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PLS2NipalsAlgorithm Class

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

Namespace:  CenterSpace.NMath.Core
Assembly:  NMath (in NMath.dll) Version: 7.4
public class PLS2NipalsAlgorithm : IPLS2Calc

The PLS2NipalsAlgorithm type exposes the following members.

Public methodPLS2NipalsAlgorithm
Constructs a PLS2NipalsAlgorithm instance;
Public propertyCoefficients
Gets the regression coefficients matrix, B, for the PLS2 calculation. B satisifies the relationship
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.)
Public propertyIsGood
Whether the most recent calculation was successful.
(Overrides IPLS2CalcIsGood.)
Public propertyMaxIterations
Gets or sets the maximum number of iterations.
Public propertyMessage
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.)
Public propertyPredictorLoadings
Gets the loadings matrix for PredictorMatrix.
(Overrides IPLS2CalcPredictorLoadings.)
Public propertyPredictorMean
Gets the vector of means for the predictor variables.
Public propertyPredictorResiduals
Gets the predictor residuals matrix.
Public propertyPredictorScores
Gets the scores matrix for PredictorMatrix.
(Overrides IPLS2CalcPredictorScores.)
Public propertyPredictorWeights
Gets the matrix of weights for the predictors.
Public propertyResponseLoadings
Gets the response loadings matrix.
Public propertyResponseMean
Gets the vector of means for the response variables.
Public propertyResponseResiduals
Gets the response residuals matrix.
Public propertyResponseScores
Gets the response scores matrix.
Public propertyResponseWeights
Gets the matrix of weights for the responses.
Public methodCalculate
Calculates the PLS2 for the given predictor and response matrices and the given number of components.
(Overrides IPLS2CalcCalculate(DoubleMatrix, DoubleMatrix, Int32).)
Public methodClone
Creates a deep copy of this PLS2NipalsAlgorithm.
(Overrides IPLS2CalcClone.)
Public methodHotellingsT2
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.)
Public methodPredict(DoubleMatrix)
Predicts the responses for a set of predictor values.
(Overrides IPLS2CalcPredict(DoubleMatrix).)
Public methodPredict(DoubleVector)
Predicts the response for the given predictor value.
(Overrides IPLS2CalcPredict(DoubleVector).)
Public methodQResiduals
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.)
During the calculation the following model for PredictorMatrix (independent variable values) is is formed:
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:
ResponseMatrix = UQ' + Yg
U is the scores matrix for ResponseMatrix, Q the loading matrix for ResponseMatrix, and Yg is the residual matrix for ResponseMatrix.
See Also