Score is approximately:
\( \sum{\#[res_i \leq simres_{i,j}] - n } \)
with the distinction that each element of sum is also scaled to take values from [0,1].
\(res_i\) is a residual for i-th observation, \(simres_{i,j}\) is the residual of j-th simulation for i-th observation, and \(n\) is the number of simulations for each observation.
Scores are calculated on the basis of simulated data, so they may differ between function calls.
# NOT RUN {library(car)
lm_model <- lm(prestige~education + women + income, data = Prestige)
lm_au <- audit(lm_model, data = Prestige, y = Prestige$prestige)
plotHalfNormal(lm_au)
# }