Find the global Empirical Bayes estimates for BMA

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

Value

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

Details

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

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.
doi:10.1198/016214507000001337

Author

Merlise Clyde clyde@stat.duke.edu

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)