statsmodels.discrete.discrete_model.MultinomialResults.f_test MultinomialResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypothese

statsmodels.discrete.discrete_model.MultinomialResults.cov_params MultinomialResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column :

statsmodels.discrete.discrete_model.MultinomialResults.conf_int MultinomialResults.conf_int(alpha=0.05, cols=None) [source]

statsmodels.discrete.discrete_model.MultinomialModel.score MultinomialModel.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.discrete.discrete_model.MultinomialModel.predict MultinomialModel.predict(params, exog=None, linear=False) [source] Predict response variable of a model given exogenous variables. Parameters: params : array-like 2d array of fitted parameters of the model. Should be in the order returned from the model. exog : array-like 1d or 2d array of exogenous values. If not supplied, the whole exog attribute of the model is used. If a 1d array is given it assumed to be 1 row of exogenous

statsmodels.discrete.discrete_model.MultinomialModel.pdf MultinomialModel.pdf(X) The probability density (mass) function of the model.

statsmodels.discrete.discrete_model.MultinomialModel.loglike MultinomialModel.loglike(params) Log-likelihood of model.

statsmodels.discrete.discrete_model.MultinomialModel.initialize MultinomialModel.initialize() [source] Preprocesses the data for MNLogit.

statsmodels.discrete.discrete_model.MultinomialModel.information MultinomialModel.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.discrete.discrete_model.MultinomialModel.hessian MultinomialModel.hessian(params) The Hessian matrix of the model