arviz.plot_separation#

arviz.plot_separation(idata=None, y=None, y_hat=None, y_hat_line=False, expected_events=False, figsize=None, textsize=None, color='C0', legend=True, ax=None, plot_kwargs=None, y_hat_line_kwargs=None, exp_events_kwargs=None, backend=None, backend_kwargs=None, show=None)[source]#

Separation plot for binary outcome models.

Model predictions are sorted and plotted using a color code according to the observed data.

Parameters
idataInferenceData

arviz.InferenceData object.

yarray, DataArray or str

Observed data. If str, idata must be present and contain the observed data group

y_hatarray, DataArray or str

Posterior predictive samples for y. It must have the same shape as y. If str or None, idata must contain the posterior predictive group.

y_hat_linebool, optional

Plot the sorted y_hat predictions.

expected_eventsbool, optional

Plot the total number of expected events.

figsizefigure size tuple, optional

If None, size is (8 + numvars, 8 + numvars)

textsize: int, optional

Text size for labels. If None it will be autoscaled based on figsize.

colorstr, optional

Color to assign to the positive class. The negative class will be plotted using the same color and an alpha=0.3 transparency.

legendbool, optional

Show the legend of the figure.

ax: axes, optional

Matplotlib axes or bokeh figures.

plot_kwargsdict, optional

Additional keywords passed to matplotlib.axes.Axes.bar() or bokeh:bokeh.plotting.Figure.vbar() for separation plot.

y_hat_line_kwargsdict, optional

Additional keywords passed to ax.plot for y_hat line.

exp_events_kwargsdict, optional

Additional keywords passed to ax.scatter for expected_events marker.

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_ppc

Plot for posterior/prior predictive checks.

References

1

Greenhill, B. et al., The Separation Plot: A New Visual Method for Evaluating the Fit of Binary Models, American Journal of Political Science, (2011) see https://doi.org/10.1111/j.1540-5907.2011.00525.x

Examples

Separation plot for a logistic regression model.

>>> import arviz as az
>>> idata = az.load_arviz_data('classification10d')
>>> az.plot_separation(idata=idata, y='outcome', y_hat='outcome', figsize=(8, 1))
../../_images/arviz-plot_separation-1.png