MultivariateRegression Class

Contains methods common to all multivariate regressions.

Inheritance Hierarchy

SystemObject
  Altaxo.Calc.Regression.MultivariateMultivariateRegression
    Altaxo.Calc.Regression.MultivariatePCRRegression
    Altaxo.Calc.Regression.MultivariatePLS1Regression
    Altaxo.Calc.Regression.MultivariatePLS2Regression

Namespace: Altaxo.Calc.Regression.Multivariate
Assembly: AltaxoCore (in AltaxoCore.dll) Version: 4.8.3448.0 (4.8.3448.0)

Syntax

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public abstract class MultivariateRegression

The MultivariateRegression type exposes the following members.

Constructors

	Name	Description
	MultivariateRegression	Initializes a new instance of the MultivariateRegression class

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Properties

	Name	Description
	CalibrationModel	Gets the calibration model of the analysis.
	InternalCalibrationModel	Gets the calibration model of the analysis.
	NumberOfFactors	Gets the number of factors calculated during the analysis.
	NumberOfSpectralResiduals	Gets the number of spectral residuals. This is normally 1, but for the PLS1 analysis, it is NumberOfY.

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Methods

	Name	Description
	AnalyzeFromPreprocessed	Creates an analysis from preprocessed spectra and preprocessed concentrations.
	AnalyzeFromPreprocessedWithoutReset	Creates an analysis from preprocessed spectra and preprocessed concentrations.
	CrossValidationIteration	Separates the spectra into a group of spectra used for calibration and the remaining spectra used for prediction. This separation is repeated until all spectra are used exactly one time for prediction.
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object)
	GetCrossPRESS	Gets the cross predicted error sum of squares (PRESS) for numberOfFactors = 0..maximalNumberOfFactors.
	GetCrossXResiduals	Calculates the spectral residuals obtained from cross validation.
	GetCrossYPredicted	Calculates the cross-predicted y values.
	GetHashCode	Serves as the default hash function. (Inherited from Object)
	GetPredictionScores	Calculates the prediction scores.
	GetPRESSFromPreprocessed	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.
	GetType	Gets the Type of the current instance. (Inherited from Object)
	GetXLeverageFromPreprocessed	Calculates the spectral leverage from preprocessed spectra.
	GetXLeverageFromRaw	Calculates the spectral leverage from raw spectra.
	InternalGetPredictionScores	Calculates the prediction scores (for use with the preprocessed spectra).
	InternalGetXLeverageFromPreprocessed	Calculates the spectral leverage values from the preprocessed spectra.
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object)
	PostprocessTargetVariablesInline(IMultivariatePreprocessingModel, IMatrixDouble)	This calculates from the predicted (but still centered) y values the raw y values.
	PostprocessTargetVariablesInline(IMatrixDouble, IReadOnlyListDouble, IReadOnlyListDouble)	This calculates from the predicted (but still centered) y values the raw y values.
	PredictedYAndSpectralResidualsFromPreprocessed	Calculates the predicted target variables and the spectral residuals from preprocessed spectra.
	PredictYFromPreprocessed	Predicts target variables from preprocessed spectra.
	PredictYFromRaw	Predicts y values from raw (unpreprocessed) spectra.
	PreprocessForAnalysis	Preprocesses the x and y matrices before usage in multivariate calibrations.
	PreprocessSpectraForAnalysis	Preprocesses the spectra. This is the common first part, without ensemble processing.
	PreprocessSpectraForPrediction(IMultivariateCalibrationModel, Double, IROMatrixDouble, Double, IMatrixDouble)	This will convert the raw spectra (horizontally in matrixX) to preprocessed spectra according to the calibration model.
	PreprocessSpectraForPrediction(ISingleSpectrumPreprocessor, IEnsembleMeanScalePreprocessor, Double, IROMatrixDouble, IReadOnlyListDouble, IReadOnlyListDouble, Double, IMatrixDouble)	Preprocesses the x and y matrices before usage in multivariate calibrations.
	PreprocessTargetVariablesForAnalysis	Preprocesses the x and y matrices before usage in multivariate calibrations.
	PreprocessTargetVariablesForAnalysisInline	Preprocess the y values for analysis (mean center; scaling is currently not used).
	Reset	Resets the regression, so that it appears newly created.
	SetCalibrationModel	Sets the calibration model data of the analysis to the provided data. This can be used to restore previously stored calibration data for use in the prediction functions.
	SpectralResidualsFromPreprocessed(IROMatrixDouble, Int32)	Calculates the spectral residuals from preprocessed spectra.
	SpectralResidualsFromPreprocessed(IROMatrixDouble, Int32, IMatrixDouble)	Calculates the spectral residuals from preprocessed spectra.
	ToString	Returns a string that represents the current object. (Inherited from Object)

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Remarks

Explanation of variables common to all methods:

NumberOfPoints: Number of measurements for calibration. Each measurement is represented by a spectrum X (or a 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 horizontally oriented, i.e. one spectrum is a row in the X matrix.

Y: Matrix of concentrations (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 consist of more than one region, these regions should be preprocessed separately. To designate them, one has to provide an array of ascending integer values. Each element of this array designates the starting index of a spectral region.

Reference

Altaxo.Calc.Regression.Multivariate Namespace