WS9

*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, 06 Dec 2009 12:44:42 -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/06/t1260128724pv1txpb3h20remo.htm/, Retrieved Sun, 06 Dec 2009 20:45:31 +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/06/t1260128724pv1txpb3h20remo.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 «

286602 283042 276687 277915 277128 277103 275037 270150 267140 264993 287259 291186 292300 288186 281477 282656 280190 280408 276836 275216 274352 271311 289802 290726 292300 278506 269826 265861 269034 264176 255198 253353 246057 235372 258556 260993 254663 250643 243422 247105 248541 245039 237080 237085 225554 226839 247934 248333 246969 245098 246263 255765 264319 268347 273046 273963 267430 271993 292710 295881 293299

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 286602 286602 0 0 0 2 283042 283653.978487577 -193.757637766118 -611.978487576924 -0.512602058586361 3 276687 278740.990211037 -626.91648573849 -2053.99021103726 -1.10434425180643 4 277915 277704.864198171 -673.55551718559 210.135801828790 -0.101330739637021 5 277128 277273.640546886 -640.785948258969 -145.640546885858 0.0584370187608226 6 277103 277214.827411661 -551.988562908826 -111.827411661256 0.137775381399521 7 275037 275811.948393772 -692.421269421379 -774.948393772297 -0.199101667700537 8 270150 271753.599507441 -1275.95775947025 -1603.59950744087 -0.781314140029168 9 267140 267985.936032108 -1721.42278317639 -845.936032107815 -0.575202190918298 10 264993 265328.515582245 -1892.03633889931 -335.515582244946 -0.215266548845033 11 287259 280406.358926374 1239.59753561958 6852.64107362596 3.8931729128653 12 291186 290440.110659114 2875.28500374179 745.88934088587 2.01424283726858 13 292300 291811.390019001 2606.24837084285 488.609980998629 -0.372527026877422 14 288186 289098.607550545 1618.16982635888 -912.607550544624 -1.16426911072216 15 281477 285178.999425359 602.536601873665 -3701.99942535885 -1.19092457735949 16 282656 283206.224384905 127.925516326566 -550.224384904784 -0.580264949079261 17 280190 281327.864600774 -244.314731512342 -1137.86460077385 -0.452964131984537 18 280408 280367.008152864 -377.432403815789 40.9918471356168 -0.160971815641822 19 276836 277424.781175635 -853.839424952912 -588.781175635382 -0.575948862545888 20 275216 275496.58372056 -1053.41932744088 -280.583720559806 -0.241500869741957 21 274352 274617.855766088 -1020.95701995824 -265.855766087961 0.0393016841926683 22 271311 276030.215661518 -568.683553263882 -4719.21566151804 0.547625224235703 23 289802 282771.491607707 788.966076137044 7030.5083922935 1.64319917819594 24 290726 288213.532132554 1650.38256178230 2512.46786744636 1.04636923700167 25 292300 289952.311980775 1666.71803985619 2347.68801922449 0.0203784280270694 26 278506 281999.064691389 -117.659049246313 -3493.06469138926 -2.15340139308426 27 269826 274836.563223571 -1413.65069880534 -5010.5632235706 -1.54252059215159 28 265861 267760.870634804 -2454.48893417320 -1899.87063480361 -1.26333867744152 29 269034 267809.600921741 -1992.19384070773 1224.39907825936 0.562640557884551 30 264176 264112.236155732 -2307.76323427986 63.7638442678025 -0.381999700049219 31 255198 257214.214674899 -3157.17150710773 -2016.21467489926 -1.02625665260686 32 253353 253392.069538115 -3280.18000254160 -39.0695381152983 -0.148704898086683 33 246057 248396.529836918 -3597.46683497079 -2339.5298369184 -0.383840074575263 34 235372 243660.007073746 -3808.10126249424 -8288.00707374609 -0.254844312051477 35 258556 248976.627828301 -2122.70582481358 9579.37217169924 2.03989541260807 36 260993 255053.955971136 -610.469965081585 5939.04402886411 1.83962445629410 37 254663 251026.739372880 -1241.29575600392 3636.26062712045 -0.773380939517793 38 250643 250518.406034935 -1105.81393404893 124.593965065388 0.163892018883985 39 243422 247728.116495114 -1415.84137125016 -4306.11649511423 -0.371828651395445 40 247105 248640.464030903 -988.115637813652 -1535.46403090263 0.517745385301904 41 248541 247026.040000542 -1103.44852647703 1514.95999945761 -0.140257801685897 42 245039 244050.964668988 -1448.79358272527 988.035331012056 -0.418794092280214 43 237080 239697.574284708 -1984.906940721 -2617.57428470779 -0.648444263383376 44 237085 236437.658312311 -2220.1657284884 647.341687689025 -0.284410628875452 45 225554 229952.287500718 -3006.90280590260 -4398.28750071783 -0.95132373108318 46 226839 234617.804240254 -1592.45202999503 -7778.80424025407 1.71075555036107 47 247934 238924.679206512 -505.905416583661 9009.32079348799 1.31590710248393 48 248333 240707.601689204 -84.528703131175 7625.39831079642 0.512423869308125 49 246969 242740.200822058 305.724540959554 4228.79917794196 0.47562544061237 50 245098 244316.037711156 539.957089869316 781.962288844438 0.283389732372138 51 246263 249456.146442621 1386.20495140250 -3193.14644262108 1.01866715220675 52 255765 255336.712012602 2211.62675979135 428.287987397754 0.998333278051667 53 264319 260770.361657864 2804.01165987218 3548.63834213641 0.719563361095417 54 268347 265434.193728728 3146.48921863369 2912.80627127159 0.415612413075281 55 273046 272774.223787166 3919.11985033031 271.776212833821 0.935383352977324 56 273963 273975.878938259 3418.56586900893 -12.8789382591303 -0.605245222300388 57 267430 275523.398699325 3074.09089325668 -8093.3986993247 -0.416427338040158 58 271993 280664.842993517 3454.45798717111 -8671.84299351723 0.460026571812748 59 292710 284483.845640238 3521.4864346501 8226.15435976162 0.0812095325190694 60 295881 288632.069257583 3636.73463081132 7248.93074241721 0.140042398042488 61 293299 290649.867902538 3338.72874859421 2649.13209746249 -0.362247822261644

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128724pv1txpb3h20remo/1ojm01260128680.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128724pv1txpb3h20remo/1ojm01260128680.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128724pv1txpb3h20remo/2rqgf1260128680.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128724pv1txpb3h20remo/2rqgf1260128680.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128724pv1txpb3h20remo/3phjn1260128680.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128724pv1txpb3h20remo/3phjn1260128680.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128724pv1txpb3h20remo/47wh41260128680.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128724pv1txpb3h20remo/47wh41260128680.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128724pv1txpb3h20remo/52jf51260128680.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128724pv1txpb3h20remo/52jf51260128680.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')

Copyright

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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