PLS2NipalsAlgorithm Class

Class PLS2NipalsAlgorithm encapsulates the Nonlinear Iterative PArtial Least Squares (NIPALS) algorithm for computing partial least squares regression components.

Inheritance Hierarchy

SystemObject
CenterSpace.NMath.CoreIPLS2Calc
CenterSpace.NMath.CorePLS2NipalsAlgorithm

Namespace: CenterSpace.NMath.Core
Assembly: NMath (in NMath.dll) Version: 7.4

Syntax

C++

Copy

[SerializableAttribute]
public class PLS2NipalsAlgorithm : IPLS2Calc

<SerializableAttribute>
Public Class PLS2NipalsAlgorithm
	Inherits IPLS2Calc

[SerializableAttribute]
public ref class PLS2NipalsAlgorithm : public IPLS2Calc

[<SerializableAttribute>]
type PLS2NipalsAlgorithm = 
    class
        inherit IPLS2Calc
    end

The PLS2NipalsAlgorithm type exposes the following members.

Constructors

	Name	Description
	PLS2NipalsAlgorithm	Constructs a PLS2NipalsAlgorithm instance;

Top

Properties

	Name	Description
	Coefficients	Gets the regression coefficients matrix, B, for the PLS2 calculation. B satisifies the relationship C# Copy ResponseVector = XB + E. where X and ResponseVector are respectively, the centered independent and dependent variables values, and EE is a noise term for the model. (Overrides IPLS2CalcCoefficients)
	IsGood	Whether the most recent calculation was successful. (Overrides IPLS2CalcIsGood)
	MaxIterations	Gets or sets the maximum number of iterations.
	Message	Gets any message that may have been generated by the algorithm. For example, if the calculation is unsuccessful, the message indicates the reason. (Overrides IPLS2CalcMessage)
	PredictorLoadings	Gets the loadings matrix for PredictorMatrix. (Overrides IPLS2CalcPredictorLoadings)
	PredictorMean	Gets the vector of means for the predictor variables.
	PredictorResiduals	Gets the predictor residuals matrix.
	PredictorScores	Gets the scores matrix for PredictorMatrix. (Overrides IPLS2CalcPredictorScores)
	PredictorWeights	Gets the matrix of weights for the predictors.
	ResponseLoadings	Gets the response loadings matrix.
	ResponseMean	Gets the vector of means for the response variables.
	ResponseResiduals	Gets the response residuals matrix.
	ResponseScores	Gets the response scores matrix.
	ResponseWeights	Gets the matrix of weights for the responses.

Top

Methods

	Name	Description
	Calculate	Calculates the PLS2 for the given predictor and response matrices and the given number of components. (Overrides IPLS2CalcCalculate(DoubleMatrix, DoubleMatrix, Int32))
	Clone	Creates a deep copy of this PLS2NipalsAlgorithm. (Overrides IPLS2CalcClone)
	HotellingsT2	Calculaties Hotelling's T2 statistic for each sample. T2 can be viewed as the squared distance from a samples projection into the subspace to the centroid of the subspace, or, more simply, the variation of the sample point within the model. (Inherited from IPLS2Calc)
	Predict(DoubleMatrix)	Predicts the responses for a set of predictor values. (Overrides IPLS2CalcPredict(DoubleMatrix))
	Predict(DoubleVector)	Predicts the response for the given predictor value. (Overrides IPLS2CalcPredict(DoubleVector))
	QResiduals	Calculates the Q residuals for in sample in the model. The Q residual for a given sample is the distance between the sample and its projection in the subspace of the model. (Inherited from IPLS2Calc)

Top

Remarks

During the calculation the following model for PredictorMatrix (independent variable values) is is formed:

Copy

PredictorMatrix = TP' + Xg

where g is the number of components specified for the model. T is called the scores matrix (the columns of T are the scores), and P is called the loadings matrix. The matrix Xg is called the residual matrix for PredictorMatrix. A corresponding model for ResponseMatrix (dependent variable values) is formed:

Copy

ResponseMatrix = UQ' + Yg

U is the scores matrix for ResponseMatrix, Q the loading matrix for ResponseMatrix, and Yg is the residual matrix for ResponseMatrix.

Reference

CenterSpace.NMath.Core Namespace