statsmodels.gam.generalized_additive_model.LogitGam¶
-
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
Names of endogenous variables.
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 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 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
Names of endogenous variables.
Names of exogenous variables.