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*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: Tue, 29 Dec 2009 04:03:45 -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/29/t12620846979mzn2rcb95lbrtu.htm/, Retrieved Sat, 25 May 2013 05:09:22 +0000   Original text written by user:   IsPrivate? No (this computation is public)   User-defined keywords:   System-generated keywords (parent): t1259940802qchy5nhwej2s3uh (pk = 63768) Estimated Impact 34   Dataseries X: » Textfile « » CSV « » Stem and Leaf « » Histogram « » Kernel Density « » Harrell-Davis Quantiles « » Central Tendency « » Variability « 8 8,1 7,7 7,5 7,6 7,8 7,8 7,8 7,5 7,5 7,1 7,5 7,5 7,6 7,7 7,7 7,9 8,1 8,2 8,2 8,2 7,9 7,3 6,9 6,6 6,7 6,9 7 7,1 7,2 7,1 6,9 7 6,8 6,4 6,7 6,6 6,4 6,3 6,2 6,5 6,8 6,8 6,4 6,1 5,8 6,1 7,2 7,3 6,9 6,1 5,8 6,2 7,1 7,7 7,9 7,7 7,4 7,5 8 8,1   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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Structural Time Series Model t Observed Level Slope Seasonal Stand. Residuals 1 8 8 0 0 0 2 8.1 8.0992534482645 0.099315146768711 0.000746551735494343 0.312144661532379 3 7.7 7.71033729004476 -0.378431686426799 -0.0103372900447557 -1.51214927172144 4 7.5 7.49518144618358 -0.219599500213875 0.00481855381641875 0.497206469501047 5 7.6 7.59543379667154 0.0916915119177725 0.00456620332845658 0.974888938016866 6 7.8 7.79997542952185 0.201528226306382 2.45704781487947e-05 0.343976132170661 7 7.8 7.8047055047012 0.00998749078310632 -0.00470550470120262 -0.599848918670261 8 7.8 7.79990695798508 -0.00440352846728825 9.30420149202435e-05 -0.0450684147140476 9 7.5 7.50581770169191 -0.286350290163513 -0.00581770169191252 -0.88297384336456 10 7.5 7.49414521701395 -0.0190105117436494 0.00585478298605043 0.837229093239738 11 7.1 7.10858667133196 -0.37576619516409 -0.00858667133196044 -1.11725325390524 12 7.5 7.484997974067 0.356316841173229 0.0150020259329987 2.2926674822242 13 7.5 7.50864261038258 0.032587076773531 -0.00864261038258466 -1.01407990410654 14 7.6 7.58491489562795 0.0751181182763844 0.0150851043720510 0.133567918038366 15 7.7 7.7084760605823 0.121471978101266 -0.00847606058229988 0.146272311098007 16 7.7 7.70545524508715 0.00261434002097083 -0.00545524508714855 -0.371955138449220 17 7.9 7.89464456833885 0.180749508662983 0.00535543166115181 0.557902812678462 18 8.1 8.09489680165998 0.199372469606793 0.00510319834001557 0.0583215090833516 19 8.2 8.21219543390285 0.121001741196476 -0.0121954339028472 -0.245434002944632 20 8.2 8.1909994410428 -0.0147791354599226 0.009000558957195 -0.425225530166752 21 8.2 8.21852473280732 0.0256162848724262 -0.0185247328073169 0.126506505635344 22 7.9 7.88249894272392 -0.319706625173862 0.0175010572760754 -1.08144919353469 23 7.3 7.33869729193712 -0.533690185774264 -0.0386972919371166 -0.670133269467272 24 6.9 6.87140224212641 -0.470291519574783 0.028597757873589 0.198546043256021 25 6.6 6.60311922778425 -0.277433260941253 -0.00311922778424517 0.604149594912838 26 6.7 6.6797710450136 0.0608008977403762 0.0202289549863973 1.06092958645746 27 6.9 6.89514738117445 0.206784535759022 0.0048526188255462 0.459307752845182 28 7 7.01767499004384 0.127210418477692 -0.0176749900438427 -0.249046153952195 29 7.1 7.09884793220702 0.0837509293643512 0.00115206779297903 -0.136111223030652 30 7.2 7.19318721825769 0.0937482246273258 0.00681278174231 0.0313085411199649 31 7.1 7.1100720075795 -0.07324315511923 -0.0100720075795037 -0.52296789058124 32 6.9 6.9055879594375 -0.197156910422666 -0.00558795943749705 -0.38806121776312 33 7 6.99642175712162 0.0747549074142069 0.00357824287837512 0.85154738176011 34 6.8 6.78424673668332 -0.196156010514188 0.0157532633166833 -0.848412853688194 35 6.4 6.44173983614233 -0.334337351626327 -0.041739836142333 -0.432743423377657 36 6.7 6.64964151052286 0.177618078097179 0.0503584894771431 1.60329587906902 37 6.6 6.62624203274371 -0.0121423223956272 -0.0262420327437118 -0.594487171643204 38 6.4 6.39927632215368 -0.215023499150526 0.000723677846320193 -0.635800922086482 39 6.3 6.28504196179206 -0.120512429444735 0.0149580382079447 0.296796859166133 40 6.2 6.21239006778479 -0.0755941333569787 -0.0123900677847891 0.140618085687078 41 6.5 6.49161311656742 0.25718047654175 0.0083868834325828 1.04216848423040 42 6.8 6.7862481165473 0.292315279718560 0.0137518834527053 0.110032561872672 43 6.8 6.80639498936865 0.0369978373522829 -0.0063949893686552 -0.799578495942069 44 6.4 6.44677828751598 -0.335055756823133 -0.0467782875159820 -1.16516196744487 45 6.1 6.08224180870059 -0.362710690296217 0.0177581912994072 -0.0866070734016949 46 5.8 5.75679966834911 -0.327750061018043 0.0432003316508939 0.109486350054354 47 6.1 6.14911730473386 0.347731453096235 -0.0491173047338628 2.11541274740739 48 7.2 7.12316501016608 0.93523583883227 0.076834989833919 1.83989654065104 49 7.3 7.34662678923473 0.267642099046376 -0.0466267892347286 -2.09154344595480 50 6.9 6.93040979468902 -0.373894943904836 -0.0304097946890245 -2.00952585364868 51 6.1 6.09048338595636 -0.809120969827583 0.00951661404364303 -1.36527912941267 52 5.8 5.80665831083743 -0.31803189808199 -0.00665831083743158 1.53770947365362 53 6.2 6.17444559647432 0.322660083497355 0.0255544035256774 2.0063694962774 54 7.1 7.06253399377584 0.850975541906616 0.0374660062241608 1.65455577486633 55 7.7 7.69946956489358 0.650976480496868 0.00053043510642342 -0.626336834773619 56 7.9 7.96149209663143 0.287541529634192 -0.0614920966314265 -1.13817113772244 57 7.7 7.68002448200592 -0.244131220656649 0.0199755179940838 -1.66504180735822 58 7.4 7.38064957142924 -0.295750353071808 0.0193504285707568 -0.161655880159988 59 7.5 7.58745451312257 0.173834195968218 -0.0874545131225718 1.47060399206747 60 8 7.89896132520757 0.302465200781383 0.101038674792426 0.402838373145633 61 8.1 8.12341871212448 0.229583418603231 -0.0234187121244858 -0.228334511141484   Charts produced by software: http://www.freestatistics.org/blog/date/2009/Dec/29/t12620846979mzn2rcb95lbrtu/1kw1j1262084620.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620846979mzn2rcb95lbrtu/1kw1j1262084620.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2009/Dec/29/t12620846979mzn2rcb95lbrtu/2z0un1262084620.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620846979mzn2rcb95lbrtu/2z0un1262084620.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2009/Dec/29/t12620846979mzn2rcb95lbrtu/3avih1262084620.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620846979mzn2rcb95lbrtu/3avih1262084620.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2009/Dec/29/t12620846979mzn2rcb95lbrtu/45ufx1262084620.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620846979mzn2rcb95lbrtu/45ufx1262084620.ps (opens in new window) Click here to open pdf file. http://www.freestatistics.org/blog/date/2009/Dec/29/t12620846979mzn2rcb95lbrtu/5401g1262084620.png (opens in new window) http://www.freestatistics.org/blog/date/2009/Dec/29/t12620846979mzn2rcb95lbrtu/5401g1262084620.ps (opens in new window) Click here to open pdf file.   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')