statsmodels.discrete.discrete_model.MNLogit

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

Multinomial logit model

Parameters

endogarray-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.

exogarray-like

A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See statsmodels.tools.add_constant.

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. Default is ‘none.’

Notes

See developer notes for further information on MNLogit internals.

Attributes

endogarray

A reference to the endogenous response variable

exogarray

A reference to the exogenous design.

Jfloat

The number of choices for the endogenous variable. Note that this is zero-indexed.

Kfloat

The actual number of parameters for the exogenous design. Includes the constant if the design has one.

namesdict

A dictionary mapping the column number in wendog to the variables in endog.

wendogarray

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.
cov_params_func_l1(likelihood_model, xopt, …)	Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit.
fit([start_params, method, maxiter, …])	Fit the model using maximum likelihood.
fit_regularized([start_params, method, …])	Fit the model using a regularized maximum likelihood.
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
hessian(params)	Multinomial logit Hessian matrix of the log-likelihood
information(params)	Fisher information matrix of model
initialize()	Preprocesses the data for MNLogit.
loglike(params)	Log-likelihood of the multinomial logit model.
loglike_and_score(params)	Returns log likelihood and score, efficiently reusing calculations.
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
score_obs(params)	Jacobian matrix for multinomial logit model log-likelihood