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Reference Guide
FourBinInstructional.C File Reference

Detailed Description

This example is a generalization of the on/off problem.

View in nbviewer Open in SWAN This example is a generalization of the on/off problem. It's a common setup for SUSY searches. Imagine that one has two variables "x" and "y" (eg. missing ET and SumET), see figure. The signal region has high values of both of these variables (top right). One can see low values of "x" or "y" acting as side-bands. If we just used "y" as a sideband, we would have the on/off problem.

  • In the signal region we observe non events and expect s+b events.
  • In the region with low values of "y" (bottom right) we observe noff events and expect tau*b events. Note the significance of tau. In the background only case:
tau ~ <expectation off> / <expectation on>

If tau is known, this model is sufficient, but often tau is not known exactly. So one can use low values of "x" as an additional constraint for tau. Note that this technique critically depends on the notion that the joint distribution for "x" and "y" can be factorized. Generally, these regions have many events, so it the ratio can be measured very precisely there. So we extend the model to describe the left two boxes... denoted with "bar".

  • In the upper left we observe nonbar events and expect bbar events
  • In the bottom left we observe noffbar events and expect tau bbar events Note again we have:
tau ~ <expectation off bar> / <expectation on bar>

One can further expand the model to account for the systematic associated to assuming the distribution of "x" and "y" factorizes (eg. that tau is the same for off/on and offbar/onbar). This can be done in several ways, but here we introduce an additional parameter rho, which so that one set of models will use tau and the other tau*rho. The choice is arbitrary, but it has consequences on the numerical stability of the algorithms. The "bar" measurements typically have more events (& smaller relative errors). If we choose

<expectation noffbar> = tau * rho * <expectation noonbar>

the product tau*rho will be known very precisely (~1/sqrt(bbar)) and the contour in those parameters will be narrow and have a non-trivial tau~1/rho shape. However, if we choose to put rho on the non/noff measurements (where the product will have an error ~1/sqrt(b)), the contours will be more amenable to numerical techniques. Thus, here we choose to define:

tau := <expectation off bar> / (<expectation on bar>)
rho := <expectation off> / (<expectation on> * tau)
^ y
|
|---------------------------+
| | |
| nonbar | non |
| bbar | s+b |
| | |
|---------------+-----------|
| | |
| noffbar | noff |
| tau bbar | tau b rho |
| | |
+-----------------------------> x

Left in this way, the problem is under-constrained. However, one may have some auxiliary measurement (usually based on Monte Carlo) to constrain rho. Let us call this auxiliary measurement that gives the nominal value of rho "rhonom". Thus, there is a 'constraint' term in the full model: P(rhonom | rho). In this case, we consider a Gaussian constraint with standard deviation sigma.

In the example, the initial values of the parameters are:

- s = 40
- b = 100
- tau = 5
- bbar = 1000
- rho = 1
(sigma for rho = 20%)

and in the toy dataset:

- non = 139
- noff = 528
- nonbar = 993
- noffbar = 4906
- rhonom = 1.27824

Note, the covariance matrix of the parameters has large off-diagonal terms. Clearly s,b are anti-correlated. Similarly, since noffbar >> nonbar, one would expect bbar,tau to be anti-correlated.

This can be seen below.

GLOBAL b bbar rho s tau
b 0.96820 1.000 0.191 -0.942 -0.762 -0.209
bbar 0.91191 0.191 1.000 0.000 -0.146 -0.912
rho 0.96348 -0.942 0.000 1.000 0.718 -0.000
s 0.76250 -0.762 -0.146 0.718 1.000 0.160
tau 0.92084 -0.209 -0.912 -0.000 0.160 1.000

Similarly, since tau*rho appears as a product, we expect rho,tau to be anti-correlated. When the error on rho is significantly larger than 1/sqrt(bbar), tau is essentially known and the correlation is minimal (tau mainly cares about bbar, and rho about b,s). In the alternate parametrization (bbar* tau * rho) the correlation coefficient for rho,tau is large (and negative).

The code below uses best-practices for RooFit & RooStats as of June 2010.

It proceeds as follows:

  • create a workspace to hold the model
  • use workspace factory to quickly create the terms of the model
  • use workspace factory to define total model (a prod pdf)
  • create a RooStats ModelConfig to specify observables, parameters of interest
  • add to the ModelConfig a prior on the parameters for Bayesian techniques note, the pdf it is factorized for parameters of interest & nuisance params
  • visualize the model
  • write the workspace to a file
  • use several of RooStats IntervalCalculators & compare results
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
[#1] INFO:ObjectHandling -- RooWorkspace::import(wspace) importing dataset modelData
[#1] INFO:Minization -- createNLL: caching constraint set under name CONSTR_OF_PDF_model_FOR_OBS_noff:noffbar:non:nonbar:rhonom with 0 entries
[#0] PROGRESS:Minization -- ProfileLikelihoodCalcultor::DoGLobalFit - find MLE
[#0] PROGRESS:Minization -- ProfileLikelihoodCalcultor::DoMinimizeNLL - using Minuit / Migrad with strategy 1
[#1] INFO:Minization -- RooMinimizer::optimizeConst: activating const optimization
[#1] INFO:Minization -- The following expressions will be evaluated in cache-and-track mode: (on,off,onbar,offbar,mcCons)
[#1] INFO:Minization --
RooFitResult: minimized FCN value: 16.2872, estimated distance to minimum: 1.21273e-07
covariance matrix quality: Full, accurate covariance matrix
Status : MINIMIZE=0
Floating Parameter FinalValue +/- Error
-------------------- --------------------------
b 8.3602e+01 +/- 1.39e+01
bbar 9.9301e+02 +/- 3.15e+01
rho 1.2783e+00 +/- 1.99e-01
s 5.5397e+01 +/- 1.78e+01
tau 4.9405e+00 +/- 1.72e-01
Bayesian Calc. only supports on parameter of interest
[#1] INFO:Minization -- createNLL picked up cached consraints from workspace with 0 entries
[#1] INFO:Minization -- RooMinimizer::optimizeConst: activating const optimization
[#1] INFO:Minization -- The following expressions will be evaluated in cache-and-track mode: (on,off,onbar,offbar,mcCons)
**********
** 1 **SET PRINT 1
**********
**********
** 2 **SET NOGRAD
**********
PARAMETER DEFINITIONS:
NO. NAME VALUE STEP SIZE LIMITS
1 b 1.17958e+02 1.38657e+01 0.00000e+00 3.00000e+02
2 bbar 1.00111e+03 3.15031e+01 5.00000e+02 2.00000e+03
3 rho 9.28979e-01 1.98664e-01 0.00000e+00 2.00000e+00
4 s 1.20959e+01 1.78108e+01 0.00000e+00 1.00000e+02
MINUIT WARNING IN PARAMETR
============== VARIABLE4 BROUGHT BACK INSIDE LIMITS.
5 tau 4.89226e+00 1.71715e-01 3.00000e+00 7.00000e+00
**********
** 3 **SET ERR 0.5
**********
**********
** 4 **SET PRINT 1
**********
**********
** 5 **SET STR 1
**********
NOW USING STRATEGY 1: TRY TO BALANCE SPEED AGAINST RELIABILITY
**********
** 6 **MIGRAD 2500 1
**********
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=18.2144 FROM MIGRAD STATUS=INITIATE 20 CALLS 21 TOTAL
EDM= unknown STRATEGY= 1 NO ERROR MATRIX
EXT PARAMETER CURRENT GUESS STEP FIRST
NO. NAME VALUE ERROR SIZE DERIVATIVE
1 b 1.17958e+02 1.38657e+01 9.47845e-02 -1.99668e-02
2 bbar 1.00111e+03 3.15031e+01 4.45476e-02 -1.41126e-01
3 rho 9.28979e-01 1.98664e-01 2.00529e-01 -1.44935e-02
4 s 1.20959e+01 1.78108e+01 5.77645e-01 -2.24297e+00
5 tau 4.89226e+00 1.71715e-01 8.60896e-02 -8.62115e-02
ERR DEF= 0.5
MIGRAD MINIMIZATION HAS CONVERGED.
MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.
COVARIANCE MATRIX CALCULATED SUCCESSFULLY
FCN=16.2872 FROM MIGRAD STATUS=CONVERGED 190 CALLS 191 TOTAL
EDM=3.60951e-06 STRATEGY= 1 ERROR MATRIX ACCURATE
EXT PARAMETER STEP FIRST
NO. NAME VALUE ERROR SIZE DERIVATIVE
1 b 8.35905e+01 1.38673e+01 7.30675e-05 2.40056e-02
2 bbar 9.93029e+02 3.15018e+01 5.19045e-05 -3.02691e-02
3 rho 1.27859e+00 1.98755e-01 1.58308e-04 1.62269e-02
4 s 5.54105e+01 1.78121e+01 6.70504e-04 3.43680e-04
5 tau 4.94037e+00 1.71699e-01 9.49080e-05 -2.33172e-02
ERR DEF= 0.5
EXTERNAL ERROR MATRIX. NDIM= 25 NPAR= 5 ERR DEF=0.5
1.930e+02 8.358e+01 -2.620e+00 -1.929e+02 -5.000e-01
8.358e+01 9.930e+02 1.248e-04 -8.355e+01 -4.940e+00
-2.620e+00 1.248e-04 4.008e-02 2.619e+00 -7.467e-07
-1.929e+02 -8.355e+01 2.619e+00 3.319e+02 4.998e-01
-5.000e-01 -4.940e+00 -7.467e-07 4.998e-01 2.955e-02
PARAMETER CORRELATION COEFFICIENTS
NO. GLOBAL 1 2 3 4 5
1 0.96819 1.000 0.191 -0.942 -0.762 -0.209
2 0.91195 0.191 1.000 0.000 -0.146 -0.912
3 0.96348 -0.942 0.000 1.000 0.718 -0.000
4 0.76233 -0.762 -0.146 0.718 1.000 0.160
5 0.92088 -0.209 -0.912 -0.000 0.160 1.000
**********
** 7 **SET ERR 0.5
**********
**********
** 8 **SET PRINT 1
**********
**********
** 9 **HESSE 2500
**********
COVARIANCE MATRIX CALCULATED SUCCESSFULLY
FCN=16.2872 FROM HESSE STATUS=OK 31 CALLS 222 TOTAL
EDM=3.6099e-06 STRATEGY= 1 ERROR MATRIX ACCURATE
EXT PARAMETER INTERNAL INTERNAL
NO. NAME VALUE ERROR STEP SIZE VALUE
1 b 8.35905e+01 1.38734e+01 1.46135e-05 -4.58641e-01
2 bbar 9.93029e+02 3.14977e+01 1.03809e-05 -3.49712e-01
3 rho 1.27859e+00 1.98839e-01 3.16616e-05 2.82321e-01
4 s 5.54105e+01 1.78190e+01 1.34101e-04 1.08422e-01
5 tau 4.94037e+00 1.71676e-01 1.89816e-05 -2.98215e-02
ERR DEF= 0.5
EXTERNAL ERROR MATRIX. NDIM= 25 NPAR= 5 ERR DEF=0.5
1.932e+02 8.358e+01 -2.623e+00 -1.931e+02 -5.000e-01
8.358e+01 9.928e+02 -2.730e-04 -8.358e+01 -4.939e+00
-2.623e+00 -2.730e-04 4.012e-02 2.623e+00 1.633e-06
-1.931e+02 -8.358e+01 2.623e+00 3.321e+02 5.000e-01
-5.000e-01 -4.939e+00 1.633e-06 5.000e-01 2.955e-02
PARAMETER CORRELATION COEFFICIENTS
NO. GLOBAL 1 2 3 4 5
1 0.96822 1.000 0.191 -0.942 -0.763 -0.209
2 0.91193 0.191 1.000 -0.000 -0.146 -0.912
3 0.96351 -0.942 -0.000 1.000 0.718 0.000
4 0.76256 -0.763 -0.146 0.718 1.000 0.160
5 0.92086 -0.209 -0.912 0.000 0.160 1.000
[#1] INFO:Minization -- RooMinimizer::optimizeConst: deactivating const optimization
[#1] INFO:Minization -- RooProfileLL::evaluate(nll_model_modelData_Profile[s]) Creating instance of MINUIT
[#1] INFO:Minization -- RooProfileLL::evaluate(nll_model_modelData_Profile[s]) determining minimum likelihood for current configurations w.r.t all observable
[#1] INFO:Minization -- RooProfileLL::evaluate(nll_model_modelData_Profile[s]) minimum found at (s=55.4077)
.
[#1] INFO:Minization -- RooProfileLL::evaluate(nll_model_modelData_Profile[s]) Creating instance of MINUIT
[#1] INFO:Minization -- RooProfileLL::evaluate(nll_model_modelData_Profile[s]) determining minimum likelihood for current configurations w.r.t all observable
[#0] ERROR:InputArguments -- RooArgSet::checkForDup: ERROR argument with name s is already in this set
[#1] INFO:Minization -- RooProfileLL::evaluate(nll_model_modelData_Profile[s]) minimum found at (s=55.4105)
..........................................................................................................................................................................................................Profile Likelihood interval on s = [12.1902, 88.6871]
Real time 0:00:00, CP time 0.720
#include "TStopwatch.h"
#include "TCanvas.h"
#include "TROOT.h"
#include "RooPlot.h"
#include "RooAbsPdf.h"
#include "RooWorkspace.h"
#include "RooDataSet.h"
#include "RooGlobalFunc.h"
#include "RooFitResult.h"
#include "RooRandom.h"
using namespace RooFit;
using namespace RooStats;
void FourBinInstructional(bool doBayesian = false, bool doFeldmanCousins = false, bool doMCMC = false)
{
// let's time this challenging example
t.Start();
// set RooFit random seed for reproducible results
// make model
RooWorkspace *wspace = new RooWorkspace("wspace");
wspace->factory("Poisson::on(non[0,1000], sum::splusb(s[40,0,100],b[100,0,300]))");
wspace->factory("Poisson::off(noff[0,5000], prod::taub(b,tau[5,3,7],rho[1,0,2]))");
wspace->factory("Poisson::onbar(nonbar[0,10000], bbar[1000,500,2000])");
wspace->factory("Poisson::offbar(noffbar[0,1000000], prod::lambdaoffbar(bbar, tau))");
wspace->factory("Gaussian::mcCons(rhonom[1.,0,2], rho, sigma[.2])");
wspace->factory("PROD::model(on,off,onbar,offbar,mcCons)");
wspace->defineSet("obs", "non,noff,nonbar,noffbar,rhonom");
wspace->factory("Uniform::prior_poi({s})");
wspace->factory("Uniform::prior_nuis({b,bbar,tau, rho})");
wspace->factory("PROD::prior(prior_poi,prior_nuis)");
// ----------------------------------
// Control some interesting variations
// define parameers of interest
// for 1-d plots
wspace->defineSet("poi", "s");
wspace->defineSet("nuis", "b,tau,rho,bbar");
// for 2-d plots to inspect correlations:
// wspace->defineSet("poi","s,rho");
// test simpler cases where parameters are known.
// wspace->var("tau")->setConstant();
// wspace->var("rho")->setConstant();
// wspace->var("b")->setConstant();
// wspace->var("bbar")->setConstant();
// inspect workspace
// wspace->Print();
// ----------------------------------------------------------
// Generate toy data
// generate toy data assuming current value of the parameters
// import into workspace.
// add Verbose() to see how it's being generated
RooDataSet *data = wspace->pdf("model")->generate(*wspace->set("obs"), 1);
// data->Print("v");
wspace->import(*data);
// ----------------------------------
// Now the statistical tests
// model config
ModelConfig *modelConfig = new ModelConfig("FourBins");
modelConfig->SetWorkspace(*wspace);
modelConfig->SetPdf(*wspace->pdf("model"));
modelConfig->SetPriorPdf(*wspace->pdf("prior"));
modelConfig->SetParametersOfInterest(*wspace->set("poi"));
modelConfig->SetNuisanceParameters(*wspace->set("nuis"));
wspace->import(*modelConfig);
wspace->writeToFile("FourBin.root");
// -------------------------------------------------
// If you want to see the covariance matrix uncomment
// wspace->pdf("model")->fitTo(*data);
// use ProfileLikelihood
ProfileLikelihoodCalculator plc(*data, *modelConfig);
plc.SetConfidenceLevel(0.95);
LikelihoodInterval *plInt = plc.GetInterval();
plInt->LowerLimit(*wspace->var("s")); // get ugly print out of the way. Fix.
// use FeldmaCousins (takes ~20 min)
FeldmanCousins fc(*data, *modelConfig);
fc.SetConfidenceLevel(0.95);
// number counting: dataset always has 1 entry with N events observed
fc.FluctuateNumDataEntries(false);
fc.UseAdaptiveSampling(true);
fc.SetNBins(40);
PointSetInterval *fcInt = NULL;
if (doFeldmanCousins) { // takes 7 minutes
fcInt = (PointSetInterval *)fc.GetInterval(); // fix cast
}
// use BayesianCalculator (only 1-d parameter of interest, slow for this problem)
BayesianCalculator bc(*data, *modelConfig);
bc.SetConfidenceLevel(0.95);
SimpleInterval *bInt = NULL;
if (doBayesian && wspace->set("poi")->getSize() == 1) {
bInt = bc.GetInterval();
} else {
cout << "Bayesian Calc. only supports on parameter of interest" << endl;
}
// use MCMCCalculator (takes about 1 min)
// Want an efficient proposal function, so derive it from covariance
// matrix of fit
RooFitResult *fit = wspace->pdf("model")->fitTo(*data, Save());
ph.SetUpdateProposalParameters(kTRUE); // auto-create mean vars and add mappings
ph.SetCacheSize(100);
MCMCCalculator mc(*data, *modelConfig);
mc.SetConfidenceLevel(0.95);
mc.SetProposalFunction(*pf);
mc.SetNumBurnInSteps(500); // first N steps to be ignored as burn-in
mc.SetNumIters(50000);
mc.SetLeftSideTailFraction(0.5); // make a central interval
MCMCInterval *mcInt = NULL;
if (doMCMC)
mcInt = mc.GetInterval();
// ----------------------------------
// Make some plots
TCanvas *c1 = (TCanvas *)gROOT->Get("c1");
if (!c1)
c1 = new TCanvas("c1");
if (doBayesian && doMCMC) {
c1->Divide(3);
c1->cd(1);
} else if (doBayesian || doMCMC) {
c1->Divide(2);
c1->cd(1);
}
lrplot->Draw();
if (doBayesian && wspace->set("poi")->getSize() == 1) {
c1->cd(2);
// the plot takes a long time and print lots of error
// using a scan it is better
bc.SetScanOfPosterior(20);
RooPlot *bplot = bc.GetPosteriorPlot();
bplot->Draw();
}
if (doMCMC) {
if (doBayesian && wspace->set("poi")->getSize() == 1)
c1->cd(3);
else
c1->cd(2);
MCMCIntervalPlot mcPlot(*mcInt);
mcPlot.Draw();
}
// ----------------------------------
// querry intervals
cout << "Profile Likelihood interval on s = [" << plInt->LowerLimit(*wspace->var("s")) << ", "
<< plInt->UpperLimit(*wspace->var("s")) << "]" << endl;
// Profile Likelihood interval on s = [12.1902, 88.6871]
if (doBayesian && wspace->set("poi")->getSize() == 1) {
cout << "Bayesian interval on s = [" << bInt->LowerLimit() << ", " << bInt->UpperLimit() << "]" << endl;
}
if (doFeldmanCousins) {
cout << "Feldman Cousins interval on s = [" << fcInt->LowerLimit(*wspace->var("s")) << ", "
<< fcInt->UpperLimit(*wspace->var("s")) << "]" << endl;
// Feldman Cousins interval on s = [18.75 +/- 2.45, 83.75 +/- 2.45]
}
if (doMCMC) {
cout << "MCMC interval on s = [" << mcInt->LowerLimit(*wspace->var("s")) << ", "
<< mcInt->UpperLimit(*wspace->var("s")) << "]" << endl;
// MCMC interval on s = [15.7628, 84.7266]
}
t.Print();
}
Authors
authors: Kyle Cranmer, Tanja Rommerskirchen

Definition in file FourBinInstructional.C.

RooWorkspace.h
RooPlot::Draw
virtual void Draw(Option_t *options=0)
Draw this plot and all of the elements it contains.
Definition: RooPlot.cxx:691
kTRUE
const Bool_t kTRUE
Definition: RtypesCore.h:91
RooStats::MCMCInterval
Definition: MCMCInterval.h:32
PointSetInterval.h
RooWorkspace::writeToFile
Bool_t writeToFile(const char *fileName, Bool_t recreate=kTRUE)
Save this current workspace into given file.
Definition: RooWorkspace.cxx:2154
RooStats::PointSetInterval::UpperLimit
Double_t UpperLimit(RooRealVar &param)
return upper limit on a given parameter
Definition: PointSetInterval.cxx:147
fc
static struct mg_connection * fc(struct mg_context *ctx)
Definition: civetweb.c:3728
RooStats::ProposalHelper::SetVariables
virtual void SetVariables(RooArgList &vars)
Definition: ProposalHelper.h:66
RooStats::ProfileLikelihoodCalculator
Definition: ProfileLikelihoodCalculator.h:28
RooStats::ModelConfig::SetWorkspace
virtual void SetWorkspace(RooWorkspace &ws)
Definition: ModelConfig.h:78
TStopwatch.h
RooStats::FeldmanCousins
Definition: FeldmanCousins.h:33
TGeant4Unit::s
static constexpr double s
Definition: TGeant4SystemOfUnits.h:168
RooStats::ProposalHelper::SetUpdateProposalParameters
virtual void SetUpdateProposalParameters(Bool_t updateParams)
Definition: ProposalHelper.h:63
RooStats::MCMCInterval::LowerLimit
virtual Double_t LowerLimit(RooRealVar &param)
get the lowest value of param that is within the confidence interval
Definition: MCMCInterval.cxx:1005
RooStats::SimpleInterval::LowerLimit
virtual Double_t LowerLimit()
Definition: SimpleInterval.h:47
x
Double_t x[n]
Definition: legend1.C:17
TCanvas.h
RooStats::LikelihoodIntervalPlot::Draw
void Draw(const Option_t *options=0)
draw the likelihood interval or contour for the 1D case a RooPlot is drawn by default of the profiled...
Definition: LikelihoodIntervalPlot.cxx:167
RooFit::MsgLevel
MsgLevel
Verbosity level for RooMsgService::StreamConfig in RooMsgService.
Definition: RooGlobalFunc.h:65
TGeant4Unit::bar
static constexpr double bar
Definition: TGeant4SystemOfUnits.h:227
RooWorkspace::set
const RooArgSet * set(const char *name)
Return pointer to previously defined named set with given nmame If no such set is found a null pointe...
Definition: RooWorkspace.cxx:977
RooStats::MCMCIntervalPlot
Definition: MCMCIntervalPlot.h:37
RooDataSet.h
b
#define b(i)
Definition: RSha256.hxx:118
RooStats::ProposalHelper::SetCacheSize
virtual void SetCacheSize(Int_t size)
Definition: ProposalHelper.h:54
RooStats::SimpleInterval::UpperLimit
virtual Double_t UpperLimit()
Definition: SimpleInterval.h:49
RooFitResult
Definition: RooFitResult.h:40
RooWorkspace::factory
RooFactoryWSTool & factory()
Return instance to factory tool.
Definition: RooWorkspace.cxx:2166
RooStats::ModelConfig::SetPdf
virtual void SetPdf(const RooAbsPdf &pdf)
Set the Pdf, add to the the workspace if not already there.
Definition: ModelConfig.h:93
RooStats::SimpleInterval
Definition: SimpleInterval.h:20
RooWorkspace::import
Bool_t import(const RooAbsArg &arg, const RooCmdArg &arg1=RooCmdArg(), const RooCmdArg &arg2=RooCmdArg(), const RooCmdArg &arg3=RooCmdArg(), const RooCmdArg &arg4=RooCmdArg(), const RooCmdArg &arg5=RooCmdArg(), const RooCmdArg &arg6=RooCmdArg(), const RooCmdArg &arg7=RooCmdArg(), const RooCmdArg &arg8=RooCmdArg(), const RooCmdArg &arg9=RooCmdArg())
Import a RooAbsArg object, e.g.
Definition: RooWorkspace.cxx:361
TROOT.h
RooStats::LikelihoodIntervalPlot
Definition: LikelihoodIntervalPlot.h:41
TStopwatch::Print
void Print(Option_t *option="") const
Print the real and cpu time passed between the start and stop events.
Definition: TStopwatch.cxx:219
RooStats::ProposalHelper
Definition: ProposalHelper.h:35
LikelihoodInterval.h
RooStats::LikelihoodInterval::LowerLimit
Double_t LowerLimit(const RooRealVar &param)
return the lower bound of the interval on a given parameter
Definition: LikelihoodInterval.h:65
MCMCIntervalPlot.h
RooFit
Definition: RooCFunction1Binding.h:29
RooStats::MCMCInterval::UpperLimit
virtual Double_t UpperLimit(RooRealVar &param)
get the highest value of param that is within the confidence interval
Definition: MCMCInterval.cxx:1021
RooAbsPdf.h
RooStats::ModelConfig::SetNuisanceParameters
virtual void SetNuisanceParameters(const RooArgSet &set)
Specify the nuisance parameters (parameters that are not POI).
Definition: ModelConfig.h:131
RooFit::FATAL
@ FATAL
Definition: RooGlobalFunc.h:65
BayesianCalculator.h
RooRandom.h
MCMCInterval.h
RooPlot.h
RooMsgService::setGlobalKillBelow
void setGlobalKillBelow(RooFit::MsgLevel level)
Definition: RooMsgService.h:160
SimpleInterval.h
TRandom::SetSeed
virtual void SetSeed(ULong_t seed=0)
Set the random generator seed.
Definition: TRandom.cxx:597
RooPlot
Definition: RooPlot.h:44
RooStats::MCMCCalculator
Definition: MCMCCalculator.h:37
RooWorkspace::pdf
RooAbsPdf * pdf(const char *name) const
Retrieve p.d.f (RooAbsPdf) with given name. A null pointer is returned if not found.
Definition: RooWorkspace.cxx:1277
y
Double_t y[n]
Definition: legend1.C:17
TStopwatch::Start
void Start(Bool_t reset=kTRUE)
Start the stopwatch.
Definition: TStopwatch.cxx:58
RooFitResult.h
RooGlobalFunc.h
RooStats::ProposalHelper::SetCovMatrix
virtual void SetCovMatrix(const TMatrixDSym &covMatrix)
Definition: ProposalHelper.h:81
MCMCCalculator.h
FeldmanCousins.h
sigma
const Double_t sigma
Definition: h1analysisProxy.h:11
RooWorkspace
Definition: RooWorkspace.h:43
RooStats::ModelConfig::SetPriorPdf
virtual void SetPriorPdf(const RooAbsPdf &pdf)
Set the Prior Pdf, add to the the workspace if not already there.
Definition: ModelConfig.h:99
TCanvas
Definition: TCanvas.h:23
RooAbsPdf::generate
RooDataSet * generate(const RooArgSet &whatVars, Int_t nEvents, const RooCmdArg &arg1, const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none())
See RooAbsPdf::generate(const RooArgSet&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&,...
Definition: RooAbsPdf.h:55
RooStats
Definition: Asimov.h:19
TStopwatch
Definition: TStopwatch.h:28
ProfileLikelihoodCalculator.h
RooStats::BayesianCalculator
Definition: BayesianCalculator.h:37
RooStats::ProposalFunction
Definition: ProposalFunction.h:48
RooWorkspace::var
RooRealVar * var(const char *name) const
Retrieve real-valued variable (RooRealVar) with given name. A null pointer is returned if not found.
Definition: RooWorkspace.cxx:1295
RooFitResult::floatParsFinal
const RooArgList & floatParsFinal() const
Definition: RooFitResult.h:110
RooDataSet
Definition: RooDataSet.h:33
RooStats::ModelConfig::SetParametersOfInterest
virtual void SetParametersOfInterest(const RooArgSet &set)
Specify parameters of interest.
Definition: ModelConfig.h:112
RooRandom::randomGenerator
static TRandom * randomGenerator()
Return a pointer to a singleton random-number generator implementation.
Definition: RooRandom.cxx:53
LikelihoodIntervalPlot.h
RooStats::PointSetInterval
Definition: PointSetInterval.h:27
RooFit::Save
RooCmdArg Save(Bool_t flag=kTRUE)
Definition: RooGlobalFunc.cxx:187
RooStats::ModelConfig
Definition: ModelConfig.h:36
RooMsgService::instance
static RooMsgService & instance()
Return reference to singleton instance.
Definition: RooMsgService.cxx:363
RooStats::ProposalHelper::GetProposalFunction
virtual ProposalFunction * GetProposalFunction()
Definition: ProposalHelper.cxx:77
RooMsgService::globalKillBelow
RooFit::MsgLevel globalKillBelow() const
Definition: RooMsgService.h:161
RooStats::LikelihoodInterval
Definition: LikelihoodInterval.h:34
RooStats::PointSetInterval::LowerLimit
Double_t LowerLimit(RooRealVar &param)
return lower limit on a given parameter
Definition: PointSetInterval.cxx:160
RooAbsCollection::getSize
Int_t getSize() const
Definition: RooAbsCollection.h:171
RooArgSet
Definition: RooArgSet.h:28
RooFitResult::covarianceMatrix
const TMatrixDSym & covarianceMatrix() const
Return covariance matrix.
Definition: RooFitResult.cxx:1108
gROOT
#define gROOT
Definition: TROOT.h:406
ProposalHelper.h
RooStats::LikelihoodInterval::UpperLimit
Double_t UpperLimit(const RooRealVar &param)
return the upper bound of the interval on a given parameter
Definition: LikelihoodInterval.h:69
c1
return c1
Definition: legend1.C:41
RooAbsPdf::fitTo
virtual RooFitResult * fitTo(RooAbsData &data, 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())
Fit PDF to given dataset.
Definition: RooAbsPdf.cxx:1261
RooWorkspace::defineSet
Bool_t defineSet(const char *name, const RooArgSet &aset, Bool_t importMissing=kFALSE)
Define a named RooArgSet with given constituents.
Definition: RooWorkspace.cxx:855