Skip to contents

Area of Applicability

Source: R/borg_aoa.R
borg_aoa.Rd

Determines where a spatial prediction model can be trusted, following Meyer & Pebesma (2021). Combines the dissimilarity index (DI) with a threshold derived from cross-validated DI values to produce a binary applicability mask.

Usage

borg_aoa(
  train,
  new,
  predictors = NULL,
  coords = NULL,
  weights = NULL,
  folds = NULL,
  threshold = NULL,
  normalize = FALSE
)

Arguments

train

Data frame of training data.

new

Data frame of prediction locations (same predictor columns).

predictors

Character vector. Predictor column names.

coords

Character vector of length 2. Coordinate columns in new for spatial mapping. Optional.

weights

Numeric vector. Variable importance weights.

folds

Optional borg_cv object or fold list. If provided, the threshold is derived from cross-validated DI (more robust).

threshold

Numeric. Manual DI threshold override. If NULL, computed as the outlier-adjusted maximum (Q75 + 1.5 * IQR rule) of the LOO training DI, or of the cross-validated DI when folds is given.

normalize

Logical. If TRUE, add a di_norm column holding the dissimilarity index rescaled by the AOA threshold, so that di_norm = 1 marks the applicability boundary and values above 1 lie outside the AOA. Makes DI comparable across models and thresholds. Default: FALSE.

Value

A data frame with class "borg_aoa" containing:

di

Dissimilarity index for each prediction point

aoa

Logical. TRUE if inside AOA (DI <= threshold)

di_norm

DI divided by the threshold (only when normalize = TRUE); the AOA boundary sits at 1

x, y

Coordinates (if coords provided)

Has autoplot() method showing the AOA map.

References

Meyer, H., & Pebesma, E. (2021). Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution, 12(9), 1620-1633. doi:10.1111/2041-210X.13650

Examples

set.seed(42)
train <- data.frame(x = runif(80, 0, 50), y = runif(80, 0, 50),
                     a = rnorm(80), b = rnorm(80))
pred <- data.frame(x = runif(200, 0, 100), y = runif(200, 0, 100),
                    a = rnorm(200), b = rnorm(200))
aoa <- borg_aoa(train, pred, predictors = c("a", "b"), coords = c("x", "y"))
table(aoa$aoa)