rf602_chi2fit.C

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/// \file
/// \ingroup tutorial_roofit_main
/// \notebook -nodraw
/// Likelihood and minimization: setting up a chi^2 fit to a binned dataset
///
/// \macro_code
/// \macro_output
///
/// \date July 2008
/// \author Wouter Verkerke
 
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooChebychev.h"
#include "RooAddPdf.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 pdf
   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 pdf
   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
   // -----------------------------------------
 
   std::unique_ptr<RooDataSet> d{model.generate(x, 10000)};
   std::unique_ptr<RooDataHist> dh{d->binnedClone()};
 
   // Construct a chi^2 of the data and the model.
   // When a pdf 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 pdf, the expected number events is used
   // instead of the observed number of events.
   model.chi2FitTo(*dh, {PrintLevel(-1)});
 
   // 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
   std::unique_ptr<RooAbsData> dsmall{d->reduce(EventRange(1, 100))};
   std::unique_ptr<RooDataHist> dhsmall{static_cast<RooDataSet&>(*dsmall).binnedClone()};
   std::unique_ptr<RooAbsReal> chi2_lowstat{model.createChi2(*dhsmall)};
   cout << chi2_lowstat->getVal() << endl;
}