7.6.3.3. statsmodels.discrete.discrete_model.MNLogit¶
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class
statsmodels.discrete.discrete_model.MNLogit(endog, exog, **kwargs)[source]¶ 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
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.
missing : str
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
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. -
__init__(endog, exog, **kwargs)¶
Methods
__init__(endog, exog, **kwargs)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])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. jac(*args, **kwds)jac is deprecated, use score_obs instead! 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 Attributes
endog_namesexog_names-