simulate_mnp_data: Simulate multinomial probit data

In choicer: Discrete Choice Models for Economic Applications

Simulate multinomial probit data

Description

Generates synthetic choice data from the MNP data-generating process estimated by run_mnprobit(): latent utility differences against the base alternative (alternative 1),

w_i = X_i \beta + \delta + \varepsilon_i, \qquad \varepsilon_i \sim N_{J-1}(0, \Sigma),

with alternative j > 1 chosen iff w_{ij} > \max(0, \max_{k \neq j} w_{ik}) and the base chosen iff all w_{ij} < 0. Covariates are Uniform(-1, 1). Choice sets are balanced (every individual faces all J alternatives), as the MNP estimator requires; there is no outside-option flag — model an outside good as a zero-covariate base alternative instead.

Usage

simulate_mnp_data(
  N = 5000,
  J = 3,
  beta = c(0.8, -0.6),
  delta = NULL,
  Sigma = matrix(c(1, 0.5, 0.5, 1.5), nrow = 2),
  seed = 123
)

Arguments

N	Number of choice situations.
J	Number of alternatives (alternative 1 is the base).
beta	Fixed coefficients for ⁠x1..x{K_x}⁠ (length K_x = length(beta)).
delta	ASCs of the differenced utilities, one per non-base alternative (length J - 1). Defaults to an alternating pattern of c(0.5, -0.5).
Sigma	Covariance matrix of the differenced errors (⁠(J-1) x (J-1)⁠).
seed	Random seed (NULL skips set.seed()).

Details

The MNP likelihood only identifies parameters up to scale, so true_params is reported on the identified scale (normalized by \sigma_{11}): beta = \beta / \sqrt{\sigma_{11}}, delta = \delta / \sqrt{\sigma_{11}}, and Sigma = \Sigma / \sigma_{11} — the scale on which run_mnprobit() reports its posterior. With the default Sigma (\sigma_{11} = 1) the DGP scale and the identified scale coincide.

Value

A choicer_sim object. true_params contains beta, delta, and Sigma on the identified scale (see Details).

Examples

sim <- simulate_mnp_data(N = 1000, J = 3, seed = 123)
print(sim)

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