statsmodels.regression.linear_model.GLSAR.fit¶
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GLSAR.
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.
**kwargs
Additional keyword arguments that contain information used when constructing a model using the formula interface.
Returns: RegressionResults
The model estimation results.
See also
RegressionResults
- The results container.
RegressionResults.get_robustcov_results
- A method to change the covariance estimator used when fitting the model.
Notes
The fit method uses the pseudoinverse of the design/exogenous variables to solve the least squares minimization.