- class statsmodels.discrete.conditional_models.ConditionalLogit(endog, exog, missing='none', **kwargs)¶
Fit a conditional logistic regression model to grouped data.
Every group is implicitly given an intercept, but the model is fit using a conditional likelihood in which the intercepts are not present. Thus, intercept estimates are not given, but the other parameter estimates can be interpreted as being adjusted for any group-level confounders.
fit([start_params, method, maxiter, …])
Fit method for likelihood based models
fit_regularized([method, alpha, …])
Return a regularized fit to a linear regression model.
from_formula(formula, data[, subset, drop_cols])
Create a Model from a formula and dataframe.
The Hessian matrix of the model.
Fisher information matrix of model.
Initialize (possibly re-initialize) a Model instance.
Log-likelihood of model.
After a model has been fit predict returns the fitted values.
Score vector of model.
Names of endogenous variables.
Names of exogenous variables.