AUC: AUC

In bigstatsr: Statistical Tools for Filebacked Big Matrices

AUC

Description

Compute the Area Under the ROC Curve (AUC) of a predictor and possibly its 95% confidence interval.

Usage

AUC(pred, target, digits = NULL)

AUCBoot(pred, target, nboot = 10000, seed = NA, digits = NULL)

Arguments

pred	Vector of predictions.
target	Vector of true labels (must have exactly two levels, no missing values).
digits	See round. Default doesn't use rounding.
nboot	Number of bootstrap samples used to evaluate the 95% CI. Default is 1e4.
seed	See set.seed. Use it for reproducibility. Default doesn't set any seed.

Details

Other packages provide ways to compute the AUC (see this answer). I chose to compute the AUC through its statistical definition as a probability:

P(score(x_{case}) > score(x_{control})).

Note that I consider equality between scores as a 50%-probability of one being greater than the other.

Value

The AUC, a probability, and possibly its 2.5% and 97.5% quantiles (95% CI).

See Also

wilcox.test

Examples

set.seed(1)

AUC(c(0, 0), 0:1) # Equality of scores
AUC(c(0.2, 0.1, 1), c(0, 0, 1)) # Perfect AUC
x <- rnorm(100)
z <- rnorm(length(x), x, abs(x))
y <- as.numeric(z > 0)
print(AUC(x, y))
print(AUCBoot(x, y))

# Partial AUC
pAUC <- function(pred, target, p = 0.1) {
  val.min <- min(target)
  q <- quantile(pred[target == val.min], probs = 1 - p)
  ind <- (target != val.min) | (pred > q)
  bigstatsr::AUC(pred[ind], target[ind]) * p
}
pAUC(x, y)
pAUC(x, y, 0.2)

bigstatsr documentation built on Sept. 11, 2024, 7:08 p.m.