PrePostNEGD.plot#

PrePostNEGD.plot(*, round_to=None, ci_prob=0.94, hdi_prob=None, kind='ribbon', ci_kind='hdi', num_samples=50, figsize=(7, 9), show=True, legend_kwargs=None)[source]#

Plot the pre-post non-equivalent group design results.

Parameters:

  • round_to (int | None) – Number of decimals used to round numerical results in the figure. Defaults to None, in which case 2 significant figures are used.

  • ci_prob (float) – Probability mass of the highest density interval drawn around the posterior predictive bands for the control and treatment groups, and around the posterior of the estimated treatment effect. Must be in (0, 1]. 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).

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

  • num_samples (int) – Number of posterior draws to overlay when kind="spaghetti". Defaults to 50.

  • figsize (tuple[float, float]) – Width and height of the figure in inches. Defaults to (7, 9).

  • 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:

Two-facet plot (top: scatter + posterior predictive bands; bottom: estimated treatment effect posterior).

Return type:

tuple[matplotlib.figure.Figure, matplotlib.axes.Axes or numpy.ndarray]