| | Name | Description |
|---|
  | 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.
|
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