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Reference Guide
IntervalExamples.C File Reference

Detailed Description

View in nbviewer Open in SWAN Example showing confidence intervals with four techniques.

An example that shows confidence intervals with four techniques. The model is a Normal Gaussian G(x|mu,sigma) with 100 samples of x. The answer is known analytically, so this is a good example to validate the RooStats tools.

  • expected interval is [-0.162917, 0.229075]
  • plc interval is [-0.162917, 0.229075]
  • fc interval is [-0.17 , 0.23] // stepsize is 0.01
  • bc interval is [-0.162918, 0.229076]
  • mcmc interval is [-0.166999, 0.230224]
pict1_IntervalExamples.C.png
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
RooDataSet::normalData[x] = 100 entries
[#1] INFO:Minization -- createNLL: caching constraint set under name CONSTR_OF_PDF_normal_FOR_OBS_x 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 --
RooFitResult: minimized FCN value: 144.292, estimated distance to minimum: 3.63481e-09
covariance matrix quality: Full, accurate covariance matrix
Status : MINIMIZE=0
Floating Parameter FinalValue +/- Error
-------------------- --------------------------
mu 3.3073e-02 +/- 9.98e-02
=== Using the following for Example G(x|mu,1) ===
Observables: RooArgSet:: = (x)
Parameters of Interest: RooArgSet:: = (mu)
PDF: RooGaussian::normal[ x=x mean=mu sigma=sigma ] = 0.999453
FeldmanCousins: ntoys per point: adaptive
FeldmanCousins: nEvents per toy will not fluctuate, will always be 100
FeldmanCousins: Model has no nuisance parameters
FeldmanCousins: # points to test = 100
NeymanConstruction: Prog: 1/100 total MC = 78 this test stat = 52.3345
mu=-0.99 [-1e+30, 1.44394] in interval = 0
NeymanConstruction: Prog: 2/100 total MC = 78 this test stat = 50.3084
mu=-0.97 [-1e+30, 1.79333] in interval = 0
NeymanConstruction: Prog: 3/100 total MC = 78 this test stat = 48.3222
mu=-0.95 [-1e+30, 2.15157] in interval = 0
NeymanConstruction: Prog: 4/100 total MC = 78 this test stat = 46.3761
mu=-0.93 [-1e+30, 1.35751] in interval = 0
NeymanConstruction: Prog: 5/100 total MC = 78 this test stat = 44.4699
mu=-0.91 [-1e+30, 3.34994] in interval = 0
NeymanConstruction: Prog: 6/100 total MC = 78 this test stat = 42.6037
mu=-0.89 [-1e+30, 2.51372] in interval = 0
NeymanConstruction: Prog: 7/100 total MC = 78 this test stat = 40.7776
mu=-0.87 [-1e+30, 2.23515] in interval = 0
NeymanConstruction: Prog: 8/100 total MC = 78 this test stat = 38.9914
mu=-0.85 [-1e+30, 1.58856] in interval = 0
NeymanConstruction: Prog: 9/100 total MC = 78 this test stat = 37.2453
mu=-0.83 [-1e+30, 1.81502] in interval = 0
NeymanConstruction: Prog: 10/100 total MC = 78 this test stat = 35.5391
mu=-0.81 [-1e+30, 2.60219] in interval = 0
NeymanConstruction: Prog: 11/100 total MC = 78 this test stat = 33.873
mu=-0.79 [-1e+30, 1.83579] in interval = 0
NeymanConstruction: Prog: 12/100 total MC = 78 this test stat = 32.2468
mu=-0.77 [-1e+30, 1.80677] in interval = 0
NeymanConstruction: Prog: 13/100 total MC = 78 this test stat = 30.6606
mu=-0.75 [-1e+30, 2.46798] in interval = 0
NeymanConstruction: Prog: 14/100 total MC = 78 this test stat = 29.1145
mu=-0.73 [-1e+30, 1.76469] in interval = 0
NeymanConstruction: Prog: 15/100 total MC = 78 this test stat = 27.6083
mu=-0.71 [-1e+30, 2.10923] in interval = 0
NeymanConstruction: Prog: 16/100 total MC = 78 this test stat = 26.1422
mu=-0.69 [-1e+30, 1.96368] in interval = 0
NeymanConstruction: Prog: 17/100 total MC = 78 this test stat = 24.716
mu=-0.67 [-1e+30, 2.46737] in interval = 0
NeymanConstruction: Prog: 18/100 total MC = 78 this test stat = 23.3298
mu=-0.65 [-1e+30, 2.22208] in interval = 0
NeymanConstruction: Prog: 19/100 total MC = 78 this test stat = 21.9837
mu=-0.63 [-1e+30, 1.92004] in interval = 0
NeymanConstruction: Prog: 20/100 total MC = 78 this test stat = 20.6775
mu=-0.61 [-1e+30, 2.09449] in interval = 0
NeymanConstruction: Prog: 21/100 total MC = 78 this test stat = 19.4114
mu=-0.59 [-1e+30, 2.82615] in interval = 0
NeymanConstruction: Prog: 22/100 total MC = 78 this test stat = 18.1852
mu=-0.57 [-1e+30, 2.44483] in interval = 0
NeymanConstruction: Prog: 23/100 total MC = 78 this test stat = 16.9991
mu=-0.55 [-1e+30, 1.47648] in interval = 0
NeymanConstruction: Prog: 24/100 total MC = 78 this test stat = 15.8529
mu=-0.53 [-1e+30, 1.64253] in interval = 0
NeymanConstruction: Prog: 25/100 total MC = 78 this test stat = 14.7467
mu=-0.51 [-1e+30, 3.23375] in interval = 0
NeymanConstruction: Prog: 26/100 total MC = 78 this test stat = 13.6806
mu=-0.49 [-1e+30, 1.36352] in interval = 0
NeymanConstruction: Prog: 27/100 total MC = 78 this test stat = 12.6544
mu=-0.47 [-1e+30, 2.24046] in interval = 0
NeymanConstruction: Prog: 28/100 total MC = 78 this test stat = 11.6683
mu=-0.45 [-1e+30, 1.99249] in interval = 0
NeymanConstruction: Prog: 29/100 total MC = 78 this test stat = 10.7221
mu=-0.43 [-1e+30, 2.54633] in interval = 0
NeymanConstruction: Prog: 30/100 total MC = 78 this test stat = 9.81595
mu=-0.41 [-1e+30, 2.19145] in interval = 0
NeymanConstruction: Prog: 31/100 total MC = 78 this test stat = 8.94979
mu=-0.39 [-1e+30, 2.25133] in interval = 0
NeymanConstruction: Prog: 32/100 total MC = 78 this test stat = 8.12363
mu=-0.37 [-1e+30, 2.63436] in interval = 0
NeymanConstruction: Prog: 33/100 total MC = 78 this test stat = 7.33748
mu=-0.35 [-1e+30, 1.7752] in interval = 0
NeymanConstruction: Prog: 34/100 total MC = 78 this test stat = 6.59132
mu=-0.33 [-1e+30, 2.63173] in interval = 0
NeymanConstruction: Prog: 35/100 total MC = 78 this test stat = 5.88516
mu=-0.31 [-1e+30, 2.2561] in interval = 0
NeymanConstruction: Prog: 36/100 total MC = 78 this test stat = 5.219
mu=-0.29 [-1e+30, 2.0388] in interval = 0
NeymanConstruction: Prog: 37/100 total MC = 234 this test stat = 4.59284
mu=-0.27 [-1e+30, 1.92574] in interval = 0
NeymanConstruction: Prog: 38/100 total MC = 78 this test stat = 4.00668
mu=-0.25 [-1e+30, 2.51905] in interval = 0
NeymanConstruction: Prog: 39/100 total MC = 234 this test stat = 3.46053
mu=-0.23 [-1e+30, 2.20004] in interval = 0
NeymanConstruction: Prog: 40/100 total MC = 234 this test stat = 2.95437
mu=-0.21 [-1e+30, 1.49924] in interval = 0
NeymanConstruction: Prog: 41/100 total MC = 234 this test stat = 2.48821
mu=-0.19 [-1e+30, 1.88454] in interval = 0
NeymanConstruction: Prog: 42/100 total MC = 78 this test stat = 2.06205
mu=-0.17 [-1e+30, 2.92073] in interval = 1
NeymanConstruction: Prog: 43/100 total MC = 234 this test stat = 1.6759
mu=-0.15 [-1e+30, 2.19199] in interval = 1
NeymanConstruction: Prog: 44/100 total MC = 78 this test stat = 1.32974
mu=-0.13 [-1e+30, 1.94832] in interval = 1
NeymanConstruction: Prog: 45/100 total MC = 78 this test stat = 1.02358
mu=-0.11 [-1e+30, 2.16863] in interval = 1
NeymanConstruction: Prog: 46/100 total MC = 78 this test stat = 0.757422
mu=-0.09 [-1e+30, 1.46141] in interval = 1
NeymanConstruction: Prog: 47/100 total MC = 78 this test stat = 0.531264
mu=-0.07 [-1e+30, 4.11006] in interval = 1
NeymanConstruction: Prog: 48/100 total MC = 78 this test stat = 0.345097
mu=-0.05 [-1e+30, 2.11353] in interval = 1
NeymanConstruction: Prog: 49/100 total MC = 78 this test stat = 0.198947
mu=-0.03 [-1e+30, 2.38127] in interval = 1
NeymanConstruction: Prog: 50/100 total MC = 78 this test stat = 0.09279
mu=-0.01 [-1e+30, 3.0189] in interval = 1
NeymanConstruction: Prog: 51/100 total MC = 78 this test stat = 0.026632
mu=0.01 [-1e+30, 2.23448] in interval = 1
NeymanConstruction: Prog: 52/100 total MC = 78 this test stat = 0.000474009
mu=0.03 [-1e+30, 2.54313] in interval = 1
NeymanConstruction: Prog: 53/100 total MC = 78 this test stat = 0.014316
mu=0.05 [-1e+30, 1.52484] in interval = 1
NeymanConstruction: Prog: 54/100 total MC = 78 this test stat = 0.0681571
mu=0.07 [-1e+30, 2.72021] in interval = 1
NeymanConstruction: Prog: 55/100 total MC = 78 this test stat = 0.161992
mu=0.09 [-1e+30, 3.26474] in interval = 1
NeymanConstruction: Prog: 56/100 total MC = 78 this test stat = 0.295842
mu=0.11 [-1e+30, 2.81134] in interval = 1
NeymanConstruction: Prog: 57/100 total MC = 78 this test stat = 0.469684
mu=0.13 [-1e+30, 2.59127] in interval = 1
NeymanConstruction: Prog: 58/100 total MC = 78 this test stat = 0.683526
mu=0.15 [-1e+30, 2.60194] in interval = 1
NeymanConstruction: Prog: 59/100 total MC = 78 this test stat = 0.937368
mu=0.17 [-1e+30, 1.94974] in interval = 1
NeymanConstruction: Prog: 60/100 total MC = 78 this test stat = 1.23121
mu=0.19 [-1e+30, 1.73838] in interval = 1
NeymanConstruction: Prog: 61/100 total MC = 702 this test stat = 1.56505
mu=0.21 [-1e+30, 1.73023] in interval = 1
NeymanConstruction: Prog: 62/100 total MC = 78 this test stat = 1.93888
mu=0.23 [-1e+30, 3.06401] in interval = 1
NeymanConstruction: Prog: 63/100 total MC = 234 this test stat = 2.35273
mu=0.25 [-1e+30, 1.63166] in interval = 0
NeymanConstruction: Prog: 64/100 total MC = 234 this test stat = 2.80658
mu=0.27 [-1e+30, 1.83441] in interval = 0
NeymanConstruction: Prog: 65/100 total MC = 234 this test stat = 3.30042
mu=0.29 [-1e+30, 2.06725] in interval = 0
NeymanConstruction: Prog: 66/100 total MC = 78 this test stat = 3.83426
mu=0.31 [-1e+30, 2.10484] in interval = 0
NeymanConstruction: Prog: 67/100 total MC = 78 this test stat = 4.4081
mu=0.33 [-1e+30, 2.1714] in interval = 0
NeymanConstruction: Prog: 68/100 total MC = 78 this test stat = 5.02195
mu=0.35 [-1e+30, 2.77418] in interval = 0
NeymanConstruction: Prog: 69/100 total MC = 78 this test stat = 5.67579
mu=0.37 [-1e+30, 2.39844] in interval = 0
NeymanConstruction: Prog: 70/100 total MC = 78 this test stat = 6.36963
mu=0.39 [-1e+30, 1.83585] in interval = 0
NeymanConstruction: Prog: 71/100 total MC = 78 this test stat = 7.10347
mu=0.41 [-1e+30, 1.92776] in interval = 0
NeymanConstruction: Prog: 72/100 total MC = 78 this test stat = 7.87731
mu=0.43 [-1e+30, 1.62539] in interval = 0
NeymanConstruction: Prog: 73/100 total MC = 78 this test stat = 8.69116
mu=0.45 [-1e+30, 1.57241] in interval = 0
NeymanConstruction: Prog: 74/100 total MC = 78 this test stat = 9.545
mu=0.47 [-1e+30, 1.9811] in interval = 0
NeymanConstruction: Prog: 75/100 total MC = 78 this test stat = 10.4388
mu=0.49 [-1e+30, 3.71619] in interval = 0
NeymanConstruction: Prog: 76/100 total MC = 78 this test stat = 11.3727
mu=0.51 [-1e+30, 2.09734] in interval = 0
NeymanConstruction: Prog: 77/100 total MC = 78 this test stat = 12.3465
mu=0.53 [-1e+30, 1.61789] in interval = 0
NeymanConstruction: Prog: 78/100 total MC = 78 this test stat = 13.3604
mu=0.55 [-1e+30, 1.75937] in interval = 0
NeymanConstruction: Prog: 79/100 total MC = 78 this test stat = 14.4142
mu=0.57 [-1e+30, 2.16051] in interval = 0
NeymanConstruction: Prog: 80/100 total MC = 78 this test stat = 15.5081
mu=0.59 [-1e+30, 2.49006] in interval = 0
NeymanConstruction: Prog: 81/100 total MC = 78 this test stat = 16.6419
mu=0.61 [-1e+30, 2.15141] in interval = 0
NeymanConstruction: Prog: 82/100 total MC = 78 this test stat = 17.8157
mu=0.63 [-1e+30, 2.63832] in interval = 0
NeymanConstruction: Prog: 83/100 total MC = 78 this test stat = 19.0296
mu=0.65 [-1e+30, 2.12006] in interval = 0
NeymanConstruction: Prog: 84/100 total MC = 78 this test stat = 20.2834
mu=0.67 [-1e+30, 1.70414] in interval = 0
NeymanConstruction: Prog: 85/100 total MC = 78 this test stat = 21.5773
mu=0.69 [-1e+30, 2.54958] in interval = 0
NeymanConstruction: Prog: 86/100 total MC = 78 this test stat = 22.9111
mu=0.71 [-1e+30, 2.27992] in interval = 0
NeymanConstruction: Prog: 87/100 total MC = 78 this test stat = 24.2849
mu=0.73 [-1e+30, 2.99068] in interval = 0
NeymanConstruction: Prog: 88/100 total MC = 78 this test stat = 25.6988
mu=0.75 [-1e+30, 1.60655] in interval = 0
NeymanConstruction: Prog: 89/100 total MC = 78 this test stat = 27.1526
mu=0.77 [-1e+30, 1.61728] in interval = 0
NeymanConstruction: Prog: 90/100 total MC = 78 this test stat = 28.6465
mu=0.79 [-1e+30, 1.92571] in interval = 0
NeymanConstruction: Prog: 91/100 total MC = 78 this test stat = 30.1803
mu=0.81 [-1e+30, 1.69221] in interval = 0
NeymanConstruction: Prog: 92/100 total MC = 78 this test stat = 31.7542
mu=0.83 [-1e+30, 3.26227] in interval = 0
NeymanConstruction: Prog: 93/100 total MC = 78 this test stat = 33.368
mu=0.85 [-1e+30, 1.75583] in interval = 0
NeymanConstruction: Prog: 94/100 total MC = 78 this test stat = 35.0218
mu=0.87 [-1e+30, 2.54103] in interval = 0
NeymanConstruction: Prog: 95/100 total MC = 78 this test stat = 36.7157
mu=0.89 [-1e+30, 2.267] in interval = 0
NeymanConstruction: Prog: 96/100 total MC = 78 this test stat = 38.4495
mu=0.91 [-1e+30, 2.31167] in interval = 0
NeymanConstruction: Prog: 97/100 total MC = 78 this test stat = 40.2234
mu=0.93 [-1e+30, 2.24794] in interval = 0
NeymanConstruction: Prog: 98/100 total MC = 78 this test stat = 42.0372
mu=0.95 [-1e+30, 1.29779] in interval = 0
NeymanConstruction: Prog: 99/100 total MC = 78 this test stat = 43.891
mu=0.97 [-1e+30, 2.00008] in interval = 0
NeymanConstruction: Prog: 100/100 total MC = 78 this test stat = 45.7849
mu=0.99 [-1e+30, 1.56062] in interval = 0
[#1] INFO:Eval -- 21 points in interval
[#1] INFO:Minization -- createNLL picked up cached consraints from workspace with 0 entries
[#1] INFO:Eval -- BayesianCalculator::GetPosteriorFunction : nll value 190.077 poi value = 0.99
[#1] INFO:Eval -- BayesianCalculator::GetPosteriorFunction : minimum of NLL vs POI for POI = 0.033079 min NLL = 144.292
[#1] INFO:Minization -- Including the following constraint terms in minimization: (prior)
[#1] INFO:Minization -- The following global observables have been defined: ()
[#1] INFO:Eval -- BayesianCalculator: Compute interval using RooFit: posteriorPdf + createCdf + RooBrentRootFinder
[#1] INFO:Eval -- BayesianCalculator::GetInterval - found a valid interval : [-0.162918 , 0.229076 ]
[#1] INFO:Minization -- Including the following constraint terms in minimization: (prior)
[#1] INFO:Minization -- The following global observables have been defined: ()
Metropolis-Hastings progress: ....................................................................................................
[#1] INFO:Eval -- Proposal acceptance rate: 16.013%
[#1] INFO:Eval -- Number of steps in chain: 16013
expected interval is [-1.42571, -1.03372]
plc interval is [-0.162917, 0.229075]
fc interval is [-0.17 , 0.23]
bc interval is [-0.162918, 0.229076]
mc interval is [-0.166999, 0.230224]
is mu=0 in the interval? 1
.
[#1] INFO:Minization -- RooProfileLL::evaluate(nll_normal_normalData_Profile[mu]) Creating instance of MINUIT
[#1] INFO:Minization -- RooProfileLL::evaluate(nll_normal_normalData_Profile[mu]) determining minimum likelihood for current configurations w.r.t all observable
[#0] ERROR:InputArguments -- RooArgSet::checkForDup: ERROR argument with name mu is already in this set
[#1] INFO:Minization -- RooProfileLL::evaluate(nll_normal_normalData_Profile[mu]) minimum found at (mu=0.033079)
..........................................................................................................................................................................................................Real time 0:00:17, CP time 17.630
#include "RooRandom.h"
#include "RooDataSet.h"
#include "RooRealVar.h"
#include "RooConstVar.h"
#include "RooAddition.h"
#include "RooDataHist.h"
#include "RooPoisson.h"
#include "RooPlot.h"
#include "TCanvas.h"
#include "TTree.h"
#include "TStyle.h"
#include "TMath.h"
#include "Math/DistFunc.h"
#include "TH1F.h"
#include "TMarker.h"
#include "TStopwatch.h"
#include <iostream>
// use this order for safety on library loading
using namespace RooFit;
using namespace RooStats;
void IntervalExamples()
{
// Time this macro
t.Start();
// set RooFit random seed for reproducible results
// make a simple model via the workspace factory
RooWorkspace *wspace = new RooWorkspace();
wspace->factory("Gaussian::normal(x[-10,10],mu[-1,1],sigma[1])");
wspace->defineSet("poi", "mu");
wspace->defineSet("obs", "x");
// specify components of model for statistical tools
ModelConfig *modelConfig = new ModelConfig("Example G(x|mu,1)");
modelConfig->SetWorkspace(*wspace);
modelConfig->SetPdf(*wspace->pdf("normal"));
modelConfig->SetParametersOfInterest(*wspace->set("poi"));
modelConfig->SetObservables(*wspace->set("obs"));
// create a toy dataset
RooDataSet *data = wspace->pdf("normal")->generate(*wspace->set("obs"), 100);
data->Print();
// for convenience later on
RooRealVar *x = wspace->var("x");
RooRealVar *mu = wspace->var("mu");
// set confidence level
double confidenceLevel = 0.95;
// example use profile likelihood calculator
ProfileLikelihoodCalculator plc(*data, *modelConfig);
plc.SetConfidenceLevel(confidenceLevel);
LikelihoodInterval *plInt = plc.GetInterval();
// example use of Feldman-Cousins
FeldmanCousins fc(*data, *modelConfig);
fc.SetConfidenceLevel(confidenceLevel);
fc.SetNBins(100); // number of points to test per parameter
fc.UseAdaptiveSampling(true); // make it go faster
// Here, we consider only ensembles with 100 events
// The PDF could be extended and this could be removed
fc.FluctuateNumDataEntries(false);
// Proof
// ProofConfig pc(*wspace, 4, "workers=4", kFALSE); // proof-lite
// ProofConfig pc(w, 8, "localhost"); // proof cluster at "localhost"
// ToyMCSampler* toymcsampler = (ToyMCSampler*) fc.GetTestStatSampler();
// toymcsampler->SetProofConfig(&pc); // enable proof
PointSetInterval *interval = (PointSetInterval *)fc.GetInterval();
// example use of BayesianCalculator
// now we also need to specify a prior in the ModelConfig
wspace->factory("Uniform::prior(mu)");
modelConfig->SetPriorPdf(*wspace->pdf("prior"));
// example usage of BayesianCalculator
BayesianCalculator bc(*data, *modelConfig);
bc.SetConfidenceLevel(confidenceLevel);
SimpleInterval *bcInt = bc.GetInterval();
// example use of MCMCInterval
MCMCCalculator mc(*data, *modelConfig);
mc.SetConfidenceLevel(confidenceLevel);
// special options
mc.SetNumBins(200); // bins used internally for representing posterior
mc.SetNumBurnInSteps(500); // first N steps to be ignored as burn-in
mc.SetNumIters(100000); // how long to run chain
mc.SetLeftSideTailFraction(0.5); // for central interval
MCMCInterval *mcInt = mc.GetInterval();
// for this example we know the expected intervals
double expectedLL =
data->mean(*x) + ROOT::Math::normal_quantile((1 - confidenceLevel) / 2, 1) / sqrt(data->numEntries());
double expectedUL =
data->mean(*x) + ROOT::Math::normal_quantile_c((1 - confidenceLevel) / 2, 1) / sqrt(data->numEntries());
// Use the intervals
std::cout << "expected interval is [" << expectedLL << ", " << expectedUL << "]" << endl;
cout << "plc interval is [" << plInt->LowerLimit(*mu) << ", " << plInt->UpperLimit(*mu) << "]" << endl;
std::cout << "fc interval is [" << interval->LowerLimit(*mu) << " , " << interval->UpperLimit(*mu) << "]" << endl;
cout << "bc interval is [" << bcInt->LowerLimit() << ", " << bcInt->UpperLimit() << "]" << endl;
cout << "mc interval is [" << mcInt->LowerLimit(*mu) << ", " << mcInt->UpperLimit(*mu) << "]" << endl;
mu->setVal(0);
cout << "is mu=0 in the interval? " << plInt->IsInInterval(RooArgSet(*mu)) << endl;
// make a reasonable style
// some plots
TCanvas *canvas = new TCanvas("canvas");
canvas->Divide(2, 2);
// plot the data
canvas->cd(1);
RooPlot *frame = x->frame();
data->plotOn(frame);
data->statOn(frame);
frame->Draw();
// plot the profile likelihood
canvas->cd(2);
plot.Draw();
// plot the MCMC interval
canvas->cd(3);
MCMCIntervalPlot *mcPlot = new MCMCIntervalPlot(*mcInt);
mcPlot->SetLineColor(kGreen);
mcPlot->SetLineWidth(2);
mcPlot->Draw();
canvas->cd(4);
RooPlot *bcPlot = bc.GetPosteriorPlot();
bcPlot->Draw();
canvas->Update();
t.Stop();
t.Print();
}
Author
Kyle Cranmer

Definition in file IntervalExamples.C.