bayes.model.selection {LearnBayes}R Documentation

Bayesian regression model selection using G priors

Description

Using Zellner's G priors, computes the log marginal density for all possible regression models

Usage

bayes.model.selection(y, X, c, constant=TRUE)

Arguments

y vector of response values
X matrix of covariates
c parameter of the G prior
constant logical variable indicating if a constant term is in the matrix X

Value

mod.prob data frame specifying the model, the value of the log marginal density and the value of the posterior model probability
converge logical vector indicating if the laplace algorithm converged for each model

Author(s)

Jim Albert

Examples

data(birdextinct)
logtime=log(birdextinct$time)
X=cbind(1,birdextinct$nesting,birdextinct$size,birdextinct$status)
bayes.model.selection(logtime,X,100)

[Package LearnBayes version 2.0 Index]