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rs401c_FeldmanCousins.C File Reference
Tutorials » RooStats Tutorials

Detailed Description

View in nbviewer Open in SWAN Produces an interval on the mean signal in a number counting experiment with known background using the Feldman-Cousins technique.

Using the RooStats FeldmanCousins tool with 200 bins it takes 1 min and the interval is [0.2625, 10.6125] with a step size of 0.075. The interval in Feldman & Cousins's original paper is [.29, 10.81] 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
All rights reserved, please read http://roofit.sourceforge.net/license.txt
DataStore poisData (Generated From )
Contains 1 entries
Observables:
1) x = 7 L(0 - 50) ""
RooWorkspace() contents
variables
---------
(mu,x)
p.d.f.s
-------
RooPoisson::pois[ x=x mean=mean ] = 0.0224772
functions
--------
RooAddition::mean[ mu + b ] = 5.5
named sets
----------
poissonProblem_Observables:(x)
poissonProblem_POI:(mu)
=== Using the following for poissonProblem ===
Observables: RooArgSet:: = (x)
Parameters of Interest: RooArgSet:: = (mu)
PDF: RooPoisson::pois[ x=x mean=mean ] = 0.0224772
FeldmanCousins: ntoys per point: adaptive
FeldmanCousins: nEvents per toy will not fluctuate, will always be 1
FeldmanCousins: Model has no nuisance parameters
FeldmanCousins: # points to test = 100
NeymanConstruction: Prog: 1/100 total MC = 240 this test stat = 1.83324
mu=0.075 [-1e+30, 1.08573] in interval = 0
NeymanConstruction: Prog: 2/100 total MC = 80 this test stat = 1.64984
mu=0.225 [-1e+30, 0.949959] in interval = 0
NeymanConstruction: Prog: 3/100 total MC = 80 this test stat = 1.4816
mu=0.375 [-1e+30, 0.827185] in interval = 0
NeymanConstruction: Prog: 4/100 total MC = 240 this test stat = 1.32721
mu=0.525 [-1e+30, 1.32721] in interval = 1
NeymanConstruction: Prog: 5/100 total MC = 80 this test stat = 1.1855
mu=0.675 [-1e+30, 2.73604] in interval = 1
NeymanConstruction: Prog: 6/100 total MC = 240 this test stat = 1.05546
mu=0.825 [-1e+30, 1.72806] in interval = 1
NeymanConstruction: Prog: 7/100 total MC = 80 this test stat = 0.936198
mu=0.975 [-1e+30, 2.32979] in interval = 1
NeymanConstruction: Prog: 8/100 total MC = 80 this test stat = 0.826909
mu=1.125 [-1e+30, 1.424] in interval = 1
NeymanConstruction: Prog: 9/100 total MC = 80 this test stat = 0.726882
mu=1.275 [-1e+30, 1.28826] in interval = 1
NeymanConstruction: Prog: 10/100 total MC = 80 this test stat = 0.635479
mu=1.425 [-1e+30, 1.81459] in interval = 1
NeymanConstruction: Prog: 11/100 total MC = 80 this test stat = 0.552124
mu=1.575 [-1e+30, 1.66456] in interval = 1
NeymanConstruction: Prog: 12/100 total MC = 80 this test stat = 0.476298
mu=1.725 [-1e+30, 1.725] in interval = 1
NeymanConstruction: Prog: 13/100 total MC = 80 this test stat = 0.40753
mu=1.875 [-1e+30, 2.05965] in interval = 1
NeymanConstruction: Prog: 14/100 total MC = 80 this test stat = 0.345393
mu=2.025 [-1e+30, 1.9066] in interval = 1
NeymanConstruction: Prog: 15/100 total MC = 80 this test stat = 0.289496
mu=2.175 [-1e+30, 1.76244] in interval = 1
NeymanConstruction: Prog: 16/100 total MC = 80 this test stat = 0.239482
mu=2.325 [-1e+30, 1.7512] in interval = 1
NeymanConstruction: Prog: 17/100 total MC = 80 this test stat = 0.195006
mu=2.475 [-1e+30, 1.27184] in interval = 1
NeymanConstruction: Prog: 18/100 total MC = 80 this test stat = 0.155824
mu=2.625 [-1e+30, 1.99639] in interval = 1
NeymanConstruction: Prog: 19/100 total MC = 80 this test stat = 0.121603
mu=2.775 [-1e+30, 1.86293] in interval = 1
NeymanConstruction: Prog: 20/100 total MC = 80 this test stat = 0.0921062
mu=2.925 [-1e+30, 1.73086] in interval = 1
NeymanConstruction: Prog: 21/100 total MC = 80 this test stat = 0.0670922
mu=3.075 [-1e+30, 1.60585] in interval = 1
NeymanConstruction: Prog: 22/100 total MC = 80 this test stat = 0.0463567
mu=3.225 [-1e+30, 2.10098] in interval = 1
NeymanConstruction: Prog: 23/100 total MC = 80 this test stat = 0.0296823
mu=3.375 [-1e+30, 1.96524] in interval = 1
NeymanConstruction: Prog: 24/100 total MC = 80 this test stat = 0.0168841
mu=3.525 [-1e+30, 1.97094] in interval = 1
NeymanConstruction: Prog: 25/100 total MC = 80 this test stat = 0.00778646
mu=3.675 [-1e+30, 2.07549] in interval = 1
NeymanConstruction: Prog: 26/100 total MC = 80 this test stat = 0.00222463
mu=3.825 [-1e+30, 1.59677] in interval = 1
NeymanConstruction: Prog: 27/100 total MC = 80 this test stat = 4.47494e-05
mu=3.975 [-1e+30, 2.06902] in interval = 1
NeymanConstruction: Prog: 28/100 total MC = 80 this test stat = 0.00110295
mu=4.125 [-1e+30, 2.39501] in interval = 1
NeymanConstruction: Prog: 29/100 total MC = 80 this test stat = 0.00526396
mu=4.275 [-1e+30, 1.6175] in interval = 1
NeymanConstruction: Prog: 30/100 total MC = 80 this test stat = 0.0123995
mu=4.425 [-1e+30, 1.70627] in interval = 1
NeymanConstruction: Prog: 31/100 total MC = 80 this test stat = 0.0223862
mu=4.575 [-1e+30, 1.79627] in interval = 1
NeymanConstruction: Prog: 32/100 total MC = 80 this test stat = 0.0351383
mu=4.725 [-1e+30, 2.04934] in interval = 1
NeymanConstruction: Prog: 33/100 total MC = 80 this test stat = 0.0505187
mu=4.875 [-1e+30, 2.94484] in interval = 1
NeymanConstruction: Prog: 34/100 total MC = 80 this test stat = 0.0684391
mu=5.025 [-1e+30, 3.0571] in interval = 1
NeymanConstruction: Prog: 35/100 total MC = 80 this test stat = 0.0888053
mu=5.175 [-1e+30, 2.27954] in interval = 1
NeymanConstruction: Prog: 36/100 total MC = 80 this test stat = 0.11153
mu=5.325 [-1e+30, 2.26305] in interval = 1
NeymanConstruction: Prog: 37/100 total MC = 80 this test stat = 0.136526
mu=5.475 [-1e+30, 2.03894] in interval = 1
NeymanConstruction: Prog: 38/100 total MC = 80 this test stat = 0.163716
mu=5.625 [-1e+30, 1.55152] in interval = 1
NeymanConstruction: Prog: 39/100 total MC = 80 this test stat = 0.193024
mu=5.775 [-1e+30, 1.81715] in interval = 1
NeymanConstruction: Prog: 40/100 total MC = 80 this test stat = 0.224377
mu=5.925 [-1e+30, 1.71475] in interval = 1
NeymanConstruction: Prog: 41/100 total MC = 80 this test stat = 0.257707
mu=6.075 [-1e+30, 2.75427] in interval = 1
NeymanConstruction: Prog: 42/100 total MC = 80 this test stat = 0.292951
mu=6.225 [-1e+30, 2.03573] in interval = 1
NeymanConstruction: Prog: 43/100 total MC = 80 this test stat = 0.330045
mu=6.375 [-1e+30, 1.92767] in interval = 1
NeymanConstruction: Prog: 44/100 total MC = 80 this test stat = 0.368932
mu=6.525 [-1e+30, 2.0545] in interval = 1
NeymanConstruction: Prog: 45/100 total MC = 80 this test stat = 0.409554
mu=6.675 [-1e+30, 2.142] in interval = 1
NeymanConstruction: Prog: 46/100 total MC = 80 this test stat = 0.45186
mu=6.825 [-1e+30, 2.72294] in interval = 1
NeymanConstruction: Prog: 47/100 total MC = 80 this test stat = 0.495797
mu=6.975 [-1e+30, 2.03823] in interval = 1
NeymanConstruction: Prog: 48/100 total MC = 80 this test stat = 0.541318
mu=7.125 [-1e+30, 2.41015] in interval = 1
NeymanConstruction: Prog: 49/100 total MC = 80 this test stat = 0.588375
mu=7.275 [-1e+30, 1.83449] in interval = 1
NeymanConstruction: Prog: 50/100 total MC = 80 this test stat = 0.636924
mu=7.425 [-1e+30, 2.80213] in interval = 1
NeymanConstruction: Prog: 51/100 total MC = 80 this test stat = 0.686922
mu=7.575 [-1e+30, 1.82973] in interval = 1
NeymanConstruction: Prog: 52/100 total MC = 80 this test stat = 0.738329
mu=7.725 [-1e+30, 1.9093] in interval = 1
NeymanConstruction: Prog: 53/100 total MC = 80 this test stat = 0.791105
mu=7.875 [-1e+30, 1.98986] in interval = 1
NeymanConstruction: Prog: 54/100 total MC = 80 this test stat = 0.845213
mu=8.025 [-1e+30, 2.81879] in interval = 1
NeymanConstruction: Prog: 55/100 total MC = 80 this test stat = 0.900613
mu=8.175 [-1e+30, 2.23216] in interval = 1
NeymanConstruction: Prog: 56/100 total MC = 80 this test stat = 0.957282
mu=8.325 [-1e+30, 1.51348] in interval = 1
NeymanConstruction: Prog: 57/100 total MC = 80 this test stat = 1.01518
mu=8.475 [-1e+30, 2.32134] in interval = 1
NeymanConstruction: Prog: 58/100 total MC = 80 this test stat = 1.07427
mu=8.625 [-1e+30, 4.56136] in interval = 1
NeymanConstruction: Prog: 59/100 total MC = 80 this test stat = 1.13452
mu=8.775 [-1e+30, 1.83847] in interval = 1
NeymanConstruction: Prog: 60/100 total MC = 80 this test stat = 1.19591
mu=8.925 [-1e+30, 1.80373] in interval = 1
NeymanConstruction: Prog: 61/100 total MC = 80 this test stat = 1.25841
mu=9.075 [-1e+30, 2.45893] in interval = 1
NeymanConstruction: Prog: 62/100 total MC = 80 this test stat = 1.32199
mu=9.225 [-1e+30, 1.95466] in interval = 1
NeymanConstruction: Prog: 63/100 total MC = 80 this test stat = 1.38662
mu=9.375 [-1e+30, 2.24356] in interval = 1
NeymanConstruction: Prog: 64/100 total MC = 80 this test stat = 1.45228
mu=9.525 [-1e+30, 2.50319] in interval = 1
NeymanConstruction: Prog: 65/100 total MC = 80 this test stat = 1.51895
mu=9.675 [-1e+30, 3.02403] in interval = 1
NeymanConstruction: Prog: 66/100 total MC = 240 this test stat = 1.5866
mu=9.825 [-1e+30, 1.60451] in interval = 1
NeymanConstruction: Prog: 67/100 total MC = 80 this test stat = 1.6552
mu=9.975 [-1e+30, 3.20706] in interval = 1
NeymanConstruction: Prog: 68/100 total MC = 80 this test stat = 1.72474
mu=10.125 [-1e+30, 2.42844] in interval = 1
[#0] PROGRESS:Generation -- generated toys: 500 / 1440
[#0] PROGRESS:Generation -- generated toys: 1000 / 1440
NeymanConstruction: Prog: 69/100 total MC = 2160 this test stat = 1.79519
mu=10.275 [-1e+30, 1.79519] in interval = 1
NeymanConstruction: Prog: 70/100 total MC = 240 this test stat = 1.86654
mu=10.425 [-1e+30, 1.86654] in interval = 1
NeymanConstruction: Prog: 71/100 total MC = 80 this test stat = 1.93876
mu=10.575 [-1e+30, 2.67618] in interval = 1
NeymanConstruction: Prog: 72/100 total MC = 80 this test stat = 2.01184
mu=10.725 [-1e+30, 1.40678] in interval = 0
NeymanConstruction: Prog: 73/100 total MC = 80 this test stat = 2.08575
mu=10.875 [-1e+30, 1.86151] in interval = 0
NeymanConstruction: Prog: 74/100 total MC = 240 this test stat = 2.16048
mu=11.025 [-1e+30, 1.7642] in interval = 0
NeymanConstruction: Prog: 75/100 total MC = 80 this test stat = 2.23601
mu=11.175 [-1e+30, 1.59869] in interval = 0
NeymanConstruction: Prog: 76/100 total MC = 240 this test stat = 2.31232
mu=11.325 [-1e+30, 1.66448] in interval = 0
NeymanConstruction: Prog: 77/100 total MC = 80 this test stat = 2.38941
mu=11.475 [-1e+30, 1.26987] in interval = 0
NeymanConstruction: Prog: 78/100 total MC = 240 this test stat = 2.46724
mu=11.625 [-1e+30, 1.40071] in interval = 0
NeymanConstruction: Prog: 79/100 total MC = 80 this test stat = 2.54582
mu=11.775 [-1e+30, 1.86704] in interval = 0
NeymanConstruction: Prog: 80/100 total MC = 80 this test stat = 2.62511
mu=11.925 [-1e+30, 1.93623] in interval = 0
NeymanConstruction: Prog: 81/100 total MC = 80 this test stat = 2.70511
mu=12.075 [-1e+30, 1.43268] in interval = 0
NeymanConstruction: Prog: 82/100 total MC = 240 this test stat = 2.7858
mu=12.225 [-1e+30, 1.49357] in interval = 0
NeymanConstruction: Prog: 83/100 total MC = 80 this test stat = 2.86717
mu=12.375 [-1e+30, 1.55534] in interval = 0
NeymanConstruction: Prog: 84/100 total MC = 80 this test stat = 2.94921
mu=12.525 [-1e+30, 1.12633] in interval = 0
NeymanConstruction: Prog: 85/100 total MC = 80 this test stat = 3.0319
mu=12.675 [-1e+30, 2.29398] in interval = 0
NeymanConstruction: Prog: 86/100 total MC = 80 this test stat = 3.11523
mu=12.825 [-1e+30, 1.7457] in interval = 0
NeymanConstruction: Prog: 87/100 total MC = 80 this test stat = 3.1992
mu=12.975 [-1e+30, 1.8108] in interval = 0
NeymanConstruction: Prog: 88/100 total MC = 80 this test stat = 3.28378
mu=13.125 [-1e+30, 2.51757] in interval = 0
NeymanConstruction: Prog: 89/100 total MC = 240 this test stat = 3.36896
mu=13.275 [-1e+30, 1.40455] in interval = 0
NeymanConstruction: Prog: 90/100 total MC = 80 this test stat = 3.45474
mu=13.425 [-1e+30, 2.01076] in interval = 0
NeymanConstruction: Prog: 91/100 total MC = 240 this test stat = 3.5411
mu=13.575 [-1e+30, 2.07896] in interval = 0
NeymanConstruction: Prog: 92/100 total MC = 80 this test stat = 3.62804
mu=13.725 [-1e+30, 2.14788] in interval = 0
NeymanConstruction: Prog: 93/100 total MC = 80 this test stat = 3.71554
mu=13.875 [-1e+30, 1.16768] in interval = 0
NeymanConstruction: Prog: 94/100 total MC = 80 this test stat = 3.80359
mu=14.025 [-1e+30, 1.70402] in interval = 0
NeymanConstruction: Prog: 95/100 total MC = 240 this test stat = 3.89219
mu=14.175 [-1e+30, 1.7663] in interval = 0
NeymanConstruction: Prog: 96/100 total MC = 240 this test stat = 3.98132
mu=14.325 [-1e+30, 1.32819] in interval = 0
NeymanConstruction: Prog: 97/100 total MC = 80 this test stat = 4.07097
mu=14.475 [-1e+30, 1.38336] in interval = 0
NeymanConstruction: Prog: 98/100 total MC = 80 this test stat = 4.16114
mu=14.625 [-1e+30, 1.43935] in interval = 0
NeymanConstruction: Prog: 99/100 total MC = 240 this test stat = 4.25182
mu=14.775 [-1e+30, 1.49612] in interval = 0
NeymanConstruction: Prog: 100/100 total MC = 240 this test stat = 4.343
mu=14.925 [-1e+30, 2.08888] in interval = 0
[#1] INFO:Eval -- 68 points in interval
is this point in the interval? 0
interval is [0.525, 10.575]
Real time 0:00:02, CP time 2.730
#include "RooGlobalFunc.h"
#include "RooStats/ConfInterval.h"
#include "RooStats/PointSetInterval.h"
#include "RooStats/ConfidenceBelt.h"
#include "RooStats/FeldmanCousins.h"
#include "RooStats/ModelConfig.h"
#include "RooWorkspace.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 "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 rs401c_FeldmanCousins()
{
// to time the macro... about 30 s
TStopwatch t;
t.Start();
// make a simple model
RooRealVar x("x", "", 1, 0, 50);
RooRealVar mu("mu", "", 2.5, 0, 15); // with a limit on mu>=0
RooConstVar b("b", "", 3.);
RooAddition mean("mean", "", RooArgList(mu, b));
RooPoisson pois("pois", "", x, mean);
RooArgSet parameters(mu);
// create a toy dataset
RooDataSet *data = pois.generate(RooArgSet(x), 1);
data->Print("v");
TCanvas *dataCanvas = new TCanvas("dataCanvas");
RooPlot *frame = x.frame();
data->plotOn(frame);
frame->Draw();
dataCanvas->Update();
RooWorkspace *w = new RooWorkspace();
ModelConfig modelConfig("poissonProblem", w);
modelConfig.SetPdf(pois);
modelConfig.SetParametersOfInterest(parameters);
modelConfig.SetObservables(RooArgSet(x));
w->Print();
//////// show use of Feldman-Cousins
RooStats::FeldmanCousins fc(*data, modelConfig);
fc.SetTestSize(.05); // set size of test
fc.UseAdaptiveSampling(true);
fc.FluctuateNumDataEntries(false); // number counting analysis: dataset always has 1 entry with N events observed
fc.SetNBins(100); // number of points to test per parameter
// use the Feldman-Cousins tool
PointSetInterval *interval = (PointSetInterval *)fc.GetInterval();
// make a canvas for plots
TCanvas *intervalCanvas = new TCanvas("intervalCanvas");
std::cout << "is this point in the interval? " << interval->IsInInterval(parameters) << std::endl;
std::cout << "interval is [" << interval->LowerLimit(mu) << ", " << interval->UpperLimit(mu) << "]" << endl;
// using 200 bins it takes 1 min and the answer is
// interval is [0.2625, 10.6125] with a step size of .075
// The interval in Feldman & Cousins's original paper is [.29, 10.81]
// Phys.Rev.D57:3873-3889,1998.
// No dedicated plotting class yet, so do it by hand:
RooDataHist *parameterScan = (RooDataHist *)fc.GetPointsToScan();
TH1F *hist = (TH1F *)parameterScan->createHistogram("mu", 30);
hist->Draw();
RooArgSet *tmpPoint;
// loop over points to test
for (Int_t i = 0; i < parameterScan->numEntries(); ++i) {
// cout << "on parameter point " << i << " out of " << parameterScan->numEntries() << endl;
// get a parameter point from the list of points to test.
tmpPoint = (RooArgSet *)parameterScan->get(i)->clone("temp");
TMarker *mark = new TMarker(tmpPoint->getRealValue("mu"), 1, 25);
if (interval->IsInInterval(*tmpPoint))
mark->SetMarkerColor(kBlue);
else
mark->SetMarkerColor(kRed);
mark->Draw("s");
// delete tmpPoint;
// delete mark;
}
t.Stop();
t.Print();
}
b
#define b(i)
Definition RSha256.hxx:100
Int_t
int Int_t
Definition RtypesCore.h:45
kRed
@ kRed
Definition Rtypes.h:66
kBlue
@ kBlue
Definition Rtypes.h:66
fc
static struct mg_connection * fc(struct mg_context *ctx)
Definition civetweb.c:3728
RooAbsCollection::getRealValue
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.
Definition RooAbsCollection.cxx:1018
RooAbsData::Print
virtual void Print(Option_t *options=0) const
Print TNamed name and title.
Definition RooAbsData.h:250
RooAbsData::plotOn
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
Definition RooAbsData.cxx:609
RooAbsData::createHistogram
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...
Definition RooAbsData.cxx:687
RooAddition
RooAddition calculates the sum of a set of RooAbsReal terms, or when constructed with two sets,...
Definition RooAddition.h:27
RooArgList
RooArgList is a container object that can hold multiple RooAbsArg objects.
Definition RooArgList.h:22
RooArgSet
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition RooArgSet.h:35
RooArgSet::clone
TObject * clone(const char *newname) const override
Definition RooArgSet.h:128
RooConstVar
RooConstVar represent a constant real-valued object.
Definition RooConstVar.h:26
RooDataHist
The RooDataHist is a container class to hold N-dimensional binned data.
Definition RooDataHist.h:45
RooDataHist::numEntries
Int_t numEntries() const override
Return the number of bins.
Definition RooDataHist.cxx:1813
RooDataHist::get
const RooArgSet * get() const override
Get bin centre of current bin.
Definition RooDataHist.h:84
RooDataSet
RooDataSet is a container class to hold unbinned data.
Definition RooDataSet.h:36
RooPlot
A RooPlot is a plot frame and a container for graphics objects within that frame.
Definition RooPlot.h:44
RooPlot::frame
static RooPlot * frame(const RooAbsRealLValue &var, Double_t xmin, Double_t xmax, Int_t nBins)
Create a new frame for a given variable in x.
Definition RooPlot.cxx:249
RooPlot::Draw
virtual void Draw(Option_t *options=0)
Draw this plot and all of the elements it contains.
Definition RooPlot.cxx:661
RooPoisson
Poisson pdf.
Definition RooPoisson.h:19
RooRealVar
RooRealVar represents a variable that can be changed from the outside.
Definition RooRealVar.h:39
RooStats::FeldmanCousins
The FeldmanCousins class (like the Feldman-Cousins technique) is essentially a specific configuration...
Definition FeldmanCousins.h:33
RooStats::ModelConfig
ModelConfig is a simple class that holds configuration information specifying how a model should be u...
Definition ModelConfig.h:30
RooStats::PointSetInterval
PointSetInterval is a concrete implementation of the ConfInterval interface.
Definition PointSetInterval.h:21
RooStats::PointSetInterval::UpperLimit
Double_t UpperLimit(RooRealVar &param)
return upper limit on a given parameter
Definition PointSetInterval.cxx:147
RooStats::PointSetInterval::LowerLimit
Double_t LowerLimit(RooRealVar &param)
return lower limit on a given parameter
Definition PointSetInterval.cxx:160
RooStats::PointSetInterval::IsInInterval
virtual Bool_t IsInInterval(const RooArgSet &) const
check if parameter is in the interval
Definition PointSetInterval.cxx:76
RooWorkspace
The RooWorkspace is a persistable container for RooFit projects.
Definition RooWorkspace.h:43
RooWorkspace::Print
void Print(Option_t *opts=0) const
Print contents of the workspace.
Definition RooWorkspace.cxx:2178
TCanvas
The Canvas class.
Definition TCanvas.h:23
TCanvas::Update
void Update() override
Update canvas pad buffers.
Definition TCanvas.cxx:2502
TH1F
1-D histogram with a float per channel (see TH1 documentation)}
Definition TH1.h:575
TH1::Draw
virtual void Draw(Option_t *option="")
Draw this histogram with options.
Definition TH1.cxx:3074
TMarker
Manages Markers.
Definition TMarker.h:22
TStopwatch
Stopwatch class.
Definition TStopwatch.h:28
TStopwatch::Start
void Start(Bool_t reset=kTRUE)
Start the stopwatch.
Definition TStopwatch.cxx:58
TStopwatch::Stop
void Stop()
Stop the stopwatch.
Definition TStopwatch.cxx:77
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
x
Double_t x[n]
Definition legend1.C:17
RooFit
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
Definition Common.h:18
RooStats
Namespace for the RooStats classes.
Definition Asimov.h:19
mark
#define mark(osub)
Definition triangle.c:1207
Author
Kyle Cranmer

Definition in file rs401c_FeldmanCousins.C.