rf602_chi2fit.C

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/// \file
/// \ingroup tutorial_roofit
/// \notebook -nodraw
///  'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #602
///
///  Setting up a chi^2 fit to a binned dataset
///
/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke 


#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
#include "RooChebychev.h"
#include "RooAddPdf.h"
#include "RooChi2Var.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
using namespace RooFit ;


void rf602_chi2fit()
{

   // S e t u p   m o d e l
   // ---------------------

   // Declare observable x
   RooRealVar x("x","x",0,10) ;

   // Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and their parameters
   RooRealVar mean("mean","mean of gaussians",5) ;
   RooRealVar sigma1("sigma1","width of gaussians",0.5) ;
   RooRealVar sigma2("sigma2","width of gaussians",1) ;

   RooGaussian sig1("sig1","Signal component 1",x,mean,sigma1) ;  
   RooGaussian sig2("sig2","Signal component 2",x,mean,sigma2) ;  
   
   // Build Chebychev polynomial p.d.f.  
   RooRealVar a0("a0","a0",0.5,0.,1.) ;
   RooRealVar a1("a1","a1",0.2,0.,1.) ;
   RooChebychev bkg("bkg","Background",x,RooArgSet(a0,a1)) ;

   // Sum the signal components into a composite signal p.d.f.
   RooRealVar sig1frac("sig1frac","fraction of component 1 in signal",0.8,0.,1.) ;
   RooAddPdf sig("sig","Signal",RooArgList(sig1,sig2),sig1frac) ;

   // Sum the composite signal and background 
   RooRealVar bkgfrac("bkgfrac","fraction of background",0.5,0.,1.) ;
   RooAddPdf  model("model","g1+g2+a",RooArgList(bkg,sig),bkgfrac) ;


   // C r e a t e   b i n n e d   d a t a s e t
   // -----------------------------------------

   RooDataSet* d = model.generate(x,10000) ;
   RooDataHist* dh = d->binnedClone() ;

   // Construct a chi^2 of the data and the model.
   // When a p.d.f. is used in a chi^2 fit, the probability density scaled
   // by the number of events in the dataset to obtain the fit function
   // If model is an extended p.d.f, the expected number events is used
   // instead of the observed number of events.
   model.chi2FitTo(*dh) ;

   // NB: It is also possible to fit a RooAbsReal function to a RooDataHist
   // using chi2FitTo(). 

   // Note that entries with zero bins are _not_ allowed 
   // for a proper chi^2 calculation and will give error
   // messages  
   RooDataSet* dsmall = (RooDataSet*) d->reduce(EventRange(1,100)) ;
   RooDataHist* dhsmall = dsmall->binnedClone() ;
   RooChi2Var chi2_lowstat("chi2_lowstat","chi2",model,*dhsmall) ;
   cout << chi2_lowstat.getVal() << endl ;


}