rf101_basics.C File Reference

Detailed Description

'BASIC FUNCTIONALITY' RooFit tutorial macro #101

Fitting, plotting, toy data generation on one-dimensional p.d.f

pdf = gauss(x,m,s)

 
 
␛[1mRooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby␛[0m 
                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 mean         1.00000e+00  2.00000e+00   -1.00000e+01  1.00000e+01
     2 sigma        3.00000e+00  9.90000e-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        1000           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=25019.2 FROM MIGRAD    STATUS=INITIATE       10 CALLS          11 TOTAL
                     EDM= unknown      STRATEGY= 1      NO ERROR MATRIX       
  EXT PARAMETER               CURRENT GUESS       STEP         FIRST   
  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE 
   1  mean         1.00000e+00   2.00000e+00   2.02430e-01  -1.99004e+02
   2  sigma        3.00000e+00   9.90000e-01   2.22742e-01   1.98823e+02
                               ERR DEF= 0.5
 MIGRAD MINIMIZATION HAS CONVERGED.
 MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.
 COVARIANCE MATRIX CALCULATED SUCCESSFULLY
 FCN=25018.5 FROM MIGRAD    STATUS=CONVERGED      32 CALLS          33 TOTAL
                     EDM=5.80039e-07    STRATEGY= 1      ERROR MATRIX ACCURATE 
  EXT PARAMETER                                   STEP         FIRST   
  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE 
   1  mean         1.01746e+00   3.00149e-02   3.29345e-04  -8.41130e-02
   2  sigma        2.97870e+00   2.19221e-02   5.32112e-04   1.48724e-01
                               ERR DEF= 0.5
 EXTERNAL ERROR MATRIX.    NDIM=  25    NPAR=  2    ERR DEF=0.5
  9.009e-04  1.839e-05 
  1.839e-05  4.806e-04 
 PARAMETER  CORRELATION COEFFICIENTS  
       NO.  GLOBAL      1      2
        1  0.02795   1.000  0.028
        2  0.02795   0.028  1.000
 **********
 **    7 **SET ERR         0.5
 **********
 **********
 **    8 **SET PRINT           1
 **********
 **********
 **    9 **HESSE        1000
 **********
 COVARIANCE MATRIX CALCULATED SUCCESSFULLY
 FCN=25018.5 FROM HESSE     STATUS=OK             10 CALLS          43 TOTAL
                     EDM=5.80381e-07    STRATEGY= 1      ERROR MATRIX ACCURATE 
  EXT PARAMETER                                INTERNAL      INTERNAL  
  NO.   NAME      VALUE            ERROR       STEP SIZE       VALUE   
   1  mean         1.01746e+00   3.00144e-02   6.58691e-05   1.01922e-01
   2  sigma        2.97870e+00   2.19217e-02   2.12845e-05  -4.31732e-01
                               ERR DEF= 0.5
 EXTERNAL ERROR MATRIX.    NDIM=  25    NPAR=  2    ERR DEF=0.5
  9.009e-04  1.792e-05 
  1.792e-05  4.806e-04 
 PARAMETER  CORRELATION COEFFICIENTS  
       NO.  GLOBAL      1      2
        1  0.02723   1.000  0.027
        2  0.02723   0.027  1.000
[#1] INFO:Minization -- RooMinimizer::optimizeConst: deactivating const optimization
RooRealVar::mean = 1.01746 +/- 0.0300144  L(-10 - 10) 
RooRealVar::sigma = 2.9787 +/- 0.0219217  L(0.1 - 10) 

 
 
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "TCanvas.h"
#include "RooPlot.h"
#include "TAxis.h"
using namespace RooFit ;
 
 
void rf101_basics()
{
   // S e t u p   m o d e l 
   // ---------------------
 
   // Declare variables x,mean,sigma with associated name, title, initial value and allowed range
   RooRealVar x("x","x",-10,10) ;
   RooRealVar mean("mean","mean of gaussian",1,-10,10) ;
   RooRealVar sigma("sigma","width of gaussian",1,0.1,10) ;
 
   // Build gaussian p.d.f in terms of x,mean and sigma
   RooGaussian gauss("gauss","gaussian PDF",x,mean,sigma) ;  
 
   // Construct plot frame in 'x'
   RooPlot* xframe = x.frame(Title("Gaussian p.d.f.")) ;
 
 
   // P l o t   m o d e l   a n d   c h a n g e   p a r a m e t e r   v a l u e s
   // ---------------------------------------------------------------------------
 
   // Plot gauss in frame (i.e. in x) 
   gauss.plotOn(xframe) ;
 
   // Change the value of sigma to 3
   sigma.setVal(3) ;
 
   // Plot gauss in frame (i.e. in x) and draw frame on canvas
   gauss.plotOn(xframe,LineColor(kRed)) ;
  
 
   // G e n e r a t e   e v e n t s 
   // -----------------------------
 
   // Generate a dataset of 1000 events in x from gauss
   RooDataSet* data = gauss.generate(x,10000) ;  
  
   // Make a second plot frame in x and draw both the 
   // data and the p.d.f in the frame
   RooPlot* xframe2 = x.frame(Title("Gaussian p.d.f. with data")) ;
   data->plotOn(xframe2) ;
   gauss.plotOn(xframe2) ;
  
 
   // F i t   m o d e l   t o   d a t a
   // -----------------------------
 
   // Fit pdf to data
   gauss.fitTo(*data) ;
 
   // Print values of mean and sigma (that now reflect fitted values and errors)
   mean.Print() ;
   sigma.Print() ;
 
   // Draw all frames on a canvas
   TCanvas* c = new TCanvas("rf101_basics","rf101_basics",800,400) ;
   c->Divide(2) ;
   c->cd(1) ; gPad->SetLeftMargin(0.15) ; xframe->GetYaxis()->SetTitleOffset(1.6) ; xframe->Draw() ;
   c->cd(2) ; gPad->SetLeftMargin(0.15) ; xframe2->GetYaxis()->SetTitleOffset(1.6) ; xframe2->Draw() ;
  
 
}

Author

07/2008 - Wouter Verkerke

Definition in file rf101_basics.C.