statsmodels.gam.generalized_additive_model.LogitGam

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

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-likelihood 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

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-likelihood 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

Properties

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