doc/v606/rf707__kernelestimation_8C.html

#ifndef __CINT__

#include "RooGlobalFunc.h"

#endif

#include "RooRealVar.h"

#include "RooDataSet.h"

#include "RooGaussian.h"

#include "RooConstVar.h"

#include "RooPolynomial.h"

#include "RooKeysPdf.h"

#include "RooNDKeysPdf.h"

#include "RooProdPdf.h"

#include "TCanvas.h"

#include "TAxis.h"

#include "TH1.h"

#include "RooPlot.h"

using namespace RooFit ;


void rf707_kernelestimation()

{

  // C r e a t e   l o w   s t a t s   1 - D   d a t a s e t

  // -------------------------------------------------------


  // Create a toy pdf for sampling

  RooRealVar x("x","x",0,20) ;

  RooPolynomial p("p","p",x,RooArgList(RooConst(0.01),RooConst(-0.01),RooConst(0.0004))) ;


  // Sample 500 events from p

  RooDataSet* data1 = p.generate(x,200) ;


  // C r e a t e   1 - D   k e r n e l   e s t i m a t i o n   p d f

  // ---------------------------------------------------------------


  // Create adaptive kernel estimation pdf. In this configuration the input data

  // is mirrored over the boundaries to minimize edge effects in distribution

  // that do not fall to zero towards the edges

  RooKeysPdf kest1("kest1","kest1",x,*data1,RooKeysPdf::MirrorBoth) ;


  // An adaptive kernel estimation pdf on the same data without mirroring option

  // for comparison

  RooKeysPdf kest2("kest2","kest2",x,*data1,RooKeysPdf::NoMirror) ;


  // Adaptive kernel estimation pdf with increased bandwidth scale factor

  // (promotes smoothness over detail preservation)

  RooKeysPdf kest3("kest1","kest1",x,*data1,RooKeysPdf::MirrorBoth,2) ;


  // Plot kernel estimation pdfs with and without mirroring over data

  RooPlot* frame = x.frame(Title("Adaptive kernel estimation pdf with and w/o mirroring"),Bins(20)) ;

  data1->plotOn(frame) ;

  kest1.plotOn(frame) ;

  kest2.plotOn(frame,LineStyle(kDashed),LineColor(kRed)) ;


  // Plot kernel estimation pdfs with regular and increased bandwidth

  RooPlot* frame2 = x.frame(Title("Adaptive kernel estimation pdf with regular, increased bandwidth")) ;

  kest1.plotOn(frame2) ;

  kest3.plotOn(frame2,LineColor(kMagenta)) ;


  // C r e a t e   l o w   s t a t s   2 - D   d a t a s e t

  // -------------------------------------------------------


  // Construct a 2D toy pdf for sampleing

  RooRealVar y("y","y",0,20) ;

  RooPolynomial py("py","py",y,RooArgList(RooConst(0.01),RooConst(0.01),RooConst(-0.0004))) ;

  RooProdPdf pxy("pxy","pxy",RooArgSet(p,py)) ;

  RooDataSet* data2 = pxy.generate(RooArgSet(x,y),1000) ;


  // C r e a t e   2 - D   k e r n e l   e s t i m a t i o n   p d f

  // ---------------------------------------------------------------


  // Create 2D adaptive kernel estimation pdf with mirroring

  RooNDKeysPdf kest4("kest4","kest4",RooArgSet(x,y),*data2,"am") ;


  // Create 2D adaptive kernel estimation pdf with mirroring and double bandwidth

  RooNDKeysPdf kest5("kest5","kest5",RooArgSet(x,y),*data2,"am",2) ;


  // Create a histogram of the data

  TH1* hh_data = data2->createHistogram("hh_data",x,Binning(10),YVar(y,Binning(10))) ;


  // Create histogram of the 2d kernel estimation pdfs

  TH1* hh_pdf = kest4.createHistogram("hh_pdf",x,Binning(25),YVar(y,Binning(25))) ;

  TH1* hh_pdf2 = kest5.createHistogram("hh_pdf2",x,Binning(25),YVar(y,Binning(25))) ;

  hh_pdf->SetLineColor(kBlue) ;

  hh_pdf2->SetLineColor(kMagenta) ;


  TCanvas* c = new TCanvas("rf707_kernelestimation","rf707_kernelestimation",800,800) ;

  c->Divide(2,2) ;

  c->cd(1) ; gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.4) ; frame->Draw() ;

  c->cd(2) ; gPad->SetLeftMargin(0.15) ; frame2->GetYaxis()->SetTitleOffset(1.8) ; frame2->Draw() ;

  c->cd(3) ; gPad->SetLeftMargin(0.15) ; hh_data->GetZaxis()->SetTitleOffset(1.4) ; hh_data->Draw("lego") ;

  c->cd(4) ; gPad->SetLeftMargin(0.20) ; hh_pdf->GetZaxis()->SetTitleOffset(2.4) ; hh_pdf->Draw("surf") ; hh_pdf2->Draw("surfsame") ;


}