/**
* Copyright (C) 2001-2017 by RapidMiner and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapidminer.com
*
* This program is free software: you can redistribute it and/or modify it under the terms of the
* GNU Affero General Public License as published by the Free Software Foundation, either version 3
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without
* even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License along with this program.
* If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.tools.math;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.List;
import org.apache.commons.math3.distribution.FDistribution;
import com.rapidminer.tools.Tools;
/**
* Determines if the null hypothesis (all actual mean values are the same) holds for the given
* values. This class uses an ANalysis Of VAriances approach to determine probability that the null
* hypothesis is wrong.
*
* @author Ingo Mierswa
*/
public class AnovaCalculator {
public static class AnovaSignificanceTestResult extends SignificanceTestResult {
private static final long serialVersionUID = 9007616378489018565L;
private double sumSquaresBetween = 0.0d;
private double sumSquaresResiduals = 0.0d;
private double meanSquaresBetween = 0.0d;
private double meanSquaresResiduals = 0.0d;
private int df1 = 0;
private int df2 = 0;
private double alpha = 0.05;;
private double fValue = 0.0d;
private double prob = 0.0d;
public AnovaSignificanceTestResult(double sumSquaresBetween, double sumSquaresResiduals, int df1, int df2,
double alpha) {
this.sumSquaresBetween = sumSquaresBetween;
this.sumSquaresResiduals = sumSquaresResiduals;
this.df1 = df1;
this.df2 = df2;
this.alpha = alpha;
this.meanSquaresBetween = sumSquaresBetween / df1;
this.meanSquaresResiduals = sumSquaresResiduals / df2;
this.fValue = meanSquaresBetween / meanSquaresResiduals;
FDistribution fDist = new FDistribution(df1, df2);
this.prob = 1.0d - fDist.cumulativeProbability(this.fValue);
}
@Override
public String getName() {
return "Anova Test";
}
@Override
public String toString() {
return "ANOVA result (f=" + Tools.formatNumber(fValue) + ", prob=" + Tools.formatNumber(prob) + ", alpha="
+ Tools.formatNumber(alpha) + ")";
}
@Override
public double getProbability() {
return prob;
}
public double getSumSquaresBetween() {
return this.sumSquaresBetween;
}
public double getSumSquaresResiduals() {
return this.sumSquaresResiduals;
}
public double getMeanSquaresBetween() {
return this.meanSquaresBetween;
}
public double getMeanSquaresResiduals() {
return this.meanSquaresResiduals;
}
public int getDf1() {
return this.df1;
}
public int getDf2() {
return this.df2;
}
public double getAlpha() {
return this.alpha;
}
public double getFValue() {
return this.fValue;
}
}
private double alpha = 0.05;
private List<TestGroup> groups = new LinkedList<TestGroup>();
public void setAlpha(double alpha) {
this.alpha = alpha;
}
public void addGroup(TestGroup group) {
groups.add(group);
}
public void addGroup(double numberOfValues, double mean, double variance) {
addGroup(new TestGroup(numberOfValues, mean, variance));
}
public void clearGroups() {
groups.clear();
}
public SignificanceTestResult performSignificanceTest() throws SignificanceCalculationException {
if (groups.size() < 2) {
throw new SignificanceCalculationException("Not enough groups added (current number of groups: " + groups.size()
+ ", must be at least 2");
}
double meanOfMeans = 0.0d;
Iterator<TestGroup> i = groups.iterator();
while (i.hasNext()) {
TestGroup group = i.next();
meanOfMeans += group.getMean();
}
meanOfMeans /= groups.size();
double sumSquaresBetween = 0.0d;
i = groups.iterator();
while (i.hasNext()) {
TestGroup group = i.next();
double diff = group.getMean() - meanOfMeans;
sumSquaresBetween += group.getNumber() * (diff * diff);
}
double sumSquaresResiduals = 0.0d;
int counterSum = 0;
i = groups.iterator();
while (i.hasNext()) {
TestGroup group = i.next();
sumSquaresResiduals += (group.getNumber() - 1) * group.getVariance();
counterSum += group.getNumber();
}
if (groups.size() == counterSum) {
throw new SignificanceCalculationException("Number of groups can't be equal to the number of examples.");
}
return new AnovaSignificanceTestResult(sumSquaresBetween, sumSquaresResiduals, groups.size() - 1, counterSum
- groups.size(), alpha);
}
}