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mcp: Regression with Multiple Change Points

Source: R/mcp-package.R
mcp-package.Rd

mcp is regression with multiple change points, inferring full Bayesian uncertainty for all parameters, including the change points themselves. At its simplest, it feels like lm() with one formula per segment. At its most flexible, it aims to be brms for change points:

  • GLMM and distributional regression: Gaussian, binomial, Bernoulli, Poisson, and Negative Binomial families. Group-level (random) effects, AR/MA, and regression on distributional parameters (e.g., sigma, shape).

  • Express expert knowledge: Specify the number of change points, the form of each segment, and restrict coefficients via priors. Compare hypotheses using loo() and hypothesis().

  • Native feel: summary(), plot_pars(), pp_check(), and as_draws_df() follow the conventions of brms, rstanarm, and posterior, adapted to change point regression. Inspect the sampled JAGS model in fit$jags_code.

You will find many aliases for change point regression in the literature, including piecewise linear, discontinuity, segmented, and broken stick regression.

See mcp() to fit models, mcp-formula for the model and formula syntax, and mcp-priors for priors.

  • Distributional regression: Model residual variance and other distributional parameters across segments, e.g., ~ sigma(1 + x) or ~ shape(1). Read more.

  • Time-series residuals: Model serial dependence using ar(p) and ma(q) terms with a generalized link-scale recurrence. Read more.

  • Group-level effects: Add hierarchical (random) intercepts, slopes, and change points, e.g., ~ 1 + (1|id) or ~ cp((1|id)) + 0 + x. Read more.

  • Families: Binomial, Bernoulli, Poisson, and negative-binomial responses with several link functions. Read more.

  • Model comparison and hypothesis testing: Compare models using PSIS-LOO (loo()) or WAIC (waic()), and test hypotheses (hypothesis()) using Savage-Dickey density ratios for point-null tests or posterior-to-prior odds for directional tests. Read more.

  • Fits, predictions, and forecasts: fitted(), predict(), and residuals(), including forecasts beyond the data. Read more.

  • Missing responses: Missing values in the response are imputed jointly with the parameters. Read more.

Options

  • mcp_quiet: Logical. The default of mcp(..., quiet). options(mcp_quiet = TRUE) suppresses routine JAGS output and mcp sampling-status messages for the session.

  • mcp_reparam: Logical. TRUE (default) lets mcp() sample some default priors on scales that mix faster. This changes only fit$jags_code and sampling efficiency, not the model, the priors, or the posterior. options(mcp_reparam = FALSE) writes the plain model, which is mostly useful for seeing how mcp works. Ignored when jags_code is supplied. Read more in From formulas to sampler code.

References

  • Lindeløv, J. K. (2020). mcp: An R Package for Regression With Multiple Change Points. OSF Preprints. doi:10.31219/osf.io/fzqxv Introduces the mcp package, formula syntax, default priors, and workflow for regression with multiple change points across generalized linear and time-series models. Newer-than-2020 versions of the paper may be available at that link. Please cite the newest version.

  • Carlin, B. P., Gelfand, A. E., & Smith, A. F. (1992). Hierarchical Bayesian Analysis of Changepoint Problems. Applied Statistics, 41(2), 389–405. doi:10.2307/2347570 Introduced Gibbs sampling for hierarchical Bayesian change-point models, the computational approach that BUGS and JAGS build on.

See also

mcp

Author

Jonas Kristoffer Lindeløv jonas@lindeloev.dk