Home » date » 2009 » Dec » 04 »

WS 9, Populair model 3

*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, 04 Dec 2009 09:02:52 -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/04/t1259942693kapxohb7eitp7zq.htm/, Retrieved Fri, 04 Dec 2009 17:04:59 +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/04/t1259942693kapxohb7eitp7zq.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 «

95.1 97 112.7 102.9 97.4 111.4 87.4 96.8 114.1 110.3 103.9 101.6 94.6 95.9 104.7 102.8 98.1 113.9 80.9 95.7 113.2 105.9 108.8 102.3 99 100.7 115.5 100.7 109.9 114.6 85.4 100.5 114.8 116.5 112.9 102 106 105.3 118.8 106.1 109.3 117.2 92.5 104.2 112.5 122.4 113.3 100 110.7 112.8 109.8 117.3 109.1 115.9 96 99.8 116.8 115.7 99.4 94.3

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	95.1	95.1	0	0	0
2	97	95.6172457653417	-0.017913717674252	1.38275423465830	0.355983429664942
3	112.7	103.510061627232	0.534092319487827	9.18993837276807	2.67772412401643
4	102.9	105.425107617692	0.641816735585187	-2.5251076176916	0.524277643898397
5	97.4	103.355196266626	0.460294618783866	-5.95519626662574	-1.24098426251121
6	111.4	105.245730983825	0.533280509231634	6.1542690161755	0.718438978345372
7	87.4	100.466672647718	0.329844159680205	-13.0666726477184	-2.75926564762998
8	96.8	97.67278452305	0.234102247883548	-0.872784523050115	-1.63863215627200
9	114.1	101.693354237365	0.336632235559500	12.4066457626347	1.98989980491086
10	110.3	105.775502195327	0.433882450243126	4.52449780467264	1.96626962392249
11	103.9	106.679544562154	0.446107526520085	-2.7795445621543	0.246334626336542
12	101.6	105.460510391342	0.402164682467852	-3.86051039134245	-0.870790832135682
13	94.6	103.148133838992	0.405379607507195	-8.54813383899242	-1.47301299519722
14	95.9	101.754421894918	0.402709771371073	-5.8544218949178	-0.984415504282895
15	104.7	99.3632542567194	0.350462643531558	5.33674574328056	-1.42367769403096
16	102.8	99.7468511491854	0.351534636626128	3.05314885081463	0.0159369830328408
17	98.1	100.877374905881	0.381386334477444	-2.77737490588053	0.374405975970588
18	113.9	102.186725638773	0.417033085067379	11.7132743612268	0.458675404508563
19	80.9	100.201722691066	0.332036963937143	-19.3017226910665	-1.21903554616217
20	95.7	99.8695020254803	0.311129466062295	-4.16950202548029	-0.342395782714928
21	113.2	100.946612877836	0.332447355007716	12.2533871221636	0.397518723956748
22	105.9	101.350879410691	0.334217126603476	4.54912058930924	0.0373398207251376
23	108.8	103.913504392772	0.382480165687309	4.88649560722841	1.15844417296230
24	102.3	104.653514692740	0.38919806339569	-2.35351469274042	0.185972813927420
25	99	105.233242031901	0.392389111935568	-6.23324203190120	0.0993119002920132
26	100.7	105.443899678798	0.389169397687618	-4.74389967879805	-0.0942976122775373
27	115.5	107.197843601367	0.418458428560663	8.30215639863339	0.696945241968241
28	100.7	105.099550922506	0.353254968432263	-4.39955092250612	-1.2629212179917
29	109.9	106.784660019047	0.392172382749667	3.11533998095294	0.663873067526193
30	114.6	105.723634008228	0.347593952942233	8.87636599177186	-0.728312784310215
31	85.4	105.223802918773	0.321815495199988	-19.8238029187726	-0.429238487890929
32	100.5	105.347361413383	0.316066024321495	-4.84736141338333	-0.101341346478198
33	114.8	104.992069304884	0.297929598235033	9.80793069511591	-0.344952271055414
34	116.5	107.620783521147	0.356050097777632	8.87921647885314	1.19994015450840
35	112.9	108.796080263285	0.374985570808506	4.10391973671507	0.421988022066659
36	102	108.002379587682	0.349450759817781	-6.00237958768213	-0.602100114128637
37	106	109.154631424205	0.366654233734571	-3.15463142420537	0.413329357891361
38	105.3	109.942599986325	0.375926629927034	-4.64259998632514	0.216233565197655
39	118.8	110.009348100003	0.368692225198402	8.79065189999748	-0.157728262228629
40	106.1	110.360999836184	0.368264442911986	-4.26099983618425	-0.0086377960010636
41	109.3	108.918062757243	0.320134614131354	0.381937242756751	-0.915032123650648
42	117.2	108.248978493781	0.292993174014727	8.95102150621929	-0.500264487858976
43	92.5	109.445912812150	0.317924742269318	-16.9459128121498	0.458898932602473
44	104.2	109.998329024923	0.324280203971745	-5.79832902492336	0.119539579511164
45	112.5	108.640179614686	0.280154718872115	3.8598203853136	-0.860136310712448
46	122.4	109.900055770153	0.304859734857201	12.4999442298470	0.501577874270787
47	113.3	110.075576228061	0.301714853313248	3.22442377193928	-0.0662506501165228
48	100	109.206026206749	0.273938130054238	-9.2060262067488	-0.599998337377564
49	110.7	110.448317590123	0.296725012273525	0.251682409877066	0.495801751623415
50	112.8	112.862498084998	0.347070434402659	-0.0624980849981567	1.08249484577425
51	109.8	109.879975041913	0.265999384561075	-0.0799750419126175	-1.69792094268066
52	117.3	112.135650742638	0.315867321998037	5.16434925736186	1.01199085651005
53	109.1	111.812627735560	0.299438038773849	-2.71262773556041	-0.324450755077545
54	115.9	110.493936631922	0.257132316540835	5.40606336807807	-0.821824416022757
55	96	110.841435145385	0.259506739129008	-14.8414351453846	0.0459592510074398
56	99.8	109.232902735312	0.21071436643755	-9.4329027353118	-0.951746156028206
57	116.8	110.12269316056	0.228205676022696	6.67730683944004	0.346469066656222
58	115.7	108.344476696577	0.17742944163946	7.35552330342254	-1.02451795269354
59	99.4	104.160922265628	0.0688897057410682	-4.76092226562805	-2.22758842502274
60	94.3	103.074919560155	0.0404780215439526	-8.77491956015468	-0.589954652290969

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942693kapxohb7eitp7zq/1i3eq1259942570.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942693kapxohb7eitp7zq/1i3eq1259942570.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942693kapxohb7eitp7zq/2mpl61259942570.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942693kapxohb7eitp7zq/2mpl61259942570.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942693kapxohb7eitp7zq/32r051259942570.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942693kapxohb7eitp7zq/32r051259942570.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942693kapxohb7eitp7zq/4citv1259942570.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942693kapxohb7eitp7zq/4citv1259942570.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942693kapxohb7eitp7zq/5skhr1259942570.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942693kapxohb7eitp7zq/5skhr1259942570.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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