% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{fauxmatching}
\alias{fauxmatching}
\title{Faux Data on Heterosexual Matching}
\format{
\code{fauxmatching} is a list containing a pair of \code{data.frame} objects: \code{Xdata} and \code{Zdata}.

\code{Xdata} is for women. Each row is a woman, each column is a variable on that women
or her partnerships. The women's ID variable s called \code{pid} and
the variable with the ID of the women's partner is called \code{pair_id}. If the women is single the men's ID is NA.
\code{Zdata} is for men. Each row is a man, each column is a variable on that men
The men's ID variable is called \code{pid}.
\describe{
\item{pair_id}{ The ID of the person's partner. This is in both \code{Xdata} and \code{Zdata}.}
\item{sampled}{ The indicator that the person was sampled directly (as distinct from being included as the match of a directly sampled
person. All single people are directly sampled.  This is in both \code{Xdata} and \code{Zdata}.}
}
}
\source{
The data set is simulation based upon an rpm model fit to data from
the 2008 SIPP.
}
\usage{
data(fauxmatching)
}
\value{
No return value, called for side effects.
}
\description{
This data set represents a simulation of a bipartite matching.
The data set is named fauxmatching.
Its primary use is to illustrate the fitting of a Revealed Preference Matchings Model (rpm).
The model assumes a one-to-one stable matching using an observed set of
matchings and a set of (possibly dyadic) covariates to 
estimate the parameters for
linear equations of utilities. This provides such data
for a matching between men and women of certain
characteristics (or shared characteristics) of people of the opposite sex.
}
\details{
The pairings are determined by the \code{pair_id} variable in \code{Xdata}. 
If that variable is NA then the women is
assumed to be single. If men are listed in \code{Zdata} and are not partnered then they are assumed single.
Weights are specified by three optional variables in \code{Xdata}.
\describe{
\item{X_w}{The weight variable for women. The sum of the weights of the sampled women is the
number of women in the population.}
\item{Z_w}{The weight variable for men. The sum of the weights of the sampled men is the
number of men in the population.}
\item{pair_w}{The weight variable for pairs.}
}
}
\examples{
library(rpm)
data(fauxmatching)
\donttest{
fit <- rpm(~match("edu") + WtoM_diff("edu",3),
          Xdata=fauxmatching$Xdata, Zdata=fauxmatching$Zdata,
          X_w="X_w", Z_w="Z_w",
          pair_w="pair_w", pair_id="pair_id", Xid="pid", Zid="pid",
          sampled="sampled")
summary(fit)
}
}
\references{
Goyal, Shuchi; Handcock, Mark S.; Jackson, Heide M.; Rendall, Michael S. and Yeung, Fiona C. (2023).
\emph{A Practical Revealed Preference Model for Separating Preferences and Availability Effects in Marriage Formation},
\emph{Journal of the Royal Statistical Society}, A. \doi{10.1093/jrsssa/qnad031} 

Dagsvik, John K. (2000) \emph{Aggregation in Matching Markets} \emph{International Economic Review}, Vol. 41, 27-57.
JSTOR: https://www.jstor.org/stable/2648822, \doi{10.1111/1468-2354.00054}

Menzel, Konrad (2015).
\emph{Large Matching Markets as Two-Sided Demand Systems}
Econometrica, Vol. 83, No. 3 (May, 2015), 897-941. \doi{10.3982/ECTA12299}
}
\keyword{datasets}
