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rf608_fitresultaspdf.C
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1 /// \file
2 /// \ingroup tutorial_roofit
3 /// \notebook
4 /// Likelihood and minimization: representing the parabolic approximation of the fit as a multi-variate Gaussian on the
5 /// parameters of the fitted pdf
6 ///
7 /// \macro_image
8 /// \macro_output
9 /// \macro_code
10 ///
11 /// \date July 2008
12 /// \author Wouter Verkerke
13 
14 #include "RooRealVar.h"
15 #include "RooDataSet.h"
16 #include "RooGaussian.h"
17 #include "RooConstVar.h"
18 #include "RooAddPdf.h"
19 #include "RooChebychev.h"
20 #include "RooFitResult.h"
21 #include "TCanvas.h"
22 #include "TAxis.h"
23 #include "RooPlot.h"
24 #include "TFile.h"
25 #include "TStyle.h"
26 #include "TH2.h"
27 #include "TH3.h"
28 
29 using namespace RooFit;
30 
32 {
33  // C r e a t e m o d e l a n d d a t a s e t
34  // -----------------------------------------------
35 
36  // Observable
37  RooRealVar x("x", "x", -20, 20);
38 
39  // Model (intentional strong correlations)
40  RooRealVar mean("mean", "mean of g1 and g2", 0, -1, 1);
41  RooRealVar sigma_g1("sigma_g1", "width of g1", 2);
42  RooGaussian g1("g1", "g1", x, mean, sigma_g1);
43 
44  RooRealVar sigma_g2("sigma_g2", "width of g2", 4, 3.0, 5.0);
45  RooGaussian g2("g2", "g2", x, mean, sigma_g2);
46 
47  RooRealVar frac("frac", "frac", 0.5, 0.0, 1.0);
48  RooAddPdf model("model", "model", RooArgList(g1, g2), frac);
49 
50  // Generate 1000 events
51  RooDataSet *data = model.generate(x, 1000);
52 
53  // F i t m o d e l t o d a t a
54  // ----------------------------------
55 
56  RooFitResult *r = model.fitTo(*data, Save());
57 
58  // C r e a t e M V G a u s s i a n p d f o f f i t t e d p a r a m e t e r s
59  // ------------------------------------------------------------------------------------
60 
61  RooAbsPdf *parabPdf = r->createHessePdf(RooArgSet(frac, mean, sigma_g2));
62 
63  // S o m e e x e c e r c i s e s w i t h t h e p a r a m e t e r p d f
64  // -----------------------------------------------------------------------------
65 
66  // Generate 100K points in the parameter space, sampled from the MVGaussian pdf
67  RooDataSet *d = parabPdf->generate(RooArgSet(mean, sigma_g2, frac), 100000);
68 
69  // Sample a 3-D histogram of the pdf to be visualized as an error ellipsoid using the GLISO draw option
70  TH3 *hh_3d = (TH3 *)parabPdf->createHistogram("mean,sigma_g2,frac", 25, 25, 25);
71  hh_3d->SetFillColor(kBlue);
72 
73  // Project 3D parameter pdf down to 3 permutations of two-dimensional pdfs
74  // The integrations corresponding to these projections are performed analytically
75  // by the MV Gaussian pdf
76  RooAbsPdf *pdf_sigmag2_frac = parabPdf->createProjection(mean);
77  RooAbsPdf *pdf_mean_frac = parabPdf->createProjection(sigma_g2);
78  RooAbsPdf *pdf_mean_sigmag2 = parabPdf->createProjection(frac);
79 
80  // Make 2D plots of the 3 two-dimensional pdf projections
81  TH2 *hh_sigmag2_frac = (TH2 *)pdf_sigmag2_frac->createHistogram("sigma_g2,frac", 50, 50);
82  TH2 *hh_mean_frac = (TH2 *)pdf_mean_frac->createHistogram("mean,frac", 50, 50);
83  TH2 *hh_mean_sigmag2 = (TH2 *)pdf_mean_sigmag2->createHistogram("mean,sigma_g2", 50, 50);
84  hh_mean_frac->SetLineColor(kBlue);
85  hh_sigmag2_frac->SetLineColor(kBlue);
86  hh_mean_sigmag2->SetLineColor(kBlue);
87 
88  // Draw the 'sigar'
89  new TCanvas("rf608_fitresultaspdf_1", "rf608_fitresultaspdf_1", 600, 600);
90  hh_3d->Draw("iso");
91 
92  // Draw the 2D projections of the 3D pdf
93  TCanvas *c2 = new TCanvas("rf608_fitresultaspdf_2", "rf608_fitresultaspdf_2", 900, 600);
94  c2->Divide(3, 2);
95  c2->cd(1);
96  gPad->SetLeftMargin(0.15);
97  hh_mean_sigmag2->GetZaxis()->SetTitleOffset(1.4);
98  hh_mean_sigmag2->Draw("surf3");
99  c2->cd(2);
100  gPad->SetLeftMargin(0.15);
101  hh_sigmag2_frac->GetZaxis()->SetTitleOffset(1.4);
102  hh_sigmag2_frac->Draw("surf3");
103  c2->cd(3);
104  gPad->SetLeftMargin(0.15);
105  hh_mean_frac->GetZaxis()->SetTitleOffset(1.4);
106  hh_mean_frac->Draw("surf3");
107 
108  // Draw the distributions of parameter points sampled from the pdf
109  TH1 *tmp1 = d->createHistogram("mean,sigma_g2", 50, 50);
110  TH1 *tmp2 = d->createHistogram("sigma_g2,frac", 50, 50);
111  TH1 *tmp3 = d->createHistogram("mean,frac", 50, 50);
112 
113  c2->cd(4);
114  gPad->SetLeftMargin(0.15);
115  tmp1->GetZaxis()->SetTitleOffset(1.4);
116  tmp1->Draw("lego3");
117  c2->cd(5);
118  gPad->SetLeftMargin(0.15);
119  tmp2->GetZaxis()->SetTitleOffset(1.4);
120  tmp2->Draw("lego3");
121  c2->cd(6);
122  gPad->SetLeftMargin(0.15);
123  tmp3->GetZaxis()->SetTitleOffset(1.4);
124  tmp3->Draw("lego3");
125 }
RooChebychev.h
RooAddPdf
RooAddPdf is an efficient implementation of a sum of PDFs of the form.
Definition: RooAddPdf.h:32
r
ROOT::R::TRInterface & r
Definition: Object.C:4
RooArgList
RooArgList is a container object that can hold multiple RooAbsArg objects.
Definition: RooArgList.h:21
RooGaussian.h
TStyle.h
x
Double_t x[n]
Definition: legend1.C:17
RooGaussian
Plain Gaussian p.d.f.
Definition: RooGaussian.h:24
RooAddPdf.h
TAttLine::SetLineColor
virtual void SetLineColor(Color_t lcolor)
Set the line color.
Definition: TAttLine.h:40
TCanvas.h
TH1::GetZaxis
TAxis * GetZaxis()
Definition: TH1.h:322
RooDataSet.h
TFile.h
RooFitResult
RooFitResult is a container class to hold the input and output of a PDF fit to a dataset.
Definition: RooFitResult.h:40
TH3
The 3-D histogram classes derived from the 1-D histogram classes.
Definition: TH3.h:31
RooFit
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
Definition: RooCFunction1Binding.h:29
RooPlot.h
TH2
Service class for 2-Dim histogram classes.
Definition: TH2.h:30
RooRealVar.h
TH2.h
RooFitResult.h
TH3.h
RooConstVar.h
RooAbsReal::createHistogram
TH1 * createHistogram(const char *varNameList, Int_t xbins=0, Int_t ybins=0, Int_t zbins=0) const
Create and fill a ROOT histogram TH1, TH2 or TH3 with the values of this function for the variables w...
Definition: RooAbsReal.cxx:1304
RooAbsPdf::createProjection
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:3417
TCanvas
The Canvas class.
Definition: TCanvas.h:23
RooAbsPdf::generate
RooDataSet * generate(const RooArgSet &whatVars, Int_t nEvents, const RooCmdArg &arg1, const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none())
See RooAbsPdf::generate(const RooArgSet&,const RooCmdArg&,const RooCmdArg&,const RooCmdArg&,...
Definition: RooAbsPdf.h:58
TAttFill::SetFillColor
virtual void SetFillColor(Color_t fcolor)
Set the fill area color.
Definition: TAttFill.h:37
TAxis.h
TH1
TH1 is the base class of all histogramm classes in ROOT.
Definition: TH1.h:58
kBlue
@ kBlue
Definition: Rtypes.h:66
d
#define d(i)
Definition: RSha256.hxx:102
c2
return c2
Definition: legend2.C:14
gPad
#define gPad
Definition: TVirtualPad.h:287
RooDataSet
RooDataSet is a container class to hold unbinned data.
Definition: RooDataSet.h:33
make_cnn_model.model
model
Definition: make_cnn_model.py:6
RooAbsPdf
Definition: RooAbsPdf.h:43
TAttAxis::SetTitleOffset
virtual void SetTitleOffset(Float_t offset=1)
Set distance between the axis and the axis title.
Definition: TAttAxis.cxx:293
RooRealVar
RooRealVar represents a variable that can be changed from the outside.
Definition: RooRealVar.h:37
RooFit::Save
RooCmdArg Save(Bool_t flag=kTRUE)
Definition: RooGlobalFunc.cxx:190
RooArgSet
RooArgSet is a container object that can hold multiple RooAbsArg objects.
Definition: RooArgSet.h:29
TH1::Draw
virtual void Draw(Option_t *option="")
Draw this histogram with options.
Definition: TH1.cxx:3050
rf608_fitresultaspdf
Definition: rf608_fitresultaspdf.py:1