pfilter: Particle filter

Description

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

Usage

pfilter(object, ...)
## S3 method for class 'pomp':
pfilter(object, params, Np, tol = 1e-17,
    max.fail = 0, pred.mean = FALSE, pred.var = FALSE,
    filter.mean = FALSE, save.states = FALSE,
    verbose = getOption("verbose"), ...)
## S3 method for class 'mif':
pfilter(object, params, Np, tol = 1e-17,
    max.fail = 0, pred.mean = FALSE, pred.var = FALSE,
    filter.mean = FALSE, \dots)

Arguments

Value

A list with the following elements:
pred.meanThe matrix of prediction means. The rows correspond to states and parameters (if appropriate), in that order, the columns to successive observations in the time series contained in object.
pred.varThe matrix of prediction variances, in the same format as pred.mean.
filter.meanThe matrix of filtering means, in the same format as pred.mean.
eff.sample.sizeA vector containing the effective number of particles at each time point.
cond.loglikA vector containing the conditional log likelihoods at each time point.
statesIf saves.states=TRUE, the array of state-vectors at each time point, for each particle. An array with dimensions nvars-by-Np-by-ntimes. In particular, states[,i,t] can be considered a sample from $f[X|y_{1:t}]$.
nfailThe number of filtering failures encountered.
loglikThe estimated log-likelihood.

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.

Examples

Run this code

## See the vignettes for examples.