statsmodels.regression.linear_model.GLS.fit¶

GLS.fit(method='pinv', cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs)¶

Full fit of the model.

The results include an estimate of covariance matrix, (whitened) residuals and an estimate of scale.

Parameters:	method (str, optional) – Can be “pinv”, “qr”. “pinv” uses the Moore-Penrose pseudoinverse to solve the least squares problem. “qr” uses the QR factorization. cov_type (str, optional) – See regression.linear_model.RegressionResults for a description of the available covariance estimators cov_kwds (list or None, optional) – See linear_model.RegressionResults.get_robustcov_results for a description required keywords for alternative covariance estimators use_t (bool, optional) – Flag indicating to use the Student’s t distribution when computing p-values. Default behavior depends on cov_type. See linear_model.RegressionResults.get_robustcov_results for implementation details.
Returns:
Return type:	A RegressionResults class instance.