statsmodels.discrete.conditional_models.ConditionalLogit

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.

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.

Attributes

endog_names

Names of endogenous variables

exog_names

Names of exogenous variables

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()	Initialize (possibly re-initialize) a Model instance.
loglike(params)	Log-likelihood of model.
predict(params[, exog])	After a model has been fit predict returns the fitted values.
score(params)	Score vector of model.

loglike_grp
score_grp