BAS {BAS}

BAS: Bayesian Model Averaging using Bayesian Adaptive Sampling

Description

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.

Author(s)

Merlise Clyde,
Maintainer: Merlise Clyde <[e-mail]>

References

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
doi:10.1198/jcgs.2010.09049

Clyde, M. and George, E. I. (2004) Model uncertainty. Statist. Sci., 19, 81-94.
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 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.

doi:10.1198/016214507000001337

See Also

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.basglm(), predict.bas(), summary.bas(), update.bas(), variable.names.pred.bas()

Examples

data("Hald")
hald.gprior =  bas.lm(Y ~ ., data=Hald, alpha=13, prior="g-prior")

# more complete demos

demo(BAS.hald)
## Not run: 
demo(BAS.USCrime)

## End(Not run)