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Reference Guide
StandardFeldmanCousinsDemo.C File Reference

Detailed Description

View in nbviewer Open in SWAN Standard demo of the Feldman-Cousins calculator StandardFeldmanCousinsDemo

This is a standard demo that can be used with any ROOT file prepared in the standard way. You specify:

  • name for input ROOT file
  • name of workspace inside ROOT file that holds model and data
  • name of ModelConfig that specifies details for calculator tools
  • name of dataset

With default parameters the macro will attempt to run the standard hist2workspace example and read the ROOT file that it produces.

The actual heart of the demo is only about 10 lines long.

The FeldmanCousins tools is a classical frequentist calculation based on the Neyman Construction. The test statistic can be generalized for nuisance parameters by using the profile likelihood ratio. But unlike the ProfileLikelihoodCalculator, this tool explicitly builds the sampling distribution of the test statistic via toy Monte Carlo.

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
=== Using the following for ModelConfig ===
Observables: RooArgSet:: = (obs_x_channel1,weightVar,channelCat)
Parameters of Interest: RooArgSet:: = (SigXsecOverSM)
Nuisance Parameters: RooArgSet:: = (alpha_syst2,alpha_syst3,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
Global Observables: RooArgSet:: = (nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
PDF: RooSimultaneous::simPdf[ indexCat=channelCat channel1=model_channel1 ] = 0.174888
FeldmanCousins: ntoys per point: adaptive
FeldmanCousins: nEvents per toy will fluctuate about expectation
FeldmanCousins: Model has nuisance parameters, will do profile construction
FeldmanCousins: # points to test = 10
lookup index = 0
NeymanConstruction: Prog: 1/10 total MC = 78 this test stat = 1.323
SigXsecOverSM=0.15 alpha_syst2=0.602204 alpha_syst3=0.227781 gamma_stat_channel1_bin_0=1.03121 gamma_stat_channel1_bin_1=1.04626 [-1e+30, 3.00321] in interval = 1
NeymanConstruction: Prog: 2/10 total MC = 78 this test stat = 0.62223
SigXsecOverSM=0.45 alpha_syst2=0.398364 alpha_syst3=0.161337 gamma_stat_channel1_bin_0=1.02058 gamma_stat_channel1_bin_1=1.03265 [-1e+30, 2.02972] in interval = 1
NeymanConstruction: Prog: 3/10 total MC = 78 this test stat = 0.185526
SigXsecOverSM=0.75 alpha_syst2=0.208894 alpha_syst3=0.0960207 gamma_stat_channel1_bin_0=1.01066 gamma_stat_channel1_bin_1=1.01935 [-1e+30, 1.36211] in interval = 1
NeymanConstruction: Prog: 4/10 total MC = 78 this test stat = 0.00588509
SigXsecOverSM=1.05 alpha_syst2=0.0280678 alpha_syst3=0.0367739 gamma_stat_channel1_bin_0=1.0014 gamma_stat_channel1_bin_1=1.00625 [-1e+30, 1.11847] in interval = 1
NeymanConstruction: Prog: 5/10 total MC = 78 this test stat = 0.0747855
SigXsecOverSM=1.35 alpha_syst2=-0.14426 alpha_syst3=-0.0333676 gamma_stat_channel1_bin_0=0.99286 gamma_stat_channel1_bin_1=0.994142 [-1e+30, 2.18053] in interval = 1
NeymanConstruction: Prog: 6/10 total MC = 78 this test stat = 0.38368
SigXsecOverSM=1.65 alpha_syst2=-0.306403 alpha_syst3=-0.0939379 gamma_stat_channel1_bin_0=0.985118 gamma_stat_channel1_bin_1=0.981397 [-1e+30, 1.59882] in interval = 1
NeymanConstruction: Prog: 7/10 total MC = 78 this test stat = 0.924288
SigXsecOverSM=1.95 alpha_syst2=-0.45999 alpha_syst3=-0.155748 gamma_stat_channel1_bin_0=0.977832 gamma_stat_channel1_bin_1=0.96943 [-1e+30, 2.13401] in interval = 1
NeymanConstruction: Prog: 8/10 total MC = 78 this test stat = 1.68871
SigXsecOverSM=2.25 alpha_syst2=-0.601904 alpha_syst3=-0.21612 gamma_stat_channel1_bin_0=0.971077 gamma_stat_channel1_bin_1=0.957715 [-1e+30, 2.12322] in interval = 1
NeymanConstruction: Prog: 9/10 total MC = 234 this test stat = 2.66932
SigXsecOverSM=2.55 alpha_syst2=-0.732241 alpha_syst3=-0.275902 gamma_stat_channel1_bin_0=0.964738 gamma_stat_channel1_bin_1=0.946434 [-1e+30, 2.20603] in interval = 0
NeymanConstruction: Prog: 10/10 total MC = 234 this test stat = 3.85849
SigXsecOverSM=2.85 alpha_syst2=-0.851873 alpha_syst3=-0.333983 gamma_stat_channel1_bin_0=0.958838 gamma_stat_channel1_bin_1=0.935421 [-1e+30, 2.28476] in interval = 0
[#1] INFO:Eval -- 8 points in interval
95% interval on SigXsecOverSM is : [0.15, 2.25]
#include "TFile.h"
#include "TROOT.h"
#include "TH1F.h"
#include "TSystem.h"
#include "RooWorkspace.h"
#include "RooAbsData.h"
using namespace RooFit;
using namespace RooStats;
void StandardFeldmanCousinsDemo(const char *infile = "", const char *workspaceName = "combined",
const char *modelConfigName = "ModelConfig", const char *dataName = "obsData")
// -------------------------------------------------------
// First part is just to access a user-defined file
// or create the standard example file if it doesn't exist
const char *filename = "";
if (!strcmp(infile, "")) {
filename = "results/example_combined_GaussExample_model.root";
bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code
// if file does not exists generate with histfactory
if (!fileExist) {
#ifdef _WIN32
cout << "HistFactory file cannot be generated on Windows - exit" << endl;
// Normally this would be run on the command line
cout << "will run standard hist2workspace example" << endl;
gROOT->ProcessLine(".! prepareHistFactory .");
gROOT->ProcessLine(".! hist2workspace config/example.xml");
cout << "\n\n---------------------" << endl;
cout << "Done creating example input" << endl;
cout << "---------------------\n\n" << endl;
} else
filename = infile;
// Try to open the file
TFile *file = TFile::Open(filename);
// if input file was specified byt not found, quit
if (!file) {
cout << "StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl;
// -------------------------------------------------------
// Tutorial starts here
// -------------------------------------------------------
// get the workspace out of the file
RooWorkspace *w = (RooWorkspace *)file->Get(workspaceName);
if (!w) {
cout << "workspace not found" << endl;
// get the modelConfig out of the file
ModelConfig *mc = (ModelConfig *)w->obj(modelConfigName);
// get the modelConfig out of the file
RooAbsData *data = w->data(dataName);
// make sure ingredients are found
if (!data || !mc) {
cout << "data or ModelConfig was not found" << endl;
// -------------------------------------------------------
// create and use the FeldmanCousins tool
// to find and plot the 95% confidence interval
// on the parameter of interest as specified
// in the model config
FeldmanCousins fc(*data, *mc);
fc.SetConfidenceLevel(0.95); // 95% interval
// fc.AdditionalNToysFactor(0.1); // to speed up the result
fc.UseAdaptiveSampling(true); // speed it up a bit
fc.SetNBins(10); // set how many points per parameter of interest to scan
fc.CreateConfBelt(true); // save the information in the belt for plotting
// Since this tool needs to throw toy MC the PDF needs to be
// extended or the tool needs to know how many entries in a dataset
// per pseudo experiment.
// In the 'number counting form' where the entries in the dataset
// are counts, and not values of discriminating variables, the
// datasets typically only have one entry and the PDF is not
// extended.
if (!mc->GetPdf()->canBeExtended()) {
if (data->numEntries() == 1)
cout << "Not sure what to do about this model" << endl;
// We can use PROOF to speed things along in parallel
// ProofConfig pc(*w, 1, "workers=4", kFALSE);
// ToyMCSampler* toymcsampler = (ToyMCSampler*) fc.GetTestStatSampler();
// toymcsampler->SetProofConfig(&pc); // enable proof
// Now get the interval
PointSetInterval *interval = fc.GetInterval();
ConfidenceBelt *belt = fc.GetConfidenceBelt();
// print out the interval on the first Parameter of Interest
cout << "\n95% interval on " << firstPOI->GetName() << " is : [" << interval->LowerLimit(*firstPOI) << ", "
<< interval->UpperLimit(*firstPOI) << "] " << endl;
// ---------------------------------------------
// No nice plots yet, so plot the belt by hand
// Ask the calculator which points were scanned
RooDataSet *parameterScan = (RooDataSet *)fc.GetPointsToScan();
RooArgSet *tmpPoint;
// make a histogram of parameter vs. threshold
TH1F *histOfThresholds =
new TH1F("histOfThresholds", "", parameterScan->numEntries(), firstPOI->getMin(), firstPOI->getMax());
// loop through the points that were tested and ask confidence belt
// what the upper/lower thresholds were.
// For FeldmanCousins, the lower cut off is always 0
for (Int_t i = 0; i < parameterScan->numEntries(); ++i) {
tmpPoint = (RooArgSet *)parameterScan->get(i)->clone("temp");
double arMax = belt->GetAcceptanceRegionMax(*tmpPoint);
double arMin = belt->GetAcceptanceRegionMax(*tmpPoint);
double poiVal = tmpPoint->getRealValue(firstPOI->GetName());
histOfThresholds->Fill(poiVal, arMax);
Kyle Cranmer

Definition in file StandardFeldmanCousinsDemo.C.

RooAbsData * data(const char *name) const
Retrieve dataset (binned or unbinned) with given name. A null pointer is returned if not found.
Definition: RooWorkspace.cxx:1368
RooAbsArg * first() const
Definition: RooAbsCollection.h:177
RooAbsData is the common abstract base class for binned and unbinned datasets.
Definition: RooAbsData.h:49
virtual Double_t getMax(const char *name=0) const
Get maximum of currently defined range.
Definition: RooAbsRealLValue.h:89
Double_t UpperLimit(RooRealVar &param)
return upper limit on a given parameter
Definition: PointSetInterval.cxx:147
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: RooArgSet.cxx:343
virtual void SetMinimum(Double_t minimum=-1111)
Definition: TH1.h:399
static struct mg_connection * fc(struct mg_context *ctx)
Definition: civetweb.c:3728
TObject * clone(const char *newname) const override
Definition: RooArgSet.h:83
The FeldmanCousins class (like the Feldman-Cousins technique) is essentially a specific configuration...
Definition: FeldmanCousins.h:33
static TFile * Open(const char *name, Option_t *option="", const char *ftitle="", Int_t compress=ROOT::RCompressionSetting::EDefaults::kUseCompiledDefault, Int_t netopt=0)
Create / open a file.
Definition: TFile.cxx:3997
virtual Bool_t AccessPathName(const char *path, EAccessMode mode=kFileExists)
Returns FALSE if one can access a file using the specified access mode.
Definition: TSystem.cxx:1294
virtual Double_t getMin(const char *name=0) const
Get miniminum of currently defined range.
Definition: RooAbsRealLValue.h:86
virtual const RooArgSet * get(Int_t index) const override
Return RooArgSet with coordinates of event 'index'.
Definition: RooDataSet.cxx:1051
void Print(Option_t *opts=0) const
Print contents of the workspace.
Definition: RooWorkspace.cxx:2194
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
Definition: RooCFunction1Binding.h:29
ConfidenceBelt is a concrete implementation of the ConfInterval interface.
Definition: ConfidenceBelt.h:156
virtual Int_t Fill(Double_t x)
Increment bin with abscissa X by 1.
Definition: TH1.cxx:3350
TObject * obj(const char *name) const
Return any type of object (RooAbsArg, RooAbsData or generic object) with given name)
Definition: RooWorkspace.cxx:2106
virtual Int_t numEntries() const
Return number of entries in dataset, i.e., count unweighted entries.
Definition: RooAbsData.cxx:307
A ROOT file is a suite of consecutive data records (TKey instances) with a well defined format.
Definition: TFile.h:54
RooAbsPdf * GetPdf() const
get model PDF (return NULL if pdf has not been specified or does not exist)
Definition: ModelConfig.h:234
R__EXTERN TSystem * gSystem
Definition: TSystem.h:559
const RooArgSet * GetParametersOfInterest() const
get RooArgSet containing the parameter of interest (return NULL if not existing)
Definition: ModelConfig.h:237
The RooWorkspace is a persistable container for RooFit projects.
Definition: RooWorkspace.h:43
Namespace for the RooStats classes.
Definition: Asimov.h:19
Definition: file.py:1
1-D histogram with a float per channel (see TH1 documentation)}
Definition: TH1.h:575
Double_t GetAcceptanceRegionMax(RooArgSet &, Double_t cl=-1., Double_t leftside=-1.)
Definition: ConfidenceBelt.cxx:101
RooDataSet is a container class to hold unbinned data.
Definition: RooDataSet.h:33
virtual const char * GetName() const
Returns name of object.
Definition: TNamed.h:47
RooRealVar represents a variable that can be changed from the outside.
Definition: RooRealVar.h:39
PointSetInterval is a concrete implementation of the ConfInterval interface.
Definition: PointSetInterval.h:21
ModelConfig is a simple class that holds configuration information specifying how a model should be u...
Definition: ModelConfig.h:30
Double_t LowerLimit(RooRealVar &param)
return lower limit on a given parameter
Definition: PointSetInterval.cxx:160
Bool_t canBeExtended() const
If true, PDF can provide extended likelihood term.
Definition: RooAbsPdf.h:238
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition: RooArgSet.h:29
#define gROOT
Definition: TROOT.h:406
virtual void Draw(Option_t *option="")
Draw this histogram with options.
Definition: TH1.cxx:3073