```/////////////////////////////////////////////////////////////////////////
//
// 'VALIDATION AND MC STUDIES' RooFit tutorial macro #803
//
// RooMCStudy: Using the randomizer and profile likelihood add-on models
//
//
// 07/2008 - Wouter Verkerke
//
/////////////////////////////////////////////////////////////////////////

#ifndef __CINT__
#include "RooGlobalFunc.h"
#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
#include "RooChebychev.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 ;

{
// 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) ;

// 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) ;

// 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) ;

// 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
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

}

```