Package index • RprobitB

Data management

Use these functions for the choice data preparation or simulation.

check_form(): Check model formula

overview_effects(): Print effect overview

create_lagged_cov(): Create lagged choice covariates

as_cov_names(): Re-label alternative specific covariates

prepare_data(): Prepare choice data for estimation

RprobitB_parameter(): Define probit model parameter

simulate_choices(): Simulate choice data

train_test(): Split choice data in train and test subset

plot(<RprobitB_data>): Visualize choice data

Model fitting

Use these function for fitting a probit model to choice data.

check_prior(): Check prior parameters

fit_model(): Fit probit model to choice data

R_hat(): Compute Gelman-Rubin statistic

update(<RprobitB_fit>): Update and re-fit probit model

transform(<RprobitB_fit>): Transform fitted probit model

Model evaluation

Use these functions for model evaluation.

coef(<RprobitB_fit>): Extract model effects

cov_mix(): Extract estimated covariance matrix of mixing distribution

point_estimates(): Compute point estimates

choice_probabilities(): Compute choice probabilities

classification(): Classify deciders preference-based

get_cov(): Extract covariates of choice occasion

predict(<RprobitB_fit>): Predict choices

plot(<RprobitB_fit>): Visualize fitted probit model

plot_roc(): Plot ROC curve

Model selection

Use these functions for model selection.

model_selection(): Compare fitted models

npar(): Extract number of model parameters

mml(): Approximate marginal model likelihood

compute_p_si(): Compute choice probabilities at posterior samples

pred_acc(): Compute prediction accuracy

Datasets

The following datasets are included in the package.

train_choice: Stated Preferences for Train Traveling

Distributions

Implementation of distributions and number generating functions.

dmvnorm(): Density of multivariate normal distribution

rdirichlet(): Draw from Dirichlet distribution

rmvnorm(): Draw from multivariate normal distribution

rtnorm(): Draw from one-sided truncated normal

rttnorm(): Draw from two-sided truncated normal

rwishart(): Draw from Wishart distribution

Posterior samplers

These functions draw from conditional posterior distributions.

d_to_gamma(): Transform threshold increments to thresholds

ll_ordered(): Log-likelihood in the ordered probit model

update_Omega(): Update class covariances

update_Sigma(): Update error term covariance matrix of multiple linear regression

update_U(): Update latent utility vector

update_U_ranked(): Update latent utility vector in the ranked probit case

update_b(): Update class means

update_d(): Update utility threshold increments

update_m(): Update class sizes

update_reg(): Update coefficient vector of multiple linear regression

update_s(): Update class weight vector

update_z(): Update class allocation vector