The solution path of a search-based detector

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.

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