Obtain posterior model probabilities after running Bayesian model selection
postProb(object, nmax, method='norm')
Arguments
object: Object of class msfit returned by modelSelection, class mixturebf returned by bfnormmix, class cilfit returned by cil
or class localtest returned by localnulltest
nmax: Maximum number of models to report (defaults to no max)
method: Only when class(object) is msfit. For 'norm' probabilities are obtained by renormalizing the stored integrated likelihoods, for 'exact' they are given by the proportion of MCMC visits to each model. 'norm' has less variability but can be biased if the chain has not converged.
Returns
A data.frame with posterior model probabilities in column pp. Column modelid indicates the indexes of the selected covariates (empty for the null model with no covariates)
For mixturebf objects, posterior probabilities for the specified number of components
For localtest objects, posterior probabilities of a local covariate effect at various regions