plot_mixture_marginal

This function plots an estimated marginal mixing distributions.

internal

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_mixture_marginal function

<dl><dt>mean</dt>
<dd>The class means.</dd>
<dt>cov</dt>
<dd>The class covariances.</dd>
<dt>weights</dt>
<dd>The class weights.</dd>
<dt>name</dt>
<dd>The covariate name.</dd></dl>

Arguments

Plot marginal mixing distributions — plot_mixture_marginal

<dl>

<dt>mean</dt>
<dd>The class means.</dd>


<dt>cov</dt>
<dd>The class covariances.</dd>


<dt>weights</dt>
<dd>The class weights.</dd>


<dt>name</dt>
<dd>The covariate name.</dd>

</dl>

plot_mixture_marginal: Plot marginal mixing distributions

Description

Usage

Value

Arguments