R/data-cvd.R

In R2MLwiN: Running 'MLwiN' from Within R

#' Data from the 1998 Scottish Health Survey on cardiovascular disease status of
#' 8804 respondents
#' 
#' Data from the 1998 Scottish Health Survey, with 8804 respondents aged between
#' 18 and 64. The outcome, \code{cvddef}, is a self-report of a doctor-diagnosed
#' cardiovascular disease (CVD) condition (angina, diabetes, hypertension, acute
#' myocardial infarction, etc.). This is a binary response, whether (1) or not
#' (0) respondents have CVD condition.
#' 
#' The \code{cvd} dataset is one of the example datasets analysed in
#' Leyland and Groenewegen (2020), and provided with the
#' multilevel-modelling software package MLwiN (Charlton et al., 2024), as
#' \code{cvd_data}.
#' 
#' @docType data
#' @format A data frame with 8804 observations on the following 9 variables:
#' \describe{
#' \item{age}{Age.}
#' \item{sex}{Gender (factor with levels: \code{male}, \code{female}).}
#' \item{sc}{Social class (factor with levels: \code{12} (social class 1 and 2),
#' \code{3} (social class 3), \code{45} (social class 4 and 5)).}
#' \item{cvddef}{Self-reported cardiovascular disease (\code{0} = does not have
#' condition, \code{1} = has condition)}
#' \item{carstair}{Carstairs score.}
#' \item{smoke}{Smoking frequency (factor with levels: \code{lite} (<10 a day),
#' \code{mod} (10-19 a day), \code{hvy} (20+ a day), \code{ex} (ex-smoker),
#' \code{nevr} (never smoked)).}
#' \item{id}{Respondent identifier.}
#' \item{area}{Postcode sector} }
#' @source Charlton, C., Rasbash, J., Browne, W.J., Healy, M. and Cameron, B. (2024)
#' \emph{MLwiN Version 3.09} Centre for Multilevel Modelling, University of
#' Bristol.
#' 
#' Leyland A.H. (2005) Socioeconomic gradients in the prevalence of cardiovascular
#' disease in Scotland: the roles of composition and context. \emph{J Epidemiol
#' Community Health} 59:799–803
#'
#' Leyland, A.H., Groenewegen, P.P. (2020). Untangling Context and Composition.
#' In: \emph{Multilevel Modelling for Public Health and Health Services Research}.
#' Springer, Cham. \doi{10.1007/978-3-030-34801-4_13}
#' @keywords datasets
#' @examples
#'
#' \dontrun{
#'
#' data(cvd, package = "R2MLwiN")
#' 
#' # Example taken from Leyland and Groenewegen (2020)
#' 
#' F1 <- logit(cvddef) ~ 1 + I(age^3) + I(age^3):I(log(age)) +
#'   sex + sex:I(age^3) + sex:I(age^3):I(log(age)) +
#'   (1 | area)
#' 
#' (mod_MQL1 <- runMLwiN(Formula = F1,
#'                       D = "Binomial",
#'                       data = cvd))
#' 
#' (mod_PQL2 <- runMLwiN(Formula = F1,
#'                       D = "Binomial",
#'                       data = cvd,
#'                       estoptions = list(
#'                         nonlinear = c(N = 1, M = 2),
#'                         startval = list(FP.b = mod_MQL1@FP,
#'                                         FP.v = mod_MQL1@FP.cov,
#'                                         RP.b = mod_MQL1@RP,
#'                                         RP.v = mod_MQL1@RP.cov))))
#' }
"cvd"