monte_carlo: Monte Carlo parameter recovery

In choicer: Discrete Choice Models for Economic Applications

Monte Carlo parameter recovery

Description

Replicates a (DGP -> fit) cycle R times with independent seeds and collects per-parameter estimates, standard errors, bias, and coverage. Returns a choicer_mc object; call summary() for aggregated statistics (mean estimate, bias, RMSE, coverage rate, convergence rate).

Usage

monte_carlo(
  sim_fun,
  fit_fun,
  R = 100,
  seed = 1L,
  parallel = FALSE,
  progress = TRUE,
  ...
)

Arguments

sim_fun	Function of seed returning a choicer_sim.
fit_fun	Function of a choicer_sim returning a choicer_fit.
R	Number of replications.
seed	Base integer seed. Replication r uses seed + r - 1L.
parallel	Logical; if TRUE and future.apply is available, run replications in parallel using the user's active future::plan().
progress	Logical; print a one-line progress update per iteration in serial mode. Ignored when parallel = TRUE.
...	Unused.

Details

Each iteration calls sim_fun(seed = seed + r - 1L), then fit_fun(sim). Write sim_fun as a closure that captures N, J, and other DGP settings and forwards seed. Write fit_fun as a closure that takes a choicer_sim and returns a fitted choicer_fit object, wrapping any data-preparation, draws, or optimizer-control setup.

Value

A choicer_mc object: a list with elements replications (a long data.table with one row per estimated parameter per replication) and meta (run metadata).

Examples

sim_fun <- function(seed) simulate_mnl_data(N = 1000, J = 4, seed = seed)
fit_fun <- function(sim) run_mnlogit(
  data = sim$data, id_col = "id", alt_col = "alt", choice_col = "choice",
  covariate_cols = c("x1", "x2"), outside_opt_label = 0L,
  include_outside_option = FALSE, use_asc = TRUE,
  control = list(print_level = 0L)
)
mc <- monte_carlo(sim_fun, fit_fun, R = 5, seed = 1L, progress = FALSE)
summary(mc)

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