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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]
␛[1mRooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby␛[0m
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 0:00:14, CP time 14.930
#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);
LikelihoodIntervalPlot plot(plInt);
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();
}
@ kGreen
Definition: Rtypes.h:64
double sqrt(double)
R__EXTERN TStyle * gStyle
Definition: TStyle.h:410
static struct mg_connection * fc(struct mg_context *ctx)
Definition: civetweb.c:3728
virtual RooPlot * statOn(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())
Add a box with statistics information to the specified frame.
virtual void Print(Option_t *options=0) const
Print TNamed name and title.
Definition: RooAbsData.h:175
virtual Int_t numEntries() const
Definition: RooAbsData.cxx:306
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
Calls RooPlot* plotOn(RooPlot* frame, const RooLinkedList& cmdList) const ;.
Definition: RooAbsData.cxx:546
Double_t mean(const RooRealVar &var, const char *cutSpec=0, const char *cutRange=0) const
Definition: RooAbsData.h:193
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
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition: RooArgSet.h:28
RooDataSet is a container class to hold unbinned data.
Definition: RooDataSet.h:33
A RooPlot is a plot frame and a container for graphics objects within that frame.
Definition: RooPlot.h:44
virtual void Draw(Option_t *options=0)
Draw this plot and all of the elements it contains.
Definition: RooPlot.cxx:712
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:35
virtual void setVal(Double_t value)
Set value of variable to 'value'.
Definition: RooRealVar.cxx:261
The RooWorkspace is a persistable container for RooFit projects.
Definition: RooWorkspace.h:43
Bool_t defineSet(const char *name, const RooArgSet &aset, Bool_t importMissing=kFALSE)
Define a named RooArgSet with given constituents.
RooRealVar * var(const char *name) const
Retrieve real-valued variable (RooRealVar) with given name. A null pointer is returned if not found.
RooFactoryWSTool & factory()
Return instance to factory tool.
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...
RooAbsPdf * pdf(const char *name) const
Retrieve p.d.f (RooAbsPdf) with given name. A null pointer is returned if not found.
virtual void SetFillColor(Color_t fcolor)
Set the fill area color.
Definition: TAttFill.h:37
The Canvas class.
Definition: TCanvas.h:27
virtual void Update()
Update canvas pad buffers.
Definition: TCanvas.cxx:2433
TVirtualPad * cd(Int_t subpadnumber=0)
Set current canvas & pad.
Definition: TCanvas.cxx:701
virtual void Divide(Int_t nx=1, Int_t ny=1, Float_t xmargin=0.01, Float_t ymargin=0.01, Int_t color=0)
Automatic pad generation by division.
Definition: TPad.cxx:1165
virtual void SetSeed(ULong_t seed=0)
Set the random generator seed.
Definition: TRandom.cxx:597
Stopwatch class.
Definition: TStopwatch.h:28
void Start(Bool_t reset=kTRUE)
Start the stopwatch.
Definition: TStopwatch.cxx:58
void Stop()
Stop the stopwatch.
Definition: TStopwatch.cxx:77
void Print(Option_t *option="") const
Print the real and cpu time passed between the start and stop events.
Definition: TStopwatch.cxx:219
void SetPadBorderMode(Int_t mode=1)
Definition: TStyle.h:338
void SetFrameFillColor(Color_t color=1)
Definition: TStyle.h:353
void SetCanvasColor(Color_t color=19)
Definition: TStyle.h:325
void SetCanvasBorderMode(Int_t mode=1)
Definition: TStyle.h:327
void SetTitleFillColor(Color_t color=1)
Definition: TStyle.h:385
void SetStatColor(Color_t color=19)
Definition: TStyle.h:371
void SetPadColor(Color_t color=19)
Definition: TStyle.h:336
double normal_quantile(double z, double sigma)
Inverse ( ) of the cumulative distribution function of the lower tail of the normal (Gaussian) distri...
double normal_quantile_c(double z, double sigma)
Inverse ( ) of the cumulative distribution function of the upper tail of the normal (Gaussian) distri...
Double_t x[n]
Definition: legend1.C:17
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
Author
Kyle Cranmer

Definition in file IntervalExamples.C.