plot.RprobitB_fit

This function is the plot method for an object of class <code>RprobitB_fit</code>.

Bayes estimation of probit choice models in cross-sectional and panel
settings. The package can analyze binary, multivariate, ordered, and ranked
choices, as well as heterogeneity of choice behavior among deciders. The
main functionality includes model fitting via Gibbs sampling, tools for
convergence diagnostic, choice data simulation, in-sample and out-of-sample
choice prediction, and model selection using information criteria and Bayes
factors. The latent class model extension facilitates preference-based
decider classification, where the number of latent classes can be inferred
via the Dirichlet process or a weight-based updating heuristic. This allows
for flexible modeling of choice behavior without the need to impose
structural constraints. For a reference on the method, see Oelschlaeger and
Bauer (2021) <https://trid.trb.org/view/1759753>.

Lennart Oelschläger

RprobitB

Bayesian Probit Choice Modeling

Dietmar Bauer

plot.RprobitB_fit function

<dl><dt>x</dt>
<dd>An object of class <code>RprobitB_fit</code>.</dd>
<dt>type</dt>
<dd>[<code>character(1)</code>] 
The type of plot, which can be one of:<ul>
<li><code>"mixture"</code> to visualize the mixing distribution,</li>
<li><code>"acf"</code> for autocorrelation plots of the Gibbs samples,</li>
<li><code>"trace"</code> for trace plots of the Gibbs samples,</li>
<li><code>"class_seq"</code> to visualize the sequence of class numbers.</li>
</ul></dd>
<dt>ignore</dt>
<dd>[<code><a href="/link/character()?package=RprobitB&version=1.2.0" data-mini-rdoc="RprobitB::character()">character()</a></code>] 
Covariate or parameter names that do not get visualized.</dd>
<dt>...</dt>
<dd>Currently not used.</dd></dl>

Arguments

Visualize fitted probit model — plot.RprobitB_fit

<dl>

<dt>x</dt>
<dd>An object of class <code>RprobitB_fit</code>.</dd>


<dt>type</dt>
<dd>[<code>character(1)</code>] 
The type of plot, which can be one of:<ul>
<li><code>"mixture"</code> to visualize the mixing distribution,</li>
<li><code>"acf"</code> for autocorrelation plots of the Gibbs samples,</li>
<li><code>"trace"</code> for trace plots of the Gibbs samples,</li>
<li><code>"class_seq"</code> to visualize the sequence of class numbers.</li>
</ul></dd>


<dt>ignore</dt>
<dd>[<code><a href='https://rdrr.io/r/base/character.html'>character()</a></code>] 
Covariate or parameter names that do not get visualized.</dd>


<dt>...</dt>
<dd>Currently not used.</dd>

</dl>

plot.RprobitB_fit: Visualize fitted probit model

Description

Usage

Value

Arguments