R: Find the global Empirical Bayes estimates for BMA

EB.global {BAS} | R Documentation

## Find the global Empirical Bayes estimates for BMA

### Description

Finds the global Empirical Bayes estimates of g in Zellner's g-prior and model probabilities

### Usage

    EB.global(object, tol = 0.1, g.0 = NULL, max.iterations = 100)

### Arguments

`object` | A 'bas' object created by `bas`
`tol` | tolerance for estimating g
`g.0` | initial value for g
`max.iterations` | Maximum number of iterations for the EM algorithm

### Details

Uses the EM algorithm in Liang et al to estimate the type II MLE of g in Zellner's g prior

### Value

An object of class 'bas' using Zellner's g prior with an estimate of g based on all models

### Author(s)

Merlise Clyde clyde@stat.duke.edu

### References

Liang, F., Paulo, R., Molina, G., Clyde, M. and Berger, J.O. (2008) Mixtures of g-priors for Bayesian Variable Selection. Journal of the American Statistical Association. 103:410-423.
https://dx.doi.org/10.1198/016214507000001337

### See Also

`bas`, `update`

### Examples

    library(MASS)
    data(UScrime)
    UScrime[,-2] = log(UScrime[,-2])
    # EB local uses a different g within each model
    crime.EBL =  bas.lm(y ~ ., data=UScrime, n.models=2^15,
                        prior="EB-local", initprobs= "eplogp")
    # use a common (global) estimate of g
    crime.EBG = EB.global(crime.EBL)

[Package BAS version 1.5.5 Index]