ProfileLikelihoodTestStat.h

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// @(#)root/roostats:$Id$
// Author: Kyle Cranmer, Lorenzo Moneta, Gregory Schott, Wouter Verkerke
// Additional Contributions: Giovanni Petrucciani
/*************************************************************************
 * 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_ProfileLikelihoodTestStat
#define ROOSTATS_ProfileLikelihoodTestStat
 
 
#include "Rtypes.h"
 
#include "RooStats/TestStatistic.h"
 
 
#include "RooRealVar.h"
 
#include "RooNLLVar.h"
 
#include "Math/MinimizerOptions.h"
 
#include "RooStats/RooStatsUtils.h"
 
 
namespace RooStats {
 
  class ProfileLikelihoodTestStat : public TestStatistic{
 
     enum LimitType {twoSided, oneSided, oneSidedDiscovery};
 
   public:
     ProfileLikelihoodTestStat() {
        // Proof constructor. Do not use.
        fPdf = 0;
        fNll = 0;
        fCachedBestFitParams = 0;
        fLastData = 0;
   fLimitType = twoSided;
   fSigned = false;
        fDetailedOutputWithErrorsAndPulls = false;
        fDetailedOutputEnabled = false;
        fDetailedOutput = NULL;
   fLOffset = RooStats::IsNLLOffset() ;
 
        fVarName = "Profile Likelihood Ratio";
        fReuseNll = false;
   fMinimizer=::ROOT::Math::MinimizerOptions::DefaultMinimizerType().c_str();
   fStrategy=::ROOT::Math::MinimizerOptions::DefaultStrategy();
        fTolerance=TMath::Max(1.,::ROOT::Math::MinimizerOptions::DefaultTolerance());
   fPrintLevel=::ROOT::Math::MinimizerOptions::DefaultPrintLevel();
 
     }
     ProfileLikelihoodTestStat(RooAbsPdf& pdf) {
       fPdf = &pdf;
       fNll = 0;
       fCachedBestFitParams = 0;
       fLastData = 0;
       fLimitType = twoSided;
       fSigned = false;
       fDetailedOutputWithErrorsAndPulls = false;
       fDetailedOutputEnabled = false;
       fDetailedOutput = NULL;
       fLOffset = RooStats::IsNLLOffset() ;
 
       fVarName = "Profile Likelihood Ratio";
       fReuseNll = false;
       fMinimizer=::ROOT::Math::MinimizerOptions::DefaultMinimizerType().c_str();
       fStrategy=::ROOT::Math::MinimizerOptions::DefaultStrategy();
       // avoid default tolerance to be too small (1. is default in RooMinimizer)
       fTolerance=TMath::Max(1.,::ROOT::Math::MinimizerOptions::DefaultTolerance());
       fPrintLevel=::ROOT::Math::MinimizerOptions::DefaultPrintLevel();
     }
 
     virtual ~ProfileLikelihoodTestStat() {
       if(fNll) delete fNll;
       if(fCachedBestFitParams) delete fCachedBestFitParams;
       if(fDetailedOutput) delete fDetailedOutput;
     }
 
     //LM use default copy constructor and assignment copying the pointers. Is this what we want ?
 
     void SetOneSided(Bool_t flag=true) {fLimitType = (flag ? oneSided : twoSided);}
     void SetOneSidedDiscovery(Bool_t flag=true) {fLimitType = (flag ? oneSidedDiscovery : twoSided);}
     void SetSigned(Bool_t flag=true) {fSigned = flag;}  // +/- t_mu instead of t_mu>0 with one-sided settings
     //void SetOneSidedDiscovery(Bool_t flag=true) {fOneSidedDiscovery = flag;}
 
     bool IsTwoSided() const { return fLimitType == twoSided; }
     bool IsOneSidedDiscovery() const { return fLimitType == oneSidedDiscovery; }
 
     static void SetAlwaysReuseNLL(Bool_t flag);
 
     void SetReuseNLL(Bool_t flag) { fReuseNll = flag ; }
     void SetLOffset(Bool_t flag=kTRUE) { fLOffset = flag ; }
 
     void SetMinimizer(const char* minimizer){ fMinimizer=minimizer;}
     void SetStrategy(Int_t strategy){fStrategy=strategy;}
     void SetTolerance(double tol){fTolerance=tol;}
     void SetPrintLevel(Int_t printlevel){fPrintLevel=printlevel;}
 
     // Main interface to evaluate the test statistic on a dataset
     virtual Double_t Evaluate(RooAbsData& data, RooArgSet& paramsOfInterest) {
        return EvaluateProfileLikelihood(0, data, paramsOfInterest);
     }
 
     // evaluate  the profile likelihood ratio (type = 0) or the minimum of likelihood (type=1) or the conditional LL (type = 2)
     virtual Double_t EvaluateProfileLikelihood(int type, RooAbsData &data, RooArgSet & paramsOfInterest);
 
     virtual void EnableDetailedOutput( bool e=true, bool withErrorsAndPulls=false ) {
        fDetailedOutputEnabled = e;
        fDetailedOutputWithErrorsAndPulls = withErrorsAndPulls;
        delete fDetailedOutput;
        fDetailedOutput = NULL;
     }
     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:
        // <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;
     }
 
     // set the conditional observables which will be used when creating the NLL
     // so the pdf's will not be normalized on the conditional observables when computing the NLL
     virtual void SetConditionalObservables(const RooArgSet& set) {fConditionalObs.removeAll(); fConditionalObs.add(set);}
 
     // set the global observables which will be used when creating the NLL
     // so the constraint pdf's will be normalized correctly on the global observables when computing the NLL
     virtual void SetGlobalObservables(const RooArgSet& set) {fGlobalObs.removeAll(); fGlobalObs.add(set);}
 
     virtual void SetVarName(const char* name) { fVarName = name; }
     virtual const TString GetVarName() const {return fVarName;}
 
     virtual RooAbsPdf * GetPdf() const { return fPdf; }
 
 
      //      const bool PValueIsRightTail(void) { return false; } // overwrites default
 
  private:
 
     RooFitResult* GetMinNLL();
 
   private:
 
      RooAbsPdf* fPdf;
      RooAbsReal* fNll; //!
      const RooArgSet* fCachedBestFitParams;
      RooAbsData* fLastData;
      //      Double_t fLastMLE;
      LimitType fLimitType;
      Bool_t fSigned;
 
      // this will store a snapshot of the unconditional nuisance
      // parameter fit.
      bool fDetailedOutputEnabled;
      bool fDetailedOutputWithErrorsAndPulls;
      RooArgSet* fDetailedOutput; //!
      RooArgSet fConditionalObs;    // conditional observables
      RooArgSet fGlobalObs;    // global observables
 
      TString fVarName;
 
      static Bool_t fgAlwaysReuseNll ;
      Bool_t fReuseNll ;
      TString fMinimizer;
      Int_t fStrategy;
      Double_t fTolerance;
      Int_t fPrintLevel;
      Bool_t fLOffset ;
 
   protected:
 
      ClassDef(ProfileLikelihoodTestStat,10)   // implements the profile likelihood ratio as a test statistic to be used with several tools
   };
}
 
 
#endif