R/data-wage1.R

In R2MLwiN: Running 'MLwiN' from Within R

#' Simulated dataset of office workers' salary and other employment details.
#' 
#' A simulated dataset of office workers' salary (and associated information)
#' in which workers exhibit multiple membership of companies worked for over
#' past year.
#' 
#' The simulated \code{wage1} dataset is one of the sample datasets provided
#' with the multilevel modelling software package MLwiN (Rasbash et al., 2009),
#' and described in Browne (2012). It consists of salary and associated
#' information for office workers, and is used by Browne (2012) as an example
#' of modelling a multiple membership structure. The dataset exhibits multiple
#' membership in that workers are clustered across the companies employing them
#' over the past year, but some have worked for more than one company during
#' that time.)
#' 
#' @docType data
#' @format A data frame with 3022 observations on the following 21 variables:
#' \describe{ \item{list("id")}{Unique office worker identifying code.}
#' \item{list("company")}{Identifying code for company worked for over the last
#' 12 months.} \item{list("company2")}{If worked for >1 company over the last
#' 12 months, identifying code for second company.} \item{list("company3")}{If
#' worked for >2 companies over the last 12 months, identifying code for third
#' company.} \item{list("company4")}{If worked for >3 companies over the last
#' 12 months, identifying code for fourth company.} \item{list("age")}{Age of
#' worker.} \item{list("parttime")}{Part or full-time, a factor with levels
#' \code{Fulltime} and \code{Parttime}.} \item{list("sex")}{Sex of worker, a
#' factor with levels \code{male} and \code{female}.} \item{list("cons")}{A
#' column of ones. If included as an explanatory variable in a regression model
#' (e.g. in MLwiN), its coefficient is the intercept.}
#' \item{list("earnings")}{Workers' earnings over the last financial year.}
#' \item{list("logearn")}{Workers' (natural) log-transformed earnings over the
#' last financial year.} \item{list("numjobs")}{The number of companies worked
#' for over the last 12 months.} \item{list("weight1")}{Proportion of time
#' worked for employer listed in \code{company}.}
#' \item{list("weight2")}{Proportion of time worked for employer listed in
#' \code{company2}.} \item{list("weight3")}{Proportion of time worked for
#' employer listed in \code{company3}.} \item{list("weight4")}{Proportion of
#' time worked for employer listed in \code{company4}.}
#' \item{list("ew1")}{Alternative (equal) weighting for \code{company}
#' (1/\code{numjobs}).} \item{list("ew2")}{Alternative (equal) weighting for
#' \code{company2} (if numjobs >1 then 1/\code{numjobs}, else 0).}
#' \item{list("ew3")}{Alternative (equal) weighting for \code{company3} (if
#' numjobs >2 then 1/\code{numjobs}, else 0).} \item{list("ew4")}{Alternative
#' (equal) weighting for \code{company4} (if numjobs >3 then 1/\code{numjobs},
#' else 0).} \item{list("age_40")}{Age of worker, centered on 40 years.} }
#' @source Browne, W. J. (2012) \emph{MCMC Estimation in MLwiN Version 2.26.}
#' University of Bristol: Centre for Multilevel Modelling.
#' 
#' Rasbash, J., Charlton, C., Browne, W.J., Healy, M. and Cameron, B. (2009)
#' \emph{MLwiN Version 2.1.} Centre for Multilevel Modelling, University of
#' Bristol.
#' @keywords datasets
#' @examples
#' 
#' \dontrun{
#' 
#' data(wage1, package = "R2MLwiN")
#' 
#' (mymodel <- runMLwiN(logearn ~ 1 + age_40 + numjobs + (1 | company) + (1 | id), 
#'   estoptions = list(EstM = 1, 
#'   mm = list(list(mmvar = list("company", "company2", "company3", "company4"),
#'   weights = list("weight1", "weight2", "weight3", "weight4")), NA)),
#'   data = wage1))
#' 
#' }
#' 
"wage1"