RWLeastSqQRPvtCalc Class Reference

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

#include <rw/analytics/lsqqrpvt.h>

Inheritance diagram for RWLeastSqQRPvtCalc:

Public Member Functions
	RWLeastSqQRPvtCalc (double tol=0.0)
virtual void	calc (const RWGenMat< double > &, const RWMathVec< double > &)
virtual RWRegressionCalc< double, double > *	clone () const
virtual bool	fail () const
double	getTolerance () const
virtual RWCString	name () const
virtual RWMathVec< double >	parameters () const
void	setTolerance (double tol)
Public Member Functions inherited from RWRegressionCalc< double, double >
	RWRegressionCalc ()
virtual void	addObsToBaseCalc (const RWAddObservations< double, double > &dataChange)
virtual void	addPredToBaseCalc (const RWAddPredictors< double, double > &dataChange)
virtual void	removeObsFromBaseCalc (const RWRemoveObservations< double, double > &dataChange)
virtual void	removePredFromBaseCalc (const RWRemovePredictors< double, double > &dataChange)
virtual void	setBaseCalc (const RWGenMat< double > &r, const RWMathVec< double > &o)

Detailed Description

Class RWLeastSqQRPvtCalc implements the calculation of linear regression parameters using QR decomposition with pivoting, as described in Section 5.5.1.2, "RWLeastSqQRPvtCalc" in the Business Analysis Module User's Guide.

Synopsis

#include <rw/analytics/lsqqrpvt.h>

RWLeastSqQRPvtCalc calc;

Example

#include <rw/analytics/lsqqrpvt.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]";
 RWLeastSqQRPvtCalc qrpvt;

 qrpvt.calc(predictorMatrix, observationVector);

 if ( !qrpvt.fail() )
  cout << "Model parameters: " << qrpvt.parameters() << endl;
 else
  cout << "Parameter calculation failed." << endl;
 return 0;
}

Constructor & Destructor Documentation

RWLeastSqQRPvtCalc::RWLeastSqQRPvtCalc

(

double

tol = 0.0

)

Constructs an RWLeastSqQRPvtCalc object with the specified tolerance parameter tol. Entries along the diagonal of the R factor of the QR decomposition less than this tolerance are treated as zero. Using this can prevent insignificant entries of R from corrupting your solution.

Member Function Documentation

virtual void RWLeastSqQRPvtCalc::calc ( const RWGenMat< double > &  , const RWMathVec< double > &    )

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 >.

virtual RWRegressionCalc<double, double>* RWLeastSqQRPvtCalc::clone ( ) 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 >.

virtual bool RWLeastSqQRPvtCalc::fail ( ) const

inlinevirtual

Returns false.

Implements RWRegressionCalc< double, double >.

double RWLeastSqQRPvtCalc::getTolerance ( ) const

inline

Gets the tolerance parameter for the QR decomposition. Entries along the diagonal of the R factor of the QR decomposition that are less than this tolerance are treated as zero. Using this can prevent insignificant entries of R from corrupting your solution.

virtual RWCString RWLeastSqQRPvtCalc::name ( ) const

inlinevirtual

Returns the name of the calculation method.

Implements RWRegressionCalc< double, double >.

virtual RWMathVec<double> RWLeastSqQRPvtCalc::parameters ( ) const

inlinevirtual

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 >.

void RWLeastSqQRPvtCalc::setTolerance ( double  tol )

inline

Sets the tolerance parameter for the QR decomposition. Entries along the diagonal of the R factor of the QR decomposition that are less than this tolerance are treated as zero. Using this can prevent insignificant entries of R from corrupting your solution.