NEWS.md
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)
.
Get 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