mcmc_mix1_wrapper: Wrapper of mcmc_mix1

Description

Wrapper of mcmc_mix1

Usage

mcmc_mix1_wrapper(
  df,
  seed,
  u_max = 2000L,
  log_diff_max = 11,
  a_psiu = 0.1,
  b_psiu = 0.9,
  m_alpha1 = 0,
  s_alpha1 = 10,
  a_theta1 = 1,
  b_theta1 = 1,
  m_alpha2 = 0,
  s_alpha2 = 10,
  positive = FALSE,
  iter = 20000L,
  thin = 1L,
  burn = 10000L,
  freq = 100L,
  invts = 1,
  mc3_or_marg = TRUE,
  x_max = 1e+05
)

Value

A list returned by mcmc_mix1

Arguments

df: A data frame with at least two columns, x & count
seed: Integer for set.seed
u_max: Scalar (default 2000), positive integer for the maximum threshold to be passed to obtain_u_set_mix1
log_diff_max: Positive real number, the value such that thresholds with profile posterior density not less than the maximum posterior density - log_diff_max will be kept
a_psiu, b_psiu, m_alpha1, s_alpha1, a_theta1, b_theta1, m_alpha2, s_alpha2: Scalars, real numbers representing the hyperparameters of the prior distributions for the respective parameters. See details for the specification of the priors.
positive: Boolean, is alpha1 positive (TRUE) or unbounded (FALSE)?
iter: Positive integer representing the length of the MCMC output
thin: Positive integer representing the thinning in the MCMC
burn: Non-negative integer representing the burn-in of the MCMC
freq: Positive integer representing the frequency of the sampled values being printed
invts: Vector of the inverse temperatures for Metropolis-coupled MCMC (if mc3_or_marg = TRUE) or power posterior (if mc3_or_marg = FALSE)
mc3_or_marg: Boolean, is Metropolis-coupled MCMC to be used? Ignored if invts = c(1.0)
x_max: Scalar (default 100000), positive integer limit for computing the normalising constant