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The solution path of a search-based detector

Source: R/diagnostics.R
cpt_solution_path.Rd

The order in which candidate changepoints entered the model, with the contrast (or split criterion) at each step. Binary segmentation splits recursively, WBS/WBS2/NOT/TGUH rank random intervals; in every case the final answer is a prefix of a path, and seeing the path shows how decisively each changepoint beat the next.

Usage

cpt_solution_path(object)

ggcpt_solution_path(object, max_steps = 40)

Arguments

object

A ggcpt object from an engine with a solution path (subset(cpt_methods(), path)$method).

max_steps

Longest prefix of the path drawn. Defaults to 40: a randomised search proposes hundreds of candidates and only the head of the ranking is readable.

Value

cpt_solution_path() returns a tibble with step, cp, contrast and, for interval-based searches, start/end of the interval that proposed it, plus a selected flag marking the changepoints in the final model. ggcpt_solution_path() draws it.

contrast is the engine's own ordering criterion, and it is a different quantity per engine: a penalty value for binseg/segneigh (from changepoint::pen.value.full()), \(|CUSUM|\) for wbs, \(|\)max.contrast\(|\) for not, and breakfast's candidate criterion for wbs2/tguh. The values order the candidates within one result; they are not comparable across engines, and the plot legend names the quantity rather than calling all of them "Contrast".

For wbs2 and tguh the path is recomputed with breakfast, because their fit objects do not keep the candidate list. wbs2's search is randomised, so its path is a second search of the same series rather than a record of the first: it can differ between calls, and selected can be FALSE throughout if the recomputed candidates miss the fit's own changepoints. Every other engine's path is read off the fit.

See also

cpt_statistic(), cpt_crops() for the penalty path of an optimal-partitioning method.

Examples

set.seed(2026)
fit <- cpt_detect(c(rnorm(150), rnorm(150, 3)), method = "binseg")
cpt_solution_path(fit)
#> # A tibble: 5 × 6
#>    step    cp contrast start   end selected
#>   <int> <int>    <dbl> <int> <int> <lgl>   
#> 1     1   150   730.      NA    NA TRUE    
#> 2     2    15     4.21    NA    NA FALSE   
#> 3     3    14     3.87    NA    NA FALSE   
#> 4     4   149     2.61    NA    NA FALSE   
#> 5     5   117     1.78    NA    NA FALSE