Multiple |
[Missing <summary> documentation for "T:Altaxo.Calc.LinearRegression.MultipleRegression"]
public static class MultipleRegression
The MultipleRegression type exposes the following members.
Name | Description | |
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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. |