RWLeastSqSVDCalc

RWLeastSqSVDCalc       RWRegressionCalc<double,double>

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• Synopsis
• Description
• Example
• Public Constructor
• Public Member Functions

Members

addObsToBaseCalc() addPredToBaseCalc() calc() clone()

fail() getTolerance() name() parameters()

removeObsFromBaseCalc() removePredFromBaseCalc() RWLeastSqSVDCalc() setBaseCalc()

setTolerance()

#include <rw/analytics/lsqsvd.h>

RWLeastSqSVDCalc calc;

Class RWLeastSqSVDCalc implements the calculation of linear regression parameters using singular value decomposition, as described in Section 5.5.1.3, "RWLeastSqSVDCalc" in the Business Analysis Module User's Guide

#include <rw/analytics/lsqsvd.h>
#include <rw/rstream.h>  // Handles proper inclusion of iostream

int main() {
 RWGenMat<double> predictorMatrix = 
                       "5x2 [1.2 2.1 8 7 3 3.2 6.4 4.6 2 2.3]";
 RWMathVec<double> observationVector = "[2.5 3.7 1.4 2.3 5.6]";
 RWLeastSqSVDCalc svd;

 svd.calc(predictorMatrix, observationVector);
 
 if ( !svd.fail() )
  cout << "Model parameters: " << svd.parameters() << endl;
 else
  cout << "Parameter calculation failed." << endl;
 return 0;
}

RWLeastSqSVDCalc();

Constructs an empty RWLeastSqSVDCalc object.

RWLeastSqSVDCalc(double tol=0.0);

Constructs an RWLeastSqSVDCalc object with the specified tolerance parameter tol. Singular values whose value is less than tol are discarded.

virtual 
void 
addObsToBaseCalc(const RWAddObservations<double,double>& r);

Recalculates the regression model using an additional set of predictor-observation data pairs. This method offers the option of using results from the base calculation to calculate the coefficients for the larger data set. Consequently, this method can be called only when a base calculation has been set using setBaseCalc(). The input variable provides the additional data. The default implementation simply recalculates the parameters from scratch using the calc() method.

virtual 
void
addPredToBaseCalc(const RWAddPredictors<double,double>& r);

Expands the regression model to include new predictor variables. This method offers the option of using results from the base calculation to calculate the coefficients for the larger predictor set. Consequently, this method can be called only when a base calculation has been set using setBaseCalc(). The input variable contains the data for the added predictor variables. The default implementation simply recalculates the parameters from scratch using the calc() method.

virtual 
void
calc(const RWGenMat<double>& xdata,
     RWMathVec<double> ydata);

Calculates the parameters for the regression model. Invoking this method does not affect the state of any existing base calculation.

virtual 
RWLeastSqSVDCalc* 
clone() const;

Allocates and creates a clone, or exact copy, of the current instance, and returns a pointer to the copy. Caller is responsible for deleting the returned object.

virtual 
bool 
fail() const;

Returns true if the calculation failed.

virtual 
RWCString 
name() const;

Returns the name of the calculation method.

virtual
RWMathVec<double>
parameters() const;

Returns the parameters from the last calculation performed. If the calculation failed and this method is called, an exception of type RWInternalErr is thrown.

virtual 
void
removeObsFromBaseCalc(const
                      RWRemoveObservations<double,double>& r);

Modifies the regression model by removing a set of contiguous predictor-observation data pairs, and recalculating the parameters. This method offers the option of using results from the base calculation to calculate the coefficients for the smaller data set. Consequently, this method can be called only when a base calculation has been set using setBaseCalc(). The input variable indicates the indices of the rows to be removed from the regression matrix and from the observation vector used in the base calculation. The default implementation simply recalculates the parameters from scratch using the calc() method.

virtual 
void
removePredFromBaseCalc(const
                       RWRemovePredictors<double,double>& r);

Shrinks the regression model to exclude previously used predictor variables. This method offers the option of using results from the base calculation to calculate the coefficients for the smaller predictor set. Consequently, this method can be called only when a base calculation has been set using setBaseCalc(). The input variable indicates the indices of the columns to be removed from the regression matrix used in the base calculation. The default implementation simply recalculates the parameters from scratch using the calc() method.

virtual 
void
setBaseCalc(const RWGenMat<double>& xdata, 
            const RWMathVec<double>& ydata);

Calculates the coefficients for the input regression data and sets the base calculation to this calculation. Deriving classes need not override this method if they do not store base calculation results. The default implementation calls the calc() method.

void   
setTolerance(double tol);
double 
getTolerance()const;

Sets and gets the tolerance parameter for the SVD decomposition. Singular values whose value is less than tol are discarded.

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