Calculates a number of goodness of fit quantities for a given RWLogisticRegression instance. More...

#include <rw/analytics/logfit.h>

Public Member Functions
	RWLogisticFitAnalysis ()

	RWLogisticFitAnalysis (const RWLogisticFitAnalysis &rhs)

	RWLogisticFitAnalysis (const RWLogisticRegression &reg)

	~RWLogisticFitAnalysis ()

double	deviance () const

double	GStatistic () const

double	GStatisticCriticalValue (double alpha=.05) const

int	GStatisticDegreesOfFreedom () const

double	GStatisticPValue () const

double	HLStatistic () const

double	HLStatisticCriticalValue (double alpha=.05) const

int	HLStatisticDegreesOfFreedom () const

const Histogram &	HLStatisticOutputHistogram () const

const Histogram &	HLStatisticPosObsHistogram () const

double	HLStatisticPValue () const

double	logLikelihood () const

size_t	modelOutputGroups () const

RWLogisticFitAnalysis &	operator= (const RWLogisticFitAnalysis &rhs)

double	PearsonStatistic () const

double	PearsonStatisticCriticalValue (double alpha=.05) const

int	PearsonStatisticDegreesOfFreedom () const

double	PearsonStatisticPValue () const

const RWMathVec< size_t >	predictorDataGroups () const

int	regressionDegreesOfFreedom () const

int	residualDegreesOfFreedom () const

void	setLogisticRegression (const RWLogisticRegression &lr)

void	setModelOutputGroups (size_t ngroups)

void	setPredictorDataGroups (const RWMathVec< size_t > &groups)

Detailed Description

Given RWLogisticRegression instance, class RWLogisticFitAnalysis calculates several goodness of fit quantities, including the G-statistic, Pearson statistic, and Hosmer and Lemeshow statistic, along with their P-values and critical values. These statistics are described in the Business Analysis Module User's Guide.

Synopsis: #include <rw/stats/logregress.h>

#include <rw/stats/logfit.h>

RWLogisticRegression lr;

RWLogisticFitAnalysis logfit(lr);

Example: #include <rw/analytics/logregress.h>

#include <rw/analytics/logfit.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;

// Create a logistic regression object.

RWLogisticRegression model(predData, obsData);

// Analyze the model; print out some summaries of the model's

// goodness of fit.

RWLogisticFitAnalysis logfit(model);

std::cout << "Pearson statistic: " << logfit.PearsonStatistic()

<< std::endl;

std::cout << "Pearson statistic P-value: "

<< logfit.PearsonStatisticPValue() << std::endl;

std::cout << "Pearson statistic 10% critical value: "

<< logfit.PearsonStatisticCriticalValue(0.10) << std::endl;

std::cout << "Pearson statistic degrees of freedom: "

<< logfit.PearsonStatisticDegreesOfFreedom() << std::endl;

std::cout << "Predictor data groups for Pearson statistic: "

<< logfit.predictorDataGroups() << std::endl;

std::cout << std::endl;

std::cout << "HL statistic: " << logfit.HLStatistic() << std::endl;

std::cout << "HL statistic P-value: " << logfit.HLStatisticPValue()

<< std::endl;

std::cout << "HL statistic 10% critical value: "

<< logfit.HLStatisticCriticalValue(0.10) << std::endl;

std::cout << "HL statistic degrees of freedom: "

<< logfit.HLStatisticDegreesOfFreedom() << std::endl;

std::cout << "Bin counts for predictions: "

<< logfit.HLStatisticOutputHistogram() << std::endl;

std::cout << "Bin counts for predictions associated\n"

<< "with positive observations: "

<< logfit.HLStatisticPosObsHistogram() << std::endl;

std::cout << std::std::endl;

return 0;

}

Constructor & Destructor Documentation

RWLogisticFitAnalysis::RWLogisticFitAnalysis ( )

Constructs an empty logistic regression fit analysis object. Behavior is undefined.

RWLogisticFitAnalysis::RWLogisticFitAnalysis ( const RWLogisticFitAnalysis & rhs )

Constructs a copy of rhs.

RWLogisticFitAnalysis::RWLogisticFitAnalysis ( const RWLogisticRegression & reg )

Constructs an analysis object for the logistic regression object reg.

RWLogisticFitAnalysis::~RWLogisticFitAnalysis ( )

inline

Destructor.

Member Function Documentation

double RWLogisticFitAnalysis::deviance ( ) const

inline

Returns the deviance from the saturated model.

double RWLogisticFitAnalysis::GStatistic ( ) const

inline

Returns the G statistic value, described in the Business Analysis Module User's Guide, relative to the intercept only model.

double RWLogisticFitAnalysis::GStatisticCriticalValue ( double alpha = .05 ) const

inline

Returns the critical value for the G statistic at the specified significance level.

int RWLogisticFitAnalysis::GStatisticDegreesOfFreedom ( ) const

inline

Returns the degrees of freedom for the G statistic test value.

double RWLogisticFitAnalysis::GStatisticPValue ( ) const

inline

Returns the P-value for the G statistic according to a chi-square distribution.

double RWLogisticFitAnalysis::HLStatistic ( ) const

inline

Returns the Hosmer-Lemeshow test statistic, described in the Business Analysis Module User's Guide.

double RWLogisticFitAnalysis::HLStatisticCriticalValue ( double alpha = .05 ) const

inline

Returns the critical value for the Hosmer-Lemeshow statistic according to a chi-square distribution.

int RWLogisticFitAnalysis::HLStatisticDegreesOfFreedom ( ) const

inline

Returns degrees of freedom for the Hosmer-Lemeshow statistic test value.

const Histogram& RWLogisticFitAnalysis::HLStatisticOutputHistogram ( ) const

inline

Returns the histogram of model predictions used in computing the Hosmer-Lemeshow statistic. This can be used to verify the exact bin boundaries, which can be critical to the statistic's value.

const Histogram& RWLogisticFitAnalysis::HLStatisticPosObsHistogram ( ) const

inline

Returns the histogram of model predictions whose corresponding observation value is positive. Seeing this histogram can be very useful for understanding the value for the statistic.

double RWLogisticFitAnalysis::HLStatisticPValue ( ) const

inline

Returns the P-value for the Hosmer-Lemeshow statistic according to a chi-square distribution.

double RWLogisticFitAnalysis::logLikelihood ( ) const

inline

Returns log likelihood of the logistic regression model.

size_t RWLogisticFitAnalysis::modelOutputGroups ( ) const

inline

Returns the number of groups used for computing the Hosmer-Lemeshow statistic.

RWLogisticFitAnalysis& RWLogisticFitAnalysis::operator= ( const RWLogisticFitAnalysis & rhs )

Sets self equal to rhs.

double RWLogisticFitAnalysis::PearsonStatistic ( ) const

inline

Returns the Pearson test statistic. See the Business Analysis Module User's Guide for more information.

double RWLogisticFitAnalysis::PearsonStatisticCriticalValue ( double alpha = .05 ) const

inline

Returns the critical value for the Pearson statistic according to a chi-square distribution.

int RWLogisticFitAnalysis::PearsonStatisticDegreesOfFreedom ( ) const

inline

Returns degrees of freedom for the Pearson statistic test value.

double RWLogisticFitAnalysis::PearsonStatisticPValue ( ) const

inline

Returns the P-value for the Pearson statistic according to a chi-square distribution.

const RWMathVec<size_t> RWLogisticFitAnalysis::predictorDataGroups ( ) const

inline

Returns the grouping used for the predictor matrix during computation of a Pearson statistic.

int RWLogisticFitAnalysis::regressionDegreesOfFreedom ( ) const

inline

Returns the regression degrees of freedom for a regression model. This is defined as the number of predictor variables in the model.

int RWLogisticFitAnalysis::residualDegreesOfFreedom ( ) const

inline

Returns the residual degrees of freedom for a regression model. This is defined as the number of predictor patterns minus the number of parameters in the regression model.

void RWLogisticFitAnalysis::setLogisticRegression ( const RWLogisticRegression & lr )

Specifies which regression model will be used for the fit analysis.

void RWLogisticFitAnalysis::setModelOutputGroups ( size_t ngroups )

Sets the grouping that should be used for the model's predicted values when computing a Hosmer-Lemeshow statistic. The model's predicted values are organized into a number of equal-mass bins; the number of bins is equal to ngroups.

void RWLogisticFitAnalysis::setPredictorDataGroups ( const RWMathVec< size_t > & groups )

Sets the grouping that should be used for the predictor matrix when computing a Pearson statistic. The length of groups should equal the number of predictor variables in the model. The element groups[k] specifies the number of equally-spaced groups to be used for predictor variable k's data. Each element of groups must have a positive (nonzero) value.

SourcePro® API Reference Guide

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation