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{id}{Unique office worker identifying code.}
#' \item{company}{Identifying code for company worked for over the last
#' 12 months.}
#' \item{company2}{If worked for >1 company over the last 12 months, identifying
#' code for second company.}
#' \item{company3}{If worked for >2 companies over the last 12 months,
#' identifying code for third company.}
#' \item{company4}{If worked for >3 companies over the last 12 months,
#' identifying code for fourth company.}
#' \item{age}{Age of worker.}
#' \item{parttime}{Part or full-time, a factor with levels \code{Fulltime} and
#' \code{Parttime}.}
#' \item{sex}{Sex of worker, a factor with levels \code{male} and
#' \code{female}.}
#' \item{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{earnings}{Workers' earnings over the last financial year.}
#' \item{logearn}{Workers' (natural) log-transformed earnings over the last
#' financial year.}
#' \item{numjobs}{The number of companies worked for over the last 12 months.}
#' \item{weight1}{Proportion of time worked for employer listed in
#' \code{company}.}
#' \item{weight2}{Proportion of time worked for employer listed in
#' \code{company2}.}
#' \item{weight3}{Proportion of time worked for employer listed in
#' \code{company3}.}
#' \item{weight4}{Proportion of time worked for employer listed in
#' \code{company4}.}
#' \item{ew1}{Alternative (equal) weighting for \code{company}
#' (1/\code{numjobs}).}
#' \item{ew2}{Alternative (equal) weighting for \code{company2} (if numjobs >1
#' then 1/\code{numjobs}, else 0).}
#' \item{ew3}{Alternative (equal) weighting for \code{company3} (if numjobs >2
#' then 1/\code{numjobs}, else 0).}
#' \item{ew4}{Alternative (equal) weighting for \code{company4} (if numjobs >3
#' then 1/\code{numjobs},
#' else 0).}
#' \item{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"