nl_loglik_numeric_hessian: Numerical Hessian of the log-likelihood via finite...

In choicer: Discrete Choice Models for Economic Applications

Numerical Hessian of the log-likelihood via finite differences

Description

Numerical Hessian of the log-likelihood via finite differences

Usage

nl_loglik_numeric_hessian(
  theta,
  X,
  alt_idx,
  choice_idx,
  nest_idx,
  M,
  weights,
  use_asc = TRUE,
  include_outside_option = FALSE,
  eps = 1e-06
)

Arguments

theta	(K + n_delta + n_nests) vector with model parameters. Order: ⁠[beta (K), delta (n_delta), lambda (n_nests)]⁠
X	sum(M) x K design matrix with covariates.
alt_idx	sum(M) x 1 vector with indices of alternatives; 1-based indexing.
choice_idx	N x 1 vector with indices of chosen alternatives; 0 for outside option, 1-based index relative to rows in X_i otherwise.
nest_idx	J x 1 vector with indices of nests for each alternative; 1-based indexing (1 to n_nests).
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.
eps	finite difference step size

Value

Hessian evaluated at input arguments

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]
d <- prepare_nl_data(dt, "id", "alt", "choice", c("x1", "x2"), "nest")
K_x <- ncol(d$X); K_l <- length(unique(d$nest_idx))
theta <- c(rep(0, K_x), rep(0.5, K_l), rep(0, J - 1))
H <- choicer:::nl_loglik_numeric_hessian(theta, d$X, d$alt_idx, d$choice_idx,
  d$nest_idx, d$M, d$weights)
dim(H)

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