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class RooStats::ProfileLikelihoodCalculator: public RooStats::CombinedCalculator


ProfileLikelihoodCalculator is a concrete implementation of CombinedCalculator (the interface class for a tools which can produce both RooStats HypoTestResults and ConfIntervals). The tool uses the profile likelihood ratio as a test statistic, and assumes that Wilks' theorem is valid. Wilks' theorem states that -2* log (profile likelihood ratio) is asymptotically distributed as a chi^2 distribution with N-dof, where N is the number of degrees of freedom. Thus, p-values can be constructed and the profile likelihood ratio can be used to construct a LikelihoodInterval. (In the future, this class could be extended to use toy Monte Carlo to calibrate the distribution of the test statistic).

Usage: It uses the interface of the CombinedCalculator, so that it can be configured by specifying:

  • a model common model (eg. a family of specific models which includes both the null and alternate),
  • a data set,
  • a set of parameters of interest. The nuisance parameters will be all other parameters of the model
  • a set of parameters of which specify the null hypothesis (including values and const/non-const status)
The interface allows one to pass the model, data, and parameters either directly or via a ModelConfig class. The alternate hypothesis leaves the parameter free to take any value other than those specified by the null hypotesis. There is therefore no need to specify the alternate parameters.

After configuring the calculator, one only needs to ask GetHypoTest() (which will return a HypoTestResult pointer) or GetInterval() (which will return an ConfInterval pointer).

The concrete implementations of this interface should deal with the details of how the nuisance parameters are dealt with (eg. integration vs. profiling) and which test-statistic is used (perhaps this should be added to the interface).

The motivation for this interface is that we hope to be able to specify the problem in a common way for several concrete calculators.



Function Members (Methods)

public:
virtual~ProfileLikelihoodCalculator()
static TClass*Class()
virtual Double_tRooStats::CombinedCalculator::ConfidenceLevel() const
virtual RooStats::HypoTestResult*GetHypoTest() const
virtual RooStats::LikelihoodInterval*GetInterval() const
virtual TClass*IsA() const
RooStats::ProfileLikelihoodCalculator&operator=(const RooStats::ProfileLikelihoodCalculator&)
RooStats::ProfileLikelihoodCalculatorProfileLikelihoodCalculator()
RooStats::ProfileLikelihoodCalculatorProfileLikelihoodCalculator(const RooStats::ProfileLikelihoodCalculator&)
RooStats::ProfileLikelihoodCalculatorProfileLikelihoodCalculator(RooAbsData& data, RooStats::ModelConfig& model, Double_t size = 0.05)
RooStats::ProfileLikelihoodCalculatorProfileLikelihoodCalculator(RooAbsData& data, RooAbsPdf& pdf, const RooArgSet& paramsOfInterest, Double_t size = 0.05, const RooArgSet* nullParams = 0)
virtual voidRooStats::CombinedCalculator::SetAlternateModel(const RooStats::ModelConfig&)
virtual voidRooStats::CombinedCalculator::SetAlternateParameters(const RooArgSet& set)
virtual voidRooStats::HypoTestCalculator::SetCommonModel(const RooStats::ModelConfig& model)
virtual voidRooStats::CombinedCalculator::SetConfidenceLevel(Double_t cl)
virtual voidRooStats::CombinedCalculator::SetData(RooAbsData& data)
virtual voidRooStats::CombinedCalculator::SetModel(const RooStats::ModelConfig& model)
virtual voidRooStats::CombinedCalculator::SetNuisanceParameters(const RooArgSet& set)
virtual voidRooStats::CombinedCalculator::SetNullModel(const RooStats::ModelConfig&)
virtual voidRooStats::CombinedCalculator::SetNullParameters(const RooArgSet& set)
virtual voidRooStats::CombinedCalculator::SetParameters(const RooArgSet& set)
virtual voidRooStats::CombinedCalculator::SetPdf(RooAbsPdf& pdf)
virtual voidRooStats::CombinedCalculator::SetTestSize(Double_t size)
virtual voidShowMembers(TMemberInspector& insp)
virtual Double_tRooStats::CombinedCalculator::Size() const
virtual voidStreamer(TBuffer& b)
voidStreamerNVirtual(TBuffer& b)

Data Members

public:
RooFitResult*fFitResultinternal result of gloabl fit
protected:
RooArgSetRooStats::CombinedCalculator::fAlternateParamsRooArgSet specifying alternate parameters for hypothesis test // Is it used ????
RooAbsData*RooStats::CombinedCalculator::fData
RooArgSetRooStats::CombinedCalculator::fNuisParamsRooArgSet specifying nuisance parameters for interval
RooArgSetRooStats::CombinedCalculator::fNullParamsRooArgSet specifying null parameters for hypothesis test
RooArgSetRooStats::CombinedCalculator::fPOIRooArgSet specifying parameters of interest for interval
RooAbsPdf*RooStats::CombinedCalculator::fPdf
Double_tRooStats::CombinedCalculator::fSizesize of the test (eg. specified rate of Type I error)

Class Charts

Inheritance Inherited Members Includes Libraries
Class Charts

Function documentation

ProfileLikelihoodCalculator()
 default constructor
ProfileLikelihoodCalculator(RooAbsData& data, RooAbsPdf& pdf, const RooArgSet& paramsOfInterest, Double_t size = 0.05, const RooArgSet* nullParams = 0)
 constructor from pdf and parameters
 the pdf must contain eventually the nuisance parameters
ProfileLikelihoodCalculator(RooAbsData& data, RooStats::ModelConfig& model, Double_t size = 0.05)
 construct from a ModelConfig. Assume data model.GetPdf() will provide full description of model including
 constraint term on the nuisances parameters
~ProfileLikelihoodCalculator()
 destructor
 cannot delete prod pdf because it will delete all the composing pdf's
    if (fOwnPdf) delete fPdf;
    fPdf = 0;
void DoReset() const
 reset and clear fit result
 to be called when a new model or data are set in the calculator
void DoGlobalFit() const
 perform a global fit of the likelihood letting with all parameter of interest and
 nuisance parameters
 keep the list of fitted parameters
LikelihoodInterval* GetInterval() const
 Main interface to get a RooStats::ConfInterval.
 It constructs a profile likelihood ratio and uses that to construct a RooStats::LikelihoodInterval.
HypoTestResult* GetHypoTest() const
 Main interface to get a HypoTestResult.
 It does two fits:
 the first lets the null parameters float, so it's a maximum likelihood estimate
 the second is to the null (fixing null parameters to their specified values): eg. a conditional maximum likelihood
 the ratio of the likelihood at the conditional MLE to the MLE is the profile likelihood ratio.
 Wilks' theorem is used to get p-values
ProfileLikelihoodCalculator()
 default constructor (needed for I/O)