func_MCMC: Function to run MCMC sampling

In BayesSurvive: Bayesian Survival Models for High-Dimensional Data

Function to run MCMC sampling

Description

This an internal function for MCMC sampling

Usage

func_MCMC(
  survObj,
  hyperpar,
  ini,
  nIter,
  thin,
  burnin,
  S,
  method,
  MRF_2b,
  MRF_G,
  output_graph_para,
  verbose,
  cpp = FALSE
)

Arguments

survObj	a list containing observed data from n subjects; t, di, X. See details for more information
hyperpar	a list containing prior parameter values
ini	a list containing prior parameters' initial values
nIter	the number of iterations of the chain
thin	thinning MCMC intermediate results to be stored
burnin	number of iterations to discard at the start of the chain. Default is 0
S	the number of subgroups
method	a method option from c("Pooled", "CoxBVSSL", "Sub-struct")
MRF_2b	two different b in MRF prior for subgraphs G_ss and G_rs
MRF_G	logical value. MRF_G = TRUE is to fix the MRF graph which is provided in the argument hyperpar, and MRF_G = FALSE is to use graphical model for learning the MRF graph
output_graph_para	allow (TRUE) or suppress (FALSE) the output for parameters 'G', 'V', 'C' and 'Sig' in the graphical model if MRF_G = FALSE
verbose	logical value to display the progress of MCMC
cpp	logical, whether to use C++ code for faster computation

Value

A list object saving the MCMC results with components including 'gamma.p', 'beta.p', 'h.p', 'gamma.margin', 'beta.margin', 's', 'eta0', 'kappa0', 'c0', 'pi.ga', 'tau', 'cb', 'accept.RW', 'log.jpost', 'log.like', 'post.gamma'

BayesSurvive documentation built on April 3, 2025, 5:58 p.m.