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Posted to dev@commons.apache.org by ps...@apache.org on 2004/05/03 05:02:25 UTC
cvs commit: jakarta-commons/math/src/java/org/apache/commons/math/stat/inference ChiSquareTest.java ChiSquareTestImpl.java
psteitz 2004/05/02 20:02:25
Added: math/src/java/org/apache/commons/math/stat/inference
ChiSquareTest.java ChiSquareTestImpl.java
Log:
Initial commit of code split off from TestStatistic. Changed observed vectors to be long[] arrays and added support for independence tests using 2-way tables.
Revision Changes Path
1.1 jakarta-commons/math/src/java/org/apache/commons/math/stat/inference/ChiSquareTest.java
Index: ChiSquareTest.java
===================================================================
/*
* Copyright 2004 The Apache Software Foundation.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.commons.math.stat.inference;
import org.apache.commons.math.MathException;
/**
* An interface for Chi-Square tests.
*
* @version $Revision: 1.1 $ $Date: 2004/05/03 03:02:25 $
*/
public interface ChiSquareTest {
/**
* Computes the <a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm">
* Chi-Square statistic</a> comparing <code>observed</code> and <code>expected</code>
* freqeuncy counts.
* <p>
* This statistic can be used to perform a Chi-Square test evaluating the null hypothesis that
* the observed counts follow the expected distribution.
* <p>
* <strong>Preconditions</strong>: <ul>
* <li>Expected counts must all be positive.
* </li>
* <li>Observed counts must all be >= 0.
* </li>
* <li>The observed and expected arrays must have the same length and
* their common length must be at least 2.
* </li></ul><p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.
*
* @param observed array of observed frequency counts
* @param expected array of expected frequency counts
* @return chiSquare statistic
* @throws IllegalArgumentException if preconditions are not met
*/
double chiSquare(double[] expected, long[] observed)
throws IllegalArgumentException;
/**
* Returns the <i>observed significance level</i>, or <a href=
* "http://www.cas.lancs.ac.uk/glossary_v1.1/hyptest.html#pvalue">
* p-value</a>, associated with a
* <a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm">
* Chi-square goodness of fit test</a> comparing the <code>observed</code>
* frequency counts to those in the <code>expected</code> array.
* <p>
* The number returned is the smallest significance level at which one can reject
* the null hypothesis that the observed counts conform to the frequency distribution
* described by the expected counts.
* <p>
* <strong>Preconditions</strong>: <ul>
* <li>Expected counts must all be positive.
* </li>
* <li>Observed counts must all be >= 0.
* </li>
* <li>The observed and expected arrays must have the same length and
* their common length must be at least 2.
* </li></ul><p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.
*
* @param observed array of observed frequency counts
* @param expected array of expected frequency counts
* @return p-value
* @throws IllegalArgumentException if preconditions are not met
* @throws MathException if an error occurs computing the p-value
*/
double chiSquareTest(double[] expected, long[] observed)
throws IllegalArgumentException, MathException;
/**
* Performs a <a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm">
* Chi-square goodness of fit test</a> evaluating the null hypothesis that the observed counts
* conform to the frequency distribution described by the expected counts, with
* significance level <code>alpha</code>. Returns true iff the null hypothesis can be rejected
* with 100 * (1 - alpha) percent confidence.
* <p>
* <strong>Example:</strong><br>
* To test the hypothesis that <code>observed</code> follows
* <code>expected</code> at the 99% level, use <p>
* <code>chiSquareTest(expected, observed, 0.01) </code>
* <p>
* <strong>Preconditions</strong>: <ul>
* <li>Expected counts must all be positive.
* </li>
* <li>Observed counts must all be >= 0.
* </li>
* <li>The observed and expected arrays must have the same length and
* their common length must be at least 2.
* <li> <code> 0 < alpha < 0.5 </code>
* </li></ul><p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.
*
* @param observed array of observed frequency counts
* @param expected array of expected frequency counts
* @param alpha significance level of the test
* @return true iff null hypothesis can be rejected with confidence
* 1 - alpha
* @throws IllegalArgumentException if preconditions are not met
* @throws MathException if an error occurs performing the test
*/
boolean chiSquareTest(double[] expected, long[] observed, double alpha)
throws IllegalArgumentException, MathException;
/**
* Computes the Chi-Square statistic associated with a
* <a href="http://www.itl.nist.gov/div898/handbook/prc/section4/prc45.htm">
* chi-square test of independence</a> based on the input <code>counts</code>
* array, viewed as a two-way table.
* <p>
* The rows of the 2-way table are <code>count[0], ... , count[count.length - 1] </code>
* <p>
* <strong>Preconditions</strong>: <ul>
* <li>All counts must be >= 0.
* </li>
* <li>The count array must be rectangular (i.e. all count[i] subarrays must have the same length).
* </li>
* <li>The 2-way table represented by <code>counts</code> must have at least 2 columns and
* at least 2 rows.
* </li>
* </li></ul><p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.
*
* @param counts array representation of 2-way table
* @return chiSquare statistic
* @throws IllegalArgumentException if preconditions are not met
*/
double chiSquare(long[][] counts)
throws IllegalArgumentException;
/**
* Returns the <i>observed significance level</i>, or <a href=
* "http://www.cas.lancs.ac.uk/glossary_v1.1/hyptest.html#pvalue">
* p-value</a>, associated with a
* <a href="http://www.itl.nist.gov/div898/handbook/prc/section4/prc45.htm">
* chi-square test of independence</a> based on the input <code>counts</code>
* array, viewed as a two-way table.
* <p>
* The rows of the 2-way table are <code>count[0], ... , count[count.length - 1] </code>
* <p>
* <strong>Preconditions</strong>: <ul>
* <li>All counts must be >= 0.
* </li>
* <li>The count array must be rectangular (i.e. all count[i] subarrays must have the same length).
* </li>
* <li>The 2-way table represented by <code>counts</code> must have at least 2 columns and
* at least 2 rows.
* </li>
* </li></ul><p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.
*
* @param counts array representation of 2-way table
* @return p-value
* @throws IllegalArgumentException if preconditions are not met
* @throws MathException if an error occurs computing the p-value
*/
double chiSquareTest(long[][] counts)
throws IllegalArgumentException, MathException;
/**
* Performs a <a href="http://www.itl.nist.gov/div898/handbook/prc/section4/prc45.htm">
* chi-square test of independence</a> evaluating the null hypothesis that the classifications
* represented by the counts in the columns of the input 2-way table are independent of the rows,
* with significance level <code>alpha</code>. Returns true iff the null hypothesis can be rejected
* with 100 * (1 - alpha) percent confidence.
* <p>
* The rows of the 2-way table are <code>count[0], ... , count[count.length - 1] </code>
* <p>
* <strong>Example:</strong><br>
* To test the null hypothesis that the counts in <code>count[0], ... , count[count.length - 1] </code>
* all correspond to the same underlying probability distribution at the 99% level, use <p>
* <code>chiSquareTest(counts, 0.01) </code>
* <p>
* <strong>Preconditions</strong>: <ul>
* <li>All counts must be >= 0.
* </li>
* <li>The count array must be rectangular (i.e. all count[i] subarrays must have the same length).
* </li>
* <li>The 2-way table represented by <code>counts</code> must have at least 2 columns and
* at least 2 rows.
* </li>
* </li></ul><p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.
*
* @param observed array of observed frequency counts
* @param expected array of exptected frequency counts
* @param alpha significance level of the test
* @return true iff null hypothesis can be rejected with confidence
* 1 - alpha
* @throws IllegalArgumentException if preconditions are not met
* @throws MathException if an error occurs performing the test
*/
boolean chiSquareTest(long[][] counts, double alpha)
throws IllegalArgumentException, MathException;
}
1.1 jakarta-commons/math/src/java/org/apache/commons/math/stat/inference/ChiSquareTestImpl.java
Index: ChiSquareTestImpl.java
===================================================================
/*
* Copyright 2004 The Apache Software Foundation.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.commons.math.stat.inference;
import java.io.Serializable;
import org.apache.commons.math.MathException;
import org.apache.commons.math.distribution.DistributionFactory;
import org.apache.commons.math.distribution.ChiSquaredDistribution;
/**
* Implements Chi-Square test statistics defined in the {@link ChiSquareTest} interface.
*
* @version $Revision: 1.1 $ $Date: 2004/05/03 03:02:25 $
*/
public class ChiSquareTestImpl implements ChiSquareTest, Serializable {
/** Serializable version identifier */
static final long serialVersionUID = 8125110460369960493L;
public ChiSquareTestImpl() {
super();
}
/**
* @param observed array of observed frequency counts
* @param expected array of expected frequency counts
* @return chi-square test statistic
* @throws IllegalArgumentException if preconditions are not met
* or length is less than 2
*/
public double chiSquare(double[] expected, long[] observed)
throws IllegalArgumentException {
double sumSq = 0.0d;
double dev = 0.0d;
if ((expected.length < 2) || (expected.length != observed.length)) {
throw new IllegalArgumentException("observed, expected array lengths incorrect");
}
if (!isPositive(expected) || !isNonNegative(observed)) {
throw new IllegalArgumentException(
"observed counts must be non-negative and expected counts must be postive");
}
for (int i = 0; i < observed.length; i++) {
dev = ((double) observed[i] - expected[i]);
sumSq += dev * dev / expected[i];
}
return sumSq;
}
/**
* @param observed array of observed frequency counts
* @param expected array of exptected frequency counts
* @return p-value
* @throws IllegalArgumentException if preconditions are not met
* @throws MathException if an error occurs computing the p-value
*/
public double chiSquareTest(double[] expected, long[] observed)
throws IllegalArgumentException, MathException {
ChiSquaredDistribution chiSquaredDistribution =
DistributionFactory.newInstance().createChiSquareDistribution((double) expected.length - 1);
return 1 - chiSquaredDistribution.cumulativeProbability(chiSquare(expected, observed));
}
/**
* @param observed array of observed frequency counts
* @param expected array of exptected frequency counts
* @param alpha significance level of the test
* @return true iff null hypothesis can be rejected with confidence
* 1 - alpha
* @throws IllegalArgumentException if preconditions are not met
* @throws MathException if an error occurs performing the test
*/
public boolean chiSquareTest(double[] expected, long[] observed, double alpha)
throws IllegalArgumentException, MathException {
if ((alpha <= 0) || (alpha > 0.5)) {
throw new IllegalArgumentException("bad significance level: " + alpha);
}
return (chiSquareTest(expected, observed) < alpha);
}
/**
* @param observed array of observed frequency counts
* @param expected array of expected frequency counts
* @return chi-square test statistic
* @throws IllegalArgumentException if preconditions are not met
*/
public double chiSquare(long[][] counts)
throws IllegalArgumentException {
checkArray(counts);
int nRows = counts.length;
int nCols = counts[0].length;
// compute row, column and total sums
double[] rowSum = new double[nRows];
double[] colSum = new double[nCols];
double total = 0.0d;
for (int row = 0; row < nRows; row++) {
for (int col = 0; col < nCols; col++) {
rowSum[row] += (double) counts[row][col];
colSum[col] += (double) counts[row][col];
total += (double) counts[row][col];
}
}
// compute expected counts and chi-square
double sumSq = 0.0d;
double expected = 0.0d;
for (int row = 0; row < nRows; row++) {
for (int col = 0; col < nCols; col++) {
expected = (rowSum[row] * colSum[col]) / total;
sumSq += (((double) counts[row][col] - expected) * ((double) counts[row][col] - expected))
/ expected;
}
}
return sumSq;
}
/**
* @param observed array of observed frequency counts
* @param expected array of exptected frequency counts
* @return p-value
* @throws IllegalArgumentException if preconditions are not met
* @throws MathException if an error occurs computing the p-value
*/
public double chiSquareTest(long[][] counts)
throws IllegalArgumentException, MathException {
checkArray(counts);
double df = ((double) counts.length -1) * ((double) counts[0].length - 1);
ChiSquaredDistribution chiSquaredDistribution =
DistributionFactory.newInstance().createChiSquareDistribution(df);
return 1 - chiSquaredDistribution.cumulativeProbability(chiSquare(counts));
}
/**
* @param observed array of observed frequency counts
* @param expected array of exptected frequency counts
* @param alpha significance level of the test
* @return true iff null hypothesis can be rejected with confidence
* 1 - alpha
* @throws IllegalArgumentException if preconditions are not met
* @throws MathException if an error occurs performing the test
*/
public boolean chiSquareTest(long[][] counts, double alpha)
throws IllegalArgumentException, MathException {
if ((alpha <= 0) || (alpha > 0.5)) {
throw new IllegalArgumentException("bad significance level: " + alpha);
}
return (chiSquareTest(counts) < alpha);
}
/**
* Checks to make sure that the input long[][] array is rectangular,
* has at least 2 rows and 2 columns, and has all non-negative entries,
* throwing IllegalArgumentException if any of these checks fail.
*
* @param in input 2-way table to check
* @throws IllegalArgumentException
*/
private void checkArray(long[][] in) throws IllegalArgumentException {
if (in.length < 2) {
throw new IllegalArgumentException("Input table must have at least two rows");
}
if (in[0].length < 2) {
throw new IllegalArgumentException("Input table must have at least two columns");
}
if (!isRectangular(in)) {
throw new IllegalArgumentException("Input table must be rectangular");
}
if (!isNonNegative(in)) {
throw new IllegalArgumentException("All entries in input 2-way table must be non-negative");
}
}
//--------------------- Private array methods -- should find a utility home for these
/**
* Returns true iff input array is rectangular.
* Throws NullPointerException if input array is null
* Throws ArrayIndexOutOfBoundsException if input array is empty
*
* @param in array to be tested
* @return true if the array is rectangular
*/
private boolean isRectangular(long[][] in) {
for (int i = 1; i < in.length; i++) {
if (in[i].length != in[0].length) {
return false;
}
}
return true;
}
/**
* Returns true iff all entries of the input array are > 0.
* Throws NullPointerException if input array is null.
* Returns true if the array is non-null, but empty
*
* @param in array to be tested
* @return true if all entries of the array are positive
*/
private boolean isPositive(double[] in) {
for (int i = 0; i < in.length; i ++) {
if (in[i] <= 0) {
return false;
}
}
return true;
}
/**
* Returns true iff all entries of the input array are >= 0.
* Throws NullPointerException if input array is null.
* Returns true if the array is non-null, but empty
*
* @param in array to be tested
* @return true if all entries of the array are non-negative
*/
private boolean isNonNegative(double[] in) {
for (int i = 0; i < in.length; i ++) {
if (in[i] < 0) {
return false;
}
}
return true;
}
/**
* Returns true iff all entries of the input array are > 0.
* Throws NullPointerException if input array is null.
* Returns true if the array is non-null, but empty
*
* @param in array to be tested
* @return true if all entries of the array are positive
*/
private boolean isPositive(long[] in) {
for (int i = 0; i < in.length; i ++) {
if (in[i] <= 0) {
return false;
}
}
return true;
}
/**
* Returns true iff all entries of the input array are >= 0.
* Throws NullPointerException if input array is null.
* Returns true if the array is non-null, but empty
*
* @param in array to be tested
* @return true if all entries of the array are non-negative
*/
private boolean isNonNegative(long[] in) {
for (int i = 0; i < in.length; i ++) {
if (in[i] < 0) {
return false;
}
}
return true;
}
/**
* Returns true iff all entries of (all subarrays of) the input array are > 0.
* Throws NullPointerException if input array is null.
* Returns true if the array is non-null, but empty
*
* @param in array to be tested
* @return true if all entries of the array are positive
*/
private boolean isPositive(long[][] in) {
for (int i = 0; i < in.length; i ++) {
for (int j = 0; j < in[i].length; j++) {
if (in[i][j] <= 0) {
return false;
}
}
}
return true;
}
/**
* Returns true iff all entries of (all subarrays of) the input array are >= 0.
* Throws NullPointerException if input array is null.
* Returns true if the array is non-null, but empty
*
* @param in array to be tested
* @return true if all entries of the array are non-negative
*/
private boolean isNonNegative(long[][] in) {
for (int i = 0; i < in.length; i ++) {
for (int j = 0; j < in[i].length; j++) {
if (in[i][j] <= 0) {
return false;
}
}
}
return true;
}
}
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