statsmodels.regression.process_regression.ProcessMLE.from_formula¶

classmethod ProcessMLE.from_formula(formula, data, subset=None, drop_cols=None, *args, **kwargs)[source]¶

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¶

Additional positional argument that are passed to the model.

**kwargs¶

These are passed to the model with one exception. The eval_env keyword is passed to patsy. It can be either a patsy: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:¶

The model instance.

Return type:¶

model

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.