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Shwws9_v2

*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: Fri, 11 Dec 2009 09:22:50 -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/11/t1260548617z528ribzkqka7ch.htm/, Retrieved Fri, 11 Dec 2009 17:23:43 +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/11/t1260548617z528ribzkqka7ch.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 «

102.1 102.86 102.99 103.73 105.02 104.43 104.63 104.93 105.87 105.66 106.76 106 107.22 107.33 107.11 108.86 107.72 107.88 108.38 107.72 108.41 109.9 111.45 112.18 113.34 113.46 114.06 115.54 116.39 115.94 116.97 115.94 115.91 116.43 116.26 116.35 117.9 117.7 117.53 117.86 117.65 116.51 115.93 115.31 115

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	102.1	102.1	0	0	0
2	102.86	102.765151457035	0.06421066028975	0.0472981722103792	0.811371258374927
3	102.99	102.937342900285	0.0830850431561682	0.0375618299775458	0.189765895420029
4	103.73	103.588003997962	0.211951644815353	0.067714864134335	0.954909891260168
5	105.02	104.812706859019	0.470198797216456	0.0812581721220051	1.65487858208892
6	104.43	104.540561632929	0.270205063345090	-0.0205258726395394	-1.19559366289446
7	104.63	104.613411613814	0.215607438579203	0.0402172397717256	-0.315576160168867
8	104.93	104.880139398616	0.229932906681037	0.0437790001326162	0.081455708723675
9	105.87	105.727522168496	0.404050369434276	0.0692493144769964	0.982188462873182
10	105.66	105.705969236383	0.283663252394629	0.00443160071438382	-0.676473794049252
11	106.76	106.609177722391	0.459174208723252	0.0775061606676195	0.984369587066293
12	106	106.133127680932	0.194036528702973	-0.0224910439118621	-1.48569097016466
13	107.22	107.125112295267	0.411194761759492	0.00451842929517610	1.45667327957439
14	107.33	107.342766908453	0.358721621445186	0.003821206714052	-0.260008449393046
15	107.11	107.230710716395	0.227692677879219	-0.0680454006315661	-0.739681292772364
16	108.86	108.642470881281	0.56135759369155	0.0835838855526111	1.86501983542233
17	107.72	107.933839215472	0.203509707592572	-0.070446551532578	-1.99747126935286
18	107.88	107.934205468626	0.146308209952554	-0.0312448268648332	-0.319764662433646
19	108.38	108.319849571243	0.213686298759239	0.0330781989645532	0.376964778916922
20	107.72	107.889039538910	0.0322513933179768	-0.0961080204849495	-1.01545858717818
21	108.41	108.265389713630	0.129124049066835	0.105662449630389	0.542271327969
22	109.9	109.714326201406	0.500715448906563	0.0362588532372428	2.08026990939708
23	111.45	111.193613654370	0.776254836292293	0.145587891498956	1.54261026183656
24	112.18	112.252279326314	0.855671935194427	-0.104203793427510	0.444689780661887
25	113.34	113.224810759244	0.888089973140158	0.102049069170236	0.196644175735931
26	113.46	113.520212788915	0.725146290757188	-0.00276096361689073	-0.85787675926491
27	114.06	114.244820601299	0.724996985899211	-0.184762113724858	-0.00083967605545427
28	115.54	115.285735604074	0.813519896172332	0.219399538641074	0.495729607223135
29	116.39	116.398029269592	0.897292134789788	-0.0409189242552296	0.467859690556891
30	115.94	116.251556685710	0.604801409303994	-0.196582891669543	-1.63528753513486
31	116.97	116.844524646464	0.601486011141343	0.126779821009020	-0.0185506130058656
32	115.94	116.300401985885	0.280522780961716	-0.234065698926343	-1.79648518578687
33	115.91	116.003649299062	0.118780668329578	-0.0299736903632424	-0.90543196184608
34	116.43	116.399524995428	0.196424933136498	-9.530930384552e-05	0.434686592306393
35	116.26	116.237925550805	0.0961101278666753	0.0615700667171395	-0.561600288915193
36	116.35	116.435811108018	0.1245715588933	-0.0970117213422119	0.159495425282006
37	117.9	117.417821519902	0.363198753510621	0.387126608350525	1.39661150407570
38	117.7	117.727551245982	0.348419018330608	-0.0221249495079620	-0.0798484402436809
39	117.53	117.826874428711	0.279472100006796	-0.270231867062211	-0.386690010795041
40	117.86	117.773280784439	0.186487873012909	0.122969793321466	-0.521370824042164
41	117.65	117.663367194782	0.103638542355162	0.0188137010861343	-0.462937653020033
42	116.51	116.923002540769	-0.132163703558486	-0.321356449630923	-1.31836801891653
43	115.93	115.88208675318	-0.386004644997404	0.146662301323176	-1.42032071118767
44	115.31	115.466804163221	-0.394182837006596	-0.153621309674475	-0.0457758208463296
45	115	115.065385319931	-0.396204205777226	-0.0645984848540805	-0.0113159082216976

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260548617z528ribzkqka7ch/1ye721260548568.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260548617z528ribzkqka7ch/1ye721260548568.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260548617z528ribzkqka7ch/25o8q1260548568.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260548617z528ribzkqka7ch/25o8q1260548568.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260548617z528ribzkqka7ch/3bxdp1260548568.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260548617z528ribzkqka7ch/3bxdp1260548568.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260548617z528ribzkqka7ch/4cq0p1260548568.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260548617z528ribzkqka7ch/4cq0p1260548568.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260548617z528ribzkqka7ch/51e4a1260548568.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260548617z528ribzkqka7ch/51e4a1260548568.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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