statsmodels.discrete.discrete_model.Logit

class statsmodels.discrete.discrete_model.Logit(endog, exog, **kwargs)

Binary choice logit model

Parameters:

endog : array-like

1-d endogenous response variable. The dependent variable.

exog : array-like

A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See statsmodels.tools.add_constant.

missing : str

Available options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none.’

Attributes

endog	(array) A reference to the endogenous response variable
exog	(array) A reference to the exogenous design.

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([start_params, method, maxiter, ...])	Fit the model using maximum likelihood.
fit_regularized([start_params, method, ...])	Fit the model using a regularized maximum likelihood.
hessian(params)	Logit model Hessian matrix of the log-likelihood
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 logit model.
loglikeobs(params)	Log-likelihood of logit model for each observation.
pdf(X)	The logistic probability density function
predict(params[, exog, linear])	Predict response variable of a model given exogenous variables.
score(params)	Logit model score (gradient) vector of the log-likelihood
score_obs(params)	Logit model Jacobian of the log-likelihood for each observation

Attributes

endog_names
exog_names