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 most influential first, with columns index, delta_n_cp, max_shift, param_shift and leverage.

Rows with leverage = NA come first, and they are the most influential of all. max_shift and param_shift are undefined for a perturbation that left the engine with no changepoints at all (there is nothing to match against and no parameters to compare), so the composite score cannot be formed for an observation whose removal destroys the segmentation entirely. The NA is kept rather than filled in with a fabricated number; read the delta_n_cp column on those rows, which says how many changepoints were lost.

An NA here is always that case. If the original fit found no changepoints then max_shift is missing for every observation, the standardisation returns zeros rather than NAs, and every leverage is finite. A perturbation whose re-fit failed is not one of these: it carries delta_n_cp = NA as well and is sorted last, since nothing is known about it.

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