interpret.predictions

interpret.predictions(
    model,
    idata,
    conditional=None,
    average_by=None,
    target='mean',
    pps=False,
    use_hdi=True,
    prob=None,
    transforms=None,
    sample_new_groups=False,
)

Compute Conditional Adjusted Predictions

Parameters

model : bambi.Model

The model for which we want to plot the predictions.

idata : arviz.InferenceData

The InferenceData object that contains the samples from the posterior distribution of the model.

conditional : (str, list, dict) = None

The covariates we would like to condition on. If dict, keys are the covariate names and values are the values to condition on.

average_by : str | list | bool | None = None

The covariates we would like to average by. The passed covariate(s) will marginalize over the other covariates in the model. If True, it averages over all covariates in the model to obtain the average estimate. Defaults to None.

target : str = 'mean'

Which model parameter to plot. Defaults to ‘mean’. Passing a parameter into target only works when pps is False as the target may not be available in the posterior predictive distribution.

pps : bool = False

Whether to plot the posterior predictive samples. Defaults to False.

use_hdi : bool = True

Whether to compute the highest density interval (defaults to True) or the quantiles.

prob : float = None

The probability for the credibility intervals. Must be between 0 and 1. Defaults to 0.94. Changing the global variable az.rcParam["stats.ci_prob"] affects this default.

transforms : dict = None

Transformations that are applied to each of the variables being plotted. The keys are the name of the variables, and the values are functions to be applied. Defaults to None.

sample_new_groups : bool = False

If the model contains group-level effects, and data is passed for unseen groups, whether to sample from the new groups. Defaults to False.

Returns

cap_data : pd.DataFrame

A DataFrame with the result of create_predictions_data and model predictions.

Raises

: ValueError

If pps is True and target is not "mean". If conditional is a list and the length is greater than 3. If prob is not > 0 and < 1.