mcmc_pol_wrapper: Wrapper of mcmc_pol

Description

Wrapper of mcmc_pol

Usage

mcmc_pol_wrapper(
  df,
  seed,
  alpha_init = 1.5,
  theta_init = 0.5,
  m_alpha = 0,
  s_alpha = 10,
  a_theta = 1,
  b_theta = 1,
  a_pseudo = 10,
  b_pseudo = 1,
  pr_power = 0.5,
  iter = 20000L,
  thin = 20L,
  burn = 100000L,
  freq = 1000L,
  invts = 1,
  mc3_or_marg = TRUE,
  x_max = 1e+05
)

Value

A list returned by mcmc_pol

Arguments

df: A data frame with at least two columns, x & count
seed: Integer for set.seed
alpha_init: Real number greater than 1, initial value of the parameter
theta_init: Real number in (0, 1], initial value of the parameter
m_alpha: Real number, mean of the prior normal distribution for alpha
s_alpha: Positive real number, standard deviation of the prior normal distribution for alpha
a_theta: Positive real number, first parameter of the prior beta distribution for theta; ignored if pr_power = 1.0
b_theta: Positive real number, second parameter of the prior beta distribution for theta; ignored if pr_power = 1.0
a_pseudo: Positive real number, first parameter of the pseudoprior beta distribution for theta in model selection; ignored if pr_power = 1.0
b_pseudo: Positive real number, second parameter of the pseudoprior beta distribution for theta in model selection; ignored if pr_power = 1.0
pr_power: Real number in [0, 1], prior probability of the discrete power law
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