HealthInsurance {nestedLogit}

Choice of Health Insurance Product

Description

A company recently introduced a new health insurance provider for its employees. At the beginning of the year the employees had to choose one of three (or four) different health plan products from this provider to best suit their needs.

This dataset was modified from its original source (McNulty, 2022) for the present purposes by adding a fourth choice, sampled randomly from the original three.

Usage

data("HealthInsurance", package = "nestedLogit")

Format

A data frame with 1448 rows and 7 columns.

product

Choice among three products, a factor with levels "A", "B", and "C".

product4

Choice among four products, a factor with levels "A", "B", "C", and "D".

age

The age of the individual, in years.

household

The number of people living with the individual in the same household.

position_level

Position level in the company at the time the choice was made, where 1 is is the lowest level and 5 is the highest, a numeric vector.

gender

The gender of the individual, a factor with levels "Female" and "Male".

absent

The number of days the individual was absent from work in the year prior to the choice,

Source

Originally taken from McNulty, K. (2022). Handbook of Regression Modeling in People Analytics, https://peopleanalytics-regression-book.org/data/health_insurance.csv.

See Also

nestedLogit.

Examples

lbinary <- logits(AB_CD = dichotomy(c("A", "B"), c("C", "D")),
                  A_B   = dichotomy("A", "B"),
                  C_D   = dichotomy("C", "D"))
as.matrix(lbinary)
health.nested <- nestedLogit(product4 ~ age  + gender * household + position_level,
                             dichotomies = lbinary, data = HealthInsurance)
                             car::Anova(health.nested)
coef(health.nested)