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 Tuesday, March 19, 2024 at 07:16 PM.
%%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;
Declare observable x
RooRealVar x("x", "x", 0, 10);
Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and their parameters
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
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
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 bkgfrac("bkgfrac", "fraction of background", 0.5, 0., 1.);
RooAddPdf model("model", "g1+g2+a", RooArgList(bkg, sig), bkgfrac);
Generate 1000 events
std::unique_ptr<RooDataSet> data{model.generate(x, 1000)};
Perform fit and save result
std::unique_ptr<RooFitResult> r{model.fitTo(*data, Save(), PrintLevel(-1))};
Summary printing: Basic info plus final values of floating fit parameters
r->Print();
Verbose printing: Basic info, values of constant parameters, initial and final values of floating parameters, global correlations
r->Print("v");
Construct 2D color plot of correlation matrix
gStyle->SetOptStat(0);
TH2 *hcorr = r->correlationHist();
Visualize ellipse corresponding to single correlation matrix element
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 basic information
cout << "EDM = " << r->edm() << endl;
cout << "-log(L) at minimum = " << r->minNll() << endl;
Access list of final fit parameter values
cout << "final value of floating parameters" << endl;
r->floatParsFinal().Print("s");
Access correlation matrix elements
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
const TMatrixDSym &cor = r->correlationMatrix();
const TMatrixDSym &cov = r->covarianceMatrix();
Print correlation, covariance matrix
cout << "correlation matrix" << endl;
cor.Print();
cout << "covariance matrix" << endl;
cov.Print();
Open new ROOT file save save result
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") ;
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
gROOT->GetListOfCanvases()->Draw()