# statsmodels.discrete.conditional_models.ConditionalMNLogit¶

class statsmodels.discrete.conditional_models.ConditionalMNLogit(endog, exog, missing='none', **kwargs)[source]

Fit a conditional multinomial logit model to grouped data.

Parameters
endogarray-like

The dependent variable, must be integer-valued, coded 0, 1, …, c-1, where c is the number of response categories.

exogarray-like

The independent variables.

groupsarray-like

Codes defining the groups. This is a required keyword parameter.

Notes

Equivalent to femlogit in Stata.

References

Gary Chamberlain (1980). Analysis of covariance with qualitative data. The Review of Economic Studies. Vol. 47, No. 1, pp. 225-238.

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 (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.