rf610_visualerror

Likelihood and minimization: visualization of errors from a covariance matrix

Author: Wouter Verkerke
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Sunday, November 27, 2022 at 11:08 AM.

In [1]:
%%cpp -d
#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;

Setup example fit

Create sum of two Gaussians pdf with factory

In [2]:
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

In [3]:
x.setBins(25);
RooAbsData *d = model.generateBinned(x, 1000);

Perform fit and save fit result

In [4]:
RooFitResult *r = model.fitTo(*d, Save());
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: activating const optimization
[#1] INFO:Minimization --  The following expressions will be evaluated in cache-and-track mode: (sig,bkg)
 **********
 **    1 **SET PRINT           1
 **********
 **********
 **    2 **SET NOGRAD
 **********
 PARAMETER DEFINITIONS:
    NO.   NAME         VALUE      STEP SIZE      LIMITS
     1 fsig         3.30000e-01  1.00000e-01    0.00000e+00  1.00000e+00
     2 m            0.00000e+00  2.00000e+00   -1.00000e+01  1.00000e+01
     3 m2          -1.00000e+00  2.00000e+00   -1.00000e+01  1.00000e+01
     4 s            2.00000e+00  5.00000e-01    1.00000e+00  5.00000e+01
     5 s2           6.00000e+00  2.50000e+00    1.00000e+00  5.00000e+01
 **********
 **    3 **SET ERR         0.5
 **********
 **********
 **    4 **SET PRINT           1
 **********
 **********
 **    5 **SET STR           1
 **********
 NOW USING STRATEGY  1: TRY TO BALANCE SPEED AGAINST RELIABILITY
 **********
 **    6 **MIGRAD        2500           1
 **********
 FIRST CALL TO USER FUNCTION AT NEW START POINT, WITH IFLAG=4.
 START MIGRAD MINIMIZATION.  STRATEGY  1.  CONVERGENCE WHEN EDM .LT. 1.00e-03
 FCN=2770.05 FROM MIGRAD    STATUS=INITIATE       20 CALLS          21 TOTAL
                     EDM= unknown      STRATEGY= 1      NO ERROR MATRIX       
  EXT PARAMETER               CURRENT GUESS       STEP         FIRST   
  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE 
   1  fsig         3.30000e-01   1.00000e-01   2.14988e-01  -9.80006e+00
   2  m            0.00000e+00   2.00000e+00   2.01358e-01  -3.95919e+01
   3  m2          -1.00000e+00   2.00000e+00   2.02430e-01   3.98515e+01
   4  s            2.00000e+00   5.00000e-01   7.46809e-02   2.95415e+01
   5  s2           6.00000e+00   2.50000e+00   1.74125e-01   4.56667e+01
                               ERR DEF= 0.5
 MIGRAD MINIMIZATION HAS CONVERGED.
 MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.
 COVARIANCE MATRIX CALCULATED SUCCESSFULLY
 FCN=2767.65 FROM MIGRAD    STATUS=CONVERGED     125 CALLS         126 TOTAL
                     EDM=1.56678e-05    STRATEGY= 1      ERROR MATRIX ACCURATE 
  EXT PARAMETER                                   STEP         FIRST   
  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE 
   1  fsig         2.98883e-01   6.74303e-02   2.39863e-03   7.00359e-03
   2  m            3.08816e-01   2.09098e-01   6.83546e-04  -2.93512e-02
   3  m2          -1.31219e+00   3.64277e-01   9.46152e-04   8.97852e-02
   4  s            1.78229e+00   2.51997e-01   9.25951e-04  -2.49363e-02
   5  s2           5.51197e+00   4.85498e-01   6.94615e-04   1.27501e-01
                               ERR DEF= 0.5
 EXTERNAL ERROR MATRIX.    NDIM=  25    NPAR=  5    ERR DEF=0.5
  4.580e-03 -3.800e-03 -1.557e-02  1.331e-02  2.642e-02 
 -3.800e-03  4.373e-02 -3.141e-03 -1.195e-02 -2.959e-02 
 -1.557e-02 -3.141e-03  1.328e-01 -4.509e-02 -1.100e-01 
  1.331e-02 -1.195e-02 -4.509e-02  6.354e-02  7.253e-02 
  2.642e-02 -2.959e-02 -1.100e-01  7.253e-02  2.358e-01 
 PARAMETER  CORRELATION COEFFICIENTS  
       NO.  GLOBAL      1      2      3      4      5
        1  0.89430   1.000 -0.269 -0.632  0.780  0.804
        2  0.43384  -0.269  1.000 -0.041 -0.227 -0.291
        3  0.70478  -0.632 -0.041  1.000 -0.491 -0.621
        4  0.78303   0.780 -0.227 -0.491  1.000  0.593
        5  0.82883   0.804 -0.291 -0.621  0.593  1.000
 **********
 **    7 **SET ERR         0.5
 **********
 **********
 **    8 **SET PRINT           1
 **********
 **********
 **    9 **HESSE        2500
 **********
 COVARIANCE MATRIX CALCULATED SUCCESSFULLY
 FCN=2767.65 FROM HESSE     STATUS=OK             31 CALLS         157 TOTAL
                     EDM=1.56685e-05    STRATEGY= 1      ERROR MATRIX ACCURATE 
  EXT PARAMETER                                INTERNAL      INTERNAL  
  NO.   NAME      VALUE            ERROR       STEP SIZE       VALUE   
   1  fsig         2.98883e-01   6.78952e-02   4.79727e-04  -4.13956e-01
   2  m            3.08816e-01   2.09026e-01   1.36709e-04   3.08865e-02
   3  m2          -1.31219e+00   3.67042e-01   1.89230e-04  -1.31599e-01
   4  s            1.78229e+00   2.53102e-01   1.85190e-04  -1.31741e+00
   5  s2           5.51197e+00   4.89300e-01   1.38923e-04  -9.54177e-01
                               ERR DEF= 0.5
 EXTERNAL ERROR MATRIX.    NDIM=  25    NPAR=  5    ERR DEF=0.5
  4.644e-03 -3.811e-03 -1.596e-02  1.350e-02  2.692e-02 
 -3.811e-03  4.370e-02 -2.970e-03 -1.206e-02 -2.967e-02 
 -1.596e-02 -2.970e-03  1.348e-01 -4.619e-02 -1.129e-01 
  1.350e-02 -1.206e-02 -4.619e-02  6.410e-02  7.402e-02 
  2.692e-02 -2.967e-02 -1.129e-01  7.402e-02  2.395e-01 
 PARAMETER  CORRELATION COEFFICIENTS  
       NO.  GLOBAL      1      2      3      4      5
        1  0.89584   1.000 -0.268 -0.638  0.782  0.807
        2  0.43319  -0.268  1.000 -0.039 -0.228 -0.290
        3  0.71012  -0.638 -0.039  1.000 -0.497 -0.628
        4  0.78518   0.782 -0.228 -0.497  1.000  0.597
        5  0.83175   0.807 -0.290 -0.628  0.597  1.000
[#1] INFO:Minimization -- RooAbsMinimizerFcn::setOptimizeConst: deactivating const optimization

Visualize fit error

Make plot frame

In [5]:
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

In [6]:
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

In [7]:
model.plotOn(frame, VisualizeError(*r, 1, false), DrawOption("L"), LineWidth(2), LineColor(kRed));
[#1] INFO:Plotting -- RooAbsReal::plotOn(model) INFO: visualizing 1-sigma uncertainties in parameters (m,s,fsig,m2,s2) from fit result fitresult_model_genData using 315 samplings.

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..)

In [8]:
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));
[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) directly selected PDF components: (bkg)
[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) indirectly selected PDF components: ()
[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) directly selected PDF components: (bkg)
[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) indirectly selected PDF components: ()
[#1] INFO:Plotting -- RooAbsReal::plotOn(model) INFO: visualizing 1-sigma uncertainties in parameters (m,s,fsig,m2,s2) from fit result fitresult_model_genData using 315 samplings.

Overlay central value

In [9]:
model.plotOn(frame);
model.plotOn(frame, Components("bkg"), LineStyle(kDashed));
d->plotOn(frame);
frame->SetMinimum(0);
[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) directly selected PDF components: (bkg)
[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) indirectly selected PDF components: ()

Visualize partial fit error

Make plot frame

In [10]:
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

In [11]:
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);
[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) directly selected PDF components: (bkg)
[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) indirectly selected PDF components: ()
[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) directly selected PDF components: (bkg)
[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) indirectly selected PDF components: ()

Make plot frame

In [12]:
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

In [13]:
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);
[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) directly selected PDF components: (bkg)
[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) indirectly selected PDF components: ()
[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) directly selected PDF components: (bkg)
[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) indirectly selected PDF components: ()

Make plot frame

In [14]:
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

In [15]:
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();
[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) directly selected PDF components: (bkg)
[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) indirectly selected PDF components: ()
[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) directly selected PDF components: (bkg)
[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) indirectly selected PDF components: ()

Draw all canvases

In [16]:
%jsroot on
gROOT->GetListOfCanvases()->Draw()