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.

missingstr

Available options are ‘none’, ‘drop’, and ‘raise’. 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 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

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.