Namespace for the RooStats classes. More...

Namespaces
namespace	HistFactory

namespace	NumberCountingUtils

Classes
class	AcceptanceRegion

class	AsymptoticCalculator
	Hypothesis Test Calculator based on the asymptotic formulae for the profile likelihood ratio. More...

class	BayesianCalculator
	BayesianCalculator is a concrete implementation of IntervalCalculator, providing the computation of a credible interval using a Bayesian method. More...

class	BernsteinCorrection
	BernsteinCorrection is a utility in RooStats to augment a nominal PDF with a polynomial correction term. More...

class	BranchStore

class	CombinedCalculator
	CombinedCalculator is an interface class for a tools which can produce both RooStats HypoTestResults and ConfIntervals. More...

class	ConfidenceBelt
	ConfidenceBelt is a concrete implementation of the ConfInterval interface. More...

class	ConfInterval
	ConfInterval is an interface class for a generic interval in the RooStats framework. More...

class	DebuggingSampler

class	DebuggingTestStat

class	DetailedOutputAggregator
	This class is designed to aid in the construction of RooDataSets and RooArgSets, particularly those naturally arising in fitting operations. More...

class	FeldmanCousins
	The FeldmanCousins class (like the Feldman-Cousins technique) is essentially a specific configuration of the more general NeymanConstruction. More...

class	FrequentistCalculator
	Does a frequentist hypothesis test. More...

class	Heaviside
	Represents the Heaviside function. More...

class	HLFactory
	HLFactory is an High Level model Factory allows you to describe your models in a configuration file (datacards) acting as an interface with the RooFactoryWSTool. More...

class	HybridCalculator
	Same purpose as HybridCalculatorOriginal, but different implementation. More...

class	HybridCalculatorOriginal
	HybridCalculatorOriginal class. More...

class	HybridPlot
	This class provides the plots for the result of a study performed with the HybridCalculatorOriginal class. More...

class	HybridResult
	Class encapsulating the result of the HybridCalculatorOriginal. More...

class	HypoTestCalculator
	HypoTestCalculator is an interface class for a tools which produce RooStats HypoTestResults. More...

class	HypoTestCalculatorGeneric
	Common base class for the Hypothesis Test Calculators. More...

class	HypoTestInverter
	A class for performing a hypothesis test inversion by scanning the hypothesis test results of a HypoTestCalculator for various values of the parameter of interest. More...

class	HypoTestInverterOriginal
	This class is now deprecated and to be replaced by the HypoTestInverter. More...

class	HypoTestInverterPlot
	Class to plot a HypoTestInverterResult, the output of the HypoTestInverter calculator. More...

class	HypoTestInverterResult
	HypoTestInverterResult class holds the array of hypothesis test results and compute a confidence interval. More...

class	HypoTestPlot
	This class provides the plots for the result of a study performed with any of the HypoTestCalculatorGeneric (e.g. More...

class	HypoTestResult
	HypoTestResult is a base class for results from hypothesis tests. More...

class	IntervalCalculator
	IntervalCalculator is an interface class for a tools which produce RooStats ConfIntervals. More...

struct	LikelihoodFunction

class	LikelihoodInterval
	LikelihoodInterval is a concrete implementation of the RooStats::ConfInterval interface. More...

class	LikelihoodIntervalPlot
	This class provides simple and straightforward utilities to plot a LikelihoodInterval object. More...

class	MarkovChain
	Stores the steps in a Markov Chain of points. More...

class	MaxLikelihoodEstimateTestStat
	MaxLikelihoodEstimateTestStat: TestStatistic that returns maximum likelihood estimate of a specified parameter. More...

class	MCMCCalculator
	Bayesian Calculator estimating an interval or a credible region using the Markov-Chain Monte Carlo method to integrate the likelihood function with the prior to obtain the posterior function. More...

class	MCMCInterval
	MCMCInterval is a concrete implementation of the RooStats::ConfInterval interface. More...

class	MCMCIntervalPlot
	This class provides simple and straightforward utilities to plot a MCMCInterval object. More...

class	MetropolisHastings
	This class uses the Metropolis-Hastings algorithm to construct a Markov Chain of data points using Monte Carlo. More...

class	MinNLLTestStat
	MinNLLTestStat is an implementation of the TestStatistic interface that calculates the minimum value of the negative log likelihood function and returns it as a test statistic. More...

class	ModelConfig
	ModelConfig is a simple class that holds configuration information specifying how a model should be used in the context of various RooStats tools. More...

class	NeymanConstruction
	NeymanConstruction is a concrete implementation of the NeymanConstruction interface that, as the name suggests, performs a NeymanConstruction. More...

class	NuisanceParametersSampler
	Helper class for ToyMCSampler. More...

class	NumberCountingPdfFactory
	A factory for building PDFs and data for a number counting combination. More...

class	NumEventsTestStat
	NumEventsTestStat is a simple implementation of the TestStatistic interface used for simple number counting. More...

class	PdfProposal
	PdfProposal is a concrete implementation of the ProposalFunction interface. More...

class	PointSetInterval
	PointSetInterval is a concrete implementation of the ConfInterval interface. More...

class	PosteriorCdfFunction

class	PosteriorFunction

class	PosteriorFunctionFromToyMC
	Posterior function obtaining sampling toy MC for the nuisance according to their pdf. More...

class	ProfileInspector
	Utility class to plot conditional MLE of nuisance parameters vs. More...

class	ProfileLikelihoodCalculator
	The ProfileLikelihoodCalculator is a concrete implementation of CombinedCalculator (the interface class for tools which can produce both a RooStats HypoTestResult and ConfInterval). More...

class	ProfileLikelihoodTestStat
	ProfileLikelihoodTestStat is an implementation of the TestStatistic interface that calculates the profile likelihood ratio at a particular parameter point given a dataset. More...

class	ProofConfig
	Holds configuration options for proof and proof-lite. More...

class	ProposalFunction
	ProposalFunction is an interface for all proposal functions that would be used with a Markov Chain Monte Carlo algorithm. More...

class	ProposalHelper

class	RatioOfProfiledLikelihoodsTestStat
	TestStatistic that returns the ratio of profiled likelihoods. More...

struct	RooStatsConfig

class	SamplingDistPlot
	This class provides simple and straightforward utilities to plot SamplingDistribution objects. More...

class	SamplingDistribution
	This class simply holds a sampling distribution of some test statistic. More...

class	SamplingSummary

class	SamplingSummaryLookup

class	SequentialProposal
	Class implementing a proposal function that samples the parameter space by moving only in one coordinate (chosen randomly) at each step. More...

class	SimpleInterval
	SimpleInterval is a concrete implementation of the ConfInterval interface. More...

class	SimpleLikelihoodRatioTestStat
	TestStatistic class that returns -log(L[null] / L[alt]) where L is the likelihood. More...

class	SPlot
	A class to calculate "sWeights" used to create an "sPlot". More...

class	TestStatistic
	TestStatistic is an interface class to provide a facility for construction test statistics distributions to the NeymanConstruction class. More...

class	TestStatSampler
	TestStatSampler is an interface class for a tools which produce RooStats SamplingDistributions. More...

class	ToyMCImportanceSampler
	ToyMCImportanceSampler is an extension of the ToyMCSampler for Importance Sampling. More...

class	ToyMCPayload

class	ToyMCSampler
	ToyMCSampler is an implementation of the TestStatSampler interface. More...

class	ToyMCStudy
	ToyMCStudy is an implementation of RooAbsStudy for toy Monte Carlo sampling. More...

class	UniformProposal
	UniformProposal is a concrete implementation of the ProposalFunction interface for use with a Markov Chain Monte Carlo algorithm. More...

class	UpperLimitMCSModule
	This class allow to compute in the ToyMcStudy framework the ProfileLikelihood upper limit for each toy-MC sample generated. More...

Enumerations
enum	toysStrategies { EQUALTOYSPERDENSITY , EXPONENTIALTOYDISTRIBUTION }

Functions
Double_t	AsimovSignificance (Double_t s, Double_t b, Double_t sigma_b=0.0)
	Compute the Asimov Median significance for a Poisson process with s = expected number of signal events, b = expected numner of background events and optionally sigma_b = expected uncertainty of backgorund events

BranchStore *	CreateBranchStore (const RooDataSet &data)

void	FactorizePdf (const RooArgSet &observables, RooAbsPdf &pdf, RooArgList &obsTerms, RooArgList &constraints)

void	FactorizePdf (RooStats::ModelConfig &model, RooAbsPdf &pdf, RooArgList &obsTerms, RooArgList &constraints)

void	FillTree (TTree &myTree, const RooDataSet &data)

TTree *	GetAsTTree (TString name, TString desc, const RooDataSet &data)

RooStatsConfig &	GetGlobalRooStatsConfig ()
	Retrieve the config object which can be used to set flags for things like offsetting the likelihood or using the error wall for the minimiser.

bool	IsNLLOffset ()
	Test of RooStats should by default offset NLL calculations.

RooWorkspace *	MakeCleanWorkspace (RooWorkspace oldWS, const char newName, bool copySnapshots, const char mcname, const char newmcname)

RooAbsPdf *	MakeNuisancePdf (const RooStats::ModelConfig &model, const char *name)

RooAbsPdf *	MakeNuisancePdf (RooAbsPdf &pdf, const RooArgSet &observables, const char *name)

RooAbsPdf *	MakeUnconstrainedPdf (const RooStats::ModelConfig &model, const char *name=NULL)

RooAbsPdf *	MakeUnconstrainedPdf (RooAbsPdf &pdf, const RooArgSet &observables, const char *name=NULL)

void	PrintListContent (const RooArgList &l, std::ostream &os=std::cout)

Double_t	PValueToSignificance (Double_t pvalue)
	returns one-sided significance corresponding to a p-value

void	RandomizeCollection (RooAbsCollection &set, Bool_t randomizeConstants=kTRUE)

void	RemoveConstantParameters (RooArgList &set)

void	RemoveConstantParameters (RooArgSet *set)

bool	SetAllConstant (const RooAbsCollection &coll, bool constant=true)

void	SetParameters (const RooArgSet desiredVals, RooArgSet paramsToChange)

Double_t	SignificanceToPValue (Double_t Z)
	returns p-value corresponding to a 1-sided significance

RooAbsPdf *	StripConstraints (RooAbsPdf &pdf, const RooArgSet &observables)

void	UseNLLOffset (bool on)
	Use an offset in NLL calculations.

Variables
const ROOT::Math::RootFinder::EType	kRootFinderType = ROOT::Math::RootFinder::kBRENT

Detailed Description

Namespace for the RooStats classes.

All the classes of the RooStats package are in the RooStats namespace. In addition the namespace contain a set of utility functions.

Enumeration Type Documentation

◆ toysStrategies

enum RooStats::toysStrategies

Enumerator
EQUALTOYSPERDENSITY
EXPONENTIALTOYDISTRIBUTION

Definition at line 20 of file ToyMCImportanceSampler.h.

Function Documentation

◆ AsimovSignificance()

Double_t RooStats::AsimovSignificance	(	Double_t	s,
		Double_t	b,
		Double_t	sigma_b = `0.0`
	)

Compute the Asimov Median significance for a Poisson process with s = expected number of signal events, b = expected numner of background events and optionally sigma_b = expected uncertainty of backgorund events

Definition at line 61 of file RooStatsUtils.cxx.

◆ CreateBranchStore()

BranchStore * RooStats::CreateBranchStore ( const RooDataSet & data )

Definition at line 274 of file RooStatsUtils.cxx.

◆ FactorizePdf() [1/2]

void RooStats::FactorizePdf	(	const RooArgSet &	observables,
		RooAbsPdf &	pdf,
		RooArgList &	obsTerms,
		RooArgList &	constraints
	)

Definition at line 93 of file RooStatsUtils.cxx.

◆ FactorizePdf() [2/2]

void RooStats::FactorizePdf	(	RooStats::ModelConfig &	model,
		RooAbsPdf &	pdf,
		RooArgList &	obsTerms,
		RooArgList &	constraints
	)

Definition at line 126 of file RooStatsUtils.cxx.

◆ FillTree()

void RooStats::FillTree	(	TTree &	myTree,
		const RooDataSet &	data
	)

Definition at line 299 of file RooStatsUtils.cxx.

◆ GetAsTTree()

TTree * RooStats::GetAsTTree	(	TString	name,
		TString	desc,
		const RooDataSet &	data
	)

Definition at line 327 of file RooStatsUtils.cxx.

◆ GetGlobalRooStatsConfig()

RooStatsConfig & RooStats::GetGlobalRooStatsConfig ( )

Retrieve the config object which can be used to set flags for things like offsetting the likelihood or using the error wall for the minimiser.

Definition at line 56 of file RooStatsUtils.cxx.

◆ IsNLLOffset()

bool RooStats::IsNLLOffset ( )

Test of RooStats should by default offset NLL calculations.

Definition at line 89 of file RooStatsUtils.cxx.

◆ MakeCleanWorkspace()

RooWorkspace * RooStats::MakeCleanWorkspace	(	RooWorkspace *	oldWS,
		const char *	newName,
		bool	copySnapshots,
		const char *	mcname,
		const char *	newmcname
	)

Definition at line 353 of file RooStatsUtils.cxx.

◆ MakeNuisancePdf() [1/2]

RooAbsPdf * RooStats::MakeNuisancePdf	(	const RooStats::ModelConfig &	model,
		const char *	name
	)

Definition at line 148 of file RooStatsUtils.cxx.

◆ MakeNuisancePdf() [2/2]

RooAbsPdf * RooStats::MakeNuisancePdf	(	RooAbsPdf &	pdf,
		const RooArgSet &	observables,
		const char *	name
	)

Definition at line 137 of file RooStatsUtils.cxx.

◆ MakeUnconstrainedPdf() [1/2]

RooAbsPdf * RooStats::MakeUnconstrainedPdf	(	const RooStats::ModelConfig &	model,
		const char *	name = `NULL`
	)

Definition at line 228 of file RooStatsUtils.cxx.

◆ MakeUnconstrainedPdf() [2/2]

RooAbsPdf * RooStats::MakeUnconstrainedPdf	(	RooAbsPdf &	pdf,
		const RooArgSet &	observables,
		const char *	name = `NULL`
	)

Definition at line 217 of file RooStatsUtils.cxx.

◆ PrintListContent()

void RooStats::PrintListContent	(	const RooArgList &	l,
		std::ostream &	os = `std::cout`
	)

Definition at line 335 of file RooStatsUtils.cxx.

◆ PValueToSignificance()

Double_t RooStats::PValueToSignificance ( Double_t pvalue )

inline

returns one-sided significance corresponding to a p-value

Definition at line 51 of file RooStatsUtils.h.

◆ RandomizeCollection()

void RooStats::RandomizeCollection	(	RooAbsCollection &	set,
		Bool_t	randomizeConstants = `kTRUE`
	)

inline

Definition at line 106 of file RooStatsUtils.h.

◆ RemoveConstantParameters() [1/2]

void RooStats::RemoveConstantParameters ( RooArgList & set )

inline

Definition at line 79 of file RooStatsUtils.h.

◆ RemoveConstantParameters() [2/2]

void RooStats::RemoveConstantParameters ( RooArgSet * set )

inline

Definition at line 69 of file RooStatsUtils.h.

◆ SetAllConstant()

bool RooStats::SetAllConstant	(	const RooAbsCollection &	coll,
		bool	constant = `true`
	)

inline

Definition at line 89 of file RooStatsUtils.h.

◆ SetParameters()

void RooStats::SetParameters	(	const RooArgSet *	desiredVals,
		RooArgSet *	paramsToChange
	)

inline

Definition at line 65 of file RooStatsUtils.h.

◆ SignificanceToPValue()

Double_t RooStats::SignificanceToPValue ( Double_t Z )

inline

returns p-value corresponding to a 1-sided significance

Definition at line 56 of file RooStatsUtils.h.

◆ StripConstraints()

RooAbsPdf * RooStats::StripConstraints	(	RooAbsPdf &	pdf,
		const RooArgSet &	observables
	)

Definition at line 157 of file RooStatsUtils.cxx.

◆ UseNLLOffset()

void RooStats::UseNLLOffset ( bool on )

Use an offset in NLL calculations.

Definition at line 84 of file RooStatsUtils.cxx.

Variable Documentation

◆ kRootFinderType

const ROOT::Math::RootFinder::EType RooStats::kRootFinderType = ROOT::Math::RootFinder::kBRENT

Definition at line 101 of file BayesianCalculator.cxx.

Namespaces

Classes

Enumerations

Functions

Variables

Detailed Description

Enumeration Type Documentation

◆ toysStrategies

Function Documentation

◆ AsimovSignificance()

◆ CreateBranchStore()

◆ FactorizePdf() [1/2]

◆ FactorizePdf() [2/2]

◆ FillTree()

◆ GetAsTTree()

◆ GetGlobalRooStatsConfig()

◆ IsNLLOffset()

◆ MakeCleanWorkspace()

◆ MakeNuisancePdf() [1/2]

◆ MakeNuisancePdf() [2/2]

◆ MakeUnconstrainedPdf() [1/2]

◆ MakeUnconstrainedPdf() [2/2]

◆ PrintListContent()

◆ PValueToSignificance()

◆ RandomizeCollection()

◆ RemoveConstantParameters() [1/2]

◆ RemoveConstantParameters() [2/2]

◆ SetAllConstant()

◆ SetParameters()

◆ SignificanceToPValue()

◆ StripConstraints()

◆ UseNLLOffset()

Variable Documentation

◆ kRootFinderType