augment.ggcpt: Augment a ggcpt object

In ggchangepoint: Combines Changepoint Analysis with 'ggplot2'

Augment a ggcpt object

Description

Returns the original data with added columns: seg_id, .fitted, .resid, and is_changepoint.

Usage

## S3 method for class 'ggcpt'
augment(x, ...)

Arguments

x	A ggcpt object.
...	Additional arguments (ignored).

Details

For a multivariate result every coordinate is returned, but the changepoints are shared across them, so seg_id and is_changepoint apply to the whole row. Each coordinate also gets its own .fitted_<name> and .resid_<name> (its segment means and residuals), while .fitted and .resid describe the univariate series the result carries: $data$value, the same series $segments$param_estimate summarises, so .resid is always value - .fitted. For most multivariate engines that series is the first coordinate; fmean, fcov, kwc and fabisearch store the cross-sectional mean rowMeans() instead, and for those .fitted/.resid describe that mean rather than any one column. Either way the two columns agree with each other, which is what makes .resid a residual. When an engine supplies its own fitted signal that signal is used for .fitted in place of the segment means, and rides along in a fitted column of its own, so for those engines the two columns agree. The engines that do this are exactly the ones cpt_methods() marks in its fitted column: smuce, hsmuce, cpop, bcp, bocpd, beast, decafs, segmented, envcpt, mcp and bfast; a formula fit carries its piecewise regression's fitted values the same way.

Value

A tibble with one row per observation: the data as the result carries it, plus four added columns. The data half depends on the result: index and value for a univariate one, index plus one column per coordinate (named as the input's columns were) for a multivariate one, and an extra fitted column for the engines that supply their own fitted signal. The added four are always the same:

seg_id

which segment the observation falls in, counting from 1.

.fitted

the segment's param_estimate: the segment mean, for every method in the package, or the engine's own fitted signal where there is one. See the details below for the multivariate case.

.resid

value - .fitted, against the univariate series the result carries.

is_changepoint

TRUE at each detected location, under the result's cp_convention.

Measured on a pelt fit the columns are index, value, seg_id, .fitted, .resid, is_changepoint.

Examples

set.seed(2026)
fit <- cpt_detect(c(rnorm(60), rnorm(60, 3)), method = "pelt")
head(augment(fit))

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