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STSM

*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: Sat, 18 Dec 2010 12:52:36 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676632yq6ebxmw3ml58wk.htm/, Retrieved Sat, 18 Dec 2010 13:50:32 +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/2010/Dec/18/t1292676632yq6ebxmw3ml58wk.htm/},
    year = {2010},
}
@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 = {2010},
    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 «

37 30 47 35 30 43 82 40 47 19 52 136 80 42 54 66 81 63 137 72 107 58 36 52 79 77 54 84 48 96 83 66 61 53 30 74 69 59 42 65 70 100 63 105 82 81 75 102 121 98 76 77 63 37 35 23 40 29 37 51 20 28 13 22 25 13 16 13 16 17 9 17 25 14 8 7 10 7 10 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	3 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Structural Time Series Model
t	Observed	Level	Slope	Seasonal	Stand. Residuals
1	37	37	0	0	0
2	30	34.0671084504094	-0.315647737896301	-0.315647736716693	-0.291557373239402
3	47	39.2754264905757	0.11301801109518	0.113018011095178	0.578107230534407
4	35	37.5988246319671	0.00609094512104394	0.0060909451210442	-0.194826690254935
5	30	34.6319916237495	-0.138701840871005	-0.138701840871004	-0.3327193574072
6	43	37.8293313222563	0.000285651134746523	0.000285651134746358	0.380389932955922
7	82	54.734562025283	0.626347555767166	0.626347555767167	1.9516353763694
8	40	49.2755392508467	0.420159259664933	0.420159259664934	-0.708355611710072
9	47	48.5140116965764	0.382764613514644	0.382764613514644	-0.138315763526530
10	19	37.4418371634876	0.0393364718001919	0.0393364718001923	-1.34590443540171
11	52	42.9493181781026	0.196170714087929	0.196170714087929	0.644219300663072
12	136	78.0934249057448	1.16140934013955	1.16140934013954	4.12554663337715
13	80	82.4051287930136	0.803640420332868	-8.84004462743343	0.530695402530692
14	42	65.051615442622	0.054273280851314	0.0542732923496264	-1.77265794694282
15	54	60.6291424886599	-0.0882433345724525	-0.0882433345724519	-0.486103539806455
16	66	62.6908736996413	-0.0332820757823487	-0.0332820757823507	0.244989301253035
17	81	69.6599818490697	0.117306975926682	0.117306975926684	0.817044166716085
18	63	67.173086793437	0.068127454075331	0.0681274540753288	-0.307662325615526
19	137	93.436457744403	0.517994320813638	0.51799432081364	3.11620637069108
20	72	85.5313682814193	0.382947131209116	0.382947131209119	-1.00607627066500
21	107	93.6608382016142	0.501077588735697	0.501077588735698	0.927553150304634
22	58	80.4581273309898	0.299769558235157	0.299769558235159	-1.64344369789465
23	36	63.9281948750631	0.0595839504322536	0.0595839504322537	-2.02042796921583
24	52	59.4900687050896	-0.00312604141474305	-0.00312604141474494	-0.54034738640311
25	79	65.1690060817906	-0.296958217294313	3.26654037666748	0.816165036492403
26	77	69.9327176397053	-0.163315956956953	-0.163315945538547	0.539272850845273
27	54	63.696619994003	-0.286952611242518	-0.286952611242513	-0.689459781792143
28	84	71.361935332189	-0.15642868646816	-0.156428686468162	0.929872419065168
29	48	62.5185472204572	-0.27701760417005	-0.277017604170044	-1.03081304168558
30	96	74.99792153124	-0.120646343560960	-0.120646343560963	1.52551249389633
31	83	77.9573560741685	-0.086122045861719	-0.0861220458617164	0.369942030387921
32	66	73.4756554444656	-0.132363334029531	-0.132363334029526	-0.529281929120724
33	61	68.7937784664887	-0.178117812451621	-0.178117812451619	-0.548659557065844
34	53	62.8678232306838	-0.234077744574632	-0.23407774457463	-0.693843428894893
35	30	50.5756044641092	-0.348675414200005	-0.348675414200005	-1.45649856561658
36	74	59.2017176099048	-0.264959393837943	-0.264959393837946	1.08452209496677
37	69	61.189327525208	-0.34037124629908	3.74408369789963	0.306763102795221
38	59	60.2507045095553	-0.351987476238834	-0.351987464594544	-0.0662477595957704
39	42	53.1654628542818	-0.452754206075758	-0.452754206075749	-0.78005018736377
40	65	57.5263499840013	-0.394569177253741	-0.394569177253741	0.569637436002642
41	70	62.1044147754013	-0.343605642903206	-0.3436056429032	0.594750080138199
42	100	76.1680237933218	-0.212868237585064	-0.212868237585066	1.73293648170817
43	63	71.2077589022676	-0.252364401924127	-0.252364401924125	-0.572834761579663
44	105	83.7167842250474	-0.152464757185518	-0.152464757185516	1.54266391728603
45	82	83.0393997660179	-0.156402951636845	-0.156402951636844	-0.0635256883153927
46	81	82.2413690241213	-0.161074345587819	-0.161074345587817	-0.077703578920588
47	75	79.5098014807554	-0.179386215058064	-0.179386215058065	-0.311435531989578
48	102	87.817101121155	-0.119908849226321	-0.119908849226324	1.02853430266919
49	121	98.1776819780824	-0.379309693215247	4.17240660863431	1.38956317156998
50	98	98.0252747333647	-0.375826144700893	-0.375826128063804	0.0256752879138793
51	76	89.551325373142	-0.471636492175208	-0.471636492175192	-0.949061242123092
52	77	84.7142597088665	-0.513397339314662	-0.513397339314667	-0.520171725959087
53	63	76.4679979380646	-0.57621152654483	-0.576211526544819	-0.92914821466339
54	37	61.6153896802288	-0.679051361969763	-0.679051361969767	-1.72302404363950
55	35	51.5421475741942	-0.741189251124894	-0.741189251124895	-1.13660580672317
56	23	40.7466735646845	-0.803861378156782	-0.803861378156778	-1.21823086562640
57	40	40.2625458498069	-0.801948473910867	-0.801948473910865	0.0387744205668611
58	29	35.8754051069844	-0.822793631044984	-0.822793631044981	-0.435015163561924
59	37	36.0806287449173	-0.81693680382169	-0.816936803821692	0.124779247320516
60	51	41.405660226982	-0.782464317764402	-0.782464317764406	0.745672601407395
61	20	30.8153767243869	-0.589909230883511	6.48900153172886	-1.27958596784119
62	28	29.5928933011181	-0.597939930575799	-0.597939922317725	-0.0725786997937341
63	13	23.1643183013491	-0.654977400054657	-0.654977400054645	-0.688443430875821
64	22	22.5639431191055	-0.654545208976197	-0.654545208976197	0.00653550317436943
65	25	23.3065338765099	-0.645145845616579	-0.645145845616572	0.168379161783554
66	13	19.3067220149444	-0.665180438385684	-0.665180438385686	-0.405776123043721
67	16	17.9072739317707	-0.66921117713715	-0.66921117713715	-0.088997803780667
68	13	15.9132662950303	-0.676071448510229	-0.676071448510227	-0.160764796809479
69	16	15.7710514277892	-0.673415470849055	-0.673415470849053	0.0648302167189355
70	17	16.0530129272959	-0.668791874403398	-0.668791874403395	0.116070036876449
71	9	13.2647382145488	-0.678852258164591	-0.678852258164592	-0.257571298837704
72	17	14.4742987179818	-0.670013553542685	-0.670013553542688	0.229532516514790
73	25	15.1084490094974	-0.691230652088033	7.60353716587865	0.168326295267939
74	14	14.5224713530272	-0.690091594327187	-0.690091587282125	0.0121933502314133
75	8	11.8921752047423	-0.706269825998804	-0.706269825998792	-0.230287014248023
76	7	9.88411967373391	-0.715057228451049	-0.71505722845105	-0.156306743395605
77	10	9.74499100434729	-0.711750641328668	-0.711750641328663	0.0695565566603299
78	7	8.5420505110587	-0.714255872149867	-0.71425587214987	-0.0595069932557826
79	10	8.89933968941821	-0.709228709422989	-0.709228709422987	0.130042001338295
80	3	6.52786353021991	-0.71659043105388	-0.716590431053878	-0.201933369779238

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676632yq6ebxmw3ml58wk/1ttdj1292676751.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676632yq6ebxmw3ml58wk/1ttdj1292676751.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676632yq6ebxmw3ml58wk/2m3cn1292676751.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676632yq6ebxmw3ml58wk/2m3cn1292676751.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676632yq6ebxmw3ml58wk/3ecb71292676751.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676632yq6ebxmw3ml58wk/3ecb71292676751.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676632yq6ebxmw3ml58wk/4ecb71292676751.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676632yq6ebxmw3ml58wk/4ecb71292676751.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676632yq6ebxmw3ml58wk/57lta1292676751.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676632yq6ebxmw3ml58wk/57lta1292676751.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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