statsmodels.discrete.discrete_model.BinaryResults.bic static BinaryResults.bic()

statsmodels.tsa.arima_model.ARIMAResults class statsmodels.tsa.arima_model.ARIMAResults(model, params, normalized_cov_params=None, scale=1.0) [source] Methods aic() arfreq() Returns the frequency of the AR roots. arparams() arroots() bic() bse() conf_int([alpha, cols, method]) Returns the confidence interval of the fitted parameters. cov_params() f_test(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint linear hypothesis. fittedvalues() forecast([steps, exog,

statsmodels.tsa.arima_process.ArmaProcess.arma2ma ArmaProcess.arma2ma(nobs=None) [source]

statsmodels.discrete.discrete_model.Logit.score_obs Logit.score_obs(params) [source] Logit model Jacobian of the log-likelihood for each observation Parameters: params: array-like : The parameters of the model Returns: jac : ndarray, (nobs, k_vars) The derivative of the loglikelihood for each observation evaluated at params. Notes for observations

statsmodels.sandbox.sysreg.SUR.whiten SUR.whiten(X) [source] SUR whiten method. Parameters: X : list of arrays Data to be whitened. Returns: If X is the exogenous RHS of the system. : ``np.dot(np.kron(cholsigmainv,np.eye(M)),np.diag(X))`` : If X is the endogenous LHS of the system. :

statsmodels.robust.robust_linear_model.RLMResults.normalized_cov_params RLMResults.normalized_cov_params()

statsmodels.discrete.discrete_model.MultinomialResults.predict MultinomialResults.predict(exog=None, transform=True, *args, **kwargs) Call self.model.predict with self.params as the first argument. Parameters: exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pa

statsmodels.regression.quantile_regression.QuantRegResults.compare_f_test QuantRegResults.compare_f_test(restricted) use F test to test whether restricted model is correct Parameters: restricted : Result instance The restricted model is assumed to be nested in the current model. The result instance of the restricted model is required to have two attributes, residual sum of squares, ssr, residual degrees of freedom, df_resid. Returns: f_value : float test statistic, F distributed p_val

statsmodels.sandbox.sysreg.Sem2SLS.fit Sem2SLS.fit() [source]

statsmodels.regression.quantile_regression.QuantRegResults.save QuantRegResults.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. No