mcp
does regression with one or Multiple Change Points (MCP) between Generalized and hierarchical Linear Segments using Bayesian inference. mcp
aims to provide maximum flexibility for analyses with a priori knowledge about the number of change points and the form of the segments in between.
Change points are also called switch points, break points, broken line regression, broken stick regression, bilinear regression, piecewise linear regression, local linear regression, segmented regression, and (performance) discontinuity models. mcp
aims to be be useful for all of them. See how mcp
compares to other R packages.
Under the hood, mcp
takes a formula-representation of linear segments and turns it into JAGS code. mcp
leverages the power of tidybayes
, bayesplot
, coda
, and loo
to make change point analysis easy and powerful.
Install the latest version of JAGS. Linux users can fetch binaries here.
Install from CRAN:
install.packages("mcp")
or install the development version from GitHub:
if (!requireNamespace("remotes")) install.packages("remotes") remotes::install_github("lindeloev/mcp")
Here are some example mcp
models. mcp
takes a list of formulas - one for each segment. The change point(s) are the x
at which data changes from being better predicted by one formula to the next. The first formula is just response ~ predictors
and the most common formula for segment 2+ would be ~ predictors
(more details here).
Scroll down to see brief introductions to each of these, or browse the website articles for more thorough worked examples and discussions.
The following model infers the two change points between three segments.
library(mcp) # Define the model model = list( response ~ 1, # plateau (int_1) ~ 0 + time, # joined slope (time_2) at cp_1 ~ 1 + time # disjoined slope (int_3, time_3) at cp_2 ) # Get example data and fit it ex = mcp_example("demo") fit = mcp(model, data = ex$data)
The default plot includes data, fitted lines drawn randomly from the posterior, and change point(s) posterior density for each chain:
plot(fit)
Use summary()
to summarise the posterior distribution as well as sampling diagnostics. They were simulated using mcp
(see mcp_example("demo")$call
) so the summary include the “true” values in the column sim
and the column match
show whether this true value is within the interval:
summary(fit)
Family: gaussian(link = 'identity') Iterations: 9000 from 3 chains. Segments: 1: response ~ 1 2: response ~ 1 ~ 0 + time 3: response ~ 1 ~ 1 + time Population-level parameters: name match sim mean lower upper Rhat n.eff cp_1 OK 30.0 30.27 23.19 38.760 1 384 cp_2 OK 70.0 69.78 69.27 70.238 1 5792 int_1 OK 10.0 10.26 8.82 11.768 1 1480 int_3 OK 0.0 0.44 -2.49 3.428 1 810 sigma_1 OK 4.0 4.01 3.43 4.591 1 3852 time_2 OK 0.5 0.53 0.40 0.662 1 437 time_3 OK -0.2 -0.22 -0.38 -0.035 1 834
rhat
is the Gelman-Rubin convergence diagnostic, eff
is the effective sample size. You may also want to do a posterior predictive check using pp_check(fit)
.
plot_pars(fit)
can be used to inspect the posteriors and convergence of all parameters. See the documentation of plot_pars()
for many other plotting options. Here, we plot just the (population-level) change points. They often have “strange” posterior distributions, highlighting the need for a computational approach:
plot_pars(fit, regex_pars = "cp_")
Use fitted(fit)
and predict(fit)
to get fits and predictions for in-sample and out-of-sample data.
We can test (joint) probabilities in the model using hypothesis()
(see more here). For example, what is the evidence (given priors) that the first change point is later than 25 against it being less than 25?
hypothesis(fit, "cp_1 > 25")
hypothesis mean lower upper p BF 1 cp_1 - 25 > 0 5.27 -1.81 13.76 0.917 11.1
For model comparisons, we can fit a null model and compare the predictive performance of the two models using (approximate) leave-one-out cross-validation (see more here). Our null model omits the first plateau and change point, essentially testing the credence of that change point:
# Define the model model_null = list( response ~ 1 + time, # intercept (int_1) and slope (time_1) ~ 1 + time # disjoined slope (int_2, time_1) ) # Fit it fit_null = mcp(model_null, ex$data)
Leveraging the power of loo::loo
, we see that the two-change-points model is preferred (it is on top), but the elpd_diff / se_diff
ratio ratio indicate that this preference is not very strong.
fit$loo = loo(fit) fit_null$loo = loo(fit_null) loo::loo_compare(fit$loo, fit_null$loo)
elpd_diff se_diff
model1 0.0 0.0
model2 -7.6 4.6
The articles on the mcp website go in-depth with the functionality of mcp
. Here is an executive summary, to give you a quick sense of what mcp can do.
About mcp models and simulating data:
int_i
(intercepts), cp_i
(change points), x_i
(slopes), phi_i
(autocorrelation), and sigma_*
(variance).ifelse
model.rel()
to specify that parameters are relative to those corresponding in the previous segments.fit$simulate()
. See examples in, e.g., mcp_examples("demo")$call
.fit$prior
.mcp(..., prior = list(cp_1 = "dnorm(0, 1)", cp_1 = "dunif(0, 45)")
.cp_i = "dirichlet(1)"
) is slow but beautiful.cp_1 = 45
.slope_1 = "slope_2"
.T(lower, upper)
, e.g., int_1 = "dnorm(0, 1) T(0, )"
. mcp
applies this automatically to change point priors to enforce order restriction. This is true for varying change points too.mcp(model, data, sample = "prior")
.ranef(fit)
.plot(fit, facet_by = "my_group")
and plot_pars(fit, pars = "varying", type = "dens_overlay", ncol = 3)
.Supported families and link functions:
mcp
currently supports specific combinations of families (gaussian()
, binomial()
, bernoulli()
, and poisson()
) and link functions (identity
, logit
, probit
, and log
).binomial(link = "logit")
for binomial change points in mcp. Also relevant for bernoulli(link = "logit")
.poisson(link = "log")
for Poisson change points in mcp.plot(fit, scale = "linear")
or predict(fit, scale = "linear")
.Model comparison and hypothesis testing:
loo(fit)
and loo::loo_compare(fit1$loo, fit2$loo)
.hypothesis(fit, "cp_1 = 40")
.hypothesis(fit, "cp_1 > 30 & cp_1 < 50")
), combined other hypotheses (hypothesis(fit, "cp_1 > 30 & int_1 > int_2")
), etc.Modeling variance and autoregression:
~ sigma(1)
models an intercept change in variance. ~ sigma(0 + x)
models increasing/decreasing variance.~ ar(N)
models Nth order autoregression on residuals. ~ar(N, 0 + x)
models increasing/decreasing autocorrelation.sigma()
and ar()
. For example, ~ x + sigma(1 + x + I(x^2))
models polynomial change in variance with x
on top of a slope on the mean.Get fitted and predicted values and intervals:
fitted(fit)
and predict(fit)
take many arguments to predict in-sample and out-of-sample values and intervals.mcp(..., cores = 3)
/ options(mcp_cores = 3)
, and/or fewer iterations, mcp(..., adapt = 500)
.mcp(..., inits = list(cp_1 = 20, int_2 = -3))
.mcp
aims to support a wide variety of models. Here are some example models for inspiration.
Find the single change point between two plateaus (simulated using mcp_example("intercepts")$call
).
model = list( y ~ 1, # plateau (int_1) ~ 1 # plateau (int_2) ) ex = mcp_example("intercepts") fit = mcp(model, ex$data, par_x = "x") plot(fit)
Here, we find the single change point between two joined slopes. While the slopes are shared by all participants, the change point varies by id
. Read more about varying change points in mcp.
model = list( y ~ 1 + x, # intercept + slope 1 + (1|id) ~ 0 + x # joined slope, varying by id ) ex = mcp_example("varying") fit = mcp(model, ex$data) plot(fit, facet_by = "id")
Summarise the varying change points using ranef()
or plot them using plot_pars(fit, "varying")
. Again, this data was simulated using mcp
(see mcp_example("varying")$call
) so the columns match
and sim
are added to show simulation values and whether they are inside the interval. Set the width
wider for a more lenient criterion.
ranef(fit, width = 0.98)
name match sim mean lower upper Rhat n.eff cp_1_id[Benny] OK -17.5 -18.1 -21.970 -14.877 1 895 cp_1_id[Bill] OK -10.5 -7.6 -10.658 -4.451 1 420 cp_1_id[Cath] OK -3.5 -2.8 -5.634 0.027 1 888 cp_1_id[Erin] OK 3.5 3.1 0.041 5.952 1 3622 cp_1_id[John] OK 10.5 11.3 7.577 14.989 1 2321 cp_1_id[Rose] OK 17.5 14.1 10.485 18.079 1 5150
mcp
supports Generalized Linear Modeling. See extended examples using binomial()
and poisson()
.
Here is a binomial change point model with three segments (see simulation code: mcp_example("binomial")$call
). We plot the 95% HDI too:
model = list( y | trials(N) ~ 1, # constant rate ~ 0 + x, # joined changing rate ~ 1 + x # disjoined changing rate ) ex = mcp_example("binomial") fit = mcp(model, ex$data, family = binomial()) plot(fit, q_fit = TRUE)
Use plot(fit, rate = FALSE)
if you want the points and fit lines on the original scale of y
rather than divided by N
.
mcp
allows for flexible time series analysis with autoregressive residuals of arbitrary order. Below, we model a change from a plateau with strong positive AR(2) residuals to a slope with medium AR(1) residuals. These data were simulated with mcp
(see simulation code: mcp_example("ar")$call
) and the generating values are in the sim
column. You can also do regression on the AR coefficients themselves using e.g., ar(1, 1 + x)
. Read more here.
model = list( price ~ 1 + ar(2), ~ 0 + time + ar(1) ) ex = mcp_example("ar") fit = mcp(model, ex$data) summary(fit)
The AR(N) parameters on intercepts are named ar[order]_[segment]
. All parameters, including the change point, are well recovered:
Population-level parameters: name match sim mean lower upper Rhat n.eff ar1_1 OK 0.7 0.741 5.86e-01 0.892 1.01 713 ar1_2 OK -0.4 -0.478 -6.88e-01 -0.255 1.00 2151 ar2_1 OK 0.2 0.145 -6.56e-04 0.284 1.01 798 cp_1 120.0 117.313 1.14e+02 118.963 1.05 241 int_1 20.0 17.558 1.51e+01 19.831 1.02 293 sigma_1 OK 5.0 4.829 4.39e+00 5.334 1.00 3750 time_2 OK 0.5 0.517 4.85e-01 0.553 1.00 661
The fit plot shows the inferred autocorrelated nature:
plot(fit_ar)
You can model variance by adding a sigma()
term to the formula. The inside sigma()
can take everything that the formulas outside do. Read more in the article on variance. The example below models two change points. The first is variance-only: variance abruptly increases and then declines linearly with x
. The second change point is the stop of the variance-decline and the onset of a slope on the mean.
Effects on variance is best visualized using prediction intervals. See more in the documentation for plot.mcpfit()
.
model = list( y ~ 1, ~ 0 + sigma(1 + x), ~ 0 + x ) ex = mcp_example("variance") fit = mcp(model, ex$data, cores = 3, adapt = 5000, iter = 5000) plot(fit, q_predict = TRUE)
Write exponents as I(x^N)
. E.g., quadratic I(x^2)
, cubic I(x^3)
, or some other power function I(x^1.5)
. The example below detects the onset of linear + quadratic growth. This is often called the BLQ model (Broken Line Quadratic) in nutrition research.
model = list( y ~ 1, ~ 0 + x + I(x^2) ) ex = mcp_example("quadratic") fit = mcp(model, ex$data) plot(fit)
You can use sin(x)
, cos(x)
, and tan(x)
to do trigonometry. This can be useful for seasonal trends and other periodic data. You can also do exp(x)
, abs(x)
, log(x)
, and sqrt(x)
, but beware that the two latter will currently fail in segment 2+. Raise an issue if you need this.
rel()
and priorsRead more about formula options and priors.
Here we find the two change points between three segments. The slope and intercept of segment 2 are parameterized relative to segment 1, i.e., modeling the change in intercept and slope since segment 1. So too with the second change point (cp_2
) which is now the distance from cp_1
.
Some of the default priors are overwritten. The first intercept (int_1
) is forced to be 10, the slopes are in segment 1 and 3 is shared. It is easy to see these effects in the (simulated) data because they violate it somewhat. The first change point has to be at x = 20
or later.
model = list( y ~ 1 + x, ~ rel(1) + rel(x), rel(1) ~ 0 + x ) prior = list( int_1 = 10, # Constant, not estimated x_3 = "x_1", # shared slope in segment 1 and 3 int_2 = "dnorm(0, 20)", cp_1 = "dunif(20, 50)" # has to occur in this interval ) ex = mcp_example("rel_prior") fit = mcp(model, ex$data, prior, iter = 10000) plot(fit)
Comparing the summary to the fitted lines in the plot, we can see that int_2
and x_2
are relative values. We also see that the “wrong” priors made it harder to recover the parameters used to simulate this code (see mcp_example("rel_prior")$simulated
which is represented in the match
and sim
columns):
summary(fit)
Population-level parameters: name match sim mean lower upper Rhat n.eff cp_1 OK 25.0 23.15 20.00 25.81 1.00 297 cp_2 40.0 51.85 47.06 56.36 1.02 428 int_1 25.0 10.00 10.00 10.00 NaN 0 int_2 OK -10.0 -6.86 -21.57 11.89 1.03 190 sigma_1 7.0 9.70 8.32 11.18 1.00 7516 x_1 1.0 1.58 1.24 1.91 1.07 120 x_2 OK -3.0 -3.28 -3.61 -2.96 1.04 293 x_3 0.5 1.58 1.24 1.91 1.07 120
Don’t be constrained by these simple mcp
functions. fit$samples
is a regular mcmc.list
object and all methods apply. You can work with the MCMC samples just as you would with brms
, rstanarm
, jags
, or other samplers using the always excellent tidybayes
:
library(tidybayes) # Extract all parameters: tidy_draws(fit$samples) %>% # tidybayes stuff here # Extract some parameters: fit$pars$model # check out which parameters are inferred. spread_draws(fit$samples, cp_1, cp_2, int_1, year_1) %>% # tidybayes stuff here
It may be convenient to use fitted(fit, summary = FALSE)
or predict(fit, summary = FALSE)
which return draws in tidybayes
format, extended with additional columns for fits and predictions. For example:
This preprint formally introduces mcp
. Find citation info at the link, call citation("mcp")
or copy-paste this into your reference manager:
@Article{,
title = {mcp: An R Package for Regression With Multiple Change Points},
author = {Jonas Kristoffer Lindeløv},
journal = {OSF Preprints},
year = {2020},
doi = {10.31219/osf.io/fzqxv},
encoding = {UTF-8},
}