ConsiderationSetMixedLogit.fit

ConsiderationSetMixedLogit.fit(choice_df=None, utility_equations=None, *, method='mcmc', progressbar=None, random_seed=None, sample_kwargs=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.

methodstr

Method used to fit the model. One of "mcmc", "map", "demz", "advi" or "fullrank_advi".

progressbarbool, optional

Show progress bar during sampling

random_seedRandomState, optional

Random seed for reproducibility

sample_kwargsdict, optional

Only used by the variational methods; forwarded to Approximation.sample.

**kwargs

Additional arguments passed to the underlying PyMC routine

Returns:

xr.DataTree

Fitted model with posterior samples