Hi My name is Vladimir Samaj. I am a student of Univerzity of Zilina. I am trying to implement Kalman Filter into my school work. I have some problems with understanding of R version of Kalman Filter in package stats( functions KalmanLike, KalmanRun, KalmanSmooth,KalmanForecast). 1) Can you tell me how are you seting the initial values of state vector in Kalman Filter? Are you using some method? 2) I have fond function StructTS in stats package. I dont understand, how exactly, are you computing(what method are you using) fitted values which are the output of this function( $fitted ) . In description od this function is that it fit a structural model for a time series by maximum likehood. Does it means, that the fitted values are fit by maximum likehood? If so how does look the likehood function? 3)Finaly, I dont understand smooting problem. What I know is that, if I have t observations of some time serie, I can use function KalmanRun to get estimates of state vector. And if I gain aditional observations of time serie( T > t ), I shoud use KalmanSmooth function to smooth estimates of state vector. I dont understand, that how shoud I "tell" to KalmanSmooth funtion that I allready did filtering and it shoud use the values from filtering to smoothing. I will be glad if you help me. I hope that my folmulations were correct. Thank you very much. [[alternative HTML version deleted]]
Have you looked at the 'dlm' package? It has a vignette to help
you learn to use it. Also, I've heard that a book about that package is
scheduled to appear in the next few months.
I have looked at the Kalman functions in the 'stats' package but
have not found documentation that seemed sufficient to get me started
using it.
RSiteSearch('Kalman', 'fun') produced 48 hits for me just
now. If
you don't find what you want with 'dlm' (and maybe even if you do),
you
may wish to examine that list, if you haven't already.
Hope this helps.
Spencer
Vladim?r ?amaj wrote:> Hi
>
> My name is Vladimir Samaj. I am a student of Univerzity of Zilina. I am
> trying to implement Kalman Filter into my school work. I have some problems
> with understanding of R version of Kalman Filter in package stats(
functions
> KalmanLike, KalmanRun, KalmanSmooth,KalmanForecast).
>
> 1) Can you tell me how are you seting the initial values of state vector in
> Kalman Filter? Are you using some method?
>
> 2) I have fond function StructTS in stats package. I dont understand, how
> exactly, are you computing(what method are you using) fitted values which
> are the output of this function( $fitted ) . In description od this
function
> is that it fit a structural model for a time series by maximum likehood.
> Does it means, that the fitted values are fit by maximum likehood? If so
how
> does look the likehood function?
>
> 3)Finaly, I dont understand smooting problem. What I know is that, if I
> have t observations of some time serie, I can use function KalmanRun to
get
> estimates of state vector. And if I gain aditional observations of time
> serie( T > t ), I shoud use KalmanSmooth function to smooth estimates of
> state vector. I dont understand, that how shoud I "tell" to
KalmanSmooth
> funtion that I allready did filtering and it shoud use the values from
> filtering to smoothing.
>
> I will be glad if you help me. I hope that my folmulations were correct.
>
> Thank you very much.
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
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> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
Vladimir- there are at least 3 packages that will facilitate state space modeling: http://cran.r-project.org/src/contrib/Descriptions/dlm.html DLM , http://cran.r-project.org/src/contrib/Descriptions/dse.html DSE , and http://cran.r-project.org/src/contrib/Descriptions/sspir.html SSPIR . In addition, I have scripts and examples for fitting state space models and running the Kalman filter and smoother for Chapter 6 of our text, http://www.stat.pitt.edu/stoffer/tsa2/ tsa2 . Go to "R CODE (Ch 6)" using the blue bar at the top. There you will find the scripts and examples. If you have any questions, feel free to contact me. Vladim?r ?amaj wrote:> > Hi > > My name is Vladimir Samaj. I am a student of Univerzity of Zilina. I am > trying to implement Kalman Filter into my school work. I have some > problems > with understanding of R version of Kalman Filter in package stats( > functions > KalmanLike, KalmanRun, KalmanSmooth,KalmanForecast). > > 1) Can you tell me how are you seting the initial values of state vector > in > Kalman Filter? Are you using some method? > > 2) I have fond function StructTS in stats package. I dont understand, how > exactly, are you computing(what method are you using) fitted values which > are the output of this function( $fitted ) . In description od this > function > is that it fit a structural model for a time series by maximum likehood. > Does it means, that the fitted values are fit by maximum likehood? If so > how > does look the likehood function? > > 3)Finaly, I dont understand smooting problem. What I know is that, if I > have t observations of some time serie, I can use function KalmanRun to > get > estimates of state vector. And if I gain aditional observations of time > serie( T > t ), I shoud use KalmanSmooth function to smooth estimates of > state vector. I dont understand, that how shoud I "tell" to KalmanSmooth > funtion that I allready did filtering and it shoud use the values from > filtering to smoothing. > > I will be glad if you help me. I hope that my folmulations were correct. > > Thank you very much. > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > >----- The power of accurate observation is commonly called cynicism by those who have not got it. George Bernard Shaw -- View this message in context: http://www.nabble.com/Kalman-Filter-tp15696135p15826374.html Sent from the R help mailing list archive at Nabble.com.