statsmodels.discrete.discrete_model.Logit

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

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.’

endog

A reference to the endogenous response variable

Type

array

exog

A reference to the exogenous design.

Type

array

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.

from_formula(formula, data[, subset, drop_cols])

Create a Model from a formula and dataframe.

hessian(params)

Logit model Hessian matrix of the log-likelihood

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 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

Names of endogenous variables

exog_names

Names of exogenous variables