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 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.
Its default value is a multivariate normal distribution with mean at center
and standard deviation 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 fixed lag used in the estimation of initial-value parameters (IVPs).
- cooling.factor
- the exponential cooling factor,
alpha
, where 0<alpha<1
.
- 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*var.factor.
- 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.
See Also
mif
, mif-methods, pomp
, pomp-class
[Package
pomp version 0.22-6
Index]