cpt_effect: Effect size at each changepoint

In ggchangepoint: Combines Changepoint Analysis with 'ggplot2'

Effect size at each changepoint

Description

The size of every change a fit reports, in the data's own units and standardised: the level before and after, their difference with an interval, the difference in noise standard deviations, the percentage change, and for count and waiting-time data the rate or hazard ratio.

Usage

cpt_effect(fit, level = 0.95, method = c("naive", "split"), seed = NULL, ...)

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

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

## S3 method for class 'ggcpt_effect'
autoplot(object, ...)

Arguments

fit	A ggcpt object.
level	Confidence level for the intervals. Defaults to 0.95.
method	"naive" (the default) measures each effect on the data that located the change. "split" locates the changepoints on the odd-numbered observations and measures their effects on the even-numbered ones, which removes the winner's curse at the price of locating on half the data (see below).
seed	Optional seed for method = "split", scoped to this call.
...	Further arguments for the re-run method = "split" makes (overriding what is recovered from the fit).
x	A ggcpt_effect object.
object	A ggcpt_effect object.

Value

A ggcpt_effect tibble with one row per changepoint (per changepoint and coordinate for a multivariate fit, per changepoint and term for a regression fit): cp (and cp_index), before, after, n_before, n_after, delta, delta_lower, delta_upper (a Welch interval), delta_std (the change in noise standard deviations, the unit cpt_detect(min_effect = ) filters on), pct_change, sd_before, sd_after, sd_ratio, the family-specific ratio where it applies (rate_ratio for Poisson counts, odds_ratio for binary data, hazard_ratio for exponential waiting times, each with an interval), selection_adjusted and method. With print() and autoplot().

The winner's curse

A change measured at a location the same data chose is biased upward: the detector put the boundary where the two sides differ most, so the difference it reports is the largest the noise allowed. The bias is worst for the marginal detections, which are exactly the ones whose size matters. method = "naive" rows carry selection_adjusted = FALSE for that reason, except at a changepoint fixed in advance with cpt_detect(fixed = ), which the data did not locate and which is TRUE. method = "split" is honest about the size (the measuring half never influenced the locations) and less precise about the location (the locating half has half the data), and it assumes the noise is independent from one observation to the next, since the two halves are interleaved.

See Also

cpt_test_at() for a change at a date fixed in advance, cpt_detect()'s min_effect.

Other inference: cpt_abruptness(), cpt_assumptions(), cpt_attribute(), cpt_attribute_event(), cpt_confint_coverage(), cpt_gof(), cpt_k_weights(), cpt_null_power(), cpt_robustness(), cpt_screen(), cpt_test_at(), cpt_test_null()

Examples

set.seed(1)
fit <- cpt_detect(c(rnorm(100, 10), rnorm(100, 12)), method = "pelt")
cpt_effect(fit)

counts <- c(rpois(100, 4), rpois(100, 8))
cpt_effect(cpt_detect(counts, method = "pelt", family = "poisson"))

ggchangepoint documentation built on Nov. 5, 2026, 9:06 a.m.