Influence diagnostics for a changepoint segmentation

Perturbs one observation at a time — deleting it, or replacing it with an outlier — re-runs the detector, and reports what changed: the number of changepoints, where they moved to, and how the segment parameters responded. This is the diagnostic family of Wilms, Killick and Matteson (2022), rendered in ggplot2 so it composes with the rest of the package.

Usage

cpt_influence(
  object,
  type = c("delete", "outlier"),
  engine = c("auto", "changepoint.influence", "recompute"),
  subset = NULL,
  outlier_sd = 5,
  seed = NULL,
  ...
)

# S3 method for class 'ggcpt_influence'
print(x, ...)

# S3 method for class 'ggcpt_influence'
autoplot(
  object,
  plot_type = c("overview", "location", "parameter", "map"),
  ...
)

# S3 method for class 'ggcpt_influence'
tidy(x, ...)

Arguments

object

A ggcpt_influence object (for autoplot()).

type

"delete" (drop the observation) or "outlier" (replace it with a large value). Defaults to "delete".

engine

Which implementation to use: "auto" (default) uses changepoint.influence when the result came from a changepoint engine and that package is installed, and the generic recomputation otherwise; "changepoint.influence" insists on the former; "recompute" insists on the latter, which works for every wired and registered method.

subset

Optional integer vector of observation positions to perturb. Influence by deletion costs one detector fit per observation, so on a long series or an expensive engine this is the argument that makes the diagnostic affordable. Defaults to every observation.

outlier_sd

For type = "outlier", how many residual standard deviations the substituted value sits above the fitted level. Defaults to 5.

seed

Optional seed, for detectors that randomise.

...

Additional arguments passed to cpt_detect() on each perturbed series.

x

A ggcpt_influence object (for print()).

plot_type

Which diagnostic to draw: "overview" (the series with the influential observations highlighted), "location" (perturbed changepoint locations against the perturbed observation), "parameter" (segment-parameter shift per perturbation) or "map" (the full influence map: perturbed observation on x, position on y, parameter shift as fill).

Value

A ggcpt_influence object: a list with

influence

a tibble with one row per perturbed observation — index, n_cp, delta_n_cp (against the unperturbed fit), max_shift (largest movement of a surviving changepoint, in positions), param_shift (largest absolute change in a segment parameter) and cpts (a list-column of the perturbed changepoint sets);

param

an \(n \times n\) matrix of per-observation segment parameters, one row per perturbation — the input to the influence map;

original, type, engine, method

with print() and autoplot() methods.

References

Wilms I, Killick R, Matteson DS (2022). “Graphical influence diagnostics for changepoint models.” Journal of Computational and Graphical Statistics, 31(3), 753–765. doi:10.1080/10618600.2021.2000873 .

See also

cpt_leverage(), cpt_sensitivity(), cpt_stability().

Examples

set.seed(2026)
fit <- cpt_detect(c(rnorm(40), rnorm(40, 4)), method = "pelt")
inf <- cpt_influence(fit)
inf
#> ggcpt_influence (delete perturbation, method: pelt, engine: changepoint.influence)
#>   Observations perturbed: 80
#>   Unperturbed changepoints: 1
#>   Perturbations changing the number of changepoints: 0 (0%)
#> 
#> Most influential observations:
#> # A tibble: 5 × 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
#> 4    77          0         0      0.0578     2.50
#> 5    26          0         0      0.0500     1.97
head(cpt_leverage(inf))
#> # A tibble: 6 × 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
#> 4    77          0         0      0.0578     2.50
#> 5    26          0         0      0.0500     1.97
#> 6    27          0         0      0.0454     1.66