PanelRegression.plot#

PanelRegression.plot(*, group='posterior', hdi_prob=0.94, show=True, legend_kwargs=None)[source]#

Plot the panel regression coefficients.

Bayesian models render a forest plot with HDI intervals; OLS models render a bar plot of point estimates. To plot only a subset of coefficients (or to customise the figure size), call plot_coefficients() directly.

Parameters:

  • group (Literal['prior', 'posterior']) – Draw group to render. "posterior" (default) plots coefficient draws after fit(); "prior" plots the coefficient draws implied by the prior phase and requires sample_prior_predictive() first — on backends without a prior phase that call raises PriorPredictiveNotSupportedException.

  • hdi_prob (float) – Probability mass of the highest density interval drawn around each posterior coefficient. Must be in (0, 1]. Ignored for OLS models. Defaults to HDI_PROB (currently 0.94).

  • 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). The existing legend is modified in place so that custom handles are preserved.

Returns:

  • fig (matplotlib.figure.Figure) – The figure that was created.

  • ax (matplotlib.axes.Axes) – The axes object containing the coefficient plot.

Return type:

tuple[Figure, Axes]