MultivariateRegression (FsODE)

FsODE

Documentation for 0.0.2

MultivariateRegression Type

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

Contains method common for all multivariate regressions.

Explanation of variables common to all methods:

NumberOfPoints: Number of measurements for calibration. Each measurement is represented by a spectrum X (or set of independent variables) and a set of corresponding concentrations Y (or dependent variables)

NumberOfX: Number of spectral values (or number of independent variables).

NumberOfY: Number of concentrations (or number of dependent variables).

NumberOfFactors: Number of main components used for prediction or calculation.

X: Matrix of spectra( or independent variables). The spectra are horizontal oriented, i.e. one spectra is a row in the X matrix.

Y: Matrix of concentration (or dependent variables). One set of concentrations is also represented by one row in the matrix.

XU: Matrix of unknown spectra (or independent variables) used for prediction of the Y variables.

SpectralRegions: If the spectra consists of more than one regions, these regions should be preprocessed separately. To designate them, one has to provide an array of ascending integer values. Each element of this region designates the starting index of a spectral region.

Constructors

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

Instance members

Instance member	Description
`this.AnalyzeFromPreprocessed` Full Usage: `this.AnalyzeFromPreprocessed` 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.	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.
`this.AnalyzeFromRaw` Full Usage: `this.AnalyzeFromRaw` Parameters: preprocessOptions : `SpectralPreprocessingOptions` - Information how to preprocess the data. spectralRegions : `int[]` - Array of ascending indices representing the starting indices of spectral regions. matrixX : `IMatrix<float>` - Matrix of preprocessed spectra (number of observations, number of wavelengths). matrixY : `IMatrix<float>` - Matrix of preprocessed y values (number of observations, number of y). maxFactors : `int` - Maximum number of factors to calculate.	Creates an analyis from the raw spectra and raw concentrations. preprocessOptions : `SpectralPreprocessingOptions` Information how to preprocess the data. spectralRegions : `int[]` Array of ascending indices representing the starting indices of spectral regions. matrixX : `IMatrix<float>` Matrix of preprocessed spectra (number of observations, number of wavelengths). matrixY : `IMatrix<float>` Matrix of preprocessed y values (number of observations, number of y). maxFactors : `int` Maximum number of factors to calculate.
`this.AnalyzeFromRaw` Full Usage: `this.AnalyzeFromRaw` Parameters: preprocessOptions : `SpectralPreprocessingOptions` - Information how to preprocess the data. xOfX : `IReadOnlyList<float>` - The spectral x variable (e.g. frequencies, wavelength). matrixX : `IMatrix<float>` - Matrix of preprocessed spectra (number of observations, number of wavelengths). matrixY : `IMatrix<float>` - Matrix of preprocessed y values (number of observations, number of y). maxFactors : `int` - Maximum number of factors to calculate.	Creates an analyis from the raw spectra and raw concentrations. preprocessOptions : `SpectralPreprocessingOptions` Information how to preprocess the data. xOfX : `IReadOnlyList<float>` The spectral x variable (e.g. frequencies, wavelength). matrixX : `IMatrix<float>` Matrix of preprocessed spectra (number of observations, number of wavelengths). matrixY : `IMatrix<float>` Matrix of preprocessed y values (number of observations, number of y). maxFactors : `int` Maximum number of factors to calculate.
`this.CalibrationModel` Full Usage: `this.CalibrationModel` Returns: `IMultivariateCalibrationModel` Modifiers: abstract	Returns the calibration model of the analysis. Returns: `IMultivariateCalibrationModel`
`this.GetPRESSFromPreprocessed` Full Usage: `this.GetPRESSFromPreprocessed` Parameters: matrixX : `IROMatrix<float>` - The preprocessed spectra. Returns: `IROVector<float>` The PRESS vector. Modifiers: abstract	Returns the predicted error sum of squares (PRESS) for this analysis. The length of the vector returned is the number of factors in the analysis plus one. matrixX : `IROMatrix<float>` The preprocessed spectra. Returns: `IROVector<float>` The PRESS vector.
`this.GetPredictionScores` Full Usage: `this.GetPredictionScores` Parameters: numberOfFactors : `int` - Number of factors used for calculation. Returns: `IROMatrix<float>` The prediction score matrix. This matrix has the dimensions (NumberOfX, NumberOfY). Modifiers: abstract	Calculates the prediction scores. numberOfFactors : `int` Number of factors used for calculation. Returns: `IROMatrix<float>` The prediction score matrix. This matrix has the dimensions (NumberOfX, NumberOfY).
`this.GetXLeverageFromPreprocessed` Full Usage: `this.GetXLeverageFromPreprocessed` Parameters: matrixX : `IROMatrix<float>` - Matrix of spectra (a spectrum = a row in the matrix). numFactors : `int` - Number of factors used for calculation. Returns: `IROMatrix<float>` Matrix of spectral leverages. Normally, this is a (NumberOfPoints,1) matrix, with exception of PLS1, where it is a (NumberOfPoints,NumberOfY) matrix. Modifiers: abstract	Calculates the spectral leverage from preprocessed spectra. matrixX : `IROMatrix<float>` Matrix of spectra (a spectrum = a row in the matrix). numFactors : `int` Number of factors used for calculation. Returns: `IROMatrix<float>` Matrix of spectral leverages. Normally, this is a (NumberOfPoints,1) matrix, with exception of PLS1, where it is a (NumberOfPoints,NumberOfY) matrix.
`this.GetXLeverageFromRaw` Full Usage: `this.GetXLeverageFromRaw` Parameters: preprocessOptions : `SpectralPreprocessingOptions` - matrixX : `IMatrix<float>` - Matrix of spectra (a spectrum = a row in the matrix). numFactors : `int` - Number of factors used for calculation. Returns: `IROMatrix<float>` Matrix of spectral leverages. Normally, this is a (NumberOfPoints,1) matrix, with exception of PLS1, where it is a (NumberOfPoints,NumberOfY) matrix. Modifiers: abstract	Calculates the spectral leverage from raw spectra. preprocessOptions : `SpectralPreprocessingOptions` matrixX : `IMatrix<float>` Matrix of spectra (a spectrum = a row in the matrix). numFactors : `int` Number of factors used for calculation. Returns: `IROMatrix<float>` Matrix of spectral leverages. Normally, this is a (NumberOfPoints,1) matrix, with exception of PLS1, where it is a (NumberOfPoints,NumberOfY) matrix.
`this.GetXLeverageFromRaw` Full Usage: `this.GetXLeverageFromRaw` Parameters: matrixX : `IMatrix<float>` - Matrix of spectra (a spectrum = a row in the matrix). numFactors : `int` - Number of factors used for calculation. Returns: `IROMatrix<float>` Matrix of spectral leverages. Normally, this is a (NumberOfPoints,1) matrix, with exception of PLS1, where it is a (NumberOfPoints,NumberOfY) matrix. Modifiers: abstract	Calculates the spectral leverage from raw spectra. matrixX : `IMatrix<float>` Matrix of spectra (a spectrum = a row in the matrix). numFactors : `int` Number of factors used for calculation. Returns: `IROMatrix<float>` Matrix of spectral leverages. Normally, this is a (NumberOfPoints,1) matrix, with exception of PLS1, where it is a (NumberOfPoints,NumberOfY) matrix.
`this.InternalCalibrationModel` Full Usage: `this.InternalCalibrationModel` Returns: `MultivariateCalibrationModel` Modifiers: abstract	Returns the calibration model of the analysis. Returns: `MultivariateCalibrationModel`
`this.NumberOfFactors` Full Usage: `this.NumberOfFactors` Returns: `int`	Returns the number of factors calculated during the analysis. Returns: `int`
`this.NumberOfSpectralResiduals` Full Usage: `this.NumberOfSpectralResiduals` Returns: `int` Modifiers: abstract	This returns the number of spectral residuals. This is normally 1, but for the PLS1 analyis, it is the NumberOfY. Returns: `int`
`this.PredictYFromPreprocessed` Full Usage: `this.PredictYFromPreprocessed` 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). 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).
`this.PredictYFromPreprocessed` Full Usage: `this.PredictYFromPreprocessed` Parameters: XU : `IROMatrix<float>` - Matrix of unknown spectra (preprocessed the same way as the calibration spectra). numFactors : `int` - Number of factors used for prediction. Returns: `IROMatrix<float>` The predicted y values. (They are centered). 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. Returns: `IROMatrix<float>` The predicted y values. (They are centered).
`this.PredictYFromRaw` Full Usage: `this.PredictYFromRaw` Parameters: XU : `IMatrix<float>` - Matrix of spectra used for prediction (number of spectra, number of wavelengths). numFactors : `int` - Number of factors used for prediction. predictedY : `IMatrix<float>` - In return, holds the predicted y values. You have to provide a matrix of dimensions (number of spectra, number of y).	Predicts y values from raw (unpreprocessed) spectra. XU : `IMatrix<float>` Matrix of spectra used for prediction (number of spectra, number of wavelengths). numFactors : `int` Number of factors used for prediction. predictedY : `IMatrix<float>` In return, holds the predicted y values. You have to provide a matrix of dimensions (number of spectra, number of y).
`this.PredictedYAndSpectralResidualsFromPreprocessed` Full Usage: `this.PredictedYAndSpectralResidualsFromPreprocessed` Parameters: XU : `IROMatrix<float>` - Spectra (horizontally oriented). numFactors : `int` - Number of factors used for calculation. 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 calculates the spectral residuals. XU : `IROMatrix<float>` Spectra (horizontally oriented). numFactors : `int` Number of factors used for calculation. predictedY : `IMatrix<float>` On return, holds the predicted y values. (They are centered). spectralResiduals : `IMatrix<float>` On return, holds the spectral residual values.
`this.PredictedYAndSpectralResidualsFromPreprocessed` Full Usage: `this.PredictedYAndSpectralResidualsFromPreprocessed` Parameters: XU : `IROMatrix<float>` - Spectra (horizontally oriented). numFactors : `int` - Number of factors used for calculation. predictedY : `byref<IMatrix<float>>` - On return, holds the predicted y values. (They are centered). If the matrix you provide has not the appropriate dimensions, it is reallocated. spectralResiduals : `byref<IMatrix<float>>` - On return, holds the spectral residual values. If the matrix you provide has not the appropriate dimensions, it is reallocated. Modifiers: abstract	This calculates the spectral residuals. The matrices are reallocated if they don't have the appropriate dimensions. XU : `IROMatrix<float>` Spectra (horizontally oriented). numFactors : `int` Number of factors used for calculation. predictedY : `byref<IMatrix<float>>` On return, holds the predicted y values. (They are centered). If the matrix you provide has not the appropriate dimensions, it is reallocated. spectralResiduals : `byref<IMatrix<float>>` On return, holds the spectral residual values. If the matrix you provide has not the appropriate dimensions, it is reallocated.
`this.PredictedYAndSpectralResidualsFromPreprocessed` Full Usage: `this.PredictedYAndSpectralResidualsFromPreprocessed` Parameters: XU : `IROMatrix<float>` - Spectra (horizontally oriented). numFactors : `int` - Number of factors used for calculation. calculatePredictedY : `bool` - If true, the predictedY is calculated. Otherwise, predictedY is null on return. predictedY : `byref<IMatrix<float>>` - On return, holds the predicted y values. (They are centered). If the matrix you provide has not the appropriate dimensions, it is reallocated. calculateSpectralResiduals : `bool` - If true, the spectral residuals are calculated. Otherwise spectralResiduals is null on return. spectralResiduals : `byref<IMatrix<float>>` - On return, holds the spectral residual values. If the matrix you provide has not the appropriate dimensions, it is reallocated. Modifiers: abstract	This calculates the spectral residuals. The matrices are reallocated if they don't have the appropriate dimensions. XU : `IROMatrix<float>` Spectra (horizontally oriented). numFactors : `int` Number of factors used for calculation. calculatePredictedY : `bool` If true, the predictedY is calculated. Otherwise, predictedY is null on return. predictedY : `byref<IMatrix<float>>` On return, holds the predicted y values. (They are centered). If the matrix you provide has not the appropriate dimensions, it is reallocated. calculateSpectralResiduals : `bool` If true, the spectral residuals are calculated. Otherwise spectralResiduals is null on return. spectralResiduals : `byref<IMatrix<float>>` On return, holds the spectral residual values. If the matrix you provide has not the appropriate dimensions, it is reallocated.
`this.Reset` Full Usage: `this.Reset` Modifiers: abstract	Resets the regression, so that it appears like newly created.
`this.SetCalibrationModel` Full Usage: `this.SetCalibrationModel` Parameters: calib : `IMultivariateCalibrationModel` - The calibration set for use in the analysis. The provided calibration model have to correspond to the type of analysis. Modifiers: abstract	This sets the calibration model data of the analysis to the provided data. This can be used to set back previously stored calibration data for use in the prediction functions. calib : `IMultivariateCalibrationModel` The calibration set for use in the analysis. The provided calibration model have to correspond to the type of analysis.
`this.SpectralResidualsFromPreprocessed` Full Usage: `this.SpectralResidualsFromPreprocessed` Parameters: XU : `IROMatrix<float>` - Spectra (horizontally oriented). numFactors : `int` - Number of factors used for calculation. spectralResiduals : `IMatrix<float>` - On return, holds the spectral residual values. Modifiers: abstract	This calculates the spectral residuals. XU : `IROMatrix<float>` Spectra (horizontally oriented). numFactors : `int` Number of factors used for calculation. spectralResiduals : `IMatrix<float>` On return, holds the spectral residual values.
`this.SpectralResidualsFromPreprocessed` Full Usage: `this.SpectralResidualsFromPreprocessed` Parameters: XU : `IROMatrix<float>` - Spectra (horizontally oriented). numFactors : `int` - Number of factors used for calculation. Returns: `IROMatrix<float>` The calculated spectral residuals. Modifiers: abstract	This calculates the spectral residuals. XU : `IROMatrix<float>` Spectra (horizontally oriented). numFactors : `int` Number of factors used for calculation. Returns: `IROMatrix<float>` The calculated spectral residuals.

Static members