MixedLM.from_formula()

statsmodels.regression.mixed_linear_model.MixedLM.from_formula

classmethod MixedLM.from_formula(formula, data, re_formula=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. re_formula : string A one-sided formula defining the variance structure of the model. The default gives a random intercept for each group. 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 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 : 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.

If re_formula is not provided, the default is a random intercept for each group.

This method currently does not correctly handle missing values, so missing values should be explicitly dropped from the DataFrame before calling this method.