arviz_plots.plot_psense_dist

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arviz_plots.plot_psense_dist#

arviz_plots.plot_psense_dist(dt, alphas=None, var_names=None, filter_vars=None, prior_var_names=None, likelihood_var_names=None, prior_coords=None, likelihood_coords=None, coords=None, sample_dims=None, kind=None, point_estimate=None, ci_kind=None, ci_prob=None, plot_collection=None, backend=None, labeller=None, aes_by_visuals=None, visuals=None, stats=None, **pc_kwargs)[source]#

Plot power scaled posteriors.

The posterior sensitivity is assessed by power-scaling the prior or likelihood and visualizing the resulting changes, using Pareto-smoothed importance sampling to avoid refitting as explained in [1].

Parameters:
dtxarray.DataTree

Input data

alphastuple of float

Lower and upper alpha values for power scaling. Defaults to (0.8, 1.25).

var_namesstr or list of str, optional

One or more variables to be plotted. 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.

prior_var_namesstr, optional

Name of the log-prior variables to include in the power scaling sensitivity diagnostic

likelihood_var_namesstr, optional

Name of the log-likelihood variables to include in the power scaling sensitivity diagnostic

prior_coordsdict, optional

Coordinates defining a subset over the group element for which to compute the log-prior sensitivity diagnostic

likelihood_coordsdict, optional

Coordinates defining a subset over the group element for which to compute the log-likelihood sensitivity diagnostic

coordsdict, optional
sample_dimsstr or sequence of hashable, optional

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

kind{“kde”, “hist”, “dot”, “ecdf”}, optional

How to represent the marginal distribution.

point_estimate{“mean”, “median”, “mode”}, optional

Which point estimate to plot. Defaults to rcParam stats.point_estimate

ci_kind{“eti”, “hdi”}, optional

Which credible interval to use. Defaults to rcParams["stats.ci_kind"]

ci_probfloat, optional

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

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 except for “remove_axis”

visualsmapping of {strmapping or False}, optional

Valid keys are:

statsmapping, optional

Valid keys are:

  • dist -> passed to kde, ecdf, …

  • credible_interval -> passed to eti or hdi

  • point_estimate -> passed to mean, median or mode

**pc_kwargs

Passed to arviz_plots.PlotCollection.wrap

Returns:
PlotCollection

References

[1]

Kallioinen et al, Detecting and diagnosing prior and likelihood sensitivity with power-scaling, Stat Comput 34, 57 (2024), https://doi.org/10.1007/s11222-023-10366-5

Examples

Select a single variable and generate a point-interval plot

>>> from arviz_plots import plot_psense_dist, style
>>> style.use("arviz-variat")
>>> from arviz_base import load_arviz_data
>>> rugby = load_arviz_data('rugby')
>>> plot_psense_dist(rugby, var_names=["sd_att"], visuals={"kde":False})
../../_images/arviz_plots-plot_psense_dist-1.png

The posterior sensitivity is assessed by power-scaling the prior or likelihood and visualizing the resulting changes. Sensitivity can then be quantified by considering how much the perturbed posteriors differ from the base posterior.

Sensitivity posterior marginals

Sensitivity posterior marginals
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