statsmodels.miscmodels.count.PoissonGMLE.jac PoissonGMLE.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.PoissonGMLE.initialize PoissonGMLE.initialize()

statsmodels.miscmodels.count.PoissonGMLE.information PoissonGMLE.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

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

statsmodels.miscmodels.count.PoissonGMLE.from_formula classmethod PoissonGMLE.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 args

statsmodels.miscmodels.count.PoissonGMLE.fit PoissonGMLE.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.PoissonGMLE.expandparams PoissonGMLE.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 general

statsmodels.genmod.families.family.Poisson.weights Poisson.weights(mu) Weights for IRLS steps Parameters: mu : array-like The transformed mean response variable in the exponential family Returns: w : array The weights for the IRLS steps Notes w = 1 / (link?(mu)**2 * variance(mu))

statsmodels.genmod.families.family.Poisson.starting_mu Poisson.starting_mu(y) Starting value for mu in the IRLS algorithm. Parameters: y : array The untransformed response variable. Returns: mu_0 : array The first guess on the transformed response variable. Notes Only the Binomial family takes a different initial value.

statsmodels.discrete.discrete_model.Poisson.score_obs Poisson.score_obs(params) [source] Poisson model Jacobian of the log-likelihood for each observation Parameters: params : array-like The parameters of the model Returns: score : ndarray (nobs, k_vars) The score vector of the model evaluated at params Notes for observations where the loglinear model is assumed