RWLogisticIterLSQ

RWLogisticIterLSQ        RWRegressionCalc<double,bool>

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

Members

addObsToBaseCalc() addPredToBaseCalc() calc()

clone() fail() name()

parameters() removeObsFromBaseCalc() removePredFromBaseCalc()

RWLogisticIterLSq() setBaseCalc()

#include <rw/analytics/loglsq.h>

RWLogisticIterLSQ c;

Class RWLogisticIterLSQ calculates model parameter estimates from logistic regression data using the iterative least squares method described in Section 5.5.2.1, "RWLogisticIterLSQ" in the Business Analysis Module User's Guide.

#include <rw/analytics/loglsq.h>
#include <rw/rstream.h>

int int main()
{
  RWGenMat<double> predData = "5x2 [1 234 2 431 3 
                                   333 4 654 5 788]";
  RWMathVec<bool> obsData(5, rwUninitialized );
  obsData[0] = obsData[3] = obsData[4] = true;
  obsData[1] = obsData[2] = false;

  RWLogisticIterLSQ iterativeLSQCalc;
  iterativeLSQCalc.calc( predData, obsData );
  if ( !iterativeLSQCalc.fail() )
  {
    cout << "Parameters: " << iterativeLSQCalc.parameters() <<
            endl;
  }
  else
  {
    cout << "Calculation failed" << endl;
  }

  return 0;
}

RWLogisticIterLSq();

Constructs an empty RWLogisticIterLSq object.

virtual 
void 
addObsToBaseCalc(const 
                 RWAddObservations<double,bool>& 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,bool>& 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<bool> ydata)=0;

Calculates the parameters for the regression model. This is a pure virtual function that must be overridden by deriving classes. Invoking this method does not affect the state of any existing base calculation.

virtual 
RWLogisticIterLSq
clone() const = 0;

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 = 0;

Returns true if the calculation failed.

virtual 
RWCString 
name() const = 0;

Returns the name of the calculation method.

virtual
RWMathVec<double>
parameters() const = 0;

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,bool>& 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,bool>& 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<bool>& 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.

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