mjmcmc {FBMS}

Main algorithm for MJMCMC (Genetically Modified MJMCMC)

Description

Main algorithm for MJMCMC (Genetically Modified MJMCMC)

Usage

mjmcmc(
  data,
  loglik.pi,
  N = 100,
  probs = NULL,
  params = NULL,
  sub = FALSE,
  verbose = TRUE
)

Arguments

data

A matrix containing the data to use in the algorithm, first column should be the dependent variable, second should be the intercept and the rest of the columns should be the independent variables.

loglik.pi

The (log) density to explore

N

The number of iterations to run for

probs

A list of the various probability vectors to use

params

A list of the various parameters for all the parts of the algorithm

sub

An indicator that if the likelihood is inexact and should be improved each model visit (EXPERIMENTAL!)

verbose

A logical denoting if messages should be printed

Value

A list containing the following elements:

models

All visited models.

accept

Average acceptance rate of the chain.

lo.models

All models visited during local optimization.

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(matrix(rnorm(600), 100), gaussian.loglik)
summary(result)
plot(result)