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PLS2CrossValidationWithJackknife Class

Class PLS2CrossValidationWithJackknife performs an evaluation of a PLS (Partial Least Squares) model with model coefficient variance estimates and confidence intervals.
Inheritance Hierarchy
SystemObject
  CenterSpace.NMath.CorePLS2CrossValidationWithJackknife

Namespace:  CenterSpace.NMath.Core
Assembly:  NMath (in NMath.dll) Version: 7.4
Syntax
[SerializableAttribute]
public class PLS2CrossValidationWithJackknife : ICloneable

The PLS2CrossValidationWithJackknife type exposes the following members.

Constructors
  NameDescription
Public methodPLS2CrossValidationWithJackknife
Default constructor. Constructs a PLS2CrossValidationWithJackknife instance that uses the "leave one out" cross validation and the Nipals algorithm. No scaling will be done and full model coefficients will be used in the jackknife coefficient variance estimate computation. LeaveOneOutSubsets
Public methodPLS2CrossValidationWithJackknife(Boolean)
Constructs a PLS2CrossValidationWithJackknife instance that uses the "leave one out" cross validation and the Nipals algorithm. full model coefficients will be used in the jackknife coefficient variance estimate computation. If true, the learning X data for each subset is scaled by dividing each variable by its sample standard deviation. The prediction data is scaled by the same amount. Note that this will impact performance.LeaveOneOutSubsets
Public methodPLS2CrossValidationWithJackknife(ICrossValidationSubsets)
Constructs a PLS2CrossValidationWithJackknife instance which uses the given subset generator and the Nipals algorithm.
Public methodPLS2CrossValidationWithJackknife(ICrossValidationSubsets, Boolean)
Constructs a PLS2CrossValidationWithJackknife instance which uses the given subset generator and the Nipals algorithm. No scaling and full model coefficients will be used in the jackknife coefficient variance estimate computation. No scaling will be done and full model coefficients will be used in the jackknife coefficient variance estimate computation.
Public methodPLS2CrossValidationWithJackknife(IPLS2Calc, ICrossValidationSubsets)
Constructs a PLS2CrossValidationWithJackknife instance which uses the given PLS calculator and subset generator. No scaling will be done and full model coefficients will be used in the jackknife coefficient variance estimate computation.
Public methodPLS2CrossValidationWithJackknife(ICrossValidationSubsets, Boolean, Boolean)
Constructs a PLS2CrossValidationWithJackknife instance which uses the given subset generator and the Nipals algorithm. No scaling and full model coefficients will be used in the jackknife coefficient variance estimate computation. No scaling will be done and full model coefficients will be used in the jackknife coefficient variance estimate computation.
Public methodPLS2CrossValidationWithJackknife(IPLS2Calc, ICrossValidationSubsets, Boolean)
Constructs a PLS2CrossValidationWithJackknife instance which uses the given PLS calculator and subset generator.
Public methodPLS2CrossValidationWithJackknife(IPLS2Calc, ICrossValidationSubsets, Boolean, Boolean)
Constructs a PLS2CrossValidationWithJackknife instance which uses the given PLS calculator and subset generator.
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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 PLS2 calculator to use for PLS2 calculations.
Public propertyCoefficients
Gets the coefficients for the full model.
Public propertyCoefficientVariance
Gets the jackknife variance estimates for the model coefficients.
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 propertyScale
Gets and sets the scale property. If true, the learning X data for each subset is scaled by dividing each variable by its sample standard deviation. The prediction data is scaled by the same amount. Note that this will impact performance.
Public propertySubsetGenerator
Gets and sets the subset generator to use to generate testing and training subsets.
Public propertyUseMeans
Gets and sets the use means property. If true the mean of the coefficients computed in the jackknife replicates will be used to compute variance estimates. If false the full model coefficients will be used.
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Methods
  NameDescription
Public methodClone
Creates a deep copy of this PLS2CrossValidationWithJackknife.
Public methodCoefficientConfidenceIntervals
Calculates the (1 - alpha)x100% confidence intervals for the model coeffficients. The i,j entry corresponds to the i,j entry of the matrix of coefficients accessed by the Coefficients property.
Public methodDoCrossValidation(DoubleMatrix, DoubleMatrix, Int32)
Perform cross validation and jackknife variance estimation on the given data using the existing calculator and subset generator.
Public methodDoCrossValidation(DoubleMatrix, DoubleMatrix, IPLS2Calc, Int32)
Performs cross validation and jackknife variance estimation on the given data using the given calculator and number of components.
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Remarks
The original (Tukey) jackknife variance estimator is defined as ((g - 1)/g)*sum(Bi - Bbar) where g is the number of subsets used in cross validation, Bi is the estimated coefficients when subset i is left out (called the jackknife replicates), and Bbar is the mean of the Bi. However, Martens and Martens (2000) defined the estimator as ((g - 1)/g)*sum(Bi - Bhat) where Bhat is the coefficient estimate using the entire data set. I.e., they use the original fitted coefficients instead of the mean of the jackknife replicates and is the default for class PLS2CrossValidationWithJackknife. However it can be made to use the orginal Tukey definition by setting the UseMean property to true.
See Also