statsmodels.discrete.discrete_model.GeneralizedPoisson.from_formula

classmethod GeneralizedPoisson.from_formula(formula, data, subset=None, drop_cols=None, *args, **kwargs)

Create a Model from a formula and dataframe.

Parameters:	formula (str or generic Formula object) – The formula specifying the model data (array-like) – The data for the model. See Notes. subset (array-like) – An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame drop_cols (array-like) – Columns to drop from the design matrix. Cannot be used to drop terms involving categoricals. args (extra arguments) – These are passed to the model kwargs (extra keyword arguments) – These are passed to the model with one exception. The eval_env keyword is passed to patsy. It can be either a patsy.EvalEnvironment object or an integer indicating the depth of the namespace to use. For example, the default eval_env=0 uses the calling namespace. If you wish to use a “clean” environment set eval_env=-1.
Returns:	model
Return type:	Model instance

Notes

data must define __getitem__ with the keys in the formula terms args and kwargs are passed on to the model instantiation. E.g., a numpy structured or rec array, a dictionary, or a pandas DataFrame.