statsmodels.regression.linear_model.WLS.fit

WLS.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:
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.


Last update: Jun 14, 2024