# BAS: Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling

The `BAS`

R package is designed to provide
an easy to use package and fast code for implementing Bayesian Model
Averaging and Model Selection in `R`

using state of the art prior
distributions for linear and generalized linear models. The prior
distributions in `BAS`

are based on Zellner’s g-prior or mixtures of
g-priors for linear and generalized linear models. These have been shown
to be consistent asymptotically for model selection and inference and
have a number of computational advantages. `BAS`

implements three main
algorithms for sampling from the space of potential models: a
deterministic algorithm for efficient enumeration, adaptive sampling
without replacement algorithm for modest problems, and a MCMC algorithm
that utilizes swapping to escape from local modes with standard
Metropolis-Hastings proposals.

## Installation

The stable version
can be installed easily in the `R`

console like any other package:

`install.packages('BAS')`

On the other hand, I welcome everyone to use the most recent version of
the package with quick-fixes, new features and probably new bugs. It’s
currently hosted on GitHub. To
get the latest development version from
GitHub, use the `devtools`

package
from CRAN and enter in
`R`

:

`devtools::install_github('merliseclyde/BAS')`

You can check out the current build status before installing.

Installing the package from source does require compilation of C and FORTRAN code as the library makes use of BLAS and LAPACK for efficient model fitting. See CRAN manuals for installing packages from source under different operating systems.

## Usage

To begin load the package:

`library(BAS)`

The two main function in `BAS`

are `bas.lm`

and `bas.glm`

for
implementing Bayesian Model Averaging and Variable Selection using
Zellner’s g-prior and mixtures of g priors. Both functions have a syntax
similar to the `lm`

and `glm`

functions respectively. We illustrate
using `BAS`

on a simple example with the famous Hald data set using the
Zellner-Siow Cauchy prior via

```
data(Hald)
hald.ZS = bas.lm(Y ~ ., data=Hald, prior="ZS-null", modelprior=uniform(), method="BAS")
```

`BAS`

has `summary`

, `plot`

`coef`

, `predict`

and `fitted`

functions
like the `lm`

/`glm`

functions. Images of the model space highlighting
which variable are important may be obtained via

`image(hald.ZS)`

Run `demo("BAS.hald")`

or `demo("BAS.USCrime")`

or see the package
vignette for more examples and options such as using MCMC for model
spaces that cannot be enumerated.

### Generalized Linear Models

`BAS`

now includes for support for binomial and binary regression,
Poisson regression, and Gamma regression using Laplace approximations to
obtain Bayes Factors used in calculating posterior probabilities of
models or sampling of models. Here is an example using the Pima diabetes
data set with the hyper-g/n prior:

```
library(MASS)
data(Pima.tr)
Pima.hgn = bas.glm(type ~ ., data=Pima.tr, method="BAS", family=binomial(),
betaprior=hyper.g.n(), modelprior=uniform())
```

Note, the syntax for specifying priors on the coefficients in `bas.glm`

uses a function with arguments to specify the hyper-parameters, rather
than a text string to specify the prior name and a separate argument for
the hyper-parameters. `bas.lm`

will be moving to this format sometime in
the future.

## Feature Requests and Issues

Feel free to report any issues or request features to be added via the github issues page.

For current documentation and vignettes see the BAS website

### Support

This material is based upon work supported by the National Science Foundation under Grant DMS-1106891. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.