StandardHypoTestDemo.C File Reference

Detailed Description

Standard tutorial macro for hypothesis test (for computing the discovery significance) using all RooStats hypotheiss tests calculators and test statistics.

Usage:

root>.L StandardHypoTestDemo.C
root> StandardHypoTestDemo("fileName","workspace name","S+B modelconfig name","B model name","data set name",calculator type, test statistic type, number of toys)

 type = 0 Freq calculator
 type = 1 Hybrid calculator
 type = 2 Asymptotic calculator
 type = 3 Asymptotic calculator using nominal Asimov data sets (not using fitted parameter values but nominal ones)

testStatType = 0 LEP
             = 1 Tevatron
             = 2 Profile Likelihood
             = 3 Profile Likelihood one sided (i.e. = 0 if mu_hat < 0)

Processing /builddir/build/BUILD/root-6.10.02/tutorials/roostats/StandardHypoTestDemo.C...

RooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby 
                Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University
                All rights reserved, please read http://roofit.sourceforge.net/license.txt


RooWorkspace(combined) combined contents

variables
---------
(Lumi,SigXsecOverSM,alpha_syst1,alpha_syst2,alpha_syst3,binWidth_obs_x_channel1_0,binWidth_obs_x_channel1_1,binWidth_obs_x_channel1_2,channelCat,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1,nom_alpha_syst1,nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1,nominalLumi,obs_x_channel1,weightVar)

p.d.f.s
-------
RooGaussian::alpha_syst1Constraint[ x=alpha_syst1 mean=nom_alpha_syst1 sigma=1 ] = 1
RooGaussian::alpha_syst2Constraint[ x=alpha_syst2 mean=nom_alpha_syst2 sigma=1 ] = 1
RooGaussian::alpha_syst3Constraint[ x=alpha_syst3 mean=nom_alpha_syst3 sigma=1 ] = 1
RooRealSumPdf::channel1_model[ binWidth_obs_x_channel1_0 * L_x_signal_channel1_overallSyst_x_Exp + binWidth_obs_x_channel1_1 * L_x_background1_channel1_overallSyst_x_StatUncert + binWidth_obs_x_channel1_2 * L_x_background2_channel1_overallSyst_x_StatUncert ] = 220
RooPoisson::gamma_stat_channel1_bin_0_constraint[ x=nom_gamma_stat_channel1_bin_0 mean=gamma_stat_channel1_bin_0_poisMean ] = 0.019943
RooPoisson::gamma_stat_channel1_bin_1_constraint[ x=nom_gamma_stat_channel1_bin_1 mean=gamma_stat_channel1_bin_1_poisMean ] = 0.039861
RooGaussian::lumiConstraint[ x=Lumi mean=nominalLumi sigma=0.1 ] = 1
RooProdPdf::model_channel1[ lumiConstraint * alpha_syst1Constraint * alpha_syst2Constraint * alpha_syst3Constraint * gamma_stat_channel1_bin_0_constraint * gamma_stat_channel1_bin_1_constraint * channel1_model(obs_x_channel1) ] = 0.174888
RooSimultaneous::simPdf[ indexCat=channelCat channel1=model_channel1 ] = 0.174888

functions
--------
RooProduct::L_x_background1_channel1_overallSyst_x_StatUncert[ Lumi * background1_channel1_overallSyst_x_StatUncert ] = 0
RooProduct::L_x_background2_channel1_overallSyst_x_StatUncert[ Lumi * background2_channel1_overallSyst_x_StatUncert ] = 100
RooProduct::L_x_signal_channel1_overallSyst_x_Exp[ Lumi * signal_channel1_overallSyst_x_Exp ] = 10
RooStats::HistFactory::FlexibleInterpVar::background1_channel1_epsilon[ paramList=(alpha_syst2) ] = 1
RooHistFunc::background1_channel1_nominal[ depList=(obs_x_channel1) depList=(obs_x_channel1) ] = 0
RooProduct::background1_channel1_overallSyst_x_Exp[ background1_channel1_nominal * background1_channel1_epsilon ] = 0
RooProduct::background1_channel1_overallSyst_x_StatUncert[ mc_stat_channel1 * background1_channel1_overallSyst_x_Exp ] = 0
RooStats::HistFactory::FlexibleInterpVar::background2_channel1_epsilon[ paramList=(alpha_syst3) ] = 1
RooHistFunc::background2_channel1_nominal[ depList=(obs_x_channel1) depList=(obs_x_channel1) ] = 100
RooProduct::background2_channel1_overallSyst_x_Exp[ background2_channel1_nominal * background2_channel1_epsilon ] = 100
RooProduct::background2_channel1_overallSyst_x_StatUncert[ mc_stat_channel1 * background2_channel1_overallSyst_x_Exp ] = 100
RooProduct::gamma_stat_channel1_bin_0_poisMean[ gamma_stat_channel1_bin_0 * gamma_stat_channel1_bin_0_tau ] = 400
RooProduct::gamma_stat_channel1_bin_1_poisMean[ gamma_stat_channel1_bin_1 * gamma_stat_channel1_bin_1_tau ] = 100
ParamHistFunc::mc_stat_channel1[ ] = 1
RooStats::HistFactory::FlexibleInterpVar::signal_channel1_epsilon[ paramList=(alpha_syst1) ] = 1
RooHistFunc::signal_channel1_nominal[ depList=(obs_x_channel1) depList=(obs_x_channel1) ] = 10
RooProduct::signal_channel1_overallNorm_x_sigma_epsilon[ SigXsecOverSM * signal_channel1_epsilon ] = 1
RooProduct::signal_channel1_overallSyst_x_Exp[ signal_channel1_nominal * signal_channel1_overallNorm_x_sigma_epsilon ] = 10

datasets
--------
RooDataSet::asimovData(obs_x_channel1,weightVar,channelCat)
RooDataSet::obsData(channelCat,obs_x_channel1)

embedded datasets (in pdfs and functions)
-----------------------------------------
RooDataHist::signal_channel1nominalDHist(obs_x_channel1)
RooDataHist::background1_channel1nominalDHist(obs_x_channel1)
RooDataHist::background2_channel1nominalDHist(obs_x_channel1)

parameter snapshots
-------------------
NominalParamValues = (nom_alpha_syst2=0[C],nom_alpha_syst3=0[C],nom_gamma_stat_channel1_bin_0=400[C],nom_gamma_stat_channel1_bin_1=100[C],weightVar=0,obs_x_channel1=1.75,Lumi=1[C],nominalLumi=1[C],alpha_syst1=0[C],nom_alpha_syst1=0[C],alpha_syst2=0,alpha_syst3=0,gamma_stat_channel1_bin_0=1,gamma_stat_channel1_bin_1=1,SigXsecOverSM=1,binWidth_obs_x_channel1_0=2[C],binWidth_obs_x_channel1_1=2[C],binWidth_obs_x_channel1_2=2[C])

named sets
----------
ModelConfig_GlobalObservables:(nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
ModelConfig_NuisParams:(alpha_syst2,alpha_syst3,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
ModelConfig_Observables:(obs_x_channel1,weightVar,channelCat)
ModelConfig_POI:(SigXsecOverSM)
globalObservables:(nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
observables:(obs_x_channel1,weightVar,channelCat)

generic objects
---------------
RooStats::ModelConfig::ModelConfig


=== Using the following for ModelConfigB_only ===
Observables:             RooArgSet:: = (obs_x_channel1,weightVar,channelCat)
Parameters of Interest:  RooArgSet:: = (SigXsecOverSM)
Nuisance Parameters:     RooArgSet:: = (alpha_syst2,alpha_syst3,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
Global Observables:      RooArgSet:: = (nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
PDF:                     RooSimultaneous::simPdf[ indexCat=channelCat channel1=model_channel1 ] = 0.174888
Snapshot:                
  1) 0x82948b80 RooRealVar:: SigXsecOverSM = 0  L(0 - 3)  "SigXsecOverSM"


=== Using the following for ModelConfig ===
Observables:             RooArgSet:: = (obs_x_channel1,weightVar,channelCat)
Parameters of Interest:  RooArgSet:: = (SigXsecOverSM)
Nuisance Parameters:     RooArgSet:: = (alpha_syst2,alpha_syst3,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
Global Observables:      RooArgSet:: = (nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
PDF:                     RooSimultaneous::simPdf[ indexCat=channelCat channel1=model_channel1 ] = 0.174888
Snapshot:                
  1) 0x82948b80 RooRealVar:: SigXsecOverSM = 1  L(0 - 3)  "SigXsecOverSM"

[#0] PROGRESS:Generation -- Test Statistic on data: 1.77404
[#1] INFO:InputArguments -- Profiling conditional MLEs for Null.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Null.
[#0] PROGRESS:Generation -- generated toys: 500 / 5000
[#0] PROGRESS:Generation -- generated toys: 1000 / 5000
[#0] PROGRESS:Generation -- generated toys: 1500 / 5000
[#0] PROGRESS:Generation -- generated toys: 2000 / 5000
[#0] PROGRESS:Generation -- generated toys: 2500 / 5000
[#0] PROGRESS:Generation -- generated toys: 3000 / 5000
[#0] PROGRESS:Generation -- generated toys: 3500 / 5000
[#0] PROGRESS:Generation -- generated toys: 4000 / 5000
[#0] PROGRESS:Generation -- generated toys: 4500 / 5000
[#1] INFO:InputArguments -- Profiling conditional MLEs for Alt.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Alt.
[#0] PROGRESS:Generation -- generated toys: 500 / 1250
[#0] PROGRESS:Generation -- generated toys: 1000 / 1250

Results HypoTestCalculator_result: 
 - Null p-value = 0.0304 +/- 0.002428
 - Significance = 1.87495 +/- 0.0352942 sigma
 - Number of Alt toys: 1250
 - Number of Null toys: 5000
 - Test statistic evaluated on data: 1.77404
 - CL_b: 0.0304 +/- 0.002428
 - CL_s+b: 0.4328 +/- 0.0140138
 - CL_s: 14.2368 +/- 1.22696
 Expected p -value and significance at -2 sigma = 0.8614 significance -1.08663 sigma 
 Expected p -value and significance at -1 sigma = 0.2238 significance 0.759422 sigma 
 Expected p -value and significance at 0 sigma = 0.0434 significance 1.71252 sigma 
 Expected p -value and significance at 1 sigma = 0.0028 significance 2.77033 sigma 
 Expected p -value and significance at 2 sigma = 0.0002 significance 3.54008 sigma 

#include "TFile.h"
#include "RooWorkspace.h"
#include "RooAbsPdf.h"
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooStats/ModelConfig.h"
#include "RooRandom.h"
#include "TGraphErrors.h"
#include "TGraphAsymmErrors.h"
#include "TCanvas.h"
#include "TLine.h"
#include "TSystem.h"
#include "TROOT.h"

#include "RooStats/AsymptoticCalculator.h"
#include "RooStats/HybridCalculator.h"
#include "RooStats/FrequentistCalculator.h"
#include "RooStats/ToyMCSampler.h"
#include "RooStats/HypoTestPlot.h"

#include "RooStats/NumEventsTestStat.h"
#include "RooStats/ProfileLikelihoodTestStat.h"
#include "RooStats/SimpleLikelihoodRatioTestStat.h"
#include "RooStats/RatioOfProfiledLikelihoodsTestStat.h"
#include "RooStats/MaxLikelihoodEstimateTestStat.h"

#include "RooStats/HypoTestInverter.h"
#include "RooStats/HypoTestInverterResult.h"
#include "RooStats/HypoTestInverterPlot.h"

using namespace RooFit;
using namespace RooStats;

struct HypoTestOptions {

   bool noSystematics = false;              // force all systematics to be off (i.e. set all nuisance parameters as constat
  double nToysRatio = 4;                   // ratio Ntoys Null/ntoys ALT
  double poiValue = -1;                    // change poi snapshot value for S+B model (needed for expected p0 values)
int  printLevel=0;
bool generateBinned = false;             // for binned generation
   bool useProof = false;                // use Proof
   bool enableDetailedOutput = false;    // for detailed output

};

HypoTestOptions optHT;

void StandardHypoTestDemo(const char* infile = "",
                          const char* workspaceName = "combined",
                          const char* modelSBName = "ModelConfig",
                          const char* modelBName = "",
                          const char* dataName = "obsData",
                          int calcType = 0, /* 0 freq 1 hybrid, 2 asymptotic */
                          int testStatType = 3,   /* 0 LEP, 1 TeV, 2 LHC, 3 LHC - one sided*/
                          int ntoys = 5000,
                          bool useNC = false,
                          const char * nuisPriorName = 0)
{

   bool noSystematics = optHT.noSystematics;
   double nToysRatio = optHT.nToysRatio;                    // ratio Ntoys Null/ntoys ALT
   double poiValue = optHT.poiValue;                    // change poi snapshot value for S+B model (needed for expected p0 values)
   int  printLevel = optHT.printLevel;
   bool generateBinned = optHT.generateBinned;             // for binned generation
   bool useProof = optHT.useProof;                // use Proof
   bool enableDetOutput = optHT.enableDetailedOutput;


  // Other Parameter to pass in tutorial
  // apart from standard for filename, ws, modelconfig and data

  // type = 0 Freq calculator
  // type = 1 Hybrid calculator
  // type = 2 Asymptotic calculator

  // testStatType = 0 LEP
  // = 1 Tevatron
  // = 2 Profile Likelihood
  // = 3 Profile Likelihood one sided (i.e. = 0 if mu < mu_hat)

  // ntoys:         number of toys to use

  // useNumberCounting:  set to true when using number counting events

  // nuisPriorName:   name of prior for the nuisance. This is often expressed as constraint term in the global model
  // It is needed only when using the HybridCalculator (type=1)
  // If not given by default the prior pdf from ModelConfig is used.

  // extra options are available as global parameters of the macro. They major ones are:

  // generateBinned       generate binned data sets for toys (default is false) - be careful not to activate with
  // a too large (>=3) number of observables
  // nToyRatio            ratio of S+B/B toys (default is 2)
  // printLevel


   // disable - can cause some problems
   //ToyMCSampler::SetAlwaysUseMultiGen(true);

   SimpleLikelihoodRatioTestStat::SetAlwaysReuseNLL(true);
   ProfileLikelihoodTestStat::SetAlwaysReuseNLL(true);
   RatioOfProfiledLikelihoodsTestStat::SetAlwaysReuseNLL(true);

   //RooRandom::randomGenerator()->SetSeed(0);

   // to change minimizers
   // ~~~{.bash}
   // ROOT::Math::MinimizerOptions::SetDefaultStrategy(0);
   // ROOT::Math::MinimizerOptions::SetDefaultMinimizer("Minuit2");
   // ROOT::Math::MinimizerOptions::SetDefaultTolerance(1);
   // ~~~

  // -------------------------------------------------------
  // First part is just to access a user-defined file
  // or create the standard example file if it doesn't exist
   const char* filename = "";
   if (!strcmp(infile,"")) {
      filename = "results/example_combined_GaussExample_model.root";
      bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code
      // if file does not exists generate with histfactory
      if (!fileExist) {
#ifdef _WIN32
         cout << "HistFactory file cannot be generated on Windows - exit" << endl;
         return;
#endif
         // Normally this would be run on the command line
         cout <<"will run standard hist2workspace example"<<endl;
         gROOT->ProcessLine(".! prepareHistFactory .");
         gROOT->ProcessLine(".! hist2workspace config/example.xml");
         cout <<"\n\n---------------------"<<endl;
         cout <<"Done creating example input"<<endl;
         cout <<"---------------------\n\n"<<endl;
      }

   }
   else
      filename = infile;

   // Try to open the file
   TFile *file = TFile::Open(filename);

   // if input file was specified byt not found, quit
   if(!file ){
      cout <<"StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl;
      return;
   }


   // -------------------------------------------------------
   // Tutorial starts here
   // -------------------------------------------------------

   // get the workspace out of the file
   RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName);
   if(!w){
      cout <<"workspace not found" << endl;
      return;
   }
   w->Print();

   // get the modelConfig out of the file
   ModelConfig* sbModel = (ModelConfig*) w->obj(modelSBName);


   // get the modelConfig out of the file
   RooAbsData* data = w->data(dataName);

   // make sure ingredients are found
   if(!data || !sbModel){
      w->Print();
      cout << "data or ModelConfig was not found" <<endl;
      return;
   }
   // make b model
   ModelConfig* bModel = (ModelConfig*) w->obj(modelBName);


   // case of no systematics
   // remove nuisance parameters from model
   if (noSystematics) {
      const RooArgSet * nuisPar = sbModel->GetNuisanceParameters();
      if (nuisPar && nuisPar->getSize() > 0) {
         std::cout << "StandardHypoTestInvDemo" << "  -  Switch off all systematics by setting them constant to their initial values" << std::endl;
         RooStats::SetAllConstant(*nuisPar);
      }
      if (bModel) {
         const RooArgSet * bnuisPar = bModel->GetNuisanceParameters();
         if (bnuisPar)
            RooStats::SetAllConstant(*bnuisPar);
      }
   }


  if (!bModel ) {
      Info("StandardHypoTestInvDemo","The background model %s does not exist",modelBName);
      Info("StandardHypoTestInvDemo","Copy it from ModelConfig %s and set POI to zero",modelSBName);
      bModel = (ModelConfig*) sbModel->Clone();
      bModel->SetName(TString(modelSBName)+TString("B_only"));
      RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
      if (!var) return;
      double oldval = var->getVal();
      var->setVal(0);
      //bModel->SetSnapshot( RooArgSet(*var, *w->var("lumi"))  );
      bModel->SetSnapshot( RooArgSet(*var)  );
      var->setVal(oldval);
  }

   if (!sbModel->GetSnapshot() || poiValue > 0) {
      Info("StandardHypoTestDemo","Model %s has no snapshot  - make one using model poi",modelSBName);
      RooRealVar * var = dynamic_cast<RooRealVar*>(sbModel->GetParametersOfInterest()->first());
      if (!var) return;
      double oldval = var->getVal();
      if (poiValue > 0)  var->setVal(poiValue);
      //sbModel->SetSnapshot( RooArgSet(*var, *w->var("lumi") ) );
      sbModel->SetSnapshot( RooArgSet(*var) );
      if (poiValue > 0) var->setVal(oldval);
      //sbModel->SetSnapshot( *sbModel->GetParametersOfInterest() );
   }





   // part 1, hypothesis testing
   SimpleLikelihoodRatioTestStat * slrts = new SimpleLikelihoodRatioTestStat(*bModel->GetPdf(), *sbModel->GetPdf());
   // null parameters must includes snapshot of poi plus the nuisance values
   RooArgSet nullParams(*bModel->GetSnapshot());
   if (bModel->GetNuisanceParameters()) nullParams.add(*bModel->GetNuisanceParameters());

   slrts->SetNullParameters(nullParams);
   RooArgSet altParams(*sbModel->GetSnapshot());
   if (sbModel->GetNuisanceParameters()) altParams.add(*sbModel->GetNuisanceParameters());
   slrts->SetAltParameters(altParams);

   ProfileLikelihoodTestStat * profll = new ProfileLikelihoodTestStat(*bModel->GetPdf());


   RatioOfProfiledLikelihoodsTestStat *
      ropl = new RatioOfProfiledLikelihoodsTestStat(*bModel->GetPdf(), *sbModel->GetPdf(), sbModel->GetSnapshot());
   ropl->SetSubtractMLE(false);

   if (testStatType == 3) profll->SetOneSidedDiscovery(1);
   profll->SetPrintLevel(printLevel);

   if (enableDetOutput) {
      slrts->EnableDetailedOutput();
      profll->EnableDetailedOutput();
      ropl->EnableDetailedOutput();
   }


   /* profll.SetReuseNLL(mOptimize);*/
   /* slrts.SetReuseNLL(mOptimize);*/
   /* ropl.SetReuseNLL(mOptimize);*/

   AsymptoticCalculator::SetPrintLevel(printLevel);

   HypoTestCalculatorGeneric *  hypoCalc = 0;
   // note here Null is B and Alt is S+B
   if (calcType == 0) hypoCalc = new  FrequentistCalculator(*data, *sbModel, *bModel);
   else if (calcType == 1) hypoCalc= new  HybridCalculator(*data, *sbModel, *bModel);
   else if (calcType == 2) hypoCalc= new  AsymptoticCalculator(*data, *sbModel, *bModel);

   if (calcType == 0) {
       ((FrequentistCalculator*)hypoCalc)->SetToys(ntoys, ntoys/nToysRatio);
       if (enableDetOutput) ((FrequentistCalculator*) hypoCalc)->StoreFitInfo(true);
   }
   if (calcType == 1) {
       ((HybridCalculator*)hypoCalc)->SetToys(ntoys, ntoys/nToysRatio);
       // n. a. yetif (enableDetOutput) ((HybridCalculator*) hypoCalc)->StoreFitInfo(true);
   }
   if (calcType == 2 ) {
      if (testStatType == 3) ((AsymptoticCalculator*) hypoCalc)->SetOneSidedDiscovery(true);
      if (testStatType != 2 && testStatType != 3)
         Warning("StandardHypoTestDemo","Only the PL test statistic can be used with AsymptoticCalculator - use by default a two-sided PL");


   }


   // check for nuisance prior pdf in case of nuisance parameters
   if (calcType == 1 && (bModel->GetNuisanceParameters() || sbModel->GetNuisanceParameters() )) {
         RooAbsPdf * nuisPdf = 0;
         if (nuisPriorName) nuisPdf = w->pdf(nuisPriorName);
         // use prior defined first in bModel (then in SbModel)
         if (!nuisPdf)  {
            Info("StandardHypoTestDemo","No nuisance pdf given for the HybridCalculator - try to deduce  pdf from the   model");
            if (bModel->GetPdf() && bModel->GetObservables() )
               nuisPdf = RooStats::MakeNuisancePdf(*bModel,"nuisancePdf_bmodel");
            else
               nuisPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisancePdf_sbmodel");
         }
         if (!nuisPdf ) {
            if (bModel->GetPriorPdf())  {
               nuisPdf = bModel->GetPriorPdf();
               Info("StandardHypoTestDemo","No nuisance pdf given - try to use %s that is defined as a prior pdf in the B model",nuisPdf->GetName());
            }
            else {
               Error("StandardHypoTestDemo","Cannot run Hybrid calculator because no prior on the nuisance parameter is specified or can be derived");
               return;
            }
         }
         assert(nuisPdf);
         Info("StandardHypoTestDemo","Using as nuisance Pdf ... " );
         nuisPdf->Print();

         const RooArgSet * nuisParams = (bModel->GetNuisanceParameters() ) ? bModel->GetNuisanceParameters() : sbModel->GetNuisanceParameters();
         RooArgSet * np = nuisPdf->getObservables(*nuisParams);
         if (np->getSize() == 0) {
            Warning("StandardHypoTestDemo","Prior nuisance does not depend on nuisance parameters. They will be smeared in their full range");
         }
         delete np;

         ((HybridCalculator*)hypoCalc)->ForcePriorNuisanceAlt(*nuisPdf);
         ((HybridCalculator*)hypoCalc)->ForcePriorNuisanceNull(*nuisPdf);
   }

   /* hypoCalc->ForcePriorNuisanceAlt(*sbModel->GetPriorPdf());*/
   /* hypoCalc->ForcePriorNuisanceNull(*bModel->GetPriorPdf());*/

   ToyMCSampler * sampler = (ToyMCSampler *)hypoCalc->GetTestStatSampler();

   if (sampler && (calcType == 0 || calcType == 1) ) {

      // look if pdf is number counting or extended
      if (sbModel->GetPdf()->canBeExtended() ) {
         if (useNC)   Warning("StandardHypoTestDemo","Pdf is extended: but number counting flag is set: ignore it ");
      }
      else {
         // for not extended pdf
         if (!useNC)  {
            int nEvents = data->numEntries();
            Info("StandardHypoTestDemo","Pdf is not extended: number of events to generate taken  from observed data set is %d",nEvents);
            sampler->SetNEventsPerToy(nEvents);
         }
         else {
            Info("StandardHypoTestDemo","using a number counting pdf");
            sampler->SetNEventsPerToy(1);
         }
      }

      if (data->isWeighted() && !generateBinned) {
         Info("StandardHypoTestDemo","Data set is weighted, nentries = %d and sum of weights = %8.1f but toy generation is unbinned - it would be faster to set generateBinned to true\n",data->numEntries(), data->sumEntries());
      }
      if (generateBinned)  sampler->SetGenerateBinned(generateBinned);

      // use PROOF
      if (useProof) {
         ProofConfig pc(*w, 0, "", kFALSE);
         sampler->SetProofConfig(&pc);    // enable proof
   }



      // set the test statistic
      if (testStatType == 0) sampler->SetTestStatistic(slrts);
      if (testStatType == 1) sampler->SetTestStatistic(ropl);
      if (testStatType == 2 || testStatType == 3) sampler->SetTestStatistic(profll);

   }

   HypoTestResult *  htr = hypoCalc->GetHypoTest();
   htr->SetPValueIsRightTail(true);
   htr->SetBackgroundAsAlt(false);
   htr->Print(); // how to get meaningful CLs at this point?

   delete sampler;
   delete slrts;
   delete ropl;
   delete profll;

   if (calcType != 2) {
      HypoTestPlot * plot = new HypoTestPlot(*htr,100);
      plot->SetLogYaxis(true);
      plot->Draw();
   }
   else {
      std::cout << "Asymptotic results " << std::endl;

   }

   // look at expected significances
   // found median of S+B distribution
   if (calcType != 2) {

      SamplingDistribution * altDist = htr->GetAltDistribution();
      HypoTestResult htExp("Expected Result");
      htExp.Append(htr);
      // find quantiles in alt (S+B) distribution
      double p[5];
      double q[5];
      for (int i = 0; i < 5; ++i) {
         double sig = -2  + i;
         p[i] = ROOT::Math::normal_cdf(sig,1);
      }
      std::vector<double> values = altDist->GetSamplingDistribution();
      TMath::Quantiles( values.size(), 5, &values[0], q, p, false);

      for (int i = 0; i < 5; ++i) {
         htExp.SetTestStatisticData( q[i] );
         double sig = -2  + i;
         std::cout << " Expected p -value and significance at " << sig << " sigma = "
                   << htExp.NullPValue() << " significance " << htExp.Significance() << " sigma " << std::endl;

      }
   }
   else {
      // case of asymptotic calculator
      for (int i = 0; i < 5; ++i) {
         double sig = -2  + i;
         // sigma is inverted here
         double pval = AsymptoticCalculator::GetExpectedPValues( htr->NullPValue(), htr->AlternatePValue(), -sig, false);
         std::cout << " Expected p -value and significance at " << sig << " sigma = "
                   << pval << " significance " << ROOT::Math::normal_quantile_c(pval,1) << " sigma " << std::endl;

      }
   }


   // write result in a file in case of toys
   bool writeResult = (calcType != 2);

   if (enableDetOutput) {
      writeResult=true;
      Info("StandardHypoTestDemo","Detailed output will be written in output result file");
   }


   if (htr != NULL && writeResult) {

      // write to a file the results
      const char *  calcTypeName = (calcType == 0) ? "Freq" : (calcType == 1) ? "Hybr" : "Asym";
      TString resultFileName = TString::Format("%s_HypoTest_ts%d_",calcTypeName,testStatType);
      //strip the / from the filename

      TString name = infile;
      name.Replace(0, name.Last('/')+1, "");
      resultFileName += name;

      TFile * fileOut = new TFile(resultFileName,"RECREATE");
      htr->Write();
      Info("StandardHypoTestDemo","HypoTestResult has been written in the file %s",resultFileName.Data());

      fileOut->Close();
   }





}

Author

Lorenzo Moneta

Definition in file StandardHypoTestDemo.C.