saved_states {pomp} | R Documentation |
Saved states
Description
Retrieve latent state trajectories from a particle filter calculation.
Usage
## S4 method for signature 'pfilterd_pomp'
saved_states(object, ..., format = c("list", "data.frame"))
## S4 method for signature 'pfilterList'
saved_states(object, ..., format = c("list", "data.frame"))
Arguments
object |
result of a filtering computation |
... |
ignored |
format |
character; format of the returned object (see below). |
Details
When one calls pfilter
with save.states=TRUE
, the latent state vector associated with each particle is saved.
This can be extracted by calling saved_states
on the ‘pfilterd.pomp’ object.
These are the unweighted particles, saved after resampling.
Value
According to the format
argument, the saved states are returned either as a list or a data frame.
If format="data.frame"
, then the returned data frame holds the state variables and (optionally) the unnormalized log weight of each particle at each observation time.
The .id
variable distinguishes particles.
If format="list"
and pfilter
was called with save.states="unweighted"
or save.states="TRUE"
, the returned list contains one element per observation time.
Each element consists of a matrix, with one row for each state variable and one column for each particle.
If pfilter
was called with save.states="weighted"
, the list itself contains two lists:
the first holds the particles as above, the second holds the corresponding unnormalized log weights.
In particular, it has one element per observation time; each element is the vector of per-particle log weights.
See Also
More on sequential Monte Carlo methods:
bsmc2()
,
cond_logLik()
,
eff_sample_size()
,
filter_mean()
,
filter_traj()
,
kalman
,
mif2()
,
pfilter()
,
pmcmc()
,
pred_mean()
,
pred_var()
,
wpfilter()
Other extraction methods:
coef()
,
cond_logLik()
,
covmat()
,
eff_sample_size()
,
filter_mean()
,
filter_traj()
,
forecast()
,
logLik
,
obs()
,
pred_mean()
,
pred_var()
,
spy()
,
states()
,
summary()
,
time()
,
timezero()
,
traces()