cond_logLik {pomp} | R Documentation |
Conditional log likelihood
Description
The estimated conditional log likelihood from a fitted model.
Usage
## S4 method for signature 'kalmand_pomp'
cond_logLik(object, ..., format = c("numeric", "data.frame"))
## S4 method for signature 'pfilterd_pomp'
cond_logLik(object, ..., format = c("numeric", "data.frame"))
## S4 method for signature 'wpfilterd_pomp'
cond_logLik(object, ..., format = c("numeric", "data.frame"))
## S4 method for signature 'bsmcd_pomp'
cond_logLik(object, ..., format = c("numeric", "data.frame"))
## S4 method for signature 'pfilterList'
cond_logLik(object, ..., format = c("numeric", "data.frame"))
Arguments
object |
result of a filtering computation |
... |
ignored |
format |
format of the returned object |
Details
The conditional likelihood is defined to be the value of the density of
evaluated at .
Here,
is the observable process, and
the data, at time
.
Thus the conditional log likelihood at time is
where is the probability density above.
Value
The numerical value of the conditional log likelihood.
Note that some methods compute not the log likelihood itself but instead a related quantity.
To keep the code simple, the cond_logLik
function is nevertheless used to extract this quantity.
When object
is of class ‘bsmcd_pomp’
(i.e., the result of a bsmc2
computation),
cond_logLik
returns the conditional log “evidence”
(see bsmc2
).
See Also
More on sequential Monte Carlo methods:
bsmc2()
,
eff_sample_size()
,
filter_mean()
,
filter_traj()
,
kalman
,
mif2()
,
pfilter()
,
pmcmc()
,
pred_mean()
,
pred_var()
,
saved_states()
,
wpfilter()
Other extraction methods:
coef()
,
covmat()
,
eff_sample_size()
,
filter_mean()
,
filter_traj()
,
forecast()
,
logLik
,
obs()
,
pred_mean()
,
pred_var()
,
saved_states()
,
spy()
,
states()
,
summary()
,
time()
,
timezero()
,
traces()