
6	103.08	103.095711249913	0.137359171386002	-0.0157112499134435	1.02151188726503
7	102.76	102.775691829632	0.0272945100648635	-0.0156918296321747	-0.958216547410568
8	102.51	102.525682955471	-0.041008229438491	-0.0156829554710754	-0.578790293818229
9	102.87	102.885692586588	0.0590525893512763	-0.0156925865878397	0.835233604496448
10	103.14	103.155696379665	0.112068676529851	-0.0156963796646531	0.438841052469211
11	103.12	103.135694604142	0.0787435722540339	-0.0156946041417716	-0.274561944132307
12	103.16	103.175694215	0.068945530657078	-0.0156942149998998	-0.0805149633735772
13	102.48	102.575864924310	-0.0942567935268288	-0.0958649243103273	-1.61460401257669
14	102.57	102.559541291563	-0.0754164330562679	0.0104587084371704	0.135582455788541
15	102.88	102.869974655270	0.0225296918861205	0.0100253447304229	0.80029042560907
16	102.63	102.619746024090	-0.0466648981590681	0.0102539759095662	-0.565977416656666
17	102.38	102.369618730044	-0.0982647580710872	0.0103812699560964	-0.422313943435951
18	101.69	101.679342265555	-0.248385199123064	0.0106577344450224	-1.22905562572630
19	101.96	101.949523026306	-0.116894197066505	0.0104769736944682	1.07673397188970
20	102.19	102.179613308323	-0.0289105295560245	0.0103866916769103	0.720542140046447
21	101.87	101.859556763784	-0.102736662499294	0.0104432362158988	-0.604633885219041
22	101.6	101.589532512824	-0.145156837857006	0.0104674871757102	-0.347431163266818
23	101.63	101.619551467726	-0.100735446744689	0.0104485322743415	0.363828098679409
24	101.22	101.209526487745	-0.179167124240111	0.0104735122548333	-0.642391691130274
25	101.21	101.278359709834	-0.117221366123082	-0.068359709833807	0.556644237990322
26	101.49	101.479211673508	-0.0390324828583465	0.0107883264921917	0.594617454136453
27	101.64	101.629353480429	0.0089910937788323	0.0106465195709939	0.392534199004252
28	101.66	101.649359642584	0.0117857285541051	0.0106403574155241	0.0228634951773747
29	101.77	101.759400667893	0.0367068525134991	0.0105993321074123	0.203988177108418
30	101.82	101.809404811985	0.040079074733847	0.0105951880150128	0.027610610650106
31	101.78	101.769386179755	0.0197672426005779	0.0106138202445912	-0.16633251119359
32	101.28	101.269295916727	-0.112060395788408	0.0107040832726320	-1.07962398180594
33	101.29	101.279311737844	-0.0811037254439684	0.0106882621560814	0.253537069583334
34	101.37	101.359327323774	-0.0402459262768263	0.010672676225837	0.334637023681791
35	101.12	101.109312177555	-0.0934413378146773	0.0106878224453378	-0.435692178920362
36	101.51	101.499338233413	0.0291626009788227	0.0106617665869096	1.00418487210583
37	102.24	102.266203225094	0.214411009207366	-0.0262032250935371	1.61463476533749
38	102.94	102.932823149750	0.326494681902529	0.00717685025042731	0.871317560731968
39	103.09	103.082725058048	0.281676616384881	0.00727494195247039	-0.366527153335146
40	103.46	103.452761687947	0.304092045455078	0.0072383120526583	0.183439465808140
41	103.64	103.632723280672	0.272609057234745	0.0072767193277125	-0.257741715202036
42	104.39	104.382833556325	0.393704706663714	0.00716644367481712	0.991580874127709
43	104.15	104.142724300902	0.232974299582409	0.00727569909844755	-1.31627989799212
44	105.21	105.202830723504	0.442726256121331	0.00716927649549986	1.71784674455270
45	105.8	105.792844868524	0.480076970899705	0.00715513147573213	0.305909538284946
46	105.91	105.902818338589	0.386221807156536	0.0071816614112087	-0.768707330585288
47	105.39	105.382769849284	0.156397238797767	0.00723015071617186	-1.88236647106921
48	105.46	105.452766398805	0.134486375664457	0.00723360119541531	-0.179460935387349
49	104.72	104.865725642857	-0.047162469548292	-0.145725642857173	-1.55911141587972
50	103.14	103.140841115573	-0.465004433973417	-0.000841115573391832	-3.28718271230301
51	102.63	102.630821263224	-0.476427293630119	-0.000821263223674354	-0.093446718219952
52	102.32	102.320876057764	-0.434196321776809	-0.000876057763634722	0.345662807929606
53	101.93	101.930886917534	-0.422984349636314	-0.000886917534437867	0.0917979615088299
54	100.62	100.620724247123	-0.647975545765978	-0.000724247123189815	-1.84242012302064
55	100.6	100.600810202774	-0.488702372453864	-0.00081020277419643	1.30438547197669
56	99.63	99.6307610320795	-0.610768309656651	-0.000761032079550974	-0.999724171123227
57	98.9	98.9007519403006	-0.641006918798332	-0.000751940300637301	-0.247662346523827
58	98.32	98.3207554124853	-0.625535032553002	-0.000755412485319058	0.126720939011964
59	99.22	99.2208202182434	-0.23864900116482	-0.00082021824338226	3.16877963671013
60	98.81	98.81081478516	-0.282104592272321	-0.000814785160120457	-0.355923617417946

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/10/t1260465098oef22y0o3ce5kev/1wsqi1260465045.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/10/t1260465098oef22y0o3ce5kev/1wsqi1260465045.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/10/t1260465098oef22y0o3ce5kev/2w0fx1260465045.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/10/t1260465098oef22y0o3ce5kev/2w0fx1260465045.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/10/t1260465098oef22y0o3ce5kev/39ov91260465045.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/10/t1260465098oef22y0o3ce5kev/39ov91260465045.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/10/t1260465098oef22y0o3ce5kev/4rlce1260465045.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/10/t1260465098oef22y0o3ce5kev/4rlce1260465045.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/10/t1260465098oef22y0o3ce5kev/5lqsx1260465045.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/10/t1260465098oef22y0o3ce5kev/5lqsx1260465045.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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