mif {pomp} | R Documentation |
The MIF algorithm for estimating the parameters of a partially-observed Markov process.
mif(object, ...) ## S4 method for signature 'pomp': mif(object, Nmif = 1, start, pars, ivps = character(0), particles, rw.sd, alg.pars, weighted = TRUE, tol = 1e-17, warn = TRUE, max.fail = 0, verbose = FALSE, .ndone = 0) ## S4 method for signature 'mif': mif(object, Nmif, start, pars, ivps, rw.sd, alg.pars, weighted = TRUE, tol = 1e-17, warn = TRUE, max.fail = 0, verbose = FALSE, .ndone = 0) ## S4 method for signature 'mif': continue(object, Nmif, ...)
object |
An object of class pomp .
|
Nmif |
The number of MIF iterations to perform. |
start |
The initial guess of the parameters. This must be a named vector. |
pars |
character vector; names of ordinary parameters to be estimated. |
ivps |
character vector; names of initial-value parameters to be estimated. |
particles |
Function of prototype particles(Np,center,sd,...) which sets up the initial particle matrix by drawing a sample of size Np from the initial particle distribution centered at center and of width sd .
If particles is not supplied by the user, the default behavior is to draw the particles from a multivariate normal distribution with mean center and standard deviation sd .
|
rw.sd |
numeric vector with names; the intensity of the random walk to be applied to parameters.
The random walk is only applied to parameters named in pars .
The algorithm requires that the random walk be nontrivial.
Thus, each element in rw.sd[pars] must be positive.
rw.sd is also used to scale the initial-value parameters (via the particles function).
Therefore, each element of rw.sd[ivps] must be positive.
|
alg.pars |
A named list of algorithm parameters.
This consists of
|
weighted |
Should a weighted average be used?
If weighted=F , the MIF update is not used;
instead, an unweighed average of the filtering means is used for the update.
|
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.
|
warn |
Should a warning be generated when a filtering failure occurs? |
max.fail |
Maximum number of filtering failures permitted. If the number of failures exceeds this number, execution will terminate with an error. |
verbose |
logical; if TRUE, print progress reports. |
.ndone |
for internal use by continue .
do not meddle with this!
|
... |
Additional arguments that can be used to override the defaults. |
To re-run a sequence of MIF iterations, one can use the mif
method on a mif
object.
The call sequence is mif(object)
.
By default, the same parameters used for the original MIF run are re-used.
If one does specify additional arguments, these will override the defaults.
An exception is that one cannot override the particles
function.
One can continue a series of MIF iterations from where one left off.
The call sequence is continue(object, Nmif)
.
This will perform Nmif
additional MIF iterations on the mif
object object
.
A call to mif
to perform Nmif=m
iterations followed by a call to continue
to perform Nmif=n
iterations will produce precisely the same effect as a single call to mif
to perform Nmif=m+n
iterations.
Additional arguments are passed to mif
.
This feature can be used to change any of the parameters (except the particles
function).
It is the user's responsibility to ensure that, if the optional particles
argument is given, that the particles
function satisfies the following conditions:
particles
has at least the following arguments:
Np
, center
, sd
, and ...
.
Np
should be assumed to be an integer; center
and sd
will be named vectors of the same length.
Additional arguments may be specified;
these will be filled with the elements of the userdata
slot of the underlying pomp
object (see pomp-class
).
particles
returns a length(center)
x Np
matrix with rownames.
Each column represents a distinct particle.
The rownames are used by the algorithms (see mif
, pfilter
).
The center of the particle distribution returned by particles
should be center
.
The width of the particle distribution should vary monotonically with sd
.
In particular, when sd=0
, the particles
should return matrices with Np
identical columns, each corresponding to the parameters specified in center
.
Aaron A. King (kingaa at umich dot edu)
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.
mif-class
, mif-methods
, pomp
, pomp-class
, pfilter
.
See the "intro_to_pomp" vignette for an example.