arviz_plots.plot_ppc_pit

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arviz_plots.plot_ppc_pit#

arviz_plots.plot_ppc_pit(dt, ci_prob=None, coverage=False, var_names=None, data_pairs=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, stats=None, **pc_kwargs)[source]#

PIT Δ-ECDF values with simultaneous confidence envelope.

For a calibrated model the Probability Integral Transform (PIT) values, $p(tilde{y}_i le y_i mid y)$, should be uniformly distributed. Where $y_i$ represents the observed data for index $i$ and $tilde y_i$ represents the posterior predictive sample at index $i$.

This plot shows the empirical cumulative distribution function (ECDF) of the PIT values. To make the plot easier to interpret, we plot the Δ-ECDF, that is, the difference between the observed ECDF and the expected CDF. Simultaneous confidence bands are computed using the method described in described in [1].

Alternatively, we can visualize the coverage of the central posterior credible intervals by setting coverage=True. This allows us to assess whether the credible intervals includes the observed values. We can obtain the coverage of the central intervals from the PIT by replacing the PIT with two times the absolute difference between the PIT values and 0.5.

For more details on how to interpret this plot, see https://arviz-devs.github.io/EABM/Chapters/Prior_posterior_predictive_checks.html#pit-ecdfs.

Parameters:
dtxarray.DataTree

Input data

ci_probfloat, optional

Indicates the probability that should be contained within the plotted credible interval. Defaults to rcParams["stats.ci_prob"]

coveragebool, optional

If True, plot the coverage of the central posterior credible intervals. Defaults to False.

data_pairsdict, optional

Dictionary of keys prior/posterior predictive data and values observed data variable names. If None, it will assume that the observed data and the predictive data have the same variable name.

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,

Group to be plotted. Defaults to “posterior_predictive”. It could also be “prior_predictive”.

coordsdict, optional

Coordinates to plot.

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:

statsmapping, optional

Valid keys are:

  • ecdf_pit -> passed to ecdf_pit. Default is {"n_simulation": 1000}.

**pc_kwargs

Passed to arviz_plots.PlotCollection.wrap

Returns:
PlotCollection

References

[1]

Säilynoja et al. Graphical test for discrete uniformity and its applications in goodness-of-fit evaluation and multiple sample comparison. Statistics and Computing 32(32). (2022) https://doi.org/10.1007/s11222-022-10090-6

Examples

Plot the ecdf-PIT for the crabs hurdle-negative-binomial dataset.

>>> from arviz_plots import plot_ppc_pit, style
>>> style.use("arviz-variat")
>>> from arviz_base import load_arviz_data
>>> dt = load_arviz_data('crabs_hurdle_nb')
>>> plot_ppc_pit(dt)
../../_images/arviz_plots-plot_ppc_pit-1.png

Plot the coverage for the crabs hurdle-negative-binomial dataset.

>>> plot_ppc_pit(dt, coverage=True)
../../_images/arviz_plots-plot_ppc_pit-2.png

Plot of the probability integral transform of the posterior predictive distribution with respect to the observed data.

PIT ECDF

PIT ECDF

Proportion of true values that fall within a given prediction interval.

Coverage ECDF

Coverage ECDF
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