plot.bma

Produces a combined plot: upper row shows prior and posterior model size
distribution, lower row shows posterior model probabilities for the best
models

hplot

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

plot.bma function

<dl><dt>x</dt>
<dd>an object of class 'bma'</dd>
<dt>...</dt>
<dd>additional arguments for <code><a href="/link/matplot?package=BMS&version=0.3.5" data-mini-rdoc="BMS::matplot">matplot</a></code></dd></dl>

Arguments

Plot Posterior Model Size and Model Probabilities — plot.bma

<dl>

<dt>x</dt>
<dd>an object of class 'bma'</dd>


<dt>...</dt>
<dd>additional arguments for <code><a href='https://rdrr.io/r/graphics/matplot.html'>matplot</a></code></dd>

</dl>

plot.bma: Plot Posterior Model Size and Model Probabilities

Description

Usage

Value

Arguments

See Also

Examples