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Reference Guide
SamplingDistribution.h
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1 // @(#)root/roostats:$Id$
2 
3 /*************************************************************************
4  * Project: RooStats *
5  * Package: RooFit/RooStats *
6  * Authors: *
7  * Kyle Cranmer, Lorenzo Moneta, Gregory Schott, Wouter Verkerke *
8  *************************************************************************
9  * Copyright (C) 1995-2008, Rene Brun and Fons Rademakers. *
10  * All rights reserved. *
11  * *
12  * For the licensing terms see $ROOTSYS/LICENSE. *
13  * For the list of contributors see $ROOTSYS/README/CREDITS. *
14  *************************************************************************/
15 
16 #ifndef ROOSTATS_SamplingDistribution
17 #define ROOSTATS_SamplingDistribution
18 
19 #include "TNamed.h"
20 
21 #include "Rtypes.h"
22 #include "RooDataSet.h"
23 
24 #include <vector>
25 
26 namespace RooStats {
27 
28  class SamplingDistribution : public TNamed {
29 
30  public:
31 
32  /// Constructor for SamplingDistribution
33  SamplingDistribution(const char *name,const char *title, std::vector<Double_t>& samplingDist, const char * varName = 0);
34  SamplingDistribution(const char *name,const char *title,
35  std::vector<Double_t>& samplingDist, std::vector<Double_t>& sampleWeights, const char * varName = 0);
36 
37 
38  SamplingDistribution(const char *name,const char *title, const char * varName = 0);
39 
40  SamplingDistribution(const char *name,const char *title, RooDataSet& dataSet, const char * columnName = 0, const char * varName = 0);
41 
42  /// Default constructor for SamplingDistribution
44 
45  /// Destructor of SamplingDistribution
46  virtual ~SamplingDistribution();
47 
48  /// get the inverse of the Cumulative distribution function
50 
51  /// get the inverse of the Cumulative distribution function
53 
54  /// get the inverse of the Cumulative distribution function
55  /// together with the inverse based on sampling variation
56  Double_t InverseCDF(Double_t pvalue, Double_t sigmaVariaton, Double_t& inverseVariation);
57 
58  /// merge two sampling distributions
59  void Add(const SamplingDistribution* other);
60 
61  /// size of samples
62  Int_t GetSize() const{return fSamplingDist.size();}
63 
64  /// Get test statistics values
65  const std::vector<Double_t> & GetSamplingDistribution() const {return fSamplingDist;}
66  /// Get the sampling weights
67  const std::vector<Double_t> & GetSampleWeights() const {return fSampleWeights;}
68 
69  const TString GetVarName() const {return fVarName;}
70 
71  /// numerical integral in these limits
72  Double_t Integral(Double_t low, Double_t high, Bool_t normalize = kTRUE, Bool_t lowClosed = kTRUE, Bool_t highClosed = kFALSE) const;
73 
74  /// numerical integral in these limits including error estimation
75  Double_t IntegralAndError(Double_t & error, Double_t low, Double_t high, Bool_t normalize = kTRUE,
76  Bool_t lowClosed = kTRUE, Bool_t highClosed = kFALSE) const;
77 
78  /// calculate CDF as a special case of Integral(...) with lower limit equal to -inf
79  Double_t CDF(Double_t x) const;
80 
81  private:
82 
83  mutable std::vector<Double_t> fSamplingDist; /// vector of points for the sampling distribution
84  mutable std::vector<Double_t> fSampleWeights; /// vector of weights for the samples
85  // store a RooRealVar that this distribution corresponds to?
86 
88 
89  mutable std::vector<Double_t> fSumW; //! Cached vector with sum of the weight used to compute integral
90  mutable std::vector<Double_t> fSumW2; //! Cached vector with sum of the weight used to compute integral error
91 
92  protected:
93 
94  /// internal function to sort values
95  void SortValues() const;
96 
97  ClassDef(SamplingDistribution,2) /// Class containing the results of the HybridCalculator
98  };
99 }
100 
101 #endif
const std::vector< Double_t > & GetSamplingDistribution() const
Get test statistics values.
std::vector< Double_t > fSumW2
Cached vector with sum of the weight used to compute integral.
TString fVarName
vector of weights for the samples
Double_t InverseCDF(Double_t pvalue)
get the inverse of the Cumulative distribution function
Basic string class.
Definition: TString.h:131
int Int_t
Definition: RtypesCore.h:41
bool Bool_t
Definition: RtypesCore.h:59
const std::vector< Double_t > & GetSampleWeights() const
Get the sampling weights.
std::vector< Double_t > fSampleWeights
vector of points for the sampling distribution
Double_t x[n]
Definition: legend1.C:17
#define ClassDef(name, id)
Definition: Rtypes.h:326
The TNamed class is the base class for all named ROOT classes.
Definition: TNamed.h:29
Double_t InverseCDFInterpolate(Double_t pvalue)
get the inverse of the Cumulative distribution function
Double_t Integral(Double_t low, Double_t high, Bool_t normalize=kTRUE, Bool_t lowClosed=kTRUE, Bool_t highClosed=kFALSE) const
numerical integral in these limits
void SortValues() const
Cached vector with sum of the weight used to compute integral error.
std::vector< Double_t > fSamplingDist
Double_t CDF(Double_t x) const
calculate CDF as a special case of Integral(...) with lower limit equal to -inf
virtual ~SamplingDistribution()
Destructor of SamplingDistribution.
Int_t GetSize() const
size of samples
void Add(const SamplingDistribution *other)
merge two sampling distributions
RooDataSet is a container class to hold unbinned data.
Definition: RooDataSet.h:31
This class simply holds a sampling distribution of some test statistic.
const Bool_t kFALSE
Definition: RtypesCore.h:88
Namespace for the RooStats classes.
Definition: Asimov.h:20
double Double_t
Definition: RtypesCore.h:55
Double_t IntegralAndError(Double_t &error, Double_t low, Double_t high, Bool_t normalize=kTRUE, Bool_t lowClosed=kTRUE, Bool_t highClosed=kFALSE) const
numerical integral in these limits including error estimation
SamplingDistribution()
Default constructor for SamplingDistribution.
const Bool_t kTRUE
Definition: RtypesCore.h:87
char name[80]
Definition: TGX11.cxx:109