rf608_fitresultaspdf.C

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/// \file
/// \ingroup tutorial_roofit
/// \notebook
///  'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #608
///
///  Representing the parabolic approximation of the fit as
///  a multi-variate Gaussian on the parameters of the fitted p.d.f.
///
/// \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 "TH3.h"
 
using namespace RooFit ;
 
 
void rf608_fitresultaspdf()
{
   // C r e a t e   m o d e l   a n d   d a t a s e t 
   // -----------------------------------------------
 
   // Observable
   RooRealVar x("x","x",-20,20) ;
 
   // Model (intentional strong correlations)
   RooRealVar mean("mean","mean of g1 and g2",0,-1,1) ;
   RooRealVar sigma_g1("sigma_g1","width of g1",2) ; 
   RooGaussian g1("g1","g1",x,mean,sigma_g1) ;
 
   RooRealVar sigma_g2("sigma_g2","width of g2",4,3.0,5.0) ;
   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) ;
 
 
   // F i t   m o d e l   t o   d a t a 
   // ----------------------------------
 
   RooFitResult* r = model.fitTo(*data,Save()) ;
 
 
   // C r e a t e M V   G a u s s i a n   p d f   o f   f i t t e d    p a r a m e t e r s
   // ------------------------------------------------------------------------------------
 
   RooAbsPdf* parabPdf = r->createHessePdf(RooArgSet(frac,mean,sigma_g2)) ;
 
 
   // S o m e   e x e c e r c i s e s   w i t h   t h e   p a r a m e t e r   p d f 
   // -----------------------------------------------------------------------------
 
   // Generate 100K points in the parameter space, sampled from the MVGaussian p.d.f.
   RooDataSet* d = parabPdf->generate(RooArgSet(mean,sigma_g2,frac),100000) ;
 
 
   // Sample a 3-D histogram of the p.d.f. to be visualized as an error ellipsoid using the GLISO draw option
   TH3* hh_3d = (TH3*) parabPdf->createHistogram("mean,sigma_g2,frac",25,25,25) ;
   hh_3d->SetFillColor(kBlue) ;  
 
 
   // Project 3D parameter p.d.f. down to 3 permutations of two-dimensional p.d.f.s 
   // The integrations corresponding to these projections are performed analytically
   // by the MV Gaussian p.d.f.
   RooAbsPdf* pdf_sigmag2_frac = parabPdf->createProjection(mean) ;
   RooAbsPdf* pdf_mean_frac    = parabPdf->createProjection(sigma_g2) ;
   RooAbsPdf* pdf_mean_sigmag2 = parabPdf->createProjection(frac) ;
 
 
   // Make 2D plots of the 3 two-dimensional p.d.f. projections
   TH2* hh_sigmag2_frac = (TH2*) pdf_sigmag2_frac->createHistogram("sigma_g2,frac",50,50) ;
   TH2* hh_mean_frac    = (TH2*) pdf_mean_frac->createHistogram("mean,frac",50,50) ;
   TH2* hh_mean_sigmag2 = (TH2*) pdf_mean_sigmag2->createHistogram("mean,sigma_g2",50,50) ;
   hh_mean_frac->SetLineColor(kBlue) ;
   hh_sigmag2_frac->SetLineColor(kBlue) ;
   hh_mean_sigmag2->SetLineColor(kBlue) ;
 
 
   // Draw the 'sigar'
   new TCanvas("rf608_fitresultaspdf_1","rf608_fitresultaspdf_1",600,600) ;
   hh_3d->Draw("iso") ; 
 
   // Draw the 2D projections of the 3D p.d.f.
   TCanvas* c2 = new TCanvas("rf608_fitresultaspdf_2","rf608_fitresultaspdf_2",900,600) ;
   c2->Divide(3,2) ;
   c2->cd(1) ; gPad->SetLeftMargin(0.15) ; hh_mean_sigmag2->GetZaxis()->SetTitleOffset(1.4) ; hh_mean_sigmag2->Draw("surf3") ; 
   c2->cd(2) ; gPad->SetLeftMargin(0.15) ; hh_sigmag2_frac->GetZaxis()->SetTitleOffset(1.4) ; hh_sigmag2_frac->Draw("surf3") ; 
   c2->cd(3) ; gPad->SetLeftMargin(0.15) ; hh_mean_frac->GetZaxis()->SetTitleOffset(1.4) ; hh_mean_frac->Draw("surf3") ; 
 
   // Draw the distributions of parameter points sampled from the p.d.f.
   TH1* tmp1 = d->createHistogram("mean,sigma_g2",50,50) ;
   TH1* tmp2 = d->createHistogram("sigma_g2,frac",50,50) ;
   TH1* tmp3 = d->createHistogram("mean,frac",50,50) ;
 
   c2->cd(4) ; gPad->SetLeftMargin(0.15) ; tmp1->GetZaxis()->SetTitleOffset(1.4) ; tmp1->Draw("lego3") ;
   c2->cd(5) ; gPad->SetLeftMargin(0.15) ; tmp2->GetZaxis()->SetTitleOffset(1.4) ; tmp2->Draw("lego3") ;
   c2->cd(6) ; gPad->SetLeftMargin(0.15) ; tmp3->GetZaxis()->SetTitleOffset(1.4) ; tmp3->Draw("lego3") ;
 
}