statsmodels.regression.linear_model.GLS.fit_regularized GLS.fit_regularized(method='coord_descent', maxiter=1000, alpha=0.0, L1_wt=1.0, start_params=None, cnvrg_tol=1e-08, zero_tol=1e-08, **kwargs) Return a regularized fit to a linear regression model. Parameters: method : string Only the coordinate descent algorithm is implemented. maxiter : integer The maximum number of iteration cycles (an iteration cycle involves running coordinate descent on all variables). alpha : scalar or array-

statsmodels.discrete.discrete_model.LogitResults.f_test LogitResults.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 hypotheses to test ca

statsmodels.discrete.discrete_model.MNLogit.pdf MNLogit.pdf(eXB) [source] NotImplemented

statsmodels.regression.mixed_linear_model.MixedLM.predict MixedLM.predict(params, exog=None, *args, **kwargs) After a model has been fit predict returns the fitted values. This is a placeholder intended to be overwritten by individual models.

statsmodels.miscmodels.count.PoissonOffsetGMLE.score_obs PoissonOffsetGMLE.score_obs(params, **kwds) Jacobian/Gradient of log-likelihood evaluated at params for each observation.

statsmodels.tools.tools.recipr statsmodels.tools.tools.recipr(X) [source] Return the reciprocal of an array, setting all entries less than or equal to 0 to 0. Therefore, it presumes that X should be positive in general.

statsmodels.iolib.table.SimpleTable.as_text SimpleTable.as_text(**fmt_dict) [source] Return string, the table as text.

statsmodels.miscmodels.tmodel.TLinearModel.hessian TLinearModel.hessian(params) Hessian of log-likelihood evaluated at params

statsmodels.discrete.discrete_model.CountResults.summary CountResults.summary(yname=None, xname=None, title=None, alpha=0.05, yname_list=None) Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns:

statsmodels.miscmodels.count.PoissonGMLE.information PoissonGMLE.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.