PLS2Regression (FsODE)

FsODE

Documentation for 0.0.2

PLS2Regression Type

Namespace: Altaxo.Calc.Regression.Multivariate
Assembly: AltaxoCore.Redist.dll
Base Type: MultivariateRegression

PLSRegression contains static methods for doing partial least squares regression analysis and prediction of the data.

Constructors

Constructor	Description
`PLS2Regression()` Full Usage: `PLS2Regression()`

Instance members

Instance member	Description
`this.CalibrationModel` Full Usage: `this.CalibrationModel` Returns: `IPLS2CalibrationModel`	Returns: `IPLS2CalibrationModel`
`this.GetPRESSFromPreprocessed` Full Usage: `this.GetPRESSFromPreprocessed` Parameters: matrixX : `IROMatrix<float>` Returns: `IROVector<float>` Modifiers: abstract	matrixX : `IROMatrix<float>` Returns: `IROVector<float>`
`this.InternalCalibrationModel` Full Usage: `this.InternalCalibrationModel` Returns: `MultivariateCalibrationModel` Modifiers: abstract	Returns: `MultivariateCalibrationModel`
`this.PRESS` Full Usage: `this.PRESS` Returns: `IROVector<float>`	Returns: `IROVector<float>`
`this.PredictedYAndSpectralResidualsFromPreprocessed` Full Usage: `this.PredictedYAndSpectralResidualsFromPreprocessed` Parameters: XU : `IROMatrix<float>` - Matrix of unknown spectra (preprocessed the same way as the calibration spectra). numFactors : `int` - Number of factors used for prediction. predictedY : `IMatrix<float>` - On return, holds the predicted y values. (They are centered). spectralResiduals : `IMatrix<float>` - On return, holds the spectral residual values. Modifiers: abstract	This predicts concentrations of unknown spectra. XU : `IROMatrix<float>` Matrix of unknown spectra (preprocessed the same way as the calibration spectra). numFactors : `int` Number of factors used for prediction. predictedY : `IMatrix<float>` On return, holds the predicted y values. (They are centered). spectralResiduals : `IMatrix<float>` On return, holds the spectral residual values.
`this.Reset` Full Usage: `this.Reset` Modifiers: abstract
`this.SetCalibrationModel` Full Usage: `this.SetCalibrationModel` Parameters: calib : `IMultivariateCalibrationModel` Modifiers: abstract	calib : `IMultivariateCalibrationModel`

Static members

Static member	Description
`PLS2Regression.CalculateXLeverageFromPreprocessed(matrixX, W, numFactors, leverage, leverageColumn)` Full Usage: `PLS2Regression.CalculateXLeverageFromPreprocessed(matrixX, W, numFactors, leverage, leverageColumn)` Parameters: matrixX : `IROMatrix<float>` W : `IROMatrix<float>` numFactors : `int` leverage : `IMatrix<float>` leverageColumn : `int`	matrixX : `IROMatrix<float>` W : `IROMatrix<float>` numFactors : `int` leverage : `IMatrix<float>` leverageColumn : `int`
`PLS2Regression.CreateFromPreprocessed(matrixX, matrixY, maxFactors)` Full Usage: `PLS2Regression.CreateFromPreprocessed(matrixX, matrixY, maxFactors)` Parameters: matrixX : `IROMatrix<float>` - The spectral matrix (each spectrum is a row in the matrix). They must at least be centered. matrixY : `IROMatrix<float>` - The matrix of concentrations (each experiment is a row in the matrix). They must at least be centered. maxFactors : `int` - Maximum number of factors for analysis. Returns: `PLS2Regression` A regression object, which holds all the loads and weights neccessary for further calculations.	Creates an analyis from preprocessed spectra and preprocessed concentrations. matrixX : `IROMatrix<float>` The spectral matrix (each spectrum is a row in the matrix). They must at least be centered. matrixY : `IROMatrix<float>` The matrix of concentrations (each experiment is a row in the matrix). They must at least be centered. maxFactors : `int` Maximum number of factors for analysis. Returns: `PLS2Regression` A regression object, which holds all the loads and weights neccessary for further calculations.
`PLS2Regression.ExecuteAnalysis(_X, _Y, numFactors, xLoads, yLoads, W, V, PRESS)` Full Usage: `PLS2Regression.ExecuteAnalysis(_X, _Y, numFactors, xLoads, yLoads, W, V, PRESS)` Parameters: _X : `IROMatrix<float>` - The X ("spectrum") matrix, centered and preprocessed. _Y : `IROMatrix<float>` - The Y ("concentration") matrix (centered). numFactors : `byref<int>` - Number of factors to calculate. xLoads : `IBottomExtensibleMatrix<float>` - Returns the matrix of eigenvectors of X. Should be initially empty. yLoads : `IBottomExtensibleMatrix<float>` - Returns the matrix of eigenvectors of Y. Should be initially empty. W : `IBottomExtensibleMatrix<float>` - Returns the matrix of weighting values. Should be initially empty. V : `IRightExtensibleMatrix<float>` - Returns the vector of cross products. Should be initially empty. PRESS : `IExtensibleVector<float>` - If not null, the PRESS value of each factor is stored (vertically) here.	Partial least squares (PLS) decomposition of the matrizes X and Y. _X : `IROMatrix<float>` The X ("spectrum") matrix, centered and preprocessed. _Y : `IROMatrix<float>` The Y ("concentration") matrix (centered). numFactors : `byref<int>` Number of factors to calculate. xLoads : `IBottomExtensibleMatrix<float>` Returns the matrix of eigenvectors of X. Should be initially empty. yLoads : `IBottomExtensibleMatrix<float>` Returns the matrix of eigenvectors of Y. Should be initially empty. W : `IBottomExtensibleMatrix<float>` Returns the matrix of weighting values. Should be initially empty. V : `IRightExtensibleMatrix<float>` Returns the vector of cross products. Should be initially empty. PRESS : `IExtensibleVector<float>` If not null, the PRESS value of each factor is stored (vertically) here.
`PLS2Regression.GetPredictionScoreMatrix(xLoads, yLoads, W, V, numFactors, predictionScores)` Full Usage: `PLS2Regression.GetPredictionScoreMatrix(xLoads, yLoads, W, V, numFactors, predictionScores)` Parameters: xLoads : `IROMatrix<float>` - Matrix of spectral loads [factors,spectral bins]. yLoads : `IROMatrix<float>` - Matrix of concentration loads[factors, number of concentrations]. W : `IROMatrix<float>` - Matrix of spectral weightings [factors,spectral bins]. V : `IROMatrix<float>` - Cross product matrix[1,factors]. numFactors : `int` - Number of factors to use to calculate the score matrix. predictionScores : `IMatrix<float>` - Output: the resulting score matrix[ spectral bins, numberOfConcentrations]	Get the prediction score matrix. To get the predictions, you have to multiply the spectras to predict by this prediction score matrix (in case of a single y-variable), this is simply the dot product. xLoads : `IROMatrix<float>` Matrix of spectral loads [factors,spectral bins]. yLoads : `IROMatrix<float>` Matrix of concentration loads[factors, number of concentrations]. W : `IROMatrix<float>` Matrix of spectral weightings [factors,spectral bins]. V : `IROMatrix<float>` Cross product matrix[1,factors]. numFactors : `int` Number of factors to use to calculate the score matrix. predictionScores : `IMatrix<float>` Output: the resulting score matrix[ spectral bins, numberOfConcentrations]
`PLS2Regression.Predict(XU, xLoads, yLoads, W, V, numFactors, predictedY, spectralResiduals)` Full Usage: `PLS2Regression.Predict(XU, xLoads, yLoads, W, V, numFactors, predictedY, spectralResiduals)` Parameters: XU : `IROMatrix<float>` xLoads : `IROMatrix<float>` yLoads : `IROMatrix<float>` W : `IROMatrix<float>` V : `IROMatrix<float>` numFactors : `int` predictedY : `IMatrix<float>` spectralResiduals : `IMatrix<float>`	XU : `IROMatrix<float>` xLoads : `IROMatrix<float>` yLoads : `IROMatrix<float>` W : `IROMatrix<float>` V : `IROMatrix<float>` numFactors : `int` predictedY : `IMatrix<float>` spectralResiduals : `IMatrix<float>`