statsmodels.genmod.families.links.Logit.inverse Logit.inverse(z) [source] Inverse of the logit transform Parameters: z : array-like The value of the logit transform at p Returns: p : array Probabilities Notes g^(-1)(z) = exp(z)/(1+exp(z))

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

statsmodels.discrete.discrete_model.Logit.hessian Logit.hessian(params) [source] Logit model Hessian matrix of the log-likelihood Parameters: params : array-like The parameters of the model Returns: hess : ndarray, (k_vars, k_vars) The Hessian, second derivative of loglikelihood function, evaluated at params Notes

statsmodels.discrete.discrete_model.Logit.from_formula classmethod Logit.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 : ext

statsmodels.discrete.discrete_model.Logit.fit_regularized Logit.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) 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, optional Initial guess of the solution for the

statsmodels.discrete.discrete_model.Logit.fit Logit.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, optional The

statsmodels.genmod.families.links.Logit.deriv2 Logit.deriv2(p) Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.genmod.families.links.Logit.deriv Logit.deriv(p) [source] Derivative of the logit transform Parameters: p: array-like : Probabilities Returns: g?(p) : array Value of the derivative of logit transform at p Notes g?(p) = 1 / (p * (1 - p)) Alias for Logit: logit = Logit()

statsmodels.discrete.discrete_model.Logit.cov_params_func_l1 Logit.cov_params_func_l1(likelihood_model, xopt, retvals) 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.