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.

missing{‘none’, ‘drop’, ‘raise’}, optional

If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised.

Attributes:

endog_names

Names of endogenous variables

exog_names

Names of exogenous variables

Methods

fit([start_params, method, maxiter, ...])	Fit the conditional model.
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)	Returns numerical approximation to the Hessian.
information(params)	Fisher information matrix of model
initialize()	Initialize (possibly re-initialize) a Model instance
loglike(params)	Log-likelihood of the conditional logistic model.
predict(params[, exog])	After a model has been fit, predict returns the fitted values
score(params)	Score vector of the conditional logistic model.

loglike_grp
score_grp

Methods

fit([start_params, method, maxiter, ...])	Fit the conditional model.
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)	Returns numerical approximation to the Hessian.
information(params)	Fisher information matrix of model
initialize()	Initialize (possibly re-initialize) a Model instance
loglike(params)	Log-likelihood of the conditional logistic model.
loglike_grp(grp, params)
predict(params[, exog])	After a model has been fit, predict returns the fitted values
score(params)	Score vector of the conditional logistic model.
score_grp(grp, params)

Properties

endog_names	Names of endogenous variables
exog_names	Names of exogenous variables