library: libPhysics #include "TRolke.h"

# TRolke

class description - source file - inheritance tree (.pdf)

## class TRolke : public TObject

| Inheritance Chart:

`TObject`

<-

`TRolke`

        protected:

                 Double_t EvalLikeMod1(Double_t mu, Int_t x, Int_t y, Int_t z, Double_t e, Double_t tau, Double_t b, Int_t m, Int_t what)
                 Double_t EvalLikeMod2(Double_t mu, Int_t x, Int_t y, Double_t em, Double_t e, Double_t sde, Double_t tau, Double_t b, Int_t what)
                 Double_t EvalLikeMod3(Double_t mu, Int_t x, Double_t bm, Double_t em, Double_t e, Double_t sde, Double_t sdb, Double_t b, Int_t what)
                 Double_t EvalLikeMod4(Double_t mu, Int_t x, Int_t y, Double_t tau, Double_t b, Int_t what)
                 Double_t EvalLikeMod5(Double_t mu, Int_t x, Double_t bm, Double_t sdb, Double_t b, Int_t what)
                 Double_t EvalLikeMod6(Double_t mu, Int_t x, Int_t z, Double_t e, Double_t b, Int_t m, Int_t what)
                 Double_t EvalLikeMod7(Double_t mu, Int_t x, Double_t em, Double_t e, Double_t sde, Double_t b, Int_t what)
          static Double_t EvalMonomial(Double_t x, const Int_t* coef, Int_t N)
          static Double_t EvalPolynomial(Double_t x, const Int_t* coef, Int_t N)
                 Double_t Interval(Int_t x, Int_t y, Int_t z, Double_t bm, Double_t em, Double_t e, Int_t mid, Double_t sde, Double_t sdb, Double_t tau, Double_t b, Int_t m)
                 Double_t LikeGradMod1(Double_t e, Double_t mu, Int_t x, Int_t y, Int_t z, Double_t tau, Int_t m)
                 Double_t Likelihood(Double_t mu, Int_t x, Int_t y, Int_t z, Double_t bm, Double_t em, Double_t e, Int_t mid, Double_t sde, Double_t sdb, Double_t tau, Double_t b, Int_t m, Int_t what)
                 Double_t LikeMod1(Double_t mu, Double_t b, Double_t e, Int_t x, Int_t y, Int_t z, Double_t tau, Int_t m)
                 Double_t LikeMod2(Double_t mu, Double_t b, Double_t e, Int_t x, Int_t y, Double_t em, Double_t tau, Double_t v)
                 Double_t LikeMod3(Double_t mu, Double_t b, Double_t e, Int_t x, Double_t bm, Double_t em, Double_t u, Double_t v)
                 Double_t LikeMod4(Double_t mu, Double_t b, Int_t x, Int_t y, Double_t tau)
                 Double_t LikeMod5(Double_t mu, Double_t b, Int_t x, Double_t bm, Double_t u)
                 Double_t LikeMod6(Double_t mu, Double_t b, Double_t e, Int_t x, Int_t z, Int_t m)
                 Double_t LikeMod7(Double_t mu, Double_t b, Double_t e, Int_t x, Double_t em, Double_t v)
                     void ProfLikeMod1(Double_t mu, Double_t& b, Double_t& e, Int_t x, Int_t y, Int_t z, Double_t tau, Int_t m)

        public:

                          TRolke(Double_t CL = 0.9, Option_t* option = "")
                          TRolke(const TRolke&)
                  virtual ~TRolke()
                 Double_t CalculateInterval(Int_t x, Int_t y, Int_t z, Double_t bm, Double_t em, Double_t e, Int_t mid, Double_t sde, Double_t sdb, Double_t tau, Double_t b, Int_t m)
           static TClass* Class()
                 Double_t GetCL() const
                 Double_t GetLowerLimit() const
                    Int_t GetSwitch() const
                 Double_t GetUpperLimit() const
          virtual TClass* IsA() const
                  TRolke& operator=(const TRolke&)
                     void SetCL(Double_t CL)
                     void SetSwitch(Int_t sw)
             virtual void ShowMembers(TMemberInspector& insp, char* parent)
             virtual void Streamer(TBuffer& b)
                     void StreamerNVirtual(TBuffer& b)

### Data Members

        protected:

          Double_t fCL          confidence level as a fraction [e.g. 90% = 0.9]
          Double_t fUpperLimit  the calculated upper limit
          Double_t fLowerLimit  the calculated lower limit
             Int_t fSwitch      0: for unbounded likelihood

## Class Description

      TRolke

      This class computes confidence intervals for the rate of a Poisson
      in the presence of background and efficiency with a fully frequentist
      treatment of the uncertainties in the efficiency and background estimate
      using the profile likelihood method.

      The signal is always assumed to be Poisson.

      The method is very similar to the one used in MINUIT (MINOS).

      Two options are offered to deal with cases where the maximum likelihood
      estimate (MLE) is not in the physical region. Version "bounded likelihood"
      is the one used by MINOS if bounds for the physical region are chosen. Versi//  on "unbounded likelihood (the default) allows the MLE to be in the
      unphysical region. It has however better coverage.
      For more details consult the reference (see below).

       It allows the following Models:

           1: Background - Poisson, Efficiency - Binomial  (cl,x,y,z,tau,m)
           2: Background - Poisson, Efficiency - Gaussian  (cl,xd,y,em,tau,sde)
           3: Background - Gaussian, Efficiency - Gaussian (cl,x,bm,em,sd)
           4: Background - Poisson, Efficiency - known     (cl,x,y,tau,e)
           5: Background - Gaussian, Efficiency - known    (cl,x,y,z,sdb,e)
           6: Background - known, Efficiency - Binomial    (cl,x,z,m,b)
           7: Background - known, Efficiency - Gaussian    (cl,x,em,sde,b)

      Parameter definition:

      cl  =  Confidence level

      x = number of observed events

      y = number of background events

      z = number of simulated signal events

      em = measurement of the efficiency.

      bm = background estimate

      tau = ratio between signal and background region (in case background is
      observed) ratio between observed and simulated livetime in case
      background is determined from MC.

      sd(x) = sigma of the Gaussian

      e = true efficiency (in case known)

      b = expected background (in case known)

      m = number of MC runs

      mid = ID number of the model ...

      For a description of the method and its properties:

      W.Rolke, A. Lopez, J. Conrad and Fred James
      "Limits and Confidence Intervals in presence of nuisance parameters"
       http://lanl.arxiv.org/abs/physics/0403059

      Should I use TRolke, TFeldmanCousins, TLimit?
      ============================================
      1. I guess TRolke makes TFeldmanCousins obsolete?

      Certainly not. TFeldmanCousins is the fully frequentist construction and
      should be used in case of no (or negligible uncertainties). It is however
      not capable of treating uncertainties in nuisance parameters.
      TRolke is desined for this case and it is shown in the reference above
      that it has good coverage properties for most cases, ie it might be
      used where FeldmannCousins can't.

      2. What are the advantages of TRolke over TLimit?

      TRolke is fully frequentist. TLimit treats nuisance parameters Bayesian.
      For a coverage study of a Bayesian method refer to
      physics/0408039 (Tegenfeldt & J.C). However, this note studies
      the coverage of Feldman&Cousins with Bayesian treatment of nuisance
      parameters. To make a long story short: using the Bayesian method you
      might introduce a small amount of over-coverage (though I haven't shown it
      for TLimit). On the other hand, coverage of course is a not so interesting
      when you consider yourself a Bayesian.

     Author: Jan Conrad (CERN)

     see example in tutorial Rolke.C

     Copyright CERN 2004                Jan.Conrad@cern.ch

TRolke(Double_t CL, Option_t * /*option*/)

~TRolke()

Double_t CalculateInterval(Int_t x, Int_t y, Int_t z, Double_t bm, Double_t em,Double_t e, Int_t mid, Double_t sde, Double_t sdb, Double_t tau, Double_t b, Int_t m)

Double_t Interval(Int_t x, Int_t y, Int_t z, Double_t bm, Double_t em,Double_t e, Int_t mid, Double_t sde, Double_t sdb, Double_t tau, Double_t b, Int_t m)

     Calculates the Confidence Interval

Double_t Likelihood(Double_t mu, Int_t x, Int_t y, Int_t z, Double_t bm,Double_t em, Double_t e, Int_t mid, Double_t sde, Double_t sdb, Double_t tau, Double_t b, Int_t m, Int_t what)

     Chooses between the different profile likelihood functions to use for the
     different models.
     Returns evaluation of the profile likelihood functions.

Double_t EvalLikeMod1(Double_t mu, Int_t x, Int_t y, Int_t z, Double_t e, Double_t tau, Double_t b, Int_t m, Int_t what)

     Calculates the Profile Likelihood for MODEL 1:
      Poisson background/ Binomial Efficiency
     what = 1: Maximum likelihood estimate is returned
     what = 2: Profile Likelihood of Maxmimum Likelihood estimate is returned.
     what = 3: Profile Likelihood of Test hypothesis is returned
     otherwise parameters as described in the beginning of the class)

Double_t LikeMod1(Double_t mu,Double_t b, Double_t e, Int_t x, Int_t y, Int_t z, Double_t tau, Int_t m)

     Profile Likelihood function for MODEL 1:
     Poisson background/ Binomial Efficiency

void ProfLikeMod1(Double_t mu,Double_t &b,Double_t &e,Int_t x,Int_t y, Int_t z,Double_t tau,Int_t m)

     Void needed to calculate estimates of efficiency and background for model 1

Double_t LikeGradMod1(Double_t e, Double_t mu, Int_t x,Int_t y,Int_t z,Double_t tau,Int_t m)

Double_t EvalLikeMod2(Double_t mu, Int_t x, Int_t y, Double_t em, Double_t e,Double_t sde, Double_t tau, Double_t b, Int_t what)

     Calculates the Profile Likelihood for MODEL 2:
      Poisson background/ Gauss Efficiency
     what = 1: Maximum likelihood estimate is returned
     what = 2: Profile Likelihood of Maxmimum Likelihood estimate is returned.
     what = 3: Profile Likelihood of Test hypothesis is returned
     otherwise parameters as described in the beginning of the class)

Double_t LikeMod2(Double_t mu, Double_t b, Double_t e,Int_t x,Int_t y,Double_t em,Double_t tau, Double_t v)

     Profile Likelihood function for MODEL 2:
     Poisson background/Gauss Efficiency

Double_t EvalLikeMod3(Double_t mu, Int_t x, Double_t bm, Double_t em, Double_t e, Double_t sde, Double_t sdb, Double_t b, Int_t what)

     Calculates the Profile Likelihood for MODEL 3:
     Gauss  background/ Gauss Efficiency
     what = 1: Maximum likelihood estimate is returned
     what = 2: Profile Likelihood of Maxmimum Likelihood estimate is returned.
     what = 3: Profile Likelihood of Test hypothesis is returned
     otherwise parameters as described in the beginning of the class)

Double_t LikeMod3(Double_t mu,Double_t b,Double_t e,Int_t x,Double_t bm,Double_t em,Double_t u,Double_t v)

     Profile Likelihood function for MODEL 3:
     Gauss background/Gauss Efficiency

Double_t EvalLikeMod4(Double_t mu, Int_t x, Int_t y, Double_t tau, Double_t b, Int_t what)

     Calculates the Profile Likelihood for MODEL 4:
     Poiss  background/Efficiency known
     what = 1: Maximum likelihood estimate is returned
     what = 2: Profile Likelihood of Maxmimum Likelihood estimate is returned.
     what = 3: Profile Likelihood of Test hypothesis is returned
     otherwise parameters as described in the beginning of the class)

Double_t LikeMod4(Double_t mu,Double_t b,Int_t x,Int_t y,Double_t tau)

     Profile Likelihood function for MODEL 4:
     Poiss background/Efficiency known

Double_t EvalLikeMod5(Double_t mu, Int_t x, Double_t bm, Double_t sdb, Double_t b, Int_t what)

     Calculates the Profile Likelihood for MODEL 5:
     Gauss  background/Efficiency known
     what = 1: Maximum likelihood estimate is returned
     what = 2: Profile Likelihood of Maxmimum Likelihood estimate is returned.
     what = 3: Profile Likelihood of Test hypothesis is returned
     otherwise parameters as described in the beginning of the class)

Double_t LikeMod5(Double_t mu,Double_t b,Int_t x,Double_t bm,Double_t u)

     Profile Likelihood function for MODEL 5:
     Gauss background/Efficiency known

Double_t EvalLikeMod6(Double_t mu, Int_t x, Int_t z, Double_t e, Double_t b, Int_t m, Int_t what)

     Calculates the Profile Likelihood for MODEL 6:
     Gauss  known/Efficiency binomial
     what = 1: Maximum likelihood estimate is returned
     what = 2: Profile Likelihood of Maxmimum Likelihood estimate is returned.
     what = 3: Profile Likelihood of Test hypothesis is returned
     otherwise parameters as described in the beginning of the class)

Double_t LikeMod6(Double_t mu,Double_t b,Double_t e,Int_t x,Int_t z,Int_t m)

     Profile Likelihood function for MODEL 6:
     background known/ Efficiency binomial

Double_t EvalLikeMod7(Double_t mu, Int_t x, Double_t em, Double_t e, Double_t sde, Double_t b, Int_t what)

     Calculates the Profile Likelihood for MODEL 7:
     background known/Efficiency Gauss
     what = 1: Maximum likelihood estimate is returned
     what = 2: Profile Likelihood of Maxmimum Likelihood estimate is returned.
     what = 3: Profile Likelihood of Test hypothesis is returned
     otherwise parameters as described in the beginning of the class)

Double_t LikeMod7(Double_t mu,Double_t b,Double_t e,Int_t x,Double_t em,Double_t v)

     Profile Likelihood function for MODEL 6:
     background known/ Efficiency binomial

Double_t EvalPolynomial(Double_t x, const Int_t coef[], Int_t N)

     evaluate polynomial

Double_t EvalMonomial(Double_t x, const Int_t coef[], Int_t N)

     evaluate mononomial

### Inline Functions

               Double_t GetUpperLimit() const
               Double_t GetLowerLimit() const
                  Int_t GetSwitch() const
                   void SetSwitch(Int_t sw)
               Double_t GetCL() const
                   void SetCL(Double_t CL)
                TClass* Class()
                TClass* IsA() const
                   void ShowMembers(TMemberInspector& insp, char* parent)
                   void Streamer(TBuffer& b)
                   void StreamerNVirtual(TBuffer& b)
                 TRolke TRolke(const TRolke&)
                TRolke& operator=(const TRolke&)

Author: Jan Conrad 9/2/2004
Last update: root/physics:$Name: $:$Id: TRolke.cxx,v 1.9 2005/06/17 14:56:08 brun Exp $
Copyright (C) 1995-2004, Rene Brun and Fons Rademakers. *