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.

scoreHalfNormal(object, ...)



modelAudit or modelFit object.


Extra arguments passed to hnp.


library(car) lm_model <- lm(prestige~education + women + income, data = Prestige) lm_au <- audit(lm_model, data = Prestige, y = Prestige$prestige) plotHalfNormal(lm_au)
#> Gaussian model (lm object)