bayesplot R package for plotting Bayesian models
Just a patch release to fix a minor bug:
Full Changelog: https://github.com/stan-dev/bayesplot/compare/v1.11.0...v1.11.1
ppc_loo_pit_qq plots by @avehtari in https://github.com/stan-dev/bayesplot/pull/307
prob is numeric for intervals plots by @tony-stone in https://github.com/stan-dev/bayesplot/pull/299
bins and breaks arguments to more histogram and hex plots by @heavywatal in https://github.com/stan-dev/bayesplot/pull/313
psis_object argument by @jgabry in https://github.com/stan-dev/bayesplot/pull/311
ppc_loo_pit_ecdf() and ppc_loo_pit_ecdf_grouped() now support discrete variables, and their default method for selecting the number of ECDF evaluation points has been updated. @TeemuSailynoja in https://github.com/stan-dev/bayesplot/pull/316
Full Changelog: https://github.com/stan-dev/bayesplot/compare/v1.10.0...v1.11.0
mcmc_rank_ecdf() for rank ecdf plots with confidence bands for
assessing if two or more chains sample the same distribution (#282,
@TeemuSailynoja)ppc_pit_ecdf(), ppc_pit_ecdf_grouped(), PIT ecdf plots with
confidence bands to assess if y and yrep contain samples from the same
distribution. (#282, @TeemuSailynoja)ppc and ppd functions now accept the new linewidth argument
introduced in ggplot2 3.4.0: ppc_bars(), ppc_bars_grouped(),
ppc_intervals(), ppc_intervals_grouped(), ppd_intervals(),
ppd_intervals_grouped().mcmc_pairs() detected hitting max_treedepth, thanks to @dmphillippo. (#281)Lots of new features in this release!
New module PPD (posterior/prior predictive distribution) with a lot of new
plotting functions with ppd_ prefix. These functions plot draws from the prior
or posterior predictive distributions (PPD) without comparing to observed data
(i.e., no y argument). Because these are not "checks" against the observed
data we use PPD instead of PPC. These plots are essentially the same as the
corresponding PPC plots but without showing any observed data (e.g.,
ppd_intervals() is like ppc_intervals() but without plotting y). See
help("PPD-overview") for details. (#151, #222)
All PPC categories now have one or more _data() functions that return the
data frame used for plotting (#97, #222). Many of these have already been in
previous releases, but the new ones in this release are:
ppc_bars_data()
ppc_error_data()
ppc_error_binnned_data()
ppc_scatter_data()
ppc_scatter_avg_data()
ppc_stat_data()
Many functions gain an argument facet_args for controlling ggplot2 faceting
(many other functions have had this argument for a long time).
The ones that just now got the argument are:
ppc_scatter()
ppc_scatter_avg_grouped()
ppc_error_hist()
ppc_error_hist_grouped()
ppc_error_scatter()
ppc_error_binned()
New plotting function ppc_km_overlay_grouped(), the grouped variant of
ppc_km_overlay(). (#260, @fweber144)
ppc_scatter(), ppc_scatter_avg(), and ppc_scatter_avg_grouped() gain an
argument ref_line, which can be set to FALSE to turn off the x=y line
drawn behind the scatterplot.
ppc_ribbon() and ppc_ribbon_grouped() gain argument y_draw that specifies whether the observed y should be plotted using a point, line, or both. (#257, @charlesm93)
mcmc_*() functions now support all draws formats from the posterior package. (#277, @Ozan147)
mcmc_dens() and mcmc_dens_overlay() gain arguments for controlling the
the density calculation. (#258)
mcmc_hist() and mcmc_dens() gain argument alpha for controlling transparency. (#244)
mcmc_areas() and mcmc_areas_ridges() gain an argument border_size for
controlling the thickness of the ridgelines. (#224)
mcmc_areas() tries to use less vertical blank space. (#218, #230)
Fix bug in color_scheme_view() minimal theme (#213).
Fix error in mcmc_acf() for certain input types. (#244, #245, @hhau)
New plotting functions ppc_dens_overlay_grouped() and ppc_ecdf_overlay_grouped()
for plotting density and cumulative distributions of the posterior predictive
distribution (versus observed data) by group. (#212)
New plotting function ppc_km_overlay() for outcome variables that are
right-censored. Empirical CCDF estimates of yrep are compared with the
Kaplan-Meier estimate of y. (#233, #234, @fweber144)
ppc_loo_pit_overlay() now uses a boundary correction for an improved kernel
density estimation. The new argument boundary_correction defaults to TRUE but
can be set to FALSE to recover the old version of the plot. (#171, #235,
@ecoronado92)
CmdStanMCMC objects (from CmdStanR) can now be used with extractor
functions nuts_params(), log_posterior(), rhat(), and
neff_ratio(). (#227)
On the y axis, ppc_loo_pit_qq(..., compare = "normal") now plots standard
normal quantiles calculated from the PIT values (instead of the standardized
PIT values). (#240, #243, @fweber144)
mcmc_rank_overlay() gains argument facet_args. (#221, @hhau)
For mcmc_intervals() the sizeof the points and interval lines can be set withmcmc_intervals(..., outer_size, inner_size, point_size)`. (#215, #228, #229)
Minor internal fixes to ensure compatibility with dplyr 1.0.0
bayesplot v1.7.0 is now on CRAN. There are a bunch of new features and fixes in this release. Release notes are available below and also at mc-stan.org/bayesplot/news.
After CRAN binaries are built (usually a few days) just use install.packages("bayesplot"). Before binaries are available the update can be installed from CRAN using
install.packages("bayesplot", type = "source", repos = "https://cran.rstudio.com/")
or from GitHub using
# note: setting build_vignettes=TRUE will be much slower and you can always access
# the vignettes online at mc-stan.org/bayesplot/articles/
devtools::install_github("stan-dev/bayesplot", ref = "v1.7.0", build_vignettes = FALSE)
The pars argument of all MCMC plotting functions now supports tidy variable
selection. See help("tidy-params", package="bayesplot") for details and
examples. (#161, #183, #188)
Two new plots have been added for inspecting the distribution of ranks. Rank histograms were introduced by the Stan team's new paper on MCMC diagnostics. (#178, #179)
mcmc_rank_hist(): A traditional traceplot (mcmc_trace()) visualizes how
sampled values the MCMC chains mix over the course of sampling. A rank
histogram (mcmc_rank_hist()) visualizes how the ranks of values from the
chains mix together. An ideal plot would show the ranks mixing or overlapping
in a uniform distribution.
mcmc_rank_overlay(): Instead of drawing each chain's histogram in a separate
panel, this plot draws the top edge of the chains' histograms in a single
panel.
Added mcmc_trace_data(), which returns the data used for plotting the trace
plots and rank histograms. (Advances #97)
ColorBrewer palettes are now available as color schemes via color_scheme_set().
For example, color_scheme_set("brewer-Spectral") will use the Spectral
palette. (#177, #190)
MCMC plots now also accept objects with an as.array method as
input (e.g., stanfit objects). (#175, #184)
mcmc_trace() gains an argument iter1 which can be used to label the traceplot starting
from the first iteration after warmup. (#14, #155, @mcol)
mcmc_areas() gains an argument area_method which controls how to draw the density
curves. The default "equal area" constrains the heights so that the curves
have the same area. As a result, a narrow interval will appear as a spike
of density, while a wide, uncertain interval is spread thin over the x axis.
Alternatively "equal height" will set the maximum height on each curve to
the same value. This works well when the intervals are about the same width.
Otherwise, that wide, uncertain interval will dominate the visual space
compared to a narrow, less uncertain interval. A compromise between the two is
"scaled height" which scales the curves from "equal height" using
height * sqrt(height). (#163, #169)
mcmc_areas() correctly plots density curves where the point estimate
does not include the highest point of the density curve.
(#168, #169, @jtimonen)
mcmc_areas_ridges() draws the vertical line at x = 0 over the curves so
that it is always visible.
mcmc_intervals() and mcmc_areas() raise a warning if prob_outer is ever
less than prob. It sorts these two values into the correct order. (#138)
MCMC parameter names are now always converted to factors prior to plotting. We use factors so that the order of parameters in a plot matches the order of the parameters in the original MCMC data. This change fixes a case where factor-conversion failed. (#162, #165, @wwiecek)
The examples in ?ppc_loo_pit_overlay() now work as expected. (#166, #167)
Added "viridisD" as an alternative name for "viridis" to the supported colors.
Added "viridisE" (the cividis version of viridis) to the supported colors.
ppc_bars() and ppc_bars_grouped() now allow negative integers as input. (#172, @jeffpollock9)
bayesplot v1.6.0 is now on CRAN. See release notes below or at mc-stan.org/bayesplot/news.
After CRAN binaries are built (usually a few days) just use install.packages("bayesplot"). Before binaries are available the update can be installed from CRAN using
install.packages("bayesplot", type = "source", repos = "https://cran.rstudio.com/")
or from GitHub using
# note: setting build_vignettes=FALSE will be much faster and you can always access
# the vignettes at mc-stan.org/bayesplot/articles/
devtools::install_github("stan-dev/bayesplot", ref = "v1.6.0", build_vignettes = TRUE)
(GitHub issue/PR numbers in parentheses)
Loading bayesplot no longer overrides the ggplot theme! Rather, it sets a theme specific for bayesplot. Some packages using bayesplot may still override the default ggplot theme (e.g., rstanarm does but only until next release), but simply loading bayesplot itself will not. There are new functions for controlling the ggplot theme for bayesplot that work like their ggplot2 counterparts but only affect plots made using bayesplot. Thanks to Malcolm Barrett. (#117, #149).
bayesplot_theme_set()
bayesplot_theme_get()
bayesplot_theme_update()
bayesplot_theme_replace()
The Visual MCMC Diagnostics vignette has been reorganized and has a lot of useful new content thanks to Martin Modrák. (#144, #153)
The LOO predictive checks
now require loo version >= 2.0.0. (#139)
Histogram plots gain a breaks argument that can be used as an alternative to binwidth. (#148)
mcmc_pairs()
now has an argument grid_args to provide a way of passing optional arguments to
gridExtra::arrangeGrob(). This can be used to add a title to the plot, for example. (#143)
ppc_ecdf_overlay()
gains an argument discrete, which is FALSE by default, but can be used to make the
Geom more appropriate for discrete data. (#145)
PPC intervals plots
and LOO predictive checks
now draw both an outer and an inner probability interval, which can be
controlled through the new argument prob_outer and the already existing
prob. This is consistent with what is produced by mcmc_intervals().
(#152, #154, @mcol)
bayesplot v1.5.0 is now on CRAN. See release notes below or at mc-stan.org/bayesplot/news.
After CRAN binaries are built (usually a few days) just use install.packages("bayesplot"). Before binaries are available the update can be installed from CRAN using
install.packages("bayesplot", type = "source", repos = "https://cran.rstudio.com/")
or from GitHub using
# note: setting build_vignettes=FALSE will be much faster and you can always access
# the vignettes at mc-stan.org/bayesplot/articles/
devtools::install_github("stan-dev/bayesplot", ref = "v1.5.0", build_vignettes = TRUE)
(GitHub issue/PR numbers in parentheses)
New package documentation website: http://mc-stan.org/bayesplot/
Two new plots that visualize posterior density using ridgelines (ggridges pkg). These work well when parameters have similar values and similar densities, as in hierarchical models. (#104)
mcmc_dens_chains() draws the kernel density of each sampling chain.mcmc_areas_ridges() draws the kernel density combined across chains._data() function to return the data plotted by
each function.mcmc_intervals() and mcmc_areas() have been rewritten. (#103)
mcmc_areas() now uses geoms from the ggridges package to draw density
curves.Added mcmc_intervals_data() and mcmc_areas_data() that return data
plotted by mcmc_intervals() and mcmc_areas(). Similarly, ppc_data()
returns data plotted ppc_hist() and other ppc plot. (Advances #97)
Added ppc_loo_pit_overlay() function for a better LOO PIT predictive check.
(#123)
Started using vdiffr to add visual unit tests to the existing PPC unit tests. (#137)
bayesplot v1.4.0 is now on CRAN.
Until CRAN binaries are built (usually a few days) you can install the update using
install.packages("bayesplot", type = "source", repos = "https://cran.rstudio.com/")
or by installing from GitHub using
devtools::install_github("stan-dev/bayesplot", ref = "v1.4.0", build_vignettes = TRUE)
(GitHub issue/PR numbers in parentheses)
mcmc_parcoord() for parallel coordinates plots of
MCMC draws (optionally including HMC/NUTS diagnostic information). (#108)mcmc_scatter gains an np argument for specifying NUTS parameters, which
allows highlighting divergences in the plot. (#112)_data don't make the plots,
they just return the data prepared for plotting (more of these to come in
future releases):
ppc_intervals_data() (#101)ppc_ribbon_data() (#101)mcmc_parcoord_data() (#108)mcmc_rhat_data() (#110)mcmc_neff_data() (#110)ppc_stat_grouped(), ppc_stat_freqpoly_grouped() gain a facet_args
argument for controlling ggplot2 faceting (many of the mcmc_ functions
already have this).divergences argument to mcmc_trace() has been deprecated in favor
of np (NUTS parameters) to match the other functions that have an np
argument.mcmc_rhat() (#105).