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MultipleRegression Class
Inheritance Hierarchy
SystemObject
  MathNet.Numerics.LinearRegressionMultipleRegression

Namespace: MathNet.Numerics.LinearRegression
Assembly: MathNet.Numerics (in MathNet.Numerics.dll) Version: 3.7
Syntax
C#
public static class MultipleRegression
Methods
  NameDescription
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(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(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(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(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(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(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(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(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(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(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(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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