doc/master/rf801__mcstudy_8C.html

#include "RooRealVar.h"

#include "RooDataSet.h"

#include "RooGaussian.h"

#include "RooChebychev.h"

#include "RooAddPdf.h"

#include "RooMCStudy.h"

#include "RooPlot.h"

#include "TCanvas.h"

#include "TAxis.h"

#include "TH2.h"

#include "RooFitResult.h"

#include "TStyle.h"

#include "TDirectory.h"


using namespace RooFit;


void rf801_mcstudy()

{

   // C r e a t e   m o d e l

   // -----------------------


   // Declare observable x

   RooRealVar x("x", "x", 0, 10);

   x.setBins(40);


   // Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and their parameters

   RooRealVar mean("mean", "mean of gaussians", 5, 0, 10);

   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, -1, 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 nbkg("nbkg", "number of background events,", 150, 0, 1000);

   RooRealVar nsig("nsig", "number of signal events", 150, 0, 1000);

   RooAddPdf model("model", "g1+g2+a", RooArgList(bkg, sig), RooArgList(nbkg, nsig));


   // C r e a t e   m a n a g e r

   // ---------------------------


   // Instantiate RooMCStudy manager on model with x as observable and given choice of fit options

   //

   // The Silence() option kills all messages below the PROGRESS level, leaving only a single message

   // per sample executed, and any error message that occur during fitting

   //

   // The Extended() option has two effects:

   //    1) The extended ML term is included in the likelihood and

   //    2) A poisson fluctuation is introduced on the number of generated events

   //

   // The FitOptions() given here are passed to the fitting stage of each toy experiment.

   // If Save() is specified, the fit result of each experiment is saved by the manager

   //

   // A Binned() option is added in this example to bin the data between generation and fitting

   // to speed up the study at the expense of some precision


   RooMCStudy *mcstudy =

      new RooMCStudy(model, x, Binned(true), Silence(), Extended(), FitOptions(Save(true), PrintEvalErrors(0)));


   // G e n e r a t e   a n d   f i t   e v e n t s

   // ---------------------------------------------


   // Generate and fit 1000 samples of Poisson(nExpected) events

   mcstudy->generateAndFit(1000);


   // E x p l o r e   r e s u l t s   o f   s t u d y

   // ------------------------------------------------


   // Make plots of the distributions of mean, the error on mean and the pull of mean

   RooPlot *frame1 = mcstudy->plotParam(mean, Bins(40));

   RooPlot *frame2 = mcstudy->plotError(mean, Bins(40));

   RooPlot *frame3 = mcstudy->plotPull(mean, Bins(40), FitGauss(true));


   // Plot distribution of minimized likelihood

   RooPlot *frame4 = mcstudy->plotNLL(Bins(40));


   // Make some histograms from the parameter dataset

   TH1 *hh_cor_a0_s1f = mcstudy->fitParDataSet().createHistogram("hh", a1, YVar(sig1frac));

   TH1 *hh_cor_a0_a1 = mcstudy->fitParDataSet().createHistogram("hh", a0, YVar(a1));


   // Access some of the saved fit results from individual toys

   TH2 *corrHist000 = mcstudy->fitResult(0)->correlationHist("c000");

   TH2 *corrHist127 = mcstudy->fitResult(127)->correlationHist("c127");

   TH2 *corrHist953 = mcstudy->fitResult(953)->correlationHist("c953");


   // Draw all plots on a canvas

   gStyle->SetOptStat(0);

   TCanvas *c = new TCanvas("rf801_mcstudy", "rf801_mcstudy", 900, 900);

   c->Divide(3, 3);

   c->cd(1);

   gPad->SetLeftMargin(0.15);

   frame1->GetYaxis()->SetTitleOffset(1.4);

   frame1->Draw();

   c->cd(2);

   gPad->SetLeftMargin(0.15);

   frame2->GetYaxis()->SetTitleOffset(1.4);

   frame2->Draw();

   c->cd(3);

   gPad->SetLeftMargin(0.15);

   frame3->GetYaxis()->SetTitleOffset(1.4);

   frame3->Draw();

   c->cd(4);

   gPad->SetLeftMargin(0.15);

   frame4->GetYaxis()->SetTitleOffset(1.4);

   frame4->Draw();

   c->cd(5);

   gPad->SetLeftMargin(0.15);

   hh_cor_a0_s1f->GetYaxis()->SetTitleOffset(1.4);

   hh_cor_a0_s1f->Draw("box");

   c->cd(6);

   gPad->SetLeftMargin(0.15);

   hh_cor_a0_a1->GetYaxis()->SetTitleOffset(1.4);

   hh_cor_a0_a1->Draw("box");

   c->cd(7);

   gPad->SetLeftMargin(0.15);

   corrHist000->GetYaxis()->SetTitleOffset(1.4);

   corrHist000->Draw("colz");

   c->cd(8);

   gPad->SetLeftMargin(0.15);

   corrHist127->GetYaxis()->SetTitleOffset(1.4);

   corrHist127->Draw("colz");

   c->cd(9);

   gPad->SetLeftMargin(0.15);

   corrHist953->GetYaxis()->SetTitleOffset(1.4);

   corrHist953->Draw("colz");


   // Make RooMCStudy object available on command line after

   // macro finishes

   gDirectory->Add(mcstudy);

}

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

RooAddPdf.h

RooChebychev.h

RooDataSet.h

RooFitResult.h

RooGaussian.h

RooMCStudy.h

RooPlot.h

RooRealVar.h

TAxis.h

TCanvas.h

TDirectory.h

gDirectory
#define gDirectory
Definition TDirectory.h:384

TH2.h

TStyle.h

gStyle
R__EXTERN TStyle * gStyle
Definition TStyle.h:436

gPad
#define gPad
Definition TVirtualPad.h:308

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:564

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:24

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

RooFitResult::correlationHist
TH2 * correlationHist(const char *name="correlation_matrix") const
Return TH2D of correlation matrix.
Definition RooFitResult.cxx:1086

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

RooMCStudy
Helper class to facilitate Monte Carlo studies such as 'goodness-of-fit' studies, that involve fittin...
Definition RooMCStudy.h:32

RooMCStudy::plotParam
RooPlot * plotParam(const RooRealVar &param, const RooCmdArg &arg1={}, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}, const RooCmdArg &arg6={}, const RooCmdArg &arg7={}, const RooCmdArg &arg8={})
Plot the distribution of the fitted value of the given parameter on a newly created frame.
Definition RooMCStudy.cxx:928

RooMCStudy::plotNLL
RooPlot * plotNLL(const RooCmdArg &arg1={}, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}, const RooCmdArg &arg6={}, const RooCmdArg &arg7={}, const RooCmdArg &arg8={})
Plot the distribution of the -log(L) values on a newly created frame.
Definition RooMCStudy.cxx:962

RooMCStudy::fitResult
const RooFitResult * fitResult(Int_t sampleNum) const
Return the RooFitResult of the fit with the given run number.
Definition RooMCStudy.cxx:831

RooMCStudy::plotError
RooPlot * plotError(const RooRealVar &param, const RooCmdArg &arg1={}, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}, const RooCmdArg &arg6={}, const RooCmdArg &arg7={}, const RooCmdArg &arg8={})
Plot the distribution of the fit errors for the specified parameter on a newly created frame.
Definition RooMCStudy.cxx:986

RooMCStudy::fitParDataSet
const RooDataSet & fitParDataSet()
Return a RooDataSet containing the post-fit parameters of each toy cycle.
Definition RooMCStudy.cxx:793

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:503

RooMCStudy::plotPull
RooPlot * plotPull(const RooRealVar &param, const RooCmdArg &arg1, const RooCmdArg &arg2={}, const RooCmdArg &arg3={}, const RooCmdArg &arg4={}, const RooCmdArg &arg5={}, const RooCmdArg &arg6={}, const RooCmdArg &arg7={}, const RooCmdArg &arg8={})
Plot the distribution of pull values for the specified parameter on a newly created frame.
Definition RooMCStudy.cxx:1087

RooPlot
Plot frame and a container for graphics objects within that frame.
Definition RooPlot.h:43

RooPlot::GetYaxis
TAxis * GetYaxis() const
Definition RooPlot.cxx:1224

RooPlot::Draw
void Draw(Option_t *options=nullptr) override
Draw this plot and all of the elements it contains.
Definition RooPlot.cxx:597

RooRealVar
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:59

TH1::GetYaxis
TAxis * GetYaxis()
Definition TH1.h:326

TH1::Draw
void Draw(Option_t *option="") override
Draw this histogram with options.
Definition TH1.cxx:3068

TH2
Service class for 2-D histogram classes.
Definition TH2.h:30

TStyle::SetOptStat
void SetOptStat(Int_t stat=1)
The type of information printed in the histogram statistics box can be selected via the parameter mod...
Definition TStyle.cxx:1640

x
Double_t x[n]
Definition legend1.C:17

RooFit
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
Definition CodegenImpl.h:64

rf801_mcstudy
Definition rf801_mcstudy.py:1