doc/master/rf610__visualerror_8C.html

#include "RooRealVar.h"

#include "RooDataHist.h"

#include "RooGaussian.h"

#include "RooAddPdf.h"

#include "RooPlot.h"

#include "TCanvas.h"

#include "TAxis.h"

#include "TAxis.h"

using namespace RooFit;


void rf610_visualerror()

{

   // S e t u p   e x a m p l e   f i t

   // ---------------------------------------


   // Create sum of two Gaussians pdf with factory

   RooRealVar x("x", "x", -10, 10);


   RooRealVar m("m", "m", 0, -10, 10);

   RooRealVar s("s", "s", 2, 1, 50);

   RooGaussian sig("sig", "sig", x, m, s);


   RooRealVar m2("m2", "m2", -1, -10, 10);

   RooRealVar s2("s2", "s2", 6, 1, 50);

   RooGaussian bkg("bkg", "bkg", x, m2, s2);


   RooRealVar fsig("fsig", "fsig", 0.33, 0, 1);

   RooAddPdf model("model", "model", RooArgList(sig, bkg), fsig);


   // Create binned dataset

   x.setBins(25);

   std::unique_ptr<RooAbsData> d{model.generateBinned(x, 1000)};


   // Perform fit and save fit result

   std::unique_ptr<RooFitResult> r{model.fitTo(*d, Save(), PrintLevel(-1))};


   // V i s u a l i z e   f i t   e r r o r

   // -------------------------------------


   // Make plot frame

   RooPlot *frame = x.frame(Bins(40), Title("P.d.f with visualized 1-sigma error band"));

   d->plotOn(frame);


   // Visualize 1-sigma error encoded in fit result 'r' as orange band using linear error propagation

   // This results in an error band that is by construction symmetric

   //

   // The linear error is calculated as

   // error(x) = Z* F_a(x) * Corr(a,a') F_a'(x)

   //

   // where     F_a(x) = [ f(x,a+da) - f(x,a-da) ] / 2,

   //

   //         with f(x) = the plotted curve

   //              'da' = error taken from the fit result

   //        Corr(a,a') = the correlation matrix from the fit result

   //                Z = requested significance 'Z sigma band'

   //

   // The linear method is fast (required 2*N evaluations of the curve, where N is the number of parameters),

   // but may not be accurate in the presence of strong correlations (~>0.9) and at Z>2 due to linear and

   // Gaussian approximations made

   //

   model.plotOn(frame, VisualizeError(*r, 1), FillColor(kOrange));


   // Calculate error using sampling method and visualize as dashed red line.

   //

   // In this method a number of curves is calculated with variations of the parameter values, as sampled

   // from a multi-variate Gaussian pdf that is constructed from the fit results covariance matrix.

   // The error(x) is determined by calculating a central interval that capture N% of the variations

   // for each value of x, where N% is controlled by Z (i.e. Z=1 gives N=68%). The number of sampling curves

   // is chosen to be such that at least 100 curves are expected to be outside the N% interval, and is minimally

   // 100 (e.g. Z=1->Ncurve=356, Z=2->Ncurve=2156)) Intervals from the sampling method can be asymmetric,

   // and may perform better in the presence of strong correlations, but may take (much) longer to calculate

   model.plotOn(frame, VisualizeError(*r, 1, false), DrawOption("L"), LineWidth(2), LineColor(kRed));


   // Perform the same type of error visualization on the background component only.

   // The VisualizeError() option can generally applied to _any_ kind of plot (components, asymmetries, efficiencies

   // etc..)

   model.plotOn(frame, VisualizeError(*r, 1), FillColor(kOrange), Components("bkg"));

   model.plotOn(frame, VisualizeError(*r, 1, false), DrawOption("L"), LineWidth(2), LineColor(kRed), Components("bkg"),

                LineStyle(kDashed));


   // Overlay central value

   model.plotOn(frame);

   model.plotOn(frame, Components("bkg"), LineStyle(kDashed));

   d->plotOn(frame);

   frame->SetMinimum(0);


   // V i s u a l i z e   p a r t i a l   f i t   e r r o r

   // ------------------------------------------------------


   // Make plot frame

   RooPlot *frame2 = x.frame(Bins(40), Title("Visualization of 2-sigma partial error from (m,m2)"));


   // Visualize partial error. For partial error visualization the covariance matrix is first reduced as follows

   //        ___                   -1

   // Vred = V22  = V11 - V12 * V22   * V21

   //

   // Where V11,V12,V21,V22 represent a block decomposition of the covariance matrix into observables that

   // are propagated (labeled by index '1') and that are not propagated (labeled by index '2'), and V22bar

   // is the Shur complement of V22, calculated as shown above

   //

   // (Note that Vred is _not_ a simple sub-matrix of V)


   // Propagate partial error due to shape parameters (m,m2) using linear and sampling method

   model.plotOn(frame2, VisualizeError(*r, RooArgSet(m, m2), 2), FillColor(kCyan));

   model.plotOn(frame2, Components("bkg"), VisualizeError(*r, RooArgSet(m, m2), 2), FillColor(kCyan));


   model.plotOn(frame2);

   model.plotOn(frame2, Components("bkg"), LineStyle(kDashed));

   frame2->SetMinimum(0);


   // Make plot frame

   RooPlot *frame3 = x.frame(Bins(40), Title("Visualization of 2-sigma partial error from (s,s2)"));


   // Propagate partial error due to yield parameter using linear and sampling method

   model.plotOn(frame3, VisualizeError(*r, RooArgSet(s, s2), 2), FillColor(kGreen));

   model.plotOn(frame3, Components("bkg"), VisualizeError(*r, RooArgSet(s, s2), 2), FillColor(kGreen));


   model.plotOn(frame3);

   model.plotOn(frame3, Components("bkg"), LineStyle(kDashed));

   frame3->SetMinimum(0);


   // Make plot frame

   RooPlot *frame4 = x.frame(Bins(40), Title("Visualization of 2-sigma partial error from fsig"));


   // Propagate partial error due to yield parameter using linear and sampling method

   model.plotOn(frame4, VisualizeError(*r, RooArgSet(fsig), 2), FillColor(kMagenta));

   model.plotOn(frame4, Components("bkg"), VisualizeError(*r, RooArgSet(fsig), 2), FillColor(kMagenta));


   model.plotOn(frame4);

   model.plotOn(frame4, Components("bkg"), LineStyle(kDashed));

   frame4->SetMinimum(0);


   TCanvas *c = new TCanvas("rf610_visualerror", "rf610_visualerror", 800, 800);

   c->Divide(2, 2);

   c->cd(1);

   gPad->SetLeftMargin(0.15);

   frame->GetYaxis()->SetTitleOffset(1.4);

   frame->Draw();

   c->cd(2);

   gPad->SetLeftMargin(0.15);

   frame2->GetYaxis()->SetTitleOffset(1.6);

   frame2->Draw();

   c->cd(3);

   gPad->SetLeftMargin(0.15);

   frame3->GetYaxis()->SetTitleOffset(1.6);

   frame3->Draw();

   c->cd(4);

   gPad->SetLeftMargin(0.15);

   frame4->GetYaxis()->SetTitleOffset(1.6);

   frame4->Draw();

}

d
#define d(i)
Definition RSha256.hxx:102

c
#define c(i)
Definition RSha256.hxx:101

RooAddPdf.h

RooDataHist.h

RooGaussian.h

RooPlot.h

RooRealVar.h

kRed
@ kRed
Definition Rtypes.h:67

kOrange
@ kOrange
Definition Rtypes.h:68

kGreen
@ kGreen
Definition Rtypes.h:67

kMagenta
@ kMagenta
Definition Rtypes.h:67

kCyan
@ kCyan
Definition Rtypes.h:67

kDashed
@ kDashed
Definition TAttLine.h:52

TAxis.h

TCanvas.h

TRangeDynCast
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Definition TCollection.h:358

r
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t r
Definition TGWin32VirtualXProxy.cxx:168

gPad
#define gPad
Definition TVirtualPad.h:314

ROOT::Detail::TRangeCast
Definition TCollection.h:311

RooAddPdf
Efficient implementation of a sum of PDFs of the form.
Definition RooAddPdf.h:33

RooArgList
RooArgList is a container object that can hold multiple RooAbsArg objects.
Definition RooArgList.h:22

RooArgSet
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition RooArgSet.h:24

RooGaussian
Plain Gaussian p.d.f.
Definition RooGaussian.h:24

RooPlot
Plot frame and a container for graphics objects within that frame.
Definition RooPlot.h:43

RooPlot::frame
static RooPlot * frame(const RooAbsRealLValue &var, double xmin, double xmax, Int_t nBins)
Create a new frame for a given variable in x.
Definition RooPlot.cxx:184

RooPlot::SetMinimum
virtual void SetMinimum(double minimum=-1111)
Set minimum value of Y axis.
Definition RooPlot.cxx:1006

RooPlot::GetYaxis
TAxis * GetYaxis() const
Definition RooPlot.cxx:1223

RooPlot::Draw
void Draw(Option_t *options=nullptr) override
Draw this plot and all of the elements it contains.
Definition RooPlot.cxx:596

RooRealVar
Variable that can be changed from the outside.
Definition RooRealVar.h:37

TAttAxis::SetTitleOffset
virtual void SetTitleOffset(Float_t offset=1)
Set distance between the axis and the axis title.
Definition TAttAxis.cxx:279

TCanvas
The Canvas class.
Definition TCanvas.h:23

RooFit::Bins
RooCmdArg Bins(Int_t nbin)
Definition RooGlobalFunc.cxx:536

RooFit::Components
RooCmdArg Components(Args_t &&... argsOrArgSet)
Definition RooGlobalFunc.h:141

RooFit::FillColor
RooCmdArg FillColor(TColorNumber color)
Definition RooGlobalFunc.cxx:245

RooFit::DrawOption
RooCmdArg DrawOption(const char *opt)
Definition RooGlobalFunc.cxx:144

RooFit::LineColor
RooCmdArg LineColor(TColorNumber color)
Definition RooGlobalFunc.cxx:229

RooFit::LineWidth
RooCmdArg LineWidth(Width_t width)
Definition RooGlobalFunc.cxx:241

RooFit::VisualizeError
RooCmdArg VisualizeError(const RooDataSet &paramData, double Z=1)
Definition RooGlobalFunc.cxx:289

RooFit::LineStyle
RooCmdArg LineStyle(Style_t style)
Definition RooGlobalFunc.cxx:233

x
Double_t x[n]
Definition legend1.C:17

RooFit
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
Definition CodegenImpl.h:67

rf610_visualerror
Definition rf610_visualerror.py:1

xmlio::Title
const char * Title
Definition TXMLSetup.cxx:67

m
TMarker m
Definition textangle.C:8