Summarize Bootstrap AUC Results

Computes aggregated statistics from bootstrap AUC iterations. This function processes the raw output of auc_parallel to produce meaningful summary metrics of the partial ROC test.

Usage

summarize_auc_results(auc_results, has_complete_auc)

Arguments

auc_results

Numeric matrix output from auc_parallel (dimensions: n_iterations x 4)

has_complete_auc

Boolean indicating whether complete AUC was computed in the bootstrap iterations (affects first summary column)

Value

A numeric matrix with 1 row and 5 columns containing:

• mean_complete_auc: Mean of complete AUC values (NA if not computed)

• mean_pauc: Mean of partial AUC values for the model

• mean_pauc_rand: Mean of partial AUC values for random model (reference)

• mean_auc_ratio: Mean of AUC ratios (model/random)

• prop_ratio_gt1: Proportion of iterations where ratio > 1 (performance better than random)

Details

This function: 1. Filters iterations with non-finite ratio values (handles bootstrap failures) 2. Computes means for each AUC metric across valid iterations 3. Calculates proportion of iterations where model outperforms random (ratio > 1). This way of computing the the p-value of the test.

Special handling: - Returns all NAs if no valid iterations exist - First column (complete AUC) depends on has_complete_auc parameter - Handles NaN/Inf values safely by filtering

Interpretation Guide

- mean_auc_ratio > 1: Model generally outperforms random predictions - prop_ratio_gt1 = 1.9: 90 - mean_pauc: Absolute performance measure (higher = better discrimination)

See also

auc_parallel for generating the input matrix

Examples

# Basic usage with simulated results
set.seed(123)
# Simulate bootstrap output (100 iterations x 4 metrics)
auc_matrix <- cbind(
  complete = rnorm(100, 0.85, 0.05),  # Complete AUC
  pmodel   = rnorm(100, 0.15, 0.03),  # Partial model AUC
  prand    = rnorm(100, 0.08, 0.02),  # Partial random AUC
  ratio    = rnorm(100, 1.9, 0.4)     # Ratio
)

# Summarize results (assuming complete AUC was computed)
summary <- summarize_auc_results(auc_matrix, has_complete_auc = TRUE)

# Typical output interpretation:
# - mean_complete_auc: 0.85 (good overall discrimination)
# - mean_pauc: 0.15 (absolute partial AUC)
# - mean_pauc_rand: 0.08 (random expectation)
# - mean_pAUCratio: 1.9 (model 90% better than random)
# - p_value: 0.98 (98% of iterations showed model > random)

# Real-world usage with actual AUC function output
# \donttest{
# First run bootstrap AUC calculation
bg_pred <- runif(1000)
test_pred <- runif(500)
auc_output <- auc_parallel(
  test_prediction = test_pred,
  prediction = bg_pred,
  iterations = 100
)

# Then summarize results (complete AUC not computed in this case)
summary <- summarize_auc_results(auc_output, has_complete_auc = FALSE)

# Print summary statistics
colnames(summary) <- c("mean_complete_auc", "mean_pauc",
                      "mean_pauc_rand", "mean_pAUCratio", "p_value")
print(summary)
#>      mean_complete_auc mean_pauc mean_pauc_rand mean_pAUCratio p_value
#> [1,]                NA 0.0507199     0.05050317       1.003634    0.31

# Expected output structure:
#      mean_complete_auc mean_pauc mean_pauc_rand mean_pAUCratio    p_value
# [1,]               NA     0.152          0.083       1.83           0.94
# }