statsmodels.miscmodels.count.PoissonGMLE.score_obs PoissonGMLE.score_obs(params, **kwds) Jacobian/Gradient of log-likelihood evaluated at params for each observation.

statsmodels.miscmodels.count.PoissonGMLE.score PoissonGMLE.score(params) Gradient of log-likelihood evaluated at params

statsmodels.miscmodels.count.PoissonOffsetGMLE.expandparams PoissonOffsetGMLE.expandparams(params) expand to full parameter array when some parameters are fixed Parameters: params : array reduced parameter array Returns: paramsfull : array expanded parameter array where fixed parameters are included Notes Calling this requires that self.fixed_params and self.fixed_paramsmask are defined. developer notes: This can be used in the log-likelihood to ... this could also be replaced by a m

statsmodels.miscmodels.count.PoissonGMLE.predict_distribution PoissonGMLE.predict_distribution(exog) [source] return frozen scipy.stats distribution with mu at estimated prediction

statsmodels.miscmodels.count.PoissonGMLE.reduceparams PoissonGMLE.reduceparams(params)

statsmodels.miscmodels.count.PoissonGMLE.predict PoissonGMLE.predict(params, exog=None, *args, **kwargs) After a model has been fit predict returns the fitted values. This is a placeholder intended to be overwritten by individual models.

statsmodels.miscmodels.count.PoissonGMLE.nloglike PoissonGMLE.nloglike(params)

statsmodels.miscmodels.count.PoissonGMLE.nloglikeobs PoissonGMLE.nloglikeobs(params) [source] Loglikelihood of Poisson model Parameters: params : array-like The parameters of the model. Returns: The log likelihood of the model evaluated at `params` : Notes

statsmodels.miscmodels.count.PoissonGMLE.loglike PoissonGMLE.loglike(params)

statsmodels.miscmodels.count.PoissonGMLE.loglikeobs PoissonGMLE.loglikeobs(params)