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, ...)

object | modelAudit or modelFit object. |
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... | 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)