| IPLS2Calc Class |
Interface for performing a Partial Least Squares (PLS) calculation.
Inheritance Hierarchy Namespace: CenterSpace.NMath.CoreAssembly: NMath (in NMath.dll) Version: 7.4
Syntax [SerializableAttribute]
public abstract class IPLS2Calc : ICloneable
<SerializableAttribute>
Public MustInherit Class IPLS2Calc
Implements ICloneable
[SerializableAttribute]
public ref class IPLS2Calc abstract : ICloneable
[<AbstractClassAttribute>]
[<SerializableAttribute>]
type IPLS2Calc =
class
interface ICloneable
end
The IPLS2Calc type exposes the following members.
Constructors | Name | Description |
---|
| IPLS2Calc | Initializes a new instance of the IPLS2Calc class |
TopProperties | Name | Description |
---|
| Coefficients |
Gets the matrix of coefficients used for making predictions.
|
| IsGood |
Indicates whether the most recent calculation was successful.
|
| 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.
|
| PredictorLoadings |
Gets a matrix whow columns are the predictor loading vectors.
|
| PredictorScores |
Gets a matrix whow columns are the predictor score vectors.
|
TopMethods | Name | Description |
---|
| Calculate |
Perform a PLS2 calculation on the given data.
|
| Clone |
A deep copy of self.
|
| 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.
|
| Predict(DoubleMatrix) |
Use the calculated model to predict the response value for each of
of the given predictor values.
|
| Predict(DoubleVector) |
Use the calculated model to predict the response value, y, from
the given value of the predictor variable.
|
| 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.
|
TopRemarks
Implementations must be able to handle dependent, or
ResponseVector, data with multiple columns (variables). That
is, the algorithm must be a PLS2 algorithm.
See Also