class statsmodels.gam.generalized_additive_model.LogitGam(endog, smoother, alpha, *args, **kwargs)[source]

Generalized Additive model for discrete Logit

This subclasses discrete_model Logit.

Warning: not all inherited methods might take correctly account of the penalization

not verified yet.

Methods

 The logistic cumulative distribution function cov_params_func_l1(likelihood_model, xopt, …) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. fit([method, trim]) minimize negative penalized log-likelihood fit_regularized([start_params, method, …]) Fit the model using a regularized maximum likelihood. from_formula(formula, data[, subset, drop_cols]) Create a Model from a formula and dataframe. hessian(params[, pen_weight]) Hessian of model at params hessian_numdiff(params[, pen_weight]) hessian based on finite difference derivative information(params) Fisher information matrix of model. Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model. loglike(params[, pen_weight]) Log-likelihood of model at params loglikeobs(params[, pen_weight]) Log-likelihood of model observations at params The logistic probability density function predict(params[, exog, linear]) Predict response variable of a model given exogenous variables. score(params[, pen_weight]) Gradient of model at params score_numdiff(params[, pen_weight, method]) score based on finite difference derivative score_obs(params[, pen_weight]) Gradient of model observations at params

Properties

 endog_names Names of endogenous variables. exog_names Names of exogenous variables.