glidertools.calibration.robust_linear_fit¶
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glidertools.calibration.
robust_linear_fit
(gld_var, gld_var_cal, interpolate_limit=3, return_figures=True, **kwargs)¶ Perform a robust linear regression using a Huber Loss Function to remove outliers. Returns a model object that behaves like a scikit-learn model object with a model.predict method.
Parameters: - gld_var (np.array, shape=[n, ]) – glider variable
- gld_var_cal (np.array, shape=[n, ]) – bottle variable on glider indicies
- fit_intercept (bool, default=False) – forces 0 intercept if False
- return_figures (bool, default=True) – create figure with metrics
- interpolate_limit (int, default=3) – glider data may have missing points. The glider data is thus interpolated to ensure that as many bottle samples as possible have a match-up with the glider.
- **kwargs (keyword=value pairs) – will be passed to the Huber Loss regression to adjust regression
Returns: model – A fitted model. Use model.predict(glider_var) to create the calibrated output.
Return type: sklearn.linear_model