RWLeastSqQRCalc Class Reference

Calculates linear regression parameters using QR decomposition. More...

#include <rw/analytics/lsqqr.h>

Inheritance diagram for RWLeastSqQRCalc:

Public Member Functions
	RWLeastSqQRCalc ()
virtual void	addPredToBaseCalc (const RWAddPredictors< double, double > &dataChange)
virtual void	calc (const RWGenMat< double > &r, const RWMathVec< double > &o)
virtual RWRegressionCalc< double, double > *	clone (void) const
virtual bool	fail (void) const
virtual RWCString	name (void) const
virtual RWMathVec< double >	parameters (void) const
virtual void	removePredFromBaseCalc (const RWRemovePredictors< double, double > &dataChange)
virtual void	setBaseCalc (const RWGenMat< double > &, const RWMathVec< double > &)
Public Member Functions inherited from RWRegressionCalc< double, double >
	RWRegressionCalc ()
virtual void	addObsToBaseCalc (const RWAddObservations< double, double > &dataChange)
virtual void	removeObsFromBaseCalc (const RWRemoveObservations< double, double > &dataChange)

Detailed Description

Class RWLeastSqQRCalc implements the calculation of linear regression parameters using QR decomposition, as described in Section 5.5.1.1, "RWLeastSqQRCalc," in the Business Analysis Module User's Guide.

Synopsis

#include <rw/analytics/lsqqr.h>
 
RWLeastSqQRCalc calc;

Example

#include <rw/analytics/lsqqr.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]";
  RWLeastSqQRCalc qr;
 
  qr.calc(predictorMatrix, observationVector);
 
  if (!qr.fail()) {
    cout << "Model parameters: " << qr.parameters() << endl;
  } else {
    cout << "Parameter calculation failed." << endl;
  }
  return 0;
}

Constructor & Destructor Documentation

RWLeastSqQRCalc()

RWLeastSqQRCalc::RWLeastSqQRCalc

(

)

Constructs an empty RWLeastSqQRCalc object.

Member Function Documentation

addPredToBaseCalc()

virtual void RWLeastSqQRCalc::addPredToBaseCalc ( const RWAddPredictors< double, double > & dataChange )

virtual

Expands the regression model to include new predictor variables. This method has 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.

Reimplemented from RWRegressionCalc< double, double >.

calc()

virtual void RWLeastSqQRCalc::calc ( const RWGenMat< double > & r, const RWMathVec< double > & o )

virtual

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

Implements RWRegressionCalc< double, double >.

clone()

virtual RWRegressionCalc< double, double > * RWLeastSqQRCalc::clone ( void ) const

inlinevirtual

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.

Implements RWRegressionCalc< double, double >.

fail()

virtual bool RWLeastSqQRCalc::fail ( void ) const

inlinevirtual

Returns true if the calculation failed.

Implements RWRegressionCalc< double, double >.

name()

virtual RWCString RWLeastSqQRCalc::name ( void ) const

inlinevirtual

Returns the name of the calculation method.

Implements RWRegressionCalc< double, double >.

parameters()

virtual RWMathVec< double > RWLeastSqQRCalc::parameters ( void ) const

virtual

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

Implements RWRegressionCalc< double, double >.

removePredFromBaseCalc()

virtual void RWLeastSqQRCalc::removePredFromBaseCalc ( const RWRemovePredictors< double, double > & dataChange )

virtual

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.

Reimplemented from RWRegressionCalc< double, double >.

setBaseCalc()

virtual void RWLeastSqQRCalc::setBaseCalc ( const RWGenMat< double > & r, const RWMathVec< double > & o )

virtual

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.

Reimplemented from RWRegressionCalc< double, double >.