arviz_plots.plot_ppc_pava

arviz_plots.plot_ppc_pava(dt, data_type='binary', n_bootstaps=1000, ci_prob=None, var_names=None, filter_vars=None, group='posterior_predictive', coords=None, sample_dims=None, plot_collection=None, backend=None, labeller=None, aes_by_visuals=None, visuals=None, **pc_kwargs)

PAV-adjusted calibration plot.

Uses the pool adjacent violators (PAV) algorithm for isotonic regression. An a 45-degree line corresponds to perfect calibration. Details are discussed in [1] and [2].

Parameters:

dtxarray.DataTree

Input data

data_typestr

Defaults to “binary”. Other options are “categorical” and “ordinal”. If “categorical”, the plot will show the “one-vs-others” calibration and generate one plot per category. If “ordinal”, the plot will display cumulative conditional event probabilities and generate (number of categories - 1) plots.

n_bootstapsint, optional

Number of bootstrap samples to use for estimating the confidence intervals. defaults to 1000.

ci_probfloat, optional

Probability for the credible interval. Defaults to rcParams["stats.ci_prob"].

num_samplesint, optional

Number of samples to use for the plot. Defaults to 100.

var_namesstr or list of str, optional

One or more variables to be plotted. Currently only one variable is supported. Prefix the variables by ~ when you want to exclude them from the plot.

filter_vars{None, “like”, “regex”}, optional, default=None

If None (default), interpret var_names as the real variables names. If “like”, interpret var_names as substrings of the real variables names. If “regex”, interpret var_names as regular expressions on the real variables names.

groupstr, optional

The group from which to get the unique values. Defaults to “posterior_predictive”. It could also be “prior_predictive”. Notice that this plots always use the “observed_data” so use with extra care if you are using “prior_predictive”.

coordsdict, optional

Coordinates to plot. CURRENTLY NOT IMPLEMENTED

sample_dimsstr or sequence of hashable, optional

Dimensions to reduce unless mapped to an aesthetic. Defaults to rcParams["data.sample_dims"]

plot_collectionPlotCollection, optional

backend{“matplotlib”, “bokeh”, “plotly”}, optional

labellerlabeller, optional

aes_by_visualsmapping of {strsequence of str}, optional

Mapping of visuals to aesthetics that should use their mapping in plot_collection when plotted. Valid keys are the same as for visuals.

visualsmapping of {strmapping or False}, optional

Valid keys are:

• lines -> passed to line_xy

• markers -> passed to scatter_xy

• reference_line -> passed to line_xy

• credible_interval -> passed to fill_between_y

• xlabel -> passed to labelled_x

• ylabel -> passed to labelled_y

• title -> passed to labelled_title

markers defaults to False, no markers are plotted. Pass an (empty) mapping to plot markers.

**pc_kwargs

Passed to arviz_plots.PlotCollection.grid

Returns:

PlotCollection

References

[1]

Säilynoja et al. Recommendations for visual predictive checks in Bayesian workflow. (2025) arXiv preprint https://arxiv.org/abs/2503.01509

[2]

Dimitriadis et al Stable reliability diagrams for probabilistic classifiers. PNAS, 118(8) (2021). https://doi.org/10.1073/pnas.2016191118

Examples

Plot the PAVA calibration plot for the rugby dataset.

>>> from arviz_plots import plot_ppc_pava, style
>>> style.use("arviz-variat")
>>> from arviz_base import load_arviz_data
>>> dt = load_arviz_data('rugby')
>>> plot_ppc_pava(dt, ci_prob=0.90)

PAV-adjusted calibration plot for binary predictions.

PAV-adjusted calibration

PAV-adjusted calibration