R/data-politics-and-need-rescaled.R

In carlislerainey/separation: Using informative priors to deal with separation in logistic regression models

#' Governors' decisions to oppose the medicaid expansion under the PPACA.
#' 
#' A data set containing the \emph{rescaled} variables that Barrilleaux and Rainey (2014) use in
#' the their main statistical model of governors' decisions to oppose the
#' Medicaid expansion under the PPACA. Most importantly, no Democratic governors
#' opposed the expansion, leading to a problem of separation. Variables are rescaled following
#' Gelman (2008) and Gelman et al. (2008).
#' 
#' @format A data frame with 50 observations of standardized version of the following 
#' 10 variables: \describe{ 
#'   \item{state}{the name of the state} 
#'   \item{oppose_expansion}{equals one if
#'   the state's governor opposed the expansion and zero otherwise} 
#'   \item{gop_governor}{equals one if the state's govenor is Republican and
#'   zero otherwise} 
#'   \item{percent_favorable_aca}{an estimate of the percent of
#'   the state's population that views the PPACA favorably} 
#'   \item{gop_leg}{equals one if Republicans control both houses of the state
#'   legislature and zero otherwise} 
#'   \item{percent_uninsured}{the percent of the
#'   state's population without health insurance} 
#'   \item{bal2012}{the state's
#'   year-end reserves as a percentage of total spending for 2012} 
#'   \item{multiplier}{the state's current Medicaid multiplier} 
#'   \item{percent_nonwhite}{the percent of the state that is non-white} 
#'   \item{percent_metro}{the percent of the state that resides in a
#'   metropolitan area}}
#'   
"politics_and_need_rescaled"