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()

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.