plot_class_allocation

This function plots the allocation of decision-maker specific coefficient vectors
<code>beta</code> given the allocation vector <code>z</code>, the class means <code>b</code>,
and the class covariance matrices <code>Omega</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_class_allocation function

<dl><dt>beta</dt>
<dd>[<code>matrix(nrow = P_r, ncol = N)</code>] 
The matrix of the decider-specific coefficient vectors.</dd>
<dt>z</dt>
<dd>[<code>numeric(N)</code>] 
The decider class allocations.</dd>
<dt>b</dt>
<dd>[<code>matrix(nrow = P_r, ncol = C)</code>] 
The matrix of class means as columns.</dd>
<dt>Omega</dt>
<dd>[<code>matrix(nrow = P_r * P_r, ncol = C)</code>] 
The matrix of vectorized class covariance matrices as columns.</dd>
<dt>...</dt>
<dd>Optional visualization parameters:<ul>
<li><code>colors</code>, a character vector of color specifications,</li>
<li><code>perc</code>, a numeric between 0 and 1 to draw the <code>perc</code> percentile
ellipsoids for the underlying Gaussian distributions (<code>perc = 0.95</code> per default),</li>
<li><code>r</code>, the current iteration number of the Gibbs sampler to be displayed in the legend,</li>
<li><code>sleep</code>, the number of seconds to pause after plotting.</li>
</ul></dd></dl>

Arguments

Plot class allocation (for P_r = 2 only) — plot_class_allocation

<dl>

<dt>beta</dt>
<dd>[<code>matrix(nrow = P_r, ncol = N)</code>] 
The matrix of the decider-specific coefficient vectors.</dd>


<dt>z</dt>
<dd>[<code>numeric(N)</code>] 
The decider class allocations.</dd>


<dt>b</dt>
<dd>[<code>matrix(nrow = P_r, ncol = C)</code>] 
The matrix of class means as columns.</dd>


<dt>Omega</dt>
<dd>[<code>matrix(nrow = P_r * P_r, ncol = C)</code>] 
The matrix of vectorized class covariance matrices as columns.</dd>


<dt>...</dt>
<dd>Optional visualization parameters:<ul>
<li><code>colors</code>, a character vector of color specifications,</li>
<li><code>perc</code>, a numeric between 0 and 1 to draw the <code>perc</code> percentile
ellipsoids for the underlying Gaussian distributions (<code>perc = 0.95</code> per default),</li>
<li><code>r</code>, the current iteration number of the Gibbs sampler to be displayed in the legend,</li>
<li><code>sleep</code>, the number of seconds to pause after plotting.</li>
</ul></dd>

</dl>

plot_class_allocation: Plot class allocation (for `P_r = 2` only)

Description

Usage

Value

Arguments

Details