RWLogisticRegressionParam Class Reference

Container class for logistic regression parameter estimates and their associated statistical quantities. More...

#include <rw/analytics/logregparam.h>

Public Member Functions
	RWLogisticRegressionParam ()
	RWLogisticRegressionParam (const RWLogisticRegressionParam &rhs)
RWInterval< double >	confidenceInterval (double alpha) const
RWLogisticRegressionParam &	operator= (const RWLogisticRegressionParam &rhs)
double	standardError () const
double	value () const
double	waldChiSqStatCriticalValue (double alpha) const
double	waldChiSqStatistic () const
double	waldChiSqStatPValue () const

Detailed Description

RWLogisticRegressionParam is the container class for logistic regression parameter estimates and their associated statistical quantities. The estimates are described in the Business Analysis Module User's Guide.

Synopsis

#include <rw/analytics/logparam.h>
#include <rw/analytics/logregress.h>
 
RWLogisticRegression lr;
RWTValVector<RWLogisticRegressionParam> p = lr.parameterEstimates();

Example

#include <rw/analytics/logregress.h>
#include <iostream>
 
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;
 
  RWLogisticRegression lr(predData, obsData);
 
  // Make sure parameter calculation succeeded.
  if (lr.fail()) {
    return 0;
  }
 
  double sigLevel = .05;
 
  // Print out model parameter estimate info.
  RWTValVector<RWLogisticRegressionParam> params =
      lr.parameterEstimates();
  for (size_t i = 0; i < params.length(); i++) {
    std::cout << "Model parameter " << i
              << (i == 0 ? " Intercept:" : ":") << std::endl;
    std::cout << "  value:                      " << params[i].value()
              << std::endl;
    std::cout << "  standard error:             "
              << params[i].standardError() << std::endl;
    std::cout << "  Wald statistic:                "
              << params[i].waldChiSqStatistic() << std::endl;
    std::cout << "  Wald statistic P-value:        "
              << params[i].waldChiSqStatPValue() << std::endl;
    std::cout << "  Wald statistic critical value: "
              << params[i].waldChiSqStatCriticalValue(sigLevel)
              << std::endl;
    std::cout << "  " << sigLevel << " confidence interval:  "
              << "["
              << params[i].confidenceInterval(sigLevel).lowerBound()
              << ", "
              << params[i].confidenceInterval(sigLevel).upperBound()
              << "]" << std::endl;
  }
 
  return 0;
}

Constructor & Destructor Documentation

RWLogisticRegressionParam() [1/2]

RWLogisticRegressionParam::RWLogisticRegressionParam ( )

inline

Constructs an empty fitted parameter object. Behavior undefined.

RWLogisticRegressionParam() [2/2]

RWLogisticRegressionParam::RWLogisticRegressionParam ( const RWLogisticRegressionParam & rhs )

inline

Constructs a copy of rhs.

Member Function Documentation

confidenceInterval()

RWInterval< double > RWLogisticRegressionParam::confidenceInterval

(

double

alpha

)

const

Returns an alpha level confidence interval for the parameter.

operator=()

RWLogisticRegressionParam & RWLogisticRegressionParam::operator=

(

const RWLogisticRegressionParam &

rhs

)

Assignment operator.

standardError()

double RWLogisticRegressionParam::standardError

(

)

const

Returns the estimated standard error for the fitted value. This is the square root of the estimated variance, V, described in the Business Analysis Module User's Guide.

value()

double RWLogisticRegressionParam::value ( ) const

inline

Returns the least squares estimate for the parameter.

waldChiSqStatCriticalValue()

double RWLogisticRegressionParam::waldChiSqStatCriticalValue ( double alpha ) const

inline

Returns the critical value for the Wald chi-square statistic at significance level alpha.

waldChiSqStatistic()

double RWLogisticRegressionParam::waldChiSqStatistic ( ) const

inline

Returns the Wald chi-square statistic for the hypothesis that the parameter is equal to 0.

waldChiSqStatPValue()

double RWLogisticRegressionParam::waldChiSqStatPValue ( ) const

inline

Returns the P-value for the parameter Wald chi-square statistic.