rf607_fitresult.C File Reference

Detailed Description

Likelihood and minimization: demonstration of options of the RooFitResult class

 
#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;
 
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 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)};
 
   // 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
   std::unique_ptr<RooFitResult> r{model.fitTo(*data, Save(), PrintLevel(-1))};
 
   // 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();
}

 
[#1] INFO:Fitting -- RooAbsPdf::fitTo(model) fixing normalization set for coefficient determination to observables in data
[#1] INFO:Fitting -- using CPU computation library compiled with -mavx512
[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_model_modelData) Summation contains a RooNLLVar, using its error level
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization
 
  RooFitResult: minimized FCN value: 1885.34, estimated distance to minimum: 0.000381082
                covariance matrix quality: Full, accurate covariance matrix
                Status : MINIMIZE=0 HESSE=0 
 
    Floating Parameter    FinalValue +/-  Error   
  --------------------  --------------------------
                    a0    7.2873e-01 +/-  1.13e-01
               bkgfrac    4.3445e-01 +/-  8.57e-02
                  mean    5.0345e+00 +/-  3.36e-02
              sig1frac    7.7758e-01 +/-  9.71e-02
                sigma1    5.2318e-01 +/-  4.55e-02
                sigma2    1.7671e+00 +/-  1.18e+00
 
 
  RooFitResult: minimized FCN value: 1885.34, estimated distance to minimum: 0.000381082
                covariance matrix quality: Full, accurate covariance matrix
                Status : MINIMIZE=0 HESSE=0 
 
    Constant Parameter    Value     
  --------------------  ------------
                    a1   -2.0000e-01
 
    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.
  --------------------  ------------  --------------------------  --------
                    a0    5.0000e-01    7.2873e-01 +/-  1.13e-01  <none>
               bkgfrac    5.0000e-01    4.3445e-01 +/-  8.57e-02  <none>
                  mean    5.0000e+00    5.0345e+00 +/-  3.36e-02  <none>
              sig1frac    8.0000e-01    7.7758e-01 +/-  9.71e-02  <none>
                sigma1    5.0000e-01    5.2318e-01 +/-  4.55e-02  <none>
                sigma2    1.0000e+00    1.7671e+00 +/-  1.18e+00  <none>
 
EDM = 0.000381082
-log(L) at minimum = 1885.34
final value of floating parameters
  1) RooRealVar::       a0 = 0.72873 +/- 0.112573
  2) RooRealVar::  bkgfrac = 0.43445 +/- 0.085744
  3) RooRealVar::     mean = 5.03451 +/- 0.0336279
  4) RooRealVar:: sig1frac = 0.777578 +/- 0.0971233
  5) RooRealVar::   sigma1 = 0.523178 +/- 0.0455077
  6) RooRealVar::   sigma2 = 1.76714 +/- 1.18159
correlation between sig1frac and a0 is  -0.383713
correlation between bkgfrac and mean is -0.0516125
correlation matrix
 
6x6 matrix is as follows
 
     |      0    |      1    |      2    |      3    |      4    |
----------------------------------------------------------------------
   0 |          1     -0.8038    -0.02304     -0.3837      0.4249 
   1 |    -0.8038           1    -0.05161      0.6011     -0.4042 
   2 |   -0.02304    -0.05161           1    -0.08752    -0.04055 
   3 |    -0.3837      0.6011    -0.08752           1      0.2836 
   4 |     0.4249     -0.4042    -0.04055      0.2836           1 
   5 |     0.8347     -0.8794      0.0146     -0.2731      0.5878 
 
 
     |      5    |
----------------------------------------------------------------------
   0 |     0.8347 
   1 |    -0.8794 
   2 |     0.0146 
   3 |    -0.2731 
   4 |     0.5878 
   5 |          1 
 
covariance matrix
 
6x6 matrix is as follows
 
     |      0    |      1    |      2    |      3    |      4    |
----------------------------------------------------------------------
   0 |    0.01295   -0.007884  -8.818e-05   -0.004281    0.002201 
   1 |  -0.007884    0.007427  -0.0001496    0.005078   -0.001585 
   2 | -8.818e-05  -0.0001496    0.001131  -0.0002885  -6.206e-05 
   3 |  -0.004281    0.005078  -0.0002885     0.00961    0.001265 
   4 |   0.002201   -0.001585  -6.206e-05    0.001265    0.002071 
   5 |     0.1142    -0.09113   0.0005905     -0.0322     0.03217 
 
 
     |      5    |
----------------------------------------------------------------------
   0 |     0.1142 
   1 |   -0.09113 
   2 |  0.0005905 
   3 |    -0.0322 
   4 |    0.03217 
   5 |      1.446 
 

Date

July 2008

Author

Wouter Verkerke

Definition in file rf607_fitresult.C.