[R] Kalman filter
David Stoffer
dsstoffer at gmail.com
Tue Nov 23 06:35:11 CET 2010
It sounds like you've looked at the DLM, DSE, and SSPIR packages. If not,
then certainly check them out. Also, we have code for filtering, smoothing
and estimation in our text- go to www.stat.pitt.edu/stoffer/tsa3/ and look
at the code for chapter 6. There's not a package for the text, but all the
code is in a compressed file that you can download. The examples are
discussed in detail in the text, but I think looking at the code (and
Appendix R on the site) will be sufficient to set up your problem.
David
Garten Stuhl wrote:
>
> Hello,
>
>
>
> I have completed my kalman filter problem with more details.
>
>
>
> The transition- and the measurement equation is given by
>
>
>
> x[t]=A[t]*x[t-1]+B[t]*epsilon[t]
>
> y[t]=C[t]*x[t]+eta[t]
>
>
>
> A, y, B and C are Matrices. Y[t] is the data input vector with 800
> elements
> (every t has one element)
>
>
>
> My Model is described by the following
> (discretisation<http://www.dict.cc/englisch-deutsch/discretisation.html>)
> stochastic differential equation
>
>
>
> Lambda[t]=lambda[t-1]+kappa*lambda[t]*delta_t+epsilon_l
>
> R[t]=R[t-1]+mu*delta_t+epsilon_r
>
> epsilon_l=sigma_l*sqroot(delta_t)
>
> epsilon_r=sigma_r*sqroot(delta_t)
>
>
>
> Ln(S[t])=lambda[t]+R[t]
>
>
>
> The paramters for estimation are:
>
> kappa
>
> mu
>
> sigma_l
>
> sigma_r
>
>
>
> The state-space-model for this problem is:
>
>
>
> x[t]=(lambda[t], R[t])’
>
> A[t]=(1-kappa+delta_t, 0; 0, 1+mu)
>
> B[t]=(1,0;0,1)
>
> epsilon[t]=(epsilon_l, epsilon_r)’
>
> C[t]=(1,1)
>
> Eta[t]=0
>
>
>
> I used serveral alternative methods (dlm, kalmanLike, fkf, kfilter) for
> parameter estimation but I don’t understand the syntax and the correct
> input
> for model estimation.
>
>
>
> Can anybody help me, which packed is the most best for my problem and how
> is
> it to control?
>
>
>
> Thanks for helping.
>
>
>
> Best,
>
> Thomas
>
> [[alternative HTML version deleted]]
>
>
> ______________________________________________
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> PLEASE do read the posting guide
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> and provide commented, minimal, self-contained, reproducible code.
>
>
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