Broken-line regression wrapper (segmented)

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.

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.

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)