interpret.comparisons

interpret.comparisons(
    model,
    idata,
    contrast,
    conditional=None,
    average_by=None,
    target='mean',
    pps=False,
    comparison='diff',
    use_hdi=True,
    prob=az.rcParams['stats.ci_prob'],
    transforms=None,
    sample_new_groups=False,
)

Compute conditional adjusted comparisons.

Parameters

model : Model

The fitted Bambi model.

idata : InferenceData

InferenceData object containing the posterior samples.

contrast : ContrastParam

Variable(s) to create contrasts for.

conditional : ConditionalParam = None

Variables to condition on for comparisons.

average_by : (str, list or None) = None

Variables to average comparisons over.

target : str = 'mean'

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

pps : bool = False

Whether to use posterior predictive samples. Default is False.

comparison : ComparisonFunc or str = 'diff'

Comparison function or string name. Built-in options: “diff” (difference), “ratio” (ratio), “lift” (relative difference). Default is “diff”. Custom functions should accept (reference, contrast) DataArrays and return a DataArray.

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, multiple nested intervals are computed.

transforms : dict or None = None

Dictionary of transformations to apply to comparisons.

sample_new_groups : bool = False

Whether to sample new group levels. Default is False.

Returns

: DataFrame

A DataFrame containing the conditional adjusted comparisons with summary statistics.

Raises

ValueError

If any prob value is not between 0 and 1.

TypeError

If comparison is not a callable or valid string.