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 apatsy.EvalEnvironment
object or an integer indicating the depth of the namespace to use. For example, the defaulteval_env=0
uses the calling namespace. If you wish to use a “clean” environment seteval_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.