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Broken-line regression wrapper (segmented)

Source: R/wrap-regression.R
segmented_wrapper.Rd

Wraps segmented::segmented() (Muggeo, 2003, 2008): maximum likelihood estimation of continuous piecewise-linear ("broken-line") regressions, with standard errors and confidence intervals for the breakpoint locations. Where the step-change engines model jumps in the level, segmented models kinks in the trend, so change_in is "slope" and the fitted broken line is stored in the fitted column for autoplot(show_fit = TRUE).

Usage

segmented_wrapper(x, npsi = 1, conf_level = 0.95, seed = NULL, ...)

Arguments

x

A numeric vector; a linear model of x on time 1:length(x) is segmented.

npsi

Number of breakpoints to estimate. Defaults to 1.

conf_level

Confidence level for breakpoint intervals. Defaults to 0.95.

seed

Optional seed (the estimator uses bootstrap restarting).

...

Additional arguments passed to segmented::segmented().

Value

A ggcpt object with ci_lower/ci_upper columns and the fitted broken line in $data$fitted. Breakpoints are rounded to the nearest index; for a continuous fit the reported location is the kink itself. A constant series has no kink and returns an empty result, rather than the arbitrary breakpoint a singular fit would give.

References

Muggeo VM (2003). “Estimating regression models with unknown break-points.” Statistics in Medicine, 22(19), 3055–3071.

Muggeo VM (2008). “segmented: An R package to fit regression models with broken-line relationships.” R News, 8(1), 20–25.

See also

Other changepoint engines: bcp_wrapper(), beast_wrapper(), bfast_wrapper(), binsegrcpp_wrapper(), bocpd_wrapper(), cpm_wrapper(), cpop_wrapper(), cpt_wrapper(), decafs_wrapper(), ecp_wrapper(), envcpt_wrapper(), esac_wrapper(), fabisearch_wrapper(), fastcpd_wrapper(), fcov_wrapper(), fmean_wrapper(), fpop_wrapper(), geomcp_wrapper(), hdcov_wrapper(), hdreg_wrapper(), idetect_wrapper(), inspect_wrapper(), kcp_wrapper(), kwc_wrapper(), mcp_wrapper(), mosum_wrapper(), network_wrapper(), not_wrapper(), npmojo_wrapper(), nsp_wrapper(), ocd_wrapper(), pilliat_wrapper(), smuce_wrapper(), sn_wrapper(), strucchange_wrapper(), taylor_wrapper(), tguh_wrapper(), trend_wrapper(), var_wrapper(), wbs2_wrapper(), wbs_wrapper(), wbsts_wrapper()

Examples

set.seed(2026)
y <- cumsum(c(rep(0.5, 100), rep(-0.3, 100))) + rnorm(200)
res <- segmented_wrapper(y, npsi = 1)
res$changepoints
#> # A tibble: 1 × 4
#>      cp cp_value ci_lower ci_upper
#>   <int>    <dbl>    <int>    <int>
#> 1   100     50.4       99      101
ggplot2::autoplot(res, show_fit = TRUE, show_ci = TRUE)