StepwiseLS Class |
Implementation of Stepwise Least Squares (Regrssion) Algorithm.
Inheritance HierarchyNamespace: FinMath.LeastSquares
Assembly: FinMath (in FinMath.dll) Version: 2.4.7-0a995bd0ea1854c2c868ec3f8dae606c5777e170
SyntaxC#
[SerializableAttribute] public class StepwiseLS : AbstractLS
The StepwiseLS type exposes the following members.
Constructors| Name | Description | |
|---|---|---|
 | StepwiseLS(Matrix, Vector) |
Creates new instance of StepwiseLS.
With default _parameters: inMask, inKeep false for all regressors, penter = 0.05, premove = 0.10, scale = false.
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| StepwiseLS(Matrix, Vector, Boolean, Boolean, Double, Double, Int32, Boolean) |
Creates new instance of StepwiseLS.
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Properties| Name | Description | |
|---|---|---|
 | InModel |
Returns boolean array.
I-th element of array is true if and only if i-th regressor was added into model.
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| InModelIndices |
Indices of regressors which were added into model.
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| InterceptTerm |
Intercept Term parameter.
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| IterationsDone |
Number of iterations completed.
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| MinimalVariance |
Represents minimal regressand variance which must be covered by regressor to be in model.
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| Parameters |
Vector of regression parameters.
(Inherited from AbstractLS.) |
| PValue |
Pvalue scores for regressors.
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| Status |
Method Status.
(Inherited from AbstractLS.) |
| StepwiseParameters |
Stepwise regression _parameters.
For in-model regressors paramters will be regression _parameters.
The StepwiseParameters value for a term not included in the final model is the coefficient estimate that would result from adding the term to the model.
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| TStatistics |
Returns vector of test statistic for each regressor.
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Methods| Name | Description | |
|---|---|---|
| CalculateR2(Matrix, Vector) |
Calculate R2 - Coefficient of determination - goodness-of-fit of the OLS regression metric.
Method use computed regression parameters.
http://en.wikipedia.org/wiki/Coefficient_of_determination
(Inherited from AbstractLS.) |
| Equals | Determines whether the specified object is equal to the current object. (Inherited from Object.) |
| Estimate(Matrix) |
Estimate regresand values, using computed regression _parameters and regressors values.
Use this this method for multiple observations.
(Overrides AbstractLSEstimate(Matrix).) |
| Estimate(Vector) |
Estimate regresand value, using computed regression _parameters and regressors value.
Use this this method for single observation.
(Overrides AbstractLSEstimate(Vector).) |
| EstimateResidual |
Calculate residuals of estimations, using computed regression _parameters, regressors and regressand value.
Use this this method for single observation.
(Overrides AbstractLSEstimateResidual(Vector, Double).) |
| EstimateResiduals |
Calculate residuals of estimations, using computed regression _parameters, regressors and regressand values.
Use this this method for multiple observations.
(Overrides AbstractLSEstimateResiduals(Matrix, Vector).) |
| GetHashCode | Serves as the default hash function. (Inherited from Object.) |
| GetType | Gets the Type of the current instance. (Inherited from Object.) |
| ToString | Returns a string that represents the current object. (Inherited from Object.) |
See Also