 | Sparse |
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 HierarchyNamespace: CenterSpace.NMath.Core
Assembly: NMath (in NMath.dll) Version: 7.4
SyntaxThe SparsePlsDa type exposes the following members.
Constructors| 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. |
Properties| 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. |
Methods| 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) |
See Also