arviz.plot_compare¶
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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 az.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. For additional documentation check the plotting method of the backend.
- showbool, optional
Call backend show function.
- Returns
- axesmatplotlib axes or bokeh figures
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)
