ROOT   Reference Guide
Searching...
No Matches
rs401d_FeldmanCousins.C File Reference

## Detailed Description

Neutrino Oscillation Example from Feldman & Cousins

This tutorial shows a more complex example using the FeldmanCousins utility to create a confidence interval for a toy neutrino oscillation experiment. The example attempts to faithfully reproduce the toy example described in Feldman & Cousins' original paper, Phys.Rev.D57:3873-3889,1998.

␛[1mRooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby␛[0m
Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University
generate toy data with nEvents = 692
**********
** 1 **SET PRINT 1
**********
**********
**********
PARAMETER DEFINITIONS:
NO. NAME VALUE STEP SIZE LIMITS
1 deltaMSq 4.00000e+01 1.95000e+01 1.00000e+00 3.00000e+02
2 sinSq2theta 6.00000e-03 2.00000e-03 0.00000e+00 2.00000e-02
**********
** 3 **SET ERR 0.5
**********
**********
** 4 **SET PRINT 1
**********
**********
** 5 **SET STR 1
**********
NOW USING STRATEGY 1: TRY TO BALANCE SPEED AGAINST RELIABILITY
**********
**********
FIRST CALL TO USER FUNCTION AT NEW START POINT, WITH IFLAG=4.
START MIGRAD MINIMIZATION. STRATEGY 1. CONVERGENCE WHEN EDM .LT. 1.00e-03
FCN=-1131.15 FROM MIGRAD STATUS=INITIATE 8 CALLS 9 TOTAL
EDM= unknown STRATEGY= 1 NO ERROR MATRIX
EXT PARAMETER CURRENT GUESS STEP FIRST
NO. NAME VALUE ERROR SIZE DERIVATIVE
1 deltaMSq 4.00000e+01 1.95000e+01 1.99953e-01 1.35503e+01
2 sinSq2theta 6.00000e-03 2.00000e-03 2.21072e-01 -1.80161e+00
ERR DEF= 0.5
MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.
COVARIANCE MATRIX CALCULATED SUCCESSFULLY
FCN=-1131.34 FROM MIGRAD STATUS=CONVERGED 32 CALLS 33 TOTAL
EDM=8.53321e-08 STRATEGY= 1 ERROR MATRIX ACCURATE
EXT PARAMETER STEP FIRST
NO. NAME VALUE ERROR SIZE DERIVATIVE
1 deltaMSq 3.75389e+01 4.12974e+00 9.32732e-04 7.25757e-03
2 sinSq2theta 6.29097e-03 8.61732e-04 2.04882e-03 6.82825e-04
ERR DEF= 0.5
EXTERNAL ERROR MATRIX. NDIM= 25 NPAR= 2 ERR DEF=0.5
1.706e+01 -1.140e-03
-1.140e-03 7.447e-07
PARAMETER CORRELATION COEFFICIENTS
NO. GLOBAL 1 2
1 0.31971 1.000 -0.320
2 0.31971 -0.320 1.000
**********
** 7 **SET ERR 0.5
**********
**********
** 8 **SET PRINT 1
**********
**********
** 9 **HESSE 1000
**********
COVARIANCE MATRIX CALCULATED SUCCESSFULLY
FCN=-1131.34 FROM HESSE STATUS=OK 10 CALLS 43 TOTAL
EDM=8.52801e-08 STRATEGY= 1 ERROR MATRIX ACCURATE
EXT PARAMETER INTERNAL INTERNAL
NO. NAME VALUE ERROR STEP SIZE VALUE
1 deltaMSq 3.75389e+01 4.12749e+00 3.73093e-05 -8.56559e-01
2 sinSq2theta 6.29097e-03 8.61259e-04 4.09765e-04 -3.79981e-01
ERR DEF= 0.5
EXTERNAL ERROR MATRIX. NDIM= 25 NPAR= 2 ERR DEF=0.5
1.705e+01 -1.133e-03
-1.133e-03 7.439e-07
PARAMETER CORRELATION COEFFICIENTS
NO. GLOBAL 1 2
1 0.31816 1.000 -0.318
2 0.31816 -0.318 1.000
[#1] INFO:Minization -- p.d.f. provides expected number of events, including extended term in likelihood.
[#1] INFO:NumericIntegration -- RooRealIntegral::init(PnmuTonePrime_Int[EPrime,LPrime]) using numeric integrator RooAdaptiveIntegratorND to calculate Int(LPrime,EPrime)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(PnmuTone_Int[E,L]) using numeric integrator RooAdaptiveIntegratorND to calculate Int(L,E)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(PnmuTone_Int[L]_Norm[E,L]) using numeric integrator RooIntegrator1D to calculate Int(L)
[#1] INFO:Minization -- createNLL picked up cached constraints from workspace with 0 entries
Metropolis-Hastings progress: ....................................................................................................
[#1] INFO:Eval -- Proposal acceptance rate: 3.3%
[#1] INFO:Eval -- Number of steps in chain: 165
[#1] INFO:NumericIntegration -- RooRealIntegral::init(product_Int[deltaMSq,sinSq2theta]_Norm[deltaMSq,sinSq2theta]) using numeric integrator RooAdaptiveIntegratorND to calculate Int(deltaMSq,sinSq2theta)
[#0] WARNING:NumericIntegration -- RooAdaptiveIntegratorND::dtor(product) WARNING: Number of suppressed warningings about integral evaluations where target precision was not reached is 1
[#1] INFO:NumericIntegration -- RooRealIntegral::init(product_Int[deltaMSq,sinSq2theta]_Norm[deltaMSq,sinSq2theta]) using numeric integrator RooAdaptiveIntegratorND to calculate Int(deltaMSq,sinSq2theta)
[#1] INFO:Eval -- cutoff = 0.166573, conf = 0.904333
[#0] WARNING:NumericIntegration -- RooAdaptiveIntegratorND::dtor(product) WARNING: Number of suppressed warningings about integral evaluations where target precision was not reached is 1
[#0] WARNING:NumericIntegration -- RooAdaptiveIntegratorND::dtor(PnmuTone) WARNING: Number of suppressed warningings about integral evaluations where target precision was not reached is 628
[#0] WARNING:NumericIntegration -- RooAdaptiveIntegratorND::dtor(PnmuTonePrime) WARNING: Number of suppressed warningings about integral evaluations where target precision was not reached is 628
Real time 0:02:38, CP time 158.690
MCMC actual confidence level: 0.904333
[#1] INFO:Minization -- RooProfileLL::evaluate(nll_model_modelData_Profile[deltaMSq,sinSq2theta]) Creating instance of MINUIT
[#1] INFO:Minization -- RooProfileLL::evaluate(nll_model_modelData_Profile[deltaMSq,sinSq2theta]) determining minimum likelihood for current configurations w.r.t all observable
[#1] INFO:Minization -- RooProfileLL::evaluate(nll_model_modelData_Profile[deltaMSq,sinSq2theta]) minimum found at (deltaMSq=37.5376, sinSq2theta=0.00629099)
..[#1] INFO:Minization -- LikelihoodInterval - Finding the contour of deltaMSq ( 0 ) and sinSq2theta ( 1 )
Real time 0:03:16, CP time 196.280
#include "RooGlobalFunc.h"
#include "RooDataSet.h"
#include "RooDataHist.h"
#include "RooRealVar.h"
#include "RooConstVar.h"
#include "RooProduct.h"
#include "RooProdPdf.h"
#include "TROOT.h"
#include "RooPolynomial.h"
#include "RooRandom.h"
#include "RooNLLVar.h"
#include "RooProfileLL.h"
#include "RooPlot.h"
#include "TCanvas.h"
#include "TH1F.h"
#include "TH2F.h"
#include "TTree.h"
#include "TMarker.h"
#include "TStopwatch.h"
#include <iostream>
// PDF class created for this macro
#if !defined(__CINT__) || defined(__MAKECINT__)
#include "../tutorials/roostats/NuMuToNuE_Oscillation.h"
#include "../tutorials/roostats/NuMuToNuE_Oscillation.cxx" // so that it can be executed directly
#else
#include "../tutorials/roostats/NuMuToNuE_Oscillation.cxx+" // so that it can be executed directly
#endif
using namespace RooFit;
using namespace RooStats;
void rs401d_FeldmanCousins(bool doFeldmanCousins = false, bool doMCMC = true)
{
// to time the macro
t.Start();
// Taken from Feldman & Cousins paper, Phys.Rev.D57:3873-3889,1998.
// e-Print: physics/9711021 (see page 13.)
//
// Quantum mechanics dictates that the probability of such a transformation is given by the formula
// $P (\nu\mu \rightarrow \nu e ) = sin^2 (2\theta) sin^2 (1.27 \Delta m^2 L /E )$
// where P is the probability for a $\nu\mu$ to transform into a $\nu e$ , L is the distance in km between
// the creation of the neutrino from meson decay and its interaction in the detector, E is the
// neutrino energy in GeV, and $\Delta m^2 = |m^2 - m^2 |$ in $(eV/c^2 )^2$ .
//
// To demonstrate how this works in practice, and how it compares to alternative approaches
// that have been used, we consider a toy model of a typical neutrino oscillation experiment.
// The toy model is defined by the following parameters: Mesons are assumed to decay to
// neutrinos uniformly in a region 600 m to 1000 m from the detector. The expected background
// from conventional $\nu e$ interactions and misidentified $\nu\mu$ interactions is assumed to be 100
// events in each of 5 energy bins which span the region from 10 to 60 GeV. We assume that
// the $\nu\mu$ flux is such that if $P (\nu\mu \rightarrow \nu e ) = 0.01$ averaged over any bin, then that bin
// would
// have an expected additional contribution of 100 events due to $\nu\mu \rightarrow \nu e$ oscillations.
// Make signal model model
RooRealVar E("E", "", 15, 10, 60, "GeV");
RooRealVar L("L", "", .800, .600, 1.0, "km"); // need these units in formula
RooRealVar deltaMSq("deltaMSq", "#Delta m^{2}", 40, 1, 300, "eV/c^{2}");
RooRealVar sinSq2theta("sinSq2theta", "sin^{2}(2#theta)", .006, .0, .02);
// RooRealVar deltaMSq("deltaMSq","#Delta m^{2}",40,20,70,"eV/c^{2}");
// RooRealVar sinSq2theta("sinSq2theta","sin^{2}(2#theta)", .006,.001,.01);
// PDF for oscillation only describes deltaMSq dependence, sinSq2theta goes into sigNorm
// 1) The code for this PDF was created by issuing these commands
// root [0] RooClassFactory x
// root [1] x.makePdf("NuMuToNuE_Oscillation","L,E,deltaMSq","","pow(sin(1.27*deltaMSq*L/E),2)")
NuMuToNuE_Oscillation PnmuTone("PnmuTone", "P(#nu_{#mu} #rightarrow #nu_{e}", L, E, deltaMSq);
// only E is observable, so create the signal model by integrating out L
RooAbsPdf *sigModel = PnmuTone.createProjection(L);
// create $\int dE' dL' P(E',L' | \Delta m^2)$.
// Given RooFit will renormalize the PDF in the range of the observables,
// the average probability to oscillate in the experiment's acceptance
// needs to be incorporated into the extended term in the likelihood.
// Do this by creating a RooAbsReal representing the integral and divide by
// the area in the E-L plane.
// The integral should be over "primed" observables, so we need
// an independent copy of PnmuTone not to interfere with the original.
// Independent copy for Integral
RooRealVar EPrime("EPrime", "", 15, 10, 60, "GeV");
RooRealVar LPrime("LPrime", "", .800, .600, 1.0, "km"); // need these units in formula
NuMuToNuE_Oscillation PnmuTonePrime("PnmuTonePrime", "P(#nu_{#mu} #rightarrow #nu_{e}", LPrime, EPrime, deltaMSq);
RooAbsReal *intProbToOscInExp = PnmuTonePrime.createIntegral(RooArgSet(EPrime, LPrime));
// Getting the flux is a bit tricky. It is more clear to include a cross section term that is not
// explicitly referred to in the text, eg.
// number events in bin = flux * cross-section for nu_e interaction in E bin * average prob nu_mu osc. to nu_e in bin
// let maxEventsInBin = flux * cross-section for nu_e interaction in E bin
// maxEventsInBin * 1% chance per bin = 100 events / bin
// therefore maxEventsInBin = 10,000.
// for 5 bins, this means maxEventsTot = 50,000
RooConstVar maxEventsTot("maxEventsTot", "maximum number of sinal events", 50000);
RooConstVar inverseArea("inverseArea", "1/(#Delta E #Delta L)",
1. / (EPrime.getMax() - EPrime.getMin()) / (LPrime.getMax() - LPrime.getMin()));
// $sigNorm = maxEventsTot \cdot \int dE dL \frac{P_{oscillate\ in\ experiment}}{Area} \cdot {sin}^2(2\theta)$
RooProduct sigNorm("sigNorm", "", RooArgSet(maxEventsTot, *intProbToOscInExp, inverseArea, sinSq2theta));
// bkg = 5 bins * 100 events / bin
RooConstVar bkgNorm("bkgNorm", "normalization for background", 500);
// flat background (0th order polynomial, so no arguments for coefficients)
RooPolynomial bkgEShape("bkgEShape", "flat bkg shape", E);
// total model
RooAddPdf model("model", "", RooArgList(*sigModel, bkgEShape), RooArgList(sigNorm, bkgNorm));
// for debugging, check model tree
// model.printCompactTree();
// model.graphVizTree("model.dot");
// turn off some messages
// --------------------------------------
// n events in data to data, simply sum of sig+bkg
Int_t nEventsData = bkgNorm.getVal() + sigNorm.getVal();
cout << "generate toy data with nEvents = " << nEventsData << endl;
// adjust random seed to get a toy dataset similar to one in paper.
// Found by trial and error (3 trials, so not very "fine tuned")
// create a toy dataset
RooDataSet *data = model.generate(RooArgSet(E), nEventsData);
// --------------------------------------
// make some plots
TCanvas *dataCanvas = new TCanvas("dataCanvas");
dataCanvas->Divide(2, 2);
// plot the PDF
dataCanvas->cd(1);
TH1 *hh = PnmuTone.createHistogram("hh", E, Binning(40), YVar(L, Binning(40)), Scaling(kFALSE));
hh->SetTitle("True Signal Model");
hh->Draw("surf");
// plot the data with the best fit
dataCanvas->cd(2);
RooPlot *Eframe = E.frame();
data->plotOn(Eframe);
model.fitTo(*data, Extended());
model.plotOn(Eframe);
model.plotOn(Eframe, Components(*sigModel), LineColor(kRed));
model.plotOn(Eframe, Components(bkgEShape), LineColor(kGreen));
model.plotOn(Eframe);
Eframe->SetTitle("toy data with best fit model (and sig+bkg components)");
Eframe->Draw();
// plot the likelihood function
dataCanvas->cd(3);
RooNLLVar nll("nll", "nll", model, *data, Extended());
RooProfileLL pll("pll", "", nll, RooArgSet(deltaMSq, sinSq2theta));
// TH1* hhh = nll.createHistogram("hhh",sinSq2theta,Binning(40),YVar(deltaMSq,Binning(40))) ;
TH1 *hhh = pll.createHistogram("hhh", sinSq2theta, Binning(40), YVar(deltaMSq, Binning(40)), Scaling(kFALSE));
hhh->SetTitle("Likelihood Function");
hhh->Draw("surf");
dataCanvas->Update();
// --------------------------------------------------------------
// show use of Feldman-Cousins utility in RooStats
// set the distribution creator, which encodes the test statistic
RooArgSet parameters(deltaMSq, sinSq2theta);
ModelConfig modelConfig;
modelConfig.SetWorkspace(*w);
modelConfig.SetPdf(model);
modelConfig.SetParametersOfInterest(parameters);
RooStats::FeldmanCousins fc(*data, modelConfig);
fc.SetTestSize(.1); // set size of test
fc.SetNBins(10); // number of points to test per parameter
// use the Feldman-Cousins tool
ConfInterval *interval = 0;
if (doFeldmanCousins)
interval = fc.GetInterval();
// ---------------------------------------------------------
// show use of ProfileLikeihoodCalculator utility in RooStats
RooStats::ProfileLikelihoodCalculator plc(*data, modelConfig);
plc.SetTestSize(.1);
ConfInterval *plcInterval = plc.GetInterval();
// --------------------------------------------
// show use of MCMCCalculator utility in RooStats
MCMCInterval *mcInt = NULL;
if (doMCMC) {
// turn some messages back on
TStopwatch mcmcWatch;
mcmcWatch.Start();
RooArgList axisList(deltaMSq, sinSq2theta);
MCMCCalculator mc(*data, modelConfig);
mc.SetNumIters(5000);
mc.SetNumBurnInSteps(100);
mc.SetUseKeys(true);
mc.SetTestSize(.1);
mc.SetAxes(axisList); // set which is x and y axis in posterior histogram
// mc.SetNumBins(50);
mcInt = (MCMCInterval *)mc.GetInterval();
mcmcWatch.Stop();
mcmcWatch.Print();
}
// -------------------------------
// make plot of resulting interval
dataCanvas->cd(4);
// first plot a small dot for every point tested
if (doFeldmanCousins) {
RooDataHist *parameterScan = (RooDataHist *)fc.GetPointsToScan();
TH2F *hist = (TH2F *)parameterScan->createHistogram("sinSq2theta:deltaMSq", 30, 30);
// hist->Draw();
TH2F *forContour = (TH2F *)hist->Clone();
// now loop through the points and put a marker if it's in the interval
RooArgSet *tmpPoint;
// loop over points to test
for (Int_t i = 0; i < parameterScan->numEntries(); ++i) {
// get a parameter point from the list of points to test.
tmpPoint = (RooArgSet *)parameterScan->get(i)->clone("temp");
if (interval) {
if (interval->IsInInterval(*tmpPoint)) {
forContour->SetBinContent(
hist->FindBin(tmpPoint->getRealValue("sinSq2theta"), tmpPoint->getRealValue("deltaMSq")), 1);
} else {
forContour->SetBinContent(
hist->FindBin(tmpPoint->getRealValue("sinSq2theta"), tmpPoint->getRealValue("deltaMSq")), 0);
}
}
delete tmpPoint;
}
if (interval) {
Double_t level = 0.5;
forContour->SetContour(1, &level);
forContour->SetLineWidth(2);
forContour->SetLineColor(kRed);
forContour->Draw("cont2,same");
}
}
MCMCIntervalPlot *mcPlot = NULL;
if (mcInt) {
cout << "MCMC actual confidence level: " << mcInt->GetActualConfidenceLevel() << endl;
mcPlot = new MCMCIntervalPlot(*mcInt);
mcPlot->Draw();
}
dataCanvas->Update();
LikelihoodIntervalPlot plotInt((LikelihoodInterval *)plcInterval);
plotInt.SetTitle("90% Confidence Intervals");
if (mcInt)
plotInt.Draw("same");
else
plotInt.Draw();
dataCanvas->Update();
/// print timing info
t.Stop();
t.Print();
}
int Int_t
Definition RtypesCore.h:45
const Bool_t kFALSE
Definition RtypesCore.h:92
double Double_t
Definition RtypesCore.h:59
const Bool_t kTRUE
Definition RtypesCore.h:91
@ kRed
Definition Rtypes.h:66
@ kGreen
Definition Rtypes.h:66
@ kMagenta
Definition Rtypes.h:66
@ kBlue
Definition Rtypes.h:66
static struct mg_connection * fc(struct mg_context *ctx)
Definition civetweb.c:3728
Double_t getRealValue(const char *name, Double_t defVal=0, Bool_t verbose=kFALSE) const
Get value of a RooAbsReal stored in set with given name.
TH1 * createHistogram(const char *name, const RooAbsRealLValue &xvar, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none()) const
Calls createHistogram(const char *name, const RooAbsRealLValue& xvar, const RooLinkedList& argList) c...
virtual RooPlot * plotOn(RooPlot *frame, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none()) const
virtual RooAbsPdf * createProjection(const RooArgSet &iset)
Return a p.d.f that represent a projection of this p.d.f integrated over given observables.
RooAbsReal is the common abstract base class for objects that represent a real value and implements f...
Definition RooAbsReal.h:61
RooAbsReal * createIntegral(const RooArgSet &iset, const RooCmdArg &arg1, const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none()) const
Create an object that represents the integral of the function over one or more observables listed in ...
RooAddPdf is an efficient implementation of a sum of PDFs of the form.
RooArgList is a container object that can hold multiple RooAbsArg objects.
Definition RooArgList.h:21
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition RooArgSet.h:29
TObject * clone(const char *newname) const override
Definition RooArgSet.h:83
RooConstVar represent a constant real-valued object.
Definition RooConstVar.h:26
The RooDataHist is a container class to hold N-dimensional binned data.
Definition RooDataHist.h:37
Int_t numEntries() const override
Return the number of bins.
const RooArgSet * get() const override
Get bin centre of current bin.
Definition RooDataHist.h:74
RooDataSet is a container class to hold unbinned data.
Definition RooDataSet.h:33
void setStreamStatus(Int_t id, Bool_t active)
(De)Activate stream with given unique ID
static RooMsgService & instance()
Return reference to singleton instance.
Class RooNLLVar implements a -log(likelihood) calculation from a dataset and a PDF.
Definition RooNLLVar.h:30
A RooPlot is a plot frame and a container for graphics objects within that frame.
Definition RooPlot.h:44
void SetTitle(const char *name)
Set the title of the RooPlot to 'title'.
Definition RooPlot.cxx:1242
virtual void Draw(Option_t *options=0)
Draw this plot and all of the elements it contains.
Definition RooPlot.cxx:691
RooPolynomial implements a polynomial p.d.f of the form.
A RooProduct represents the product of a given set of RooAbsReal objects.
Definition RooProduct.h:29
Class RooProfileLL implements the profile likelihood estimator for a given likelihood and set of para...
static TRandom * randomGenerator()
Return a pointer to a singleton random-number generator implementation.
Definition RooRandom.cxx:53
RooRealVar represents a variable that can be changed from the outside.
Definition RooRealVar.h:39
ConfInterval is an interface class for a generic interval in the RooStats framework.
virtual Bool_t IsInInterval(const RooArgSet &) const =0
check if given point is in the interval
The FeldmanCousins class (like the Feldman-Cousins technique) is essentially a specific configuration...
This class provides simple and straightforward utilities to plot a LikelihoodInterval object.
LikelihoodInterval is a concrete implementation of the RooStats::ConfInterval interface.
Bayesian Calculator estimating an interval or a credible region using the Markov-Chain Monte Carlo me...
This class provides simple and straightforward utilities to plot a MCMCInterval object.
void SetLineColor(Color_t color)
void Draw(const Option_t *options=NULL)
MCMCInterval is a concrete implementation of the RooStats::ConfInterval interface.
virtual Double_t GetActualConfidenceLevel()
virtual Double_t GetKeysPdfCutoff() { return fKeysCutoff; }
ModelConfig is a simple class that holds configuration information specifying how a model should be u...
Definition ModelConfig.h:30
virtual void SetWorkspace(RooWorkspace &ws)
Definition ModelConfig.h:66
virtual void SetParametersOfInterest(const RooArgSet &set)
Specify parameters of interest.
virtual void SetPdf(const RooAbsPdf &pdf)
Set the Pdf, add to the the workspace if not already there.
Definition ModelConfig.h:81
The ProfileLikelihoodCalculator is a concrete implementation of CombinedCalculator (the interface cla...
The RooWorkspace is a persistable container for RooFit projects.
virtual void SetLineWidth(Width_t lwidth)
Set the line width.
Definition TAttLine.h:43
virtual void SetLineColor(Color_t lcolor)
Set the line color.
Definition TAttLine.h:40
The Canvas class.
Definition TCanvas.h:23
Definition TCanvas.cxx:708
void Update() override
Definition TCanvas.cxx:2504
TH1 is the base class of all histogram classes in ROOT.
Definition TH1.h:58
virtual void SetTitle(const char *title)
Definition TH1.cxx:6678
TObject * Clone(const char *newname=0) const
Make a complete copy of the underlying object.
Definition TH1.cxx:2740
virtual void SetContour(Int_t nlevels, const Double_t *levels=0)
Set the number and values of contour levels.
Definition TH1.cxx:8331
virtual void Draw(Option_t *option="")
Draw this histogram with options.
Definition TH1.cxx:3073
virtual Int_t FindBin(Double_t x, Double_t y=0, Double_t z=0)
Return Global bin number corresponding to x,y,z.
Definition TH1.cxx:3680
2-D histogram with a float per channel (see TH1 documentation)}
Definition TH2.h:251
virtual void SetBinContent(Int_t bin, Double_t content)
Set bin content.
Definition TH2.cxx:2507
void Divide(Int_t nx=1, Int_t ny=1, Float_t xmargin=0.01, Float_t ymargin=0.01, Int_t color=0) override
virtual void SetSeed(ULong_t seed=0)
Set the random generator seed.
Definition TRandom.cxx:608
Stopwatch class.
Definition TStopwatch.h:28
void Start(Bool_t reset=kTRUE)
Start the stopwatch.
void Stop()
Stop the stopwatch.
void Print(Option_t *option="") const
Print the real and cpu time passed between the start and stop events.
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
Namespace for the RooStats classes.
Definition Asimov.h:19

Definition in file rs401d_FeldmanCousins.C.