ROOT   Reference Guide
rf316_llratioplot.C File Reference

## Detailed Description

Multidimensional models: using the likelihood ratio technique to construct a signal enhanced one-dimensional projection of a multi-dimensional pdf

␛[1mRooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby␛[0m
Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University
[#1] INFO:Plotting -- RooAbsReal::plotOn(model) plot on x integrates over variables (y,z)
[#1] INFO:InputArguments -- The formula llratio>0.7 claims to use the variables (x,y,z,llratio) but only (llratio) seem to be in use.
inputs: llratio>0.7
[#1] INFO:InputArguments -- The formula llratio>0.7 claims to use the variables (x,y,z,llratio) but only (llratio) seem to be in use.
inputs: llratio>0.7
[#1] INFO:Plotting -- RooAbsReal::plotOn(model) plot on x averages using data variables (y,z)
[#1] INFO:Plotting -- RooAbsReal::plotOn(model) only the following components of the projection data will be used: (y,z)
[#1] INFO:Plotting -- RooDataWeightedAverage::ctor(modelDataWgtAvg) constructing data weighted average of function model_Norm[x] over 767 data points of (y,z) with a total weight of 767
[#1] INFO:Minization -- The following expressions have been identified as constant and will be precalculated and cached: (gy,gz,py,pz)
.................................................................................................................................................
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooConstVar.h"
#include "RooPolynomial.h"
#include "RooProdPdf.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
using namespace RooFit;
{
// C r e a t e 3 D p d f a n d d a t a
// -------------------------------------------
// Create observables
RooRealVar x("x", "x", -5, 5);
RooRealVar y("y", "y", -5, 5);
RooRealVar z("z", "z", -5, 5);
// Create signal pdf gauss(x)*gauss(y)*gauss(z)
RooGaussian gx("gx", "gx", x, RooConst(0), RooConst(1));
RooGaussian gy("gy", "gy", y, RooConst(0), RooConst(1));
RooGaussian gz("gz", "gz", z, RooConst(0), RooConst(1));
RooProdPdf sig("sig", "sig", RooArgSet(gx, gy, gz));
// Create background pdf poly(x)*poly(y)*poly(z)
RooPolynomial px("px", "px", x, RooArgSet(RooConst(-0.1), RooConst(0.004)));
RooPolynomial py("py", "py", y, RooArgSet(RooConst(0.1), RooConst(-0.004)));
RooPolynomial pz("pz", "pz", z);
RooProdPdf bkg("bkg", "bkg", RooArgSet(px, py, pz));
// Create composite pdf sig+bkg
RooRealVar fsig("fsig", "signal fraction", 0.1, 0., 1.);
RooAddPdf model("model", "model", RooArgList(sig, bkg), fsig);
RooDataSet *data = model.generate(RooArgSet(x, y, z), 20000);
// P r o j e c t p d f a n d d a t a o n x
// -------------------------------------------------
// Make plain projection of data and pdf on x observable
RooPlot *frame = x.frame(Title("Projection of 3D data and pdf on X"), Bins(40));
data->plotOn(frame);
model.plotOn(frame);
// D e f i n e p r o j e c t e d s i g n a l l i k e l i h o o d r a t i o
// ----------------------------------------------------------------------------------
// Calculate projection of signal and total likelihood on (y,z) observables
// i.e. integrate signal and composite model over x
RooAbsPdf *sigyz = sig.createProjection(x);
RooAbsPdf *totyz = model.createProjection(x);
// Construct the log of the signal / signal+background probability
RooFormulaVar llratio_func("llratio", "log10(@0)-log10(@1)", RooArgList(*sigyz, *totyz));
// P l o t d a t a w i t h a L L r a t i o c u t
// -------------------------------------------------------
// Calculate the llratio value for each event in the dataset
// Extract the subset of data with large signal likelihood
RooDataSet *dataSel = (RooDataSet *)data->reduce(Cut("llratio>0.7"));
// Make plot frame
RooPlot *frame2 = x.frame(Title("Same projection on X with LLratio(y,z)>0.7"), Bins(40));
// Plot select data on frame
dataSel->plotOn(frame2);
// M a k e M C p r o j e c t i o n o f p d f w i t h s a m e L L r a t i o c u t
// ---------------------------------------------------------------------------------------------
// Generate large number of events for MC integration of pdf projection
RooDataSet *mcprojData = model.generate(RooArgSet(x, y, z), 10000);
// Calculate LL ratio for each generated event and select MC events with llratio)0.7
RooDataSet *mcprojDataSel = (RooDataSet *)mcprojData->reduce(Cut("llratio>0.7"));
// Project model on x, integrating projected observables (y,z) with Monte Carlo technique
// on set of events with the same llratio cut as was applied to data
model.plotOn(frame2, ProjWData(*mcprojDataSel));
TCanvas *c = new TCanvas("rf316_llratioplot", "rf316_llratioplot", 800, 400);
c->Divide(2);
c->cd(1);
frame->GetYaxis()->SetTitleOffset(1.4);
frame->Draw();
c->cd(2);
frame2->GetYaxis()->SetTitleOffset(1.4);
frame2->Draw();
}
#define c(i)
Definition: RSha256.hxx:101
RooAbsData * reduce(const RooCmdArg &arg1, const RooCmdArg &arg2=RooCmdArg(), const RooCmdArg &arg3=RooCmdArg(), const RooCmdArg &arg4=RooCmdArg(), const RooCmdArg &arg5=RooCmdArg(), const RooCmdArg &arg6=RooCmdArg(), const RooCmdArg &arg7=RooCmdArg(), const RooCmdArg &arg8=RooCmdArg())
Create a reduced copy of this dataset.
Definition: RooAbsData.cxx:382
virtual RooPlot * plotOn(RooPlot *frame, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none()) const
Definition: RooAbsData.cxx:547
virtual RooAbsPdf * createProjection(const RooArgSet &iset)
Return a p.d.f that represent a projection of this p.d.f integrated over given observables.
Definition: RooAbsPdf.cxx:3378
RooAddPdf is an efficient implementation of a sum of PDFs of the form.
RooArgList is a container object that can hold multiple RooAbsArg objects.
Definition: RooArgList.h:21
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition: RooArgSet.h:29
RooDataSet is a container class to hold unbinned data.
Definition: RooDataSet.h:33
virtual RooAbsArg * addColumn(RooAbsArg &var, Bool_t adjustRange=kTRUE)
Add a column with the values of the given (function) argument to this dataset.
A RooFormulaVar is a generic implementation of a real-valued object, which takes a RooArgList of serv...
Definition: RooFormulaVar.h:30
Plain Gaussian p.d.f.
Definition: RooGaussian.h:24
A RooPlot is a plot frame and a container for graphics objects within that frame.
Definition: RooPlot.h:44
TAxis * GetYaxis() const
Definition: RooPlot.cxx:1263
virtual void Draw(Option_t *options=0)
Draw this plot and all of the elements it contains.
Definition: RooPlot.cxx:691
RooPolynomial implements a polynomial p.d.f of the form.
Definition: RooPolynomial.h:28
RooProdPdf is an efficient implementation of a product of PDFs of the form.
Definition: RooProdPdf.h:37
RooRealVar represents a variable that can be changed from the outside.
Definition: RooRealVar.h:39
virtual void SetTitleOffset(Float_t offset=1)
Set distance between the axis and the axis title.
Definition: TAttAxis.cxx:293
The Canvas class.
Definition: TCanvas.h:23
RooConstVar & RooConst(Double_t val)
RooCmdArg Bins(Int_t nbin)
RooCmdArg ProjWData(const RooAbsData &projData, Bool_t binData=kFALSE)
RooCmdArg Cut(const char *cutSpec)
Double_t y[n]
Definition: legend1.C:17
Double_t x[n]
Definition: legend1.C:17
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
const char * Title
Definition: TXMLSetup.cxx:68
Date
July 2008

Definition in file rf316_llratioplot.C.