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

SparsePlsDa 
Constructs a SparsePlsDa instance using default settings.
 
SparsePlsDa(Int32, Double) 
Constructs a SparsePlsDa instance. The sparse PLS fit algorithm is executed using
the specified maximum iterations.
 
SparsePlsDa(DoubleMatrix, Factor, Int32, Int32, Int32, Double) 
Constructs a SparsePlsDa for the given data and options.

Name  Description  

Calculator 
Gets and sets the calculator.
(Inherited from PLS2.)  
CenteredScaledX 
Matrix of scaled, centered X values.
 
CenteredScaledY 
Matrix of scaled centered Y values.
 
CMatrix 
Matrix of coefficients used internally for prediction.
 
IndicatorMatrix 
Gets the indicator matrix (dummy block matrix) used in the
calculation. The indicator matrix has G columns, where
G is the number of classes containing ones and zeros. The
gth column is one and the others zero for observations of
class g.
 
IsGood 
Whether the calculation was successful.
(Inherited from PLS2.)  
KeepX 
Get and sets the number of X variables kept in the model for each component.
 
Message 
Gets any message that may have been generated by the algorithm. For
example, if the calculation is unsuccessful, the message indicate the
reason.
(Inherited from PLS2.)  
NumComponents 
Gets and sets the number of predictor variable components to use
in the calculation.
(Inherited from PLS2.)  
X 
Gets the predictor matrix.
(Inherited from PLS2.)  
XLoadings 
Gets the matrix of X loadings.
 
XVariates 
Gets the matrix of X variates or scores.
 
Y 
Gets the response matrix.
(Inherited from PLS2.)  
YFactor 
Gets the catagorical response varible used in the calculation
as a Factor .
 
YLoadings 
Gets the matrix of Y loadings.
 
YVariates 
Gets the matrix of Y variates or scores.

Name  Description  

Calculate(DataFrame, DataFrame, Int32) 
Calculates the partial least squares fit.
(Inherited from PLS2.)  
Calculate(DoubleMatrix, DoubleMatrix, Int32) 
Calculates the partial least squares fit.
(Inherited from PLS2.)  
Calculate(DoubleMatrix, Factor, Int32, Int32) 
Performs the sparse Partial Least squares calculation for the given
data.
 
Clone 
Creates a deep copy of this PLS2.
(Inherited from PLS2.)  
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 PLS2.)  
Predict(DoubleMatrix) 
Predict the responses for a set of predictor values.
(Inherited from PLS2.)  
Predict(DoubleVector) 
Calculates the predicted value of the response variable
for the given value of the predictor variable.
(Inherited from PLS2.)  
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 PLS2.) 