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

Based on the RatioFinder code available in the RooStatsCms package developed by Gregory Schott and Danilo Piparo Ported and adapted to RooStats by Gregory Schott Some contributions to this class have been written by Matthias Wolf (error estimation)

Definition at line 26 of file HypoTestInverterResult.h.

Public Types
enum	InterpolOption_t { kLinear , kSpline }

Public Types inherited from TObject
enum	{ kIsOnHeap = 0x01000000 , kNotDeleted = 0x02000000 , kZombie = 0x04000000 , kInconsistent = 0x08000000 , kBitMask = 0x00ffffff }

enum	{ kSingleKey = BIT(0) , kOverwrite = BIT(1) , kWriteDelete = BIT(2) }

enum	EDeprecatedStatusBits { kObjInCanvas = BIT(3) }

enum	EStatusBits { kCanDelete = BIT(0) , kMustCleanup = BIT(3) , kIsReferenced = BIT(4) , kHasUUID = BIT(5) , kCannotPick = BIT(6) , kNoContextMenu = BIT(8) , kInvalidObject = BIT(13) }

Public Member Functions
	HypoTestInverterResult (const char *name, const RooRealVar &scannedVariable, double cl)
	constructor

	HypoTestInverterResult (const char *name=0)
	default constructor

	HypoTestInverterResult (const HypoTestInverterResult &other, const char *name)
	copy constructor

virtual	~HypoTestInverterResult ()
	destructor

bool	Add (const HypoTestInverterResult &otherResult)
	merge with the content of another HypoTestInverterResult object

bool	Add (Double_t x, const HypoTestResult &result)
	add the result of a single point (an HypoTestRsult)

int	ArraySize () const
	number of entries in the results array

double	CLb (int index) const
	return the observed CLb value for the i-th entry

double	CLbError (int index) const
	return the observed CLb value for the i-th entry

double	CLs (int index) const
	return the observed CLb value for the i-th entry

double	CLsError (int index) const
	return the observed CLb value for the i-th entry

double	CLsplusb (int index) const
	return the observed CLsplusb value for the i-th entry

double	CLsplusbError (int index) const
	return the observed CLsplusb value for the i-th entry

int	ExclusionCleanup ()
	remove points that appear to have failed.

int	FindIndex (double xvalue) const
	find the index corresponding at the poi value xvalue If no points is found return -1 Note that a tolerance is used of 10^-12 to find the closest point

double	FindInterpolatedLimit (double target, bool lowSearch=false, double xmin=1, double xmax=0)
	interpolate to find a limit value Use a linear or a spline interpolation depending on the interpolation option

SamplingDistribution *	GetAltTestStatDist (int index) const

SamplingDistribution *	GetBackgroundTestStatDist (int index) const
	get the background test statistic distribution

double	GetExpectedLowerLimit (double nsig=0, const char *opt="") const
	get Limit value corresponding at the desired nsigma level (0) is median -1 sigma is 1 sigma

SamplingDistribution *	GetExpectedPValueDist (int index) const
	return expected distribution of p-values (Cls or Clsplusb)

double	GetExpectedUpperLimit (double nsig=0, const char *opt="") const
	get Limit value corresponding at the desired nsigma level (0) is median -1 sigma is 1 sigma

InterpolOption_t	GetInterpolationOption () const

HypoTestResult *	GetLastResult () const

double	GetLastXValue () const

double	GetLastYError () const

double	GetLastYValue () const

SamplingDistribution *	GetLowerLimitDistribution () const
	get expected lower limit distributions implemented using interpolation The size for the sampling distribution is given (by default is given by the average number of toy/point)

SamplingDistribution *	GetNullTestStatDist (int index) const
	same in terms of alt and null

HypoTestResult *	GetResult (int index) const
	return a pointer to the i^th result object

SamplingDistribution *	GetSignalAndBackgroundTestStatDist (int index) const
	get the signal and background test statistic distribution

SamplingDistribution *	GetUpperLimitDistribution () const
	get expected upper limit distributions implemented using interpolation

double	GetXValue (int index) const
	function to return the value of the parameter of interest for the i^th entry in the results

double	GetYError (int index) const
	function to return the estimated error on the value of the confidence level for the i^th entry in the results

double	GetYValue (int index) const
	function to return the value of the confidence level for the i^th entry in the results

bool	IsOneSided () const
	query if one sided result

bool	IsTwoSided () const
	query if two sided result

Double_t	LowerLimit ()
	lower and upper bound of the confidence interval (to get upper/lower limits, multiply the size( = 1-confidence level ) by 2

Double_t	LowerLimitEstimatedError ()
	rough estimation of the error on the computed bound of the confidence interval Estimate of lower limit error function evaluates only a rough error on the lower limit.

HypoTestInverterResult &	operator= (const HypoTestInverterResult &other)
	operator =

void	SetCLsCleanupThreshold (Double_t th)
	set CLs threshold for exclusion cleanup function

virtual void	SetConfidenceLevel (Double_t cl)
	set the confidence level for the interval (eg. 0.95 for a 95% Confidence Interval)

void	SetInterpolationOption (InterpolOption_t opt)
	set the interpolation option, linear (kLinear ) or spline (kSpline)

virtual void	SetTestSize (Double_t size)
	set the size of the test (rate of Type I error) (eg. 0.05 for a 95% Confidence Interval)

Double_t	UpperLimit ()

Double_t	UpperLimitEstimatedError ()
	Estimate of lower limit error function evaluates only a rough error on the lower limit.

void	UseCLs (bool on=true)
	flag to switch between using CLsb (default) or CLs as confidence level

Public Member Functions inherited from RooStats::SimpleInterval
	SimpleInterval (const char *name, const RooRealVar &var, Double_t lower, Double_t upper, Double_t cl)
	Alternate constructor.

	SimpleInterval (const char *name=0)
	Default constructor.

	SimpleInterval (const SimpleInterval &other, const char *name)
	fParameters.add( other.fParameters );

virtual	~SimpleInterval ()
	Destructor.

Bool_t	CheckParameters (const RooArgSet &) const
	check if parameters are correct (i.e. they are the POI of this interval)

virtual Double_t	ConfidenceLevel () const
	return confidence level

virtual RooArgSet *	GetParameters () const
	return cloned list of parameters

virtual Bool_t	IsInInterval (const RooArgSet &) const
	Method to determine if a parameter point is in the interval.

SimpleInterval &	operator= (const SimpleInterval &other)

Public Member Functions inherited from RooStats::ConfInterval
	ConfInterval (const char *name=0)
	constructor given name and title

virtual	~ConfInterval ()
	destructor

ConfInterval &	operator= (const ConfInterval &other)
	operator=

Public Member Functions inherited from TNamed
	TNamed ()

	TNamed (const char name, const char title)

	TNamed (const TNamed &named)
	TNamed copy ctor.

	TNamed (const TString &name, const TString &title)

virtual	~TNamed ()
	TNamed destructor.

virtual void	Clear (Option_t *option="")
	Set name and title to empty strings ("").

virtual TObject *	Clone (const char *newname="") const
	Make a clone of an object using the Streamer facility.

virtual Int_t	Compare (const TObject *obj) const
	Compare two TNamed objects.

virtual void	Copy (TObject &named) const
	Copy this to obj.

virtual void	FillBuffer (char *&buffer)
	Encode TNamed into output buffer.

virtual const char *	GetName () const
	Returns name of object.

virtual const char *	GetTitle () const
	Returns title of object.

virtual ULong_t	Hash () const
	Return hash value for this object.

virtual Bool_t	IsSortable () const

virtual void	ls (Option_t *option="") const
	List TNamed name and title.

TNamed &	operator= (const TNamed &rhs)
	TNamed assignment operator.

virtual void	Print (Option_t *option="") const
	Print TNamed name and title.

virtual void	SetName (const char *name)
	Set the name of the TNamed.

virtual void	SetNameTitle (const char name, const char title)
	Set all the TNamed parameters (name and title).

virtual void	SetTitle (const char *title="")
	Set the title of the TNamed.

virtual Int_t	Sizeof () const
	Return size of the TNamed part of the TObject.

Public Member Functions inherited from TObject
	TObject ()
	TObject constructor.

	TObject (const TObject &object)
	TObject copy ctor.

virtual	~TObject ()
	TObject destructor.

void	AbstractMethod (const char *method) const
	Use this method to implement an "abstract" method that you don't want to leave purely abstract.

virtual void	AppendPad (Option_t *option="")
	Append graphics object to current pad.

virtual void	Browse (TBrowser *b)
	Browse object. May be overridden for another default action.

ULong_t	CheckedHash ()
	Check and record whether this class has a consistent Hash/RecursiveRemove setup (*) and then return the regular Hash value for this object.

virtual const char *	ClassName () const
	Returns name of class to which the object belongs.

virtual void	Delete (Option_t *option="")
	Delete this object.

virtual Int_t	DistancetoPrimitive (Int_t px, Int_t py)
	Computes distance from point (px,py) to the object.

virtual void	Draw (Option_t *option="")
	Default Draw method for all objects.

virtual void	DrawClass () const
	Draw class inheritance tree of the class to which this object belongs.

virtual TObject *	DrawClone (Option_t *option="") const
	Draw a clone of this object in the current selected pad for instance with: `gROOT->SetSelectedPad(gPad)`.

virtual void	Dump () const
	Dump contents of object on stdout.

virtual void	Error (const char method, const char msgfmt,...) const
	Issue error message.

virtual void	Execute (const char method, const char params, Int_t *error=0)
	Execute method on this object with the given parameter string, e.g.

virtual void	Execute (TMethod method, TObjArray params, Int_t *error=0)
	Execute method on this object with parameters stored in the TObjArray.

virtual void	ExecuteEvent (Int_t event, Int_t px, Int_t py)
	Execute action corresponding to an event at (px,py).

virtual void	Fatal (const char method, const char msgfmt,...) const
	Issue fatal error message.

virtual TObject *	FindObject (const char *name) const
	Must be redefined in derived classes.

virtual TObject *	FindObject (const TObject *obj) const
	Must be redefined in derived classes.

virtual Option_t *	GetDrawOption () const
	Get option used by the graphics system to draw this object.

virtual const char *	GetIconName () const
	Returns mime type name of object.

virtual char *	GetObjectInfo (Int_t px, Int_t py) const
	Returns string containing info about the object at position (px,py).

virtual Option_t *	GetOption () const

virtual UInt_t	GetUniqueID () const
	Return the unique object id.

virtual Bool_t	HandleTimer (TTimer *timer)
	Execute action in response of a timer timing out.

Bool_t	HasInconsistentHash () const
	Return true is the type of this object is known to have an inconsistent setup for Hash and RecursiveRemove (i.e.

virtual void	Info (const char method, const char msgfmt,...) const
	Issue info message.

virtual Bool_t	InheritsFrom (const char *classname) const
	Returns kTRUE if object inherits from class "classname".

virtual Bool_t	InheritsFrom (const TClass *cl) const
	Returns kTRUE if object inherits from TClass cl.

virtual void	Inspect () const
	Dump contents of this object in a graphics canvas.

void	InvertBit (UInt_t f)

virtual Bool_t	IsEqual (const TObject *obj) const
	Default equal comparison (objects are equal if they have the same address in memory).

virtual Bool_t	IsFolder () const
	Returns kTRUE in case object contains browsable objects (like containers or lists of other objects).

R__ALWAYS_INLINE Bool_t	IsOnHeap () const

R__ALWAYS_INLINE Bool_t	IsZombie () const

void	MayNotUse (const char *method) const
	Use this method to signal that a method (defined in a base class) may not be called in a derived class (in principle against good design since a child class should not provide less functionality than its parent, however, sometimes it is necessary).

virtual Bool_t	Notify ()
	This method must be overridden to handle object notification.

void	Obsolete (const char method, const char asOfVers, const char *removedFromVers) const
	Use this method to declare a method obsolete.

void	operator delete (void *ptr)
	Operator delete.

void	operator delete[] (void *ptr)
	Operator delete [].

void *	operator new (size_t sz)

void *	operator new (size_t sz, void *vp)

void *	operator new[] (size_t sz)

void *	operator new[] (size_t sz, void *vp)

TObject &	operator= (const TObject &rhs)
	TObject assignment operator.

virtual void	Paint (Option_t *option="")
	This method must be overridden if a class wants to paint itself.

virtual void	Pop ()
	Pop on object drawn in a pad to the top of the display list.

virtual Int_t	Read (const char *name)
	Read contents of object with specified name from the current directory.

virtual void	RecursiveRemove (TObject *obj)
	Recursively remove this object from a list.

void	ResetBit (UInt_t f)

virtual void	SaveAs (const char filename="", Option_t option="") const
	Save this object in the file specified by filename.

virtual void	SavePrimitive (std::ostream &out, Option_t *option="")
	Save a primitive as a C++ statement(s) on output stream "out".

void	SetBit (UInt_t f)

void	SetBit (UInt_t f, Bool_t set)
	Set or unset the user status bits as specified in f.

virtual void	SetDrawOption (Option_t *option="")
	Set drawing option for object.

virtual void	SetUniqueID (UInt_t uid)
	Set the unique object id.

virtual void	SysError (const char method, const char msgfmt,...) const
	Issue system error message.

R__ALWAYS_INLINE Bool_t	TestBit (UInt_t f) const

Int_t	TestBits (UInt_t f) const

virtual void	UseCurrentStyle ()
	Set current style settings in this object This function is called when either TCanvas::UseCurrentStyle or TROOT::ForceStyle have been invoked.

virtual void	Warning (const char method, const char msgfmt,...) const
	Issue warning message.

virtual Int_t	Write (const char *name=0, Int_t option=0, Int_t bufsize=0)
	Write this object to the current directory.

virtual Int_t	Write (const char *name=0, Int_t option=0, Int_t bufsize=0) const
	Write this object to the current directory.

Protected Attributes
double	fCLsCleanupThreshold

TList	fExpPValues
	list of HypoTestResult for each point

bool	fFittedLowerLimit

bool	fFittedUpperLimit

bool	fInterpolateLowerLimit
	two sided scan (look for lower/upper limit)

bool	fInterpolateUpperLimit

InterpolOption_t	fInterpolOption

bool	fIsTwoSided

double	fLowerLimitError
	interpolation option (linear or spline)

double	fUpperLimitError

bool	fUseCLs

std::vector< double >	fXValues
	number of points used to build expected p-values

TList	fYObjects

Protected Attributes inherited from RooStats::SimpleInterval
Double_t	fConfidenceLevel

Double_t	fLowerLimit

RooArgSet	fParameters

Double_t	fUpperLimit

Protected Attributes inherited from TNamed
TString	fName

TString	fTitle

Static Protected Attributes
static double	fgAsymptoticMaxSigma = 5

static int	fgAsymptoticNumPoints = 11
	max sigma value used to scan asymptotic expected p values

Private Member Functions
double	CalculateEstimatedError (double target, bool lower=true, double xmin=1, double xmax=0)
	Return an error estimate on the upper(lower) limit.

int	FindClosestPointIndex (double target, int mode=0, double xtarget=0)

double	GetExpectedLimit (double nsig, bool lower, const char *opt="") const
	get expected limit (lower/upper) depending on the flag for asymptotic is a special case (the distribution is generated an step in sigma values) distinguish asymptotic looking at the hypotest results if option = "P" get expected limit using directly quantiles of p value distribution else (default) find expected limit by obtaining first a full limit distributions The last one is in general more correct

double	GetGraphX (const TGraph &g, double y0, bool lowSearch, double &xmin, double &xmax) const
	return the X value of the given graph for the target value y0 the graph is evaluated using linear interpolation by default.

double	GetGraphX (const TGraph &g, double y0, bool lowSearch=true) const

SamplingDistribution *	GetLimitDistribution (bool lower) const
	get the limit distribution (lower/upper depending on the flag) by interpolating the expected p values for each point

Friends
class	HypoTestInverter
	list of expected sampling distribution for each point

class	HypoTestInverterOriginal

class	HypoTestInverterPlot

Additional Inherited Members
Static Public Member Functions inherited from TObject
static Long_t	GetDtorOnly ()
	Return destructor only flag.

static Bool_t	GetObjectStat ()
	Get status of object stat flag.

static void	SetDtorOnly (void *obj)
	Set destructor only flag.

static void	SetObjectStat (Bool_t stat)
	Turn on/off tracking of objects in the TObjectTable.

Protected Types inherited from TObject
enum	{ kOnlyPrepStep = BIT(3) }

Protected Member Functions inherited from TObject
virtual void	DoError (int level, const char location, const char fmt, va_list va) const
	Interface to ErrorHandler (protected).

void	MakeZombie ()

#include <RooStats/HypoTestInverterResult.h>

Inheritance diagram for RooStats::HypoTestInverterResult:

[legend]

Member Enumeration Documentation

◆ InterpolOption_t

enum RooStats::HypoTestInverterResult::InterpolOption_t

Enumerator
kLinear
kSpline

Definition at line 162 of file HypoTestInverterResult.h.

Constructor & Destructor Documentation

◆ HypoTestInverterResult() [1/3]

HypoTestInverterResult::HypoTestInverterResult ( const char * name = 0 )

explicit

default constructor

Definition at line 58 of file HypoTestInverterResult.cxx.

◆ HypoTestInverterResult() [2/3]

HypoTestInverterResult::HypoTestInverterResult	(	const char *	name,
		const RooRealVar &	scannedVariable,
		double	cl
	)

constructor

Definition at line 152 of file HypoTestInverterResult.cxx.

◆ HypoTestInverterResult() [3/3]

HypoTestInverterResult::HypoTestInverterResult	(	const HypoTestInverterResult &	other,
		const char *	name
	)

copy constructor

Definition at line 81 of file HypoTestInverterResult.cxx.

◆ ~HypoTestInverterResult()

HypoTestInverterResult::~HypoTestInverterResult ( )

virtual

destructor

Definition at line 181 of file HypoTestInverterResult.cxx.

Member Function Documentation

◆ Add() [1/2]

bool HypoTestInverterResult::Add ( const HypoTestInverterResult & otherResult )

merge with the content of another HypoTestInverterResult object

Merge this HypoTestInverterResult with another HypoTestInverterResult passed as argument The merge is done by combining the HypoTestResult when the same point value exist in both results.

If results exist at different points these are added in the new result NOTE: Merging of the expected p-values obtained with pseudo-data. When expected p-values exist in the result (i.e. when rebuild option is used when getting the expected limit distribution in the HYpoTestInverter) then the expected p-values are also merged. This is equivalent at merging the pseudo-data. However there can be an inconsistency if the expected p-values have been obtained with different toys. In this case the merge is done but a warning message is printed.

Definition at line 321 of file HypoTestInverterResult.cxx.

◆ Add() [2/2]

bool HypoTestInverterResult::Add	(	Double_t	x,
		const HypoTestResult &	result
	)

add the result of a single point (an HypoTestRsult)

Add a single point result (an HypoTestResult)

Definition at line 410 of file HypoTestInverterResult.cxx.

◆ ArraySize()

int RooStats::HypoTestInverterResult::ArraySize ( ) const

inline

number of entries in the results array

Definition at line 94 of file HypoTestInverterResult.h.

◆ CalculateEstimatedError()

Double_t HypoTestInverterResult::CalculateEstimatedError	(	double	target,
		bool	lower = `true`,
		double	xmin = `1`,
		double	xmax = `0`
	)

private

Return an error estimate on the upper(lower) limit.

This is the error on either CLs or CLsplusb divided by an estimate of the slope at this point.

Definition at line 974 of file HypoTestInverterResult.cxx.

◆ CLb()

double HypoTestInverterResult::CLb ( int index ) const

return the observed CLb value for the i-th entry

function to return the observed CLb value for the i-th entry

Definition at line 479 of file HypoTestInverterResult.cxx.

◆ CLbError()

double HypoTestInverterResult::CLbError ( int index ) const

return the observed CLb value for the i-th entry

function to return the error on the observed CLb value for the i-th entry

Definition at line 515 of file HypoTestInverterResult.cxx.

◆ CLs()

double HypoTestInverterResult::CLs ( int index ) const

return the observed CLb value for the i-th entry

function to return the observed CLs value for the i-th entry

Definition at line 503 of file HypoTestInverterResult.cxx.

◆ CLsError()

double HypoTestInverterResult::CLsError ( int index ) const

return the observed CLb value for the i-th entry

function to return the error on the observed CLs value for the i-th entry

Definition at line 539 of file HypoTestInverterResult.cxx.

◆ CLsplusb()

double HypoTestInverterResult::CLsplusb ( int index ) const

return the observed CLsplusb value for the i-th entry

function to return the observed CLs+b value for the i-th entry

Definition at line 491 of file HypoTestInverterResult.cxx.

◆ CLsplusbError()

double HypoTestInverterResult::CLsplusbError ( int index ) const

return the observed CLsplusb value for the i-th entry

function to return the error on the observed CLs+b value for the i-th entry

Definition at line 527 of file HypoTestInverterResult.cxx.

◆ ExclusionCleanup()

int HypoTestInverterResult::ExclusionCleanup ( )

remove points that appear to have failed.

Remove problematic points from this result.

This function can be used to clean up a result that has failed fits, spiking CLs or similar problems. It removes

Points where CLs is not falling monotonously. These may result from a lack of numerical precision.
Points where CLs spikes to more than 0.999.
Points with very low CLs. These are not needed to run the inverter, which speeds up the process.
Points where CLs < 0. These occur when fits fail.

Definition at line 200 of file HypoTestInverterResult.cxx.

◆ FindClosestPointIndex()

int HypoTestInverterResult::FindClosestPointIndex	(	double	target,
		int	mode = `0`,
		double	xtarget = `0`
	)

private

if mode = 0 find closest point to target in Y, the object closest to the target which is 3 sigma from the target and has smaller error
if mode = 1 find 2 closest point to target in X and between these two take the one closer to the target
if mode = 2 as in mode = 1 but return the lower point not the closest one
if mode = 3 as in mode = 1 but return the upper point not the closest one

Definition at line 885 of file HypoTestInverterResult.cxx.

◆ FindIndex()

int HypoTestInverterResult::FindIndex ( double xvalue ) const

find the index corresponding at the poi value xvalue If no points is found return -1 Note that a tolerance is used of 10^-12 to find the closest point

Definition at line 566 of file HypoTestInverterResult.cxx.

◆ FindInterpolatedLimit()

double HypoTestInverterResult::FindInterpolatedLimit	(	double	target,
		bool	lowSearch = `false`,
		double	xmin = `1`,
		double	xmax = `0`
	)

interpolate to find a limit value Use a linear or a spline interpolation depending on the interpolation option

Definition at line 712 of file HypoTestInverterResult.cxx.

◆ GetAltTestStatDist()

SamplingDistribution * RooStats::HypoTestInverterResult::GetAltTestStatDist ( int index ) const

inline

Definition at line 140 of file HypoTestInverterResult.h.

◆ GetBackgroundTestStatDist()

SamplingDistribution * HypoTestInverterResult::GetBackgroundTestStatDist ( int index ) const

get the background test statistic distribution

Definition at line 1108 of file HypoTestInverterResult.cxx.

◆ GetExpectedLimit()

double HypoTestInverterResult::GetExpectedLimit	(	double	nsig,
		bool	lower,
		const char *	opt = `""`
	)		const

private

get expected limit (lower/upper) depending on the flag for asymptotic is a special case (the distribution is generated an step in sigma values) distinguish asymptotic looking at the hypotest results if option = "P" get expected limit using directly quantiles of p value distribution else (default) find expected limit by obtaining first a full limit distributions The last one is in general more correct

Definition at line 1314 of file HypoTestInverterResult.cxx.

◆ GetExpectedLowerLimit()

double HypoTestInverterResult::GetExpectedLowerLimit	(	double	nsig = `0`,
		const char *	opt = `""`
	)		const

get Limit value corresponding at the desired nsigma level (0) is median -1 sigma is 1 sigma

Get the expected lower limit nsig is used to specify which expected value of the UpperLimitDistribution For example.

nsig = 0 (default value) returns the expected value
nsig = -1 returns the lower band value at -1 sigma
nsig + 1 return the upper value
opt = "" (default) : compute limit by interpolating all the p values, find the corresponding limit distribution and then find the quantiles in the limit distribution ioption = "P" is the method used for plotting. One Finds the corresponding nsig quantile in the p values and then interpolates them

Definition at line 1283 of file HypoTestInverterResult.cxx.

◆ GetExpectedPValueDist()

SamplingDistribution * HypoTestInverterResult::GetExpectedPValueDist ( int index ) const

return expected distribution of p-values (Cls or Clsplusb)

get the expected p-value distribution at the scanned point index

Definition at line 1127 of file HypoTestInverterResult.cxx.

◆ GetExpectedUpperLimit()

double HypoTestInverterResult::GetExpectedUpperLimit	(	double	nsig = `0`,
		const char *	opt = `""`
	)		const

get Limit value corresponding at the desired nsigma level (0) is median -1 sigma is 1 sigma

Get the expected upper limit nsig is used to specify which expected value of the UpperLimitDistribution For example.

nsig = 0 (default value) returns the expected value
nsig = -1 returns the lower band value at -1 sigma
nsig + 1 return the upper value
opt is an option specifying the type of method used for computing the upper limit
opt = "" (default) : compute limit by interpolating all the p values, find the corresponding limit distribution and then find the quantiles in the limit distribution ioption = "P" is the method used for plotting. One Finds the corresponding nsig quantile in the p values and then interpolates them

Definition at line 1301 of file HypoTestInverterResult.cxx.

◆ GetGraphX() [1/2]

double HypoTestInverterResult::GetGraphX	(	const TGraph &	graph,
		double	y0,
		bool	lowSearch,
		double &	axmin,
		double &	axmax
	)		const

private

return the X value of the given graph for the target value y0 the graph is evaluated using linear interpolation by default.

if option = "S" a TSpline3 is used

Definition at line 583 of file HypoTestInverterResult.cxx.

◆ GetGraphX() [2/2]

double RooStats::HypoTestInverterResult::GetGraphX	(	const TGraph &	g,
		double	y0,
		bool	lowSearch = `true`
	)		const

inlineprivate

Definition at line 181 of file HypoTestInverterResult.h.

◆ GetInterpolationOption()

InterpolOption_t RooStats::HypoTestInverterResult::GetInterpolationOption ( ) const

inline

Definition at line 167 of file HypoTestInverterResult.h.

◆ GetLastResult()

HypoTestResult * RooStats::HypoTestInverterResult::GetLastResult ( ) const

inline

Definition at line 91 of file HypoTestInverterResult.h.

◆ GetLastXValue()

double RooStats::HypoTestInverterResult::GetLastXValue ( ) const

inline

Definition at line 87 of file HypoTestInverterResult.h.

◆ GetLastYError()

double RooStats::HypoTestInverterResult::GetLastYError ( ) const

inline

Definition at line 89 of file HypoTestInverterResult.h.

◆ GetLastYValue()

double RooStats::HypoTestInverterResult::GetLastYValue ( ) const

inline

Definition at line 85 of file HypoTestInverterResult.h.

◆ GetLimitDistribution()

SamplingDistribution * HypoTestInverterResult::GetLimitDistribution ( bool lower ) const

private

get the limit distribution (lower/upper depending on the flag) by interpolating the expected p values for each point

Definition at line 1183 of file HypoTestInverterResult.cxx.

◆ GetLowerLimitDistribution()

SamplingDistribution * RooStats::HypoTestInverterResult::GetLowerLimitDistribution ( ) const

inline

get expected lower limit distributions implemented using interpolation The size for the sampling distribution is given (by default is given by the average number of toy/point)

Definition at line 147 of file HypoTestInverterResult.h.

◆ GetNullTestStatDist()

SamplingDistribution * RooStats::HypoTestInverterResult::GetNullTestStatDist ( int index ) const

inline

same in terms of alt and null

Definition at line 137 of file HypoTestInverterResult.h.

◆ GetResult()

HypoTestResult * HypoTestInverterResult::GetResult ( int index ) const

return a pointer to the i^th result object

get the HypoTestResult object at the given index point

Definition at line 551 of file HypoTestInverterResult.cxx.

◆ GetSignalAndBackgroundTestStatDist()

SamplingDistribution * HypoTestInverterResult::GetSignalAndBackgroundTestStatDist ( int index ) const

get the signal and background test statistic distribution

Definition at line 1118 of file HypoTestInverterResult.cxx.

◆ GetUpperLimitDistribution()

SamplingDistribution * RooStats::HypoTestInverterResult::GetUpperLimitDistribution ( ) const

inline

get expected upper limit distributions implemented using interpolation

Definition at line 151 of file HypoTestInverterResult.h.

◆ GetXValue()

double HypoTestInverterResult::GetXValue ( int index ) const

function to return the value of the parameter of interest for the i^th entry in the results

Definition at line 432 of file HypoTestInverterResult.cxx.

◆ GetYError()

double HypoTestInverterResult::GetYError ( int index ) const

function to return the estimated error on the value of the confidence level for the i^th entry in the results

Definition at line 462 of file HypoTestInverterResult.cxx.

◆ GetYValue()

double HypoTestInverterResult::GetYValue ( int index ) const

function to return the value of the confidence level for the i^th entry in the results

Definition at line 445 of file HypoTestInverterResult.cxx.

◆ IsOneSided()

bool RooStats::HypoTestInverterResult::IsOneSided ( ) const

inline

query if one sided result

Definition at line 111 of file HypoTestInverterResult.h.

◆ IsTwoSided()

bool RooStats::HypoTestInverterResult::IsTwoSided ( ) const

inline

query if two sided result

Definition at line 113 of file HypoTestInverterResult.h.

◆ LowerLimit()

Double_t HypoTestInverterResult::LowerLimit ( )

virtual

lower and upper bound of the confidence interval (to get upper/lower limits, multiply the size( = 1-confidence level ) by 2

Reimplemented from RooStats::SimpleInterval.

Definition at line 940 of file HypoTestInverterResult.cxx.

◆ LowerLimitEstimatedError()

Double_t HypoTestInverterResult::LowerLimitEstimatedError ( )

rough estimation of the error on the computed bound of the confidence interval Estimate of lower limit error function evaluates only a rough error on the lower limit.

need to have compute first lower limit

Be careful when using this estimation

Definition at line 1087 of file HypoTestInverterResult.cxx.

◆ operator=()

HypoTestInverterResult & HypoTestInverterResult::operator= ( const HypoTestInverterResult & other )

operator =

Definition at line 111 of file HypoTestInverterResult.cxx.

◆ SetCLsCleanupThreshold()

void RooStats::HypoTestInverterResult::SetCLsCleanupThreshold ( Double_t th )

inline

set CLs threshold for exclusion cleanup function

Definition at line 105 of file HypoTestInverterResult.h.

◆ SetConfidenceLevel()

virtual void RooStats::HypoTestInverterResult::SetConfidenceLevel ( Double_t cl )

inlinevirtual

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

Reimplemented from RooStats::SimpleInterval.

Definition at line 102 of file HypoTestInverterResult.h.

◆ SetInterpolationOption()

void RooStats::HypoTestInverterResult::SetInterpolationOption ( InterpolOption_t opt )

inline

set the interpolation option, linear (kLinear ) or spline (kSpline)

Definition at line 165 of file HypoTestInverterResult.h.

◆ SetTestSize()

virtual void RooStats::HypoTestInverterResult::SetTestSize ( Double_t size )

inlinevirtual

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

Definition at line 99 of file HypoTestInverterResult.h.

◆ UpperLimit()

Double_t HypoTestInverterResult::UpperLimit ( )

virtual

Reimplemented from RooStats::SimpleInterval.

Definition at line 956 of file HypoTestInverterResult.cxx.

◆ UpperLimitEstimatedError()

Double_t HypoTestInverterResult::UpperLimitEstimatedError ( )

Estimate of lower limit error function evaluates only a rough error on the lower limit.

Be careful when using this estimation

Definition at line 1097 of file HypoTestInverterResult.cxx.

◆ UseCLs()

void RooStats::HypoTestInverterResult::UseCLs ( bool on = true )

inline

flag to switch between using CLsb (default) or CLs as confidence level

Definition at line 108 of file HypoTestInverterResult.h.

Friends And Related Symbol Documentation

◆ HypoTestInverter

friend class HypoTestInverter

friend

list of expected sampling distribution for each point

Definition at line 210 of file HypoTestInverterResult.h.

◆ HypoTestInverterOriginal

friend class HypoTestInverterOriginal

friend

Definition at line 212 of file HypoTestInverterResult.h.

◆ HypoTestInverterPlot

friend class HypoTestInverterPlot

friend

Definition at line 211 of file HypoTestInverterResult.h.

Member Data Documentation

◆ fCLsCleanupThreshold

double RooStats::HypoTestInverterResult::fCLsCleanupThreshold

protected

Definition at line 200 of file HypoTestInverterResult.h.

◆ fExpPValues

TList RooStats::HypoTestInverterResult::fExpPValues

protected

list of HypoTestResult for each point

Definition at line 208 of file HypoTestInverterResult.h.

◆ fFittedLowerLimit

bool RooStats::HypoTestInverterResult::fFittedLowerLimit

protected

Definition at line 193 of file HypoTestInverterResult.h.

◆ fFittedUpperLimit

bool RooStats::HypoTestInverterResult::fFittedUpperLimit

protected

Definition at line 194 of file HypoTestInverterResult.h.

◆ fgAsymptoticMaxSigma

double HypoTestInverterResult::fgAsymptoticMaxSigma = 5

staticprotected

Definition at line 202 of file HypoTestInverterResult.h.

◆ fgAsymptoticNumPoints

int HypoTestInverterResult::fgAsymptoticNumPoints = 11

staticprotected

max sigma value used to scan asymptotic expected p values

Definition at line 203 of file HypoTestInverterResult.h.

◆ fInterpolateLowerLimit

bool RooStats::HypoTestInverterResult::fInterpolateLowerLimit

protected

two sided scan (look for lower/upper limit)

Definition at line 191 of file HypoTestInverterResult.h.

◆ fInterpolateUpperLimit

bool RooStats::HypoTestInverterResult::fInterpolateUpperLimit

protected

Definition at line 192 of file HypoTestInverterResult.h.

◆ fInterpolOption

InterpolOption_t RooStats::HypoTestInverterResult::fInterpolOption

protected

Definition at line 195 of file HypoTestInverterResult.h.

◆ fIsTwoSided

bool RooStats::HypoTestInverterResult::fIsTwoSided

protected

Definition at line 190 of file HypoTestInverterResult.h.

◆ fLowerLimitError

double RooStats::HypoTestInverterResult::fLowerLimitError

protected

interpolation option (linear or spline)

Definition at line 197 of file HypoTestInverterResult.h.

◆ fUpperLimitError

double RooStats::HypoTestInverterResult::fUpperLimitError

protected

Definition at line 198 of file HypoTestInverterResult.h.

◆ fUseCLs

bool RooStats::HypoTestInverterResult::fUseCLs

protected

Definition at line 189 of file HypoTestInverterResult.h.

◆ fXValues

std::vector<double> RooStats::HypoTestInverterResult::fXValues

protected

number of points used to build expected p-values

Definition at line 205 of file HypoTestInverterResult.h.

◆ fYObjects

TList RooStats::HypoTestInverterResult::fYObjects

protected

Definition at line 207 of file HypoTestInverterResult.h.

Libraries for RooStats::HypoTestInverterResult:

[legend]

The documentation for this class was generated from the following files:

roofit/roostats/inc/RooStats/HypoTestInverterResult.h
roofit/roostats/src/HypoTestInverterResult.cxx

Public Types

Public Member Functions

Protected Attributes

Static Protected Attributes

Private Member Functions

Friends

Additional Inherited Members

Member Enumeration Documentation

◆ InterpolOption_t

Constructor & Destructor Documentation

◆ HypoTestInverterResult() [1/3]

◆ HypoTestInverterResult() [2/3]

◆ HypoTestInverterResult() [3/3]

◆ ~HypoTestInverterResult()

Member Function Documentation

◆ Add() [1/2]

◆ Add() [2/2]

◆ ArraySize()

◆ CalculateEstimatedError()

◆ CLb()

◆ CLbError()

◆ CLs()

◆ CLsError()

◆ CLsplusb()

◆ CLsplusbError()

◆ ExclusionCleanup()

◆ FindClosestPointIndex()

◆ FindIndex()

◆ FindInterpolatedLimit()

◆ GetAltTestStatDist()

◆ GetBackgroundTestStatDist()

◆ GetExpectedLimit()

◆ GetExpectedLowerLimit()

◆ GetExpectedPValueDist()

◆ GetExpectedUpperLimit()

◆ GetGraphX() [1/2]

◆ GetGraphX() [2/2]

◆ GetInterpolationOption()

◆ GetLastResult()

◆ GetLastXValue()

◆ GetLastYError()

◆ GetLastYValue()

◆ GetLimitDistribution()

◆ GetLowerLimitDistribution()

◆ GetNullTestStatDist()

◆ GetResult()

◆ GetSignalAndBackgroundTestStatDist()

◆ GetUpperLimitDistribution()

◆ GetXValue()

◆ GetYError()

◆ GetYValue()

◆ IsOneSided()

◆ IsTwoSided()

◆ LowerLimit()

◆ LowerLimitEstimatedError()

◆ operator=()

◆ SetCLsCleanupThreshold()

◆ SetConfidenceLevel()

◆ SetInterpolationOption()

◆ SetTestSize()

◆ UpperLimit()

◆ UpperLimitEstimatedError()

◆ UseCLs()

Friends And Related Symbol Documentation

◆ HypoTestInverter

◆ HypoTestInverterOriginal

◆ HypoTestInverterPlot

Member Data Documentation

◆ fCLsCleanupThreshold

◆ fExpPValues

◆ fFittedLowerLimit

◆ fFittedUpperLimit

◆ fgAsymptoticMaxSigma

◆ fgAsymptoticNumPoints

◆ fInterpolateLowerLimit

◆ fInterpolateUpperLimit

◆ fInterpolOption

◆ fIsTwoSided

◆ fLowerLimitError

◆ fUpperLimitError