rf607_fitresult.C

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/// \file
/// \ingroup tutorial_roofit
/// \notebook
///  'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #607
///
///  Demonstration of options of the RooFitResult class
///
/// \macro_image
/// \macro_output
/// \macro_code
/// \author 07/2008 - 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() ;

}