statsmodels.discrete.discrete_model.NegativeBinomial.jac NegativeBinomial.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7

statsmodels.genmod.families.links.NegativeBinomial.inverse_deriv NegativeBinomial.inverse_deriv(z) [source] Derivative of the inverse of the negative binomial transform Parameters: z : array-like Usually the linear predictor for a GLM or GEE model Returns: The value of the inverse of the derivative of the negative binomial : link :

statsmodels.genmod.families.links.NegativeBinomial.inverse NegativeBinomial.inverse(z) [source] Inverse of the negative binomial transform Parameters: z : array-like The value of the inverse of the negative binomial link at p. Returns : ??- : p : array Mean parameters Notes g^(-1)(z) = exp(z)/(alpha*(1-exp(z)))

statsmodels.discrete.discrete_model.NegativeBinomial.initialize NegativeBinomial.initialize() Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.

statsmodels.discrete.discrete_model.NegativeBinomial.information NegativeBinomial.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.discrete.discrete_model.NegativeBinomial.hessian NegativeBinomial.hessian(params) The Hessian matrix of the model

statsmodels.discrete.discrete_model.NegativeBinomial.from_formula classmethod NegativeBinomial.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

statsmodels.discrete.discrete_model.NegativeBinomial.fit_regularized NegativeBinomial.fit_regularized(start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, **kwargs) [source]

statsmodels.genmod.families.family.NegativeBinomial.fitted NegativeBinomial.fitted(lin_pred) Fitted values based on linear predictors lin_pred. Parameters: lin_pred : array Values of the linear predictor of the model. dot(X,beta) in a classical linear model. Returns: mu : array The mean response variables given by the inverse of the link function.

statsmodels.discrete.discrete_model.NegativeBinomial.fit NegativeBinomial.fit(start_params=None, method='bfgs', maxiter=35, full_output=1, disp=1, callback=None, cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs) [source]