mjmcmc {FBMS} | R Documentation |
Main algorithm for MJMCMC (Genetically Modified MJMCMC)
Description
Main algorithm for MJMCMC (Genetically Modified MJMCMC)
Usage
mjmcmc(
data,
loglik.pi = gaussian.loglik,
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, 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)