https://github.com/cran/RecordLinkage
Tip revision: 616a49b14b7b48118cdaf06908e06151054cbcea authored by ORPHANED on 23 August 2019, 14:41:04 UTC
version 0.4-11.2
version 0.4-11.2
Tip revision: 616a49b
getParetoThreshold.Rd
\encoding{latin1}
\name{getParetoThreshold}
\Rdversion{1.1}
\alias{getParetoThreshold}
\alias{getParetoThreshold-methods}
\alias{getParetoThreshold,RecLinkData-method}
\alias{getParetoThreshold,RLBigData-method}
\title{
Estimate Threshold from Pareto Distribution
}
\description{
Calculates a classification threshold based on a generalized Pareto
distribution (GPD) fitted to the weights distribution of the given data pairs.
}
\usage{
getParetoThreshold(rpairs, quantil = 0.95, interval = NA)
\S4method{getParetoThreshold}{RecLinkData}(rpairs, quantil = 0.95, interval = NA)
\S4method{getParetoThreshold}{RLBigData}(rpairs, quantil = 0.95, interval = NA)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{rpairs}{
A \code{"\link{RecLinkData}"} or \code{"\linkS4class{RLBigData}"} object with weights.
The data for which to compute a threshold.
}
\item{quantil}{
A real number between 0 and 1. The quantil which to compute.
}
\item{interval}{
A numeric vector denoting the interval on which to fit
a GPD.
}
}
\details{
This threshold calculation is based on the assumption that the distribution
of weights exihibit a `fat tail' which can be fitted by a generalized Pareto
distribution (GPD). The limits of the interval which is subject to the
fitting are usually determined by reviewing a mean residual life plot of
the data. If the limits are not externally supplied, a MRL plot is displayed
from which the endpoints can be selected by mouse input. If only one endpoint
is selected or supplied, the greater endpoint is set to the maximum weight.
A suitable interval is characterized by a relatively long, approximately
linear segment of the plot.
}
\value{
A classification threshold.
}
\references{
Sariyar M., Borg A. and Pommerening M.: Controlling false match rates in
record linkage using extreme value theory. Journal of Biomedical Informatics
(article in press), \url{http://dx.doi.org/10.1016/j.jbi.2011.02.008}.
}
\author{
Andreas Borg, Murat Sariyar
}
\note{
The quality of matching varies, poor results can occur in some cases.
Evaluate carefully before applying to a real case.
}
\seealso{
\code{\link{emWeights}} and \code{\link{epiWeights}} for calculating weights,
\code{\link{emClassify}} and \code{\link{epiClassify}} for classifying with
the returned threshold.
}
\examples{
data(RLdata500)
rpairs=compare.dedup(RLdata500, identity=identity.RLdata500, strcmp=TRUE,
blockfld=list(1,3,5:7))
rpairs=epiWeights(rpairs)
# leave out argument interval to choose from plot
\dontrun{threshold=getParetoThreshold(rpairs,interval=c(0.68, 0.79))}
\dontrun{summary(epiClassify(rpairs,threshold))}
}
\keyword{models}
\keyword{classif}