statsmodels.gam.generalized_additive_model.LogitGam¶
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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.
Attributes
endog_names
Names of endogenous variables exog_names
Names of exogenous variables Methods
cdf
(X)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
()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-likelihodo of model at params loglikeobs
(params[, pen_weight])Log-likelihood of model observations at params pdf
(X)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