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SparsePlsDa Class

Class for performing Discriminant Analysis (DA) using sparse Partial Least Squares (sPLS). This is a classical sPLS regression, but where the response variable is catagorical. The response vector Y is qualitative and is recoded as a dummy block matrix where each of the response categories are coded via an indicator variable. PLS-DA is then run as if Y was a continuous matrix.
Inheritance Hierarchy

Namespace:  CenterSpace.NMath.Core
Assembly:  NMath (in NMath.dll) Version: 7.3
Syntax
public class SparsePlsDa : PLS2

The SparsePlsDa type exposes the following members.

Constructors
  NameDescription
Public methodSparsePlsDa
Constructs a SparsePlsDa instance using default settings.
Public methodSparsePlsDa(Int32, Double)
Constructs a SparsePlsDa instance. The sparse PLS fit algorithm is executed using the specified maximum iterations.
Public methodSparsePlsDa(DoubleMatrix, Factor, Int32, Int32, Int32, Double)
Constructs a SparsePlsDa for the given data and options.
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Properties
  NameDescription
Public propertyCalculator
Gets and sets the calculator.
(Inherited from PLS2.)
Public propertyCenteredScaledX
Matrix of scaled, centered X values.
Public propertyCenteredScaledY
Matrix of scaled centered Y values.
Public propertyCMatrix
Matrix of coefficients used internally for prediction.
Public propertyIndicatorMatrix
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.
Public propertyIsGood
Whether the calculation was successful.
(Inherited from PLS2.)
Public propertyKeepX
Get and sets the number of X variables kept in the model for each component.
Public propertyMessage
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.)
Public propertyNumComponents
Gets and sets the number of predictor variable components to use in the calculation.
(Inherited from PLS2.)
Public propertyX
Gets the predictor matrix.
(Inherited from PLS2.)
Public propertyXLoadings
Gets the matrix of X loadings.
Public propertyXVariates
Gets the matrix of X variates or scores.
Public propertyY
Gets the response matrix.
(Inherited from PLS2.)
Public propertyYFactor
Gets the catagorical response varible used in the calculation as a Factor .
Public propertyYLoadings
Gets the matrix of Y loadings.
Public propertyYVariates
Gets the matrix of Y variates or scores.
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Methods
  NameDescription
Public methodCalculate(DataFrame, DataFrame, Int32)
Calculates the partial least squares fit.
(Inherited from PLS2.)
Public methodCalculate(DoubleMatrix, DoubleMatrix, Int32)
Calculates the partial least squares fit.
(Inherited from PLS2.)
Public methodCalculate(DoubleMatrix, Factor, Int32, Int32)
Performs the sparse Partial Least squares calculation for the given data.
Public methodClone
Creates a deep copy of this PLS2.
(Inherited from PLS2.)
Public methodHotellingsT2
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.)
Public methodPredict(DoubleMatrix)
Predict the responses for a set of predictor values.
(Inherited from PLS2.)
Public methodPredict(DoubleVector)
Calculates the predicted value of the response variable for the given value of the predictor variable.
(Inherited from PLS2.)
Public methodQResiduals
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.)
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See Also