SparsePls Class 
Namespace: CenterSpace.NMath.Core
The SparsePls type exposes the following members.
Name  Description  

SparsePls(SparsePLSMode, Int32, Int32, Int32, Double) 
Constructs a SparsePls object from the given parameters.
 
SparsePls(DoubleMatrix, DoubleMatrix, Int32, Int32, Int32, SparsePLSMode, Int32, Double) 
Constructs a SparsePls object from the given parameters and performs
the sparse PLS calculation on the given data. The data is first centered and
scaled by standard deviation.

Name  Description  

CenteredScaledX 
Matrix of scaled, centered X values.
 
CenteredScaledY 
Matrix of scaled centered Y values.
 
CMatrix 
Matrix of coefficients used internally for prediction.
 
Coefficients 
Coefficient matrix that may be used for prediction.
(Overrides IPLS2CalcCoefficients.)  
IsGood 
Indicates whether the most recent calculation was successful. For
SparsePls a return value of false most likely indicates that the
iterative algorithm did not converge before reaching the maximum
number of iterations. Check the Message property for
further information in this case.
(Overrides IPLS2CalcIsGood.)  
Iterations 
Number of iterations of the algorthm for each component
 
KeepX 
Get and sets the number of X variables kept in the model for each component.
 
KeepY 
Get and sets the number of Y variables kept in the model for each component.
 
MaxIterations 
Gets the max iterations performed by the iterative algorithm.
 
Message 
Gets any message that may have been generated by the algorithm. For example,
if the calculation is unsuccessful, the message should indicate the
reason.
(Overrides IPLS2CalcMessage.)  
Mode 
Gets and sets the PLS mode.
 
NumComponents 
Gets the number components.
 
PredictorLoadings 
Gets the predictor variable loadings. This is an alias for
XLoadings.
(Overrides IPLS2CalcPredictorLoadings.)  
PredictorScores 
Gets the predictor variable scores. This is an alias for
XVariates.
(Overrides IPLS2CalcPredictorScores.)  
Tolerance 
Gets the tolerance used for convergence determination of the
iterative algorithm.
 
XLoadings 
Gets the matrix of X loadings.
 
XVariates 
Gets the matrix of X variates or scores.
 
YLoadings 
Gets the matrix of Y loadings.
 
YVariates 
Gets the matrix of Y variates or scores.

Name  Description  

Calculate 
Calculates the sparse PLS fit for the given data and number of components.
X and Y values are first centered and scaled by their standard deviations.
(Overrides IPLS2CalcCalculate(DoubleMatrix, DoubleMatrix, Int32).)  
Clone 
Creates a deep copy of self.
(Overrides IPLS2CalcClone.)  
HotellingsT2 
Calculaties Hotelling's T2 statistic for each sample. T2 can be viewed as the
squared distance from a samples projection into the subspace to the centroid
of the subspace, or, more simply, the variation of the sample point within
the model.
(Inherited from IPLS2Calc.)  
Predict(DoubleMatrix) 
Uses the calculated model to predict the observed values from
a matrix of predictor values.
(Overrides IPLS2CalcPredict(DoubleMatrix).)  
Predict(DoubleVector) 
Used the calculated model to predict the observed value from the given
predictor vector.
(Overrides IPLS2CalcPredict(DoubleVector).)  
QResiduals 
Calculates the Q residuals for in sample in the model. The Q residual
for a given sample is the distance between the sample and its projection
in the subspace of the model.
(Inherited from IPLS2Calc.) 