The flowchart of
{RprobitB}.{RprobitB} implements 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. See the
vignette on the model definition for details about the probit
model.
Working with {RprobitB} follows a structured workflow.
The main functions fall into three categories: data management, model
fitting, and model evaluation, as illustrated in the flowchart below. A
typical workflow proceeds as follows:
Prepare a choice data set via the prepare_data()
function or simulate data via simulate_choices(). Both
functions return an RprobitB_data object that can be fed
into the estimation routine. The train_test() allows to
split the data into an estimation and a validation part. See the
vignette on choice data for details.
The estimation routine is called fit_model() and
returns an RprobitB_fit object. The
transform_fit() function allows to change normalization of
the model after a model has been fitted. The details are documented in
the vignettes on
model fitting and on
modeling heterogeneity.
The RprobitB_fit object can be fed into
coef() to show the covariate effects on the choices and
into predict() to compute choice probabilities and forecast
choice behavior if choice characteristics would change, see the
vignette on choice prediction. The classification()
function allows for preference-based decider classification. The
function model_selection() compares
RprobitB_fit objects by computing different model selection
criteria, see the
vignette on model selection.
{RprobitB}.