pfilter {pomp} | R Documentation |
Run a particle filter.
pfilter(object, ...) ## S4 method for signature 'pomp': pfilter(object, params, Np, tol = 1e-17, warn = TRUE, max.fail = 0, pred.mean = FALSE, pred.var = FALSE, filter.mean = FALSE, .rw.sd, verbose = FALSE, ...) ## S4 method for signature 'mif': pfilter(object, params, Np, tol = 1e-17, warn = TRUE, max.fail = 0, pred.mean = FALSE, pred.var = FALSE, filter.mean = FALSE, ...)
object |
An object of class pomp or inheriting class pomp .
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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.
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Np |
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.
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tol |
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) .
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warn |
Should filtering failures generate warnings? |
max.fail |
The maximum number of filtering failures allowed. If the number of filtering failures exceeds this number, execution will terminate with an error. |
pred.mean |
If TRUE , the prediction means are calculated for the state variables and parameters.
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pred.var |
If TRUE , the prediction variances are calculated for the state variables and parameters.
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filter.mean |
If TRUE , the filtering means are calculated for the state variables and parameters.
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.rw.sd |
For internal use with the MIF algorithm.
If TRUE , the specified random walk SD is used.
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verbose |
If TRUE , progress information is reported as pfilter works.
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... |
Additional arguments unused at present. |
A list with the following elements:
pred.mean |
The nvars+npars x ntimes matrix of prediction means, where ntimes is the length of the time series contained in object .
The rows correspond to states and parameters, in that order.
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pred.variance |
The matrix of prediction variances, in the same format as pred.mean .
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filter.mean |
The matrix of filtering means, in the same format as pred.mean .
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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. |
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.