mxl_elasticities_parallel: Compute aggregate elasticities for mixed logit model

In choicer: Discrete Choice Models for Economic Applications

Compute aggregate elasticities for mixed logit model

Description

Computes the aggregate elasticity matrix (weighted average of individual elasticities) for the Mixed Logit model. The elasticity E(i,j) represents the percentage change in the probability of choosing alternative i when the attribute of alternative j changes by 1%.

Usage

mxl_elasticities_parallel(
  theta,
  X,
  W,
  alt_idx,
  choice_idx,
  M,
  weights,
  eta_draws,
  rc_dist,
  elast_var_idx,
  is_random_coef,
  rc_correlation = TRUE,
  rc_mean = FALSE,
  use_asc = TRUE,
  include_outside_option = FALSE,
  gen_seed = -1L,
  gen_scramble = 1L,
  gen_S = 0L
)

Arguments

theta	parameter vector (beta, [mu], L, delta)
X	design matrix for fixed coefficients; sum(M_i) x K_x
W	design matrix for random coefficients; sum(M_i) x K_w or J x K_w
alt_idx	sum(M) x 1 vector with indices of alternatives; 1-based indexing
choice_idx	N x 1 vector (kept for API consistency, not used)
M	N x 1 vector with number of alternatives for each individual
weights	N x 1 vector with weights for each observation
eta_draws	Array with draws; K_w x S x N
rc_dist	K_w vector indicating distribution (0=normal, 1=log-normal)
elast_var_idx	1-based index of the variable for elasticity computation
is_random_coef	TRUE if variable is in W (random coef), FALSE if in X (fixed coef)
rc_correlation	whether random coefficients are correlated
rc_mean	whether mu parameters are estimated
use_asc	whether ASCs are included
include_outside_option	whether outside option is included
gen_seed	Integer master seed for the on-the-fly Halton generator. < 0 (default) uses the materialized eta_draws cube; >= 0 generates draws on the fly from this seed.
gen_scramble	Integer scramble mode for on-the-fly generation: 0 = identity permutations (plain Halton, compat), 1 = seeded position-wise digit permutations.
gen_S	Integer number of draws per individual, used only when gen_seed >= 0.

Value

J x J matrix of aggregate elasticities

Examples

library(data.table)
set.seed(42)
N <- 50; J <- 3
dt <- data.table(id = rep(1:N, each = J), alt = rep(1:J, N))
dt[, `:=`(x1 = rnorm(.N), w1 = rnorm(.N))]
dt[, choice := 0L]
dt[, choice := sample(c(1L, rep(0L, J - 1))), by = id]
d <- prepare_mxl_data(dt, "id", "alt", "choice", "x1", "w1")
eta <- get_halton_normals(50, d$N, ncol(d$W))
fit <- run_mxlogit(input_data = d, eta_draws = eta)
elas <- choicer:::mxl_elasticities_parallel(coef(fit), d$X, d$W, d$alt_idx,
  d$choice_idx, d$M, d$weights, eta, rc_dist = rep(0L, ncol(d$W)),
  elast_var_idx = 1L, is_random_coef = FALSE,
  rc_correlation = FALSE, rc_mean = FALSE)
elas

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