statsmodels.discrete.conditional_models.ConditionalMNLogit

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

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.

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

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