P-value stability

Computes the proportion of perturbation iterations in which each predictor is statistically significant (p-value below alpha). The intercept is excluded. Values near 0 or 1 indicate stable decisions; values near 0.5 indicate high instability.

Usage

pvalue_stability(diag_obj)

Arguments

diag_obj

A reprostat object from run_diagnostics.

Value

A named numeric vector of significance frequencies in \([0, 1]\), excluding the intercept. All NaN for backend = "glmnet" (p-values are not defined).

Examples

set.seed(1)
d <- run_diagnostics(mpg ~ wt + hp, data = mtcars, B = 50)
pvalue_stability(d)
#>   wt   hp 
#> 1.00 0.96