arviz_plots.plot_compare

arviz_plots.plot_compare(cmp_df, similar_shade=True, relative_scale=False, backend=None, visuals=None, **pc_kwargs)

Summary plot for model comparison.

Models are compared based on their expected log pointwise predictive density (ELPD).

The ELPD is estimated either by Pareto smoothed importance sampling leave-one-out cross-validation (LOO). Details are presented in [1] and [2].

Parameters:

comp_dfpandas.DataFrame

Result of the arviz_stats.compare method.

similar_shadebool, optional

If True, a shade is drawn to indicate models with similar predictive performance to the best model. Defaults to True.

relative_scalebool, optional.

If True scale the ELPD values relative to the best model. Defaults to False.

backend{“bokeh”, “matplotlib”, “plotly”}

Select plotting backend. Defaults to rcParams[“plot.backend”].

figsizetuple of (float, float), optional

If None, size is (10, num of models) inches.

visualsmapping of {strmapping or False}, optional

Valid keys are:

• point_estimate -> passed to scatter

• error_bar -> passed to line

• ref_line -> passed to line

• shade -> passed to fill_between_y

• labels -> passed to xticks and yticks

• title -> passed to title

• ticklabels -> passed to yticks

**pc_kwargs

Passed to arviz_plots.PlotCollection

Returns:

axes :bokeh figure, matplotlib axes or plotly figure

See also

arviz_stats.compare

Summary plot for model comparison.

arviz_stats.loo

Compute the ELPD using Pareto smoothed importance sampling Leave-one-out cross-validation method.

References

[1]

Vehtari et al. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing. 27(5) (2017). https://doi.org/10.1007/s11222-016-9696-4. arXiv preprint https://arxiv.org/abs/1507.04544.

[2]

Vehtari et al. Pareto Smoothed Importance Sampling. Journal of Machine Learning Research, 25(72) (2024) https://jmlr.org/papers/v25/19-556.html arXiv preprint https://arxiv.org/abs/1507.02646