Simulate mixed logit data

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.

Value

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

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.

Examples

# \donttest{
sim <- simulate_mxl_data(N = 1000, J = 4, seed = 123)
print(sim)
#> <choicer_sim: mxl>
#>   settings:
#>     N = 1000
#>     J = 4
#>     K_x = 2
#>     K_w = 2
#>     outside_option = TRUE
#>     vary_choice_set = TRUE
#>   rows in $data: 3989
#>   true_params: beta, delta, Sigma, L_params, mu, rc_dist, rc_correlation
# }