| pfilter {pomp} | R Documentation |
Run a plain vanilla particle filter. Resampling is performed after each observation.
pfilter(object, ...)
## S4 method for signature '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"), ...)
## S4 method for signature 'mif':
pfilter(object, params, Np, tol = 1e-17,
max.fail = 0, pred.mean = FALSE, pred.var = FALSE,
filter.mean = FALSE, ...)
object |
An object of class pomp or inheriting class pomp.
|
params |
A npars x Np matrix containing the parameters corresponding to the initial state values in xstart.
This must have a 'rownames' attribute.
It is permissible to supply params as a named numeric vector, i.e., without a dim attribute.
In this case, all particles will inherit the same parameter values.
|
Np |
integer; number of particles to use.
When object is of class mif, this is by default the same number of particles used in the mif iterations.
|
tol |
positive numeric scalar; particles with log likelihood below 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 log likelihood at that time point is set to be log(tol).
|
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. |
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 |
logical; if TRUE, the state-vector for each particle is saved and returned.
|
verbose |
logical; if TRUE, progress information is reported as pfilter works.
|
... |
Additional arguments unused at present. |
A list with the following elements:
pred.mean |
The 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.var |
The matrix of prediction variances, in the same format as pred.mean.
|
filter.mean |
The matrix of filtering means, in the same format as pred.mean.
|
eff.sample.size |
A vector containing the effective number of particles at each time point. |
cond.loglik |
A vector containing the conditional log likelihoods at each time point. |
states |
If 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}].
|
nfail |
The number of filtering failures encountered. |
loglik |
The estimated log-likelihood. |
Aaron A. King kingaa at umich dot edu
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 the vignettes for examples.