Rank observations by influence

Orders the observations of a cpt_influence() result by how much perturbing them disturbs the segmentation, most influential first. The composite leverage score is the sum of three standardised components — the change in the number of changepoints, the largest movement of a changepoint, and the largest change in a segment parameter — so an observation that shifts a location without changing the count is still ranked.

Usage

cpt_leverage(object, ...)

Arguments

object

A ggcpt_influence object, or a ggcpt object (in which case cpt_influence() is run first).

...

Passed to cpt_influence() when object is a ggcpt.

Value

A tibble ordered by leverage, with columns index, delta_n_cp, max_shift, param_shift and leverage.

Examples

set.seed(2026)
fit <- cpt_detect(c(rnorm(40), rnorm(40, 4)), method = "pelt")
head(cpt_leverage(fit), 3)
#> # A tibble: 3 × 5
#>   index delta_n_cp max_shift param_shift leverage
#>   <int>      <int>     <dbl>       <dbl>    <dbl>
#> 1    40          0         1      0.0201     8.90
#> 2    15          0         0      0.0642     2.93
#> 3     6          0         0      0.0635     2.88