| | Name | Description |
|---|
  | DirectMethodT(IEnumerableTupleT, T, Boolean, DirectRegressionMethod) |
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 the cholesky decomposition of the normal equations.
|
| DirectMethodT(MatrixT, MatrixT, DirectRegressionMethod) |
Find the model parameters β such that X*β with predictor X becomes as close to response Y as possible, with least squares residuals.
|
| DirectMethodT(MatrixT, VectorT, DirectRegressionMethod) |
Find the model parameters β such that X*β with predictor X becomes as close to response Y as possible, with least squares residuals.
|
| DirectMethodT(T, T, Boolean, DirectRegressionMethod) |
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.
|
| NormalEquationsT(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 the cholesky decomposition of the normal equations.
|
| NormalEquationsT(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 the cholesky decomposition of the normal equations.
|
| NormalEquationsT(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 the cholesky decomposition of the normal equations.
|
| NormalEquationsT(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 the cholesky decomposition of the normal equations.
|
| NormalEquationsT(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 the cholesky decomposition of the normal equations.
|
| QRT(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 an orthogonal decomposition and is therefore more numerically stable than the normal equations but also slower.
|
| QRT(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 an orthogonal decomposition and is therefore more numerically stable than the normal equations but also slower.
|
| QRT(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 an orthogonal decomposition and is therefore more numerically stable than the normal equations but also slower.
|
| QRT(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 an orthogonal decomposition and is therefore more numerically stable than the normal equations but also slower.
|
| QRT(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 an orthogonal decomposition and is therefore more numerically stable than the normal equations but also slower.
|
| 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.
|
Methods