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:

methodstr, optional: Can be “pinv”, “qr”. “pinv” uses the Moore-Penrose pseudoinverse to solve the least squares problem. “qr” uses the QR factorization.
cov_typestr, optional: See regression.linear_model.RegressionResults for a description of the available covariance estimators.
cov_kwdslist or None, optional: See linear_model.RegressionResults.get_robustcov_results for a description required keywords for alternative covariance estimators.
use_tbool, 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.
**kwargs: Additional keyword arguments that contain information used when constructing a model using the formula interface.

Returns: