submitted/aliaksah-EMJMCMC2016-04a333b/README.md

In aliaksah/EMJMCMC2016: EMJMCMC

EMJMCMC2016

A package with mode jumping MCMC for Bayesian variable selection and averaging within GLMM

Generalized linear mixed models (GLMM) are addressed for inference and prediction in a wide range of different applications providing a powerful scientific tool for the researchers and analysts coming from different fields. In most of these fields more and more sources of data are becoming available introducing a variety of hypothetical explanatory variables for these models to be considered. Selection of an optimal combination of these variables is thus becoming crucial in a Bayesian setting. The posterior distribution of the models can be viewed as a relevant measure for the model evidence, based on the observed data. The number of models to select from is exponential in the number of candidate variables, moreover the search space in this context is often extremely non-concave and has numerous local extrema or statistically speaking modes. Hence efficient search algorithms have to be adopted for evaluating the posterior distribution within a reasonable amount of time. In this paper we introduce and implement efficient mode jumping MCMC algorithms for calculating posterior probabilities of the models for generalized linear models with a random effect. Marginal likelihoods of models, given the specific choice of priors and any choice of covariates, can be efficiently calculated using the integrated nested Laplace approximations approach (INLA) for the class of models addressed, however for some particular cases exact results are also available. We further apply the suggested algorithm to some simulated data, the famous U.S. crime data, gene expression data, and real epigenetic data and compare its performance to some of the existing approaches like BAS, RS or MC3.

aliaksah/EMJMCMC2016 documentation built on July 27, 2023, 5:48 a.m.