RWLogisticFitAnalysis

Does not inherit

• Local Index
• Synopsis
• Description
• Example
• Public Constructors
• Public Member Functions
• Public Member Operator

Members

deviance() GStatistic() GStatisticCriticalValue() GStatisticDegreesOfFreedom() GStatisticPValue() HLStatistic() HLStatisticCriticalValue()

HLStatisticDegreesOfFreedom() HLStatisticOutputHistogram() HLStatisticPosObsHistogram() HLStatisticPValue() logLikelihood() modelOutputGroups() operator=()

PearsonStatistic() PearsonStatisticCriticalValue() PearsonStatisticDegreesOfFreedom() PearsonStatisticPValue() predictorDataGroups() regressionDegreesOfFreedom() residualDegreesOfFreedom()

RWLogisticFitAnalysis() setLogisticRegression() setModelOutputGroups() setPredictorDataGroups()

#include <rw/stats/logregress.h>
#include <rw/stats/logfit.h>   
RWLogisticRegression lr;
RWLogisticFitAnalysis logfit(lr);

Given an instance of the class RWLogisticRegression, 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 Section 3.3.3.3, "Hosmer-Lemeshow Statistic," in the Business Analysis Module User's Guide.

#include <rw/analytics/logregress.h> 
#include <rw/analytics/logfit.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;

  // 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);

  cout << "Pearson statistic: " << logfit.PearsonStatistic() <<
          endl;
  cout << "Pearson statistic P-value: " <<
          logfit.PearsonStatisticPValue() << endl;
  cout << "Pearson statistic 10% critical value: " <<
          logfit.PearsonStatisticCriticalValue(0.10) << endl;
  cout << "Pearson statistic degrees of freedom: " << 
          logfit.PearsonStatisticDegreesOfFreedom() << endl;
  cout << "Predictor data groups for Pearson statistic: " <<
          logfit.predictorDataGroups() << endl;
  cout << endl;

  cout << "HL statistic: " << logfit.HLStatistic() << endl;
  cout << "HL statistic P-value: " << logfit.HLStatisticPValue()
       << endl;
  cout << "HL statistic 10% critical value: " <<
          logfit.HLStatisticCriticalValue(0.10) << endl;
  cout << "HL statistic degrees of freedom: " <<
          logfit.HLStatisticDegreesOfFreedom() << endl;
  cout << "Bin counts for predictions: " <<
          logfit.HLStatisticOutputHistogram() << endl;
  cout << "Bin counts for predictions associated \n
           with positive observations: "  << 
           logfit.HLStatisticPosObsHistogram() << endl;
  cout << endl;

  return 0;
}

RWLogisticFitAnalysis();

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

RWLogisticFitAnalysis(const RWLogisticFitAnalysis& t);

Constructs a copy of t.

RWLogisticFitAnalysis(const RWLogisticRegression& lr);

Constructs an analysis object for the logistic regression object lr.

double 
deviance() const;

Returns the deviance from the saturated model.

double 
GStatistic() const;

Returns the G statistic value, described in Section 3.3.3.1, "G Statistic," in the Business Analysis Module User's Guide, relative to the intercept only model.

double 
GStatisticCriticalValue(double alpha=.05) const;

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

int 
GStatisticDegreesOfFreedom() const;

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

double 
GStatisticPValue() const;

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

double 
HLStatistic() const;

Returns the Hosmer-Lemeshow test statistic, described in Section 3.3.3.3, "Hosmer-Lemeshow Statistic," in the Business Analysis Module User's Guide.

double 
HLStatisticCriticalValue(double alpha=.05) const;

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

int 
HLStatisticDegreesOfFreedom() const;

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

const Histogram& 
HLStatisticOutputHistogram() const;

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& 
HLStatisticPosObsHistogram() const;

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 
HLStatisticPValue() const;

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

double 
logLikelihood() const;

Returns log likelihood of the logistic regression model.

size_t
modelOutputGroups() const;

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

double 
PearsonStatistic() const;

Returns the Pearson test statistic. See Section 3.3.3.2, "Pearson Statistic," in the Business Analysis Module User's Guide for more information.

double 
PearsonStatisticCriticalValue(double alpha=.05) const;

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

int 
PearsonStatisticDegreesOfFreedom() const;

Returns degrees of freedom for the Pearson statistic test value.

double 
PearsonStatisticPValue() const;

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

const RWMathVec<size_t>
predictorDataGroups() const;

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

int 
regressionDegreesOfFreedom() const;

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

int 
residualDegreesOfFreedom() const;

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
setLogisticRegression(const RWLogisticRegression & lr);

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

void 
setModelOutputGroups(size_t groups);

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 groups.

void 
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.

RWLogisticFitAnalysis& 
operator=(const RWLogisticFitAnalysis& lf);

Sets self equal to lf.

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