R: Bayesian regression model selection using G priors

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 2.15 Index]