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, ...)

## Arguments

object modelAudit or modelFit object. Extra arguments passed to hnp.

## Examples

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)