Discrete Choice Models for Economic Applications

Documentation for package ‘choicer’ version 0.2.0

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-- B --

blp	BLP contraction mapping
blp.choicer_mnl	BLP contraction mapping for multinomial logit model
blp.choicer_mxl	BLP contraction mapping for mixed logit model
blp.choicer_nl	BLP contraction mapping for nested logit model
blp_contraction	BLP95 contraction mapping to find delta given target shares

-- C --

coef.choicer_fit	Extract coefficients from a choicer_fit object
coef.choicer_hb	Extract posterior means from a hierarchical Bayes fit
coef.choicer_mnp	Extract coefficients from a choicer_mnp object
consumer_surplus	Expected consumer surplus
consumer_surplus.choicer_hmnl	Expected consumer surplus
consumer_surplus.choicer_hmnp	Expected consumer surplus
consumer_surplus.choicer_mnl	Expected consumer surplus
consumer_surplus.choicer_mxl	Expected consumer surplus
consumer_surplus.choicer_nl	Expected consumer surplus

-- D --

diversion_ratios	Compute aggregate diversion ratios
diversion_ratios.choicer_hb	Compute aggregate diversion ratios
diversion_ratios.choicer_mnl	Diversion ratios for multinomial logit model
diversion_ratios.choicer_mxl	Diversion ratios for mixed logit model
diversion_ratios.choicer_nl	Diversion ratios for nested logit model

-- E --

elasticities	Compute aggregate elasticities
elasticities.choicer_hb	Compute aggregate elasticities
elasticities.choicer_mnl	Elasticities for multinomial logit model
elasticities.choicer_mxl	Elasticities for mixed logit model
elasticities.choicer_nl	Elasticities for nested logit model
ess	Rank-normalized effective sample size (bulk and tail)

-- G --

get_halton_normals	Halton draws for mixed logit
gof	Goodness of fit for a fitted choice model
gof.choicer_fit	Goodness of fit for a fitted choice model

-- L --

logLik.choicer_fit	Extract log-likelihood from a choicer_fit object
logsum	Expected logsum (inclusive value) per choice situation
logsum.choicer_hmnl	Expected logsum (inclusive value) per choice situation
logsum.choicer_hmnp	Expected logsum (inclusive value) per choice situation
logsum.choicer_mnl	Expected logsum (inclusive value) per choice situation
logsum.choicer_mxl	Expected logsum (inclusive value) per choice situation
logsum.choicer_nl	Expected logsum (inclusive value) per choice situation

-- M --

mcse	Monte Carlo standard error of posterior summaries
mc_asymptotics	Asymptotic diagnostics for a Monte Carlo study
mode_choice	Intercity travel mode choice
monte_carlo	Monte Carlo parameter recovery
mxl_blp_contraction	BLP contraction mapping for mixed logit

-- N --

new_choicer_sim	Construct a 'choicer_sim' object
nl_blp_contraction	BLP95 contraction mapping for the Nested Logit model
nobs.choicer_fit	Extract number of observations from a choicer_fit object
nobs.choicer_hb	Number of choice situations behind a hierarchical Bayes fit
nobs.choicer_mnp	Extract number of observations from a choicer_mnp object

-- P --

ppc_shares	Posterior-predictive share check for hierarchical Bayes fits
predict.choicer_hb	Posterior choice probabilities and shares for hierarchical Bayes fits
predict.choicer_mnl	Predict from a multinomial logit model
predict.choicer_mxl	Predict from a mixed logit model
predict.choicer_nl	Predict from a nested logit model
prepare_hmnl_data	Prepare inputs for hierarchical multinomial logit estimation
prepare_hmnp_data	Prepare inputs for hierarchical multinomial probit estimation
prepare_mnl_data	Prepare inputs for multinomial logit estimation
prepare_mnp_data	Prepare inputs for Bayesian multinomial probit estimation
prepare_mxl_data	Prepare inputs for mixed logit estimation
prepare_nl_data	Prepare inputs for nested logit estimation
print.choicer_cs	Print a consumer surplus summary
print.choicer_fit	Print a choicer_fit object
print.choicer_gof	Print goodness-of-fit measures
print.choicer_hb	Print a hierarchical Bayes fit
print.choicer_mnp	Print a choicer_mnp object
print.choicer_wtp	Print a WTP table
print.summary.choicer_hb	Print the summary of a hierarchical Bayes fit
print.summary.choicer_mnl	Print summary for multinomial logit model
print.summary.choicer_mnp	Print summary for Bayesian multinomial probit model
print.summary.choicer_mxl	Print summary for mixed logit model
print.summary.choicer_nl	Print summary for nested logit model

-- R --

recovery_table	Parameter recovery table
recovery_table.choicer_fit	Parameter recovery table
recovery_table.choicer_hb	Parameter recovery table
recovery_table.choicer_mc	Parameter recovery table
recovery_table.choicer_mnp	Parameter recovery table
rhat	Split-\widehat{R} convergence diagnostic
run_hmnlogit	Fit a hierarchical Bayesian multinomial logit (HMNL)
run_hmnprobit	Fit a hierarchical Bayesian multinomial probit (HMNP)
run_mnlogit	Runs multinomial logit estimation
run_mnprobit	Runs Bayesian multinomial probit estimation
run_mxlogit	Runs mixed logit estimation
run_nestlogit	Runs nested logit estimation

-- S --

sample_by_choice	Draw a choice-based sample stratified by the chosen alternative
set_num_threads	Set the number of OpenMP threads used by choicer
simulate_hmnl_data	Simulate hierarchical multinomial logit data
simulate_hmnp_data	Simulate hierarchical multinomial probit data
simulate_mnl_data	Simulate multinomial logit data
simulate_mnp_data	Simulate multinomial probit data
simulate_mxl_data	Simulate mixed logit data
simulate_nl_data	Simulate nested logit data
summary.choicer_hb	Summarize a hierarchical Bayes fit
summary.choicer_mnl	Summary for multinomial logit model
summary.choicer_mnp	Summary for Bayesian multinomial probit model
summary.choicer_mxl	Summary for mixed logit model
summary.choicer_nl	Summary for nested logit model

-- T --

thread_info	Query choicer OpenMP thread settings
traceplot	Traceplot for a hierarchical Bayes fit
traceplot.choicer_hb	Traceplot method for hierarchical Bayes fits

-- V --

vcov.choicer_fit	Extract variance-covariance matrix from a choicer_fit object
vcov.choicer_hb	Posterior covariance of the population coefficients
vcov.choicer_mnp	Extract variance-covariance matrix from a choicer_mnp object

-- W --

wesml_vcov	Robust (sandwich) variance for a weighted / choice-based logit fit
wesml_vcov.choicer_mnl	Robust (sandwich) variance for a weighted / choice-based logit fit
wesml_vcov.choicer_mxl	Robust (sandwich) variance for a weighted / choice-based logit fit
wesml_vcov.choicer_nl	Robust (sandwich) variance for a weighted / choice-based logit fit
wesml_weights	WESML weights for choice-based (endogenous stratified) samples
wtp	Compute willingness to pay
wtp.choicer_fit	Compute willingness to pay
wtp.choicer_hb	Compute willingness to pay
wtp.choicer_mxl	Compute willingness to pay