mcmc_rank_ecdf() for rank ecdf plots with
confidence bands for assessing if two or more chains sample the same
distribution (#282,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, 1)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)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,
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)
Compatibility with dplyr 1.0.0 (#219)
Release requested by CRAN to fix errors at https://cran.r-project.org/web/checks/check_results_bayesplot.html due to matrices also inheriting from “array” in R 4.0.
(GitHub issue/PR numbers in parentheses)
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)
(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)
(GitHub issue/PR numbers in parentheses)
New package documentation website: https://mc-stan.org/bayesplot/
Two new plots that visualize posterior density using ridgelines. 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(). (Advances
#97)
New ppc_data() function returns the data plotted by
many of the PPC plotting functions. (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)
(GitHub issue/PR numbers in parentheses)
New plotting function 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)
New functions with names ending with suffix _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).
The divergences argument to
mcmc_trace() has been deprecated in favor of
np (NUTS parameters) to match the other functions that have
an np argument.
Fixed an issue where duplicated rhat values would break
mcmc_rhat() (#105).
(GitHub issue/PR numbers in parentheses)
bayesplot::theme_default() is now set as the default
ggplot2 plotting theme when bayesplot is loaded, which
makes changing the default theme using ggplot2::theme_set()
possible. Thanks to @gavinsimpson. (#87)
mcmc_hist() and mcmc_hist_by_chain()
now take a freq argument that defaults to TRUE
(behavior is like freq argument to R’s hist
function).
Using a ts object for y in PPC plots no
longer results in an error. Thanks to @helske. (#94)
mcmc_intervals() doesn’t use round lineends anymore
as they slightly exaggerate the width of the intervals. Thanks to @tjmahr. (#96)
A lot of new stuff in this release. (GitHub issue/PR numbers in parentheses)
Avoid error in some cases when divergences is
specified in call to mcmc_trace() but there are not
actually any divergent transitions.
The merge_chains argument to
mcmc_nuts_energy() now defaults to
FALSE.
For mcmc_*() functions, transformations are recycled
if transformations argument is specified as a single
function rather than a named list. Thanks to @tklebel. (#64)
For ppc_violin_grouped() there is now the option of
showing y as a violin, points, or both. Thanks to @silberzwiebel.
(#74)
color_scheme_get() now has an optional argument
i for selecting only a subset of the colors.
New color schemes: darkgray, orange, viridis, viridisA, viridisB, viridisC. The viridis schemes are better than the other schemes for trace plots (the colors are very distinct from each other).
mcmc_pairs(), which is essentially a ggplot2+grid
implementation of rstan’s pairs.stanfit() method.
(#67)
mcmc_hex(), which is similar to
mcmc_scatter() but using geom_hex() instead of
geom_point(). This can be used to avoid overplotting.
(#67)
overlay_function() convenience function. Example
usage: add a Gaussian (or any distribution) density curve to a plot made
with mcmc_hist().
mcmc_recover_scatter() and
mcmc_recover_hist(), which are similar to
mcmc_recover_intervals() and compare estimates to “true”
values used to simulate data. (#81, #83)
New PPC category Discrete with functions:
ppc_rootogram() for use with models for count data.
Thanks to
ppc_bars(), ppc_bars_grouped() for use
with models for ordinal, categorical and multinomial data. Thanks to
@silberzwiebel.
(#73)New PPC category LOO (thanks to suggestions from @avehtari) with functions:
ppc_loo_pit() for assessing the calibration of marginal
predictions. (#72)ppc_loo_intervals(), ppc_loo_ribbon() for
plotting intervals of the LOO predictive distribution. (#72)(GitHub issue/PR numbers in parentheses)
Images in vignettes should now render properly using
png device. Thanks to TJ Mahr. (#51)
xaxis_title(FALSE) and
yaxis_title(FALSE) now set axis titles to NULL
rather than changing theme elements to element_blank().
This makes it easier to add axis titles to plots that don’t have them by
default. Thanks to Bill Harris. (#53)
Add argument divergences to
mcmc_trace() function. For models fit using HMC/NUTS this
can be used to display divergences as a rug at the bottom of the trace
plot. (#42)
The stat argument for all ppc_stat_*()
functions now accepts a function instead of only the name of a function.
(#31)
ppc_error_hist_grouped() for plotting predictive
errors by level of a grouping variable. (#40)
mcmc_recover_intervals)( for comparing MCMC
estimates to “true” parameter values used to simulate the data.
(#56)
bayesplot_grid() for juxtaposing plots and enforcing
shared axis limits. (#59)
Initial CRAN release