statsmodels.discrete.discrete_model.DiscreteModel.pdf DiscreteModel.pdf(X) [source] The probability density (mass) function of the model.

statsmodels.discrete.discrete_model.DiscreteModel.loglike DiscreteModel.loglike(params) Log-likelihood of model.

statsmodels.discrete.discrete_model.DiscreteModel.initialize DiscreteModel.initialize() [source] 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.DiscreteModel.information DiscreteModel.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

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

statsmodels.discrete.discrete_model.DiscreteModel.from_formula classmethod DiscreteModel.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.DataF

statsmodels.discrete.discrete_model.DiscreteModel.fit_regularized DiscreteModel.fit_regularized(start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=True, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, qc_verbose=False, **kwargs) [source] Fit the model using a regularized maximum likelihood. The regularization method AND the solver used is determined by the argument method. Parameters: start_params : array-like, o

statsmodels.discrete.discrete_model.DiscreteModel.fit DiscreteModel.fit(start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) [source] Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.base.model.LikelihoodModel.fit Fit method for likelihood based models Parameters: start_params : array-like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method : str

statsmodels.discrete.discrete_model.DiscreteModel.cov_params_func_l1 DiscreteModel.cov_params_func_l1(likelihood_model, xopt, retvals) [source] Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero?d values set to np.nan.

statsmodels.discrete.discrete_model.DiscreteModel.cdf DiscreteModel.cdf(X) [source] The cumulative distribution function of the model.