| cond_logLik | R Documentation |
The estimated conditional log likelihood from a fitted model.
## 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"))
object |
result of a filtering computation |
... |
ignored |
format |
format of the returned object |
The conditional likelihood is defined to be the value of the density of
Y(t_k) | Y(t_1),\dots,Y(t_{k-1})
evaluated at Y(t_k) = y^*_k.
Here, Y(t_k) is the observable process, and y^*_k the data, at time t_k.
Thus the conditional log likelihood at time t_k is
\ell_k(\theta) = \log f[Y(t_k)=y^*_k \vert Y(t_1)=y^*_1, \dots, Y(t_{k-1})=y^*_{k-1}],
where f is the probability density above.
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).
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()
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