mxl_diversion_ratios_parallel: Diversion ratios for Mixed Logit (simulated,...

In choicer: Discrete Choice Models for Economic Applications

Diversion ratios for Mixed Logit (simulated, derivative-based)

Description

Computes the matrix of attribute-based diversion ratios for a fitted Mixed Logit model. DR(k, j) is the fraction of demand lost by alternative j that is captured by alternative k when a marginal change in alternative j's elast_var attribute reduces s_j.

Usage

mxl_diversion_ratios_parallel(
  theta,
  X,
  W,
  alt_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
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 perturbed variable
is_random_coef	TRUE if the variable is in W (random coef), FALSE if in X (fixed)
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.

Details

In MNL the per-draw realized coefficient is a constant, so it cancels in the ratio and the result is independent of the variable chosen. In MXL, the realized coefficient \beta_{ik}^s varies across individuals and draws, so the diversion ratio depends on which attribute is perturbed. For a variable with a fixed coefficient the dependence again vanishes (the constant cancels); for a random-coefficient variable it does not.

Value

J x J (or (J+1) x (J+1)) matrix of diversion ratios with zero diagonal.

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