InversePropensityWeighting.plot#
- InversePropensityWeighting.plot(*args, show=True, kind='ribbon', ci_kind='hdi', ci_prob=0.94, num_samples=50, **kwargs)#
Plot the model.
- Parameters:
*args (
Any) – Additional positional arguments passed to _bayesian_plot or _ols_plot.show (
bool) – Whether to automatically display the plot. Defaults to True. Set to False if you want to modify the figure before displaying it.kind (
Literal['ribbon','histogram','spaghetti']) – Type of visualization. Default is “ribbon”.ci_kind (
Literal['hdi','eti']) – Type of interval for ribbon plots. Default is “hdi”.ci_prob (
float) – The size of the credible interval. Default is 0.94.num_samples (
int) – Number of posterior samples to plot for spaghetti visualization. Default is 50.**kwargs (
Any) – Additional keyword arguments passed to _bayesian_plot or _ols_plot. Can include deprecated interval and hdi_prob for backward compatibility.
- Returns:
Tuple of figure and axes objects (format depends on experiment type).
- Return type:
Notes
Internally, this function dispatches to either _bayesian_plot or _ols_plot depending on the model type. The kind, ci_kind, ci_prob, and num_samples parameters are passed through to the underlying plotting methods, which can use them when calling plot_xY().