discrete.discrete_model.DiscreteModel()

statsmodels.discrete.discrete_model.DiscreteModel

class statsmodels.discrete.discrete_model.DiscreteModel(endog, exog, **kwargs) [source]

Abstract class for discrete choice models.

This class does not do anything itself but lays out the methods and call signature expected of child classes in addition to those of statsmodels.model.LikelihoodModel.

Attributes

endog_names	Names of endogenous variables
exog_names	Names of exogenous variables

Methods

cdf(X)	The cumulative distribution function of the model.
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([start_params, method, maxiter, ...])	Fit the model using maximum 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)	The Hessian matrix of the model
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)	Log-likelihood of model.
pdf(X)	The probability density (mass) function of the model.
predict(params[, exog, linear])	Predict response variable of a model given exogenous variables.
score(params)	Score vector of model.

Methods

cdf(X)	The cumulative distribution function of the model.
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([start_params, method, maxiter, ...])	Fit the model using maximum 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)	The Hessian matrix of the model
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)	Log-likelihood of model.
pdf(X)	The probability density (mass) function of the model.
predict(params[, exog, linear])	Predict response variable of a model given exogenous variables.
score(params)	Score vector of model.

Attributes

endog_names	Names of endogenous variables
exog_names	Names of exogenous variables