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:

endog : array_like

The response variable, must contain only 0 and 1.

exog : array_like

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

groups : array_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)

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.