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Algorithms and components for machine learning and statistics.
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/*
* File: AbstractPairwiseMultipleHypothesisComparison.java
* Authors: Kevin R. Dixon
* Company: Sandia National Laboratories
* Project: Cognitive Foundry
*
* Copyright Jun 1, 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.collection.CollectionUtil;
import gov.sandia.cognition.math.matrix.Matrix;
import gov.sandia.cognition.math.matrix.MatrixFactory;
import gov.sandia.cognition.util.ObjectUtil;
import java.util.ArrayList;
import java.util.Collection;
/**
* A multiple-hypothesis comparison algorithm based on making multiple
* pair-wise null-hypothesis comparisons.
* @param
* Type of statistic returned by the test
* @author Kevin R. Dixon
* @since 3.3.0
*/
public abstract class AbstractPairwiseMultipleHypothesisComparison
extends AbstractMultipleHypothesisComparison, StatisticType>
{
/**
* Default pair-wise confidence test: Student's Paired t-test.
*/
public static final NullHypothesisEvaluator> DEFAULT_PAIRWISE_TEST =
StudentTConfidence.INSTANCE;
// WilcoxonSignedRankConfidence.INSTANCE;
/**
* Confidence test used for pair-wise null-hypothesis tests.
*/
protected NullHypothesisEvaluator> pairwiseTest;
/**
* Creates a new instance of BonferroniCorrection
* @param pairwiseTest
* Confidence test used for pair-wise null-hypothesis tests.
*/
public AbstractPairwiseMultipleHypothesisComparison(
final NullHypothesisEvaluator> pairwiseTest)
{
this.setPairwiseTest( pairwiseTest );
}
@Override
public AbstractPairwiseMultipleHypothesisComparison clone()
{
@SuppressWarnings("unchecked")
AbstractPairwiseMultipleHypothesisComparison clone =
(AbstractPairwiseMultipleHypothesisComparison) super.clone();
clone.setPairwiseTest( ObjectUtil.cloneSafe( this.getPairwiseTest() ) );
return clone;
}
/**
* Getter for pairwiseTest
* @return
* Confidence test used for pair-wise null-hypothesis tests.
*/
public NullHypothesisEvaluator> getPairwiseTest()
{
return this.pairwiseTest;
}
/**
* Setter for pairwiseTest
* @param pairwiseTest
* Confidence test used for pair-wise null-hypothesis tests.
*/
public void setPairwiseTest(
final NullHypothesisEvaluator> pairwiseTest)
{
this.pairwiseTest = pairwiseTest;
}
/**
* Result from a pairwise multiple-comparison statistic.
*/
public static abstract class Statistic
extends AbstractMultipleHypothesisComparison.Statistic
{
/**
* Results from the pair-wise confidence tests.
*/
protected ArrayList> pairwiseTestStatistics;
/**
* Creates a new instance of StudentizedMultipleComparisonStatistic
* @param data
* Data from each treatment to consider
* @param uncompensatedAlpha
* Uncompensated alpha (p-value threshold) for the multiple comparison
* test
* @param pairwiseTest
* Confidence test used for pair-wise null-hypothesis tests.
*/
public Statistic(
final Collection> data,
final double uncompensatedAlpha,
final NullHypothesisEvaluator> pairwiseTest )
{
this.treatmentCount = data.size();
this.uncompensatedAlpha = uncompensatedAlpha;
this.computePairwiseTestResults(data, pairwiseTest);
}
@Override
public AbstractPairwiseMultipleHypothesisComparison.Statistic clone()
{
AbstractPairwiseMultipleHypothesisComparison.Statistic clone =
(AbstractPairwiseMultipleHypothesisComparison.Statistic) super.clone();
clone.pairwiseTestStatistics = ObjectUtil.cloneSmartElementsAsArrayList(
this.getPairwiseTestStatistics() );
return clone;
}
/**
* Computes the pair-wise confidence test results
* @param data
* Data from each treatment to consider
* @param pairwiseTest
* Confidence test used for pair-wise null-hypothesis tests.
*/
protected void computePairwiseTestResults(
final Collection> data,
final NullHypothesisEvaluator> pairwiseTest )
{
ArrayList> treatments =
CollectionUtil.asArrayList(data);
final int K = treatments.size();
Matrix Z = MatrixFactory.getDefault().createMatrix(K, K);
Matrix P = MatrixFactory.getDefault().createMatrix(K, K);
ArrayList> stats =
new ArrayList>( K );
for( int i = 0; i < K; i++ )
{
ArrayList comp =
new ArrayList( K );
for( int j = 0; j < K; j++ )
{
comp.add( null );
}
stats.add( comp );
}
for( int i = 0; i < K; i++ )
{
// Comparisons to ourselves are perfect
Z.setElement(i, i, 0.0);
P.setElement(i, i, 1.0);
Collection datai = treatments.get(i);
for( int j = i+1; j < K; j++ )
{
Collection dataj = treatments.get(j);
ConfidenceStatistic comparison =
pairwiseTest.evaluateNullHypothesis(datai, dataj);
final double pij = comparison.getNullHypothesisProbability();
final double zij = comparison.getTestStatistic();
Z.setElement(i, j, zij);
Z.setElement(j, i, zij);
P.setElement(i, j, pij);
P.setElement(j, i, pij);
stats.get(i).set(j, comparison );
stats.get(j).set(i, comparison );
}
}
this.testStatistics = Z;
this.nullHypothesisProbabilities = P;
this.pairwiseTestStatistics = stats;
}
/**
* Getter for pairwiseTestStatistics
* @return
* Results from the pair-wise confidence tests.
*/
public ArrayList> getPairwiseTestStatistics()
{
return this.pairwiseTestStatistics;
}
@Override
public boolean acceptNullHypothesis(
int i,
int j)
{
return this.getNullHypothesisProbability(i, j) >= this.getAdjustedAlpha(i, j);
}
/**
* Gets the adjusted alpha (p-value threshold) for the given comparison
* @param i
* First treatment to compare
* @param j
* Second treatment to compare
* @return
* Adjusted alpha (p-value threshold) for the given comparison
*/
public abstract double getAdjustedAlpha(
int i,
int j );
}
}