rf601_intminuit.C

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/// \file
/// \ingroup tutorial_roofit
/// \notebook -js
///  'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #601
///
///  Interactive minimization with MINUIT
///
/// \macro_image
/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke 
 
 
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
#include "RooProdPdf.h"
#include "RooAddPdf.h"
#include "RooMinimizer.h"
#include "RooFitResult.h"
#include "RooPlot.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "TH1.h"
using namespace RooFit ;
 
 
void rf601_intminuit()
{
   // S e t u p   p d f   a n d   l i k e l i h o o d 
   // -----------------------------------------------
 
   // Observable
   RooRealVar x("x","x",-20,20) ;
 
   // Model (intentional strong correlations)
   RooRealVar mean("mean","mean of g1 and g2",0) ;
   RooRealVar sigma_g1("sigma_g1","width of g1",3) ;
   RooGaussian g1("g1","g1",x,mean,sigma_g1) ;
 
   RooRealVar sigma_g2("sigma_g2","width of g2",4,3.0,6.0) ;
   RooGaussian g2("g2","g2",x,mean,sigma_g2) ;
 
   RooRealVar frac("frac","frac",0.5,0.0,1.0) ;
   RooAddPdf model("model","model",RooArgList(g1,g2),frac) ;
 
   // Generate 1000 events
   RooDataSet* data = model.generate(x,1000) ;
   
   // Construct unbinned likelihood of model w.r.t. data
   RooAbsReal* nll = model.createNLL(*data) ;
 
   // I n t e r a c t i v e   m i n i m i z a t i o n ,   e r r o r   a n a l y s i s
   // -------------------------------------------------------------------------------
 
   // Create MINUIT interface object
   RooMinimizer m(*nll) ;
 
   // Activate verbose logging of MINUIT parameter space stepping
   m.setVerbose(kTRUE) ;
 
   // Call MIGRAD to minimize the likelihood
   m.migrad() ;
 
   // Print values of all parameters, that reflect values (and error estimates)
   // that are back propagated from MINUIT
   model.getParameters(x)->Print("s") ;
 
   // Disable verbose logging
   m.setVerbose(kFALSE) ;
 
   // Run HESSE to calculate errors from d2L/dp2
   m.hesse() ;
 
   // Print value (and error) of sigma_g2 parameter, that reflects
   // value and error back propagated from MINUIT
   sigma_g2.Print() ;
 
   // Run MINOS on sigma_g2 parameter only
   m.minos(sigma_g2) ;
 
   // Print value (and error) of sigma_g2 parameter, that reflects
   // value and error back propagated from MINUIT
   sigma_g2.Print() ;
 
 
 
   // S a v i n g   r e s u l t s ,   c o n t o u r   p l o t s 
   // ---------------------------------------------------------
 
   // Save a snapshot of the fit result. This object contains the initial
   // fit parameters, the final fit parameters, the complete correlation
   // matrix, the EDM, the minimized FCN , the last MINUIT status code and
   // the number of times the RooFit function object has indicated evaluation
   // problems (e.g. zero probabilities during likelihood evaluation)
   RooFitResult* r = m.save() ;
 
   // Make contour plot of mx vs sx at 1,2,3 sigma
   RooPlot* frame = m.contour(frac,sigma_g2,1,2,3) ;
   frame->SetTitle("Minuit contour plot") ;
 
   // Print the fit result snapshot
   r->Print("v") ;
 
 
 
   // C h a n g e   p a r a m e t e r   v a l u e s ,   f l o a t i n g
   // -----------------------------------------------------------------
 
   // At any moment you can manually change the value of a (constant)
   // parameter
   mean = 0.3 ;
   
   // Rerun MIGRAD,HESSE
   m.migrad() ;
   m.hesse() ;
   frac.Print() ;
 
   // Now fix sigma_g2
   sigma_g2.setConstant(kTRUE) ;
 
   // Rerun MIGRAD,HESSE
   m.migrad() ;
   m.hesse() ;
   frac.Print() ;
 
 
 
   new TCanvas("rf601_intminuit","rf601_intminuit",600,600) ;
   gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;
 
}