This release contains no user-facing changes. The test suite suite is now compatible with dplyr 1.0.8, which caused the test suite to fail. This, in turn, would trigger the removal of mcp from CRAN.
This is mostly a bug fix release.
ex = mcp_example("demo", with_fit = TRUE) is the new
interface that replaces the ex_* datasets in prior
versions. This reduces clutter of the namespace/documentation and the
size of the package. It also gives the user richer details on the
simulation and analyses. For “demo”, the ex_demo dataset is
now ex$data and the ex_fit is
ex$fit.
Nicer printing of lists and texts all over. E.g., try
print(demo_fit$jags_code) and
print(demo_fit$pars).
tidybayes >= 3.0.0 and
dplyr >= 1.0.6Get fits and predictions for in-sample and out-of-sample data. Read more in the article on these functions.
predict(fit) to get predicted values and
quantiles.fitted(fit) to get estimated values and
quantiles.residuals(fit) to get residuals and quantiles.All of the above functions include many arguments that align with
(and extends) the options already in plot.mcpfit(),
including getting fits/predictions for sigma
(which_y = "sigma"), for the prior
(prior = TRUE), and arbitrary quantiles
(probs = c(0.1, 0.5, 0.999)). Use the newdata
argument to get out-of-sample fitted/predicted values. Set
summary = FALSE to get per-draw values.
Added support for weighted regression for gaussian families:
model = list(y | weights(weight_column) ~ 1 + x). Weights
are visualized as dot sizes in plot(fit).
Support for more link functions across families (e.g.,
family = gaussian(link = "log")):
gaussian: “identity”, “log”binomial: “logit”, “probit”, “identity”bernoulli: “logit”, “probit”, “identity”poisson: “log”, “identity”New argument scale in fitted(),
plot(), and fit$simulate(). When
scale = "response" (default), they return fits on the
observed scale. When scale = "linear", they return fits on
the parameter scale where the linear trends are. Useful for model
understanding and debugging.
Use pp_check(fit) to do prior/posterior predictive
checking. See pp_check(fit, type = "x") for a list of plot
types. pp_check(fit, facet_by = "varying_column") facets by
a data column.
Improvements to plot():
plot(fit, facet_by = "varying_column"). Previous releases
only displayed population-level change points.rate = FALSE for
binomial models.plot(fit) are not located
directly on the x-axis. They were “floating” 5% above the x-axis in the
previous releases.New argument nsamples reduces the number of samples
used in most functions to speed up processing.
nsamples = NULL uses all samples for maximum
accuracy.
New argument arma in many functions toggles whether
autoregressive effects should be modelled.
Although the API is still in alpha, feel free to try extracting
samples using mcp:::tidy_samples(fit). This is useful for
further processing using tidybayes, bayesplot,
etc. and is used extensively internally in mcp. One useful
feature is computing absolute values for varying change points:
mcp:::tidy_samples(fit, population = FALSE, absolute = TRUE).
Feedback is appreciated before tidy_samples will to become
part of the mcp API in a future release.
plot(fit) are now scaled to
20% of the plot for each chain X changepoint combo. This addresses a
common problem where a wide posterior was almost invisibly low when a
narrow posterior was present. This means that heights should only be
compared within each chain x changepoint combo - not
across.plot.mcpfit().ranef() and
fixef() returns.mcp robust and
agile to develop. mcp now use defensive programming with
helpful error messages. The Test suite includes 3600+ tests.plot(), predict(), etc. are now
considerably faster for AR(N) due to vectorization of the underlying
code.The API and internal structure should be stable now. v0.2.0 will be released on CRAN.
I(x^2),
I(x^3.24), sin(x), sqrt(x),
etc.family = gaussian() using
~ sigma([formula here]).~ ar(order, formula), typically like
y ~ 1 + x + ar(2) for AR(2). Simulate AR(N) models from
scratch or given known data with fit$simulate(). The article on
AR(N) has more details and examples. AR(N) models are popular to
detect changes in time-series.plot().
plot(fit, cp_dens = FALSE).plot(fit, q_fit = TRUE).
plot(fit, q_fit = c(0.025, 0.5, 0.975)) plots 95% HDI and
the median.plot(fit, q_predict = TRUE).plot(fit, geom_data = "line")). The latter is useful for
time series. Disable using geom_data = FALSE.options(mc.cores = 3) for considerable speed gains
for the rest of the session. All vignettes/articles have been updated to
recommend this as a default, though serial sampling is still the
technical default. mcp(..., cores = 3) does the same thing
on a call-by-ball basis.fit$simulate() adds the simulation parameters as an
attribute (attr(y, "simulate")) to the predicted variable.
summary() recognizes this and adds the simulated values to
the results table (columns sim and match) so
that one can inspect whether the values were recovered.plot(fit, which_y = "sigma") to plot the residual
standard deviation on the y-axis. It works for AR(N) as well, e.g.,
which_y = "ar1", which_y = "ar2", etc. This is
useful to visualize change points in variance and autocorrelation. The
vignettes on variance and autocorrelations have been updated with worked
examples.prior = list(cp_1 = "dirichlet(1)", cp_2 = ...). Read pros
and cons here.mcp(..., sample = "prior") or
mcp(..., sample = "both") and most methods can now take the
prior: plot(fit, prior = TRUE),
plot_pars(fit, prior = TRUE),
summary(fit, prior = TRUE),
ranef(fit, prior = TRUE).mcp can now be cited! Call citation("mcp")
or see the pre-print here: https://osf.io/fzqxv.fit$func_y()
–> fit$simulate().plot() only visualize the total fit while
plot_pars() only visualize individual parameters. These
functions were mixed in plot() previously.update has been discarded from
mcp() (it’s all on adapt now) and
inits has been added.mcp more future proof.
The biggest internal change is that rjags and
future replace the dclone package. Among other
things, this gives faster and cleaner installations.First public release.
loo and
hypothesis