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

In BMAmevt: Multivariate Extremes: Bayesian Estimation of the Spectral Measure

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))
)

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.

Value

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

See Also

marginal.lkl, MCpriorIntFun .

Examples

## Not run: 

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") ,
          )

## End(Not run)

BMAmevt documentation built on April 21, 2023, 9:07 a.m.