rf202_extendedmlfit.C

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/// \file
/// \ingroup tutorial_roofit
/// \notebook -js
///  'ADDITION AND CONVOLUTION' RooFit tutorial macro #202
///
///  Setting up an extended maximum likelihood fit
///
/// \macro_image
/// \macro_output
/// \macro_code
/// \author 07/2008 - Wouter Verkerke 
 
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooChebychev.h"
#include "RooAddPdf.h"
#include "RooExtendPdf.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
using namespace RooFit ;
 
 
void rf202_extendedmlfit()
{
 
   // S e t u p   c o m p o n e n t   p d f s 
   // ---------------------------------------
 
   // 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) ;
   RooRealVar sigma1("sigma1","width of gaussians",0.5) ;
   RooRealVar sigma2("sigma2","width of gaussians",1) ;
 
   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",0.2,0.,1.) ;
   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) ;
 
   //----------------
   // M E T H O D   1
   //================
 
 
   // C o n s t r u c t   e x t e n d e d   c o m p o s i t e   m o d e l 
   // -------------------------------------------------------------------
 
   // Sum the composite signal and background into an extended pdf nsig*sig+nbkg*bkg
   RooRealVar nsig("nsig","number of signal events",500,0.,10000) ;
   RooRealVar nbkg("nbkg","number of background events",500,0,10000) ;
   RooAddPdf  model("model","(g1+g2)+a",RooArgList(bkg,sig),RooArgList(nbkg,nsig)) ;
 
 
 
   // S a m p l e ,   f i t   a n d   p l o t   e x t e n d e d   m o d e l 
   // ---------------------------------------------------------------------
 
   // Generate a data sample of expected number events in x from model
   // = model.expectedEvents() = nsig+nbkg
   RooDataSet *data = model.generate(x) ;
 
   // Fit model to data, extended ML term automatically included
   model.fitTo(*data) ; 
 
   // Plot data and PDF overlaid, use expected number of events for p.d.f projection normalization
   // rather than observed number of events (==data->numEntries())
   RooPlot* xframe = x.frame(Title("extended ML fit example")) ;
   data->plotOn(xframe) ;
   model.plotOn(xframe,Normalization(1.0,RooAbsReal::RelativeExpected)) ;
 
   // Overlay the background component of model with a dashed line
   model.plotOn(xframe,Components(bkg),LineStyle(kDashed),Normalization(1.0,RooAbsReal::RelativeExpected)) ;
 
   // Overlay the background+sig2 components of model with a dotted line
   model.plotOn(xframe,Components(RooArgSet(bkg,sig2)),LineStyle(kDotted),Normalization(1.0,RooAbsReal::RelativeExpected)) ;
 
   // Print structure of composite p.d.f.
   model.Print("t") ;
 
 
   //----------------
   // M E T H O D   2
   //================
 
   // C o n s t r u c t   e x t e n d e d   c o m p o n e n t s   f i r s t
   // ---------------------------------------------------------------------
 
   // Associated nsig/nbkg as expected number of events with sig/bkg
   RooExtendPdf esig("esig","extended signal p.d.f",sig,nsig) ;
   RooExtendPdf ebkg("ebkg","extended background p.d.f",bkg,nbkg) ;
 
 
   // S u m   e x t e n d e d   c o m p o n e n t s   w i t h o u t   c o e f s 
   // -------------------------------------------------------------------------
 
   // Construct sum of two extended p.d.f. (no coefficients required)
   RooAddPdf  model2("model2","(g1+g2)+a",RooArgList(ebkg,esig)) ;
 
 
 
   // Draw the frame on the canvas
   new TCanvas("rf202_composite","rf202_composite",600,600) ;
   gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.4) ; xframe->Draw() ;
 
  
}