RooMultiVarGaussian *MultiVarGaussian(RooDataSet *dataset){ RooArgSet *args = (RooArgSet *) dataset->get()->Clone(); // argSet.remove(*chain->GetNLLVar(), kFALSE, kTRUE); RooArgSet *mu = (RooArgSet *)args->Clone(); TMatrixDSym *matrix = dataset->covarianceMatrix(); RooMultiVarGaussian *g = new RooMultiVarGaussian("multi","",RooArgList(*args), RooArgList(*mu),*matrix); return g; return NULL; }
void statTest(double mu_pe, double mu_hyp, ModelConfig *mc , RooDataSet *data ){ int nToyMC = 5; // set roofit seed RooRandom::randomGenerator()->SetSeed(); cout << endl; cout << endl; cout << "Will generate " << nToyMC << " pseudo-experiments for : " << endl; cout << " - mu[pseudo-data] = " << mu_pe << endl; cout << " - mu[stat-test] = " << mu_hyp << endl; cout << endl; // Check number of POI (for Wald approx) RooArgSet *ParamOfInterest = (RooArgSet*) mc->GetParametersOfInterest(); int nPOI = ParamOfInterest->getSize(); if(nPOI>1){ cout <<"not sure what to do with other parameters of interest, but here are their values"<<endl; mc->GetParametersOfInterest()->Print("v"); } RooRealVar* firstPOI = (RooRealVar*) ParamOfInterest->first(); RooAbsPdf *simPdf = (mc->GetPdf()); //PrintAllParametersAndValues( *mc->GetGlobalObservables() ); //PrintAllParametersAndValues( *mc->GetObservables() ); firstPOI->setVal(0.0); // FIXME //simPdf->fitTo( *data, Hesse(kTRUE), Minos(kTRUE), PrintLevel(1) ); simPdf->fitTo( *data ); // set up the sampler ToyMCSampler sampler; sampler.SetPdf(*mc->GetPdf()); sampler.SetObservables(*mc->GetObservables()); sampler.SetNToys(nToyMC); sampler.SetGlobalObservables(*mc->GetGlobalObservables()); sampler.SetParametersForTestStat(*mc->GetParametersOfInterest()); RooArgSet* poiset = dynamic_cast<RooArgSet*>(ParamOfInterest->Clone()); // only unconditional fit MinNLLTestStat *minNll = new MinNLLTestStat(*mc->GetPdf()); minNll->EnableDetailedOutput(true); sampler.AddTestStatistic(minNll); // enable PROOF if desired //ProofConfig pc(*w, 8, "workers=8", kFALSE); //sampler.SetProofConfig(&pc); // evaluate the test statistics - this is where most of our time will be spent cout << "Generating " << nToyMC << " toys...this will take a few minutes" << endl; TStopwatch *mn_t = new TStopwatch; mn_t->Start(); RooDataSet* sd = sampler.GetSamplingDistributions(*poiset); cout << "Toy generation complete :" << endl; // stop timing mn_t->Stop(); cout << " total CPU time: " << mn_t->CpuTime() << endl; cout << " total real time: " << mn_t->RealTime() << endl; // now sd contains all information about our test statistics, including detailed output // we might eg. want to explore the results either directly, or first converting to a TTree // do the conversion TFile f("mytoys.root", "RECREATE"); TTree *toyTree = RooStats::GetAsTTree("toyTree", "TTree created from test statistics", *sd); // save result to file, but in general do whatever you like f.cd(); toyTree->Write(); f.Close(); /* TFile* tmpFile = new TFile("mytoys.root","READ"); TTree* myTree = (TTree*)tmpFile->Get("toyTree"); // get boundaries for histograms TIter nextLeaf( (myTree->GetListOfLeaves())->MakeIterator() ); TObject* leafObj(0); map<TString, float> xMaxs; map<TString, float> xMins; for(int i(0); i<myTree->GetEntries(); i++) { myTree->GetEntry(i); nextLeaf = ( (myTree->GetListOfLeaves())->MakeIterator() ); while( (leafObj = nextLeaf.Next()) ) { TString name(leafObj->GetName()); float value(myTree->GetLeaf( leafObj->GetName() )->GetValue()); if(value > xMaxs[name]) { xMaxs[name] = value; } if(value < xMins[name]) { xMins[name] = value; } } // loop over leaves } // loop over tree entries // plot everything in the tree myTree->GetEntry(0); nextLeaf = ( (myTree->GetListOfLeaves())->MakeIterator() ); leafObj = 0; // make a histogram per leaf map<TString, TH1F*> hists; myTree->GetEntry(0); while( (leafObj = nextLeaf.Next()) ) { if(!leafObj) { continue; } //cout << leafObj->GetName() << endl; TString name(leafObj->GetName()); // special ones : fit related things if(name.Contains("covQual")) { hists[name] = new TH1F(name,name,5,0,5); continue; } if(name.Contains("fitStatus")) { hists[name] = new TH1F(name,name,5,0,5); continue; } int nbin(500); float histMin( xMins[name] - 0.1*fabs(xMins[name]) ); float histMax( xMaxs[name] + 0.1*fabs(xMaxs[name]) ); if(name.Contains("ATLAS_norm")) { // floating normalization factors histMin = 0; histMax = 10; } else if(name.Contains("gamma_stat")) { // statistical nus param if(name.Contains("globObs")) { // get custom range for sampling histMin = int( xMins[name] - 0.1*fabs(xMins[name]) ); histMax = int( xMaxs[name] + 0.1*fabs(xMaxs[name]) ); } // use small range for pull and error else { nbin = 100; histMin = 0.0; histMax = 2.0; } } else if(name.Contains("_err")) { // errors on nus param nbin = 100; histMin = 0.0; histMax = 2.0; } else if(name.Contains("fitCond") || name.Contains("fitUncond") || name.Contains("globObs")) { // fit pulls nbin = 500; histMin = -5; histMax = 5; } hists[name] = new TH1F(name,name,nbin,histMin,histMax); } // loop over leaves to declare histos // loop over entries and fill histograms for(int i(0); i<myTree->GetEntries(); i++) { myTree->GetEntry(i); nextLeaf = ( (myTree->GetListOfLeaves())->MakeIterator() ); while( (leafObj = nextLeaf.Next()) ) { TString name(leafObj->GetName()); if(hists.find(name) == hists.end()) { continue; } hists[name]->Fill( myTree->GetLeaf( leafObj->GetName() )->GetValue() ); } // loop over leaves } // loop over tree entries // overflow and underflow for(map<TString,TH1F*>::iterator ihist(hists.begin()); ihist!=hists.end(); ihist++) { if(ihist->second->GetBinContent(0)>0) { ihist->second->SetBinContent(1, ihist->second->GetBinContent(0) + ihist->second->GetBinContent(1) ); // fix err } int nBinx = ihist->second->GetNbinsX(); if(ihist->second->GetBinContent(nBinx)>0) { ihist->second->SetBinContent(nBinx-1, ihist->second->GetBinContent(nBinx) + ihist->second->GetBinContent(nBinx-1) ); // fix err } } // save the results TString dirName(OutputDir+"/PlotsStatisticalTest/GlobalFit"); if(drawPlots) { system(TString("mkdir -vp "+dirName)); } TCanvas* canvas = new TCanvas("pulls"); TLegend *leg = new TLegend(0.67, 0.64, 0.87, 0.86); LegendStyle(leg); for(map<TString,TH1F*>::iterator ihist(hists.begin()); ihist!=hists.end(); ihist++) { if( (ihist->first).Contains("fitCond_") ) { continue; } // skip unconditional fit - get it explicitly canvas->Clear(); leg->Clear(); TString niceName(ihist->first); niceName.ReplaceAll("fitUncond_",""); //niceName.ReplaceAll("SD_TS0_",""); // not good if have multiple test statistics // conditional fit information ihist->second->SetLineColor(kGray+2); ihist->second->SetTitle(niceName); ihist->second->SetLineStyle(kSolid); ihist->second->SetLineWidth(2); if((ihist->first).Contains("fit") && !(ihist->first).Contains("_err") && !(ihist->first).Contains("Qual") && !(ihist->first).Contains("Status")) { ihist->second->Rebin(4); } // ihist->second->GetXaxis()->SetTitle(""); // ihist->second->GetYaxis()->SetTitle(""); if(niceName.Contains("globObs")) { leg->AddEntry( ihist->second, "Sampling", "l" ); // add value of mu } else { leg->AddEntry( ihist->second, "Unconditional Fit", "l" ); // add value of mu } TString condName(ihist->first); condName.ReplaceAll("fitUncond","fitCond"); // uncomditional fit information if(hists.find(condName) != hists.end() && condName != ihist->first) { hists[condName]->SetLineColor(kGray+2); hists[condName]->SetLineStyle(kDashed); hists[condName]->SetLineWidth(2); if(!(ihist->first).Contains("_err")) { hists[condName]->Rebin(4); } leg->AddEntry( hists[condName], "Conditional Fit", "l" ); if( hists[condName]->GetMaximum() > ihist->second->GetMaximum() ) { ihist->second->SetMaximum( hists[condName]->GetMaximum() ); } } ihist->second->SetMaximum( 1.2 * ihist->second->GetMaximum() ); canvas->cd(); ihist->second->Draw(); leg->Draw(); if(hists[condName] && condName != ihist->first) { hists[condName]->Draw("same"); } if(drawPlots) { canvas->Print(dirName+"/"+niceName+".eps"); canvas->Print(dirName+"/"+niceName+".png"); } MainDirStatTest->cd(); canvas->Write(); gROOT->cd(); } */ return; }