variable.names.bma

model.frame.bma

Simple utilities retrieving variable names and design matrix from a bma
object

utilities

Bayesian Model Averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, hyper-g and empirical priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Post-processing functions allow for inferring posterior inclusion and model probabilities, various moments, coefficient and predictive densities. Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison. Also includes Bayesian normal-conjugate linear model with Zellner's g prior, and assorted methods.

Stefan Zeugner

Bayesian Model Averaging Library

variable.names.bma function

<dl><dt>object</dt>
<dd>A <code>bma</code> object (as produced by <code>bms</code>)</dd>
<dt>...</dt>
<dd>further arguments passed to or from other methods</dd></dl>

Arguments

Variable names and design matrix — variable.names.bma

<dl>

<dt>object</dt>
<dd>A <code>bma</code> object (as produced by <code>bms</code>)</dd>


<dt>...</dt>
<dd>further arguments passed to or from other methods</dd>

</dl>

variable.names.bma: Variable names and design matrix

Description

Usage

Arguments

Details

See Also

Examples