arviz_plots.plot_dist

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arviz_plots.plot_dist#

arviz_plots.plot_dist(dt, var_names=None, filter_vars=None, group='posterior', 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 1D marginal densities in the style of John K. Kruschke’s book [1].

Generate faceted plots with: a graphical representation of 1D marginal densities (as KDE, histogram, ECDF or dotplot), a credible interval and a point estimate.

Parameters:
dtxarray.DataTree or dict of {strxarray.DataTree}

Input data. In case of dictionary input, the keys are taken to be model names. In such cases, a dimension “model” is generated and can be used to map to aesthetics.

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”}, default=None

If None, 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, default “posterior”

Group to be plotted.

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 density. Defaults to rcParams["plot.density_kind"]

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”}, 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.

With a single model, no aesthetic mappings are generated by default, each variable+coord combination gets a plot but they all look the same, unless there are user provided aesthetic mappings. With multiple models, plot_dist maps “color” and “y” to the “model” dimension.

By default, all aesthetics but “y” are mapped to the density representation, and if multiple models are present, “color” and “y” are mapped to the credible interval and the point estimate.

When “point_estimate” key is provided but “point_estimate_text” isn’t, the values assigned to the first are also used for the second.

visualsmapping of {strmapping or False}, optional

Valid keys are:

  • dist -> depending on the value of kind passed to:

    • “kde” -> passed to line_xy

    • “ecdf” -> passed to ecdf_line

    • “hist” -> passed to :func: step_hist

  • face -> visual that fills the area under the marginal distribution representation.

    Defaults to False. Depending on the value of kind it is passed to:

  • credible_interval -> passed to line_x

  • point_estimate -> passed to scatter_x

  • point_estimate_text -> passed to point_estimate_text

  • title -> passed to labelled_title

  • rug -> passed to scatter_x. Defaults to False.

  • remove_axis -> not passed anywhere, can only be False to skip calling this function

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

See also

Introduction to batteries-included plots

General introduction to batteries-included plotting functions, common use and logic overview

References

[1]

Kruschke. Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan. Academic Press, 2014. ISBN 978-0-12-405888-0. https://www.sciencedirect.com/book/9780124058880

Examples

Map the color to the variable, and have the mapping apply to the title too instead of only the density representation:

>>> from arviz_plots import plot_dist, style
>>> style.use("arviz-variat")
>>> from arviz_base import load_arviz_data
>>> non_centered = load_arviz_data('non_centered_eight')
>>> pc = plot_dist(
>>>     non_centered,
>>>     coords={"school": ["Choate", "Deerfield", "Hotchkiss"]},
>>>     aes={"color": ["__variable__"]},
>>>     aes_by_visuals={"title": ["color"]},
>>> )
../../_images/arviz_plots-plot_dist-1.png

Faceted ECDF plots for 1D marginals of the distribution

Posterior ECDFs

Posterior ECDFs

Faceted histogram plots for 1D marginals of the distribution. The point_estimate_text option is set to False to omit that visual from the plot.

Posterior Histograms

Posterior Histograms

KDE plot of the variable mu from the centered eight model. The sample_dims parameter is used to restrict the KDE computation along the draw dimension only.”

Posterior KDEs

Posterior KDEs

Full marginal distribution comparison between different models

Posterior KDEs for two models

Posterior KDEs for two models

Draw lines on plots to highlight specific thresholds, targets, or important values.

Add Lines

Add Lines
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