/usr/include/root/RooStats/HypoTestResult.h is in libroot-roofit-dev 5.34.30-0ubuntu8.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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// 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);
// copy constructo
HypoTestResult(const HypoTestResult& other);
// constructor from name, null and alternate p values
HypoTestResult(const char* name, Double_t nullp, Double_t altp);
// destructor
virtual ~HypoTestResult();
// assignment operator
HypoTestResult & operator=(const HypoTestResult& other);
// 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; }
RooDataSet* GetNullDetailedOutput(void) const { return fNullDetailedOutput; }
RooDataSet* GetAltDetailedOutput(void) const { return fAltDetailedOutput; }
RooDataSet* GetFitInfo(void) const { return fFitInfo; }
Double_t GetTestStatisticData(void) const { return fTestStatisticData; }
const RooArgList* GetAllTestStatisticsData(void) const { return fAllTestStatisticsData; }
Bool_t HasTestStatisticData(void) const;
void SetAltDistribution(SamplingDistribution *alt);
void SetNullDistribution(SamplingDistribution *null);
void SetAltDetailedOutput(RooDataSet* d) { fAltDetailedOutput = d; }
void SetNullDetailedOutput(RooDataSet* d) { fNullDetailedOutput = d; }
void SetFitInfo(RooDataSet* d) { fFitInfo = d; }
void SetTestStatisticData(const Double_t tsd);
void SetAllTestStatisticsData(const RooArgList* 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;
/// The error on the Null p-value
Double_t NullPValueError() const;
/// The error on the significance, computed from NullPValueError via error propagation
Double_t SignificanceError() const;
void Print(const Option_t* = "") const;
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
const RooArgList* fAllTestStatisticsData; // for the case of multiple test statistics, holds all the results
SamplingDistribution *fNullDistr;
SamplingDistribution *fAltDistr;
RooDataSet* fNullDetailedOutput;
RooDataSet* fAltDetailedOutput;
RooDataSet* fFitInfo;
Bool_t fPValueIsRightTail;
Bool_t fBackgroundIsAlt;
ClassDef(HypoTestResult,3) // Base class to represent results of a hypothesis test
};
}
#endif
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