thresholdSelect {spatstat.explore} | R Documentation |
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
X |
Point pattern (object of class |
Z |
Spatial covariate with numerical values.
Either a pixel image (object of class |
method |
Character string (partially matched) specifying the method to be used to select the optimal threshold value. See Details. |
Zname |
Optional character string giving a short name for the covariate. |
Details
The spatial covariate Z
is assumed to have some utility as a
predictor of the point pattern X
.
This code chooses the best threshold value v
for converting the
numerical predictor 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 method
. Options are:
-
method="Y"
(the default): the Youden criterion -
method="LL"
: log-likelihood -
method="AR"
: the Akman-Raftery criterion -
method="t"
: the Studentised Weights-of-Evidence contrast -
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 "bw.optim"
(see bw.optim.object
)
which can be plotted to show the criterion used to select
the threshold.
Author(s)
Adrian Baddeley Adrian.Baddeley@curtin.edu.au.
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. Natural Resources Research 30 923–969.
See Also
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))