rf606_nllerrorhandling.C

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/// \file
/// \ingroup tutorial_roofit
/// \notebook -js
///  'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #606
///
///  Understanding and customizing error handling in likelihood evaluations
///
/// \macro_image
/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke


#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooArgusBG.h"
#include "RooNLLVar.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
using namespace RooFit ;


void rf606_nllerrorhandling()
{
   // C r e a t e   m o d e l  a n d   d a t a s e t
   // ----------------------------------------------

   // Observable
   RooRealVar m("m","m",5.20,5.30) ;

   // Parameters
   RooRealVar m0("m0","m0",5.291,5.20,5.30) ;
   RooRealVar k("k","k",-30,-50,-10) ;

   // Pdf
   RooArgusBG argus("argus","argus",m,m0,k) ;

   // Sample 1000 events in m from argus
   RooDataSet* data = argus.generate(m,1000) ;



   // P l o t   m o d e l   a n d   d a t a
   // --------------------------------------

   RooPlot* frame1 = m.frame(Bins(40),Title("Argus model and data")) ;
   data->plotOn(frame1) ;
   argus.plotOn(frame1) ;



   // F i t   m o d e l   t o   d a t a
   // ---------------------------------

   // The ARGUS background shape has a sharp kinematic cutoff at m=m0
   // and is prone to evaluation errors if the cutoff parameter m0
   // is floated: when the pdf cutoff value is lower than that in data
   // events with m>m0 will have zero probability

   // Perform unbinned ML fit. Print detailed error messages for up to
   // 10 events per likelihood evaluation. The default error handling strategy
   // is to return a very high value of the likelihood to MINUIT if errors occur,
   // which will force MINUIT to retreat from the problematic area

   argus.fitTo(*data,PrintEvalErrors(10)) ;

   // Perform another fit. In this configuration only the number of errors per
   // likelihood evaluation is shown, if it is greater than zero. The
   // EvalErrorWall(kFALSE) arguments disables the default error handling strategy
   // and will cause the actual (problematic) value of the likelihood to be passed
   // to MINUIT.
   //
   // NB: Use of this option is NOT recommended as default strategy as broken -log(L) values
   // can often be lower than 'good' ones because offending events are removed.
   // This may effectively create a false minimum in problem areas. This is clearly
   // illustrated in the second plot

   m0.setError(0.1) ;
   argus.fitTo(*data,PrintEvalErrors(0),EvalErrorWall(kFALSE)) ;



   // P l o t   l i k e l i h o o d   a s   f u n c t i o n   o f   m 0
   // ------------------------------------------------------------------

   // Construct likelihood function of model and data
   RooNLLVar nll("nll","nll",argus,*data) ;

   // Plot likelihood in m0 in range that includes problematic values
   // In this configuration no messages are printed for likelihood evaluation errors,
   // but if an likelihood value evaluates with error, the corresponding value
   // on the curve will be set to the value given in EvalErrorValue().

   RooPlot* frame2 = m0.frame(Range(5.288,5.293),Title("-log(L) scan vs m0, problematic regions masked")) ;
   nll.plotOn(frame2,PrintEvalErrors(-1),ShiftToZero(),EvalErrorValue(nll.getVal()+10),LineColor(kRed)) ;
   frame2->SetMaximum(15) ;
   frame2->SetMinimum(0) ;


   TCanvas* c = new TCanvas("rf606_nllerrorhandling","rf606_nllerrorhandling",1200,400) ;
   c->Divide(2) ;
   c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame1->GetYaxis()->SetTitleOffset(1.4) ; frame1->Draw() ;
   c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.4) ; frame2->Draw() ;

}