RegressionDiscontinuity.plot#

RegressionDiscontinuity.plot(*, round_to=2, ci_prob=0.94, hdi_prob=None, kind='ribbon', ci_kind='hdi', num_samples=50, figsize=None, show=True, legend_kwargs=None)[source]#

Plot the regression discontinuity results.

Parameters:

  • round_to (int | None) – Number of decimals used to round numerical results in the figure title (e.g. the Bayesian \(R^2\)). Defaults to 2. Use None to render raw numbers.

  • ci_prob (float) – Probability mass of the highest density interval drawn around the posterior predictive band, and the central credible interval reported in the figure title for the discontinuity at threshold. Must be in (0, 1]. Ignored for OLS models. Defaults to HDI_PROB (currently 0.94).

  • hdi_prob (float | None) – Deprecated. Use ci_prob instead.

  • kind (Literal['ribbon', 'histogram', 'spaghetti']) – How posterior uncertainty is rendered. Defaults to "ribbon" (mean + credible band). "spaghetti" draws individual posterior predictive lines. "histogram" uses plotnine two-dimensional histogram layers.

  • ci_kind (Literal['hdi', 'eti']) – Credible interval type when kind="ribbon". Defaults to "hdi".

  • num_samples (int) – Number of posterior draws when kind="spaghetti". Defaults to 50. Ignored for other kinds.

  • figsize (tuple[float, float] | None) – Width and height of the figure in inches.

  • show (bool) – Whether to automatically display the plot. Defaults to True.

  • legend_kwargs (dict[str, Any] | None) – Keyword arguments to adjust legend placement and styling. Supported keys: loc, bbox_to_anchor, fontsize, frameon, title (bbox_transform is accepted alongside bbox_to_anchor). Applied to the rendered matplotlib legend.

Returns:

Matplotlib figure and axes for the rendered plot.

Return type:

tuple[matplotlib.figure.Figure, matplotlib.axes.Axes]