LearnBayes (version 2.15.1)

bayes.model.selection: 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

Examples

# NOT RUN {
data(birdextinct)
logtime=log(birdextinct$time)
X=cbind(1,birdextinct$nesting,birdextinct$size,birdextinct$status)
bayes.model.selection(logtime,X,100)
# }