BAS {BAS} | R Documentation

## BAS: Bayesian Model Averaging using Bayesian Adaptive Sampling

### Description

Package for 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.

### Details

Package: | BAS
Depends: | R (>= 2.8)
License: | GPL-3
URL: http://www.stat.duke.edu/~clyde

Index:

### Author(s)

Merlise Clyde,
Maintainer: Merlise Clyde <clyde@stat.duke.edu>

### 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
http://dx.doi.org/10.1198/jcgs.2010.09049

Clyde, M. and George, E. I. (2004) Model uncertainty. Statist. Sci., 19, 81-94.
http://dx.doi.org/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. (2015) Mixtures of g-priors in Generalized Linear Models. http://arxiv.org/abs/1503.06913

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.
http://dx.doi.org/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`, `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)

[Package BAS version 1.5.3 Index]