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Structural maandelijkse huwelijken

*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: Sun, 19 Dec 2010 09:00:25 +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/19/t129274914435nokj4pilt22e0.htm/, Retrieved Sun, 19 Dec 2010 09:59:11 +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/19/t129274914435nokj4pilt22e0.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 «
3111 3995 5245 5588 10681 10516 7496 9935 10249 6271 3616 3724 2886 3318 4166 6401 9209 9820 7470 8207 9564 5309 3385 3706 2733 3045 3449 5542 10072 9418 7516 7840 10081 4956 3641 3970 2931 3170 3889 4850 8037 12370 6712 7297 10613 5184 3506 3810 2692 3073 3713 4555 7807 10869 9682 7704 9826 5456 3677 3431 2765 3483 3445 6081 8767 9407 6551 12480 9530 5960 3252 3717 2642 2989 3607 5366 8898 9435 7328 8594 11349 5797 3621 3851
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
131113111000
239953612.8827132778488.485601609452139.6419794182420.620189542035713
352455006.45705586261081.2711499928189.10848137015120.572527068887661
455885743.84885630487862.125116581925-99.9421133704833-0.215281102215225
5106819642.940233469042812.7211173582559.0318018873491.86347004929820
61051611121.04724207871955.49656800710-396.734039870185-0.814360226394706
774968825.7241469518-777.585402586055-665.404896527415-2.59759688442302
899359281.9559906364716.2985863715707460.0759247737710.754510604173224
91024910056.1528182779503.99951020950474.31627652311110.463476299882457
1062717365.66708819162-1551.39659789881-595.132195777487-1.95328656788902
1136164025.62514647689-2702.15611401201-129.949019976698-1.09359249350097
1237243044.82566983615-1594.71609317863410.0264870834641.05242470700327
1328862486.99169218527-943.350248229637240.7124384721270.678557196445312
1433182905.40921354793-135.699925167198247.0070930576140.726065431327129
1541663737.89494658164444.853435525258296.4568896921170.56071052595083
1664016377.463067975721806.62825228939-285.1313620209201.29363633827189
1792098490.685317756021996.4021415759675.7958503378950.178869417356362
1898209763.73784502511549.84297605897157.021975453824-0.423405146846143
1974709186.05330072487234.051729130800-1418.05246931372-1.25048210857331
2082078276.98282280701-473.93103701909490.3921004331875-0.672827970709785
2195648563.54853643257-2.75179268249906893.7290215418470.447757498171943
2253096381.09526547425-1353.07287452423-766.23079770436-1.2832538466505
2333853992.71691558194-1994.03137885859-462.53688099803-0.609117291309971
2437062985.20391654292-1384.49182222250582.7031038171110.58013292969877
2527332482.90683865989-839.434633585993125.3260020192490.532934471486548
2630452683.53531007380-213.210724730631226.3035511622680.583363512011349
2734493405.59943920103346.098657533172-80.46201155133740.534857700407542
2855425401.354976141611352.49251879154-84.14864810444850.960372141135581
29100728652.615965505182513.150791292491162.848930101811.09755123053373
3094189541.189572140841523.1164154425895.9834983456691-0.938194337190754
3175169353.0419373926479.62237999601-1605.19275633586-0.991419371310645
3278408410.24716402446-389.062976705659-377.104253989255-0.825579731308563
33100818419.58764217559-145.5975115763171607.286874475110.231364888819556
3449566223.907636558-1398.15090957433-989.438051854332-1.19033085292967
3536414325.72522003029-1703.32713323455-616.880953781665-0.290064996583784
3639703238.19767537723-1327.96454301442648.263888171350.357711172156726
3729312703.31359699742-843.190162248884119.2157520696480.465387849269072
3831702829.66577232126-258.374032740858212.5872993252360.550849466717508
3938893983.96087908827587.470541468975-281.2113749386110.80579458235679
4048505264.870704534891007.32103940934-508.0443843702750.400751969095235
4180376489.296340828281139.221107834061518.629427954060.125042567003883
421237010742.69214211413026.787240388091211.709764157761.78898381848514
4367129759.16905103896596.752782097315-2510.72086596432-2.30796268443930
4472978350.23046327393-620.07791911936-784.324438665361-1.15641550144057
45106138200.4942155982-334.5231211994072349.401243054060.271369112718619
4651846521.2315631491-1150.72649051446-1156.85866327522-0.775666692127054
4735064464.29799100156-1700.20885484243-836.850789054728-0.522438247213406
4838103100.71491926352-1496.13658083475664.1187088751280.194467277053269
4926922453.83481865599-980.292614557232123.8325955261450.49209310630182
5030732725.90165906752-225.489507274648181.6853907198000.713900863345525
5137133750.94333846790524.035897149407-202.7316056051610.713021622804519
5245555079.657518978691009.39869587636-631.9791201777560.462930348129642
5378076832.955271630721459.66748888408874.8682437300420.427520908671153
54108698613.179046822881653.409497741532213.234633229480.183710129756517
55968211385.65698820432329.24534408472-1852.439508158120.641722926439211
56770410059.3187176853119.888134200352-1868.38793580562-2.09946329941921
5798267831.79311468685-1299.717723122502307.13586034375-1.34911383900893
5854566306.45038806238-1436.13364121289-820.378547042119-0.129651744926658
5936774602.27645309638-1598.09125948034-889.562088178773-0.154025322410853
6034312917.08268668110-1650.74161669649525.54055657661-0.0501450902934945
6127652439.02982233213-941.20474857984169.3897104679170.675208864974534
6234833036.25713096270-15.0678939724961243.5851302637760.877526374428028
6334453670.20234969692374.101696986122-310.7213580023810.370012529483473
6460816194.042783553051668.03785286121-398.5347660679241.23314967659956
6587678163.634817334551850.10197382981563.2797749329820.173014768934893
6694078214.341749782764.5572702297961431.15646748956-1.02988108971288
6765517852.619582537685.7453517588755-1152.38354595725-0.644468027014579
681248012141.25525629462619.53841609243-218.8703459516392.40746254299608
6995309351.01383181355-643.280446847622897.279008057147-3.10085235725312
7059607006.63878269136-1669.13408875546-820.769986925271-0.975110854149383
7132524349.17660043382-2265.00816273368-965.921265147465-0.566765490942552
7237173029.0424727204-1694.97407893364562.3061554714140.542600600387632
7326422429.10663139549-1034.1257213004867.3748505505730.628219483552403
7429892565.09861081663-330.488691603592269.5012465763330.667305291446075
7536073909.19209567202673.674180482145-522.8067076830060.95451709240853
7653665516.295154385071234.74618543120-273.9244597382830.534382161751387
7788987618.759903898141757.663349909551164.146331046780.497148175427193
7894358432.836908484951189.201349915791127.04301909470-0.539563338112475
7973289519.740077827561127.63291556398-2178.21376343124-0.0584518175544107
8085948327.30874113942-268.547958961703573.672040946175-1.32641207593223
81113499351.0037102388509.2893074522631826.895030554440.739233760317641
8257977312.85060997764-1024.07333385544-1178.46392321495-1.45770336996176
8336215075.71319716074-1754.31216075792-1293.96449646832-0.69459643242334
8438513410.20838367394-1700.81546488972429.0114756520040.0508986538457064
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/19/t129274914435nokj4pilt22e0/1jme91292749221.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t129274914435nokj4pilt22e0/1jme91292749221.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t129274914435nokj4pilt22e0/2cvvc1292749221.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t129274914435nokj4pilt22e0/2cvvc1292749221.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t129274914435nokj4pilt22e0/3cvvc1292749221.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t129274914435nokj4pilt22e0/3cvvc1292749221.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t129274914435nokj4pilt22e0/45muf1292749221.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t129274914435nokj4pilt22e0/45muf1292749221.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t129274914435nokj4pilt22e0/55muf1292749221.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t129274914435nokj4pilt22e0/55muf1292749221.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')
 





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