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 dont 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]] > > > ______________________________________________ > R-help@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. > > -- View this message in context: http://r.789695.n4.nabble.com/Kalman-filter-tp3049591p3054858.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@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.
