run_mcmc_p: Run MCMC for multiple scenarios with provided data with...

In psborrow: Bayesian Dynamic Borrowing with Propensity Score

Run MCMC for multiple scenarios with provided data with parallel processing

Description

Run MCMC for multiple scenarios with provided data with parallel processing

Usage

run_mcmc_p(
  dt,
  priorObj,
  n.chains,
  n.adapt,
  n.burn,
  n.iter,
  seed,
  path,
  n.cores = 2
)

Arguments

dt	a list of matrix containing simulated time-to-events information
priorObj	an object of class .priorClass generated in set_prior
n.chains	number of parallel chains for the model
n.adapt	number of iterations for adaptation
n.burn	number of iterations discarded as burn-in
n.iter	number of iterations to monitor
seed	the seed of random number generator. Default is the first element of .Random.seed
path	file name for saving the output including folder path
n.cores	number of processes to parallelize over (default = 2)

Value

a data.frame containing summary statistics of the posterior distribution for each simulation

Examples

# similar to run_mcmc

psborrow documentation built on March 7, 2023, 8:32 p.m.