blp_contraction: BLP95 contraction mapping to find delta given target shares

In choicer: Discrete Choice Models for Economic Applications

BLP95 contraction mapping to find delta given target shares

Description

BLP95 contraction mapping to find delta given target shares

Usage

blp_contraction(
  delta,
  target_shares,
  X,
  beta,
  alt_idx,
  M,
  weights,
  include_outside_option = FALSE,
  tol = 1e-08,
  max_iter = 1000L
)

Arguments

delta	J x 1 vector with initial guess for deltas (ASCs)
target_shares	J x 1 vector with target shares for each alternative
X	sum(M) x K design matrix with covariates. M[i] x K matrix for individual i
beta	K x 1 vector with model parameters
alt_idx	sum(M) x 1 vector with indices of alternatives within each choice set; 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
include_outside_option	whether to include outside option normalized to 0 (if so, the outside option is not included in the data)
tol	convergence tolerance
max_iter	maximum number of iterations

Value

vector with contraction's delta (ASCs) output

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), x2 = rnorm(.N))]
dt[, choice := 0L]
dt[, choice := sample(c(1L, rep(0L, J - 1))), by = id]
fit <- run_mnlogit(dt, "id", "alt", "choice", c("x1", "x2"))
beta <- coef(fit)[fit$param_map$beta]
delta <- blp_contraction(rep(0, J), rep(1/J, J), fit$data$X,
  beta, fit$data$alt_idx, fit$data$M, fit$data$weights)
delta

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