On Mon, 11 Oct 2004, Heng Sun wrote: > From the help document on KalmanLike, KalmanRun, etc., > I see the linear Gaussian state space model is > > a <- T a + R e > y = Z' a + eta > > following the book of Durbin and Koopman. > > In practice, it is useful to run Kalman > filtering/smoothing/forecasting with exogenous factor: > > a <- T a + L b + R e > y = Z' a + M b + eta > > where b is some known vector (a function of time). > > Some other software like S-plus and Mathematica > include the above exogenous factor. SsfPack by > Koopman, etal. also has the factor built in the model > to accommodate practical uses. > > So what is the rationale for R to leave off the > exogenous factor? Is there a feasible way to convert > the general model to the simple model in R?
What is the rationale for your raising this? KalmanLike, KalmanRun, etc were written for R 1.5.0 as part of the ts package (see my article in R-news), and the ts applications (see the See Also section) do not need a so-called `exogenous factor' (which is not a `factor'). R does not pretend to have facilities for whatever subject area you mean (but do not say) by `in practice'. That's what addon packages are for (and some do touch on this area). We have no idea who [EMAIL PROTECTED]' is: it is courteous to use a signature and give your affiliation. -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
