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SparsePLSDACrossValidation Class

Class SparsePLSDACrossValidation performs an evaluation of a PLS (Partial Least Squares) model.
Inheritance Hierarchy
SystemObject
  CenterSpace.NMath.CoreSparsePLSDACrossValidation

Namespace:  CenterSpace.NMath.Core
Assembly:  NMath (in NMath.dll) Version: 7.3
Syntax
[SerializableAttribute]
public class SparsePLSDACrossValidation : ICloneable

The SparsePLSDACrossValidation type exposes the following members.

Constructors
  NameDescription
Public methodSparsePLSDACrossValidation
Default constructor. Constructs a SparsePLSDACrossValidation instance that uses the "leave one out" cross validation. LeaveOneOutSubsets
Public methodSparsePLSDACrossValidation(ICrossValidationSubsets)
Constructs a SparsePLSDACrossValidation instance which uses the given subset generator and the Nipals algorithm.
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Properties
  NameDescription
Public propertyAverageMeanSqrError
Gets the average of the mean square errors for each training/testing subsets pair.
Public propertyCalculator
Gets and sets the SparsePLS calculator to use for the sparse PLS calculations.
Public propertyIsGood
Whether all the PLS2 calculations were successful.
Public propertyMessage
Gets any message that may have been generated by the computation. For example, if the calculation is unsuccessful, the message indicates the reason.
Public propertyResults
Gets the results of the cross validation for each training/testing subsets pair.
Public propertySubsetGenerator
Gets and sets the subset generator to use to generate testing and training subsets.
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Methods
  NameDescription
Public methodClone
Creates a deep copy of this SparsePLSDACrossValidation.
Public methodDoCrossValidation
Perform cross validation on the given data using the existing calculator and subset generator.
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Remarks
Evaluation consists of dividing the data into two subsets - a training subset and a testing subset. A PLS calculation is performed on the the training subset and the resulting model is used to predict the values of the dependent variables in the testing set. The mean square error between the actual and predicted dependent values is then calculated. Usually, the data is divided up into several training and testing subsets and calculations are done on each of these. In this case the average mean square error over each PLS calculation is reported (the individual mean square errors are available as well).

The subsets to use in the cross validation are specifed by providing an implementation of the ICrossValidationSubsets interface. Classes that implement this interface generate training and testing subsets from PLS data.

See Also