Computes the AUC / C-statistic as the Mann-Whitney U statistic: the
probability that a randomly chosen case (y == 1) is assigned a higher
predicted value than a randomly chosen non-case (y == 0), with ties
counting as one half. This needs no external package. An AUC of 0.5 is chance;
1 is perfect separation.
Arguments
- prob
Numeric vector of predicted probabilities (or any score where
larger means more case-like).
- y
Observed binary outcome as 0/1 numeric or logical, the same length as
prob.
Value
A single number in [0, 1], or NA_real_ if either class is
absent.
References
Merlo, J., Wagner, P., Ghith, N., & Leckie, G. (2016). An original stepwise
multilevel logistic regression analysis of discriminatory accuracy: the case of
neighbourhoods and health. PLOS ONE, 11(4), e0153778.
Examples
maihda_auc(c(0.1, 0.4, 0.35, 0.8), c(0, 0, 1, 1))
#> [1] 0.75
