 | PLS2CrossValidationWithJackknife Class |
Class PLS2CrossValidationWithJackknife performs an evaluation of a PLS (Partial Least
Squares) model with model coefficient variance estimates and confidence intervals.
Inheritance HierarchyNamespace: CenterSpace.NMath.Core
Assembly: NMath (in NMath.dll) Version: 7.4
SyntaxThe PLS2CrossValidationWithJackknife type exposes the following members.
Constructors| Name | Description | |
|---|---|---|
 | PLS2CrossValidationWithJackknife |
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 |
| PLS2CrossValidationWithJackknife(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 |
| PLS2CrossValidationWithJackknife(ICrossValidationSubsets) |
Constructs a PLS2CrossValidationWithJackknife instance which uses the given subset
generator and the Nipals algorithm.
|
| PLS2CrossValidationWithJackknife(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.
|
| PLS2CrossValidationWithJackknife(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.
|
| PLS2CrossValidationWithJackknife(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.
|
| PLS2CrossValidationWithJackknife(IPLS2Calc, ICrossValidationSubsets, Boolean) |
Constructs a PLS2CrossValidationWithJackknife instance which uses the given PLS calculator
and subset generator.
|
| PLS2CrossValidationWithJackknife(IPLS2Calc, ICrossValidationSubsets, Boolean, Boolean) |
Constructs a PLS2CrossValidationWithJackknife instance which uses the given PLS calculator
and subset generator.
|
Properties| Name | Description | |
|---|---|---|
 | AverageMeanSqrError |
Gets the average of the mean square errors for each training/testing
subsets pair.
|
| Calculator |
Gets and sets the PLS2 calculator to use for PLS2 calculations.
|
| Coefficients |
Gets the coefficients for the full model.
|
| CoefficientVariance |
Gets the jackknife variance estimates for the model coefficients.
|
| IsGood |
Whether all the PLS2 calculations were successful.
|
| Message |
Gets any message that may have been generated by the computation. For
example, if the calculation is unsuccessful, the message indicates the
reason.
|
| Results |
Gets the results of the cross validation for each training/testing
subsets pair.
|
| Scale |
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.
|
| SubsetGenerator |
Gets and sets the subset generator to use to generate testing
and training subsets.
|
| UseMeans |
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.
|
Methods| Name | Description | |
|---|---|---|
| Clone |
Creates a deep copy of this PLS2CrossValidationWithJackknife.
|
| CoefficientConfidenceIntervals |
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.
|
| DoCrossValidation(DoubleMatrix, DoubleMatrix, Int32) |
Perform cross validation and jackknife variance estimation on the given data
using the existing calculator and subset generator.
|
| DoCrossValidation(DoubleMatrix, DoubleMatrix, IPLS2Calc, Int32) |
Performs cross validation and jackknife variance estimation on the given data using the
given calculator and number of components.
|
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