Bayesian Estimation for the Beta-Danish Distribution — bayes

Samples from the posterior of the Beta-Danish or Exponentiated Danish parameters using a random-walk Metropolis sampler with vague \(\Gamma(0.01, 0.01)\) priors on the positive parameters.

Usage

bayes_betadanish(
  time,
  status = NULL,
  submodel = TRUE,
  burnin = 5000,
  mcmc = 15000,
  tune = 0.5,
  theta_init = NULL,
  seed = NULL,
  verbose = 0
)

Arguments

time: Numeric vector of observed times.
status: Numeric vector of event indicators (1 = event, 0 = right-censored).
submodel: Logical; TRUE for the 3-parameter ED submodel, FALSE for the 4-parameter full model.
burnin: Burn-in iterations.
mcmc: Post-burnin iterations.
tune: Random-walk tuning parameter.
theta_init: Optional starting values on the log scale.
seed: Optional integer seed.
verbose: Integer; passed to MCMCmetrop1R (0 = silent).

Value

An object of class "bd_bayes" with components draws (mcmc object), summary, HPD, submodel, call.

Details

Requires MCMCpack and coda (Suggests).

Examples

if (FALSE) { # \dontrun{
set.seed(1)
dat <- rbetadanish(100, a = 1.5, b = 2, c = 3, k = 0.5)
fit <- bayes_betadanish(time = dat, submodel = TRUE,
                        burnin = 500, mcmc = 1500)
fit$summary
} # }