PLS2CrossValidationWithJackknife Class 
Namespace: CenterSpace.NMath.Core
The PLS2CrossValidationWithJackknife type exposes the following members.
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.

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.

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.
