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Calculates a number of goodness of fit quantities for a given RWLogisticRegression instance. More...
#include <rw/analytics/logfit.h>
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.
#include <rw/stats/logregress.h> #include <rw/stats/logfit.h> RWLogisticRegression lr; RWLogisticFitAnalysis logfit(lr);
#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; }
| 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.
| 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.
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