Description Usage Arguments Value Re-running PMCMC Iterations Filtering failures Author(s) References See Also
The Particle MCMC algorithm for estimating the parameters of a
partially-observed Markov process. Running pmcmc
causes a particle
random-walk Metropolis-Hastings Markov chain algorithm to run for the
specified number of proposals.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## S4 method for signature 'data.frame'
pmcmc(data, Nmcmc = 1, proposal, Np,
tol = 1e-17, max.fail = Inf, params, rinit, rprocess, dmeasure,
dprior, ..., verbose = getOption("verbose", FALSE))
## S4 method for signature 'pomp'
pmcmc(data, Nmcmc = 1, proposal, Np, tol = 1e-17,
max.fail = Inf, ..., verbose = getOption("verbose", FALSE))
## S4 method for signature 'pfilterd_pomp'
pmcmc(data, Nmcmc = 1, proposal, Np, tol,
max.fail = Inf, ..., verbose = getOption("verbose", FALSE))
## S4 method for signature 'pmcmcd_pomp'
pmcmc(data, Nmcmc, proposal, ...,
verbose = getOption("verbose", FALSE))
|
data |
either a data frame holding the time series data, or an object of class ‘pomp’, i.e., the output of another pomp calculation. |
Nmcmc |
The number of PMCMC iterations to perform. |
proposal |
optional function that draws from the proposal distribution. Currently, the proposal distribution must be symmetric for proper inference: it is the user's responsibility to ensure that it is. Several functions that construct appropriate proposal function are provided: see MCMC proposals for more information. |
Np |
the number of particles to use.
This may be specified as a single positive integer, in which case the same number of particles will be used at each timestep.
Alternatively, if one wishes the number of particles to vary across timesteps, one may specify length(time(object,t0=TRUE)) or as a function taking a positive integer argument.
In the latter case, |
tol |
positive numeric scalar;
particles with likelihood less than |
max.fail |
integer; the maximum number of filtering failures allowed (see below).
If the number of filtering failures exceeds this number, execution will terminate with an error.
By default, |
params |
optional; named numeric vector of parameters.
This will be coerced internally to storage mode |
rinit |
simulator of the initial-state distribution.
This can be furnished either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library.
Setting |
rprocess |
simulator of the latent state process, specified using one of the rprocess plugins.
Setting |
dmeasure |
evaluator of the measurement model density, specified either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library.
Setting |
dprior |
optional; prior distribution density evaluator, specified either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library.
For more information, see here.
Setting |
... |
additional arguments supply new or modify existing model characteristics or components.
See When named arguments not recognized by |
verbose |
logical; if |
An object of class ‘pmcmcd_pomp’.
To re-run a sequence of PMCMC
iterations, one can use the pmcmc
method on a ‘pmcmc’ object.
By default, the same parameters used for the original PMCMC run are re-used
(except for tol
, max.fail
, and verbose
, the defaults
of which are shown above). If one does specify additional arguments, these
will override the defaults.
If the degree of disagreement between model and data becomes sufficiently large, a “filtering failure” results.
A filtering failure occurs when, at some time point, none of the Np
particles is compatible with the data.
In particular, if the conditional likelihood of a particle at any time is below the tolerance value tol
, then that particle is considered to be uninformative and its likelihood is taken to be zero.
A filtering failure occurs when this is the case for all particles.
A warning is generated when this occurs unless the cumulative number of failures exceeds max.fail
, in which case an error is generated.
Edward L. Ionides, Aaron A. King, Sebastian Funk
C. Andrieu, A. Doucet, and R. Holenstein (2010) Particle Markov chain Monte Carlo methods. Journal of the Royal Statistical Society, Series B, 72: 269<e2><80><93>342.
C. Andrieu and G.O. Roberts (2009) The pseudo-marginal approach for computation Annals of Statistics, 37:697-725.
MCMC proposals
Other particle filter methods: bsmc2
,
cond.logLik
, eff.sample.size
,
filter.mean
, filter.traj
,
mif2
, pfilter
,
pred.mean
, pred.var
Other pomp parameter estimation methods: abc
,
bsmc2
, kalman
,
mif2
, nlf
,
pomp2-package
, probe.match
,
spect.match
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