doc/v630/rf802__mcstudy__addons_8C.html

#include "RooRealVar.h"

#include "RooDataSet.h"

#include "RooGaussian.h"

#include "RooChebychev.h"

#include "RooAddPdf.h"

#include "RooMCStudy.h"

#include "RooChi2MCSModule.h"

#include "RooPlot.h"

#include "TCanvas.h"

#include "TAxis.h"

#include "TH1.h"

#include "TDirectory.h"

#include "TLegend.h"


using namespace RooFit;


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, -2., 1.8);

   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);


   // Number of bins for chi2 plots

   int nBins = 100;


   // Fill histograms with distributions chi2 and prob(chi2,ndf) that

   // are calculated by RooChiMCSModule

   TH1 *hist_chi2 = mcs->fitParDataSet().createHistogram("chi2", AutoBinning(nBins));

   hist_chi2->SetTitle("#chi^{2} values of all toy runs;#chi^{2}");

   TH1 *hist_prob = mcs->fitParDataSet().createHistogram("prob", AutoBinning(nBins));

   hist_prob->SetTitle("Corresponding #chi^{2} probability;Prob(#chi^{2},ndof)");


   // 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", 2.);

   RooGaussian gauss2("gauss2", "gaussian PDF2", x, mean2, sigma);


   // Create study manager with separate generation and fit model. This configuration

   // is set up to generate biased fits as the fit and generator model have different means,

   // and the mean parameter is limited to [-2., 1.8], so it just misses the optimal

   // mean value of 2 in the data.

   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);


   // Request a the pull plot of mean. The pulls will be one-sided because

   // `mean` is limited to 1.8.

   // Note that RooFit will have trouble to compute the pulls because the parameters

   // are called `mean` in the fit, but `mean2` in the generator model. It is not obvious

   // that these are related. RooFit will nevertheless compute pulls, but complain that

   // this is risky.

   auto pullMeanFrame = mcs2->plotPull(mean);


   // Fill histograms with distributions chi2 and prob(chi2,ndf) that

   // are calculated by RooChiMCSModule

   TH1 *hist2_chi2 = mcs2->fitParDataSet().createHistogram("chi2", AutoBinning(nBins));

   TH1 *hist2_prob = mcs2->fitParDataSet().createHistogram("prob", AutoBinning(nBins));

   hist2_chi2->SetLineColor(kRed);

   hist2_prob->SetLineColor(kRed);


   TLegend leg;

   leg.AddEntry(hist_chi2, "Optimal fit", "L");

   leg.AddEntry(hist2_chi2, "Biased fit", "L");

   leg.SetBorderSize(0);

   leg.SetFillStyle(0);


   TCanvas *c = new TCanvas("rf802_mcstudy_addons", "rf802_mcstudy_addons", 800, 400);

   c->Divide(3);

   c->cd(1);

   gPad->SetLeftMargin(0.15);

   hist_chi2->GetYaxis()->SetTitleOffset(1.4);

   hist_chi2->Draw();

   hist2_chi2->Draw("esame");

   leg.DrawClone();

   c->cd(2);

   gPad->SetLeftMargin(0.15);

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

   hist_prob->Draw();

   hist2_prob->Draw("esame");

   c->cd(3);

   pullMeanFrame->Draw();


   // 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

RooChi2MCSModule.h

RooDataSet.h

RooGaussian.h

RooMCStudy.h

RooPlot.h

RooRealVar.h

kRed
@ kRed
Definition Rtypes.h:66

TAxis.h

TCanvas.h

TDirectory.h

gDirectory
#define gDirectory
Definition TDirectory.h:384

TH1.h

TLegend.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

RooChi2MCSModule
RooChi2MCSModule is an add-on module to RooMCStudy that calculates the chi-squared of fitted p....
Definition RooChi2MCSModule.h:22

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::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:1097

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

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

TAttFill::SetFillStyle
virtual void SetFillStyle(Style_t fstyle)
Set the fill area style.
Definition TAttFill.h:39

TAttLine::SetLineColor
virtual void SetLineColor(Color_t lcolor)
Set the line color.
Definition TAttLine.h:40

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::SetTitle
void SetTitle(const char *title) override
Change/set the title.
Definition TH1.cxx:6707

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

TLegend
This class displays a legend box (TPaveText) containing several legend entries.
Definition TLegend.h:23

TLegend::AddEntry
TLegendEntry * AddEntry(const TObject *obj, const char *label="", Option_t *option="lpf")
Add a new entry to this legend.
Definition TLegend.cxx:317

sigma
const Double_t sigma
Definition h1analysisProxy.h:11

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

leg
leg
Definition legend1.C:34

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

rf802_mcstudy_addons
Definition rf802_mcstudy_addons.py:1