nl_diversion_ratios_parallel: Compute Nested Logit diversion ratios (parallelized over...

In choicer: Discrete Choice Models for Economic Applications

Compute Nested Logit diversion ratios (parallelized over individuals)

Description

Computes the diversion ratio matrix DR(j->k) for the Nested Logit model. Entry (k, j) = fraction of demand lost by alternative j captured by k. Reduces to the MNL diversion ratios when all lambda = 1.

Usage

nl_diversion_ratios_parallel(
  theta,
  X,
  alt_idx,
  nest_idx,
  M,
  weights,
  use_asc = TRUE,
  include_outside_option = FALSE
)

Arguments

theta	(K + n_non_singleton_nests + n_delta) vector with model parameters. Order: ⁠[beta (K), lambda (non-singleton), delta]⁠.
X	sum(M) x K design matrix with covariates.
alt_idx	sum(M) x 1 vector with indices of alternatives; 1-based indexing.
nest_idx	J x 1 vector with nest indices for each alternative; 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.
use_asc	whether to use alternative-specific constants.
include_outside_option	whether to include outside option normalized to V=0, lambda=1.

Value

J x J matrix where entry (k, j) = DR(j->k). Diagonal is 0.

Examples

library(data.table)
set.seed(42)
N <- 50; J <- 4
dt <- data.table(id = rep(1:N, each = J), alt = rep(1:J, N))
dt[, `:=`(x1 = rnorm(.N), x2 = rnorm(.N))]
dt[, nest := ifelse(alt <= 2, "A", "B")]
dt[, choice := 0L]
dt[, choice := sample(c(1L, rep(0L, J - 1))), by = id]
fit <- run_nestlogit(dt, "id", "alt", "choice", c("x1", "x2"), "nest")
dr <- choicer:::nl_diversion_ratios_parallel(coef(fit), fit$data$X, fit$data$alt_idx,
  fit$data$nest_idx, fit$data$M, fit$data$weights)
dr

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