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MultipleRegression Class

[Missing <summary> documentation for "T:Altaxo.Calc.LinearRegression.MultipleRegression"]

Inheritance Hierarchy
SystemObject
  Altaxo.Calc.LinearRegressionMultipleRegression

Namespace: Altaxo.Calc.LinearRegression
Assembly: AltaxoCore (in AltaxoCore.dll) Version: 4.8.3179.0 (4.8.3179.0)
Syntax
C#
public static class MultipleRegression

The MultipleRegression type exposes the following members.

Methods
 NameDescription
Public methodStatic memberDirectMethodT(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.
Public methodStatic memberDirectMethodT(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.
Public methodStatic memberDirectMethodT(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.
Public methodStatic memberDirectMethodT(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.
Public methodStatic memberNormalEquationsT(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.
Public methodStatic memberNormalEquationsT(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.
Public methodStatic memberNormalEquationsT(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.
Public methodStatic memberNormalEquationsT(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.
Public methodStatic memberNormalEquationsT(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.
Public methodStatic memberQRT(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.
Public methodStatic memberQRT(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.
Public methodStatic memberQRT(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.
Public methodStatic memberQRT(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.
Public methodStatic memberQRT(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.
Public methodStatic memberSvdT(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.
Public methodStatic memberSvdT(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.
Public methodStatic memberSvdT(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.
Public methodStatic memberSvdT(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.
Public methodStatic memberSvdT(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.
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