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.
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.