mif-class {pomp}R Documentation

The "mif" class

Description

The mif class holds a fitted model and is created by a call to mif. See mif for usage.

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 initial-value parameters (IVPs). These are parameters which are to be estimated using fixed-lag smoothing.
pars
A character vector containing the names of parameters to be estimated using MIF.
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. This function can be optionally specified by the user.
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 fixed lag used in the estimation of initial-value parameters (IVPs); and cooling.factor, the exponential cooling factor, where 0<cooling.factor<1.
random.walk.sd
A named vector containing the random-walk variance to be used for ordinary parameters.
pred.mean
Matrix of prediction means. See pfilter.
pred.var
Matrix of prediction variances. See pfilter.
filter.mean
Matrix of filtering means. See pfilter.
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.
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.
loglik
A numeric value containing the value of the log likelihood, as evaluated for the random-parameter model. Note that this will not be equal to the log likelihood for the fixed-parameter model.
data, times, t0, rprocess, dprocess, dmeasure, rmeasure, skeleton.type, skeleton, initializer, states, params, statenames, paramnames, covarnames, tcovar, covar, PACKAGE, 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.

A. A. King, E. L. Ionides, M. Pascual, and M. J. Bouma, Inapparent infections and cholera dynamics, Nature, 454:877–880, 2008.

See Also

mif, mif-methods, pomp, pomp-class


[Package pomp version 0.24-7 Index]