statsmodels.regression.rolling.RollingWLS.from_formula

classmethod RollingWLS.from_formula(formula, data, window, weights=None, subset=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.