lAccept.ratio: Acceptance probability in the MCMC algorithm.

Description

Logarithm of the acceptance probability

Usage

lAccept.ratio(
  cur.par,
  prop.par,
  llh.cur,
  lprior.cur,
  dat,
  likelihood,
  proposal,
  prior,
  Hpar,
  MCpar
)

Value

The log-acceptance probability.

Arguments

cur.par: The current parameter in the Markov chain
prop.par: The candidate parameter
dat: An angular data set, e.g., constructed by cons.angular.dat: A matrix which rows are the Cartesian coordinates of points on the unit simplex (summing to one).
likelihood: The likelihood function. Should be of type
function(x, par, log, vectorial), where log and vectorial are logical flags indicating respectively if the result is to be returned on the log-scale, and if the value is a vector of length nrow(x) or a single number (the likelihood, or the log-likelihood, for the data set x). See dpairbeta or dnestlog for templates.
proposal: The proposal function: of type
function(type = c("r","d"), cur.par, prop.par, MCpar, log). Should return the (logarithm of) the proposal density for the move cur.par --> prop.par if type == "d". If type =="r", proposal must return a candidate parameter, depending on cur.par, as a vector. See proposal.pb or proposal.nl for templates.
prior: The prior distribution: of type
function(type=c("r","d"), n ,par, Hpar, log, dimData ), where dimData is the dimension of the sample space (e.g., for the two-dimensional simplex (triangle), dimData=3. Should return either a matrix with n rows containing a random parameter sample generated under the prior (if type == "d"), or the density of the parameter par (the logarithm of the density if log==TRUE. See prior.pb and prior.nl for templates.
Hpar: A list containing Hyper-parameters to be passed to prior.
MCpar: A list containing MCMC tuning parameters to be passed to proposal.

Details

lAccept.ratio is a functional: likelihood,proposal,prior are user defined functions. Should not be called directly, but through the MCMC sampler posteriorMCMC generating the posterior.