statsmodels.discrete.discrete_model.MultinomialModel

class statsmodels.discrete.discrete_model.MultinomialModel(endog, exog, offset=None, check_rank=True, **kwargs)[source]
Attributes
endog_names

Names of endogenous variables.

exog_names

Names of exogenous variables.

Methods

cdf(X)

The cumulative distribution function of the model.

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_constrained(constraints[, start_params])

fit_constraint that returns a results instance

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)

The Hessian matrix of the model.

information(params)

Fisher information matrix of model.

initialize()

Preprocesses the data for MNLogit.

loglike(params)

Log-likelihood of model.

pdf(X)

The probability density (mass) function of the model.

predict(params[, exog, which, linear])

Predict response variable of a model given exogenous variables.

score(params)

Score vector of model.

Properties

endog_names

Names of endogenous variables.

exog_names

Names of exogenous variables.