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Reference Guide
rf802_mcstudy_addons.C File Reference

Detailed Description

View in nbviewer Open in SWAN Validation and MC studies: RooMCStudy - using separate fit and generator models, using the chi^2 calculator model Running a biased fit model against an optimal fit.

RooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby
Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University
All rights reserved, please read http://roofit.sourceforge.net/license.txt
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[#0] WARNING:Generation -- The fit parameter 'mean' is not in the model that was used to generate toy data. The parameter 'mean2'=2 was found at the same position in the generator model. It will be used to compute pulls.
If this is not desired, the parameters of the generator model need to be renamed or reordered.
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
#include "RooChebychev.h"
#include "RooAddPdf.h"
#include "RooMCStudy.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
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");
hist_chi2->SetTitle("#chi^{2} values of all toy runs;#chi^{2}");
TH1 *hist_prob = mcs->fitParDataSet().createHistogram("prob");
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");
TH1 *hist2_prob = mcs2->fitParDataSet().createHistogram("prob");
hist2_chi2->SetLineColor(kRed);
hist2_prob->SetLineColor(kRed);
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);
}
Date
July 2008
Author
Wouter Verkerke

Definition in file rf802_mcstudy_addons.C.

c
#define c(i)
Definition: RSha256.hxx:101
RooDataSet::createHistogram
TH2F * createHistogram(const RooAbsRealLValue &var1, const RooAbsRealLValue &var2, const char *cuts="", const char *name="hist") const
Create a TH2F histogram of the distribution of the specified variable using this dataset.
Definition: RooDataSet.cxx:1437
RooChebychev.h
TDirectory.h
RooChi2MCSModule
RooChi2MCSModule is an add-on module to RooMCStudy that calculates the chi-squared of fitted p....
Definition: RooChi2MCSModule.h:22
RooMCStudy.h
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=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none())
Plot the distribution of pull values for the specified parameter on a newly created frame.
Definition: RooMCStudy.cxx:1217
TLegend.h
RooGaussian.h
x
Double_t x[n]
Definition: legend1.C:17
RooGaussian
Plain Gaussian p.d.f.
Definition: RooGaussian.h:24
RooAddPdf.h
TAttLine::SetLineColor
virtual void SetLineColor(Color_t lcolor)
Set the line color.
Definition: TAttLine.h:40
TCanvas.h
RooDataSet.h
TH1::SetTitle
virtual void SetTitle(const char *title)
See GetStatOverflows for more information.
Definition: TH1.cxx:6655
TGeant4Unit::gauss
static constexpr double gauss
Definition: TGeant4SystemOfUnits.h:263
TH1::GetYaxis
TAxis * GetYaxis()
Definition: TH1.h:321
RooFit
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
Definition: RooCFunction1Binding.h:29
RooFit::FitModel
RooCmdArg FitModel(RooAbsPdf &pdf)
Definition: RooGlobalFunc.cxx:267
RooPlot.h
gDirectory
#define gDirectory
Definition: TDirectory.h:236
RooMCStudy::fitParDataSet
const RooDataSet & fitParDataSet()
Return a RooDataSet containing the post-fit parameters of each toy cycle.
Definition: RooMCStudy.cxx:981
RooRealVar.h
kRed
@ kRed
Definition: Rtypes.h:66
RooFit::Binned
RooCmdArg Binned(Bool_t flag=kTRUE)
Definition: RooGlobalFunc.cxx:274
RooConstVar.h
RooChi2MCSModule.h
sigma
const Double_t sigma
Definition: h1analysisProxy.h:11
TCanvas
The Canvas class.
Definition: TCanvas.h:23
TAxis.h
TH1
TH1 is the base class of all histogramm classes in ROOT.
Definition: TH1.h:58
leg
leg
Definition: legend1.C:34
gPad
#define gPad
Definition: TVirtualPad.h:287
RooMCStudy::generateAndFit
Bool_t generateAndFit(Int_t nSamples, Int_t nEvtPerSample=0, Bool_t keepGenData=kFALSE, const char *asciiFilePat=0)
Generate and fit 'nSamples' samples of 'nEvtPerSample' events.
Definition: RooMCStudy.cxx:660
RooMCStudy::addModule
void addModule(RooAbsMCStudyModule &module)
Insert given RooMCStudy add-on module to the processing chain of this MCStudy object.
Definition: RooMCStudy.cxx:445
TLegend
This class displays a legend box (TPaveText) containing several legend entries.
Definition: TLegend.h:23
RooFit::Silence
RooCmdArg Silence(Bool_t flag=kTRUE)
Definition: RooGlobalFunc.cxx:266
TAttAxis::SetTitleOffset
virtual void SetTitleOffset(Float_t offset=1)
Set distance between the axis and the axis title.
Definition: TAttAxis.cxx:293
RooRealVar
RooRealVar represents a variable that can be changed from the outside.
Definition: RooRealVar.h:37
TH1.h
TH1::Draw
virtual void Draw(Option_t *option="")
Draw this histogram with options.
Definition: TH1.cxx:3050