Implementation of Bayesian Model Averaging in linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are of the form of Zellner's g-prior or mixtures of g-priors. Options include the Zellner-Siow Cauchy Priors, the Liang et al hyper-g priors, Local and Global Empirical Bayes estimates of g, and other default model selection criteria such as AIC and BIC. Sampling probabilities may be updated based on the sampled models.
Merlise Clyde,
Maintainer: Merlise Clyde <clyde@stat.duke.edu>
_PACKAGE
Clyde, M. Ghosh, J. and Littman, M. (2010) Bayesian Adaptive
Sampling for Variable Selection and Model Averaging. Journal of
Computational Graphics and Statistics. 20:80-101
tools:::Rd_expr_doi("10.1198/jcgs.2010.09049")
Clyde, M. and George, E. I. (2004) Model uncertainty. Statist. Sci., 19,
81-94.
tools:::Rd_expr_doi("10.1214/088342304000000035")
Clyde, M. (1999) Bayesian Model Averaging and Model Search Strategies (with discussion). In Bayesian Statistics 6. J.M. Bernardo, A.P. Dawid, J.O. Berger, and A.F.M. Smith eds. Oxford University Press, pages 157-185.
Li, Y. and Clyde, M. (2018) Mixtures of g-priors in Generalized Linear Models. Journal of the American Statistical Association, 113:524, 1828-1845 tools:::Rd_expr_doi("10.1080/01621459.2018.1469992")
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.
tools:::Rd_expr_doi("10.1198/016214507000001337")
bas.lm bas.glm
Other bas methods:
bas.lm(),
coef.bas(),
confint.coef.bas(),
confint.pred.bas(),
diagnostics(),
fitted.bas(),
force.heredity.bas(),
image.bas(),
plot.confint.bas(),
predict.bas(),
predict.basglm(),
summary.bas(),
update.bas(),
variable.names.pred.bas()
data("Hald")
hald.gprior = bas.lm(Y ~ ., data=Hald, alpha=13, prior="g-prior")
# more complete demos
demo(BAS.hald)
if (FALSE) {
demo(BAS.USCrime)
}
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