HypoTestInverterResult.h

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// @(#)root/roostats:$Id$
// Author: Kyle Cranmer, Lorenzo Moneta, Gregory Schott, Wouter Verkerke
/*************************************************************************
 * 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_HypoTestInverterResult
#define ROOSTATS_HypoTestInverterResult



#ifndef ROOSTATS_SimpleInterval
#include "RooStats/SimpleInterval.h"
#endif

#include "RooStats/HypoTestResult.h"

class RooRealVar;

namespace RooStats {

class SamplingDistribution;

/**
   HypoTestInverterResult class: holds the array of hypothesis test results and compute a confidence interval.
   Based on the RatioFinder code available in the RooStatsCms package developed by Gregory Schott and Danilo Piparo
   Ported and adapted to RooStats by Gregory Schott
   Some contributions to this class have been written by Matthias Wolf (error estimation)

   \ingroup Roostats
**/


class HypoTestInverterResult : public SimpleInterval {

public:

   /// default constructor
   explicit HypoTestInverterResult(const char* name = 0);

   /// constructor
   HypoTestInverterResult( const char* name, 
                           const RooRealVar& scannedVariable,
                           double cl ) ;

   HypoTestInverterResult( const HypoTestInverterResult& other, const char* name );

   /// destructor
   virtual ~HypoTestInverterResult();

   /// operator =
   HypoTestInverterResult& operator = (const HypoTestInverterResult& other);

   /// remove points that appear to have failed.
   int ExclusionCleanup();

   /// merge with the content of another HypoTestInverterResult object
   bool Add( const HypoTestInverterResult& otherResult );

   ///add the result of a single point (an HypoTestRsult) 
   bool Add( Double_t x, const HypoTestResult & result ); 

   /// function to return the value of the parameter of interest for the i^th entry in the results
   double GetXValue( int index ) const ;

   /// function to return the value of the confidence level for the i^th entry in the results
   double GetYValue( int index ) const ;

   /// function to return the estimated error on the value of the confidence level for the i^th entry in the results
   double GetYError( int index ) const ;

   /// return the observed CLsplusb value  for the i-th entry
   double CLsplusb( int index) const; 
 
   /// return the observed CLb value  for the i-th entry
   double CLb( int index) const; 

   /// return the observed CLb value  for the i-th entry
   double CLs( int index) const; 

   /// return the observed CLsplusb value  for the i-th entry
   double CLsplusbError( int index) const; 
 
   /// return the observed CLb value  for the i-th entry
   double CLbError( int index) const; 

   /// return the observed CLb value  for the i-th entry
   double CLsError( int index) const; 
   
   /// return a pointer to the i^th result object
   HypoTestResult* GetResult( int index ) const ;   

   double GetLastYValue( ) const  { return GetYValue(  fXValues.size()-1); }

   double GetLastXValue( ) const  { return GetXValue(  fXValues.size()-1); }

   double GetLastYError( ) const  { return GetYError(  fXValues.size()-1); }

   HypoTestResult * GetLastResult( ) const  { return GetResult(  fXValues.size()-1); }

   /// number of entries in the results array
   int ArraySize() const { return fXValues.size(); };

   int FindIndex(double xvalue) const;

   /// set the size of the test (rate of Type I error) (eg. 0.05 for a 95% Confidence Interval)
   virtual void SetTestSize( Double_t size ) { fConfidenceLevel = 1.-size; }

   /// set the confidence level for the interval (eg. 0.95 for a 95% Confidence Interval)
   virtual void SetConfidenceLevel( Double_t cl ) { fConfidenceLevel = cl; }

   /// set CLs threshold for exclusion cleanup function 
   inline void SetCLsCleanupThreshold( Double_t th ) { fCLsCleanupThreshold = th; }

   /// flag to switch between using CLsb (default) or CLs as confidence level
   void UseCLs( bool on = true ) { fUseCLs = on; }  

   /// query if one sided result
   bool IsOneSided() const { return !fIsTwoSided; }
   /// query if two sided result
   bool IsTwoSided() const { return fIsTwoSided; }

   /// lower and upper bound of the confidence interval (to get upper/lower limits, multiply the size( = 1-confidence level ) by 2
   Double_t LowerLimit();
   Double_t UpperLimit();

   /// rough estimation of the error on the computed bound of the confidence interval 
   /// Estimate of lower limit error
   ///function evaluates only a rought error on the lower limit. Be careful when using this estimation
   Double_t LowerLimitEstimatedError();

   /// Estimate of lower limit error
   ///function evaluates only a rought error on the lower limit. Be careful when using this estimation
   Double_t UpperLimitEstimatedError();

   /// return expected distribution of p-values (Cls or Clsplusb)
   
   SamplingDistribution * GetExpectedPValueDist(int index) const; 

   SamplingDistribution * GetBackgroundTestStatDist(int index ) const; 

   SamplingDistribution * GetSignalAndBackgroundTestStatDist(int index) const; 

   /// same in terms of alt and null
   SamplingDistribution * GetNullTestStatDist(int index) const { 
      return  GetSignalAndBackgroundTestStatDist(index);
   }
   SamplingDistribution * GetAltTestStatDist(int index) const { 
      return  GetBackgroundTestStatDist(index);
   }

   /// get expected lower limit distributions
   /// implemented using interpolation 
   ///  The size for the sampling distribution is given (by default is given by the average number of toy/point)
   SamplingDistribution* GetLowerLimitDistribution() const { return GetLimitDistribution(true); }

   /// get expected upper limit distributions
   /// implemented using interpolation
   SamplingDistribution* GetUpperLimitDistribution() const { return GetLimitDistribution(false); }

   /// get Limit value correspnding at the desired nsigma level (0) is median -1 sigma is 1 sigma
   double GetExpectedLowerLimit(double nsig = 0, const char * opt = "" ) const ; 

   /// get Limit value correspnding at the desired nsigma level (0) is median -1 sigma is 1 sigma
   double GetExpectedUpperLimit(double nsig = 0, const char * opt = "") const ; 


   double FindInterpolatedLimit(double target, bool lowSearch = false, double xmin=1, double xmax=0);

   enum InterpolOption_t { kLinear, kSpline };

   /// set the interpolation option, linear (kLinear ) or spline  (kSpline)
   void SetInterpolationOption( InterpolOption_t opt) { fInterpolOption = opt; }
   
   InterpolOption_t GetInterpolationOption() const { return fInterpolOption; }

private:


   double CalculateEstimatedError(double target, bool lower = true, double xmin = 1, double xmax = 0);

   int FindClosestPointIndex(double target, int mode = 0, double xtarget = 0);

   SamplingDistribution* GetLimitDistribution(bool lower ) const;

   double GetExpectedLimit(double nsig, bool lower, const char * opt = "" ) const ; 

   double GetGraphX(const TGraph & g, double y0, bool lowSearch, double &xmin, double &xmax) const;
   double GetGraphX(const TGraph & g, double y0, bool lowSearch = true) const { 
      double xmin=1; double xmax = 0;
      return GetGraphX(g,y0,lowSearch,xmin,xmax);
   }

 
protected:

   bool fUseCLs; 
   bool fIsTwoSided;                  /// two sided scan (look for lower/upper limit) 
   bool fInterpolateLowerLimit;
   bool fInterpolateUpperLimit;
   bool fFittedLowerLimit;
   bool fFittedUpperLimit;
   InterpolOption_t fInterpolOption;  /// interpolatation option (linear or spline)

   double fLowerLimitError;
   double fUpperLimitError;

   double fCLsCleanupThreshold;

   static double fgAsymptoticMaxSigma;  /// max sigma value used to scan asymptotic expected p values 

   std::vector<double> fXValues;

   TList fYObjects;       /// list of HypoTestResult for each point
   TList fExpPValues;     /// list of expected sampling distribution for each point

   friend class HypoTestInverter;
   friend class HypoTestInverterPlot;
   friend class HypoTestInverterOriginal;

   ClassDef(HypoTestInverterResult,5)  /// HypoTestInverterResult class      
};
}

#endif