threshold for the feature distance. If the mahalanobis distance of a candidate point's features to the corresponding profile's mean is larger than this value, then that candidate point will be ignored during fitting.
threshold for point distance: If the mahalanobis distance of a candidate point to its corresponding marginal distribution is larger than this value, then that candidate point will be ignored during fitting.
bounds to apply on the model coefficients. In other words, by setting this to n, all coefficients of the fitting result will be restricted to the interval [-n, n].
threshold for the feature distance.
threshold for the feature distance. If the mahalanobis distance of a candidate point's features to the corresponding profile's mean is larger than this value, then that candidate point will be ignored during fitting.
bounds to apply on the model coefficients.
bounds to apply on the model coefficients. In other words, by setting this to n, all coefficients of the fitting result will be restricted to the interval [-n, n].
threshold for point distance: If the mahalanobis distance of a candidate point to its corresponding marginal distribution is larger than this value, then that candidate point will be ignored during fitting.
Fitting Configuration, specifying thresholds and bounds.
threshold for the feature distance. If the mahalanobis distance of a candidate point's features to the corresponding profile's mean is larger than this value, then that candidate point will be ignored during fitting.
threshold for point distance: If the mahalanobis distance of a candidate point to its corresponding marginal distribution is larger than this value, then that candidate point will be ignored during fitting.
bounds to apply on the model coefficients. In other words, by setting this to n, all coefficients of the fitting result will be restricted to the interval [-n, n].