arviz.plot_compare#

arviz.plot_compare(comp_df, insample_dev=True, plot_standard_error=True, plot_ic_diff=True, order_by_rank=True, figsize=None, textsize=None, labeller=None, plot_kwargs=None, ax=None, backend=None, backend_kwargs=None, show=None)[source]#

Summary plot for model comparison.

This plot is in the style of the one used in the book Statistical Rethinking (Chapter 6) by Richard McElreath.

Parameters

comp_dfpd.DataFrame: Result of the arviz.compare() method
insample_devbool, optional: Plot in-sample deviance, that is the value of the information criteria without the penalization given by the effective number of parameters (pIC). Defaults to True
plot_standard_errorbool, optional: Plot the standard error of the information criteria estimate. Defaults to True
plot_ic_diffbool, optional: Plot standard error of the difference in information criteria between each model and the top-ranked model. Defaults to True
order_by_rankbool: If True (default) ensure the best model is used as reference.
figsizetuple, optional: If None, size is (6, num of models) inches
textsize: float: Text size scaling factor for labels, titles and lines. If None it will be autoscaled based on figsize.
labellerlabeller instance, optional: Class providing the method model_name_to_str to generate the labels in the plot. Read the Label guide for more details and usage examples.
plot_kwargsdict, optional: Optional arguments for plot elements. Currently accepts ‘color_ic’, ‘marker_ic’, ‘color_insample_dev’, ‘marker_insample_dev’, ‘color_dse’, ‘marker_dse’, ‘ls_min_ic’ ‘color_ls_min_ic’, ‘fontsize’
ax: axes, optional: Matplotlib axes or bokeh figures.
backend: str, optional: Select plotting backend {“matplotlib”,”bokeh”}. Default “matplotlib”.
backend_kwargs: bool, optional: These are kwargs specific to the backend being used, passed to matplotlib.pyplot.subplots() or bokeh.plotting.figure().
showbool, optional: Call backend show function.

Returns

axesmatplotlib axes or bokeh figures

See also

plot_elpd: Plot pointwise elpd differences between two or more models.
compare: Compare models based on PSIS-LOO loo or WAIC waic cross-validation.
loo: Compute Pareto-smoothed importance sampling leave-one-out cross-validation (PSIS-LOO-CV).
waic: Compute the widely applicable information criterion.

Notes

Defaults to comparing Leave-one-out (psis-loo) if present in comp_df column, otherwise compares Widely Applicable Information Criterion (WAIC)

Examples

Show default compare plot

>>> import arviz as az
>>> model_compare = az.compare({'Centered 8 schools': az.load_arviz_data('centered_eight'),
>>>                  'Non-centered 8 schools': az.load_arviz_data('non_centered_eight')})
>>> az.plot_compare(model_compare)

Plot standard error and information criteria difference only

>>> az.plot_compare(model_compare, insample_dev=False)

arviz.plot_bpv

arviz.plot_density