Function reference
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check_form()
- Check the model formula
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overview_effects()
- Effect overview
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create_lagged_cov()
- Create lagged choice covariates
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as_cov_names()
- Relabel the alternative specific covariates to the required format
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missing_data()
- Handle missing choice data
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prepare_data()
- Prepare empirical choice data for estimation
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RprobitB_parameter()
- Create object of class
RprobitB_parameter
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simulate_choices()
- Simulate choice data
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train_test()
- Split choice data set in two parts
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plot(<RprobitB_data>)
- Plot method for
RprobitB_data
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check_prior()
- Check prior parameters
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mcmc()
- Probit model fitting via Markov chain Monte Carlo simulation
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nested_model()
- Estimating a nested model
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transform(<RprobitB_fit>)
- Transform an
RprobitB_fit
object
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coef(<RprobitB_fit>)
- Linear coefficients
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cov_mix()
- Estimated covariance matrix of the mixing distribution
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point_estimates()
- Compute point estimates based on Gibbs samples of an
RprobitB_fit
object
-
choice_probabilities()
- Return choice probabilities of an
RprobitB_fit
.
-
preference_classification()
- Classify deciders based on their preferences
-
get_cov()
- Get covariates of choice situation
-
predict(<RprobitB_fit>)
- Choice prediction
-
plot(<RprobitB_fit>)
- Plot method for
RprobitB_fit
-
model_selection()
- Compare fitted models
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AIC()
- Akaike's Information Criterion
-
BIC()
- Bayesian Information Criterion
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logLik()
- Log-likelihood value
-
nobs()
- Number of observations
-
npar()
- Number of model parameters
-
mml()
- Marginal model likelihood
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compute_p_si()
- Compute probability for each observed choice at posterior samples
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pred_acc()
- Prediction accuracy
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choice_berserk
- Choice of berserking
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choice_chess_opening
- Choice of a chess opening
-
model_elec
- Mixed probit model for multivariate choice between electricity suppliers
-
model_train
- Probit model for binary choice between Train trip alternatives
-
model_train_sparse
- Probit model for binary choice between Train trip alternatives with the price as the only explanatory variable
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dmvnorm()
- Density of multivariate normal distribution
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rdirichlet()
- Draw from Dirichlet distribution
-
rmvnorm()
- Draw from multivariate normal distribution
-
rtnorm()
- Draw from truncated normal
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rwishart()
- Draw from Wishart distribution
Posterior samplers
These functions draw from the conditional posterior distributions of the model parameters.
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update_Omega()
- Update class covariances
-
update_Sigma()
- Update error term covariance matrix of multiple linear regression
-
update_U()
- Update latent utility vector
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update_b()
- Update class means
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update_m()
- Update class sizes
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update_reg()
- Update coefficient vector of multiple linear regression
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update_s()
- Update class weight vector
-
update_z()
- Update class allocation vector
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R_hat()
- Compute Gelman-Rubin statistic
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delta()
- Matrix difference operator
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euc_dist()
- Euclidean distance
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is_covariance_matrix()
- Check covariance matrix properties
-
pprint()
- Print abbreviated matrices and vectors