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Joinpoint Regression++ change-point detector

Source: R/hcp_joinpoint.R
hcp_joinpoint.Rd

Piecewise linear change-point detection based on Joinpoint Regression++.

The detector searches for a globally optimal set of joinpoints with dynamic programming, fits linear segments between candidate breaks, and compares models with 0 to k_max joinpoints using BIC, BIC3, and a weighted BIC (WBIC) criterion.

This implementation is intended for univariate numeric series and follows the same family of ideas used by the National Cancer Institute Joinpoint Regression Program, but it is documented here as a Joinpoint Regression++ variant because it replaces brute-force breakpoint enumeration with dynamic programming and weighted model selection while keeping a lightweight Harbinger-compatible API.

Usage

hcp_joinpoint(min_between = 2, min_end = 2, k_max = 5, log_transform = FALSE)

Arguments

min_between

Minimum number of observations between consecutive joinpoints.

min_end

Minimum number of observations required in the first and last segments.

k_max

Maximum number of joinpoints considered during model selection.

log_transform

Logical indicating whether the series should be log transformed before fitting. This is useful for multiplicative trends and growth-rate interpretation.

Value

An hcp_joinpoint object.

References

  • Kim HJ, Fay MP, Feuer EJ, Midthune DN (2000). Permutation Tests for Joinpoint Regression with Applications to Cancer Rates. Statistics in Medicine, 19(3), 335-351. <doi:10.1002/(SICI)1097-0258(20000215)19:3<335::AID-SIM336>3.0.CO;2-Z>

  • Kim HJ, Chen HS, Midthune D, Wheeler B, Buckman DW, Green D, Byrne J, Luo J, Feuer EJ (2023). Data-driven choice of a model selection method in joinpoint regression. Journal of Applied Statistics, 50(9), 1992-2013. doi:10.1080/02664763.2022.2063265

  • National Cancer Institute. Joinpoint Trend Analysis Software. https://surveillance.cancer.gov/joinpoint/