Added OneWayAnova methods to TestUtils and updated User Guide
to cover One-way Anova tests. JIRA: MATH-173 git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@618114 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Phil Steitz
parent
821bca564f
commit
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@ -16,6 +16,7 @@
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*/
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package org.apache.commons.math.stat.inference;
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import java.util.Collection;
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import org.apache.commons.math.MathException;
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import org.apache.commons.math.stat.descriptive.StatisticalSummary;
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@ -45,6 +46,10 @@ public class TestUtils {
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private static UnknownDistributionChiSquareTest unknownDistributionChiSquareTest =
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new ChiSquareTestImpl();
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/** Singleton OneWayAnova instance using default implementation. */
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private static OneWayAnova oneWayAnova =
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new OneWayAnovaImpl();
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/**
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* Set the (singleton) TTest instance.
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*
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@ -102,6 +107,27 @@ public class TestUtils {
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return unknownDistributionChiSquareTest;
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}
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/**
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* Set the (singleton) OneWayAnova instance
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*
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* @param oneWayAnova the new instance to use
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* @since 1.2
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*/
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public static void setOneWayAnova(OneWayAnova oneWayAnova) {
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TestUtils.oneWayAnova = oneWayAnova;
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}
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/**
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* Return a (singleton) OneWayAnova instance. Does not create a new instance.
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*
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* @return a OneWayAnova instance
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* @since 1.2
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*/
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public static OneWayAnova getOneWayAnova() {
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return oneWayAnova;
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}
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/**
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* @see org.apache.commons.math.stat.inference.TTest#homoscedasticT(double[], double[])
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*/
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@ -321,6 +347,8 @@ public class TestUtils {
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/**
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* @see org.apache.commons.math.stat.inference.UnknownDistributionChiSquareTest#chiSquareDataSetsComparison(long[], long[])
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*
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* @since 1.2
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*/
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public static double chiSquareDataSetsComparison(long[] observed1, long[] observed2)
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throws IllegalArgumentException {
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@ -329,6 +357,8 @@ public class TestUtils {
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/**
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* @see org.apache.commons.math.stat.inference.UnknownDistributionChiSquareTest#chiSquareTestDataSetsComparison(long[], long[])
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*
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* @since 1.2
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*/
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public static double chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)
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throws IllegalArgumentException, MathException {
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@ -338,12 +368,43 @@ public class TestUtils {
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/**
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* @see org.apache.commons.math.stat.inference.UnknownDistributionChiSquareTest#chiSquareTestDataSetsComparison(long[], long[], double)
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*
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* @since 1.2
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*/
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public static boolean chiSquareTestDataSetsComparison(long[] observed1, long[] observed2,
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double alpha)
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throws IllegalArgumentException, MathException {
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return unknownDistributionChiSquareTest.chiSquareTestDataSetsComparison(observed1, observed2, alpha);
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}
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/**
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* @see org.apache.commons.math.stat.inference.OneWayAnova#anovaFValue(Collection)
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*
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* @since 1.2
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*/
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public static double oneWayAnovaFValue(Collection categoryData)
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throws IllegalArgumentException, MathException {
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return oneWayAnova.anovaFValue(categoryData);
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}
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/**
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* @see org.apache.commons.math.stat.inference.OneWayAnova#anovaPValue(Collection)
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*
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* @since 1.2
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*/
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public static double oneWayAnovaPValue(Collection categoryData)
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throws IllegalArgumentException, MathException {
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return oneWayAnova.anovaPValue(categoryData);
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}
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/**
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* @see org.apache.commons.math.stat.inference.OneWayAnova#anovaTest(Collection,double)
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*
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* @since 1.2
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*/
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public static boolean oneWayAnovaTest(Collection categoryData, double alpha)
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throws IllegalArgumentException, MathException {
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return oneWayAnova.anovaTest(categoryData, alpha);
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}
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}
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@ -16,6 +16,9 @@
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*/
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package org.apache.commons.math.stat.inference;
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import java.util.ArrayList;
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import java.util.List;
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import junit.framework.Test;
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import junit.framework.TestCase;
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import junit.framework.TestSuite;
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@ -440,4 +443,26 @@ public class TestUtilsTest extends TestCase {
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assertFalse(TestUtils.pairedTTest(sample1, sample3, .001));
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assertTrue(TestUtils.pairedTTest(sample1, sample3, .002));
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}
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private double[] classA =
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{93.0, 103.0, 95.0, 101.0};
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private double[] classB =
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{99.0, 92.0, 102.0, 100.0, 102.0};
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private double[] classC =
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{110.0, 115.0, 111.0, 117.0, 128.0};
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private List classes = new ArrayList();
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private OneWayAnova oneWayAnova = new OneWayAnovaImpl();
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public void testOneWayAnovaUtils() throws Exception {
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classes.add(classA);
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classes.add(classB);
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classes.add(classC);
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assertEquals(oneWayAnova.anovaFValue(classes),
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TestUtils.oneWayAnovaFValue(classes), 10E-12);
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assertEquals(oneWayAnova.anovaPValue(classes),
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TestUtils.oneWayAnovaPValue(classes), 10E-12);
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assertEquals(oneWayAnova.anovaTest(classes, 0.01),
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TestUtils.oneWayAnovaTest(classes, 0.01));
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}
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}
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@ -29,7 +29,7 @@
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<p>
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The statistics package provides frameworks and implementations for
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basic Descriptive statistics, frequency distributions, bivariate regression,
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and t- and chi-square test statistics.
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and t-, chi-square and ANOVA test statistics.
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</p>
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<p>
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<a href="#1.2 Descriptive statistics">Descriptive statistics</a><br></br>
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@ -399,30 +399,36 @@ System.out.println(regression.getSlopeStdErr());
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<a href="../apidocs/org/apache/commons/math/stat/inference/">
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org.apache.commons.math.stat.inference</a> package provide
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<a href="http://www.itl.nist.gov/div898/handbook/prc/section2/prc22.htm">
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Student's t</a> and
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Student's t</a>,
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<a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm">
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Chi-Square</a> test statistics as well as
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Chi-Square</a> and
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<a href="http://www.itl.nist.gov/div898/handbook/prc/section4/prc43.htm">
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One-Way ANOVA</a> test statistics as well as
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<a href="http://www.cas.lancs.ac.uk/glossary_v1.1/hyptest.html#pvalue">
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p-values</a> associated with <code>t-</code> and
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<code>Chi-Square</code> tests. The interfaces are
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p-values</a> associated with <code>t-</code>,
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<code>Chi-Square</code> and <code>One-Way ANOVA</code> tests. The
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interfaces are
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<a href="../apidocs/org/apache/commons/math/stat/inference/TTest.html">
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TTest</a> and
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TTest</a>,
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<a href="../apidocs/org/apache/commons/math/stat/inference/ChiSquareTest.html">
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ChiSquareTest</a>, with
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provided implementations
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ChiSquareTest</a>, and
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<a href="../apidocs/org/apache/commons/math/stat/inference/OneWayAnova.html">
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OneWayAnova</a> with provided implementations
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<a href="../apidocs/org/apache/commons/math/stat/inference/TTestImpl.html">
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TTestImpl</a> and
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TTestImpl</a>,
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<a href="../apidocs/org/apache/commons/math/stat/inference/ChiSquareTestImpl.html">
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ChiSquareTestImpl</a>.
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Abstract and default factories are provided, with configuration
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optional using commons-discovery to specify the concrete factory. The
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ChiSquareTestImpl</a> and
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<a href="../apidocs/org/apache/commons/math/stat/inference/OneWayAnovaImpl.html">
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OneWayAnovaImpl</a>, respectively.
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The
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<a href="../apidocs/org/apache/commons/math/stat/inference/TestUtils.html">
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TestUtils</a> class provides static methods to get test instances or
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to compute test statistics directly. The examples below all use the
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static methods in <code>TestUtils</code> to execute tests. To get
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test object instances, either use e.g.,
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<code>TestUtils.getTTest()</code> or use the factory directly, e.g.,
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<code>TestFactory.newInstance().createChiSquareTest()</code>.
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<code>TestUtils.getTTest()</code> or use the implementation constructors
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directly, e.g.,
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<code>new TTestImpl()</code>.
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</p>
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<p>
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<strong>Implementation Notes</strong>
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@ -448,8 +454,8 @@ System.out.println(regression.getSlopeStdErr());
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assumptions of the parametric t-test procedure, as discussed
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<a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
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here</a></li>
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<li>p-values returned by both t- and chi-square tests are exact, based
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on numerical approximations to the t- and chi-square distributions in the
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<li>p-values returned by t-, chi-square and Anova tests are exact, based
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on numerical approximations to the t-, chi-square and F distributions in the
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<code>distributions</code> package. </li>
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<li>p-values returned by t-tests are for two-sided tests and the boolean-valued
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methods supporting fixed significance level tests assume that the hypotheses
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@ -512,6 +518,7 @@ TestUtils.tTest(mu, observed, alpha);
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To test, for example at the 95% level of confidence, use
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<code>alpha = 0.05</code>
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</dd>
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<br></br>
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<dt><strong>Two-Sample t-tests</strong></dt>
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<br></br>
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<dd><strong>Example 1:</strong> Paired test evaluating
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@ -584,7 +591,8 @@ TestUtils.tTest(sample1, sample2, .05);
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replace "t" at the beginning of the method name with "homoscedasticT"
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</p>
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</dd>
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<dt>Computing <code>chi-square</code> test statistics</dt>
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<br></br>
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<dt><strong>Chi-square tests</strong></dt>
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<br></br>
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<dd>To compute a chi-square statistic measuring the agreement between a
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<code>long[]</code> array of observed counts and a <code>double[]</code>
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@ -644,7 +652,41 @@ TestUtils.chiSquareTest(counts, alpha);
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The boolean value returned will be <code>true</code> iff the null
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hypothesis can be rejected with confidence <code>1 - alpha</code>.
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</dd>
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</dl>
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<br></br>
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<dt><strong><One-Way Anova tests</strong></dt>
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<br></br>
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<dd>To conduct a One-Way Analysis of Variance (ANOVA) to evaluate the
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null hypothesis that the means of a collection of univariate datasets
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are the same, start by loading the datasets into a collection, e.g.
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<source>
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double[] classA =
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{93.0, 103.0, 95.0, 101.0, 91.0, 105.0, 96.0, 94.0, 101.0 };
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double[] classB =
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{99.0, 92.0, 102.0, 100.0, 102.0, 89.0 };
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double[] classC =
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{110.0, 115.0, 111.0, 117.0, 128.0, 117.0 };
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List classes = new ArrayList();
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classes.add(classA);
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classes.add(classB);
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classes.add(classC);
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</source>
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Then you can compute ANOVA F- or p-values associated with the
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null hypothesis that the class means are all the same
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using a <code>OneWayAnova</code> instance or <code>TestUtils</code>
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methods:
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<source>
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double fStatistic = TestUtils.oneWayAnovaFValue(classes); // F-value
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double pValue = TestUtils.oneWayAnovaPValue(classes); // P-value
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</source>
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To test perform a One-Way Anova test with signficance level set at 0.01
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(so the test will, assuming assumptions are met, reject the null
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hypothesis incorrectly only about one in 100 times), use
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<source>
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TestUtils.oneWayAnovaTest(classes, 0.01); // returns a boolean
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// true means reject null hypothesis
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</source>
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</dd>
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</dl>
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</p>
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</subsection>
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</section>
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