interpret.plot_predictions

interpret.plot_predictions(
    model,
    idata,
    conditional=None,
    average_by=None,
    target='mean',
    pps=False,
    use_hdi=True,
    prob=az.rcParams['stats.ci_prob'],
    transforms=None,
    sample_new_groups=False,
    fig_kwargs=None,
    subplot_kwargs=None,
)

Plot conditional adjusted predictions.

Parameters

model : Model

The fitted Bambi model.

idata : InferenceData

InferenceData object containing the posterior samples.

conditional : ConditionalParam = None

Variables to condition on for predictions.

average_by : str or list or bool or None = None

Variables to average predictions over.

target : str = 'mean'

The target parameter to predict. Default is “mean”.

pps : bool = False

Whether to use posterior predictive samples. Default is False.

use_hdi : bool = True

Whether to use highest density interval. Default is True.

prob : float or list[float] = az.rcParams['stats.ci_prob']

Probability or list of probabilities for credible intervals. Default is from arviz rcParams. When a list is provided, nested bands with decreasing opacity are drawn.

transforms : dict or None = None

Dictionary of transformations to apply to predictions.

sample_new_groups : bool = False

Whether to sample new group levels. Default is False.

fig_kwargs : dict or None = None

Additional keyword arguments for figure customization. Use the ‘theme’ key to pass a dictionary of matplotlib rc parameters.

subplot_kwargs : dict or None = None

Overrides default plotting sequence (main, group, panel).

Returns

: Plot

A Seaborn objects Plot. In Jupyter notebooks, the plot automatically displays. In scripts, call .show() to display. The returned Plot object can be customized before displaying using method chaining (e.g., .label(), .theme()).

Raises

ValueError

If more than 3 conditional variables are provided without averaging.