HealthInsurance: Choice of Health Insurance Product
In nestedLogit: Nested Dichotomy Logistic Regression Models

HealthInsurance

R Documentation

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.

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)

nestedLogit documentation built on July 9, 2023, 6:35 p.m.