bmrvarx: PX-DA MCMC routine to implement a bmrvarx model

In nickseedorff/bmrarm: Bayesian Mixed Response Autoregressive Models

PX-DA MCMC routine to implement a bmrvarx model

Description

PX-DA MCMC routine to implement a bmrvarx model

Usage

bmrvarx(
  formula,
  data,
  ordinal_outcomes,
  sig_prior = 1e+06,
  nsim = 1000,
  burn_in = 100,
  thin = 10,
  seed = 14,
  max_iter_rej = 500,
  N_burn_trunc = 10
)

Arguments

formula	an object of class "formula"; a symbolic description of the model to be fitted
data	a dataframe containing outcome variables, covariates, and a patient or subject identifier
ordinal_outcomes	a character string containing the names of the ordinal outcomes
sig_prior	scalar, prior variance on the regression coefficients
nsim	positive integer, number of iterations with default of 1000
burn_in	positive integer, number of iterations to remove with default of 100
thin	positive integer, specifiers the period of saving samples. Default of 10
seed	positive integer, seed for random number generation
max_iter_rej	maximum number of rejection algorithm attempts for multivariate truncated normal
N_burn_trunc	integer, number of burn-in draws from the truncated multivariate normal Gibbs sampler

Value

bmrvarx

nickseedorff/bmrarm documentation built on April 17, 2025, 9:43 p.m.