EB.global: Find the global Empirical Bayes estimates for BMA
In BAS: Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling

EB.global

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.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1198/016214507000001337")}

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)

BAS documentation built on Sept. 30, 2024, 9:19 a.m.