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rf802_mcstudy_addons.C File Reference
Tutorials » RooFit Tutorials

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.

␛[1mRooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby␛[0m
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 "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);
// 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);
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
kRed
@ kRed
Definition Rtypes.h:66
gDirectory
#define gDirectory
Definition TDirectory.h:385
gPad
#define gPad
Definition TVirtualPad.h:288
RooChi2MCSModule
RooChi2MCSModule is an add-on module to RooMCStudy that calculates the chi-squared of fitted p....
Definition RooChi2MCSModule.h:22
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:1428
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:981
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
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
RooRealVar
RooRealVar represents a variable that can be changed from the outside.
Definition RooRealVar.h:39
TAttAxis::SetTitleOffset
virtual void SetTitleOffset(Float_t offset=1)
Set distance between the axis and the axis title.
Definition TAttAxis.cxx:302
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
virtual void SetTitle(const char *title)
See GetStatOverflows for more information.
Definition TH1.cxx:6667
TH1::GetYaxis
TAxis * GetYaxis()
Definition TH1.h:321
TH1::Draw
virtual void Draw(Option_t *option="")
Draw this histogram with options.
Definition TH1.cxx:3074
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:330
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 Common.h:18
rf802_mcstudy_addons
Definition rf802_mcstudy_addons.py:1
Date
July 2008
Author
Wouter Verkerke

Definition in file rf802_mcstudy_addons.C.