summary.choicer_nl: Summary for nested logit model

In choicer: Discrete Choice Models for Economic Applications

Summary for nested logit model

Description

Triggers lazy Hessian computation if standard errors have not been computed yet.

Usage

## S3 method for class 'choicer_nl'
summary(object, gof = TRUE, ...)

Arguments

object	A choicer_nl object.
gof	Logical; compute goodness-of-fit measures (McFadden R-squared, hit rate) for the summary footer. Involves an in-sample prediction pass (for mixed logit, a full simulation over draws); set to FALSE to skip.
...	Additional arguments (ignored).

Value

A summary.choicer_nl object (includes a gof element with goodness-of-fit measures from gof; its fields are NA when the model was fitted with keep_data = FALSE).

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(
  data = dt, id_col = "id", alt_col = "alt", choice_col = "choice",
  covariate_cols = c("x1", "x2"), nest_col = "nest"
)
summary(fit)

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