statsmodels.discrete.discrete_model.MultinomialResults.get_margeff MultinomialResults.get_margeff(at='overall', method='dydx', atexog=None, dummy=False, count=False) Get marginal effects of the fitted model. Parameters: at : str, optional Options are: ?overall?, The average of the marginal effects at each observation. ?mean?, The marginal effects at the mean of each regressor. ?median?, The marginal effects at the median of each regressor. ?zero?, The marginal effects at zero for each regr

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

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

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.from_formula classmethod MultinomialModel.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas