void upper_limit_Bayesian_MCMC(Model* model,double confidence,int Niters){
cout<<"///////////////////////////////////////////////////////////////////////////////////////////"<<endl;
cout<<"Calculating upper limit with the MCMC method"<<endl;
cout<<"///////////////////////////////////////////////////////////////////////////////////////////"<<endl;
RooWorkspace* wspace = new RooWorkspace("wspace");
ModelConfig* modelConfig = new ModelConfig("bayes");
modelConfig->SetWorkspace(*wspace);
modelConfig->SetPdf(*model->get_complete_likelihood());
modelConfig->SetPriorPdf(*model->get_POI_prior());
modelConfig->SetParametersOfInterest(*model->get_POI_set());
// modelConfig->SetNuisanceParameters();
modelConfig->SetNuisanceParameters(*model->get_nuisance_set());
//configure the calculator
//model->Print();
cout<<" POI size "<<model->get_POI_set()->getSize()<<endl;
RooRealVar* firstPOI = (RooRealVar*) modelConfig->GetParametersOfInterest()->first();
MCMCCalculator mcmccalc(*model->get_data(), *modelConfig );
mcmccalc.SetTestSize(1-confidence);
mcmccalc.SetLeftSideTailFraction(0);
mcmccalc.SetNumIters(Niters);
MCMCInterval* interval = mcmccalc.GetInterval();
double ul = interval->UpperLimit(*firstPOI);
cout<<"UpperLimit: "<<ul<<endl;
}
double Tprime::printMcmcUpperLimit( double peak, std::string filename ) {
//
// print out the upper limit on the first Parameter of Interest
//
RooStats::ModelConfig * _mc = (RooStats::ModelConfig *)pWs->genobj("ModelConfig");
RooRealVar * firstPOI = (RooRealVar*) _mc->GetParametersOfInterest()->first();
double _limit = mcInt->UpperLimit(*firstPOI);
cout << "\n95% upper limit on " <<firstPOI->GetName()<<" is : "<<
_limit <<endl;
if (filename.size()!=0) {
std::ofstream aFile;
// append to file if exists
aFile.open(filename.c_str(), std::ios_base::app);
char buf[1024];
sprintf(buf, "%7.1f %7.6f", peak, _limit);
aFile << buf << std::endl;
// close outfile here so it is safe even if subsequent iterations crash
aFile.close();
}
return _limit;
}
double TwoBody::printMcmcUpperLimit( double peak, ModelConfig &_mc, std::string filename ){
//
// print out the upper limit on the first Parameter of Interest
//
std::string _legend = "[TwoBody::printMcmcUpperLimit]: ";
char buf[1024];
double _limit = numeric_limits<double>::max();
if (mcInt){
//mc.SetWorkspace(*ws);
RooRealVar * firstPOI = (RooRealVar*) _mc.GetParametersOfInterest()->first();
_limit = mcInt->UpperLimit(*firstPOI);
std::cout << "\n95% upper limit on " << firstPOI->GetName() << " is : " <<
_limit << endl;
if (bMcmcConverged){
sprintf(buf, "%7.1f %7.6f", peak, _limit);
}
else{
sprintf(buf, "# %7.1f %7.6f # MCMC did not converge", peak, _limit);
}
}
else{
sprintf(buf, "# MCMC did not converge");
}
if (filename.size()!=0){
std::ofstream aFile;
// append to file if exists
aFile.open(filename.c_str(), std::ios_base::app);
aFile << buf << std::endl;
// close outfile here so it is safe even if subsequent iterations crash
aFile.close();
}
return _limit;
}
MCMCInterval * TwoBody::GetMcmcInterval(ModelConfig _mc,
double conf_level,
int n_iter,
int n_burn,
double left_side_tail_fraction,
int n_bins){
// use MCMCCalculator (takes about 1 min)
// Want an efficient proposal function, so derive it from covariance
// matrix of fit
std::string legend = "[TwoBody::GetMcmcInterval]: ";
// FIXME: testing: this proposal function seems fairly robust
SequentialProposal sp(0.5);
MCMCCalculator mcmc( *data, _mc );
mcmc.SetConfidenceLevel(conf_level);
mcmc.SetNumIters(n_iter); // Metropolis-Hastings algorithm iterations
mcmc.SetProposalFunction(sp);
mcmc.SetNumBurnInSteps(n_burn); // first N steps to be ignored as burn-in
mcmc.SetLeftSideTailFraction(left_side_tail_fraction);
mcmc.SetNumBins(n_bins);
// FIXME: testing good initial values - don't seem to do anything different
//ws->var("ratio")->setVal(0.01);
//ws->var("beta_nsig_dielectron")->setRange(-3.0, 3.0);
//ws->var("beta_nbkg_dielectron")->setRange(-3.0, 3.0);
//ws->var("beta_mass_dielectron")->setRange(-3.0, 3.0);
//mcInt = mcmc.GetInterval();
try {
delete mcInt;
mcInt = mcmc.GetInterval();
} catch ( std::length_error &ex) {
mcInt = 0;
}
cerr<<legend<<endl;
// check if limit makes sense
bMcmcConverged = false; // default
if (mcInt){
RooRealVar * p_first_poi = (RooRealVar*) _mc.GetParametersOfInterest()->first();
double poi_limit = mcInt->UpperLimit(*p_first_poi);
double u_poi_min = p_first_poi->getMin();
double u_poi_max = p_first_poi->getMax();
double u_poi_gap = (u_poi_max-poi_limit)/(u_poi_max-u_poi_min);
std::cout << legend << "POI upper limit: " << poi_limit << std::endl;
std::cout << legend << "POI range: [" << u_poi_min << ", " << u_poi_max << "]" << std::endl;
std::cout << legend << "POI upper gap (fraction of range): " << u_poi_gap << std::endl;
if (u_poi_gap<0.2){
std::cout << legend
<< "POI limit too close to the upper boundary, MCMC probably failed!!!" << std::endl;
std::cout << legend
<< "returning interval and setting fail flag" << std::endl;
bMcmcConverged = false;
}
else{
bMcmcConverged = true;
}
}
else std::cout << "No interval found!" << std::endl;
return mcInt;
}
//
// 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();
}
Int_t Tprime::RunMcmc( std::string channel, // ejets, mujets, combined
std::string mode, // observed, expected
double peak, // resonance mass
std::string suffix, // suffix for output file names
Int_t ntoys, // number of pseudoexperiments for expected limit
Int_t mcmc_iter, // number of MCMC iterations
Int_t mcmc_burnin, // number of MCMC burn in steps to be discarded
std::string inputdir // input dir name
) {
//
// Bayesian MCMC calculation
//
std::string legend = "[tprime::RunMcmc()]: ";
// print out inputs
std::cout << legend << std::endl;
std::cout << legend << "Input parameters specified. Some of them are not used and defaults are entered" << std::endl;
std::cout << legend << "------------------------------" << std::endl;
std::cout << legend << "channel: " << channel << std::endl;
std::cout << legend << "mode: " << mode << std::endl;
std::cout << legend << "input directory: " << inputdir << std::endl;
std::cout << legend << "resonance peak mass: " << peak << std::endl;
std::cout << legend << "suffix: ->" << suffix << "<-" << std::endl;
std::cout << legend << "number of pseudo-experiments: "<< ntoys << std::endl;
std::cout << legend << std::endl;
std::cout << legend << "Bayesian MCMC parameters" << std::endl;
std::cout << legend << "------------------------------" << std::endl;
std::cout << legend << "number of iterations: " << mcmc_iter << std::endl;
std::cout << legend << "number of burn-in steps to discard: " << mcmc_burnin << std::endl;
std::cout << legend << std::endl;
// compose the workspace file name
char buf[1024];
sprintf(buf, "%sresults_%04.0f/tprime_%s_tprimeCrossSection_model.root", inputdir.c_str(), peak, channel.c_str());
std::string _file = buf;
std::cout << legend << "guessed name of the file with the workspace: >" << _file << "<" << std::endl;
//load workspace
LoadWorkspace(_file, channel);
// change POI range
double poiUpper = GetPoiUpper(channel, peak);
std::cout << legend << "setting POI range to [0; " << poiUpper << "]" << std::endl;
pWs->var("xsec")->setRange(0.0, poiUpper);
// timer
TStopwatch t;
t.Start();
int pe_counter = 0;
std::vector<Double_t> _limits;
while (pe_counter < ntoys) {
// for mass limit, add k-factor systematics to the nsig systematics
// FIXME: this is a correlated part of the uncertainty!!!
// - different uncertainties for graviton and Z' models
if ( mode.find("mass_") != std::string::npos ) {
std::cout << legend << std::endl;
std::cout << legend << "this a mass limit calculation," << std::endl;
std::cout << legend << "I would add k-factor uncertainty to the nsig uncertainty" << std::endl;
std::cout << legend << "I would do it " << ntoys << " times, so one can average. " << pe_counter+1 << " of " << ntoys << std::endl;
std::cout << legend << "Not implemented yet " << std::endl;
std::cout << legend << std::endl;
//Double_t kfactor_err = GetKfactorUncertainty(peak, mode);
//double nsig_kappa = ws->var("nsig_kappa_dimuon")->getVal();
//nsig_kappa = sqrt(nsig_kappa*nsig_kappa + kfactor_err*kfactor_err);
//ws->var("nsig_kappa_dimuon")->setVal(nsig_kappa);
//ntoys = 1;
}
else if ( mode.find("expected") != std::string::npos ) {
std::cout << legend << std::endl;
std::cout << legend << "this is pseudoexperiment " << pe_counter+1 << " of " << ntoys << std::endl;
std::cout << legend << "for the expected limit estimate" << std::endl;
std::cout << legend << std::endl;
// prepare PE data
GetPseudoData();
}
else { // "regular" observed limit
std::cout << legend << std::endl;
std::cout << legend << "calculating an observed limit..." << std::endl;
std::cout << legend << "I will do it " << ntoys << " times, so one can average. " << pe_counter+1 << " of " << ntoys << std::endl;
std::cout << legend << std::endl;
GetWorkspaceData("obsData");
//ntoys = 1;
}
mcInt = GetMcmcInterval(0.95, // conf level
mcmc_iter, // number of iterations
mcmc_burnin, // number of burn-in to discard
0.0, // left side tail fraction, 0 for upper limit
100); // number of bins in posterior, only for plotting
++pe_counter;
if (!mcInt) {
continue;
}
else {
std::string _outfile = "tprime_"+channel+"_xsec_mcmc_limit_" + suffix + ".ascii";
printMcmcUpperLimit( peak, _outfile );
// limits for averaging/medianing
RooStats::ModelConfig * pSbModel = GetModelConfig("ModelConfig");
RooRealVar * firstPOI = (RooRealVar*) pSbModel->GetParametersOfInterest()->first();
double _limit = mcInt->UpperLimit(*firstPOI);
_limits.push_back(_limit);
} // end of valid mcInt block
} // end of while
// write median limit to a file
if (_limits.size() > 0) {
Double_t _median_limit = TMath::Median(_limits.size(), &_limits[0]);
std::vector<Double_t> _mass_limit;
_mass_limit.push_back(peak);
_mass_limit.push_back(_median_limit);
std::string _outfile = "tprime_"+channel+"_xsec_mcmc_median_limit_" + suffix + ".ascii";
PrintToFile(_outfile, _mass_limit, "# mass median_limit");
}
std::string _outfile = "tprime_"+channel+"_xsec_mcmc_posterior_" + suffix + ".pdf";
makeMcmcPosteriorPlot( _outfile );
// timer
t.Print();
return 0;
}
void rs101_limitexample()
{
// --------------------------------------
// An example of setting a limit in a number counting experiment with uncertainty on background and signal
// to time the macro
TStopwatch t;
t.Start();
// --------------------------------------
// The Model building stage
// --------------------------------------
RooWorkspace* wspace = new RooWorkspace();
wspace->factory("Poisson::countingModel(obs[150,0,300], sum(s[50,0,120]*ratioSigEff[1.,0,3.],b[100]*ratioBkgEff[1.,0.,3.]))"); // counting model
// wspace->factory("Gaussian::sigConstraint(ratioSigEff,1,0.05)"); // 5% signal efficiency uncertainty
// wspace->factory("Gaussian::bkgConstraint(ratioBkgEff,1,0.1)"); // 10% background efficiency uncertainty
wspace->factory("Gaussian::sigConstraint(gSigEff[1,0,3],ratioSigEff,0.05)"); // 5% signal efficiency uncertainty
wspace->factory("Gaussian::bkgConstraint(gSigBkg[1,0,3],ratioBkgEff,0.2)"); // 10% background efficiency uncertainty
wspace->factory("PROD::modelWithConstraints(countingModel,sigConstraint,bkgConstraint)"); // product of terms
wspace->Print();
RooAbsPdf* modelWithConstraints = wspace->pdf("modelWithConstraints"); // get the model
RooRealVar* obs = wspace->var("obs"); // get the observable
RooRealVar* s = wspace->var("s"); // get the signal we care about
RooRealVar* b = wspace->var("b"); // get the background and set it to a constant. Uncertainty included in ratioBkgEff
b->setConstant();
RooRealVar* ratioSigEff = wspace->var("ratioSigEff"); // get uncertain parameter to constrain
RooRealVar* ratioBkgEff = wspace->var("ratioBkgEff"); // get uncertain parameter to constrain
RooArgSet constrainedParams(*ratioSigEff, *ratioBkgEff); // need to constrain these in the fit (should change default behavior)
RooRealVar * gSigEff = wspace->var("gSigEff"); // global observables for signal efficiency
RooRealVar * gSigBkg = wspace->var("gSigBkg"); // global obs for background efficiency
gSigEff->setConstant();
gSigBkg->setConstant();
// Create an example dataset with 160 observed events
obs->setVal(160.);
RooDataSet* data = new RooDataSet("exampleData", "exampleData", RooArgSet(*obs));
data->add(*obs);
RooArgSet all(*s, *ratioBkgEff, *ratioSigEff);
// not necessary
modelWithConstraints->fitTo(*data, RooFit::Constrain(RooArgSet(*ratioSigEff, *ratioBkgEff)));
// Now let's make some confidence intervals for s, our parameter of interest
RooArgSet paramOfInterest(*s);
ModelConfig modelConfig(wspace);
modelConfig.SetPdf(*modelWithConstraints);
modelConfig.SetParametersOfInterest(paramOfInterest);
modelConfig.SetNuisanceParameters(constrainedParams);
modelConfig.SetObservables(*obs);
modelConfig.SetGlobalObservables( RooArgSet(*gSigEff,*gSigBkg));
modelConfig.SetName("ModelConfig");
wspace->import(modelConfig);
wspace->import(*data);
wspace->SetName("w");
wspace->writeToFile("rs101_ws.root");
// First, let's use a Calculator based on the Profile Likelihood Ratio
//ProfileLikelihoodCalculator plc(*data, *modelWithConstraints, paramOfInterest);
ProfileLikelihoodCalculator plc(*data, modelConfig);
plc.SetTestSize(.05);
ConfInterval* lrinterval = plc.GetInterval(); // that was easy.
// Let's make a plot
TCanvas* dataCanvas = new TCanvas("dataCanvas");
dataCanvas->Divide(2,1);
dataCanvas->cd(1);
LikelihoodIntervalPlot plotInt((LikelihoodInterval*)lrinterval);
plotInt.SetTitle("Profile Likelihood Ratio and Posterior for S");
plotInt.Draw();
// Second, use a Calculator based on the Feldman Cousins technique
FeldmanCousins fc(*data, modelConfig);
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
fc.SetTestSize(.05);
// fc.SaveBeltToFile(true); // optional
ConfInterval* fcint = NULL;
fcint = fc.GetInterval(); // that was easy.
RooFitResult* fit = modelWithConstraints->fitTo(*data, Save(true));
// Third, use a Calculator based on Markov Chain monte carlo
// Before configuring the calculator, let's make a ProposalFunction
// that will achieve a high acceptance rate
ProposalHelper ph;
ph.SetVariables((RooArgSet&)fit->floatParsFinal());
ph.SetCovMatrix(fit->covarianceMatrix());
ph.SetUpdateProposalParameters(true);
ph.SetCacheSize(100);
ProposalFunction* pdfProp = ph.GetProposalFunction(); // that was easy
MCMCCalculator mc(*data, modelConfig);
mc.SetNumIters(20000); // steps to propose in the chain
mc.SetTestSize(.05); // 95% CL
mc.SetNumBurnInSteps(40); // ignore first N steps in chain as "burn in"
mc.SetProposalFunction(*pdfProp);
mc.SetLeftSideTailFraction(0.5); // find a "central" interval
MCMCInterval* mcInt = (MCMCInterval*)mc.GetInterval(); // that was easy
// Get Lower and Upper limits from Profile Calculator
cout << "Profile lower limit on s = " << ((LikelihoodInterval*) lrinterval)->LowerLimit(*s) << endl;
cout << "Profile upper limit on s = " << ((LikelihoodInterval*) lrinterval)->UpperLimit(*s) << endl;
// Get Lower and Upper limits from FeldmanCousins with profile construction
if (fcint != NULL) {
double fcul = ((PointSetInterval*) fcint)->UpperLimit(*s);
double fcll = ((PointSetInterval*) fcint)->LowerLimit(*s);
cout << "FC lower limit on s = " << fcll << endl;
cout << "FC upper limit on s = " << fcul << endl;
TLine* fcllLine = new TLine(fcll, 0, fcll, 1);
TLine* fculLine = new TLine(fcul, 0, fcul, 1);
fcllLine->SetLineColor(kRed);
fculLine->SetLineColor(kRed);
fcllLine->Draw("same");
fculLine->Draw("same");
dataCanvas->Update();
}
// Plot MCMC interval and print some statistics
MCMCIntervalPlot mcPlot(*mcInt);
mcPlot.SetLineColor(kMagenta);
mcPlot.SetLineWidth(2);
mcPlot.Draw("same");
double mcul = mcInt->UpperLimit(*s);
double mcll = mcInt->LowerLimit(*s);
cout << "MCMC lower limit on s = " << mcll << endl;
cout << "MCMC upper limit on s = " << mcul << endl;
cout << "MCMC Actual confidence level: "
<< mcInt->GetActualConfidenceLevel() << endl;
// 3-d plot of the parameter points
dataCanvas->cd(2);
// also plot the points in the markov chain
RooDataSet * chainData = mcInt->GetChainAsDataSet();
assert(chainData);
std::cout << "plotting the chain data - nentries = " << chainData->numEntries() << std::endl;
TTree* chain = RooStats::GetAsTTree("chainTreeData","chainTreeData",*chainData);
assert(chain);
chain->SetMarkerStyle(6);
chain->SetMarkerColor(kRed);
chain->Draw("s:ratioSigEff:ratioBkgEff","nll_MarkovChain_local_","box"); // 3-d box proportional to posterior
// the points used in the profile construction
RooDataSet * parScanData = (RooDataSet*) fc.GetPointsToScan();
assert(parScanData);
std::cout << "plotting the scanned points used in the frequentist construction - npoints = " << parScanData->numEntries() << std::endl;
// getting the tree and drawing it -crashes (very strange....);
// TTree* parameterScan = RooStats::GetAsTTree("parScanTreeData","parScanTreeData",*parScanData);
// assert(parameterScan);
// parameterScan->Draw("s:ratioSigEff:ratioBkgEff","","goff");
TGraph2D *gr = new TGraph2D(parScanData->numEntries());
for (int ievt = 0; ievt < parScanData->numEntries(); ++ievt) {
const RooArgSet * evt = parScanData->get(ievt);
double x = evt->getRealValue("ratioBkgEff");
double y = evt->getRealValue("ratioSigEff");
double z = evt->getRealValue("s");
gr->SetPoint(ievt, x,y,z);
// std::cout << ievt << " " << x << " " << y << " " << z << std::endl;
}
gr->SetMarkerStyle(24);
gr->Draw("P SAME");
delete wspace;
delete lrinterval;
delete mcInt;
delete fcint;
delete data;
// print timing info
t.Stop();
t.Print();
}