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Added OneWayAnova methods to TestUtils and updated User Guide
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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
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psteitz committed Feb 3, 2008
1 parent 821bca5 commit 61fabe8
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Showing 3 changed files with 147 additions and 19 deletions.
63 changes: 62 additions & 1 deletion src/java/org/apache/commons/math/stat/inference/TestUtils.java
View file
Expand Up @@ -16,6 +16,7 @@
*/
package org.apache.commons.math.stat.inference;

import java.util.Collection;
import org.apache.commons.math.MathException;
import org.apache.commons.math.stat.descriptive.StatisticalSummary;

Expand Down Expand Up @@ -45,6 +46,10 @@ protected TestUtils() {
private static UnknownDistributionChiSquareTest unknownDistributionChiSquareTest =
new ChiSquareTestImpl();

/** Singleton OneWayAnova instance using default implementation. */
private static OneWayAnova oneWayAnova =
new OneWayAnovaImpl();

/**
* Set the (singleton) TTest instance.
*
Expand Down Expand Up @@ -102,6 +107,27 @@ public static UnknownDistributionChiSquareTest getUnknownDistributionChiSquareTe
return unknownDistributionChiSquareTest;
}

/**
* Set the (singleton) OneWayAnova instance
*
* @param oneWayAnova the new instance to use
* @since 1.2
*/
public static void setOneWayAnova(OneWayAnova oneWayAnova) {
TestUtils.oneWayAnova = oneWayAnova;
}

/**
* Return a (singleton) OneWayAnova instance. Does not create a new instance.
*
* @return a OneWayAnova instance
* @since 1.2
*/
public static OneWayAnova getOneWayAnova() {
return oneWayAnova;
}


/**
* @see org.apache.commons.math.stat.inference.TTest#homoscedasticT(double[], double[])
*/
Expand Down Expand Up @@ -321,6 +347,8 @@ public static double chiSquareTest(long[][] counts)

/**
* @see org.apache.commons.math.stat.inference.UnknownDistributionChiSquareTest#chiSquareDataSetsComparison(long[], long[])
*
* @since 1.2
*/
public static double chiSquareDataSetsComparison(long[] observed1, long[] observed2)
throws IllegalArgumentException {
Expand All @@ -329,6 +357,8 @@ public static double chiSquareDataSetsComparison(long[] observed1, long[] observ

/**
* @see org.apache.commons.math.stat.inference.UnknownDistributionChiSquareTest#chiSquareTestDataSetsComparison(long[], long[])
*
* @since 1.2
*/
public static double chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)
throws IllegalArgumentException, MathException {
Expand All @@ -338,12 +368,43 @@ public static double chiSquareTestDataSetsComparison(long[] observed1, long[] ob

/**
* @see org.apache.commons.math.stat.inference.UnknownDistributionChiSquareTest#chiSquareTestDataSetsComparison(long[], long[], double)
*
* @since 1.2
*/
public static boolean chiSquareTestDataSetsComparison(long[] observed1, long[] observed2,
double alpha)
throws IllegalArgumentException, MathException {
return unknownDistributionChiSquareTest.chiSquareTestDataSetsComparison(observed1, observed2, alpha);
}


/**
* @see org.apache.commons.math.stat.inference.OneWayAnova#anovaFValue(Collection)
*
* @since 1.2
*/
public static double oneWayAnovaFValue(Collection categoryData)
throws IllegalArgumentException, MathException {
return oneWayAnova.anovaFValue(categoryData);
}

/**
* @see org.apache.commons.math.stat.inference.OneWayAnova#anovaPValue(Collection)
*
* @since 1.2
*/
public static double oneWayAnovaPValue(Collection categoryData)
throws IllegalArgumentException, MathException {
return oneWayAnova.anovaPValue(categoryData);
}

/**
* @see org.apache.commons.math.stat.inference.OneWayAnova#anovaTest(Collection,double)
*
* @since 1.2
*/
public static boolean oneWayAnovaTest(Collection categoryData, double alpha)
throws IllegalArgumentException, MathException {
return oneWayAnova.anovaTest(categoryData, alpha);
}

}
25 changes: 25 additions & 0 deletions src/test/org/apache/commons/math/stat/inference/TestUtilsTest.java
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Expand Up @@ -16,6 +16,9 @@
*/
package org.apache.commons.math.stat.inference;

import java.util.ArrayList;
import java.util.List;

import junit.framework.Test;
import junit.framework.TestCase;
import junit.framework.TestSuite;
Expand Down Expand Up @@ -440,4 +443,26 @@ public void testPaired() throws Exception {
assertFalse(TestUtils.pairedTTest(sample1, sample3, .001));
assertTrue(TestUtils.pairedTTest(sample1, sample3, .002));
}

private double[] classA =
{93.0, 103.0, 95.0, 101.0};
private double[] classB =
{99.0, 92.0, 102.0, 100.0, 102.0};
private double[] classC =
{110.0, 115.0, 111.0, 117.0, 128.0};

private List classes = new ArrayList();
private OneWayAnova oneWayAnova = new OneWayAnovaImpl();

public void testOneWayAnovaUtils() throws Exception {
classes.add(classA);
classes.add(classB);
classes.add(classC);
assertEquals(oneWayAnova.anovaFValue(classes),
TestUtils.oneWayAnovaFValue(classes), 10E-12);
assertEquals(oneWayAnova.anovaPValue(classes),
TestUtils.oneWayAnovaPValue(classes), 10E-12);
assertEquals(oneWayAnova.anovaTest(classes, 0.01),
TestUtils.oneWayAnovaTest(classes, 0.01));
}
}
78 changes: 60 additions & 18 deletions xdocs/userguide/stat.xml
View file
Expand Up @@ -29,7 +29,7 @@
<p>
The statistics package provides frameworks and implementations for
basic Descriptive statistics, frequency distributions, bivariate regression,
and t- and chi-square test statistics.
and t-, chi-square and ANOVA test statistics.
</p>
<p>
<a href="#1.2 Descriptive statistics">Descriptive statistics</a><br></br>
Expand Down Expand Up @@ -399,30 +399,36 @@ System.out.println(regression.getSlopeStdErr());
<a href="../apidocs/org/apache/commons/math/stat/inference/">
org.apache.commons.math.stat.inference</a> package provide
<a href="http://www.itl.nist.gov/div898/handbook/prc/section2/prc22.htm">
Student's t</a> and
Student's t</a>,
<a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm">
Chi-Square</a> test statistics as well as
Chi-Square</a> and
<a href="http://www.itl.nist.gov/div898/handbook/prc/section4/prc43.htm">
One-Way ANOVA</a> test statistics as well as
<a href="http://www.cas.lancs.ac.uk/glossary_v1.1/hyptest.html#pvalue">
p-values</a> associated with <code>t-</code> and
<code>Chi-Square</code> tests. The interfaces are
p-values</a> associated with <code>t-</code>,
<code>Chi-Square</code> and <code>One-Way ANOVA</code> tests. The
interfaces are
<a href="../apidocs/org/apache/commons/math/stat/inference/TTest.html">
TTest</a> and
TTest</a>,
<a href="../apidocs/org/apache/commons/math/stat/inference/ChiSquareTest.html">
ChiSquareTest</a>, with
provided implementations
ChiSquareTest</a>, and
<a href="../apidocs/org/apache/commons/math/stat/inference/OneWayAnova.html">
OneWayAnova</a> with provided implementations
<a href="../apidocs/org/apache/commons/math/stat/inference/TTestImpl.html">
TTestImpl</a> and
TTestImpl</a>,
<a href="../apidocs/org/apache/commons/math/stat/inference/ChiSquareTestImpl.html">
ChiSquareTestImpl</a>.
Abstract and default factories are provided, with configuration
optional using commons-discovery to specify the concrete factory. The
ChiSquareTestImpl</a> and
<a href="../apidocs/org/apache/commons/math/stat/inference/OneWayAnovaImpl.html">
OneWayAnovaImpl</a>, respectively.
The
<a href="../apidocs/org/apache/commons/math/stat/inference/TestUtils.html">
TestUtils</a> class provides static methods to get test instances or
to compute test statistics directly. The examples below all use the
static methods in <code>TestUtils</code> to execute tests. To get
test object instances, either use e.g.,
<code>TestUtils.getTTest()</code> or use the factory directly, e.g.,
<code>TestFactory.newInstance().createChiSquareTest()</code>.
<code>TestUtils.getTTest()</code> or use the implementation constructors
directly, e.g.,
<code>new TTestImpl()</code>.
</p>
<p>
<strong>Implementation Notes</strong>
Expand All @@ -448,8 +454,8 @@ System.out.println(regression.getSlopeStdErr());
assumptions of the parametric t-test procedure, as discussed
<a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
here</a></li>
<li>p-values returned by both t- and chi-square tests are exact, based
on numerical approximations to the t- and chi-square distributions in the
<li>p-values returned by t-, chi-square and Anova tests are exact, based
on numerical approximations to the t-, chi-square and F distributions in the
<code>distributions</code> package. </li>
<li>p-values returned by t-tests are for two-sided tests and the boolean-valued
methods supporting fixed significance level tests assume that the hypotheses
Expand Down Expand Up @@ -512,6 +518,7 @@ TestUtils.tTest(mu, observed, alpha);
To test, for example at the 95% level of confidence, use
<code>alpha = 0.05</code>
</dd>
<br></br>
<dt><strong>Two-Sample t-tests</strong></dt>
<br></br>
<dd><strong>Example 1:</strong> Paired test evaluating
Expand Down Expand Up @@ -584,7 +591,8 @@ TestUtils.tTest(sample1, sample2, .05);
replace "t" at the beginning of the method name with "homoscedasticT"
</p>
</dd>
<dt>Computing <code>chi-square</code> test statistics</dt>
<br></br>
<dt><strong>Chi-square tests</strong></dt>
<br></br>
<dd>To compute a chi-square statistic measuring the agreement between a
<code>long[]</code> array of observed counts and a <code>double[]</code>
Expand Down Expand Up @@ -644,7 +652,41 @@ TestUtils.chiSquareTest(counts, alpha);
The boolean value returned will be <code>true</code> iff the null
hypothesis can be rejected with confidence <code>1 - alpha</code>.
</dd>
</dl>
<br></br>
<dt><strong><One-Way Anova tests</strong></dt>
<br></br>
<dd>To conduct a One-Way Analysis of Variance (ANOVA) to evaluate the
null hypothesis that the means of a collection of univariate datasets
are the same, start by loading the datasets into a collection, e.g.
<source>
double[] classA =
{93.0, 103.0, 95.0, 101.0, 91.0, 105.0, 96.0, 94.0, 101.0 };
double[] classB =
{99.0, 92.0, 102.0, 100.0, 102.0, 89.0 };
double[] classC =
{110.0, 115.0, 111.0, 117.0, 128.0, 117.0 };
List classes = new ArrayList();
classes.add(classA);
classes.add(classB);
classes.add(classC);
</source>
Then you can compute ANOVA F- or p-values associated with the
null hypothesis that the class means are all the same
using a <code>OneWayAnova</code> instance or <code>TestUtils</code>
methods:
<source>
double fStatistic = TestUtils.oneWayAnovaFValue(classes); // F-value
double pValue = TestUtils.oneWayAnovaPValue(classes); // P-value
</source>
To test perform a One-Way Anova test with signficance level set at 0.01
(so the test will, assuming assumptions are met, reject the null
hypothesis incorrectly only about one in 100 times), use
<source>
TestUtils.oneWayAnovaTest(classes, 0.01); // returns a boolean
// true means reject null hypothesis
</source>
</dd>
</dl>
</p>
</subsection>
</section>
Expand Down

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