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Structurele tijdreeksanalyse

*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 13:17:38 -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/t12599579270sn5baasod7iqjo.htm/, Retrieved Fri, 04 Dec 2009 21:18:54 +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/t12599579270sn5baasod7iqjo.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 «

96.2 96.8 109.9 88 91.1 106.4 68.6 100.1 108 106 108.6 91.5 99.2 98 96.6 102.8 96.9 110 70.5 101.9 109.6 107.8 113 93.8 108 102.8 116.3 89.2 106.7 112.1 74.2 108.8 111.5 118.8 118.9 97.6 116.4 107.9 121.2 97.9 113.4 117.6 79.6 115.9 115.7 129.1 123.3 96.7 121.2 118.2 102.1 125.4 116.7 121.3 85.3 114.2 124.4 131 118.3 99.6

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	96.2	96.2	0	0	0
2	96.8	96.1618718674227	-0.0126865109502198	0.490971693927525	0.0860215771604872
3	109.9	97.8564999577522	0.415593165275395	9.04013220229752	1.88304573603355
4	88	97.4162637686966	0.243871635195932	-8.77666105585891	-0.467456494185525
5	91.1	95.9179594030788	-0.0478297635721764	-3.649505879799	-0.896635600476273
6	106.4	97.4000538363749	0.17240902805175	7.82665540008514	0.887236300630742
7	68.6	93.0761071637469	-0.39705910807483	-20.3647862701227	-3.02520531147466
8	100.1	92.4915161993549	-0.418355535605214	7.81037705660163	-0.144907068863579
9	108	94.2436324968791	-0.193690153377554	11.0653809205561	1.89289485129042
10	106	96.3097186045542	0.0226756511316319	6.53086081937811	2.18718107318629
11	108.6	98.5589473476668	0.222548005517190	6.59344450769942	2.35656974113319
12	91.5	98.7959961106165	0.223783704608746	-7.32048451049012	0.0165692232562231
13	99.2	99.1946555421075	0.23879406333943	-0.361311224508351	0.251425387781672
14	98	99.5063921747267	0.244841122722130	-1.65666462809965	0.103254253493782
15	96.6	98.5311789397447	0.148176546067776	0.429629625185758	-1.64136365338194
16	102.8	99.177374137127	0.186162272036182	2.68368415669078	0.658413845685761
17	96.9	99.6074663127151	0.204282120150003	-3.17105764207554	0.326128408301993
18	110	99.8058849262497	0.203853090494484	10.2055444122462	-0.00803834350682169
19	70.5	99.3895806803128	0.158724877253652	-27.643454931696	-0.874814263939484
20	101.9	99.214910696614	0.134415793260070	3.37457341488604	-0.482935013575488
21	109.6	99.474842162035	0.143636019143187	9.8592391982428	0.185851046967658
22	107.8	100.019340977883	0.173418338886763	6.91563510066174	0.603463025216093
23	113	100.842876017541	0.222414465362532	10.7366334685519	0.989712451552453
24	93.8	101.332643981784	0.242913984386474	-8.1209633866507	0.409803892376178
25	108	102.166037360142	0.289173366611608	4.52930922631014	0.910942360346007
26	102.8	102.746020876557	0.312140769626003	-0.58890729822394	0.448987937159778
27	116.3	104.251771807746	0.406425923644514	9.419881862065	1.83654106272736
28	89.2	103.747071849922	0.33460597814422	-12.5487334222503	-1.39746185411897
29	106.7	104.156940012441	0.340529275078233	2.37835433058455	0.115256056455591
30	112.1	104.262550827013	0.322044120001502	8.35104105603378	-0.359528535384603
31	74.2	104.292926254431	0.299068910279756	-29.4558214110489	-0.446067606127425
32	108.8	104.631647896674	0.302198267893774	4.08186950604718	0.0605569658307478
33	111.5	104.819655511912	0.293166485923085	6.92876961709984	-0.173977527971904
34	118.8	105.741217924979	0.342978007734423	11.6964125290637	0.954345355142754
35	118.9	106.602049505399	0.384106530537516	11.1798385589076	0.783587309827782
36	97.6	107.222912647773	0.402938249704697	-10.1319977051131	0.357005752894727
37	116.4	108.150064416429	0.44467166797434	7.1262473391355	0.788577828270827
38	107.9	108.890722497397	0.468232998680826	-1.62449478113955	0.444878281818144
39	121.2	109.560412632243	0.484257925108361	11.2083084306094	0.302742568850661
40	97.9	110.163421018652	0.493695495040898	-12.5176635821778	0.178470242512235
41	113.4	110.714557173504	0.498256934967044	2.56243432593885	0.0863503235913144
42	117.6	111.084296498494	0.488057057509401	6.79100019038001	-0.193258835069785
43	79.6	111.379627302688	0.472766923304771	-31.3667180926021	-0.289865666753695
44	115.9	111.848514414966	0.472459186529135	4.05979741397394	-0.00583501386620065
45	115.7	112.185954999426	0.46175170138517	3.80319938640297	-0.202995190229790
46	129.1	113.087966185628	0.496659804722177	15.0697168732532	0.661574159950877
47	123.3	113.727488602608	0.507984918249084	9.266931309824	0.214560500698876
48	96.7	113.805698924191	0.473924118810532	-16.1868955791862	-0.64521022119424
49	121.2	114.150456645875	0.463689844631275	7.32562972000344	-0.193895446725384
50	118.2	115.003727334308	0.4945524558364	2.36345909196876	0.584900465992555
51	102.1	113.704295236885	0.352442535840324	-7.76838182324776	-2.69398427703633
52	125.4	115.231496429980	0.445501248769139	7.6561465727521	1.76441185270405
53	116.7	115.971275072241	0.46881352250293	0.0992920029997628	0.442045513572269
54	121.3	116.382844534991	0.464278470066391	5.03960894674692	-0.0859985382032313
55	85.3	116.816002999918	0.461812925503101	-31.4494268200874	-0.0467563664472438
56	114.2	116.703532201626	0.416312728777668	-1.27488356153166	-0.862879213363571
57	124.4	117.207534760243	0.42326039503103	7.00485787476883	0.131756650380654
58	131	117.499488072170	0.412857129964083	13.781422607914	-0.197284711475872
59	118.3	117.078653088423	0.346807506490044	3.00478326692709	-1.25253114634782
60	99.6	116.927219368448	0.307335914110379	-16.2614276120992	-0.748532767038808

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599579270sn5baasod7iqjo/1q3u61259957855.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599579270sn5baasod7iqjo/1q3u61259957855.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599579270sn5baasod7iqjo/2eh9m1259957855.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599579270sn5baasod7iqjo/2eh9m1259957855.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599579270sn5baasod7iqjo/3zuf51259957855.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599579270sn5baasod7iqjo/3zuf51259957855.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599579270sn5baasod7iqjo/4s9mk1259957855.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599579270sn5baasod7iqjo/4s9mk1259957855.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599579270sn5baasod7iqjo/5rp4k1259957855.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599579270sn5baasod7iqjo/5rp4k1259957855.ps (open in new window)

Parameters (Session):

par1 = FALSE ; par2 = 0.0 ; par3 = 0 ; par4 = 0 ; par5 = 1 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = 12 ; par2 = 0.0 ; par3 = 0 ; par4 = 0 ; par5 = 1 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

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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