R: Select Threshold to Convert Numerical Predictor to Binary...

thresholdSelect {spatstat.core}

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 `"ppp"`).
`Z`	Spatial covariate with numerical values. Either a pixel image (object of class `"im"`), a distance function (object of class `"distfun"`) or a `function(x,y)` in the R language.
`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 numerical value giving the selected threshold. The result also belongs to the class "bw.optim" which can be plotted (the plot shows 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.

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))

[Package spatstat.core version 2.3-2 Index]