mcmc.erlang: Does a metropolis hastings for the Erlang distribution

In coarseDataTools: Analysis of Coarsely Observed Data

Description

Does a metropolis hastings for the Erlang distribution

Usage

mcmc.erlang(
  dat,
  prior.par1,
  prior.par2,
  init.pars,
  verbose,
  burnin,
  n.samples,
  sds = c(1, 1)
)

Arguments

dat	the data to fit
prior.par1	mean of priors. A negative binomial (for shape) and a normal for log(scale)
prior.par2	dispersion parameters for priors, dispersion for negative binomial, log scale sd for normal
init.pars	the starting parameters on the reporting scale
verbose	how often to print an update
burnin	how many burnin iterations to do
n.samples	the number of samples to keep and report back
sds	the standard deviations for the proposal distribution

Value

a matrix of n.samples X 2 parameters, on the estimation scale

coarseDataTools documentation built on Dec. 11, 2021, 9:29 a.m.