gov.sandia.cognition.statistics.method

Class TukeyKramerConfidence.Statistic

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.method.AbstractMultipleHypothesisComparison.Statistic

gov.sandia.cognition.statistics.method.TukeyKramerConfidence.Statistic

All Implemented Interfaces:

MultipleHypothesisComparison.Statistic, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

TukeyKramerConfidence

public static class TukeyKramerConfidence.Statistic
extends AbstractMultipleHypothesisComparison.Statistic

Statistic from Tukey-Kramer's multiple comparison test

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected double	meanSquaredResiduals Mean-squared difference over all subjects
protected Matrix	standardErrors Gets the standard errors in the experiment
protected java.util.ArrayList<java.lang.Integer>	subjectCounts Number of subjects in each treatment
protected java.util.ArrayList<java.lang.Double>	treatmentMeans Mean for each treatment

Fields inherited from class gov.sandia.cognition.statistics.method.AbstractMultipleHypothesisComparison.Statistic

nullHypothesisProbabilities, testStatistics, treatmentCount, uncompensatedAlpha

Constructor Summary

Constructors

Constructor and Description
Statistic(double uncompensatedAlpha, java.util.ArrayList<java.lang.Integer> subjectCounts, java.util.ArrayList<java.lang.Double> treatmentMeans, double meanSquaredResiduals) Creates a new instance of Statistic

Method Summary

Modifier and Type	Method and Description
boolean	acceptNullHypothesis(int i, int j) Determines if the (i,j) null hypothesis should be accepted (true) or rejected (false) .
TukeyKramerConfidence.Statistic	clone() This makes public the clone method on the Object class and removes the exception that it throws.
protected Matrix	computeNullHypothesisProbabilities(java.util.ArrayList<java.lang.Integer> subjectCounts, Matrix Z) Computes null-hypothesis probability for the (i,j) treatment comparison
Matrix	computeTestStatistics(java.util.ArrayList<java.lang.Integer> subjectCounts, java.util.ArrayList<java.lang.Double> treatmentMeans, double meanSquaredResiduals) Computes the test statistic for all treatments
double	getMeanSquaredResiduals() Getter for meanSquaredResiduals
Matrix	getStandardErrors() Getter for standardErrors
java.util.ArrayList<java.lang.Integer>	getSubjectCounts() Getter for subjectCounts
java.util.ArrayList<java.lang.Double>	getTreatmentMeans() Getter for treatmentMeans

Methods inherited from class gov.sandia.cognition.statistics.method.AbstractMultipleHypothesisComparison.Statistic

getNullHypothesisProbability, getTestStatistic, getTreatmentCount, getUncompensatedAlpha, toString

Methods inherited from class java.lang.Object

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

Field Detail

subjectCounts

protected java.util.ArrayList<java.lang.Integer> subjectCounts

Number of subjects in each treatment

treatmentMeans

protected java.util.ArrayList<java.lang.Double> treatmentMeans

Mean for each treatment

meanSquaredResiduals

protected double meanSquaredResiduals

Mean-squared difference over all subjects

standardErrors

protected Matrix standardErrors

Gets the standard errors in the experiment

Constructor Detail

Statistic

public Statistic(double uncompensatedAlpha,
                 java.util.ArrayList<java.lang.Integer> subjectCounts,
                 java.util.ArrayList<java.lang.Double> treatmentMeans,
                 double meanSquaredResiduals)

Creates a new instance of Statistic

Parameters:

uncompensatedAlpha - Uncompensated alpha (p-value threshold) for the multiple comparison test

subjectCounts - Number of subjects in each treatment

treatmentMeans - Mean for each treatment

meanSquaredResiduals - Mean-squared difference over all subjects

Method Detail

computeTestStatistics

public Matrix computeTestStatistics(java.util.ArrayList<java.lang.Integer> subjectCounts,
                                    java.util.ArrayList<java.lang.Double> treatmentMeans,
                                    double meanSquaredResiduals)

Computes the test statistic for all treatments

Parameters:

subjectCounts - Number of subjects in each treatment

treatmentMeans - Mean for each treatment

meanSquaredResiduals - Mean-squared difference over all subjects

Returns:

Test statistics, where the (i,j) element compares treatment "i" to treatment "j", the statistic is symmetric

computeNullHypothesisProbabilities

protected Matrix computeNullHypothesisProbabilities(java.util.ArrayList<java.lang.Integer> subjectCounts,
                                                    Matrix Z)

Computes null-hypothesis probability for the (i,j) treatment comparison

Parameters:

subjectCounts - Number of subjects in the experiment

Z - Test statistic for the (i,j) treatment comparison

Returns:

Null-hypothesis probability for the (i,j) treatment comparison

clone

public TukeyKramerConfidence.Statistic 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 CloneableSerializable

Overrides:

clone in class AbstractMultipleHypothesisComparison.Statistic

Returns:

A clone of this object.

getSubjectCounts

public java.util.ArrayList<java.lang.Integer> getSubjectCounts()

Getter for subjectCounts

Returns:

Number of subjects in the experiment

getTreatmentMeans

public java.util.ArrayList<java.lang.Double> getTreatmentMeans()

Getter for treatmentMeans

Returns:

Mean for each treatment

getMeanSquaredResiduals

public double getMeanSquaredResiduals()

Getter for meanSquaredResiduals

Returns:

Mean-squared difference over all subjects

acceptNullHypothesis

public boolean acceptNullHypothesis(int i,
                                    int j)

Description copied from interface: MultipleHypothesisComparison.Statistic

Determines if the (i,j) null hypothesis should be accepted (true) or rejected (false) . Rejecting a null hypothesis typically means that there is a significant difference between the (i,j) treatment.

Parameters:

i - First treatment index

j - Second treatment index

Returns:

True if we accept the null hypothesis, false if we reject the null hypothesis

getStandardErrors

public Matrix getStandardErrors()

Getter for standardErrors

Returns:

Gets the standard errors in the experiment