predict.choicer_hb: Posterior choice probabilities and shares for hierarchical...

In choicer: Discrete Choice Models for Economic Applications

Posterior choice probabilities and shares for hierarchical Bayes fits

Description

Computes counterfactual choice probabilities, integrating over the posterior draws and (at the population level) over the random-coefficient distribution: for each kept draw (b_r, W_r, \delta_r, \ldots) one \beta \sim N(b_r, W_r) is drawn and the model probabilities are averaged. Alternatives in newdata that were not in the estimation sample receive a posterior-predictive \delta_{new} \sim N(z_{new}'\theta_r, \sigma_{d,r}^2) — the entry counterfactual unlocked by the random-effects \delta. Price or subsidy counterfactuals are just modified covariate columns in newdata.

Usage

## S3 method for class 'choicer_hb'
predict(
  object,
  newdata = NULL,
  level = c("population", "individual"),
  n_draws = 200L,
  aggregate = TRUE,
  ...
)

Arguments

object	A choicer_hmnl or choicer_hmnp fit.
newdata	Data frame with the estimation columns (choice column not required). NULL (default) predicts on the estimation data.
level	"population" (default) integrates over N(b, W); "individual" uses the respondent-level \beta_i (requires the prediction rows to belong to estimation respondents; fully posterior-integrated when the fit kept keep_beta_i = "draws").
n_draws	Number of posterior draws to integrate over (thinned evenly from the kept draws; default 200).
aggregate	If TRUE (default) return a per-alternative posterior share table (including the outside option); if FALSE return the posterior-mean probability per row of the prediction data.
...	Ignored.

Details

HMNL probabilities are closed-form logit; HMNP probabilities use the 1-D Gauss-Hermite representation of the iid-probit integral P(j) = \int \phi(u) \prod_{k \ne j} \Phi(V_j - V_k + u) du.

Value

With aggregate = TRUE, a data.table with columns alternative, share (posterior mean), sd, lower, upper (95% equal-tailed interval); the posterior share draws are attached as attr(, "draws"). With aggregate = FALSE, a numeric vector of posterior-mean choice probabilities, one per prediction row.

Examples

sim <- simulate_hmnl_data(N = 100, T = 3, J = 4, seed = 42)
fit <- suppressWarnings(run_hmnlogit(sim$data, "task", "alt", "choice", c("x1", "x2"),
                    person_col = "pid", alt_covariate_cols = "z1",
                    mcmc = list(R = 500, burn = 200)))
predict(fit)                       # posterior shares, estimation data
cf <- sim$data
cf$x1 <- cf$x1 + 0.5               # a counterfactual attribute change
predict(fit, newdata = cf)

choicer documentation built on Sept. 5, 2026, 1:07 a.m.