doc/v630/rf803__mcstudy__addons2_8C.html

#include "RooRealVar.h"

#include "RooDataSet.h"

#include "RooGaussian.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(true),

                                    FitOptions(Extended(true), 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", AutoBinning(40), AutoBinning(40));

   TH1 *z_vs_ngen = mcs->fitParDataSet().createHistogram("ngen,significance_nullhypo_nsig", AutoBinning(40), AutoBinning(40));

   TH1 *errnsig_vs_ngen = mcs->fitParDataSet().createHistogram("ngen,nsigerr", AutoBinning(40), AutoBinning(40));

   TH1 *errnsig_vs_nsig = mcs->fitParDataSet().createHistogram("nsig,nsigerr", AutoBinning(40), AutoBinning(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);

}

c
#define c(i)
Definition RSha256.hxx:101

RooAddPdf.h

RooChebychev.h

RooDLLSignificanceMCSModule.h

RooDataSet.h

RooGaussian.h

RooMCStudy.h

RooPlot.h

RooRandomizeParamMCSModule.h

RooRealVar.h

TAxis.h

TCanvas.h

TDirectory.h

gDirectory
#define gDirectory
Definition TDirectory.h:384

TH1.h

gPad
#define gPad
Definition TVirtualPad.h:305

RooAbsData::createHistogram
TH1 * createHistogram(const char *name, const RooAbsRealLValue &xvar, const RooCmdArg &arg1={}, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}, const RooCmdArg &arg6={}, const RooCmdArg &arg7={}, const RooCmdArg &arg8={}) const
Calls createHistogram(const char *name, const RooAbsRealLValue& xvar, const RooLinkedList& argList) c...
Definition RooAbsData.cxx:562

RooAddPdf
Efficient implementation of a sum of PDFs of the form.
Definition RooAddPdf.h:33

RooArgList
RooArgList is a container object that can hold multiple RooAbsArg objects.
Definition RooArgList.h:22

RooArgSet
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition RooArgSet.h:55

RooChebychev
Chebychev polynomial p.d.f.
Definition RooChebychev.h:25

RooDLLSignificanceMCSModule
RooDLLSignificanceMCSModule is an add-on modules to RooMCStudy that calculates the significance of a ...
Definition RooDLLSignificanceMCSModule.h:23

RooGaussian
Plain Gaussian p.d.f.
Definition RooGaussian.h:24

RooMCStudy
RooMCStudy is a helper class to facilitate Monte Carlo studies such as 'goodness-of-fit' studies,...
Definition RooMCStudy.h:32

RooMCStudy::fitParDataSet
const RooDataSet & fitParDataSet()
Return a RooDataSet containing the post-fit parameters of each toy cycle.
Definition RooMCStudy.cxx:803

RooMCStudy::generateAndFit
bool generateAndFit(Int_t nSamples, Int_t nEvtPerSample=0, bool keepGenData=false, const char *asciiFilePat=nullptr)
Generate and fit 'nSamples' samples of 'nEvtPerSample' events.
Definition RooMCStudy.cxx:514

RooMCStudy::addModule
void addModule(RooAbsMCStudyModule &module)
Insert given RooMCStudy add-on module to the processing chain of this MCStudy object.
Definition RooMCStudy.cxx:306

RooRandomizeParamMCSModule
Add-on module to RooMCStudy that allows you to randomize input generation parameters.
Definition RooRandomizeParamMCSModule.h:24

RooRandomizeParamMCSModule::sampleSumUniform
void sampleSumUniform(const RooArgSet &paramSet, double lo, double hi)
Request uniform smearing of sum of parameters in paramSet uniform smearing in range [lo,...
Definition RooRandomizeParamMCSModule.cxx:137

RooRealVar
RooRealVar represents a variable that can be changed from the outside.
Definition RooRealVar.h:37

TAttAxis::SetTitleOffset
virtual void SetTitleOffset(Float_t offset=1)
Set distance between the axis and the axis title.
Definition TAttAxis.cxx:298

TCanvas
The Canvas class.
Definition TCanvas.h:23

TH1
TH1 is the base class of all histogram classes in ROOT.
Definition TH1.h:58

TH1::GetYaxis
TAxis * GetYaxis()
Definition TH1.h:323

TH1::Draw
void Draw(Option_t *option="") override
Draw this histogram with options.
Definition TH1.cxx:3067

c1
return c1
Definition legend1.C:41

c2
return c2
Definition legend2.C:14

RooFit
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
Definition JSONIO.h:26

rf803_mcstudy_addons2
Definition rf803_mcstudy_addons2.py:1