/usr/include/root/RooStats/RatioOfProfiledLikelihoodsTestStat.h is in libroot-roofit-dev 5.34.00-2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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// Authors: Kyle Cranmer, Sven Kreiss June 2010
/*************************************************************************
* 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. *
*************************************************************************/
#ifndef ROOSTATS_RatioOfProfiledLikelihoodsTestStat
#define ROOSTATS_RatioOfProfiledLikelihoodsTestStat
//_________________________________________________
/*
BEGIN_HTML
<p>
TestStatistic that returns the ratio of profiled likelihoods.
</p>
END_HTML
*/
//
#ifndef ROOT_Rtypes
#include "Rtypes.h"
#endif
#ifndef ROO_NLL_VAR
#include "RooNLLVar.h"
#endif
#ifndef ROOSTATS_TestStatistic
#include "RooStats/TestStatistic.h"
#endif
#ifndef ROOSTATS_ProfileLikelihoodTestStat
#include "RooStats/ProfileLikelihoodTestStat.h"
#endif
namespace RooStats {
class RatioOfProfiledLikelihoodsTestStat: public TestStatistic {
public:
RatioOfProfiledLikelihoodsTestStat() :
fNullProfile(),
fAltProfile(),
fAltPOI(NULL),
fSubtractMLE(true),
fDetailedOutputEnabled(false),
fDetailedOutput(NULL)
{
// Proof constructor. Don't use.
}
RatioOfProfiledLikelihoodsTestStat(RooAbsPdf& nullPdf, RooAbsPdf& altPdf,
const RooArgSet* altPOI=0) :
fNullProfile(nullPdf),
fAltProfile(altPdf),
fSubtractMLE(true),
fDetailedOutputEnabled(false),
fDetailedOutput(NULL)
{
/*
Calculates the ratio of profiled likelihoods.
By default the calculation is:
Lambda(mu_alt , conditional MLE for alt nuisance)
log --------------------------------------------
Lambda(mu_null , conditional MLE for null nuisance)
where Lambda is the profile likeihood ratio, so the
MLE for the null and alternate are subtracted off.
If SetSubtractMLE(false) then it calculates:
L(mu_alt , conditional MLE for alt nuisance)
log --------------------------------------------
L(mu_null , conditional MLE for null nuisance)
The values of the parameters of interest for the alternative
hypothesis are taken at the time of the construction.
If empty, it treats all free parameters as nuisance parameters.
The value of the parameters of interest for the null hypotheses
are given at each call of Evaluate(data,nullPOI).
*/
if(altPOI)
fAltPOI = (RooArgSet*) altPOI->snapshot();
else
fAltPOI = new RooArgSet(); // empty set
}
//__________________________________________
~RatioOfProfiledLikelihoodsTestStat(void) {
if(fAltPOI) delete fAltPOI;
if(fDetailedOutput) delete fDetailedOutput;
}
//__________________________________________
Double_t ProfiledLikelihood(RooAbsData& data, RooArgSet& poi, RooAbsPdf& pdf) {
// returns -logL(poi, conditonal MLE of nuisance params)
// it does not subtract off the global MLE
// because nuisance parameters of null and alternate may not
// be the same.
RooAbsReal* nll = pdf.createNLL(data, RooFit::CloneData(kFALSE));
RooAbsReal* profile = nll->createProfile(poi);
// make sure we set the variables attached to this nll
RooArgSet* attachedSet = nll->getVariables();
*attachedSet = poi;
// now evaluate profile to set nuisance to conditional MLE values
double nllVal = profile->getVal();
// but we may want the nll value without subtracting off the MLE
if(!fSubtractMLE) nllVal = nll->getVal();
delete attachedSet;
delete profile;
delete nll;
return nllVal;
}
//__________________________________________
virtual Double_t Evaluate(RooAbsData& data, RooArgSet& nullParamsOfInterest) {
// evaluate the ratio of profile likelihood
int type = (fSubtractMLE) ? 0 : 2;
// null
double nullNLL = fNullProfile.EvaluateProfileLikelihood(type, data, nullParamsOfInterest);
const RooArgSet *nullset = fNullProfile.GetDetailedOutput();
// alt
double altNLL = fAltProfile.EvaluateProfileLikelihood(type, data, *fAltPOI);
const RooArgSet *altset = fAltProfile.GetDetailedOutput();
if (fDetailedOutput != NULL) {
delete fDetailedOutput;
fDetailedOutput = NULL;
}
if (fDetailedOutputEnabled) {
fDetailedOutput = new RooArgSet();
RooRealVar* var(0);
for(TIterator *it = nullset->createIterator();(var = dynamic_cast<RooRealVar*>(it->Next()));) {
RooRealVar* cloneVar = new RooRealVar(TString::Format("nullprof_%s", var->GetName()),
TString::Format("%s for null", var->GetTitle()), var->getVal());
fDetailedOutput->addOwned(*cloneVar);
}
for(TIterator *it = altset->createIterator();(var = dynamic_cast<RooRealVar*>(it->Next()));) {
RooRealVar* cloneVar = new RooRealVar(TString::Format("altprof_%s", var->GetName()),
TString::Format("%s for null", var->GetTitle()), var->getVal());
fDetailedOutput->addOwned(*cloneVar);
}
}
/*
// set variables back to where they were
nullParamsOfInterest = *saveNullPOI;
*allVars = *saveAll;
delete saveAll;
delete allVars;
*/
return nullNLL -altNLL;
}
virtual void EnableDetailedOutput( bool e=true ) {
fDetailedOutputEnabled = e;
fNullProfile.EnableDetailedOutput(fDetailedOutputEnabled);
fAltProfile.EnableDetailedOutput(fDetailedOutputEnabled);
}
static void SetAlwaysReuseNLL(Bool_t flag) { fgAlwaysReuseNll = flag ; }
void SetReuseNLL(Bool_t flag) {
fNullProfile.SetReuseNLL(flag);
fAltProfile.SetReuseNLL(flag);
}
void SetMinimizer(const char* minimizer){
fNullProfile.SetMinimizer(minimizer);
fAltProfile.SetMinimizer(minimizer);
}
void SetStrategy(Int_t strategy){
fNullProfile.SetStrategy(strategy);
fAltProfile.SetStrategy(strategy);
}
void SetTolerance(Double_t tol){
fNullProfile.SetTolerance(tol);
fAltProfile.SetTolerance(tol);
}
void SetPrintLevel(Int_t printLevel){
fNullProfile.SetPrintLevel(printLevel);
fAltProfile.SetPrintLevel(printLevel);
}
virtual const RooArgSet* GetDetailedOutput(void) const {
// Returns detailed output. The value returned by this function is updated after each call to Evaluate().
// The returned RooArgSet contains the following for the alternative and null hypotheses:
// <ul>
// <li> the minimum nll, fitstatus and convergence quality for each fit </li>
// <li> for each fit and for each non-constant parameter, the value, error and pull of the parameter are stored </li>
// </ul>
return fDetailedOutput;
}
virtual const TString GetVarName() const { return "log(L(#mu_{1},#hat{#nu}_{1}) / L(#mu_{0},#hat{#nu}_{0}))"; }
// const bool PValueIsRightTail(void) { return false; } // overwrites default
void SetSubtractMLE(bool subtract){fSubtractMLE = subtract;}
private:
ProfileLikelihoodTestStat fNullProfile;
ProfileLikelihoodTestStat fAltProfile;
RooArgSet* fAltPOI;
Bool_t fSubtractMLE;
static Bool_t fgAlwaysReuseNll ;
bool fDetailedOutputEnabled;
RooArgSet* fDetailedOutput;
protected:
ClassDef(RatioOfProfiledLikelihoodsTestStat,3)
};
}
#endif
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