| | Name | Description |
|---|
  | SvdT(IEnumerableTupleT, T, Boolean) |
Find the model parameters β such that their linear combination with all predictor-arrays in X become as close to their response in Y as possible, with least squares residuals.
Uses a singular value decomposition and is therefore more numerically stable (especially if ill-conditioned) than the normal equations or QR but also slower.
|
| SvdT(IEnumerableValueTupleT, T, Boolean) |
Find the model parameters β such that their linear combination with all predictor-arrays in X become as close to their response in Y as possible, with least squares residuals.
Uses a singular value decomposition and is therefore more numerically stable (especially if ill-conditioned) than the normal equations or QR but also slower.
|
| SvdT(MatrixT, MatrixT) |
Find the model parameters β such that X*β with predictor X becomes as close to response Y as possible, with least squares residuals.
Uses a singular value decomposition and is therefore more numerically stable (especially if ill-conditioned) than the normal equations or QR but also slower.
|
| SvdT(MatrixT, VectorT) |
Find the model parameters β such that X*β with predictor X becomes as close to response Y as possible, with least squares residuals.
Uses a singular value decomposition and is therefore more numerically stable (especially if ill-conditioned) than the normal equations or QR but also slower.
|
| SvdT(T, T, Boolean) |
Find the model parameters β such that their linear combination with all predictor-arrays in X become as close to their response in Y as possible, with least squares residuals.
Uses a singular value decomposition and is therefore more numerically stable (especially if ill-conditioned) than the normal equations or QR but also slower.
|
Overload List