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verbetering workshop

*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, 05 Dec 2009 09:00:54 -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/05/t1260028897cea2wu14s537ruu.htm/, Retrieved Sat, 05 Dec 2009 17:01:44 +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/05/t1260028897cea2wu14s537ruu.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 «

29.837 29.571 30.167 30.524 30.996 31.033 31.198 30.937 31.649 33.115 34.106 33.926 33.382 32.851 32.948 36.112 36.113 35.210 35.193 34.383 35.349 37.058 38.076 36.630 36.045 35.638 35.114 35.465 35.254 35.299 35.916 36.683 37.288 38.536 38.977 36.407 34.955 34.951 32.680 34.791 34.178 35.213 34.871 35.299 35.443 37.108 36.419 34.471 33.868 34.385 33.643 34.627 32.919 35.500 36.110 37.086 37.711 40.427 39.884 38.512 38.767

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	29.837	29.837	0	0	0
2	29.571	29.5904101308721	-0.0133242641154774	-0.0194101308721427	-0.17267886155883
3	30.167	30.1047348709764	-0.00590938438004926	0.0622651290236483	0.605236883566329
4	30.524	30.501641415157	-0.00417903817515478	0.0223585848429797	0.465018942038387
5	30.996	30.9600424996861	-0.00214542395880076	0.0359575003138614	0.533814451603976
6	31.033	31.043723916878	-0.0017382534660389	-0.0107239168779781	0.0990378455712055
7	31.198	31.1855986527485	-0.00105329284670538	0.01240134725154	0.165718174423698
8	30.937	30.9703722757926	-0.00206851236682920	-0.0333722757925578	-0.247137480390282
9	31.649	31.5683254629245	0.000760661631391484	0.0806745370755246	0.692367551695651
10	33.115	32.9669743553856	0.00732074457535167	0.148025644614448	1.61301391693064
11	34.106	34.0269515623201	0.0122377149963033	0.0790484376798525	1.21464204037963
12	33.926	33.9662886000061	0.0118987770802128	-0.0402886000060978	-0.0841180582279495
13	33.382	33.6425632170615	0.0253993900842476	-0.260563217061456	-0.454947411146312
14	32.851	32.9963494146691	0.0118802614321193	-0.145349414669093	-0.682761335064029
15	32.948	32.8862609121358	0.0104368688178878	0.0617390878641806	-0.138488356549693
16	36.112	35.6011716934161	0.0204899103276316	0.510828306583931	3.11982486439929
17	36.113	36.1232866813358	0.0216534239626073	-0.0102866813358405	0.578398052153586
18	35.21	35.3959929638746	0.0197223411603790	-0.185992963874635	-0.86342346026361
19	35.193	35.1273930394818	0.0189295563011004	0.0656069605182098	-0.332379312508679
20	34.383	34.5168948730419	0.0171866649800633	-0.133894873041891	-0.72560701664154
21	35.349	35.2129543572521	0.0190679567375678	0.136045642747878	0.782616549966365
22	37.058	36.7644373884433	0.0234988685972021	0.293562611556678	1.76682355991305
23	38.076	37.8789140331341	0.0267545980794345	0.197085966865948	1.25817438197546
24	36.63	36.8881578316953	0.026927063691355	-0.258157831695279	-1.17198633735714
25	36.045	36.3208491121141	0.0360231237032054	-0.27584911211414	-0.728019554868669
26	35.638	35.8028441233771	0.0303057283601594	-0.164844123377064	-0.604216925552623
27	35.114	35.4922457904971	0.0269064083517163	-0.378245790497122	-0.385678633268819
28	35.465	35.1078416922563	0.0251432391818264	0.357158307743745	-0.474070437016384
29	35.254	35.1309489535522	0.0251389230540861	0.123051046447826	-0.00234747396731997
30	35.299	35.3786071674434	0.0255792414598661	-0.079607167443401	0.256492010881536
31	35.916	35.6643018539543	0.0261433222416476	0.251698146045702	0.299795559696390
32	36.683	36.6809883039277	0.0283903192065436	0.00201169607229421	1.14162873663866
33	37.288	37.3092512456262	0.0298066248179012	-0.0212512456261751	0.691426217013282
34	38.536	38.2783273461031	0.0321700172215651	0.257672653896938	1.08289814159513
35	38.977	38.6287451142632	0.032820453995601	0.348254885736786	0.366842156635779
36	36.407	36.9702696155171	0.0354773476946675	-0.563269615517122	-1.94993671953989
37	34.955	35.4687542868128	0.0472402791711909	-0.513754286812814	-1.82215152704289
38	34.951	35.07878508354	0.0445433039359075	-0.12778508354	-0.489271776903668
39	32.68	33.3147926521765	0.0296570253826546	-0.634792652176517	-2.04713670542767
40	34.791	34.1383983148975	0.0333005671247641	0.652601685102509	0.91392495796003
41	34.178	34.0906078429291	0.0331142109332699	0.0873921570709282	-0.093497319211094
42	35.213	35.0898105368989	0.0348343563975296	0.123189463101124	1.11357427407276
43	34.871	34.8665057845926	0.0343493886836417	0.00449421540735816	-0.297500737433527
44	35.299	35.3057854497249	0.0351771726574544	-0.00678544972484978	0.466664131864935
45	35.443	35.6174933975068	0.0357874675447234	-0.174493397506826	0.318735557420587
46	37.108	36.7253405555779	0.0381806037267405	0.382659444422126	1.23593266476961
47	36.419	36.023511746824	0.0372140943133252	0.395488253175966	-0.852612568478296
48	34.471	35.0022252219839	0.0391053647949012	-0.531225221983899	-1.22132885973800
49	33.868	34.3673970309607	0.0419179790588139	-0.499397030960679	-0.787685747978609
50	34.385	34.1755654701942	0.0409393911958756	0.209434529805770	-0.264536020689637
51	33.643	34.3482376603558	0.0418350390695929	-0.705237660355794	0.149409793657213
52	34.627	34.0284823411038	0.040171274224534	0.59851765889616	-0.415727421471527
53	32.919	33.1735109419128	0.0379213262905128	-0.25451094191284	-1.03196235268091
54	35.5	34.8910700360098	0.0408777505529137	0.60892996399022	1.93610259308469
55	36.11	35.9834883657279	0.0427021565001535	0.126511634272121	1.21189797710201
56	37.086	36.9988758029568	0.0445614216097969	0.087124197043184	1.12100074569387
57	37.711	37.9519740959720	0.0464443933682000	-0.240974095972049	1.04722999082765
58	40.427	39.6105191373181	0.0495555495900842	0.816480862681941	1.85825418146147
59	39.884	39.4368915368361	0.0493750269561307	0.447108463163941	-0.257076126273345
60	38.512	39.0578136710353	0.0500524703842053	-0.545813671035337	-0.494451743531779
61	38.767	39.2268060893307	0.0497749784745955	-0.459806089330737	0.138046923122567

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260028897cea2wu14s537ruu/1kgi81260028851.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260028897cea2wu14s537ruu/1kgi81260028851.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260028897cea2wu14s537ruu/2lr6v1260028851.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260028897cea2wu14s537ruu/2lr6v1260028851.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260028897cea2wu14s537ruu/3rgoq1260028851.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260028897cea2wu14s537ruu/3rgoq1260028851.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260028897cea2wu14s537ruu/432t61260028851.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260028897cea2wu14s537ruu/432t61260028851.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260028897cea2wu14s537ruu/5y3yp1260028851.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/05/t1260028897cea2wu14s537ruu/5y3yp1260028851.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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