Simulate nested logit data — simulate_nl

Generates synthetic choice data with nested logit probabilities computed analytically (log-sum-exp over inclusive values), then samples choices from the implied multinomial. The outside option (j = 0) sits in a singleton nest with lambda = 1.

Usage

simulate_nl_data(
  N = 10000,
  beta = c(1.5, -0.8),
  delta = c(`1` = 0.5, `2` = 0.3, `3` = -0.2, `4` = -0.5, `5` = 0.4),
  nests = list(c(1, 2), c(3, 4, 5)),
  lambdas = c(0.8, 0.2),
  seed = 123
)

Arguments

N: Number of choice situations.
beta: Fixed coefficients for covariates X, W (length 2 by default).
delta: Named numeric vector of ASCs for inside alternatives.
nests: List of integer vectors defining nest membership for inside alternatives.
lambdas: Numeric vector of dissimilarity parameters, one per nest.
seed: Random seed (NULL skips set.seed()).

Value

A choicer_sim object. true_params includes beta, delta, lambdas; settings includes the nest_structure. The returned data retains a nest column (integer, with 0L for the outside option) for convenient use with run_nestlogit().

Note

Unlike simulate_mnl_data() and simulate_mxl_data(), this function does not expose outside_option or vary_choice_set flags. The outside option (j = 0) is always present as a singleton nest with lambda = 1, and every individual faces the full set of inside alternatives. Add these flags if downstream use cases need them.

Examples

# \donttest{
sim <- simulate_nl_data(N = 2000, seed = 123)
print(sim)
#> <choicer_sim: nl>
#>   settings:
#>     N = 2000
#>     J_inside = 5
#>     nest_structure = (1,2), (3,4,5)
#>   rows in $data: 12000
#>   true_params: beta, delta, lambdas
# }