BAS: Bayesian Model Averaging using Bayesian Adaptive Sampling
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.
| Package: | BAS |
| Depends: | R (>= 2.8) |
| License: | GPL-3 |
| URL: https://www.stat.duke.edu/~clyde | |
Index:
Merlise Clyde,
Maintainer: Merlise Clyde <clyde@stat.duke.edu>
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
https://dx.doi.org/10.1198/jcgs.2010.09049
Clyde, M. and George, E. I. (2004) Model uncertainty. Statist. Sci., 19,
81-94.
https://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. https://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.
https://dx.doi.org/10.1198/016214507000001337
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()
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.