/// \file /// \ingroup tutorial_roofit /// \notebook /// Likelihood and minimization: demonstration of options of the RooFitResult class /// /// \macro_image /// \macro_output /// \macro_code /// /// \date 07/2008 /// \author Wouter Verkerke #include "RooRealVar.h" #include "RooDataSet.h" #include "RooGaussian.h" #include "RooConstVar.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; void rf607_fitresult() { // C r e a t e p d f , d a t a // -------------------------------- // 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 p.d.f. 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 p.d.f. 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 RooDataSet *data = model.generate(x, 1000); // F i t p d f t o d a t a , s a v e f i t r e s u l t // ------------------------------------------------------------- // Perform fit and save result RooFitResult *r = model.fitTo(*data, Save()); // P r i n t f i t r e s u l t s // --------------------------------- // 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"); // V i s u a l i z e c o r r e l a t i o n m a t r i x // ------------------------------------------------------- // 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"); // A c c e s s f i t r e s u l t i n f o r m a t i o n // --------------------------------------------------------- // 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(); // P e r s i s t f i t r e s u l t i n r o o t f i l e // ------------------------------------------------------------- // 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(); }