# statsmodels.discrete.conditional_models.ConditionalLogit¶

class statsmodels.discrete.conditional_models.ConditionalLogit(endog, exog, missing='none', **kwargs)[source]

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.

Parameters
endogarray_like

The response variable, must contain only 0 and 1.

exogarray_like

The array of covariates. Do not include an intercept in this array.

groupsarray_like

Codes defining the groups. This is a required keyword parameter.

Methods

 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. hessian(params) The Hessian matrix of the model. information(params) Fisher information matrix of model. Initialize (possibly re-initialize) a Model instance. loglike(params) Log-likelihood of model. loglike_grp(grp, params) predict(params[, exog]) After a model has been fit predict returns the fitted values. score(params) Score vector of model. score_grp(grp, params)

Properties

 endog_names Names of endogenous variables. exog_names Names of exogenous variables.