PLS1NipalsAlgorithm Class

Class PLS1NipalsAlgorithm encapsulates the Nonlinear Iterative PArtial Least Squares (NIPALS) algorithm for computing partial least squares regression components.

Inheritance Hierarchy

SystemObject
CenterSpace.NMath.CoreIPLS1Calc
CenterSpace.NMath.CorePLS1NipalsAlgorithm

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

Syntax

C++

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[SerializableAttribute]
public class PLS1NipalsAlgorithm : IPLS1Calc

<SerializableAttribute>
Public Class PLS1NipalsAlgorithm
	Inherits IPLS1Calc

[SerializableAttribute]
public ref class PLS1NipalsAlgorithm : public IPLS1Calc

[<SerializableAttribute>]
type PLS1NipalsAlgorithm = 
    class
        inherit IPLS1Calc
    end

The PLS1NipalsAlgorithm type exposes the following members.

Constructors

	Name	Description
	PLS1NipalsAlgorithm	Constructs an instance of the PLS1NipalsAlgorithm class.

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Properties

	Name	Description
	IsGood	Whether the most recent calculation was successful. (Overrides IPLS1CalcIsGood)
	Loadings	Gets the loadings matrix for PredictorMatrix. The loadings matrix is described in the class summary. (Overrides IPLS1CalcLoadings)
	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 IPLS1CalcMessage)
	PredictorMean	Gets the vector of means for the predictor variables.
	RegressionVector	Gets the vector of regression, r, which can be used for making predictions as follows: Let ybar and xbar be the means of the response and predictor variables, respectively, used to create the model. Then the predicted response, yhat, for a predictor vector, z is given by the formula C# Copy yhat = ybar + (z - xbar)'r
	ResponseMean	Gets the vector of means for the response variables.
	ResponseWeights	Gets the vector of response weights. The ith element of this vector corresponds to the regression coefficient calculated by ordinary linear regression of the response vector on the ith score vector.
	Scores	Gets the scores matrix for PredictorMatrix. The scores matrix is described in the class summary. (Overrides IPLS1CalcScores)
	Weights	Returns the matrix of weights computed by the algorithm.

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Methods

	Name	Description
	Calculate	Calculates a partial least squares from the given data and number of components. (Overrides IPLS1CalcCalculate(DoubleMatrix, DoubleVector, Int32))
	Clone	Creates a deep copy of this PLS1NipalsAlgorithm. (Overrides IPLS1CalcClone)
	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 IPLS1Calc)
	OnDeserialized	Sets most of the attributes only if isGood_
	OnSerializing	Conditionally sets most of the values for serialization only if isGood_
	Predict(DoubleMatrix)	Use the calculated model to predict the response values, ResponseVector, from the given set of predictor variables. (Overrides IPLS1CalcPredict(DoubleMatrix))
	Predict(DoubleVector)	Use the calculated model to predict the response value, y, from the given value for the predictor variable. (Overrides IPLS1CalcPredict(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. (Overrides IPLS1CalcQResiduals)

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Remarks

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

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PredictorMatrix = TP' + Xg

where

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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.

Reference

CenterSpace.NMath.Core Namespace