A plain vanilla sequential Monte Carlo (particle filter) algorithm. Resampling is performed at each observation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  ## S4 method for signature 'pomp'
pfilter(object, params, Np, tol = 1e17,
max.fail = Inf, pred.mean = FALSE, pred.var = FALSE,
filter.mean = FALSE, save.states = FALSE,
save.params = FALSE, seed = NULL,
verbose = getOption("verbose"), ...)
## S4 method for signature 'pfilterd.pomp'
pfilter(object, params, Np, tol, ...)
## S4 method for signature 'pfilterd.pomp'
logLik(object, ...)
## S4 method for signature 'pfilterd.pomp'
cond.logLik(object, ...)
## S4 method for signature 'pfilterd.pomp'
eff.sample.size(object, ...)
## S4 method for signature 'pfilterd.pomp'
pred.mean(object, pars, ...)
## S4 method for signature 'pfilterd.pomp'
pred.var(object, pars, ...)
## S4 method for signature 'pfilterd.pomp'
filter.mean(object, pars, ...)

object 
An object of class 
params 
A 
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.
If the number of filtering failures exceeds this number, execution will terminate with an error.
By default, 
pred.mean 
logical; if 
pred.var 
logical; if 
filter.mean 
logical; if 
save.states, save.params 
logical.
If 
seed 
optional; an object specifying if and how the random number generator should be initialized (‘seeded’).
If 
verbose 
logical; if 
pars 
Names of parameters. 
... 
additional arguments that override the defaults. 
An object of class pfilterd.pomp
.
This class inherits from class pomp
and contains the following additional slots:
matrices of prediction means, variances, and filter means, respectively.
In each of these, the rows correspond to states and parameters (if appropriate), in that order, the columns to successive observations in the time series contained in object
.
numeric vector containing the effective number of particles at each time point.
numeric vector containing the conditional log likelihoods at each time point.
If pfilter
was called with save.states=TRUE
, this is the list of statevectors at each time point, for each particle.
It is a lengthntimes
list of nvars
byNp
arrays.
In particular, saved.states[[t]][,i]
can be considered a sample from f[X_ty_{1:t}].
If pfilter
was called with save.params=TRUE
, this is the list of parametervectors at each time point, for each particle.
It is a lengthntimes
list of npars
byNp
arrays.
In particular, saved.params[[t]][,i]
is the parameter portion of the ith particle at time t.
the state of the random number generator at the time pfilter
was called.
If the argument seed
was specified, this is a copy;
if not, this is the internal state of the random number generator at the time of call.
the number of particles used, failure tolerance, and number of filtering failures, respectively.
the estimated loglikelihood.
These can be accessed using the $
operator as if the returned object were a list.
Note that if the argument params
is a named vector, then these parameters are included in the params
slot of the returned pfilterd.pomp
object.
Extracts the estimated log likelihood.
Extracts the estimated conditional log likelihood
ell_t(theta)=Prob[y_t  y_1, …, y_(t1)],
where y_t are the data, at time t.
Extracts the (timedependent) estimated effective sample size, computed as
1/(sum(w_it^2)),
where w_it is the normalized weight of particle i at time t.
Extract the mean and variance of the approximate prediction distribution. This prediction distribution is that of
X_t  y_1,…,y_(t1),
where X_t, y_t are the state vector and data, respectively, at time t.
Extract the mean of the filtering distribution, which is that of
X_t  y_1,…,y_t,
where X_t, y_t are the state vector and data, respectively, at time t.
Aaron A. King kingaa at umich dot edu
M. S. Arulampalam, S. Maskell, N. Gordon, & T. Clapp. A Tutorial on Particle Filters for Online Nonlinear, NonGaussian Bayesian Tracking. IEEE Trans. Sig. Proc. 50:174–188, 2002.
pomp
, mif
, pmcmc
, bsmc2
,
and the tutorials on the package website.
1 2 3 4 5 6 7 8 9 10  pompExample(gompertz)
pf < pfilter(gompertz,Np=1000) ## use 1000 particles
plot(pf)
logLik(pf)
cond.logLik(pf) ## conditional loglikelihoods
eff.sample.size(pf) ## effective sample size
logLik(pfilter(pf)) ## run it again with 1000 particles
## run it again with 2000 particles
pf < pfilter(pf,Np=2000,filter.mean=TRUE)
fm < filter.mean(pf) ## extract the filtering means

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