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/*
* File: MultipleHypothesisComparison.java
* Authors: Kevin R. Dixon
* Company: Sandia National Laboratories
* Project: Cognitive Foundry
*
* Copyright May 24, 2011, Sandia Corporation.
* Under the terms of Contract DE-AC04-94AL85000, there is a non-exclusive
* license for use of this work by or on behalf of the U.S. Government.
* Export of this program may require a license from the United States
* Government. See CopyrightHistory.txt for complete details.
*
*/
package gov.sandia.cognition.statistics.method;
import gov.sandia.cognition.annotation.PublicationReference;
import gov.sandia.cognition.annotation.PublicationReferences;
import gov.sandia.cognition.annotation.PublicationType;
import gov.sandia.cognition.util.CloneableSerializable;
import java.util.Collection;
/**
* Describes the functionality of an algorithm for accepting or rejecting
* multiple null hypothesis at the same time. These are typically run as a
* post-hoc test after an ANOVA or Friedman's test. The multiple comparison
* tests indicate which treatments are significantly different from each other
* once an ANOVA or Friedman's test has indicated that there are significant
* differences.
* @param
* Data associated with each treatment, such as Double or Collection of Double
* @author Kevin R. Dixon
* @since 3.3.0
*/
@PublicationReferences(
references={
@PublicationReference(
author="Juliet Popper Shaffer",
title="Multiple Hypothesis Testing",
type=PublicationType.Journal,
year=1995,
publication="Annual Review of Psychology",
url="http://www.annualreviews.org/doi/pdf/10.1146/annurev.ps.46.020195.003021"
)
,
@PublicationReference(
author="Janez Demsar",
title="Statistical Comparisons of Classifiers over Multiple Data Sets",
type=PublicationType.Journal,
publication="Journal of Machine Learning Research",
year=2006,
url="http://www.jmlr.org/papers/volume7/demsar06a/demsar06a.pdf"
)
,
@PublicationReference(
author={
"Salvador Garcia",
"Francisco Herrera"
},
title="An Extension on \"Statistical Comparisons of Classifiers over Multiple Data Sets\" for all Pairwise Comparisons",
type=PublicationType.Journal,
publication="Journal of Machine Learning Research",
year=2008,
url="http://150.214.190.154/publications/ficheros/2008-Garcia-JMLR.pdf"
)
,
@PublicationReference(
author="Wikipedia",
title="Multiple comparisons",
type=PublicationType.WebPage,
year=2011,
url="http://en.wikipedia.org/wiki/Multiple_comparisons"
)
}
)
public interface MultipleHypothesisComparison
extends CloneableSerializable
{
/**
* Default uncompensatedAlpha, {@value}.
*/
public static final double DEFAULT_UNCOMPENSATED_ALPHA = 0.05;
/**
* Evaluates the null hypotheses associated with the given collection
* of data.
* @param data
* Data from each treatment to consider
* @return
* Statistic that summarizes the multiple comparison test
*/
public MultipleHypothesisComparison.Statistic evaluateNullHypotheses(
Collection data );
/**
* Evaluates the null hypotheses associated with the given collection
* of data.
* @param data
* Data from each treatment to consider
* @param uncompensatedAlpha
* Uncompensated alpha (p-value threshold) for the multiple comparison
* test, must be [0,1]
* @return
* Statistic that summarizes the multiple comparison test
*/
public MultipleHypothesisComparison.Statistic evaluateNullHypotheses(
Collection data,
double uncompensatedAlpha );
/**
* Statistic associated with the multiple hypothesis comparison
*/
public interface Statistic
extends CloneableSerializable
{
/**
* Gets the number of treatments being compared
* @return
* Number of treatments being compared
*/
public int getTreatmentCount();
/**
* Gets the uncompensated alpha (p-value threshold) for the multiple
* comparison test
* @return
* Uncompensated alpha (p-value threshold) for the multiple comparison
* test
*/
public double getUncompensatedAlpha();
/**
* Gets the test statistic associated with the (i,j) treatment
* comparison
* @param i
* First treatment index
* @param j
* Second treatment index
* @return
* Test statistic associated with the (i,j) treatment comparison
*/
public double getTestStatistic(
int i,
int j );
/**
* Gets the Null Hypothesis probability associated with the (i,j)
* treatment comparison
* @param i
* First treatment index
* @param j
* Second treatment index
* @return
* Null Hypothesis probability associated with the (i,j)
* treatment comparison
*/
public double getNullHypothesisProbability(
int i,
int j );
/**
* 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.
* @param i
* First treatment index
* @param j
* Second treatment index
* @return
* True if we accept the null hypothesis, false if we reject the
* null hypothesis
*/
public boolean acceptNullHypothesis(
int i,
int j );
}
}