statsmodels.discrete.discrete_model.MNLogit

class statsmodels.discrete.discrete_model.MNLogit(endog, exog)

Multinomial logit model

Parameters:

endog : array-like

endog is an 1-d vector of the endogenous response. endog can contain strings, ints, or floats. Note that if it contains strings, every distinct string will be a category. No stripping of whitespace is done.

exog : array-like

exog is an n x p array where n is the number of observations and p is the number of regressors including the intercept if one is included in the data.

Notes

See developer notes for further information on MNLogit internals.

Attributes

endog	array	A reference to the endogenous response variable
exog	array	A reference to the exogenous design.
J	float	The number of choices for the endogenous variable. Note that this is zero-indexed.
K	float	The actual number of parameters for the exogenous design. Includes the constant if the design has one.
names	dict	A dictionary mapping the column number in wendog to the variables in endog.
wendog	array	An n x j array where j is the number of unique categories in endog. Each column of j is a dummy variable indicating the category of each observation. See names for a dictionary mapping each column to its category.

Methods

cdf(X)	Multinomial logit cumulative distribution function.
fit([start_params, method, maxiter, ...])	Fit the model using maximum likelihood.
hessian(params)	Multinomial logit Hessian matrix of the log-likelihood
information(params)	Fisher information matrix of model
initialize()	Preprocesses the data for MNLogit.
jac(params)	Jabobian matrix for multinomial logit model log-likelihood
loglike(params)	Log-likelihood of the multinomial logit model.
loglikeobs(params)	Log-likelihood of the multinomial logit model for each observation.
pdf(eXB)	NotImplemented
predict(params[, exog, linear])	Predict response variable of a model given exogenous variables.
score(params)	Score matrix for multinomial logit model log-likelihood

Attributes

endog_names
exog_names