'Get marginal effects of the fitted model.
Parameters
at : str, optional
Options are:
- \'overall\', The average of the marginal effects at each
observation.
- \'mean\', The marginal effects at the mean of each regressor.
- \'median\', The marginal effects at the median of each regressor.
- \'zero\', The marginal effects at zero for each regressor.
- \'all\', The marginal effects at each observation. If `at` is all
only margeff will be available from the returned object.
Note that if `exog` is specified, then marginal effects for all
variables not specified by `exog` are calculated using the `at`
option.
method : str, optional
Options are:
- \'dydx\' - dy/dx - No transformation is made and marginal effects
are returned.  This is the default.
- \'eyex\' - estimate elasticities of variables in `exog` --
d(lny)/d(lnx)
- \'dyex\' - estimate semielasticity -- dy/d(lnx)
- \'eydx\' - estimate semeilasticity -- d(lny)/dx
Note that tranformations are done after each observation is
calculated.  Semi-elasticities for binary variables are computed
using the midpoint method. \'dyex\' and \'eyex\' do not make sense
for discrete variables.
atexog : array-like, optional
Optionally, you can provide the exogenous variables over which to
get the marginal effects.  This should be a dictionary with the key
as the zero-indexed column number and the value of the dictionary.
Default is None for all independent variables less the constant.
dummy : bool, optional
If False, treats binary variables (if present) as continuous.  This
is the default.  Else if True, treats binary variables as
changing from 0 to 1.  Note that any variable that is either 0 or 1
is treated as binary.  Each binary variable is treated separately
for now.
count : bool, optional
If False, treats count variables (if present) as continuous.  This
is the default.  Else if True, the marginal effect is the
change in probabilities when each observation is increased by one.
Returns
DiscreteMargins : marginal effects instance
Returns an object that holds the marginal effects, standard
errors, confidence intervals, etc. See
`statsmodels.discrete.discrete_margins.DiscreteMargins` for more
information.
Notes
When using after Poisson, returns the expected number of events
per period, assuming that the model is loglinear.'

def get_margeff(self, at='overall', method='dydx', atexog=None, dummy=False, count=False):

    from statsmodels.discrete.discrete_margins import DiscreteMargins
    return DiscreteMargins(self, (at, method, atexog, dummy, count))
