pomp (version 0.53-5)

pfilter: Particle filter

Description

Run a plain vanilla particle filter. Resampling is performed at each observation.

Usage

## S3 method for class 'pomp':
pfilter(object, params, Np, tol = 1e-17,
    max.fail = Inf, pred.mean = FALSE, pred.var = FALSE,
    filter.mean = FALSE, save.states = FALSE,
    save.params = FALSE, seed = NULL,
    verbose = getOption("verbose"), ...)
## S3 method for class 'pfilterd.pomp':
pfilter(object, params, Np, tol, \dots)

Arguments

object

An object of class pomp or inheriting class pomp.

params

A npars x Np numeric matrix containing the parameters corresponding to the initial state values in xstart. This must have a rownames attribute. If it desired that all particles should share t

Np

the number of particles to use. This may be specified as a single positive integer, in which case the same number of particles will be used at each timestep. Alternatively, if one wishes the number of particles to vary across timesteps, one may sp

tol

positive numeric scalar; particles with likelihood less than tol are considered to be lost. A filtering failure occurs when, at some time point, all particles are lost. When all particles are lost, the conditional lik

max.fail

integer; the maximum number of filtering failures allowed. If the number of filtering failures exceeds this number, execution will terminate with an error. By default, max.fail is set to infinity, so no error can be triggered.

pred.mean

logical; if TRUE, the prediction means are calculated for the state variables and parameters.

pred.var

logical; if TRUE, the prediction variances are calculated for the state variables and parameters.

filter.mean

logical; if TRUE, the filtering means are calculated for the state variables and parameters.

save.states, save.params

logical. If save.states=TRUE, the state-vector for each particle at each time is saved in the saved.states slot of the returned pfilterd.pomp object.

seed

optional; an object specifying if and how the random number generator should be initialized (seeded). If seed is an integer, it is passed to set.seed prior to any simulation and is returned as the see

verbose

logical; if TRUE, progress information is reported as pfilter works.

...

By default, when pfilter pfilter is run on a pfilterd.pomp object, the settings in the original call are re-used. This default behavior can be overridden by changing the settings (see Examples below).

Value

• An object of class pfilterd.pomp. This class inherits from class pomp and contains the following additional slots: [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object] These can be accessed using the $ operator as if the returned object were a list. In addition, logLik returns the log likelihood. Note that if the argument params is a named vector, then these parameters are included in the params slot of the returned pfilterd.pomp object. That is coef(pfilter(obj,params=theta))==theta if theta is a named vector of parameters.

References

M. S. Arulampalam, S. Maskell, N. Gordon, & T. Clapp. A Tutorial on Particle Filters for Online Nonlinear, Non-Gaussian Bayesian Tracking. IEEE Trans. Sig. Proc. 50:174--188, 2002.

See Also

pomp, mif, pmcmc, bsmc

Examples

pompExample(gompertz)
pf <- pfilter(gompertz,Np=1000)	## use 1000 particles
logLik(pf)
cond.logLik(pf)			## conditional log-likelihoods
eff.sample.size(pf)
logLik(pfilter(gompertz))      	## run it again with 1000 particles
logLik(pfilter(gompertz,Np=2000))    	## run it with 2000 particles and get the filtering means

## See the vignettes for more examples.