rf314_paramfitrange.C File Reference

Detailed Description

'MULTIDIMENSIONAL MODELS' RooFit tutorial macro #314

Working with parametrized ranges in a fit. This an example of a fit with an acceptance that changes per-event

pdf = exp(-t/tau) with t[tmin,5]

where t and tmin are both observables in the dataset

Processing /builddir/build/BUILD/root-6.10.00/tutorials/roofit/rf314_paramfitrange.C...

RooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby 
                Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University
                All rights reserved, please read http://roofit.sourceforge.net/license.txt

[#1] INFO:Minization -- RooMinimizer::optimizeConst: activating const optimization
 **********
 **    1 **SET PRINT           1
 **********
 **********
 **    2 **SET NOGRAD
 **********
 PARAMETER DEFINITIONS:
    NO.   NAME         VALUE      STEP SIZE      LIMITS
     1 tau         -1.54000e+00  7.20000e-01   -1.00000e+01 -1.00000e-01
 **********
 **    3 **SET ERR         0.5
 **********
 **********
 **    4 **SET PRINT           1
 **********
 **********
 **    5 **SET STR           1
 **********
 NOW USING STRATEGY  1: TRY TO BALANCE SPEED AGAINST RELIABILITY
 **********
 **    6 **MIGRAD         500           1
 **********
 FIRST CALL TO USER FUNCTION AT NEW START POINT, WITH IFLAG=4.
 START MIGRAD MINIMIZATION.  STRATEGY  1.  CONVERGENCE WHEN EDM .LT. 1.00e-03
 FCN=2824.02 FROM MIGRAD    STATUS=INITIATE        4 CALLS           5 TOTAL
                     EDM= unknown      STRATEGY= 1      NO ERROR MATRIX       
  EXT PARAMETER               CURRENT GUESS       STEP         FIRST   
  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE 
   1  tau         -1.54000e+00   7.20000e-01   2.12947e-01  -4.56069e+01
                               ERR DEF= 0.5
 MIGRAD MINIMIZATION HAS CONVERGED.
 MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.
 COVARIANCE MATRIX CALCULATED SUCCESSFULLY
 FCN=2823.97 FROM MIGRAD    STATUS=CONVERGED      12 CALLS          13 TOTAL
                     EDM=6.75021e-08    STRATEGY= 1      ERROR MATRIX ACCURATE 
  EXT PARAMETER                                   STEP         FIRST   
  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE 
   1  tau         -1.53353e+00   2.21980e-02   2.34054e-04   4.07752e-02
                               ERR DEF= 0.5
 EXTERNAL ERROR MATRIX.    NDIM=  25    NPAR=  1    ERR DEF=0.5
  4.928e-04 
 **********
 **    7 **SET ERR         0.5
 **********
 **********
 **    8 **SET PRINT           1
 **********
 **********
 **    9 **HESSE         500
 **********
 COVARIANCE MATRIX CALCULATED SUCCESSFULLY
 FCN=2823.97 FROM HESSE     STATUS=OK              5 CALLS          18 TOTAL
                     EDM=6.74739e-08    STRATEGY= 1      ERROR MATRIX ACCURATE 
  EXT PARAMETER                                INTERNAL      INTERNAL  
  NO.   NAME      VALUE            ERROR       STEP SIZE       VALUE   
   1  tau         -1.53353e+00   2.21980e-02   4.68108e-05   7.90063e-01
                               ERR DEF= 0.5
 EXTERNAL ERROR MATRIX.    NDIM=  25    NPAR=  1    ERR DEF=0.5
  4.928e-04 
[#1] INFO:Minization -- RooMinimizer::optimizeConst: deactivating const optimization
[#1] INFO:Plotting -- RooPlot::updateFitRangeNorm: New event count of 5000 will supercede previous event count of 10000 for normalization of PDF projections

  RooFitResult: minimized FCN value: 2823.97, estimated distance to minimum: 6.74739e-08
                covariance matrix quality: Full, accurate covariance matrix
                Status : MINIMIZE=0 HESSE=0 

    Floating Parameter  InitialValue    FinalValue +/-  Error     GblCorr.
  --------------------  ------------  --------------------------  --------
                   tau   -1.5400e+00   -1.5335e+00 +/-  2.22e-02  <none>

#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
#include "RooExponential.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
#include "RooFitResult.h"

using namespace RooFit ;


void rf314_paramfitrange()
{

   // D e f i n e   o b s e r v a b l e s   a n d   d e c a y   p d f
   // ---------------------------------------------------------------

   // Declare observables
   RooRealVar t("t","t",0,5) ;
   RooRealVar tmin("tmin","tmin",0,0,5) ;

   // Make parametrized range in t : [tmin,5]
   t.setRange(tmin,RooConst(t.getMax())) ;

   // Make pdf
   RooRealVar tau("tau","tau",-1.54,-10,-0.1) ;
   RooExponential model("model","model",t,tau) ;



   // C r e a t e   i n p u t   d a t a
   // ------------------------------------

   // Generate complete dataset without acceptance cuts (for reference)
   RooDataSet* dall = model.generate(t,10000) ;

   // Generate a (fake) prototype dataset for acceptance limit values
   RooDataSet* tmp = RooGaussian("gmin","gmin",tmin,RooConst(0),RooConst(0.5)).generate(tmin,5000) ;

   // Generate dataset with t values that observe (t>tmin)
   RooDataSet* dacc = model.generate(t,ProtoData(*tmp)) ;



   // F i t   p d f   t o   d a t a   i n   a c c e p t a n c e   r e g i o n
   // -----------------------------------------------------------------------

   RooFitResult* r = model.fitTo(*dacc,Save()) ;



   // P l o t   f i t t e d   p d f   o n   f u l l   a n d   a c c e p t e d   d a t a
   // ---------------------------------------------------------------------------------

   // Make plot frame, add datasets and overlay model
   RooPlot* frame = t.frame(Title("Fit to data with per-event acceptance")) ;
   dall->plotOn(frame,MarkerColor(kRed),LineColor(kRed)) ;
   model.plotOn(frame) ;
   dacc->plotOn(frame) ;

   // Print fit results to demonstrate absence of bias
   r->Print("v") ;


   new TCanvas("rf314_paramranges","rf314_paramranges",600,600) ;
   gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;

   return ;
}

Author

07/2008 - Wouter Verkerke

Definition in file rf314_paramfitrange.C.