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RooStats::SamplingDistribution Class Reference

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

The distribution can either be an empirical distribution (eg. the samples themselves) or a weighted set of points (eg. for the FFT method). The class supports merging.

Definition at line 28 of file SamplingDistribution.h.

Public Member Functions

 SamplingDistribution ()
 Default constructor for SamplingDistribution.
 
 SamplingDistribution (const char *name, const char *title, const char *varName=0)
 SamplingDistribution constructor (with name and title)
 
 SamplingDistribution (const char *name, const char *title, RooDataSet &dataSet, const char *columnName=0, const char *varName=0)
 Creates a SamplingDistribution from a RooDataSet for debugging purposes; e.g.
 
 SamplingDistribution (const char *name, const char *title, std::vector< Double_t > &samplingDist, const char *varName=0)
 Constructor for SamplingDistribution.
 
 SamplingDistribution (const char *name, const char *title, std::vector< Double_t > &samplingDist, std::vector< Double_t > &sampleWeights, const char *varName=0)
 SamplingDistribution constructor.
 
virtual ~SamplingDistribution ()
 Destructor of SamplingDistribution.
 
void Add (const SamplingDistribution *other)
 merge two sampling distributions
 
Double_t CDF (Double_t x) const
 calculate CDF as a special case of Integral(...) with lower limit equal to -inf
 
const std::vector< Double_t > & GetSampleWeights () const
 Get the sampling weights.
 
const std::vector< Double_t > & GetSamplingDistribution () const
 Get test statistics values.
 
Int_t GetSize () const
 size of samples
 
const TString GetVarName () const
 
Double_t Integral (Double_t low, Double_t high, Bool_t normalize=kTRUE, Bool_t lowClosed=kTRUE, Bool_t highClosed=kFALSE) const
 numerical integral in these limits
 
Double_t IntegralAndError (Double_t &error, Double_t low, Double_t high, Bool_t normalize=kTRUE, Bool_t lowClosed=kTRUE, Bool_t highClosed=kFALSE) const
 numerical integral in these limits including error estimation
 
Double_t InverseCDF (Double_t pvalue)
 get the inverse of the Cumulative distribution function
 
Double_t InverseCDF (Double_t pvalue, Double_t sigmaVariaton, Double_t &inverseVariation)
 get the inverse of the Cumulative distribution function together with the inverse based on sampling variation
 
Double_t InverseCDFInterpolate (Double_t pvalue)
 get the inverse of the Cumulative distribution function
 
- 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 TObjectClone (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.
 
TNamedoperator= (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 TObjectDrawClone (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 TObjectFindObject (const char *name) const
 Must be redefined in derived classes.
 
virtual TObjectFindObject (const TObject *obj) const
 Must be redefined in derived classes.
 
virtual Option_tGetDrawOption () 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_tGetOption () 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)
 
Bool_t IsDestructed () const
 IsDestructed.
 
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 [].
 
voidoperator new (size_t sz)
 
voidoperator new (size_t sz, void *vp)
 
voidoperator new[] (size_t sz)
 
voidoperator new[] (size_t sz, void *vp)
 
TObjectoperator= (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 Member Functions

void SortValues () const
 Cached vector with sum of the weight used to compute integral error.
 
- 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 ()
 

Private Attributes

std::vector< Double_tfSampleWeights
 vector of points for the sampling distribution
 
std::vector< Double_tfSamplingDist
 
std::vector< Double_tfSumW
 
std::vector< Double_tfSumW2
 Cached vector with sum of the weight used to compute integral.
 
TString fVarName
 vector of weights for the samples
 

Additional Inherited Members

- 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)
}
 
- Static Public Member Functions inherited from TObject
static Longptr_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 Attributes inherited from TNamed
TString fName
 
TString fTitle
 

#include <RooStats/SamplingDistribution.h>

Inheritance diagram for RooStats::SamplingDistribution:
[legend]

Constructor & Destructor Documentation

◆ SamplingDistribution() [1/5]

SamplingDistribution::SamplingDistribution ( const char *  name,
const char *  title,
std::vector< Double_t > &  samplingDist,
const char *  varName = 0 
)

Constructor for SamplingDistribution.

SamplingDistribution constructor.

Definition at line 43 of file SamplingDistribution.cxx.

◆ SamplingDistribution() [2/5]

SamplingDistribution::SamplingDistribution ( const char *  name,
const char *  title,
std::vector< Double_t > &  samplingDist,
std::vector< Double_t > &  sampleWeights,
const char *  varName = 0 
)

SamplingDistribution constructor.

Definition at line 60 of file SamplingDistribution.cxx.

◆ SamplingDistribution() [3/5]

SamplingDistribution::SamplingDistribution ( const char *  name,
const char *  title,
const char *  varName = 0 
)

SamplingDistribution constructor (with name and title)

Definition at line 75 of file SamplingDistribution.cxx.

◆ SamplingDistribution() [4/5]

SamplingDistribution::SamplingDistribution ( const char *  name,
const char *  title,
RooDataSet dataSet,
const char *  _columnName = 0,
const char *  varName = 0 
)

Creates a SamplingDistribution from a RooDataSet for debugging purposes; e.g.

if you need a Gaussian type SamplingDistribution you can generate it from a Gaussian pdf and use the resulting RooDataSet with this constructor.

The result is the projected distribution onto varName marginalizing the other variables.

If varName is not given, the first variable will be used. This is useful mostly for RooDataSets with only one observable.

Definition at line 93 of file SamplingDistribution.cxx.

◆ SamplingDistribution() [5/5]

SamplingDistribution::SamplingDistribution ( )

Default constructor for SamplingDistribution.

SamplingDistribution default constructor.

Definition at line 134 of file SamplingDistribution.cxx.

◆ ~SamplingDistribution()

SamplingDistribution::~SamplingDistribution ( )
virtual

Destructor of SamplingDistribution.

SamplingDistribution destructor.

Definition at line 142 of file SamplingDistribution.cxx.

Member Function Documentation

◆ Add()

void SamplingDistribution::Add ( const SamplingDistribution other)

merge two sampling distributions

Merge SamplingDistributions (does nothing if NULL is given).

If variable name was not set before, it is copied from the added SamplingDistribution.

Definition at line 153 of file SamplingDistribution.cxx.

◆ CDF()

Double_t SamplingDistribution::CDF ( Double_t  x) const

calculate CDF as a special case of Integral(...) with lower limit equal to -inf

returns the closed integral [-inf,x]

Definition at line 310 of file SamplingDistribution.cxx.

◆ GetSampleWeights()

const std::vector< Double_t > & RooStats::SamplingDistribution::GetSampleWeights ( ) const
inline

Get the sampling weights.

Definition at line 67 of file SamplingDistribution.h.

◆ GetSamplingDistribution()

const std::vector< Double_t > & RooStats::SamplingDistribution::GetSamplingDistribution ( ) const
inline

Get test statistics values.

Definition at line 65 of file SamplingDistribution.h.

◆ GetSize()

Int_t RooStats::SamplingDistribution::GetSize ( ) const
inline

size of samples

Definition at line 62 of file SamplingDistribution.h.

◆ GetVarName()

const TString RooStats::SamplingDistribution::GetVarName ( ) const
inline

Definition at line 69 of file SamplingDistribution.h.

◆ Integral()

Double_t SamplingDistribution::Integral ( Double_t  low,
Double_t  high,
Bool_t  normalize = kTRUE,
Bool_t  lowClosed = kTRUE,
Bool_t  highClosed = kFALSE 
) const

numerical integral in these limits

Returns the integral in the open/closed/mixed interval.

Default is [low,high) interval. Normalization can be turned off.

Definition at line 188 of file SamplingDistribution.cxx.

◆ IntegralAndError()

Double_t SamplingDistribution::IntegralAndError ( Double_t error,
Double_t  low,
Double_t  high,
Bool_t  normalize = kTRUE,
Bool_t  lowClosed = kTRUE,
Bool_t  highClosed = kFALSE 
) const

numerical integral in these limits including error estimation

Returns the integral in the open/closed/mixed interval.

Default is [low,high) interval. Normalization can be turned off. compute also the error on the integral

Definition at line 238 of file SamplingDistribution.cxx.

◆ InverseCDF() [1/2]

Double_t SamplingDistribution::InverseCDF ( Double_t  pvalue)

get the inverse of the Cumulative distribution function

returns the inverse of the cumulative distribution function

Definition at line 317 of file SamplingDistribution.cxx.

◆ InverseCDF() [2/2]

Double_t SamplingDistribution::InverseCDF ( Double_t  pvalue,
Double_t  sigmaVariaton,
Double_t inverseVariation 
)

get the inverse of the Cumulative distribution function together with the inverse based on sampling variation

returns the inverse of the cumulative distribution function, with variations depending on number of samples

Definition at line 326 of file SamplingDistribution.cxx.

◆ InverseCDFInterpolate()

Double_t SamplingDistribution::InverseCDFInterpolate ( Double_t  pvalue)

get the inverse of the Cumulative distribution function

returns the inverse of the cumulative distribution function

Definition at line 406 of file SamplingDistribution.cxx.

◆ SortValues()

void SamplingDistribution::SortValues ( ) const
protected

Cached vector with sum of the weight used to compute integral error.

first need to sort the values and then compute the running sum of the weights and of the weight square needed later for computing the integral

internal function to sort values

Definition at line 200 of file SamplingDistribution.cxx.

Member Data Documentation

◆ fSampleWeights

std::vector<Double_t> RooStats::SamplingDistribution::fSampleWeights
mutableprivate

vector of points for the sampling distribution

Definition at line 84 of file SamplingDistribution.h.

◆ fSamplingDist

std::vector<Double_t> RooStats::SamplingDistribution::fSamplingDist
mutableprivate

Definition at line 83 of file SamplingDistribution.h.

◆ fSumW

std::vector<Double_t> RooStats::SamplingDistribution::fSumW
mutableprivate

Definition at line 89 of file SamplingDistribution.h.

◆ fSumW2

std::vector<Double_t> RooStats::SamplingDistribution::fSumW2
mutableprivate

Cached vector with sum of the weight used to compute integral.

Definition at line 90 of file SamplingDistribution.h.

◆ fVarName

TString RooStats::SamplingDistribution::fVarName
private

vector of weights for the samples

Definition at line 87 of file SamplingDistribution.h.

Libraries for RooStats::SamplingDistribution:

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