postProb: Obtain posterior model probabilities

In modelSelection: High-Dimensional Model Selection

Obtain posterior model probabilities

Description

Obtain posterior model probabilities after running Bayesian model selection

Usage

  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.

Value

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 msfit_ggm objects, a list with posterior probabilities (element "pp") and the corresponding model (element "modelid") indicating what edges are non-zero

For mixturebf objects, posterior probabilities for the specified number of components

For localtest objects, posterior probabilities of a local covariate effect at various regions

Author(s)

David Rossell

See Also

modelSelection to perform model selection

Examples

#See help(modelSelection)

modelSelection documentation built on May 16, 2026, 5:06 p.m.