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Parallel AUC and partial AUC calculation with optimized memory usage

Source: R/RcppExports.R
auc_parallel.Rd

Computes bootstrap estimates of partial and complete AUC using parallel processing and optimized binning.

Usage

auc_parallel(
  test_prediction,
  prediction,
  threshold = 5,
  sample_percentage = 50,
  iterations = 500L,
  compute_full_auc = TRUE,
  n_bins = 500L
)

Arguments

test_prediction

Numeric vector of test prediction values

prediction

Numeric vector of model predictions (background suitability data)

threshold

Percentage threshold for partial AUC calculation (default = 5.0)

sample_percentage

Percentage of test data to sample in each iteration (default = 50.0)

iterations

Number of bootstrap iterations (default = 500)

compute_full_auc

Boolean indicating whether to compute complete AUC (default = TRUE)

n_bins

Number of bins for discretization (default = 500)

Value

A numeric matrix with `iterations` rows and 4 columns containing:

  • auc_complete: Complete AUC (NA when compute_full_auc = FALSE)

  • auc_pmodel: Partial AUC for the model (sensitivity > 1 - threshold/100)

  • auc_prand: Partial AUC for random model (reference)

  • ratio: Ratio of model AUC to random AUC (model/reference)

Details

This function implements a highly optimized AUC calculation pipeline: 1. Cleans input data (removes non-finite values) 2. Combines background and test predictions 3. Performs range-based binning (discretization) 4. Computes cumulative distribution of background predictions 5. Runs bootstrap iterations in parallel: - Samples test predictions - Computes sensitivity-specificity curves - Calculates partial and complete AUC

Key optimizations: - OpenMP parallelization for binning and bootstrap - Vectorized operations using Armadillo

Partial AUC

The partial AUC focuses on the high-sensitivity region defined by: Sensitivity > 1 - (threshold/100)

See also

summarize_auc_results for results processing, trap_roc for integration method

Examples

# Basic usage with random data
set.seed(123)
bg_pred <- runif(1000)   # bg predictions
test_pred <- runif(500)     # Test predictions

# Compute only partial AUC metrics (500 iterations)
results <- auc_parallel(test_pred, bg_pred,
                            threshold = 5.0,
                            iterations = 100)  # Reduced for example

# View first 5 iterations
head(results, 5)
#>          [,1]     [,2]      [,3]      [,4]
#> [1,] 0.484496 0.063468 0.0628875 1.0092308
#> [2,] 0.522732 0.035312 0.0353520 0.9988685
#> [3,] 0.488304 0.062234 0.0619520 1.0045519
#> [4,] 0.497354 0.056516 0.0563180 1.0035157
#> [5,] 0.508788 0.051704 0.0515955 1.0021029

# Summarize results (assume complete AUC was not computed)
summary <- summarize_auc_results(results, has_complete_auc = FALSE)

# Interpretation:
# - auc_pmodel: Model's partial AUC (higher is better)
# - auc_prand: Random model's partial AUC
# - ratio: Model AUC / Random AUC (>1 indicates better than random)

# Compute both partial and complete AUC
full_results <- auc_parallel(test_pred, bg_pred,
                                 compute_full_auc = TRUE,
                                 iterations = 100)