R: Confidence Interval for Threshold of Numerical Predictor

thresholdCI {spatstat.core} | R Documentation

## Confidence Interval for Threshold of Numerical Predictor

### Description

Given a point pattern and a spatial covariate that has some predictive value for the point pattern, compute a confidence interval for the optimal value of the threshold that should be used to convert the covariate to a binary predictor.

### Usage

    thresholdCI(X, Z, confidence = 0.95, nsim = 1000, parametric = FALSE)

### 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.
`confidence` | Confidence level. A number between 0 and 1.
`nsim` | Number of bootstrap simulations to perform.
`parametric` | Logical value specifying whether to use the parametric bootstrap.

### Details

The spatial covariate `Z` is assumed to have some utility as a predictor of the point pattern `X`.

This code computes a bootstrap confidence interval for the best threshold value z for converting the numerical predictor to a binary predictor, for use in techniques such as Weights of Evidence.

### Value

A matrix containing upper and lower limits for the threshold `z` and the corresponding upper and lower limits for the fraction of area of the study region.

### 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

`thresholdSelect`

### Examples

      gold <- rescale(murchison$gold, 1000, "km")
      faults <- rescale(murchison$faults, 1000, "km")
      distfault <- distfun(faults)
      thresholdCI(gold, distfault, nsim=100)

[Package spatstat.core version 2.4-4 Index]