marginal.lkl {BMAmevt}  R Documentation 
Estimates the marginal likelihood of a model, proceeding by simple MonteCarlo 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 Hyperparameters 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 , 773795.
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)