statsmodels.miscmodels.count.PoissonZiGMLE.initialize PoissonZiGMLE.initialize()

statsmodels.miscmodels.count.PoissonZiGMLE.jac PoissonZiGMLE.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7. Jacobian/Gradient of log-likelihood evaluated at params for each observation.

statsmodels.miscmodels.count.PoissonZiGMLE.hessian PoissonZiGMLE.hessian(params) Hessian of log-likelihood evaluated at params

statsmodels.miscmodels.count.PoissonZiGMLE.fit PoissonZiGMLE.fit(start_params=None, method='nm', maxiter=500, full_output=1, disp=1, callback=None, retall=0, **kwargs) Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.LikelihoodModel.fit

statsmodels.miscmodels.count.PoissonZiGMLE.from_formula classmethod PoissonZiGMLE.from_formula(formula, data, subset=None, *args, **kwargs) 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. 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 a

statsmodels.miscmodels.count.PoissonZiGMLE.expandparams PoissonZiGMLE.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 more gene

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

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

statsmodels.miscmodels.count.PoissonOffsetGMLE.predict PoissonOffsetGMLE.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.PoissonOffsetGMLE.reduceparams PoissonOffsetGMLE.reduceparams(params)