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) ;
}
void rf802_mcstudy_addons()
{
// C r e a t e m o d e l
// -----------------------
// Observables, parameters
RooRealVar x("x","x",-10,10) ;
x.setBins(10) ;
RooRealVar mean("mean","mean of gaussian",0) ;
RooRealVar sigma("sigma","width of gaussian",5,1,10) ;
// Create Gaussian pdf
RooGaussian gauss("gauss","gaussian PDF",x,mean,sigma) ;
// C r e a t e m a n a g e r w i t h c h i ^ 2 a d d - o n m o d u l e
// ----------------------------------------------------------------------------
// Create study manager for binned likelihood fits of a Gaussian pdf in 10 bins
RooMCStudy* mcs = new RooMCStudy(gauss,x,Silence(),Binned()) ;
// Add chi^2 calculator module to mcs
RooChi2MCSModule chi2mod ;
mcs->addModule(chi2mod) ;
// Generate 1000 samples of 1000 events
mcs->generateAndFit(2000,1000) ;
// Fill histograms with distributions chi2 and prob(chi2,ndf) that
// are calculated by RooChiMCSModule
TH1* hist_chi2 = mcs->fitParDataSet().createHistogram("chi2") ;
TH1* hist_prob = mcs->fitParDataSet().createHistogram("prob") ;
// C r e a t e m a n a g e r w i t h s e p a r a t e f i t m o d e l
// ----------------------------------------------------------------------------
// Create alternate pdf with shifted mean
RooRealVar mean2("mean2","mean of gaussian 2",0.5) ;
RooGaussian gauss2("gauss2","gaussian PDF2",x,mean2,sigma) ;
// Create study manager with separate generation and fit model. This configuration
// is set up to generate bad fits as the fit and generator model have different means
// and the mean parameter is not floating in the fit
RooMCStudy* mcs2 = new RooMCStudy(gauss2,x,FitModel(gauss),Silence(),Binned()) ;
// Add chi^2 calculator module to mcs
RooChi2MCSModule chi2mod2 ;
mcs2->addModule(chi2mod2) ;
// Generate 1000 samples of 1000 events
mcs2->generateAndFit(2000,1000) ;
// Fill histograms with distributions chi2 and prob(chi2,ndf) that
// are calculated by RooChiMCSModule
TH1* hist2_chi2 = mcs2->fitParDataSet().createHistogram("chi2") ;
TH1* hist2_prob = mcs2->fitParDataSet().createHistogram("prob") ;
hist2_chi2->SetLineColor(kRed) ;
hist2_prob->SetLineColor(kRed) ;
TCanvas* c = new TCanvas("rf802_mcstudy_addons","rf802_mcstudy_addons",800,400) ;
c->Divide(2) ;
c->cd(1) ; gPad->SetLeftMargin(0.15) ; hist_chi2->GetYaxis()->SetTitleOffset(1.4) ; hist_chi2->Draw() ; hist2_chi2->Draw("esame") ;
c->cd(2) ; gPad->SetLeftMargin(0.15) ; hist_prob->GetYaxis()->SetTitleOffset(1.4) ; hist_prob->Draw() ; hist2_prob->Draw("esame") ;
// Make RooMCStudy object available on command line after
// macro finishes
gDirectory->Add(mcs) ;
}
