R/HealthInsurance.R

In nestedLogit: Nested Dichotomy Logistic Regression Models

#' @name HealthInsurance
#' @docType data
#' 
#' @title 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.
#' \describe{
#'   \item{product}{Choice among three products, a factor with levels \code{"A"}, \code{"B"}, 
#'   and \code{"C"}.}
#'   \item{product4}{Choice among four products, a factor with levels \code{"A"}, \code{"B"}, 
#'   \code{"C"}, and \code{"D"}.}
#'   \item{age}{The age of the individual, in years.}
#'   \item{household}{The number of people living with the individual in the 
#'   same household.}
#'   \item{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.}
#'   \item{gender}{The gender of the individual, a factor with levels \code{"Female"}
#'   and \code{"Male"}.}
#'   \item{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). 
#' \emph{Handbook of Regression Modeling in People Analytics},
#' \url{https://peopleanalytics-regression-book.org/data/health_insurance.csv}.
#' 
#' @seealso \code{\link{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)
"HealthInsurance"