Selection stability

Measures how consistently each predictor is selected across perturbation iterations. The definition depends on the modeling backend:

Usage

selection_stability(diag_obj)

Arguments

diag_obj

A reprostat object from run_diagnostics.

Value

A named numeric vector of selection stability values in \([0, 1]\), excluding the intercept.

Details

"lm", "glm", "rlm"

Sign consistency: the proportion of perturbation iterations in which the estimated coefficient has the same sign as in the base fit. A value of 1 means the direction of the effect is perfectly stable; 0.5 means the sign is random. Returns NA for a predictor whose base-fit coefficient is exactly zero.

"glmnet"

Non-zero selection frequency: the proportion of perturbation iterations in which the coefficient is non-zero (i.e. the variable survives the regularisation penalty).

The intercept is always excluded.

Examples

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