Class PLS2SimplsAlgorithm encapsulates the Straightforward IMplementation of Partial Least Squares, or SIMPLS, algorithm (de Jong, 1993) for computing partial least squares regression components.

Namespace:  CenterSpace.NMath.Stats
Assembly:  NMathStats (in NMathStats.dll) Version: 3.4.0.0

Syntax

C#
[SerializableAttribute]
public class PLS2SimplsAlgorithm : IPLS2Calc
Visual Basic (Declaration)
<SerializableAttribute> _
Public Class PLS2SimplsAlgorithm _
	Inherits IPLS2Calc
Visual C++
[SerializableAttribute]
public ref class PLS2SimplsAlgorithm : public IPLS2Calc

Remarks

The SIMPLS is used to find a predictive linear model of the form Y = XB, where Y is a matrix of response variables, X is a matrix of predictor variables, and B is the matrix of regression coefficients. This is achieved by compressing the predictor data matrix
CopyC#
X = [x1, x2,..., xp]
 that contains the p values of n samples, into a set of c latent variable or factor scores
CopyC#
T = [t1, t2,...,tc]

These factor scores are then used to fit a set of n observations to m response variables

CopyC#
Y = [y1, y2,...,ym]

The relationship between the X and T is T = XR, where R is the matrix of predictor weights. Factor scores U and weights Q for the response variable Y are also computed and satisfy U = Y0Q, where Y0 is the matrix of centered response data.

The algorithm requires the computation of a dominant eigenvector at each iteration. The iterative Power Method is used to calculate this eigenvector and the maximum number of iterations and convergance tolerance may be specified for instances of this class through either through the contructor or properties.

Inheritance Hierarchy

System..::.Object
  CenterSpace.NMath.Stats..::.IPLS2Calc
    CenterSpace.NMath.Stats..::.PLS2SimplsAlgorithm

See Also