BMAmevt (version 1.0.5)

marginal.lkl.nl: Marginal likelihoods of the PB and NL models.

Description

Wrappers for marginal.lkl, in the specific cases of the PB and NL models, with parameter likelihood set to dpairbeta or dnestlog, and prior set to prior.pb or prior.nl. See MCpriorIntFun for more details.

Usage

marginal.lkl.nl(
  dat,
  Nsim = 10000,
  displ = TRUE,
  Hpar = get("nl.Hpar"),
  Nsim.min = Nsim,
  precision = 0,
  show.progress = floor(seq(1, Nsim, length.out = 20))
)
marginal.lkl.pb(
  dat,
  Nsim = 10000,
  displ = TRUE,
  Hpar = get("pb.Hpar"),
  Nsim.min = Nsim,
  precision = 0,
  show.progress = floor(seq(1, Nsim, length.out = 20))
)

Value

The list returned by marginal.lkl, i.e., the one returned by MCpriorIntFun

Arguments

dat

The angular data set relative to which the marginal model likelihood is to be computed

Nsim

Total number of iterations to perform.

displ

logical. If TRUE, a plot is produced, showing the temporal evolution of the cumulative mean, with approximate confidence intervals of \(+/-2\) estimated standard errors.

Hpar

A list containing Hyper-parameters to be passed to prior.

Nsim.min

The minimum number of iterations to be performed.

precision

the desired relative precision. See MCpriorIntFun.

show.progress

An vector of integers containing the times (iteration numbers) at which a message showing progression will be printed on the standard output.

See Also

marginal.lkl, MCpriorIntFun .

Examples

if (FALSE) {

marginal.lkl.pb(dat=Leeds ,
         Nsim=20e+3 ,
         displ=TRUE, Hpar = get("pb.Hpar") ,
          )

marginal.lkl.nl(dat=Leeds ,
         Nsim=10e+3 ,
         displ=TRUE, Hpar = get("nl.Hpar") ,
          )
}