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:

A RegressionResults class instance.

See also

regression.linear_model.RegressionResults, regression.linear_model.RegressionResults.get_robustcov_results

Notes

The fit method uses the pseudoinverse of the design/exogenous variables to solve the least squares minimization.