mjmcmc: Main algorithm for MJMCMC (Genetically Modified MJMCMC)

In FBMS: Flexible Bayesian Model Selection and Model Averaging

Main algorithm for MJMCMC (Genetically Modified MJMCMC)

Description

Main algorithm for MJMCMC (Genetically Modified MJMCMC)

Usage

mjmcmc(
  x,
  y,
  N = 1000,
  probs = NULL,
  params = NULL,
  loglik.pi = NULL,
  mlpost_params = list(family = "gaussian", beta_prior = list(type = "g-prior")),
  intercept = TRUE,
  fixed = 0,
  sub = FALSE,
  verbose = TRUE
)

Arguments

x	matrix containing the design matrix with data to use in the algorithm,
y	response variable
N	The number of MJMCMC iterations to run for (default 100)
probs	A list of various probability vectors used by GMJMCMC, generated by gen.probs.mjmcmc.
params	A list of various parameter vectors used by MJMCMC, generated by gen.params.mjmcmc.
loglik.pi	A function specifying the marginal log-posterior of the model up to a constant, including the logarithm of the model prior: \log p(M|Y) = \text{const} + \log p(Y|M) + \log p(M). Typically assumes a Gaussian model with Zellner's g prior with g = max(n,p^2) by default.
mlpost_params	All parameters for the estimator function loglik.pi
intercept	Logical. Whether to include an intercept in the design matrix. Default is TRUE. No variable selection is performed on the intercept.
fixed	Integer specifying the number of leading columns in the design matrix to always include in the model. Default is 0.
sub	Logical. If TRUE, uses subsampling or a stochastic approximation approach to the likelihood rather than the full likelihood. Default is FALSE.
verbose	Logical. Whether to print messages during execution. Default is TRUE for gmjmcmc and FALSE for the parallel version.

Value

A list containing the following elements:

models	All visited models in both mjmcmc and local optimization.
accept	Average acceptance rate of the chain.
mc.models	All models visited during mjmcmc iterations.
best.crit	The highest log marginal probability of the visited models.
marg.probs	Marginal probabilities of the features.
model.probs	Marginal probabilities of all of the visited models.
model.probs.idx	Indices of unique visited models.
populations	The covariates represented as a list of features.

Examples

result <- mjmcmc(
y = matrix(rnorm(100), 100),
x = matrix(rnorm(600), 100),
loglik.pi = gaussian.loglik)
summary(result)
plot(result)

FBMS documentation built on Sept. 13, 2025, 1:09 a.m.