rf610_visualerror.C

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/// \file
/// \ingroup tutorial_roofit
/// \notebook -js
///  'LIKELIHOOD AND MINIMIZATION' RooFit tutorial macro #610
///
///  Visualization of errors from a covariance matrix
///
/// \macro_image
/// \macro_output
/// \macro_code
/// \author 04/2009 - Wouter Verkerke


#include "RooRealVar.h"
#include "RooDataHist.h"
#include "RooGaussian.h"
#include "RooConstVar.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 p.d.f. 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) ;
   RooAbsData* d = model.generateBinned(x,1000) ;

   // Perform fit and save fit result
   RooFitResult* r = model.fitTo(*d,Save()) ;


   // 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 p.d.f. 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,kFALSE),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,kFALSE),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() ;
}