Output for get_margeff (marginal effects of poisson estimates)

[B-Benja]

Firs of all, thank you very much for the very useful library. It works very well with OLS outputs, but is it also possible to get marginal effects for a poisson regression as a stargazer (table & latex) output?

Sample code: reg_ols = smf.ols(formula= 'Count_data ~ Variable1Variable2', data=df).fit(disp=0) reg_poisson = smf.poisson(formula= 'Count_data ~ Variable1Variable2', data=df).fit() margins_poisson = reg_poisson.get_margeff(at='overall', method='dydx') reg_results = Stargazer([reg_ols, margins_poisson])

Currently, it results in 'Please use trained OLS models as inputs'. Thanks in advance for any advise and help!

[phipz]

This is certainly not an elegant solution, but a quick fix:

import copy
from statsmodels.formula.api import logit

def ameWrapper(input_model):
    ame_model = copy.deepcopy(input_model)
    ame_model.params[1:] = input_model.get_margeff().margeff
    ame_model.pvalues[1:] = input_model.get_margeff().pvalues
    ame_model.bse[1:] = input_model.get_margeff().margeff_se
    ame_model.params[0] = 0
    ame_model.pvalues[0] = 1
    ame_model.bse[0] = 0
    return ame_model

model1 = logit("dv ~ iv1", data=modeldata).fit()
model2 = logit("dv ~ iv1 + iv2", data=modeldata).fit()

ame_models = [ameWrapper(model1), ameWrapper(model2)]
ame_stargazer = Stargazer(ame_models)

Don't forget to delete the Intecept row in your output.

[toobaz]

@phipz Thanks! I implemented something similar, to be used as follows:

from utils import MarginalEffects

mest = MarginalEffects(est)
ame_stargazer = Stargazer([mest])

This is just a commit as of now, will be included in next release. Feedback welcome!