mif-class {pomp}R Documentation

The "mif" class

Description

The MIF algorithm: maximum likelihood via iterated filtering. The mif class holds a fitted model.

Objects from the Class

Objects can be created by calls to the mif method on an pomp object. Such a call uses the MIF algorithm to fit the model parameters.

Slots

A mif object is derived from a pomp object and therefore has all the slots of such an object. See pomp-class for details. A full description of slots in a mif object follows.

ivps
A character vector containing the names of variables appearing in coef which are to be estimated as initial-value parameters (IVPs).
pars
A character vector containing the names of ordinary parameters appearing in coef which are to be estimated.
Nmif
Number of MIF iterations that have been completed.
particles
A function of prototype particles(Np,center,sd,...) that draws particles from a distribution centered on center and with width proportional to sd.
alg.pars
A named list of algorithm parameters. This consists of
Np
the number of particles to use in filtering
var.factor
the scaling coefficient relating the width of the initial particle distribution to rw.sd
ic.lag
the timepoint for fixed-lag smoothing of initial-value parameters (IVPs)
cooling.factor
normal-bracket41bracket-normal
the exponential cooling factor, a, where the exponential cooling factor, a, where eqn
coef
A named vector containing the parameter estimate.
random.walk.sd
A named vector containing the random-walk variance to be used for ordinary parameters. The width of the initial distribution of particles will be random.walk.sd*CC.
pred.mean
Matrix of prediction means. See pfilter.
pred.var
Matrix of prediction variances. See pfilter.
filter.mean
Matrix of filtering means. See pfilter.
conv.rec
The "convergence record": a matrix containing a record of the parameter values, log likelihoods, and other pertinent information, with one row for each MIF iteration.
eff.sample.size
A vector containing the effective number of particles at each time point. See pfilter.
cond.loglik
A vector containing the conditional log likelihoods at each time point. See pfilter.
loglik
A numeric value containing the value of the log likelihood, as evaluated for the random-parameter model.
data
Inherited from the pomp class.
times
Inherited from the pomp class.
t0
Inherited from the pomp class.
rprocess
Inherited from the pomp class.
dprocess
Inherited from the pomp class.
rmeasure
Inherited from the pomp class.
dmeasure
Inherited from the pomp class.
userdata
Inherited from the pomp class.

Extends

Class pomp, directly. See pomp-class.

Methods

See mif, mif-methods, particles-mif, pfilter-mif.

Author(s)

Aaron A. King (kingaa at umich dot edu)

References

E. L. Ionides, C. Bret{'o}, & A. A. King, Inference for nonlinear dynamical systems, Proc. Natl. Acad. Sci. U.S.A., 103:18438–18443, 2006.

See Also

mif, mif-methods, pomp, pomp-class


[Package pomp version 0.17-3 Index]