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##' The functions generate parameters samples approximating the posterior distribution in the PB model or the NL model.
##'
##' The two functions are wrappers simplifying the use of
##' \code{\link{posteriorMCMC}} for the two models implemented in this package.
##' @title MCMC posterior samplers for the pairwise beta and the negative logistic models.
##' @inheritParams posteriorMCMC
##' @param ... Additional arguments to be passed to
##' \code{\link{posteriorMCMC}} instead of their
##' default values (must not contain any of
##' \code{"prior",
##' "likelihood", "proposal",
##' "name.model"} or \code{"class"}).
##' @return an object with class attributes \code{"postsample"} and
##' \code{"PBNLpostsample"}: The posterior sample and some statistics
##' as returned by function \code{\link{posteriorMCMC}}
##' @seealso \code{\link{posteriorMCMC}}
##' @note For the Leeds data set, and for simulated data sets with
##' similar features, setting \code{Nsim=50e+3} and \code{Nbin=15e+3}
##' is enough (possibly too much),
##' with respect to the Heidelberger and Welch tests implemented in
##' \code{\link[coda]{heidel.diag}}.
##' @examples
##' \dontrun{
##' data(Leeds)
##' data(pb.Hpar)
##' data(pb.MCpar)
##' data(nl.Hpar)
##' data(nl.MCpar)
##' pPB <- posteriorMCMC.pb(Nsim=5e+3, dat=Leeds, Hpar=pb.Hpar,
##' MCpar=pb.MCpar)
##'
##' dim(pPB[1])
##' pPB[-(1:3)]
##'
##' pNL <- posteriorMCMC.nl(Nsim=5e+3, dat=Leeds, Hpar=nl.Hpar,
##' MCpar=nl.MCpar)
##'
##' dim(pNL[1])
##' pNL[-(1:3)]
##' }
##' @export
posteriorMCMC.pb <-
function(Nsim,
dat,
Hpar,
MCpar,
...
)
{
postsample <-
posteriorMCMC(Nsim=Nsim,
dat=dat,
prior=prior.pb,
proposal=proposal.pb,
likelihood=dpairbeta,
Hpar=Hpar,
MCpar=MCpar,
name.model="pairbeta",
class="PBNLpostsample",
...)
return(postsample)
}
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