# AUC: AUC In bigstatsr: Statistical Tools for Filebacked Big Matrices

## Description

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

## Usage

 ```1 2 3``` ```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).

wilcox.test

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ```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) ```

### Example output

```sh: 1: cannot create /dev/null: Permission denied
sh: 1: wc: Permission denied
 0.5
 1
 0.8071659
Mean       2.5%      97.5%         Sd
0.80608943 0.70540804 0.88921345 0.04767838
 0.00962963
 0.05802469
```

bigstatsr documentation built on April 5, 2021, 5:08 p.m.