Returns the original data with added columns: seg_id, .fitted,
.resid, and is_changepoint.
Usage
# S3 method for class 'ggcpt'
augment(x, ...)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_idwhich segment the observation falls in, counting from 1.
.fittedthe 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..residvalue - .fitted, against the univariate series the result carries.is_changepointTRUEat each detected location, under the result'scp_convention.
Measured on a pelt fit the columns are index,
value, seg_id, .fitted, .resid,
is_changepoint.
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 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, beast, decafs, segmented,
mcp and bfast.