gov.sandia.cognition.statistics.distribution

Class StudentizedRangeDistribution

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.AbstractDistribution<NumberType>

gov.sandia.cognition.statistics.AbstractClosedFormUnivariateDistribution<java.lang.Double>

gov.sandia.cognition.statistics.distribution.StudentizedRangeDistribution

All Implemented Interfaces:

Vectorizable, ClosedFormDistribution<java.lang.Double>, ClosedFormUnivariateDistribution<java.lang.Double>, Distribution<java.lang.Double>, DistributionWithMean<java.lang.Double>, UnivariateDistribution<java.lang.Double>, CloneableSerializable, Randomized, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

StudentizedRangeDistribution.CDF

@PublicationReference(author="David M. Lane",title="Studentized Range Distribution",type=WebPage,year=2011,url="http://davidmlane.com/hyperstat/A47912.html") @PublicationReference(author="Wikipedia",title="Studentized range",type=WebPage,year=2011,url="http://en.wikipedia.org/wiki/Studentized_range")
public class StudentizedRangeDistribution
extends AbstractClosedFormUnivariateDistribution<java.lang.Double>
implements Randomized

Implementation of the Studentized Range distribution, which defines the population correction factor when performing multiple comparisons. In other words, if you compute the p-value of 20 objects, there's a 65% chance of finding at least one with a p-value of less than 0.05. The Studentized Range compensates for this chance when conducting multiple comparisons.

Since:

3.1

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	StudentizedRangeDistribution.APStat This is a translation of Fortran code taken from http://lib.stat.cmu.edu/apstat/, and the comments on the individual functions in this class are taken directly from the original.
static class	StudentizedRangeDistribution.CDF CDF of the StudentizedRangeDistribution
protected static class	StudentizedRangeDistribution.SampleRange Computes the estimate of the Studentized Range for a single sample

Field Summary

Fields

Modifier and Type	Field and Description
static double	DEFAULT_DEGREES_OF_FREEDOM Default degrees of freedom, 1d/0d.
static int	DEFAULT_NUM_SAMPLES Number of samples to draw for Monte Carlo estimates, 1000.
static int	DEFAULT_TREATMENT_COUNT Default treatment count, 2.
protected double	degreesOfFreedom Number of subjects in each treatment minus one.
protected java.util.Random	random Random number generator for Monte Carlo simulations
protected int	treatmentCount Number of comparisons made

Constructor Summary

Constructors

Constructor and Description
StudentizedRangeDistribution() Default constructor
StudentizedRangeDistribution(int treatmentCount, double degreesOfFreedom) Creates a new instance of StudentizedRangeDistribution
StudentizedRangeDistribution(StudentizedRangeDistribution other) Copy constructor

Method Summary

Modifier and Type	Method and Description
StudentizedRangeDistribution	clone() This makes public the clone method on the Object class and removes the exception that it throws.
void	convertFromVector(Vector parameters) Converts the object from a Vector of parameters.
Vector	convertToVector() Converts the object to a vector.
StudentizedRangeDistribution.CDF	getCDF() Gets the CDF of a scalar distribution.
double	getDegreesOfFreedom() Getter for degreesOfFreedom
java.lang.Double	getMaxSupport() Gets the minimum support (domain or input) of the distribution.
java.lang.Double	getMean() Gets the arithmetic mean, or "first central moment" or "expectation", of the distribution.
double	getMeanAsDouble() Gets the mean of the distribution as a double.
java.lang.Double	getMinSupport() Gets the minimum support (domain or input) of the distribution.
java.util.Random	getRandom() Gets the random number generator used by this object.
int	getTreatmentCount() Getter for treatmentCount
double	getVariance() Gets the variance of the distribution.
void	sampleInto(java.util.Random random, int sampleCount, java.util.Collection<? super java.lang.Double> output) Draws multiple random samples from the distribution and puts the result into the given collection.
void	setDegreesOfFreedom(double degreesOfFreedom) Setter for degreesOfFreedom
void	setRandom(java.util.Random random) Sets the random number generator used by this object.
void	setTreatmentCount(int treatmentCount) Setter for treatmentCount

Methods inherited from class gov.sandia.cognition.statistics.AbstractDistribution

sample, sample

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface gov.sandia.cognition.statistics.Distribution

sample, sample

Field Detail

DEFAULT_TREATMENT_COUNT

public static final int DEFAULT_TREATMENT_COUNT

Default treatment count, 2.

See Also:

Constant Field Values

DEFAULT_DEGREES_OF_FREEDOM

public static final double DEFAULT_DEGREES_OF_FREEDOM

Default degrees of freedom, 1d/0d.

See Also:

Constant Field Values

DEFAULT_NUM_SAMPLES

public static final int DEFAULT_NUM_SAMPLES

Number of samples to draw for Monte Carlo estimates, 1000.

See Also:

Constant Field Values

treatmentCount

protected int treatmentCount

Number of comparisons made

degreesOfFreedom

protected double degreesOfFreedom

Number of subjects in each treatment minus one.

random

protected java.util.Random random

Random number generator for Monte Carlo simulations

Constructor Detail

StudentizedRangeDistribution

public StudentizedRangeDistribution()

Default constructor

StudentizedRangeDistribution

public StudentizedRangeDistribution(int treatmentCount,
                                    double degreesOfFreedom)

Creates a new instance of StudentizedRangeDistribution

Parameters:

treatmentCount - Number of comparisons made

degreesOfFreedom - Number of subjects in each treatment minus one.

StudentizedRangeDistribution

public StudentizedRangeDistribution(StudentizedRangeDistribution other)

Copy constructor

Parameters:

other - StudentizedRangeDistribution to copy

Method Detail

clone

public StudentizedRangeDistribution clone()

Description copied from class: AbstractCloneableSerializable

This makes public the clone method on the Object class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call super.clone() to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in ObjectUtil may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use super.clone() as the basis for your implementation.

Specified by:

clone in interface Vectorizable

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class AbstractClosedFormUnivariateDistribution<java.lang.Double>

Returns:

A clone of this object.

sampleInto

public void sampleInto(java.util.Random random,
                       int sampleCount,
                       java.util.Collection<? super java.lang.Double> output)

Description copied from interface: Distribution

Draws multiple random samples from the distribution and puts the result into the given collection.

Specified by:

sampleInto in interface Distribution<java.lang.Double>

Parameters:

random - Random number generator to use.

sampleCount - The number of samples to draw. Cannot be negative.

output - The collection to add the samples into.

getCDF

public StudentizedRangeDistribution.CDF getCDF()

Description copied from interface: UnivariateDistribution

Gets the CDF of a scalar distribution.

Specified by:

getCDF in interface ClosedFormUnivariateDistribution<java.lang.Double>

Specified by:

getCDF in interface UnivariateDistribution<java.lang.Double>

Returns:

CDF of the scalar distribution.

getMean

public java.lang.Double getMean()

Description copied from interface: DistributionWithMean

Gets the arithmetic mean, or "first central moment" or "expectation", of the distribution.

Specified by:

getMean in interface DistributionWithMean<java.lang.Double>

Returns:

Mean of the distribution.

getMeanAsDouble

public double getMeanAsDouble()

Description copied from interface: UnivariateDistribution

Gets the mean of the distribution as a double.

Specified by:

getMeanAsDouble in interface UnivariateDistribution<java.lang.Double>

Returns:

The mean as a double.

convertToVector

public Vector convertToVector()

Description copied from interface: Vectorizable

Converts the object to a vector.

Specified by:

convertToVector in interface Vectorizable

Returns:

The Vector form of the object.

convertFromVector

public void convertFromVector(Vector parameters)

Description copied from interface: Vectorizable

Converts the object from a Vector of parameters.

Specified by:

convertFromVector in interface Vectorizable

Parameters:

parameters - The parameters to incorporate.

getMinSupport

public java.lang.Double getMinSupport()

Description copied from interface: UnivariateDistribution

Gets the minimum support (domain or input) of the distribution.

Specified by:

getMinSupport in interface UnivariateDistribution<java.lang.Double>

Returns:

Minimum support.

getMaxSupport

public java.lang.Double getMaxSupport()

Description copied from interface: UnivariateDistribution

Gets the minimum support (domain or input) of the distribution.

Specified by:

getMaxSupport in interface UnivariateDistribution<java.lang.Double>

Returns:

Minimum support.

getVariance

public double getVariance()

Description copied from interface: UnivariateDistribution

Gets the variance of the distribution. This is sometimes called the second central moment by more pedantic people, which is equivalent to the square of the standard deviation.

Specified by:

getVariance in interface UnivariateDistribution<java.lang.Double>

Returns:

Variance of the distribution.

getTreatmentCount

public int getTreatmentCount()

Getter for treatmentCount

Returns:

Number of comparisons made

setTreatmentCount

public void setTreatmentCount(int treatmentCount)

Setter for treatmentCount

Parameters:

treatmentCount - Number of comparisons made

getDegreesOfFreedom

public double getDegreesOfFreedom()

Getter for degreesOfFreedom

Returns:

Number of subjects in each treatment minus one.

setDegreesOfFreedom

public void setDegreesOfFreedom(double degreesOfFreedom)

Setter for degreesOfFreedom

Parameters:

degreesOfFreedom - Number of subjects in each treatment minus one.

getRandom

public java.util.Random getRandom()

Description copied from interface: Randomized

Gets the random number generator used by this object.

Specified by:

getRandom in interface Randomized

Returns:

The random number generator used by this object.

setRandom

public void setRandom(java.util.Random random)

Description copied from interface: Randomized

Sets the random number generator used by this object.

Specified by:

setRandom in interface Randomized

Parameters:

random - The random number generator for this object to use.