doc/v610/rf803__mcstudy__addons2_8C.html

#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
#include "RooChebychev.h"
#include "RooAddPdf.h"
#include "RooMCStudy.h"
#include "RooRandomizeParamMCSModule.h"
#include "RooDLLSignificanceMCSModule.h"
#include "RooPlot.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "TH1.h"
#include "TDirectory.h"
using namespace RooFit ;
void rf803_mcstudy_addons2()
{
   // C r e a t e   m o d e l
   // -----------------------
   // Simulation of signal and background of top quark decaying into
   // 3 jets with background
   // Observable
   RooRealVar mjjj("mjjj","m(3jet) (GeV)",100,85.,350.) ;
   // Signal component (Gaussian)
   RooRealVar mtop("mtop","m(top)",162) ;
   RooRealVar wtop("wtop","m(top) resolution",15.2) ;
   RooGaussian sig("sig","top signal",mjjj,mtop,wtop) ;
   // Background component (Chebychev)
   RooRealVar c0("c0","Chebychev coefficient 0",-0.846,-1.,1.) ;
   RooRealVar c1("c1","Chebychev coefficient 1", 0.112,-1.,1.) ;
   RooRealVar c2("c2","Chebychev coefficient 2", 0.076,-1.,1.) ;
   RooChebychev bkg("bkg","combinatorial background",mjjj,RooArgList(c0,c1,c2)) ;
   // Composite model
   RooRealVar nsig("nsig","number of signal events",53,0,1e3) ;
   RooRealVar nbkg("nbkg","number of background events",103,0,5e3) ;
   RooAddPdf model("model","model",RooArgList(sig,bkg),RooArgList(nsig,nbkg)) ;
   // C r e a t e   m a n a g e r
   // ---------------------------
   // Configure manager to perform binned extended likelihood fits (Binned(),Extended()) on data generated
   // with a Poisson fluctuation on Nobs (Extended())
   RooMCStudy* mcs = new RooMCStudy(model,mjjj,Binned(),Silence(),Extended(kTRUE),
                  FitOptions(Extended(kTRUE),PrintEvalErrors(-1))) ;
   // C u s t o m i z e   m a n a g e r
   // ---------------------------------
   // Add module that randomizes the summed value of nsig+nbkg
   // sampling from a uniform distribution between 0 and 1000
   //
   // In general one can randomize a single parameter, or a
   // sum of N parameters, using either a uniform or a Gaussian
   // distribution. Multiple randomization can be executed
   // by a single randomizer module

   RooRandomizeParamMCSModule randModule ;
   randModule.sampleSumUniform(RooArgSet(nsig,nbkg),50,500) ;
   mcs->addModule(randModule) ;
   // Add profile likelihood calculation of significance. Redo each
   // fit while keeping parameter nsig fixed to zero. For each toy,
   // the difference in -log(L) of both fits is stored, as well
   // a simple significance interpretation of the delta(-logL)
   // using Dnll = 0.5 sigma^2
   RooDLLSignificanceMCSModule sigModule(nsig,0) ;
   mcs->addModule(sigModule) ;
   // R u n   m a n a g e r ,   m a k e   p l o t s
   // ---------------------------------------------
   // Run 1000 experiments. This configuration will generate a fair number
   // of (harmless) MINUIT warnings due to the instability of the Chebychev polynomial fit
   // at low statistics.
   mcs->generateAndFit(500) ;
   // Make some plots
   TH1* dll_vs_ngen = mcs->fitParDataSet().createHistogram("ngen,dll_nullhypo_nsig",-40,-40) ;
   TH1* z_vs_ngen = mcs->fitParDataSet().createHistogram("ngen,significance_nullhypo_nsig",-40,-40) ;
   TH1* errnsig_vs_ngen = mcs->fitParDataSet().createHistogram("ngen,nsigerr",-40,-40) ;
   TH1* errnsig_vs_nsig = mcs->fitParDataSet().createHistogram("nsig,nsigerr",-40,-40) ;
   // Draw plots on canvas
   TCanvas* c = new TCanvas("rf803_mcstudy_addons2","rf802_mcstudy_addons2",800,800) ;
   c->Divide(2,2) ;
   c->cd(1) ; gPad->SetLeftMargin(0.15) ; dll_vs_ngen->GetYaxis()->SetTitleOffset(1.6) ; dll_vs_ngen->Draw("box") ;
   c->cd(2) ; gPad->SetLeftMargin(0.15) ; z_vs_ngen->GetYaxis()->SetTitleOffset(1.6) ; z_vs_ngen->Draw("box") ;
   c->cd(3) ; gPad->SetLeftMargin(0.15) ; errnsig_vs_ngen->GetYaxis()->SetTitleOffset(1.6) ; errnsig_vs_ngen->Draw("box") ;
   c->cd(4) ; gPad->SetLeftMargin(0.15) ; errnsig_vs_nsig->GetYaxis()->SetTitleOffset(1.6) ; errnsig_vs_nsig->Draw("box") ;

   // Make RooMCStudy object available on command line after
   // macro finishes
   gDirectory->Add(mcs) ;
}