pred_acc

This function computes the prediction accuracy of an <code>RprobitB_fit</code>
object. Prediction accuracy means the share of choices that are correctly
predicted by the model, where prediction is based on the maximum choice
probability.

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

pred_acc function

<dl><dt>x</dt>
<dd>An object of class <code>RprobitB_fit</code>.</dd>
<dt>...</dt>
<dd>Optionally specify more <code>RprobitB_fit</code> objects.</dd></dl>

Arguments

Compute prediction accuracy — pred_acc

<dl>

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


<dt>...</dt>
<dd>Optionally specify more <code>RprobitB_fit</code> objects.</dd>

</dl>

pred_acc: Compute prediction accuracy

Description

Usage

Value

Arguments