statsmodelsdiscretediscrete_modelLogit

1-d endogenous response variable. The dependent variable.

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

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.

The logistic cumulative distribution function

Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit.

([start_params, method, maxiter, …])

Fit the model using maximum likelihood.

Fit the model using a regularized maximum likelihood.

(formula, data[, subset, drop_cols])

Create a Model from a formula and dataframe.

Logit model Hessian matrix of the log-likelihood

Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.

Log-likelihood of logit model for each observation.

The logistic probability density function

Predict response variable of a model given exogenous variables.

Logit model score (gradient) vector of the log-likelihood

Logit model Jacobian of the log-likelihood for each observation

The logistic cumulative distribution function

Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit.

([start_params, method, maxiter, …])

Fit the model using maximum likelihood.

Fit the model using a regularized maximum likelihood.

(formula, data[, subset, drop_cols])

Create a Model from a formula and dataframe.

Logit model Hessian matrix of the log-likelihood

Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model.

Log-likelihood of logit model for each observation.

The logistic probability density function

Predict response variable of a model given exogenous variables.

Logit model score (gradient) vector of the log-likelihood

Logit model Jacobian of the log-likelihood for each observation

Regression with Discrete Dependent Variable

statsmodels.discrete.discrete_model.Logit.cdf