statsmodels.regression.linear_model.RegressionResults.predict RegressionResults.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 pass

statsmodels.regression.linear_model.RegressionResults.normalized_cov_params RegressionResults.normalized_cov_params()

statsmodels.regression.linear_model.RegressionResults.get_robustcov_results RegressionResults.get_robustcov_results(cov_type='HC1', use_t=None, **kwds) [source] create new results instance with robust covariance as default Parameters: cov_type : string the type of robust sandwich estimator to use. see Notes below use_t : bool If true, then the t distribution is used for inference. If false, then the normal distribution is used. kwds : depends on cov_type Required or optional arguments

statsmodels.regression.linear_model.RegressionResults.load classmethod RegressionResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.regression.linear_model.RegressionResults.initialize RegressionResults.initialize(model, params, **kwd)

statsmodels.regression.linear_model.RegressionResults.conf_int RegressionResults.conf_int(alpha=0.05, cols=None) [source] Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The alpha level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return Notes The confidence interval is based on Student?s t-distribution.

statsmodels.regression.linear_model.RegressionResults.cov_params RegressionResults.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 : ar

statsmodels.regression.linear_model.RegressionResults.f_test RegressionResults.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

statsmodels.regression.linear_model.RegressionResults.compare_lm_test RegressionResults.compare_lm_test(restricted, demean=True, use_lr=False) [source] Use Lagrange Multiplier 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. demean : bo

statsmodels.regression.linear_model.RegressionResults.compare_f_test RegressionResults.compare_f_test(restricted) [source] 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