simulate_mxl_data: Simulate mixed logit data

In choicer: Discrete Choice Models for Economic Applications

Simulate mixed logit data

Description

Generates synthetic choice data with random coefficients drawn from a multivariate normal (optionally log-normal per dimension) and an additional mean shifter mu. Random coefficients are parameterized via the lower Cholesky factor of Sigma. Covariates are Uniform(-1, 1) by default; columns named in price_cols are drawn as -Uniform(0.1, 3) to mimic strictly-negative price variables.

Usage

simulate_mxl_data(
  N = 5000,
  J = 4,
  beta = c(0.8, -0.6),
  delta = NULL,
  mu = NULL,
  Sigma = matrix(c(1, 0.5, 0.5, 1.5), nrow = 2),
  rc_dist = NULL,
  rc_correlation = NULL,
  price_cols = NULL,
  seed = 123,
  outside_option = TRUE,
  vary_choice_set = TRUE
)

Arguments

N	Number of choice situations.
J	Number of inside alternatives.
beta	Fixed coefficients for ⁠x1..x{K_x}⁠ (length K_x = length(beta)).
delta	ASCs for inside alternatives (length J). Defaults to an alternating pattern of c(0.5, -0.5).
mu	Mean shifter for random coefficients (length K_w = ncol(Sigma)). Defaults to a zero vector.
Sigma	Covariance matrix of random coefficients (square, ⁠K_w x K_w⁠).
rc_dist	Integer vector (length K_w): 0L for normal, 1L for log-normal. Default NULL is treated as all-normal.
rc_correlation	Logical; if NULL (default) it is auto-detected from the off-diagonal entries of Sigma.
price_cols	Character vector of ⁠w*⁠ column names to draw as -Uniform(0.1, 3) instead of Uniform(-1, 1). Default NULL.
seed	Random seed (NULL skips set.seed()).
outside_option	Logical; include outside option with alt = 0.
vary_choice_set	Logical; if TRUE (default) choice set size is sampled uniformly from 2:J.

Details

Random coefficients are constructed to match the estimator's parameterization in src/mxlogit.cpp. For every dimension the raw draw is L %*% eta where eta ~ N(0, I). A normal random coefficient (rc_dist = 0) is then ⁠gamma_k = mu_k + (L %*% eta)_k⁠. A log-normal random coefficient (rc_dist = 1) follows the shifted log-normal ⁠beta_k = exp(mu_k) + exp((L %*% eta)_k)⁠ – not the textbook exp(mu_k + sigma_k * eta) – so mu_k in true_params$mu is on the same scale the estimator recovers and recovery_table() can compare like-for-like.

Value

A choicer_sim object. true_params includes beta, delta, Sigma, L_params (packed Cholesky parameters), mu, rc_dist, rc_correlation.

Examples

sim <- simulate_mxl_data(N = 1000, J = 4, seed = 123)
print(sim)

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