rs102_hypotestwithshapes.C

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/// \file
/// \ingroup tutorial_roostats
/// \notebook -js
/// A typical search for a new particle by studying an invariant mass distribution
///
/// The macro creates a simple signal model and two background models,
/// which are added to a RooWorkspace.
/// The macro creates a toy dataset, and then uses a RooStats
/// ProfileLikleihoodCalculator to do a hypothesis test of the
/// background-only and signal+background hypotheses.
/// In this example, shape uncertainties are not taken into account, but
/// normalization uncertainties are.
///
/// \macro_image
/// \macro_output
/// \macro_code
///
/// \author Kyle Cranmer
 
#include "RooDataSet.h"
#include "RooRealVar.h"
#include "RooGaussian.h"
#include "RooAddPdf.h"
#include "RooProdPdf.h"
#include "RooAddition.h"
#include "RooProduct.h"
#include "TCanvas.h"
#include "RooChebychev.h"
#include "RooAbsPdf.h"
#include "RooFit.h"
#include "RooFitResult.h"
#include "RooPlot.h"
#include "RooAbsArg.h"
#include "RooWorkspace.h"
#include "RooStats/ProfileLikelihoodCalculator.h"
#include "RooStats/HypoTestResult.h"
#include <string>
 
// use this order for safety on library loading
using namespace RooFit;
using namespace RooStats;
 
// see below for implementation
void AddModel(RooWorkspace *);
void AddData(RooWorkspace *);
void DoHypothesisTest(RooWorkspace *);
void MakePlots(RooWorkspace *);
 
//____________________________________
void rs102_hypotestwithshapes()
{
 
   // The main macro.
 
   // Create a workspace to manage the project.
   RooWorkspace *wspace = new RooWorkspace("myWS");
 
   // add the signal and background models to the workspace
   AddModel(wspace);
 
   // add some toy data to the workspace
   AddData(wspace);
 
   // inspect the workspace if you wish
   //  wspace->Print();
 
   // do the hypothesis test
   DoHypothesisTest(wspace);
 
   // make some plots
   MakePlots(wspace);
 
   // cleanup
   delete wspace;
}
 
//____________________________________
void AddModel(RooWorkspace *wks)
{
 
   // Make models for signal (Higgs) and background (Z+jets and QCD)
   // In real life, this part requires an intelligent modeling
   // of signal and background -- this is only an example.
 
   // set range of observable
   Double_t lowRange = 60, highRange = 200;
 
   // make a RooRealVar for the observable
   RooRealVar invMass("invMass", "M_{inv}", lowRange, highRange, "GeV");
 
   // --------------------------------------
   // make a simple signal model.
   RooRealVar mH("mH", "Higgs Mass", 130, 90, 160);
   RooRealVar sigma1("sigma1", "Width of Gaussian", 12., 2, 100);
   RooGaussian sigModel("sigModel", "Signal Model", invMass, mH, sigma1);
   // we will test this specific mass point for the signal
   mH.setConstant();
   // and we assume we know the mass resolution
   sigma1.setConstant();
 
   // --------------------------------------
   // make zjj model.  Just like signal model
   RooRealVar mZ("mZ", "Z Mass", 91.2, 0, 100);
   RooRealVar sigma1_z("sigma1_z", "Width of Gaussian", 10., 6, 100);
   RooGaussian zjjModel("zjjModel", "Z+jets Model", invMass, mZ, sigma1_z);
   // we know Z mass
   mZ.setConstant();
   // assume we know resolution too
   sigma1_z.setConstant();
 
   // --------------------------------------
   // make QCD model
   RooRealVar a0("a0", "a0", 0.26, -1, 1);
   RooRealVar a1("a1", "a1", -0.17596, -1, 1);
   RooRealVar a2("a2", "a2", 0.018437, -1, 1);
   RooRealVar a3("a3", "a3", 0.02, -1, 1);
   RooChebychev qcdModel("qcdModel", "A  Polynomial for QCD", invMass, RooArgList(a0, a1, a2));
 
   // let's assume this shape is known, but the normalization is not
   a0.setConstant();
   a1.setConstant();
   a2.setConstant();
 
   // --------------------------------------
   // combined model
 
   // Setting the fraction of Zjj to be 40% for initial guess.
   RooRealVar fzjj("fzjj", "fraction of zjj background events", .4, 0., 1);
 
   // Set the expected fraction of signal to 20%.
   RooRealVar fsigExpected("fsigExpected", "expected fraction of signal events", .2, 0., 1);
   fsigExpected.setConstant(); // use mu as main parameter, so fix this.
 
   // Introduce mu: the signal strength in units of the expectation.
   // eg. mu = 1 is the SM, mu = 0 is no signal, mu=2 is 2x the SM
   RooRealVar mu("mu", "signal strength in units of SM expectation", 1, 0., 2);
 
   // Introduce ratio of signal efficiency to nominal signal efficiency.
   // This is useful if you want to do limits on cross section.
   RooRealVar ratioSigEff("ratioSigEff", "ratio of signal efficiency to nominal signal efficiency", 1., 0., 2);
   ratioSigEff.setConstant(kTRUE);
 
   // finally the signal fraction is the product of the terms above.
   RooProduct fsig("fsig", "fraction of signal events", RooArgSet(mu, ratioSigEff, fsigExpected));
 
   // full model
   RooAddPdf model("model", "sig+zjj+qcd background shapes", RooArgList(sigModel, zjjModel, qcdModel),
                   RooArgList(fsig, fzjj));
 
   // interesting for debugging and visualizing the model
   //  model.printCompactTree("","fullModel.txt");
   //  model.graphVizTree("fullModel.dot");
 
   wks->import(model);
}
 
//____________________________________
void AddData(RooWorkspace *wks)
{
   // Add a toy dataset
 
   Int_t nEvents = 150;
   RooAbsPdf *model = wks->pdf("model");
   RooRealVar *invMass = wks->var("invMass");
 
   RooDataSet *data = model->generate(*invMass, nEvents);
 
   wks->import(*data, Rename("data"));
}
 
//____________________________________
void DoHypothesisTest(RooWorkspace *wks)
{
 
   // Use a RooStats ProfileLikleihoodCalculator to do the hypothesis test.
   ModelConfig model;
   model.SetWorkspace(*wks);
   model.SetPdf("model");
 
   // plc.SetData("data");
 
   ProfileLikelihoodCalculator plc;
   plc.SetData(*(wks->data("data")));
 
   // here we explicitly set the value of the parameters for the null.
   // We want no signal contribution, eg. mu = 0
   RooRealVar *mu = wks->var("mu");
   //   RooArgSet* nullParams = new RooArgSet("nullParams");
   //   nullParams->addClone(*mu);
   RooArgSet poi(*mu);
   RooArgSet *nullParams = (RooArgSet *)poi.snapshot();
   nullParams->setRealValue("mu", 0);
 
   // plc.SetNullParameters(*nullParams);
   plc.SetModel(model);
   // NOTE: using snapshot will import nullparams
   // in the WS and merge with existing "mu"
   // model.SetSnapshot(*nullParams);
 
   // use instead setNuisanceParameters
   plc.SetNullParameters(*nullParams);
 
   // We get a HypoTestResult out of the calculator, and we can query it.
   HypoTestResult *htr = plc.GetHypoTest();
   cout << "-------------------------------------------------" << endl;
   cout << "The p-value for the null is " << htr->NullPValue() << endl;
   cout << "Corresponding to a significance of " << htr->Significance() << endl;
   cout << "-------------------------------------------------\n\n" << endl;
}
 
//____________________________________
void MakePlots(RooWorkspace *wks)
{
 
   // Make plots of the data and the best fit model in two cases:
   // first the signal+background case
   // second the background-only case.
 
   // get some things out of workspace
   RooAbsPdf *model = wks->pdf("model");
   RooAbsPdf *sigModel = wks->pdf("sigModel");
   RooAbsPdf *zjjModel = wks->pdf("zjjModel");
   RooAbsPdf *qcdModel = wks->pdf("qcdModel");
 
   RooRealVar *mu = wks->var("mu");
   RooRealVar *invMass = wks->var("invMass");
   RooAbsData *data = wks->data("data");
 
   // --------------------------------------
   // Make plots for the Alternate hypothesis, eg. let mu float
 
   mu->setConstant(kFALSE);
 
   model->fitTo(*data, Save(kTRUE), Minos(kFALSE), Hesse(kFALSE), PrintLevel(-1));
 
   // plot sig candidates, full model, and individual components
   new TCanvas();
   RooPlot *frame = invMass->frame();
   data->plotOn(frame);
   model->plotOn(frame);
   model->plotOn(frame, Components(*sigModel), LineStyle(kDashed), LineColor(kRed));
   model->plotOn(frame, Components(*zjjModel), LineStyle(kDashed), LineColor(kBlack));
   model->plotOn(frame, Components(*qcdModel), LineStyle(kDashed), LineColor(kGreen));
 
   frame->SetTitle("An example fit to the signal + background model");
   frame->Draw();
   //  cdata->SaveAs("alternateFit.gif");
 
   // --------------------------------------
   // Do Fit to the Null hypothesis.  Eg. fix mu=0
 
   mu->setVal(0);          // set signal fraction to 0
   mu->setConstant(kTRUE); // set constant
 
   model->fitTo(*data, Save(kTRUE), Minos(kFALSE), Hesse(kFALSE), PrintLevel(-1));
 
   // plot signal candidates with background model and components
   new TCanvas();
   RooPlot *xframe2 = invMass->frame();
   data->plotOn(xframe2, DataError(RooAbsData::SumW2));
   model->plotOn(xframe2);
   model->plotOn(xframe2, Components(*zjjModel), LineStyle(kDashed), LineColor(kBlack));
   model->plotOn(xframe2, Components(*qcdModel), LineStyle(kDashed), LineColor(kGreen));
 
   xframe2->SetTitle("An example fit to the background-only model");
   xframe2->Draw();
   //  cbkgonly->SaveAs("nullFit.gif");
}