rf314_paramfitrange.C File Reference

Detailed Description

Multidimensional models: 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

 
#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)
   std::unique_ptr<RooDataSet> dall{model.generate(t, 10000)};
 
   // Generate a (fake) prototype dataset for acceptance limit values
   std::unique_ptr<RooDataSet> tmp{RooGaussian("gmin", "gmin", tmin, 0.0, 0.5).generate(tmin, 5000)};
 
   // Generate dataset with t values that observe (t>tmin)
   std::unique_ptr<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
   // -----------------------------------------------------------------------
 
   std::unique_ptr<RooFitResult> r{model.fitTo(*dacc, Save(), PrintLevel(-1))};
 
   // 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;
}

 
[#1] INFO:Fitting -- RooAbsPdf::fitTo(model_over_model_Int[t]) fixing normalization set for coefficient determination to observables in data
[#1] INFO:Fitting -- using CPU computation library compiled with -mavx512
[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_model_over_model_Int[t]_modelData) Summation contains a RooNLLVar, using its error level
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization
[#1] INFO:Plotting -- RooPlot::updateFitRangeNorm: New event count of 5000 will supersede previous event count of 10000 for normalization of PDF projections
 
  RooFitResult: minimized FCN value: 2823.97, estimated distance to minimum: 3.17108e-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>
 

Date

July 2008

Author

Wouter Verkerke

Definition in file rf314_paramfitrange.C.