auc_metrics: Calculate Partial and complete Area Under the Curve (AUC)...

In fpROC: Fast Partial Receiver Operating Characteristic (ROC) Test for Ecological Niche Modeling

Calculate Partial and complete Area Under the Curve (AUC) Metrics

Description

Computes partial AUC ratios between model predictions and random curves at a specified threshold, with options for sampling and iterations. Handles both numeric vectors and SpatRaster inputs.

Usage

auc_metrics(
  test_prediction,
  prediction,
  threshold = 5,
  sample_percentage = 50,
  iterations = 500,
  compute_full_auc = TRUE
)

Arguments

test_prediction	Numeric vector of test prediction values (e.g., model outputs)
prediction	Numeric vector or SpatRaster object containing prediction values
threshold	Percentage threshold for partial AUC calculation (default = 5)
sample_percentage	Percentage of test data to sample (default = 50)
iterations	Number of iterations for estimating bootstrap statistics (default = 500)
compute_full_auc	Logical. If TRUE, the complete AUC values will be computed

Details

Partial ROC is calculated following Peterson et al. (2008; \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ecolmodel.2007.11.008")}). The function calculates partial AUC ratios by:

1. Validating input types and completeness

2. Handling NA values and SpatRaster conversion

3. Checking for prediction variability

4. Computing AUC metrics using optimized C++ code

When prediction values have no variability (all equal), the function returns NA values with a warning.

Value

A list containing:

• If input has no variability: List with NA values for AUC metrics

• Otherwise: Matrix of AUC results.

References

Peterson, A.T. et al. (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol. Modell., 213, 63–72.

Examples

# With numeric vectors
test_data <- rnorm(100)
pred_data <- rnorm(100)
result <- fpROC::auc_metrics(test_prediction = test_data, prediction = pred_data)

# With SpatRaster
library(terra)
r <- terra::rast(ncol=10, nrow=10)
values(r) <- rnorm(terra::ncell(r))
result <- fpROC::auc_metrics(test_prediction = test_data, prediction = r)

fpROC documentation built on Aug. 8, 2025, 6:47 p.m.