rf403_weightedevts.C: 'DATA AND CATEGORIES' RooFit tutorial macro #403
//////////////////////////////////////////////////////////////////////////
//
// 'DATA AND CATEGORIES' RooFit tutorial macro #403
//
// Using weights in unbinned datasets
//
//
//
// 07/2008 - Wouter Verkerke
//
/////////////////////////////////////////////////////////////////////////
#ifndef __CINT__
#include "RooGlobalFunc.h"
#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooDataHist.h"
#include "RooGaussian.h"
#include "RooFormulaVar.h"
#include "RooGenericPdf.h"
#include "RooPolynomial.h"
#include "RooChi2Var.h"
#include "RooMinuit.h"
#include "TCanvas.h"
#include "RooPlot.h"
#include "RooFitResult.h"
using namespace RooFit ;
void rf403_weightedevts()
{
// C r e a t e o b s e r v a b l e a n d u n w e i g h t e d d a t a s e t
// -------------------------------------------------------------------------------
// Declare observable
RooRealVar x("x","x",-10,10) ;
x.setBins(40) ;
// Construction a uniform pdf
RooPolynomial p0("px","px",x) ;
// Sample 1000 events from pdf
RooDataSet* data = p0.generate(x,1000) ;
// C a l c u l a t e w e i g h t a n d m a k e d a t a s e t w e i g h t e d
// -----------------------------------------------------------------------------------
// Construct formula to calculate (fake) weight for events
RooFormulaVar wFunc("w","event weight","(x*x+10)",x) ;
// Add column with variable w to previously generated dataset
RooRealVar* w = (RooRealVar*) data->addColumn(wFunc) ;
// Dataset d is now a dataset with two observable (x,w) with 1000 entries
data->Print() ;
// Instruct dataset d in interpret w as event weight rather than as observable
data->setWeightVar(*w) ;
// Dataset d is now a dataset with one observable (x) with 1000 entries and a sum of weights of ~430K
data->Print() ;
// U n b i n n e d M L f i t t o w e i g h t e d d a t a
// ---------------------------------------------------------------
// Construction quadratic polynomial pdf for fitting
RooRealVar a0("a0","a0",1) ;
RooRealVar a1("a1","a1",0,-1,1) ;
RooRealVar a2("a2","a2",1,0,10) ;
RooPolynomial p2("p2","p2",x,RooArgList(a0,a1,a2),0) ;
// Fit quadratic polynomial to weighted data
// NOTE: Maximum likelihood fit to weighted data does in general
// NOT result in correct error estimates, unless individual
// event weights represent Poisson statistics themselves.
// In general, parameter error reflect precision of SumOfWeights
// events rather than NumEvents events. See comparisons below
RooFitResult* r_ml_wgt = p2.fitTo(*data,Save()) ;
// P l o t w e i g h e d d a t a a n d f i t r e s u l t
// ---------------------------------------------------------------
// Construct plot frame
RooPlot* frame = x.frame(Title("Unbinned ML fit, binned chi^2 fit to weighted data")) ;
// Plot data using sum-of-weights-squared error rather than Poisson errors
data->plotOn(frame,DataError(RooAbsData::SumW2)) ;
// Overlay result of 2nd order polynomial fit to weighted data
p2.plotOn(frame) ;
// M L F i t o f p d f t o e q u i v a l e n t u n w e i g h t e d d a t a s e t
// -----------------------------------------------------------------------------------------
// Construct a pdf with the same shape as p0 after weighting
RooGenericPdf genPdf("genPdf","x*x+10",x) ;
// Sample a dataset with the same number of events as data
RooDataSet* data2 = genPdf.generate(x,1000) ;
// Sample a dataset with the same number of weights as data
RooDataSet* data3 = genPdf.generate(x,43000) ;
// Fit the 2nd order polynomial to both unweighted datasets and save the results for comparison
RooFitResult* r_ml_unw10 = p2.fitTo(*data2,Save()) ;
RooFitResult* r_ml_unw43 = p2.fitTo(*data3,Save()) ;
// C h i 2 f i t o f p d f t o b i n n e d w e i g h t e d d a t a s e t
// ------------------------------------------------------------------------------------
// Construct binned clone of unbinned weighted dataset
RooDataHist* binnedData = data->binnedClone() ;
binnedData->Print("v") ;
// Perform chi2 fit to binned weighted dataset using sum-of-weights errors
//
// NB: Within the usual approximations of a chi2 fit, a chi2 fit to weighted
// data using sum-of-weights-squared errors does give correct error
// estimates
RooChi2Var chi2("chi2","chi2",p2,*binnedData,DataError(RooAbsData::SumW2)) ;
RooMinuit m(chi2) ;
m.migrad() ;
m.hesse() ;
// Plot chi^2 fit result on frame as well
RooFitResult* r_chi2_wgt = m.save() ;
p2.plotOn(frame,LineStyle(kDashed),LineColor(kRed)) ;
// C o m p a r e f i t r e s u l t s o f c h i 2 , M L f i t s t o ( u n ) w e i g h t e d d a t a
// ---------------------------------------------------------------------------------------------------------------
// Note that ML fit on 1Kevt of weighted data is closer to result of ML fit on 43Kevt of unweighted data
// than to 1Kevt of unweighted data, whereas the reference chi^2 fit with SumW2 error gives a result closer to
// that of an unbinned ML fit to 1Kevt of unweighted data.
cout << "==> ML Fit results on 1K unweighted events" << endl ;
r_ml_unw10->Print() ;
cout << "==> ML Fit results on 43K unweighted events" << endl ;
r_ml_unw43->Print() ;
cout << "==> ML Fit results on 1K weighted events with a summed weight of 43K" << endl ;
r_ml_wgt->Print() ;
cout << "==> Chi2 Fit results on 1K weighted events with a summed weight of 43K" << endl ;
r_chi2_wgt->Print() ;
new TCanvas("rf403_weightedevts","rf403_weightedevts",600,600) ;
frame->Draw() ;
}
Last change: Wed Dec 17 10:56:34 2008
Last generated: 2008-12-17 10:56
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