NEWS.md

bayesplot 1.6.0.9000

(GitHub issue/PR numbers in parentheses)

• 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)

• New convenience functions facet_relabel_gg() and facet_vars which provide info about and allow changing the labels used for faceted plots. (advances #75, @silberzwiebel)

bayesplot 1.6.0

(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 1.5.0

(GitHub issue/PR numbers in parentheses)

• New package documentation website: http://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.
  • Both functions have a _data() function to return the data plotted by each function.

• mcmc_intervals() and mcmc_areas() have been rewritten. (#103)

  • They now use a discrete y-axis. Previously, they used a continuous scale with numeric breaks relabelled with parameter names; this design
    caused some unexpected behavior when customizing these plots.
  • 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)

bayesplot 1.4.0

(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).

bayesplot 1.3.0

(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)

bayesplot 1.2.0

A lot of new stuff in this release. (GitHub issue/PR numbers in parentheses)

Fixes

• 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.

New features in existing functions

• 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).

New functions

• 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 @paul-buerkner. (#28)
  • 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)

bayesplot 1.1.0

(GitHub issue/PR numbers in parentheses)

Fixes

• 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)

New features in existing functions

• 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)

New functions

• 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)

bayesplot 1.0.0

Initial CRAN release