statsmodels.robust.robust_linear_model.RLM.hessian RLM.hessian(params) The Hessian matrix of the model

statsmodels.robust.robust_linear_model.RLM.from_formula classmethod RLM.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.DataFrame args : extr

statsmodels.robust.robust_linear_model.RLM.fit RLM.fit(maxiter=50, tol=1e-08, scale_est='mad', init=None, cov='H1', update_scale=True, conv='dev') [source] Fits the model using iteratively reweighted least squares. The IRLS routine runs until the specified objective converges to tol or maxiter has been reached. Parameters: conv : string Indicates the convergence criteria. Available options are ?coefs? (the coefficients), ?weights? (the weights in the iteration), ?sresid? (the standardized

statsmodels.robust.robust_linear_model.RLM.deviance RLM.deviance(tmp_results) [source] Returns the (unnormalized) log-likelihood from the M estimator.

statsmodels.regression.linear_model.RegressionResults.wald_test RegressionResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. 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 can be given as a string. See the examples. tu

statsmodels.regression.linear_model.RegressionResults.t_test RegressionResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A

statsmodels.regression.linear_model.RegressionResults.summary2 RegressionResults.summary2(yname=None, xname=None, title=None, alpha=0.05, float_format='%.4f') [source] Experimental summary function to summarize the regression results Parameters: xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) title : string, optional Title for the top table. If not None, then this

statsmodels.regression.linear_model.RegressionResults.summary RegressionResults.summary(yname=None, xname=None, title=None, alpha=0.05) [source] 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.regression.linear_model.RegressionResults.save RegressionResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes

statsmodels.regression.linear_model.RegressionResults.remove_data RegressionResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time an