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.

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.