void plot_CL_chi2_roofit(char * filename, double min, double max, double initial, double ndof_min, double ndof_max, char * plot, char * var = "chi2")
{
//gStyle->SetOptStat(0);
//gStyle->SetOptFit(1);
//gStyle->SetStatFontSize(0.02);
TFile * _file0 = TFile::Open(filename);
TTree * t = (TTree*)_file0->Get("tuple");
RooRealVar * chi2 = new RooRealVar(var, "#chi^{2}", min, max);
RooRealVar * ndof = new RooRealVar("ndof", "ndof", initial, ndof_min, ndof_max);
RooChiSquarePdf * pdf = new RooChiSquarePdf("pdf", "pdf", *chi2, *ndof);
RooDataSet * data = new RooDataSet("data", "data", RooArgSet(*chi2), RooFit::Import(*t));
pdf->fitTo(*data);
char formula[30];
sprintf(formula, "TMath::Prob(%s,ndof)", var);
RooFormulaVar * CL_ndof_eff_formula = new RooFormulaVar("CL","CL(#chi^{2})",formula, RooArgList(*chi2, *ndof));
RooRealVar * CL_ndof_eff = (RooRealVar*) data->addColumn(*CL_ndof_eff_formula);
CL_ndof_eff->setRange(0, 1);
RooUniform * uniform = new RooUniform("uniform", "uniform", *CL_ndof_eff);
uniform->fitTo(*data);
//RooFormulaVar * CL_ndof_min_formula = new RooFormulaVar("CL","CL(#chi^{2})","TMath::Prob(chi2,39)", RooArgList(*chi2));
//RooRealVar * CL_ndof_min = (RooRealVar*) data->addColumn(*CL_ndof_min_formula);
//CL_ndof_min->setRange(0, 1);
RooPlot * frame0 = chi2->frame(RooFit::Bins(25));
data->plotOn(frame0);
pdf->plotOn(frame0);
pdf->paramOn(frame0, RooFit::Format("NELU", RooFit::AutoPrecision(2)), RooFit::Layout(0.6,0.95,0.75));
data->statOn(frame0, RooFit::Format("NELU", RooFit::AutoPrecision(2)), RooFit::Layout(0.6,0.95,0.95));
RooPlot * frame1 = CL_ndof_eff->frame(RooFit::Bins(10));
data->plotOn(frame1);
uniform->plotOn(frame1);
TCanvas * c = new TCanvas("c","c",1200, 600);
c->Divide(2,1);
c->cd(1);
frame0->Draw();
c->cd(2);
frame1->Draw();
/*
char buf[30];
sprintf(buf, "TMath::Prob(chi2,%f)>>h1", f1->GetParameter(0));
cout << buf << endl;
c->Modified();
c->Update();
c->cd(2);
t->Draw("TMath::Prob(chi2,ndof-8)>>h0");
t->Draw(buf);
h1->Draw();
h1->Fit("pol0");
h0->Draw("same");
h1->GetXaxis()->SetTitle("CL(#chi^{2})");
h1->GetYaxis()->SetTitle("Number of toys / 0.1");
h1->SetMinimum(0);
h1->SetMaximum(2*t->GetEntries()/nbins);
*/
c->SaveAs(plot);
}
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 rf106_plotdecoration()
{
// S e t u p m o d e l
// ---------------------
// Create observables
RooRealVar x("x","x",-10,10) ;
// Create Gaussian
RooRealVar sigma("sigma","sigma",1,0.1,10) ;
RooRealVar mean("mean","mean",-3,-10,10) ;
RooGaussian gauss("gauss","gauss",x,mean,sigma) ;
// Generate a sample of 1000 events with sigma=3
RooDataSet* data = gauss.generate(x,1000) ;
// Fit pdf to data
gauss.fitTo(*data) ;
// P l o t p . d . f a n d d a t a
// -------------------------------------
// Overlay projection of gauss on data
RooPlot* frame = x.frame(Name("xframe"),Title("RooPlot with decorations"),Bins(40)) ;
data->plotOn(frame) ;
gauss.plotOn(frame) ;
// A d d b o x w i t h p d f p a r a m e t e r s
// -----------------------------------------------------
// Left edge of box starts at 55% of Xaxis)
gauss.paramOn(frame,Layout(0.55)) ;
// A d d b o x w i t h d a t a s t a t i s t i c s
// -------------------------------------------------------
// X size of box is from 55% to 99% of Xaxis range, top of box is at 80% of Yaxis range)
data->statOn(frame,Layout(0.55,0.99,0.8)) ;
// A d d t e x t a n d a r r o w
// -----------------------------------
// Add text to frame
TText* txt = new TText(2,100,"Signal") ;
txt->SetTextSize(0.04) ;
txt->SetTextColor(kRed) ;
frame->addObject(txt) ;
// Add arrow to frame
TArrow* arrow = new TArrow(2,100,-1,50,0.01,"|>") ;
arrow->SetLineColor(kRed) ;
arrow->SetFillColor(kRed) ;
arrow->SetLineWidth(3) ;
frame->addObject(arrow) ;
// P e r s i s t f r a m e w i t h a l l d e c o r a t i o n s i n R O O T f i l e
// ---------------------------------------------------------------------------------------------
TFile f("rf106_plotdecoration.root","RECREATE") ;
frame->Write() ;
f.Close() ;
// To read back and plot frame with all decorations in clean root session do
// root> TFile f("rf106_plotdecoration.root") ;
// root> xframe->Draw() ;
new TCanvas("rf106_plotdecoration","rf106_plotdecoration",600,600) ;
gPad->SetLeftMargin(0.15) ; frame->GetYaxis()->SetTitleOffset(1.6) ; frame->Draw() ;
}