Zbi_Zgamma.C File Reference

Detailed Description

Demonstrate Z_Bi = Z_Gamma

[#1] INFO:NumericIntegration -- RooRealIntegral::init(GENPROJ_[py_X_prior_b_X_px]_Norm[b]_denominator_Int[b]) using numeric integrator RooIntegrator1D to calculate Int(b)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(GENPROJ_[py_X_prior_b]_Norm[b]_denominator_Int[b]) using numeric integrator RooIntegrator1D to calculate Int(b)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(SPECINT[GENPROJ_[py_X_prior_b]_Norm[b]_X_px_NORM[x]]_Int[b]) using numeric integrator RooIntegrator1D to calculate Int(b)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(GENPROJ_[py_X_prior_b]_Norm[b]_denominator_Int[b]) using numeric integrator RooIntegrator1D to calculate Int(b)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(GENPROJ_[py_X_prior_b]_Norm[b]_denominator_Int[b]) using numeric integrator RooIntegrator1D to calculate Int(b)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(GENPROJ_[py_X_prior_b]_Norm[b]_denominator_Int[b]) using numeric integrator RooIntegrator1D to calculate Int(b)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(SPECINT[GENPROJ_[py_X_prior_b]_Norm[b]_X_px_cdf_NORM[x_prime]]_Int[b]) using numeric integrator RooIntegrator1D to calculate Int(b)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(GENPROJ_[py_X_prior_b]_Norm[b]_denominator_Int[b]) using numeric integrator RooIntegrator1D to calculate Int(b)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(SPECINT[GENPROJ_[py_X_prior_b]_Norm[b]_X_px_cdf_Int[x_prime|CDF]_Norm[x_prime]]_Int[b|CDF]) using numeric integrator RooIntegrator1D to calculate Int(b)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(GENPROJ_[py_X_prior_b]_Norm[b]_denominator_Int[b]) using numeric integrator RooIntegrator1D to calculate Int(b)
Hybrid p-value = 0.999226
Z_Gamma Significance  = 3.1655
Z_Bi significance estimation: 3.10804

 
#include "RooRealVar.h"
#include "RooProdPdf.h"
#include "RooWorkspace.h"
#include "RooDataSet.h"
#include "TCanvas.h"
#include "TH1.h"
 
using namespace RooFit;
using namespace RooStats;
 
void Zbi_Zgamma()
{
 
   // Make model for prototype on/off problem
   // Pois(x | s+b) * Pois(y | tau b )
   // for Z_Gamma, use uniform prior on b.
   RooWorkspace *w1 = new RooWorkspace("w");
   w1->factory("Poisson::px(x[150,0,500],sum::splusb(s[0,0,100],b[100,0,300]))");
   w1->factory("Poisson::py(y[100,0,500],prod::taub(tau[1.],b))");
   w1->factory("Uniform::prior_b(b)");
 
   // construct the Bayesian-averaged model (eg. a projection pdf)
   // p'(x|s) = \int db p(x|s+b) * [ p(y|b) * prior(b) ]
   w1->factory("PROJ::averagedModel(PROD::foo(px|b,py,prior_b),b)");
 
   // plot it, blue is averaged model, red is b known exactly
   RooPlot *frame = w1->var("x")->frame();
   w1->pdf("averagedModel")->plotOn(frame);
   w1->pdf("px")->plotOn(frame, LineColor(kRed));
   frame->Draw();
 
   // compare analytic calculation of Z_Bi
   // with the numerical RooFit implementation of Z_Gamma
   // for an example with x = 150, y = 100
 
   // numeric RooFit Z_Gamma
   w1->var("y")->setVal(100);
   w1->var("x")->setVal(150);
   std::unique_ptr<RooAbsReal> cdf{w1->pdf("averagedModel")->createCdf(*w1->var("x"))};
   cdf->getVal(); // get ugly print messages out of the way
 
   cout << "Hybrid p-value = " << cdf->getVal() << endl;
   cout << "Z_Gamma Significance  = " << PValueToSignificance(1 - cdf->getVal()) << endl;
 
   // analytic Z_Bi
   double Z_Bi = NumberCountingUtils::BinomialWithTauObsZ(150, 100, 1);
   std::cout << "Z_Bi significance estimation: " << Z_Bi << std::endl;
 
   // OUTPUT
   // Hybrid p-value = 0.999058
   // Z_Gamma Significance  = 3.10804
   // Z_Bi significance estimation: 3.10804
}

Authors

Kyle Cranmer, Wouter Verkerke

Definition in file Zbi_Zgamma.C.