statsmodels.discrete.discrete_model.Logit

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

Binary choice logit model

Parameters :

endog : array-like

1-d array of the response variable.

exog : array-like

exog is an n x p array where n is the number of observations and p is the number of regressors including the intercept if one is included in the data.

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
fit([start_params, method, maxiter, ...])	Fit the model using maximum likelihood.
hessian(params)	Logit model Hessian matrix of the log-likelihood
information(params)	Fisher information matrix of model
initialize()	Initialize is called by
jac(params)	Logit model Jacobian of the log-likelihood for each observation
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

Attributes

endog_names
exog_names