 ROOT   Reference Guide rf607_fitresult.C
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1/// \file
2/// \ingroup tutorial_roofit
3/// \notebook
4/// Likelihood and minimization: demonstration of options of the RooFitResult class
5///
6/// \macro_image
7/// \macro_output
8/// \macro_code
9///
10/// \date July 2008
11/// \author Wouter Verkerke
12
13#include "RooRealVar.h"
14#include "RooDataSet.h"
15#include "RooGaussian.h"
16#include "RooConstVar.h"
18#include "RooChebychev.h"
19#include "RooFitResult.h"
20#include "TCanvas.h"
21#include "TAxis.h"
22#include "RooPlot.h"
23#include "TFile.h"
24#include "TStyle.h"
25#include "TH2.h"
26#include "TMatrixDSym.h"
27
28using namespace RooFit;
29
30void rf607_fitresult()
31{
32 // C r e a t e p d f , d a t a
33 // --------------------------------
34
35 // Declare observable x
36 RooRealVar x("x", "x", 0, 10);
37
38 // Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and their parameters
39 RooRealVar mean("mean", "mean of gaussians", 5, -10, 10);
40 RooRealVar sigma1("sigma1", "width of gaussians", 0.5, 0.1, 10);
41 RooRealVar sigma2("sigma2", "width of gaussians", 1, 0.1, 10);
42
43 RooGaussian sig1("sig1", "Signal component 1", x, mean, sigma1);
44 RooGaussian sig2("sig2", "Signal component 2", x, mean, sigma2);
45
46 // Build Chebychev polynomial pdf
47 RooRealVar a0("a0", "a0", 0.5, 0., 1.);
48 RooRealVar a1("a1", "a1", -0.2);
49 RooChebychev bkg("bkg", "Background", x, RooArgSet(a0, a1));
50
51 // Sum the signal components into a composite signal pdf
52 RooRealVar sig1frac("sig1frac", "fraction of component 1 in signal", 0.8, 0., 1.);
53 RooAddPdf sig("sig", "Signal", RooArgList(sig1, sig2), sig1frac);
54
55 // Sum the composite signal and background
56 RooRealVar bkgfrac("bkgfrac", "fraction of background", 0.5, 0., 1.);
57 RooAddPdf model("model", "g1+g2+a", RooArgList(bkg, sig), bkgfrac);
58
59 // Generate 1000 events
60 RooDataSet *data = model.generate(x, 1000);
61
62 // F i t p d f t o d a t a , s a v e f i t r e s u l t
63 // -------------------------------------------------------------
64
65 // Perform fit and save result
66 RooFitResult *r = model.fitTo(*data, Save());
67
68 // P r i n t f i t r e s u l t s
69 // ---------------------------------
70
71 // Summary printing: Basic info plus final values of floating fit parameters
72 r->Print();
73
74 // Verbose printing: Basic info, values of constant parameters, initial and
75 // final values of floating parameters, global correlations
76 r->Print("v");
77
78 // V i s u a l i z e c o r r e l a t i o n m a t r i x
79 // -------------------------------------------------------
80
81 // Construct 2D color plot of correlation matrix
83 TH2 *hcorr = r->correlationHist();
84
85 // Visualize ellipse corresponding to single correlation matrix element
86 RooPlot *frame = new RooPlot(sigma1, sig1frac, 0.45, 0.60, 0.65, 0.90);
87 frame->SetTitle("Covariance between sigma1 and sig1frac");
88 r->plotOn(frame, sigma1, sig1frac, "ME12ABHV");
89
90 // A c c e s s f i t r e s u l t i n f o r m a t i o n
91 // ---------------------------------------------------------
92
93 // Access basic information
94 cout << "EDM = " << r->edm() << endl;
95 cout << "-log(L) at minimum = " << r->minNll() << endl;
96
97 // Access list of final fit parameter values
98 cout << "final value of floating parameters" << endl;
99 r->floatParsFinal().Print("s");
100
101 // Access correlation matrix elements
102 cout << "correlation between sig1frac and a0 is " << r->correlation(sig1frac, a0) << endl;
103 cout << "correlation between bkgfrac and mean is " << r->correlation("bkgfrac", "mean") << endl;
104
105 // Extract covariance and correlation matrix as TMatrixDSym
106 const TMatrixDSym &cor = r->correlationMatrix();
107 const TMatrixDSym &cov = r->covarianceMatrix();
108
109 // Print correlation, covariance matrix
110 cout << "correlation matrix" << endl;
111 cor.Print();
112 cout << "covariance matrix" << endl;
113 cov.Print();
114
115 // P e r s i s t f i t r e s u l t i n r o o t f i l e
116 // -------------------------------------------------------------
117
118 // Open new ROOT file save save result
119 TFile f("rf607_fitresult.root", "RECREATE");
120 r->Write("rf607");
121 f.Close();
122
123 // In a clean ROOT session retrieve the persisted fit result as follows:
124 // RooFitResult* r = gDirectory->Get("rf607") ;
125
126 TCanvas *c = new TCanvas("rf607_fitresult", "rf607_fitresult", 800, 400);
127 c->Divide(2);
128 c->cd(1);
130 hcorr->GetYaxis()->SetTitleOffset(1.4);
131 hcorr->Draw("colz");
132 c->cd(2);
134 frame->GetYaxis()->SetTitleOffset(1.6);
135 frame->Draw();
136}
ROOT::R::TRInterface & r
Definition: Object.C:4
#define f(i)
Definition: RSha256.hxx:104
#define c(i)
Definition: RSha256.hxx:101
R__EXTERN TStyle * gStyle
Definition: TStyle.h:412
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
Chebychev polynomial p.d.f.
Definition: RooChebychev.h:25
RooDataSet is a container class to hold unbinned data.
Definition: RooDataSet.h:33
RooFitResult is a container class to hold the input and output of a PDF fit to a dataset.
Definition: RooFitResult.h:40
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
void SetTitle(const char *name)
Set the title of the RooPlot to 'title'.
Definition: RooPlot.cxx:1242
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
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
A ROOT file is a suite of consecutive data records (TKey instances) with a well defined format.
Definition: TFile.h:54
TAxis * GetYaxis()
Definition: TH1.h:321
virtual void Draw(Option_t *option="")
Draw this histogram with options.
Definition: TH1.cxx:3073
Service class for 2-Dim histogram classes.
Definition: TH2.h:30
void Print(Option_t *name="") const
Print the matrix as a table of elements.
virtual Int_t Write(const char *name=0, Int_t option=0, Int_t bufsize=0)
Write this object to the current directory.
Definition: TObject.cxx:798
virtual void Print(Option_t *option="") const
This method must be overridden when a class wants to print itself.
Definition: TObject.cxx:552
void SetOptStat(Int_t stat=1)
The type of information printed in the histogram statistics box can be selected via the parameter mod...
Definition: TStyle.cxx:1589
RooCmdArg Save(Bool_t flag=kTRUE)
Double_t x[n]
Definition: legend1.C:17
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...