#include <itkTDistribution.h>

Detailed Description

TDistribution class defines the interface for a univariate Student-t distribution (pdfs, cdfs, etc.).

TDistribution provides access to the probability density function (pdf), the cumulative distribution function (cdf), and the inverse cumulative distribution function for a Student-t distribution.

The EvaluatePDF(), EvaluateCDF, EvaluateInverseCDF() methods are all virtual, allowing algorithms to be written with an abstract interface to a distribution (with said distribution provided to the algorithm at run-time). Static methods, not requiring an instance of the distribution, are also provided. The static methods allow for optimized access to distributions when the distribution is known a priori to the algorithm.

TDistributions are univariate. Multivariate versions may be provided under a separate superclass (since the parameters to the pdf and cdf would have to be vectors not scalars).

TDistributions can be used for t tests.

Note: This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on the National Centers for Biomedical Computing can be obtained from http://commonfund.nih.gov/bioinformatics.

Definition at line 59 of file itkTDistribution.h.

Inheritance diagram for itk::Statistics::TDistribution:

Collaboration diagram for itk::Statistics::TDistribution:

Public Types
using	ConstPointer = SmartPointer< const Self >

using	Pointer = SmartPointer< Self >

using	Self = TDistribution

using	Superclass = ProbabilityDistribution

Public Types inherited from itk::Statistics::ProbabilityDistribution
using	ConstPointer = SmartPointer< const Self >

using	ParametersType = Array< double >

using	Pointer = SmartPointer< Self >

using	Self = ProbabilityDistribution

using	Superclass = Object

Public Types inherited from itk::Object
using	ConstPointer = SmartPointer< const Self >

using	Pointer = SmartPointer< Self >

using	Self = Object

using	Superclass = LightObject

Public Types inherited from itk::LightObject
using	ConstPointer = SmartPointer< const Self >

using	Pointer = SmartPointer< Self >

using	Self = LightObject

Public Member Functions
double	EvaluateCDF (double x) const override

double	EvaluateCDF (double x, const ParametersType &) const override

virtual double	EvaluateCDF (double x, SizeValueType degreesOfFreedom) const

double	EvaluateInverseCDF (double p) const override

double	EvaluateInverseCDF (double p, const ParametersType &) const override

virtual double	EvaluateInverseCDF (double p, SizeValueType degreesOfFreedom) const

double	EvaluatePDF (double x) const override

double	EvaluatePDF (double x, const ParametersType &) const override

virtual double	EvaluatePDF (double x, SizeValueType degreesOfFreedom) const

virtual SizeValueType	GetDegreesOfFreedom () const

double	GetMean () const override

const char *	GetNameOfClass () const override

SizeValueType	GetNumberOfParameters () const override

double	GetVariance () const override

bool	HasMean () const override

bool	HasVariance () const override

virtual void	SetDegreesOfFreedom (SizeValueType)

Public Member Functions inherited from itk::Statistics::ProbabilityDistribution
virtual const ParametersType &	GetParameters () const

virtual void	SetParameters (const ParametersType &params)

Public Member Functions inherited from itk::Object
unsigned long	AddObserver (const EventObject &event, Command *cmd) const

unsigned long	AddObserver (const EventObject &event, std::function< void(const EventObject &)> function) const

LightObject::Pointer	CreateAnother () const override

virtual void	DebugOff () const

virtual void	DebugOn () const

Command *	GetCommand (unsigned long tag)

bool	GetDebug () const

MetaDataDictionary &	GetMetaDataDictionary ()

const MetaDataDictionary &	GetMetaDataDictionary () const

virtual ModifiedTimeType	GetMTime () const

virtual const TimeStamp &	GetTimeStamp () const

bool	HasObserver (const EventObject &event) const

void	InvokeEvent (const EventObject &)

void	InvokeEvent (const EventObject &) const

virtual void	Modified () const

void	Register () const override

void	RemoveAllObservers ()

void	RemoveObserver (unsigned long tag) const

void	SetDebug (bool debugFlag) const

void	SetReferenceCount (int) override

void	UnRegister () const noexcept override

void	SetMetaDataDictionary (const MetaDataDictionary &rhs)

void	SetMetaDataDictionary (MetaDataDictionary &&rrhs)

virtual void	SetObjectName (std::string _arg)

virtual const std::string &	GetObjectName () const

Public Member Functions inherited from itk::LightObject
Pointer	Clone () const

virtual void	Delete ()

virtual int	GetReferenceCount () const

void	Print (std::ostream &os, Indent indent=0) const

Static Public Member Functions
static double	CDF (double x, const ParametersType &)

static double	CDF (double x, SizeValueType degreesOfFreedom)

static double	InverseCDF (double p, const ParametersType &)

static double	InverseCDF (double p, SizeValueType degreesOfFreedom)

static Pointer	New ()

static double	PDF (double x, const ParametersType &)

static double	PDF (double x, SizeValueType degreesOfFreedom)

Static Public Member Functions inherited from itk::Object
static bool	GetGlobalWarningDisplay ()

static void	GlobalWarningDisplayOff ()

static void	GlobalWarningDisplayOn ()

static Pointer	New ()

static void	SetGlobalWarningDisplay (bool val)

Static Public Member Functions inherited from itk::LightObject
static void	BreakOnError ()

static Pointer	New ()

Protected Member Functions
void	PrintSelf (std::ostream &os, Indent indent) const override

	TDistribution ()

	~TDistribution () override=default

Protected Member Functions inherited from itk::Statistics::ProbabilityDistribution
	ProbabilityDistribution ()

	~ProbabilityDistribution () override

Protected Member Functions inherited from itk::Object
	Object ()

bool	PrintObservers (std::ostream &os, Indent indent) const

virtual void	SetTimeStamp (const TimeStamp &timeStamp)

	~Object () override

Protected Member Functions inherited from itk::LightObject
virtual LightObject::Pointer	InternalClone () const

	LightObject ()

virtual void	PrintHeader (std::ostream &os, Indent indent) const

virtual void	PrintTrailer (std::ostream &os, Indent indent) const

virtual	~LightObject ()

Additional Inherited Members
Protected Attributes inherited from itk::Statistics::ProbabilityDistribution
ParametersType	m_Parameters {}

Protected Attributes inherited from itk::LightObject
std::atomic< int >	m_ReferenceCount {}

Member Typedef Documentation

◆ ConstPointer

using itk::Statistics::TDistribution::ConstPointer = SmartPointer<const Self>

Definition at line 68 of file itkTDistribution.h.

◆ Pointer

using itk::Statistics::TDistribution::Pointer = SmartPointer<Self>

Definition at line 67 of file itkTDistribution.h.

◆ Self

using itk::Statistics::TDistribution::Self = TDistribution

Standard class type aliases

Definition at line 65 of file itkTDistribution.h.

◆ Superclass

using itk::Statistics::TDistribution::Superclass = ProbabilityDistribution

Definition at line 66 of file itkTDistribution.h.

Constructor & Destructor Documentation

◆ TDistribution()

itk::Statistics::TDistribution::TDistribution ( )

protected

◆ ~TDistribution()

itk::Statistics::TDistribution::~TDistribution ( )

overrideprotecteddefault

Member Function Documentation

◆ CDF() [1/2]

static double itk::Statistics::TDistribution::CDF	(	double	x,
		const ParametersType &
	)

static

Static method to evaluate the cumulative distribution function (cdf) of a Student-t with a specified number of degrees of freedom. The static method provides optimized access without requiring an instance of the class. The degrees of freedom are passed as a parameters vector.

This is based on Abramowitz and Stegun 26.7.1. Accuracy is approximately 10^-14.

◆ CDF() [2/2]

static double itk::Statistics::TDistribution::CDF	(	double	x,
		SizeValueType	degreesOfFreedom
	)

static

Static method to evaluate the cumulative distribution function (cdf) of a Student-t with a specified number of degrees of freedom. The static method provides optimized access without requiring an instance of the class.

This is based on Abramowitz and Stegun 26.7.1. Accuracy is approximately 10^-14.

◆ EvaluateCDF() [1/3]

double itk::Statistics::TDistribution::EvaluateCDF ( double x ) const

overridevirtual

Evaluate the cumulative distribution function (cdf). The parameters of the distribution are assigned via SetParameters().

Implements itk::Statistics::ProbabilityDistribution.

◆ EvaluateCDF() [2/3]

double itk::Statistics::TDistribution::EvaluateCDF	(	double	x,
		const ParametersType &
	)		const

overridevirtual

Evaluate the cumulative distribution function (cdf). The parameters for the distribution are passed as a parameters vector. The ordering of the parameters is (degreesOfFreedom).

Implements itk::Statistics::ProbabilityDistribution.

◆ EvaluateCDF() [3/3]

virtual double itk::Statistics::TDistribution::EvaluateCDF	(	double	x,
		SizeValueType	degreesOfFreedom
	)		const

virtual

Evaluate the cumulative distribution function (cdf). The parameters of the distribution are passed as separate parameters.

◆ EvaluateInverseCDF() [1/3]

double itk::Statistics::TDistribution::EvaluateInverseCDF ( double p ) const

overridevirtual

Evaluate the inverse cumulative distribution function (inverse cdf). Parameter p must be between 0.0 and 1.0. The parameters of the distribution are assigned via SetParameters().

Implements itk::Statistics::ProbabilityDistribution.

◆ EvaluateInverseCDF() [2/3]

double itk::Statistics::TDistribution::EvaluateInverseCDF	(	double	p,
		const ParametersType &
	)		const

overridevirtual

Evaluate the inverse cumulative distribution function (inverse cdf). Parameter p must be between 0.0 and 1.0. The parameters for the distribution are passed as a parameters vector. The ordering of the parameters is (degrees of freedom).

Implements itk::Statistics::ProbabilityDistribution.

◆ EvaluateInverseCDF() [3/3]

virtual double itk::Statistics::TDistribution::EvaluateInverseCDF	(	double	p,
		SizeValueType	degreesOfFreedom
	)		const

virtual

Evaluate the inverse cumulative distribution function (inverse cdf). Parameter p must be between 0.0 and 1.0. The parameters of the distribution are passed as separate parameters.

◆ EvaluatePDF() [1/3]

double itk::Statistics::TDistribution::EvaluatePDF ( double x ) const

overridevirtual

Evaluate the probability density function (pdf). The parameters of the distribution are assigned via SetParameters().

Implements itk::Statistics::ProbabilityDistribution.

◆ EvaluatePDF() [2/3]

double itk::Statistics::TDistribution::EvaluatePDF	(	double	x,
		const ParametersType &
	)		const

overridevirtual

Evaluate the probability density function (pdf). The parameters for the distribution are passed as a parameters vector. The ordering of the parameters is (degrees of freedom).

Implements itk::Statistics::ProbabilityDistribution.

◆ EvaluatePDF() [3/3]

virtual double itk::Statistics::TDistribution::EvaluatePDF	(	double	x,
		SizeValueType	degreesOfFreedom
	)		const

virtual

Evaluate the probability density function (pdf). The parameters of the distribution are passed as separate parameters.

◆ GetDegreesOfFreedom()

virtual SizeValueType itk::Statistics::TDistribution::GetDegreesOfFreedom ( ) const

virtual

Get the number of degrees of freedom in the t distribution. Defaults to 1

◆ GetMean()

double itk::Statistics::TDistribution::GetMean ( ) const

overridevirtual

Get the mean of the distribution.

Implements itk::Statistics::ProbabilityDistribution.

◆ GetNameOfClass()

const char* itk::Statistics::TDistribution::GetNameOfClass ( ) const

overridevirtual

See also: LightObject::GetNameOfClass()

Reimplemented from itk::Statistics::ProbabilityDistribution.

◆ GetNumberOfParameters()

SizeValueType itk::Statistics::TDistribution::GetNumberOfParameters ( ) const

inlineoverridevirtual

Return the number of parameters. For a univariate Student-t distribution, the number of parameters is 1 (degrees of freedom)

Implements itk::Statistics::ProbabilityDistribution.

Definition at line 79 of file itkTDistribution.h.

◆ GetVariance()

double itk::Statistics::TDistribution::GetVariance ( ) const

overridevirtual

Get the variance of the distribution. If the variance does not exist, then quiet_NaN is returned.

Implements itk::Statistics::ProbabilityDistribution.

◆ HasMean()

bool itk::Statistics::TDistribution::HasMean ( ) const

inlineoverridevirtual

Does the Student-t distribution have a mean?

Implements itk::Statistics::ProbabilityDistribution.

Definition at line 146 of file itkTDistribution.h.

◆ HasVariance()

bool itk::Statistics::TDistribution::HasVariance ( ) const

overridevirtual

Does the Student-t distribution have a variance? Variance is only defined for degrees of freedom greater than 2

Implements itk::Statistics::ProbabilityDistribution.

◆ InverseCDF() [1/2]

static double itk::Statistics::TDistribution::InverseCDF	(	double	p,
		const ParametersType &
	)

static

Static method to evaluate the inverse cumulative distribution function of a Student-t with a specified number of degrees of freedom. The static method provides optimized access without requiring an instance of the class. Parameter p must be between 0.0 and 1.0. The degrees of freedom are passed as a parameters vector.

This is based on Abramowitz and Stegun 26.7.5 followed by a few Newton iterations to improve the precision at low degrees of freedom. Accuracy is approximately 10^-10.

◆ InverseCDF() [2/2]

static double itk::Statistics::TDistribution::InverseCDF	(	double	p,
		SizeValueType	degreesOfFreedom
	)

static

Static method to evaluate the inverse cumulative distribution function of a Student-t with a specified number of degrees of freedom. The static method provides optimized access without requiring an instance of the class. Parameter p must be between 0.0 and 1.0.

This is based on Abramowitz and Stegun 26.7.5 followed by a few Newton iterations to improve the precision at low degrees of freedom. Accuracy is approximately 10^-10.

◆ New()

static Pointer itk::Statistics::TDistribution::New ( )

static

Method for creation through the object factory.

◆ PDF() [1/2]

static double itk::Statistics::TDistribution::PDF	(	double	x,
		const ParametersType &
	)

static

Static method to evaluate the probability density function (pdf) of a Student-t with a specified number of degrees of freedom. The static method provides optimized access without requiring an instance of the class. The degrees of freedom for the distribution are passed in a parameters vector.

◆ PDF() [2/2]

static double itk::Statistics::TDistribution::PDF	(	double	x,
		SizeValueType	degreesOfFreedom
	)

static

Static method to evaluate the probability density function (pdf) of a Student-t with a specified number of degrees of freedom. The static method provides optimized access without requiring an instance of the class.

◆ PrintSelf()

void itk::Statistics::TDistribution::PrintSelf	(	std::ostream &	os,
		Indent	indent
	)		const

overrideprotectedvirtual

Methods invoked by Print() to print information about the object including superclasses. Typically not called by the user (use Print() instead) but used in the hierarchical print process to combine the output of several classes.

Reimplemented from itk::Statistics::ProbabilityDistribution.

◆ SetDegreesOfFreedom()

virtual void itk::Statistics::TDistribution::SetDegreesOfFreedom ( SizeValueType )

virtual

Set the number of degrees of freedom in the Student-t distribution. Defaults to 1

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

itkTDistribution.h

Detailed Description

Public Types

Public Member Functions

Static Public Member Functions

Protected Member Functions

Additional Inherited Members

Member Typedef Documentation

◆ ConstPointer

◆ Pointer

◆ Self

◆ Superclass

Constructor & Destructor Documentation

◆ TDistribution()

◆ ~TDistribution()

Member Function Documentation

◆ CDF() [1/2]

◆ CDF() [2/2]

◆ EvaluateCDF() [1/3]

◆ EvaluateCDF() [2/3]

◆ EvaluateCDF() [3/3]

◆ EvaluateInverseCDF() [1/3]

◆ EvaluateInverseCDF() [2/3]

◆ EvaluateInverseCDF() [3/3]

◆ EvaluatePDF() [1/3]

◆ EvaluatePDF() [2/3]

◆ EvaluatePDF() [3/3]

◆ GetDegreesOfFreedom()

◆ GetMean()

◆ GetNameOfClass()

◆ GetNumberOfParameters()

◆ GetVariance()

◆ HasMean()

◆ HasVariance()

◆ InverseCDF() [1/2]

◆ InverseCDF() [2/2]

◆ New()

◆ PDF() [1/2]

◆ PDF() [2/2]

◆ PrintSelf()

◆ SetDegreesOfFreedom()