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.

Attributes:¶

endog_names

Names of endogenous variables.

exog_names

Names of exogenous variables.

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.

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.

Properties

endog_names	Names of endogenous variables.
exog_names	Names of exogenous variables.