marginal.lkl {BMAmevt} | R Documentation |
Estimates the marginal likelihood of a model, proceeding by simple Monte-Carlo integration under the prior distribution.
marginal.lkl(dat, likelihood, prior, Nsim = 300, displ = TRUE, Hpar, Nsim.min = Nsim, precision = 0, show.progress = floor(seq(1, Nsim, length.out = 20)))
dat |
The angular data set relative to which the marginal model likelihood is to be computed |
likelihood |
The likelihood function of the model.
See |
prior |
The prior distribution: of type |
Nsim |
Total number of iterations to perform. |
displ |
logical. If |
Hpar |
A list containing Hyper-parameters to be passed to
|
Nsim.min |
The minimum number of iterations to be performed. |
precision |
the desired relative precision. See
|
show.progress |
An vector of integers containing the times (iteration numbers) at which a message showing progression will be printed on the standard output. |
The function is a wrapper calling MCpriorIntFun
with parameter FUN
set to likelihood
.
The list returned by MCpriorIntFun
. The estimate is the list's element named emp.mean
.
The estimated standard deviations of the estimates produced by this function should be handled with care:For "larger" models than the Pairwise Beta or the NL models, the likelihood may have infinite second moment under the prior distribution. In such a case, it is recommended to resort to more sophisticated integration methods, e.g. by sampling from a mixture of the prior and the posterior distributions. See the reference below for more details.
KASS, R. and RAFTERY, A. (1995). Bayes factors. Journal of the american statistical association , 773-795.
marginal.lkl.pb
, marginal.lkl.nl
for direct use with the implemented models.
## Not run: lklNL= marginal.lkl(dat=Leeds, likelihood=dnestlog, prior=prior.nl, Nsim=20e+3, displ=TRUE, Hpar=nl.Hpar, ) ## End(Not run)