arviz.plot_compare#

arviz.plot_compare(comp_df, insample_dev=False, plot_standard_error=True, plot_ic_diff=False, order_by_rank=True, legend=False, title=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.

Models are compared based on their expected log pointwise predictive density (ELPD). This plot is in the style of the one used in [2]. Chapter 6 in the first edition or 7 in the second.

Parameters:

comp_dfpandas.DataFrame: Result of the arviz.compare() method.
insample_devbool, default False: Plot in-sample ELPD, that is the value of the information criteria without the penalization given by the effective number of parameters (p_loo or p_waic).
plot_standard_errorbool, default True: Plot the standard error of the ELPD.
plot_ic_diffbool, default False: Plot standard error of the difference in ELPD between each model and the top-ranked model.
order_by_rankbool, default True: If True ensure the best model is used as reference.
legendbool, default False: Add legend to figure.
figsize(float, float), optional: If None, size is (6, num of models) inches.
titlebool, default True: Show a tittle with a description of how to interpret the plot.
textsizefloat, optional: Text size scaling factor for labels, titles and lines. If None it will be autoscaled based on figsize.
labellerLabeller, optional: Class providing the method make_label_vert to generate the labels in the plot titles. 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’
axmatplotlib Axes or Bokeh Figure, optional: Matplotlib axes or bokeh figure.
backend{“matplotlib”, “bokeh”}, default “matplotlib”: Select plotting backend.
backend_kwargsbool, optional: These are kwargs specific to the backend being used, passed to matplotlib.pyplot.subplots() or bokeh.plotting.figure. For additional documentation check the plotting method of the backend.
showbool, optional: Call backend show function.

Returns:

axesmatplotlib Axes or Bokeh Figure

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

The ELPD is estimated either by Pareto smoothed importance sampling leave-one-out cross-validation (LOO) or using the widely applicable information criterion (WAIC). We recommend LOO in line with the work presented by [1].

References

[1]

Vehtari et al. (2016). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC https://arxiv.org/abs/1507.04544

[2]

McElreath R. (2022). Statistical Rethinking A Bayesian Course with Examples in R and Stan, Second edition, CRC Press.

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)

Include the in-sample ELDP

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