 | MultipleRegression Class |
Inheritance HierarchyNamespace: MathNet.Numerics.LinearRegression
Assembly: MathNet.Numerics (in MathNet.Numerics.dll) Version: 3.7
SyntaxC#
public static class MultipleRegression
Methods| Name | Description | |
|---|---|---|
  | 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(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(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(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(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(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(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(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(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(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(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(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.
|
See Also