rf607_fitresult

Likelihood and minimization: demonstration of options of the RooFitResult class

Author: Wouter Verkerke
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Sunday, November 27, 2022 at 11:08 AM.

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%%cpp -d
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooAddPdf.h"
#include "RooChebychev.h"
#include "RooFitResult.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
#include "TFile.h"
#include "TStyle.h"
#include "TH2.h"
#include "TMatrixDSym.h"

using namespace RooFit;

Create pdf, data

Declare observable x

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

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

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RooRealVar mean("mean", "mean of gaussians", 5, -10, 10);
RooRealVar sigma1("sigma1", "width of gaussians", 0.5, 0.1, 10);
RooRealVar sigma2("sigma2", "width of gaussians", 1, 0.1, 10);

RooGaussian sig1("sig1", "Signal component 1", x, mean, sigma1);
RooGaussian sig2("sig2", "Signal component 2", x, mean, sigma2);

Build Chebychev polynomial pdf

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RooRealVar a0("a0", "a0", 0.5, 0., 1.);
RooRealVar a1("a1", "a1", -0.2);
RooChebychev bkg("bkg", "Background", x, RooArgSet(a0, a1));

Sum the signal components into a composite signal pdf

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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

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RooRealVar bkgfrac("bkgfrac", "fraction of background", 0.5, 0., 1.);
RooAddPdf model("model", "g1+g2+a", RooArgList(bkg, sig), bkgfrac);

Generate 1000 events

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RooDataSet *data = model.generate(x, 1000);

Fit pdf to data, save fitresult

Perform fit and save result

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RooFitResult *r = model.fitTo(*data, Save());

Summary printing: Basic info plus final values of floating fit parameters

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r->Print();

Verbose printing: Basic info, values of constant parameters, initial and final values of floating parameters, global correlations

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r->Print("v");

Visualize correlation matrix

Construct 2D color plot of correlation matrix

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gStyle->SetOptStat(0);
TH2 *hcorr = r->correlationHist();

Visualize ellipse corresponding to single correlation matrix element

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RooPlot *frame = new RooPlot(sigma1, sig1frac, 0.45, 0.60, 0.65, 0.90);
frame->SetTitle("Covariance between sigma1 and sig1frac");
r->plotOn(frame, sigma1, sig1frac, "ME12ABHV");

Access fit result information

Access basic information

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cout << "EDM = " << r->edm() << endl;
cout << "-log(L) at minimum = " << r->minNll() << endl;

Access list of final fit parameter values

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cout << "final value of floating parameters" << endl;
r->floatParsFinal().Print("s");

Access correlation matrix elements

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cout << "correlation between sig1frac and a0 is  " << r->correlation(sig1frac, a0) << endl;
cout << "correlation between bkgfrac and mean is " << r->correlation("bkgfrac", "mean") << endl;

Extract covariance and correlation matrix as TMatrixDSym

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const TMatrixDSym &cor = r->correlationMatrix();
const TMatrixDSym &cov = r->covarianceMatrix();

Print correlation, covariance matrix

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cout << "correlation matrix" << endl;
cor.Print();
cout << "covariance matrix" << endl;
cov.Print();

Persist fit result in root file

Open new ROOT file save save result

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TFile f("rf607_fitresult.root", "RECREATE");
r->Write("rf607");
f.Close();

In a clean ROOT session retrieve the persisted fit result as follows: RooFitResult* r = gDirectory->Get("rf607") ;

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TCanvas *c = new TCanvas("rf607_fitresult", "rf607_fitresult", 800, 400);
c->Divide(2);
c->cd(1);
gPad->SetLeftMargin(0.15);
hcorr->GetYaxis()->SetTitleOffset(1.4);
hcorr->Draw("colz");
c->cd(2);
gPad->SetLeftMargin(0.15);
frame->GetYaxis()->SetTitleOffset(1.6);
frame->Draw();

Draw all canvases

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gROOT->GetListOfCanvases()->Draw()