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ROOT » ROOFIT » ROOSTATS » RooStats::ToyMCSampler

class RooStats::ToyMCSampler: public RooStats::TestStatSampler

ToyMCSampler is a simple implementation of the TestStatSampler interface. It generates Toy Monte Carlo for a given parameter point, and evaluates a test statistic that the user specifies (passed via the RooStats::TestStatistic interface). Development notes: We need to provide a nice way for the user to:

specify the number of toy experiments (needed to probe a given confidence level)
specify if the number of events per toy experiment should be fixed (conditioning) or floating (unconditional)
specify if any auxiliary observations should be fixed (conditioning) or floating (unconditional)
specify if nuisance paramters should be part of the toy MC: eg: integrated out (Bayesian marginalization)

All of these should be made fairly explicit in the interface.

Function Members (Methods)

public:

virtual	~ToyMCSampler()
virtual RooStats::SamplingDistribution*	AppendSamplingDistribution(RooArgSet& allParameters, RooStats::SamplingDistribution* last, Int_t additionalMC)
static TClass*	Class()
virtual Double_t	ConfidenceLevel() const
virtual Double_t	EvaluateTestStatistic(RooAbsData& data, RooArgSet& allParameters)
virtual RooAbsData*	GenerateToyData(RooArgSet& allParameters) const
virtual RooStats::SamplingDistribution*	GetSamplingDistribution(RooArgSet& allParameters)
virtual const RooAbsArg*	GetTestStatistic() const
virtual void	Initialize(RooAbsArg&, RooArgSet&, RooArgSet&)
virtual TClass*	IsA() const
string	MakeName(RooArgSet&)
RooStats::TestStatSampler&	RooStats::TestStatSampler::operator=(const RooStats::TestStatSampler&)
virtual void	SetConfidenceLevel(Double_t cl)
virtual void	SetData(RooAbsData& data)
virtual void	SetData(const char* name)
virtual void	SetExtended(const Bool_t isExtended)
virtual void	SetNEventsPerToy(const Int_t nevents)
virtual void	SetNToys(const Int_t ntoy)
virtual void	SetNuisanceParameters(RooArgSet& set)
virtual void	SetObservables(RooArgSet& set)
virtual void	SetParameters(RooArgSet& set)
virtual void	SetPdf(RooAbsPdf& pdf)
virtual void	SetPdf(const char* name)
virtual void	SetTestSize(Double_t size)
virtual void	SetTestStatistic(RooAbsArg&) const
virtual void	ShowMembers(TMemberInspector& insp, char* parent)
virtual void	Streamer(TBuffer& b)
void	StreamerNVirtual(TBuffer& b)
RooStats::ToyMCSampler	ToyMCSampler(RooStats::TestStatistic& ts)
RooStats::ToyMCSampler	ToyMCSampler(const RooStats::ToyMCSampler&)

Data Members

private:

Int_t	fCounter	counter for naming sampling dist objects
const char*	fDataName	name of data set in workspace
Bool_t	fExtended	if nEvents should fluctuate
RooDataSet*	fLastDataSet	work around for memory issues in nllvar->setData(data, noclone)
Int_t	fNevents	number of events per toy (may be ignored depending on settings)
Int_t	fNtoys	number of toys to generate
RooArgSet*	fNuisParams	RooArgSet specifying nuisance parameters for interval
RooArgSet*	fObservables	RooArgSet specifying the observables in the dataset (needed to evaluate the test statistic)
Bool_t	fOwnsWorkspace	flag if this object owns its workspace
RooArgSet*	fPOI	RooArgSet specifying parameters of interest for interval
const char*	fPdfName	name of common PDF in workspace
TRandom*	fRand	random generator
Double_t	fSize
RooStats::TestStatistic*	fTestStat	pointer to the test statistic that is being sampled
TString	fVarName	name of test statistic
RooWorkspace*	fWS	a workspace that owns all the components to be used by the calculator

Class Charts

Inheritance Inherited Members Includes Libraries

Function documentation

ToyMCSampler(TestStatistic &ts)

virtual ~ToyMCSampler()

SamplingDistribution* AppendSamplingDistribution(RooArgSet& allParameters, RooStats::SamplingDistribution* last, Int_t additionalMC)

 Extended interface to append to sampling distribution more samples

SamplingDistribution* GetSamplingDistribution(RooArgSet& allParameters)

 Main interface to get a SamplingDistribution

RooAbsData* GenerateToyData(RooArgSet& allParameters) const

 This method generates a toy dataset for the given parameter point.

SetParameters(&allParameters, parameters)

string MakeName(RooArgSet& )

 helper method to create meaningful names for sampling dist

Double_t EvaluateTestStatistic(RooAbsData& data, RooArgSet& allParameters)

 Main interface to evaluate the test statistic on a dataset

const RooAbsArg* GetTestStatistic() const

 Get the TestStatistic

Double_t ConfidenceLevel() const

 Get the Confidence level for the test

{return 1.-fSize;}

void Initialize(RooAbsArg& , RooArgSet& , RooArgSet& )

 Common Initialization

void SetNEventsPerToy(const Int_t nevents)

void SetExtended(const Bool_t isExtended)

void SetData(RooAbsData& data)

 Set the DataSet, add to the the workspace if not already there

void SetPdf(RooAbsPdf& pdf)

 Set the Pdf, add to the the workspace if not already there

void SetData(const char* name)

 specify the name of the dataset in the workspace to be used

{fDataName = name;}

void SetPdf(const char* name)

 specify the name of the PDF in the workspace to be used

{fPdfName = name;}

void SetNuisanceParameters(RooArgSet& set)

 specify the nuisance parameters (eg. the rest of the parameters)

{fNuisParams = &set;}

void SetObservables(RooArgSet& set)

 specify the observables in the dataset (needed to evaluate the test statistic)

{fObservables = &set;}

void SetTestSize(Double_t size)

 set the size of the test (rate of Type I error) ( Eg. 0.05 for a 95% Confidence Interval)

{fSize = size;}

void SetConfidenceLevel(Double_t cl)

 set the confidence level for the interval (eg. 0.95 for a 95% Confidence Interval)

{fSize = 1.-cl;}

void SetTestStatistic(RooAbsArg& ) const

 Set the TestStatistic (want the argument to be a function of the data & parameter points

{}