NestedLogit.fit

NestedLogit.fit(choice_df=None, utility_equations=None, progressbar=None, random_seed=None, **kwargs)

Fit the discrete choice model.

Parameters:

choice_dfpd.DataFrame, optional

New choice data. If None, uses data from initialization.

utility_equationslist[str], optional

New utility equations. If None, uses equations from initialization.

progressbarbool, optional

Show progress bar during sampling

random_seedRandomState, optional

Random seed for reproducibility

**kwargs

Additional arguments passed to pm.sample()

Returns:

az.InferenceData

Fitted model with posterior samples