GLMMselect {GLMMselect}

GLMMselect: Bayesian model selection method for generalized linear mixed models

Description

GLMMselect: Bayesian model selection method for generalized linear mixed models

Usage

GLMMselect(
  Y,
  X,
  Sigma,
  Z,
  family,
  prior,
  offset = NULL,
  NumofModel = 10,
  pip_fixed = 0.5,
  pip_random = 0.5
)

Arguments

Y

A numeric vector of observations.

X

A matrix of covariates.

Sigma

A list of covariance matrices for random effects.

Z

A list of design matrices for random effects.

family

A description of the error distribution to be used in the model.

prior

The prior distribution for variance component of random effects.

offset

This can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector of length equal to the number of observations.

NumofModel

The number of models with the largest posterior probabilities being printed out.

pip_fixed

The cutoff that if the posterior inclusion probability of fixed effects is larger than it, the fixed effects will be included in the best model.

pip_random

The cutoff that if the posterior inclusion probability of random effects is larger than it, the random effects will be included in the best model.

Value

A list of the indices of covariates and random effects which are in the best model.

Examples

library(GLMMselect)

data("Y");data("X");data("Z");data("Sigma")
Model_selection_output <- GLMMselect(Y=Y, X=X, Sigma=Sigma,
                         Z=Z, family="poisson", prior="AR", offset=NULL)