filter_traj {pomp} | R Documentation |
Filtering trajectories
Description
Drawing from the smoothing distribution
Usage
## S4 method for signature 'pfilterd_pomp'
filter_traj(object, vars, ..., format = c("array", "data.frame"))
## S4 method for signature 'listie'
filter_traj(object, vars, ..., format = c("array", "data.frame"))
## S4 method for signature 'pmcmcd_pomp'
filter_traj(object, vars, ...)
Arguments
object |
result of a filtering computation |
vars |
optional character; names of variables |
... |
ignored |
format |
format of the returned object |
Details
The smoothing distribution is the distribution of
X(t_k) | Y(t_1)=y^*_1, \dots, Y(t_n)=y^*_n,
where X(t_k)
is the latent state process and Y(t_k)
is the observable process at time t_k
, and n
is the number of observations.
To draw samples from this distribution, one can run a number of independent particle filter (pfilter
) operations, sampling the full trajectory of one randomly-drawn particle from each one.
One should view these as weighted samples from the smoothing distribution, where the weights are the likelihoods returned by each of the pfilter
computations.
One accomplishes this by setting filter.traj = TRUE
in each pfilter
computation and extracting the trajectory using the filter_traj
command.
In particle MCMC (pmcmc
), the tracking of an individual trajectory is performed automatically.
See Also
More on sequential Monte Carlo methods:
bsmc2()
,
cond_logLik()
,
eff_sample_size()
,
filter_mean()
,
kalman
,
mif2()
,
pfilter()
,
pmcmc()
,
pred_mean()
,
pred_var()
,
saved_states()
,
wpfilter()
Other extraction methods:
coef()
,
cond_logLik()
,
covmat()
,
eff_sample_size()
,
filter_mean()
,
forecast()
,
logLik
,
obs()
,
pred_mean()
,
pred_var()
,
saved_states()
,
spy()
,
states()
,
summary()
,
time()
,
timezero()
,
traces()