diversion_ratios.choicer_mxl: Diversion ratios for mixed logit model

In choicer: Discrete Choice Models for Economic Applications

Diversion ratios for mixed logit model

Description

Computes the attribute-based diversion ratio matrix. Entry (k, j) is the fraction of demand lost by alternative j that is captured by alternative k when a marginal change in alternative j's wrt_var attribute reduces s_j.

Usage

## S3 method for class 'choicer_mxl'
diversion_ratios(object, wrt_var, is_random_coef = FALSE, ...)

Arguments

object	A choicer_mxl object fitted with keep_data = TRUE.
wrt_var	Variable used to perturb alternative j's utility: a column name (character) or 1-based index. Indexes into X columns for fixed coefficients, or W columns for random coefficients (when is_random_coef = TRUE).
is_random_coef	Logical. TRUE if the variable has a random coefficient (is in W), FALSE if fixed (in X). Default FALSE.
...	Additional arguments (ignored).

Details

Unlike MNL, the MXL diversion ratio depends on which variable is perturbed: the realised coefficient \beta_{ik}^s varies across individuals and draws and does not cancel in the ratio. For a variable with a fixed coefficient the result is independent of the variable (\beta cancels); for a random-coefficient variable it is not.

Value

A J x J diversion ratio matrix with alternative labels. Cross-products are averaged across simulation draws inside the integration to avoid Jensen-style bias.

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), w1 = rnorm(.N))]
dt[, choice := 0L]
dt[, choice := sample(c(1L, rep(0L, J - 1))), by = id]
fit <- run_mxlogit(
  data = dt, id_col = "id", alt_col = "alt", choice_col = "choice",
  covariate_cols = "x1", random_var_cols = "w1", S = 50L
)
diversion_ratios(fit, "x1")
diversion_ratios(fit, "w1", is_random_coef = TRUE)

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