mcmc_bin_arms: MCMC for binary regression - ARMS version

In DouglasMesquita/FLAMES: FLAMES: Flexible Link functions with AsyMptotES

MCMC for binary regression - ARMS version

Description

Perform MCMC with ARMS algorithm

Usage

mcmc_bin_arms(y, X, nsim, burnin, lag, inv_link_f, type, sample_c,
  sample_d, sigma_beta, a_c, b_c, a_d, b_d, a_lambda, b_lambda, p_c, p_d,
  p_prop, p_beta, p_df, p_lambda, const)

Arguments

y	Bernoulli observed values
X	Covariate matrix
nsim	Sample size required for MCMC
burnin	Burn in for MCMC
lag	Lag for MCMC
inv_link_f	Inverse link function
type	"logit", "probit", "cauchit", "robit", "cloglog" or "loglog"
sample_c	Should c be sampled?
sample_d	Should d be sampled?
sigma_beta	Variance of beta prior
a_c	Shape1 for c prior
b_c	Shape2 for c prior
a_d	Shape1 for d prior
b_d	Shape2 for d prior
a_lambda	Inferior limit for lambda
b_lambda	Superior limit for lambda
p_c	To restore c
p_d	To restore d
p_prop	To restore p
p_beta	To restore beta
p_df	To restore df
p_lambda	To restore lambda
const	A constant to help on sampling degrees of freedom \tilde{df} = df/c

Value

Chains of all parameters

DouglasMesquita/FLAMES documentation built on May 28, 2022, 8:35 a.m.