Calibrate Dissimilarity Index to Prediction Error

Models the relationship between the Dissimilarity Index (DI) and prediction error using isotonic regression. Enables pixel-level uncertainty estimation: for any new prediction point, its DI can be mapped to an expected error.

Usage

borg_error_profile(
  data,
  folds,
  formula,
  predictors = NULL,
  metric = c("rmse", "mae"),
  fit_fun = stats::lm,
  n_bins = 10
)

Arguments

data: Data frame of training data.
folds: A borg_cv object or fold list.
formula: Model formula.
predictors: Character vector. Predictor columns for DI computation. If NULL, auto-detected.
metric: Character. "rmse" or "mae".
fit_fun: Function. Default: lm.
n_bins: Integer. Number of DI bins. Default: 10.

Value

A list with class "borg_error_profile" containing:

profile: Data frame: di_bin, mean_di, mean_error, n_obs
raw: Data frame: di, error (per observation)
iso_fit: Isotonic regression fit for predicting error from DI

Has an autoplot() method and a predict() method.

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)
d <- data.frame(x = runif(150, 0, 100), y = runif(150, 0, 100),
                 a = rnorm(150), b = rnorm(150))
d$z <- d$a * 2 + rnorm(150, sd = 0.5)
cv <- borg_cv(d, coords = c("x", "y"), target = "z")
ep <- borg_error_profile(d, cv, z ~ a + b, predictors = c("a", "b"))
ep