// @(#)root/roostats:$Id: HypoTestResult.h 39928 2011-06-24 08:18:59Z moneta $ // Author: Kyle Cranmer, Lorenzo Moneta, Gregory Schott, Wouter Verkerke, Sven Kreiss /************************************************************************* * Copyright (C) 1995-2008, Rene Brun and Fons Rademakers. * * All rights reserved. * * * * For the licensing terms see $ROOTSYS/LICENSE. * * For the list of contributors see $ROOTSYS/README/CREDITS. * *************************************************************************/ //_________________________________________________ /* BEGIN_HTML <p> The p-value of the null for a given test statistic is rigorously defined and this is the starting point for the following conventions. </p> <h3>Conventions used in this class</h3> <p> The p-value for the null and alternate are on the <b>same side</b> of the observed value of the test statistic. This is the more standard convention and avoids confusion when doing inverted tests. </p> <p> For exclusion, we also want the formula CLs = CLs+b / CLb to hold which therefore defines our conventions for CLs+b and CLb. CLs was specifically invented for exclusion and therefore all quantities need be related through the assignments as they are for exclusion: <b>CLs+b = p_{s+b}; CLb = p_b</b>. This is derived by considering the scenarios of a powerful and not powerful inverted test, where for the not so powerful test, CLs must be close to one. </p> <p> For results of Hypothesis tests, CLs has no similar direct interpretation as for exclusion and can be larger than one. </p> END_HTML */ // #ifndef ROOSTATS_HypoTestResult #define ROOSTATS_HypoTestResult #ifndef ROOT_TNamed #include "TNamed.h" #endif #ifndef ROOSTATS_RooStatsUtils #include "RooStats/RooStatsUtils.h" #endif #ifndef ROOSTATS_SamplingDistribution #include "RooStats/SamplingDistribution.h" #endif namespace RooStats { class HypoTestResult : public TNamed { public: // default constructor explicit HypoTestResult(const char* name = 0); // constructor from name, null and alternate p values HypoTestResult(const char* name, Double_t nullp, Double_t altp); // destructor virtual ~HypoTestResult(); // add values from another HypoTestResult virtual void Append(const HypoTestResult *other); // Return p-value for null hypothesis virtual Double_t NullPValue() const { return fNullPValue; } // Return p-value for alternate hypothesis virtual Double_t AlternatePValue() const { return fAlternatePValue; } // Convert NullPValue into a "confidence level" virtual Double_t CLb() const { return !fBackgroundIsAlt ? NullPValue() : AlternatePValue(); } // Convert AlternatePValue into a "confidence level" virtual Double_t CLsplusb() const { return !fBackgroundIsAlt ? AlternatePValue() : NullPValue(); } // CLs is simply CLs+b/CLb (not a method, but a quantity) virtual Double_t CLs() const { double thisCLb = CLb(); if (thisCLb == 0) { std::cout << "Error: Cannot compute CLs because CLb = 0. Returning CLs = -1\n"; return -1; } double thisCLsb = CLsplusb(); return thisCLsb / thisCLb; } // familiar name for the Null p-value in terms of 1-sided Gaussian significance virtual Double_t Significance() const {return RooStats::PValueToSignificance( NullPValue() ); } SamplingDistribution* GetNullDistribution(void) const { return fNullDistr; } SamplingDistribution* GetAltDistribution(void) const { return fAltDistr; } Double_t GetTestStatisticData(void) const { return fTestStatisticData; } Bool_t HasTestStatisticData(void) const; void SetAltDistribution(SamplingDistribution *alt); void SetNullDistribution(SamplingDistribution *null); void SetTestStatisticData(const Double_t tsd); void SetPValueIsRightTail(Bool_t pr); Bool_t GetPValueIsRightTail(void) const { return fPValueIsRightTail; } void SetBackgroundAsAlt(Bool_t l = kTRUE) { fBackgroundIsAlt = l; } Bool_t GetBackGroundIsAlt(void) const { return fBackgroundIsAlt; } /// The error on the "confidence level" of the null hypothesis Double_t CLbError() const; /// The error on the "confidence level" of the alternative hypothesis Double_t CLsplusbError() const; /// The error on the ratio CLs+b/CLb Double_t CLsError() const; Double_t NullPValueError() const; void Print(const Option_t* = "") const { // Print out some information about the results // Note: use Alt/Null labels for the hypotheses here as the Null // might be the s+b hypothesis. cout << endl << "Results " << GetName() << ": " << endl; if(HasTestStatisticData() && fNullDistr) { cout << " - Null p-value = " << NullPValue() << " +/- " << NullPValueError() << endl; cout << " - Significance = " << Significance() << " sigma" << endl; } if(fAltDistr) cout << " - Number of Alt toys: " << fAltDistr->GetSize() << std::endl; if(fNullDistr) cout << " - Number of Null toys: " << fNullDistr->GetSize() << std::endl; if(HasTestStatisticData()) cout << " - Test statistic evaluated on data: " << fTestStatisticData << std::endl; if(HasTestStatisticData() && fNullDistr) cout << " - CL_b: " << CLb() << " +/- " << CLbError() << std::endl; if(HasTestStatisticData() && fAltDistr) cout << " - CL_s+b: " << CLsplusb() << " +/- " << CLsplusbError() << std::endl; if(HasTestStatisticData() && fAltDistr && fNullDistr) cout << " - CL_s: " << CLs() << " +/- " << CLsError() << std::endl; return; } private: void UpdatePValue(const SamplingDistribution* distr, Double_t &pvalue, Double_t &perror, Bool_t pIsRightTail); protected: mutable Double_t fNullPValue; // p-value for the null hypothesis (small number means disfavored) mutable Double_t fAlternatePValue; // p-value for the alternate hypothesis (small number means disfavored) mutable Double_t fNullPValueError; // error of p-value for the null hypothesis (small number means disfavored) mutable Double_t fAlternatePValueError; // error of p-value for the alternate hypothesis (small number means disfavored) Double_t fTestStatisticData; // result of the test statistic evaluated on data SamplingDistribution *fNullDistr; SamplingDistribution *fAltDistr; Bool_t fPValueIsRightTail; Bool_t fBackgroundIsAlt; ClassDef(HypoTestResult,2) // Base class to represent results of a hypothesis test }; } #endif