I am attempting to write a wrapper for the statsmodels formula API (this is a simplified version, the function does more than this):
import statsmodels.formula.api as smf
def wrapper(formula, data, **kwargs):
return smf.logit(formula, data).fit(**kwargs)
model = wrapper('y ~ x + square(x)', data=df)
statsmodels uses the patsy package to parse the formulas and create the design matrix. patsy allows user functions as part of formulas and obtains or evaluates the user function in the user namespace or environment.
as reference see eval_env keyword in http://patsy.readthedocs.org/en/latest/API-reference.html
from_formula is the method of models that implements the formula interface to patsy. It use
eval_env to provide the necessary information to patsy, which by default is the calling environment of the user. This can be overwritten by the user with the corresponding keyword argument.
The simplest way to define the eval_env is as an integer that indicates the stacklevel that patsy should use. from_formula is incrementing it to take account of the additional level in the statsmodels methods.
According to the comments, eval_env = 2 will use the next higher level from the level that creates the model, e.g. with
model = smf.logit(..., eval_env=2).
This creates to model, calls patsy and creates the design matrix,
model.fit() will estimate it and returns the results instance.