nl_predict_shares: Prediction of market shares for the Nested Logit model

In choicer: Discrete Choice Models for Economic Applications

Prediction of market shares for the Nested Logit model

Description

Prediction of market shares for the Nested Logit model

Usage

nl_predict_shares(
  theta,
  X,
  alt_idx,
  M,
  weights,
  nest_idx,
  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.
M	N x 1 vector with number of alternatives for each individual.
weights	N x 1 vector with weights for each observation.
nest_idx	J x 1 vector with nest indices for each alternative; 1-based indexing.
use_asc	whether to use alternative-specific constants.
include_outside_option	whether to include outside option normalized to V=0, lambda=1.

Value

vector with predicted market shares (outside-option share first when present).

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")
shares <- choicer:::nl_predict_shares(coef(fit), fit$data$X, fit$data$alt_idx,
  fit$data$M, fit$data$weights, fit$data$nest_idx, use_asc = TRUE)
shares

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