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\name{thresholdSelect}
\alias{thresholdSelect}
\title{
Select Threshold to Convert Numerical Predictor to Binary Predictor
}
\description{
Given a point pattern and a spatial covariate that has some predictive
value for the point pattern,
determine the optimal value of the threshold for converting
the covariate to a binary predictor.
}
\usage{
thresholdSelect(X, Z, method = c("Y", "LL", "AR", "t", "C"), Zname)
}
\arguments{
\item{X}{
Point pattern (object of class \code{"ppp"}).
}
\item{Z}{
Spatial covariate with numerical values.
Either a pixel image (object of class \code{"im"}),
a distance function (object of class \code{"distfun"})
or a \code{function(x,y)} in the \R language.
}
\item{method}{
Character string (partially matched)
specifying the method to be used to select the
optimal threshold value. See Details.
}
\item{Zname}{
Optional character string giving a short name for the covariate.
}
}
\details{
The spatial covariate \code{Z} is assumed to have some utility as a
predictor of the point pattern \code{X}.
This code chooses the best threshold value \eqn{v} for converting the
numerical predictor \code{Z} to a binary predictor, for use in
techniques such as Weights of Evidence.
The best threshold is selected by maximising the criterion
specified by the argument \code{method}. Options are:
\itemize{
\item \code{method="Y"} (the default): the Youden criterion
\item \code{method="LL"}: log-likelihood
\item \code{method="AR"}: the Akman-Raftery criterion
\item \code{method="t"}: the Studentised Weights-of-Evidence contrast
\item \code{method="C"}: the Weights-of-Evidence contrast
}
These criteria are explained in Baddeley et al (2021).
}
\value{
A single numerical value giving the selected bandwidth.
The result also belongs to the class \code{"bw.optim"}
(see \code{\link[spatstat.explore]{bw.optim.object}})
which can be plotted to show the criterion used to select
the threshold.
}
\references{
Baddeley, A., Brown, W., Milne, R.K., Nair, G.,
Rakshit, S., Lawrence, T., Phatak, A. and Fu, S.C. (2021)
Optimal thresholding of predictors in mineral prospectivity analysis.
\emph{Natural Resources Research} \bold{30} 923--969.
}
\author{
\adrian.
}
\seealso{
\code{\link{thresholdCI}}
}
\examples{
gold <- rescale(murchison$gold, 1000, "km")
faults <- rescale(murchison$faults, 1000, "km")
distfault <- distfun(faults)
z <- thresholdSelect(gold, distfault)
z
plot(z, xlim=c(0, 20))
}
\keyword{spatial}
\keyword{models}
\keyword{nonparametric}
|
