OrdinaryLS Methods |
The OrdinaryLS type exposes the following members.
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.
(Inherited from AbstractLS.) |
| Estimate(Vector) |
Estimate regresand value, using computed regression parameters and regressors value.
Use this this method for single observation.
(Inherited from AbstractLS.) |
| EstimateResidual(Vector, Double) |
Calculate residuals of estimations, using computed regression parameters, regressors and regressand value.
Use this this method for single observation.
(Inherited from AbstractLS.) |
| EstimateResiduals(Matrix, Vector) |
Calculate residuals of estimations, using computed regression parameters, regressors and regressand values.
Use this this method for multiple observations.
(Inherited from AbstractLS.) |
 | FitOLS |
Static method, which can be used to simply calculate OLS parametesr.
|
| Forget(Matrix, Vector) |
Update OLS parameters according new observations.
It is useful for estimation on sliding window.
|
| Forget(Vector, Double) |
Update OLS parameters according new observations.
It is useful for estimation on sliding window.
|
| 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.) |
| Update(Matrix, Vector) |
Update OLS parameters according new observations.
It is useful for estimation on sliding window.
|
| Update(Vector, Double) |
Update OLS parameters according new observations.
It is useful for estimation on sliding window.
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See Also