rf314_paramfitrange.C

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/// \file
/// \ingroup tutorial_roofit
/// \notebook -js
///  'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #314
///
///  Working with parametrized ranges in a fit. This an example of a
///  fit with an acceptance that changes per-event
///
///   pdf = exp(-t/tau) with t[tmin,5]
///
///   where t and tmin are both observables in the dataset
///
/// \macro_image
/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke


#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
#include "RooExponential.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
#include "RooFitResult.h"

using namespace RooFit ;


void rf314_paramfitrange()
{

   // D e f i n e   o b s e r v a b l e s   a n d   d e c a y   p d f
   // ---------------------------------------------------------------

   // Declare observables
   RooRealVar t("t","t",0,5) ;
   RooRealVar tmin("tmin","tmin",0,0,5) ;

   // Make parametrized range in t : [tmin,5]
   t.setRange(tmin,RooConst(t.getMax())) ;

   // Make pdf
   RooRealVar tau("tau","tau",-1.54,-10,-0.1) ;
   RooExponential model("model","model",t,tau) ;



   // C r e a t e   i n p u t   d a t a
   // ------------------------------------

   // Generate complete dataset without acceptance cuts (for reference)
   RooDataSet* dall = model.generate(t,10000) ;

   // Generate a (fake) prototype dataset for acceptance limit values
   RooDataSet* tmp = RooGaussian("gmin","gmin",tmin,RooConst(0),RooConst(0.5)).generate(tmin,5000) ;

   // Generate dataset with t values that observe (t>tmin)
   RooDataSet* dacc = model.generate(t,ProtoData(*tmp)) ;



   // F i t   p d f   t o   d a t a   i n   a c c e p t a n c e   r e g i o n
   // -----------------------------------------------------------------------

   RooFitResult* r = model.fitTo(*dacc,Save()) ;



   // P l o t   f i t t e d   p d f   o n   f u l l   a n d   a c c e p t e d   d a t a
   // ---------------------------------------------------------------------------------

   // Make plot frame, add datasets and overlay model
   RooPlot* frame = t.frame(Title("Fit to data with per-event acceptance")) ;
   dall->plotOn(frame,MarkerColor(kRed),LineColor(kRed)) ;
   model.plotOn(frame) ;
   dacc->plotOn(frame) ;

   // Print fit results to demonstrate absence of bias
   r->Print("v") ;


   new TCanvas("rf314_paramranges","rf314_paramranges",600,600) ;
   gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;

   return ;
}