void central_interval_Profile_Likelihood(Model* model,double confidence){
cout<<"///////////////////////////////////////////////////////////////////////////////////////////"<<endl;
cout<<"Calculating central confidence interval with the Profile Likelihood method"<<endl;
cout<<"///////////////////////////////////////////////////////////////////////////////////////////"<<endl;
// //define paramsOfInterest
RooArgSet *paramsOfInterest=new RooArgSet("paramsOfInterest");
paramsOfInterest->addClone(*model->get_POI()); //clone because we need s for complete likelihood
//get the calculator
ProfileLikelihoodCalculator plc(*model->get_data(),*model->get_complete_likelihood(), *paramsOfInterest);
// //get the confidence interval
LikelihoodInterval* lrint = (LikelihoodInterval*) plc.GetInterval();
lrint->SetConfidenceLevel(confidence);
double ll=lrint->LowerLimit(*model->get_POI());
double ul=lrint->UpperLimit(*model->get_POI());
cout<<confidence<<"% inter val is "<<ll<<" , "<<ul<<endl;
}
void StandardProfileLikelihoodDemo(const char* infile = "",
const char* workspaceName = "combined",
const char* modelConfigName = "ModelConfig",
const char* dataName = "obsData"){
double confLevel = optPL.confLevel;
double nScanPoints = optPL.nScanPoints;
bool plotAsTF1 = optPL.plotAsTF1;
double poiXMin = optPL.poiMinPlot;
double poiXMax = optPL.poiMaxPlot;
bool doHypoTest = optPL.doHypoTest;
double nullParamValue = optPL.nullValue;
// -------------------------------------------------------
// 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;
return;
#endif
// 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;
return;
}
// -------------------------------------------------------
// Tutorial starts here
// -------------------------------------------------------
// get the workspace out of the file
RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName);
if(!w){
cout <<"workspace not found" << endl;
return;
}
// 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){
w->Print();
cout << "data or ModelConfig was not found" <<endl;
return;
}
// ---------------------------------------------
// create and use the ProfileLikelihoodCalculator
// to find and plot the 95% confidence interval
// on the parameter of interest as specified
// in the model config
ProfileLikelihoodCalculator pl(*data,*mc);
pl.SetConfidenceLevel(confLevel); // 95% interval
LikelihoodInterval* interval = pl.GetInterval();
// print out the interval on the first Parameter of Interest
RooRealVar* firstPOI = (RooRealVar*) mc->GetParametersOfInterest()->first();
cout << "\n>>>> RESULT : " << confLevel*100 << "% interval on " <<firstPOI->GetName()<<" is : ["<<
interval->LowerLimit(*firstPOI) << ", "<<
interval->UpperLimit(*firstPOI) <<"]\n "<<endl;
// make a plot
cout << "making a plot of the profile likelihood function ....(if it is taking a lot of time use less points or the TF1 drawing option)\n";
LikelihoodIntervalPlot plot(interval);
plot.SetNPoints(nScanPoints); // do not use too many points, it could become very slow for some models
if (poiXMin < poiXMax) plot.SetRange(poiXMin, poiXMax);
TString opt;
if (plotAsTF1) opt += TString("tf1");
plot.Draw(opt); // use option TF1 if too slow (plot.Draw("tf1")
// if requested perform also an hypothesis test for the significance
if (doHypoTest) {
RooArgSet nullparams("nullparams");
nullparams.addClone(*firstPOI);
nullparams.setRealValue(firstPOI->GetName(), nullParamValue);
pl.SetNullParameters(nullparams);
std::cout << "Perform Test of Hypothesis : null Hypothesis is " << firstPOI->GetName() << nullParamValue << std::endl;
auto result = pl.GetHypoTest();
std::cout << "\n>>>> Hypotheis Test Result \n";
result->Print();
}
}
//
// calculation of the limit: assumes that wspace is set up and observations
// contained in data
//
MyLimit computeLimit (RooWorkspace* wspace, RooDataSet* data, StatMethod method, bool draw) {
// let's time this challenging example
TStopwatch t;
//
// get nominal signal
//
RooRealVar exp_sig(*wspace->var("s"));
double exp_sig_val = exp_sig.getVal();
std::cout << "exp_sig = " << exp_sig_val << std::endl;
/////////////////////////////////////////////////////
// Now the statistical tests
// model config
std::cout << wspace->pdf("model") << " "
<< wspace->pdf("prior") << " "
<< wspace->set("poi") << " "
<< wspace->set("nuis") << std::endl;
ModelConfig modelConfig("RA4abcd");
modelConfig.SetWorkspace(*wspace);
modelConfig.SetPdf(*wspace->pdf("model"));
modelConfig.SetPriorPdf(*wspace->pdf("prior"));
modelConfig.SetParametersOfInterest(*wspace->set("poi"));
modelConfig.SetNuisanceParameters(*wspace->set("nuis"));
//////////////////////////////////////////////////
// If you want to see the covariance matrix uncomment
// wspace->pdf("model")->fitTo(*data);
// use ProfileLikelihood
if ( method == ProfileLikelihoodMethod ) {
ProfileLikelihoodCalculator plc(*data, modelConfig);
plc.SetConfidenceLevel(0.95);
RooFit::MsgLevel msglevel = RooMsgService::instance().globalKillBelow();
RooMsgService::instance().setGlobalKillBelow(RooFit::FATAL);
LikelihoodInterval* plInt = plc.GetInterval();
RooMsgService::instance().setGlobalKillBelow(RooFit::FATAL);
plInt->LowerLimit( *wspace->var("s") ); // get ugly print out of the way. Fix.
// RooMsgService::instance().setGlobalKillBelow(RooFit::DEBUG);
if ( draw ) {
TCanvas* c = new TCanvas("ProfileLikelihood");
LikelihoodIntervalPlot* lrplot = new LikelihoodIntervalPlot(plInt);
lrplot->Draw();
}
// RooMsgService::instance().setGlobalKillBelow(msglevel);
double lowLim = plInt->LowerLimit(*wspace->var("s"));
double uppLim = plInt->UpperLimit(*wspace->var("s"));
// double exp_sig_val = wspace->var("s")->getVal();
// double exp_sig_val = exp_sig.getVal();
cout << "Profile Likelihood interval on s = [" <<
lowLim << ", " <<
uppLim << "]" << " " << exp_sig_val << endl;
// MyLimit result(plInt->IsInInterval(exp_sig),
MyLimit result(exp_sig_val>lowLim&&exp_sig_val<uppLim,lowLim,uppLim);
// std::cout << "isIn " << result << std::endl;
delete plInt;
// delete modelConfig;
return result;
}
// use FeldmaCousins (takes ~20 min)
if ( method == FeldmanCousinsMethod ) {
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(100);
PointSetInterval* fcInt = NULL;
fcInt = (PointSetInterval*) fc.GetInterval(); // fix cast
double lowLim = fcInt->LowerLimit(*wspace->var("s"));
double uppLim = fcInt->UpperLimit(*wspace->var("s"));
// double exp_sig_val = wspace->var("s")->getVal();
cout << "Feldman Cousins interval on s = [" << lowLim << " " << uppLim << endl;
// std::cout << "isIn " << result << std::endl;
MyLimit result(exp_sig_val>lowLim&&exp_sig_val<uppLim,
fcInt->LowerLimit(*wspace->var("s")),fcInt->UpperLimit(*wspace->var("s")));
delete fcInt;
return result;
}
// use BayesianCalculator (only 1-d parameter of interest, slow for this problem)
if ( method == BayesianMethod ) {
BayesianCalculator bc(*data, modelConfig);
bc.SetConfidenceLevel(0.95);
bc.SetLeftSideTailFraction(0.5);
SimpleInterval* bInt = NULL;
if( wspace->set("poi")->getSize() == 1) {
bInt = bc.GetInterval();
if ( draw ) {
TCanvas* c = new TCanvas("Bayesian");
// the plot takes a long time and print lots of error
// using a scan it is better
bc.SetScanOfPosterior(50);
RooPlot* bplot = bc.GetPosteriorPlot();
bplot->Draw();
}
cout << "Bayesian interval on s = [" <<
bInt->LowerLimit( ) << ", " <<
bInt->UpperLimit( ) << "]" << endl;
// std::cout << "isIn " << result << std::endl;
MyLimit result(bInt->IsInInterval(exp_sig),
bInt->LowerLimit(),bInt->UpperLimit());
delete bInt;
return result;
} else {
cout << "Bayesian Calc. only supports on parameter of interest" << endl;
return MyLimit();
}
}
// use MCMCCalculator (takes about 1 min)
// Want an efficient proposal function, so derive it from covariance
// matrix of fit
if ( method == MCMCMethod ) {
RooFitResult* fit = wspace->pdf("model")->fitTo(*data,Save());
ProposalHelper ph;
ph.SetVariables((RooArgSet&)fit->floatParsFinal());
ph.SetCovMatrix(fit->covarianceMatrix());
ph.SetUpdateProposalParameters(kTRUE); // auto-create mean vars and add mappings
ph.SetCacheSize(100);
ProposalFunction* pf = ph.GetProposalFunction();
MCMCCalculator mc(*data, modelConfig);
mc.SetConfidenceLevel(0.95);
mc.SetProposalFunction(*pf);
mc.SetNumBurnInSteps(100); // first N steps to be ignored as burn-in
mc.SetNumIters(100000);
mc.SetLeftSideTailFraction(0.5); // make a central interval
MCMCInterval* mcInt = NULL;
mcInt = mc.GetInterval();
MCMCIntervalPlot mcPlot(*mcInt);
mcPlot.Draw();
cout << "MCMC interval on s = [" <<
mcInt->LowerLimit(*wspace->var("s") ) << ", " <<
mcInt->UpperLimit(*wspace->var("s") ) << "]" << endl;
// std::cout << "isIn " << result << std::endl;
MyLimit result(mcInt->IsInInterval(exp_sig),
mcInt->LowerLimit(*wspace->var("s")),mcInt->UpperLimit(*wspace->var("s")));
delete mcInt;
return result;
}
t.Print();
// delete modelConfig;
return MyLimit();
}
void IntervalExamples()
{
// Time this macro
TStopwatch t;
t.Start();
// set RooFit random seed for reproducible results
RooRandom::randomGenerator()->SetSeed(3001);
// 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
gStyle->SetCanvasColor(0);
gStyle->SetCanvasBorderMode(0);
gStyle->SetPadBorderMode(0);
gStyle->SetPadColor(0);
gStyle->SetCanvasColor(0);
gStyle->SetTitleFillColor(0);
gStyle->SetFillColor(0);
gStyle->SetFrameFillColor(0);
gStyle->SetStatColor(0);
// 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();
}
void exercise_3() {
//Open the rootfile and get the workspace from the exercise_0
TFile fIn("exercise_0.root");
fIn.cd();
RooWorkspace *w = (RooWorkspace*)fIn.Get("w");
//You can set constant parameters that are known
//If you leave them floating, the fit procedure will determine their uncertainty
w->var("mean")->setConstant(kFALSE); //don't fix the mean, it's what we want to know the interval for!
w->var("sigma")->setConstant(kTRUE);
w->var("tau")->setConstant(kTRUE);
w->var("Nsig")->setConstant(kTRUE);
w->var("Nbkg")->setConstant(kTRUE);
//Set the RooModelConfig and let it know what the content of the workspace is about
ModelConfig model;
model.SetWorkspace(*w);
model.SetPdf("PDFtot");
//Let the model know what is the parameter of interest
RooRealVar* mean = w->var("mean");
mean->setRange(120., 130.); //this is mostly for plotting reasons
RooArgSet poi(*mean);
// set confidence level
double confidenceLevel = 0.68;
//Build the profile likelihood calculator
ProfileLikelihoodCalculator plc;
plc.SetData(*(w->data("PDFtotData")));
plc.SetModel(model);
plc.SetParameters(poi);
plc.SetConfidenceLevel(confidenceLevel);
//Get the interval
LikelihoodInterval* plInt = plc.GetInterval();
//Now let's do the same for the Bayesian Calculator
//Now we also need to specify a prior in the ModelConfig
//To be quicker, we'll use the PDF factory facility of RooWorkspace
//NB!! For simplicity, we are using a flat prior, but this doesn't mean it's the best choice!
w->factory("Uniform::prior(mean)");
model.SetPriorPdf(*w->pdf("prior"));
//Construct the bayesian calculator
BayesianCalculator bc(*(w->data("PDFtotData")), model);
bc.SetConfidenceLevel(confidenceLevel);
bc.SetParameters(poi);
SimpleInterval* bcInt = bc.GetInterval();
// Let's make a plot
TCanvas dataCanvas("dataCanvas");
dataCanvas.Divide(2,1);
dataCanvas.cd(1);
LikelihoodIntervalPlot plotInt((LikelihoodInterval*)plInt);
plotInt.SetTitle("Profile Likelihood Ratio and Posterior for mH");
plotInt.SetMaximum(3.);
plotInt.Draw();
dataCanvas.cd(2);
RooPlot *bcPlot = bc.GetPosteriorPlot();
bcPlot->Draw();
dataCanvas.SaveAs("exercise_3.gif");
//Now print the interval for mH for the two methods
cout << "PLC interval is [" << plInt->LowerLimit(*mean) << ", " <<
plInt->UpperLimit(*mean) << "]" << endl;
cout << "Bayesian interval is [" << bcInt->LowerLimit() << ", " <<
bcInt->UpperLimit() << "]" << endl;
}
void CountingModelCross_2( int nobs_4m = 27, // number of observed events
double b_4m = 0.09, // number of background events
double sigmab_4m = 0.16, // relative uncertainty in b 1
int nobs_2e2m = 30, // number of observed events
double b_2e2m = 0.47, // number of background events
double sigmab_2e2m = 0.26, // relative uncertainty in b 2
int nobs_4e = 8, // number of observed events
double b_4e = 0.44, // number of background events
double sigmab_4e = 0.35 // relative uncertainty in b 2
)
{
RooWorkspace w("w");
// make Poisson model * Gaussian constraint
w.factory("prod:s_4m(cross[34.7,0.,10000000],BR_4m[0.5,0,1],Acc_4m[0.5,0.,1.],Lumi[2.229,0.,10000])");
w.factory("prod:s_2e2m(cross[34.7,0.,10000000],BR_2e2m[0.5,0,1],Acc_2e2m[0.5,0.,1.],Lumi[2.229,0.,10000])");
w.factory("prod:s_4e(cross[34.7,0.,10000000],BR_4e[0.5,0,1],Acc_4e[0.5,0.,1.],Lumi[2.229,0.,10000])");
w.factory("sum:nexp_4m(s_4m,b_4m[1,0,10])");
w.factory("sum:nexp_2e2m(s_2e2m,b_2e2m[1,0,10])");
w.factory("sum:nexp_4e(s_4e,b_4e[1,0,10])");
w.var("b_4m")->setVal(b_4m);
w.var("b_2e2m")->setVal(b_2e2m);
w.var("b_4e")->setVal(b_4e);
// Poisson of (n | s+b)
w.factory("Poisson:pdf_4m(nobs_4m[0,50],nexp_4m)");
w.factory("Poisson:pdf_2e2m(nobs_2e2m[0,50],nexp_2e2m)");
w.factory("Poisson:pdf_4e(nobs_4e[0,50],nexp_4e)");
w.factory("Gaussian:constraint_4m(b0_4m[0,10],b_4m,sigmab_4m[1])");
w.factory("Gaussian:constraint_2e2m(b0_2e2m[0,10],b_2e2m,sigmab_2e2m[1])");
w.factory("Gaussian:constraint_4e(b0_4e[0,10],b_4e,sigmab_4e[1])");
w.factory("Gaussian:constraintAcc_4m(Acc0_4m[0,1],Acc_4m,sigmaAcc_4m[1])");
w.factory("Gaussian:constraintAcc_2e2m(Acc0_2e2m[0,1],Acc_2e2m,sigmaAcc_2e2m[1])");
w.factory("Gaussian:constraintAcc_4e(Acc0_4e[0,1],Acc_4e,sigmaAcc_4e[1])");
w.factory("PROD:model_4m(pdf_4m,constraint_4m,constraintAcc_4m)");
w.factory("PROD:model_2e2m(pdf_2e2m,constraint_2e2m,constraintAcc_2e2m)");
w.factory("PROD:model_4e(pdf_4e,constraint_4e,constraintAcc_4e)");
w.factory("PROD:model(model_4m,model_2e2m,model_4e)");
w.var("b0_4m")->setVal(b_4m);
w.var("b0_2e2m")->setVal(b_2e2m);
w.var("b0_4e")->setVal(b_4e);
w.var("b0_4m")->setConstant(true); // needed for being treated as global observables
w.var("b0_2e2m")->setConstant(true); // needed for being treated as global observables
w.var("b0_4e")->setConstant(true); // needed for being treated as global observables
// w.var("Acc_4m")->setVal(0.844);
// w.var("Acc_2e2m")->setVal(0.694);
// w.var("Acc_4e")->setVal(0.60);
Float_t Acc_4m = 0.844*0.5388;
Float_t Acc_2e2m = 0.694*0.5388;
Float_t Acc_4e = 0.60*0.5388;
// Float_t Acc_4m = 0.844;
// Float_t Acc_2e2m = 0.694;
// Float_t Acc_4e = 0.60;
Float_t sigmaAcc_4m = 0.0097 ;
Float_t sigmaAcc_2e2m = 0.0097 ;
Float_t sigmaAcc_4e = 0.0097 ;
Float_t BRele = 0.03363;
Float_t BRmu = 0.03366;
Float_t BR_4m = BRmu*BRmu;
Float_t BR_2e2m = 2*BRmu*BRele;
Float_t BR_4e = BRele*BRele;
Float_t BR = BR_4m+BR_2e2m+BR_4e;
cout<<"BR sum"<<BR<<" BRmean "<<(BR_4m + BR_4e + BR_2e2m)/3.<<" BR_4m "<<BR_4m<<" BR_2e2m "<<BR_2e2m<<" BR_4e "<<BR_4e<<endl;
w.var("BR_4m")->setVal(BR_4m);
w.var("BR_2e2m")->setVal(BR_2e2m);
w.var("BR_4e")->setVal(BR_4e);
w.var("BR_4m")->setConstant(true); // needed for being treated as global observables
w.var("BR_2e2m")->setConstant(true); // needed for being treated as global observables
w.var("BR_4e")->setConstant(true); // needed for being treated as global observables
w.var("Lumi")->setVal(2.560);
w.var("Lumi")->setConstant(true);
w.var("sigmab_4m")->setVal(sigmab_4m*b_4m);
w.var("sigmab_2e2m")->setVal(sigmab_2e2m*b_2e2m);
w.var("sigmab_4e")->setVal(sigmab_4e*b_4e);
w.var("sigmaAcc_4m")->setVal(sigmaAcc_4m*Acc_4m);
w.var("sigmaAcc_2e2m")->setVal(sigmaAcc_2e2m*Acc_2e2m);
w.var("sigmaAcc_4e")->setVal(sigmaAcc_4e*Acc_4e);
ModelConfig mc("ModelConfig",&w);
mc.SetPdf(*w.pdf("model"));
mc.SetParametersOfInterest(*w.var("cross"));
mc.SetObservables(RooArgSet(*w.var("nobs_4m"),*w.var("nobs_2e2m"),*w.var("nobs_4e")));
// mc.SetNuisanceParameters(RooArgSet(*w.var("b_4m"),*w.var("b_2e2m"),*w.var("b_4e")));
//mc.SetObservables(RooArgSet(*w.var("nobs_4m"),*w.var("nobs_2e2m")));
// mc.SetNuisanceParameters(RooArgSet(*w.var("b_4m"),*w.var("b_2e2m"),*w.var("Acc_4m"),*w.var("Acc_2e2m")));
mc.SetNuisanceParameters(RooArgSet(*w.var("b_4m"),*w.var("b_2e2m"),*w.var("b_4e"),*w.var("Acc_4m"),*w.var("Acc_2e2m"),*w.var("Acc_4e")));
mc.SetParametersOfInterest(*w.var("cross"));
// these are needed for the hypothesis tests
mc.SetSnapshot(*w.var("cross"));
// mc.SetGlobalObservables(RooArgSet(*w.var("b0_4m"),*w.var("b0_2e2m"),*w.var("b0_4e")));
mc.Print();
// import model in the workspace
w.import(mc);
// make data set with the namber of observed events
RooDataSet data("data","", RooArgSet(*w.var("nobs_4m"),*w.var("nobs_2e2m"),*w.var("nobs_4e")));
//RooDataSet data("data","", RooArgSet(*w.var("nobs_4m"),*w.var("nobs_2e2m")));
w.var("nobs_4m")->setVal(nobs_4m);
data.add(*w.var("nobs_4m") );
w.var("nobs_2e2m")->setVal(nobs_2e2m);
data.add(*w.var("nobs_2e2m") );
w.var("nobs_4e")->setVal(nobs_4e);
data.add(*w.var("nobs_4e") );
// import data set in workspace and save it in a file
ProfileLikelihoodCalculator pl(data, mc);
pl.SetConfidenceLevel(0.68); // 95% interval
LikelihoodInterval* interval = pl.GetInterval();
// print out the iterval on the first Parameter of Interest
RooRealVar* firstPOI = (RooRealVar*) mc.GetParametersOfInterest()->first();
cout << "\n65% interval on " <<firstPOI->GetName()<<" is : ["<<
interval->LowerLimit(*firstPOI) << ", "<<
interval->UpperLimit(*firstPOI) <<"] "<<endl;
std::cout<<"Cross "<<mc.GetParametersOfInterest()->getRealValue("cross")*BR<<std::endl;
w.import(data);
w.Print();
TString fileName = "CountingModel.root";
// write workspace in the file (recreate file if already existing)
w.writeToFile(fileName, true);
}
