ChiSquareTest.java
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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
*
* https://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.
*/
/*
* This is not the original file distributed by the Apache Software Foundation
* It has been modified by the Hipparchus project
*/
package org.hipparchus.stat.inference;
import org.hipparchus.distribution.continuous.ChiSquaredDistribution;
import org.hipparchus.exception.LocalizedCoreFormats;
import org.hipparchus.exception.MathIllegalArgumentException;
import org.hipparchus.exception.MathIllegalStateException;
import org.hipparchus.exception.NullArgumentException;
import org.hipparchus.stat.LocalizedStatFormats;
import org.hipparchus.util.FastMath;
import org.hipparchus.util.MathArrays;
import org.hipparchus.util.MathUtils;
/**
* Implements Chi-Square test statistics.
* <p>
* This implementation handles both known and unknown distributions.
* <p>
* Two samples tests can be used when the distribution is unknown <i>a priori</i>
* but provided by one sample, or when the hypothesis under test is that the two
* samples come from the same underlying distribution.
*/
public class ChiSquareTest { // NOPMD - this is not a Junit test class, PMD false positive here
/** Empty constructor.
* <p>
* This constructor is not strictly necessary, but it prevents spurious
* javadoc warnings with JDK 18 and later.
* </p>
* @since 3.0
*/
public ChiSquareTest() { // NOPMD - unnecessary constructor added intentionally to make javadoc happy
// nothing to do
}
/**
* 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>
* frequency 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.
* <p>
* <strong>Note: </strong>This implementation rescales the
* <code>expected</code> array if necessary to ensure that the sum of the
* expected and observed counts are equal.
*
* @param observed array of observed frequency counts
* @param expected array of expected frequency counts
* @return chiSquare test statistic
* @throws MathIllegalArgumentException if <code>observed</code> has negative entries
* @throws MathIllegalArgumentException if <code>expected</code> has entries that are
* not strictly positive
* @throws MathIllegalArgumentException if the arrays length is less than 2
*/
public double chiSquare(final double[] expected, final long[] observed)
throws MathIllegalArgumentException {
if (expected.length < 2) {
throw new MathIllegalArgumentException(LocalizedCoreFormats.DIMENSIONS_MISMATCH,
expected.length, 2);
}
MathUtils.checkDimension(expected.length, observed.length);
MathArrays.checkPositive(expected);
MathArrays.checkNonNegative(observed);
double sumExpected = 0d;
double sumObserved = 0d;
for (int i = 0; i < observed.length; i++) {
sumExpected += expected[i];
sumObserved += observed[i];
}
double ratio = 1.0d;
boolean rescale = false;
if (FastMath.abs(sumExpected - sumObserved) > 10E-6) {
ratio = sumObserved / sumExpected;
rescale = true;
}
double sumSq = 0.0d;
for (int i = 0; i < observed.length; i++) {
if (rescale) {
final double dev = observed[i] - ratio * expected[i];
sumSq += dev * dev / (ratio * expected[i]);
} else {
final double dev = observed[i] - expected[i];
sumSq += dev * dev / expected[i];
}
}
return sumSq;
}
/**
* 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.
* <p>
* <strong>Note: </strong>This implementation rescales the
* <code>expected</code> array if necessary to ensure that the sum of the
* expected and observed counts are equal.
*
* @param observed array of observed frequency counts
* @param expected array of expected frequency counts
* @return p-value
* @throws MathIllegalArgumentException if <code>observed</code> has negative entries
* @throws MathIllegalArgumentException if <code>expected</code> has entries that are
* not strictly positive
* @throws MathIllegalArgumentException if the arrays length is less than 2
* @throws MathIllegalStateException if an error occurs computing the p-value
*/
public double chiSquareTest(final double[] expected, final long[] observed)
throws MathIllegalArgumentException, MathIllegalStateException {
final ChiSquaredDistribution distribution = new ChiSquaredDistribution(expected.length - 1.0);
return 1.0 - distribution.cumulativeProbability(chiSquare(expected, observed));
}
/**
* 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
* <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>
* <li><code> 0 < alpha < 0.5</code></li>
* </ul>
* <p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.
* <p>
* <strong>Note: </strong>This implementation rescales the
* <code>expected</code> array if necessary to ensure that the sum of the
* expected and observed counts are equal.
*
* @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 MathIllegalArgumentException if <code>observed</code> has negative entries
* @throws MathIllegalArgumentException if <code>expected</code> has entries that are
* not strictly positive
* @throws MathIllegalArgumentException if the arrays length is less than 2
* @throws MathIllegalArgumentException if <code>alpha</code> is not in the range (0, 0.5]
* @throws MathIllegalStateException if an error occurs computing the p-value
*/
public boolean chiSquareTest(final double[] expected, final long[] observed,
final double alpha)
throws MathIllegalArgumentException, MathIllegalStateException {
if ((alpha <= 0) || (alpha > 0.5)) {
throw new MathIllegalArgumentException(LocalizedStatFormats.OUT_OF_BOUND_SIGNIFICANCE_LEVEL,
alpha, 0, 0.5);
}
return chiSquareTest(expected, observed) < alpha;
}
/**
* 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>
* </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 test statistic
* @throws NullArgumentException if the array is null
* @throws MathIllegalArgumentException if the array is not rectangular
* @throws MathIllegalArgumentException if {@code counts} has negative entries
*/
public double chiSquare(final long[][] counts)
throws MathIllegalArgumentException, NullArgumentException {
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] += counts[row][col];
colSum[col] += counts[row][col];
total += counts[row][col];
}
}
// compute expected counts and chi-square
double sumSq = 0.0d;
for (int row = 0; row < nRows; row++) {
for (int col = 0; col < nCols; col++) {
final double expected = (rowSum[row] * colSum[col]) / total;
sumSq += ((counts[row][col] - expected) *
(counts[row][col] - expected)) / expected;
}
}
return sumSq;
}
/**
* 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>
* </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 NullArgumentException if the array is null
* @throws MathIllegalArgumentException if the array is not rectangular
* @throws MathIllegalArgumentException if {@code counts} has negative entries
* @throws MathIllegalStateException if an error occurs computing the p-value
*/
public double chiSquareTest(final long[][] counts)
throws MathIllegalArgumentException, NullArgumentException, MathIllegalStateException {
checkArray(counts);
double df = ((double) counts.length -1) * ((double) counts[0].length - 1);
final ChiSquaredDistribution distribution = new ChiSquaredDistribution(df);
return 1 - distribution.cumulativeProbability(chiSquare(counts));
}
/**
* 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 <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>
* </ul>
* <p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.
*
* @param counts array representation of 2-way table
* @param alpha significance level of the test
* @return true iff null hypothesis can be rejected with confidence
* 1 - alpha
* @throws NullArgumentException if the array is null
* @throws MathIllegalArgumentException if the array is not rectangular
* @throws MathIllegalArgumentException if {@code counts} has any negative entries
* @throws MathIllegalArgumentException if <code>alpha</code> is not in the range (0, 0.5]
* @throws MathIllegalStateException if an error occurs computing the p-value
*/
public boolean chiSquareTest(final long[][] counts, final double alpha)
throws MathIllegalArgumentException, NullArgumentException, MathIllegalStateException {
if ((alpha <= 0) || (alpha > 0.5)) {
throw new MathIllegalArgumentException(LocalizedStatFormats.OUT_OF_BOUND_SIGNIFICANCE_LEVEL,
alpha, 0, 0.5);
}
return chiSquareTest(counts) < alpha;
}
/**
* Computes a
* <a href="http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/chi2samp.htm">
* Chi-Square two sample test statistic</a> comparing bin frequency counts
* in <code>observed1</code> and <code>observed2</code>.
* <p>
* The sums of frequency counts in the two samples are not required to be the
* same. The formula used to compute the test statistic is
* </p>
* <code>
* ∑[(K * observed1[i] - observed2[i]/K)<sup>2</sup> / (observed1[i] + observed2[i])]
* </code>
* <p>
* where
* </p>
* <code>K = √[∑(observed2 / ∑(observed1)]</code>
* <p>
* This statistic can be used to perform a Chi-Square test evaluating the
* null hypothesis that both observed counts follow the same distribution.
* </p>
* <p><strong>Preconditions</strong>:</p>
* <ul>
* <li>Observed counts must be non-negative.</li>
* <li>Observed counts for a specific bin must not both be zero.</li>
* <li>Observed counts for a specific sample must not all be 0.</li>
* <li>The arrays <code>observed1</code> and <code>observed2</code> 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.
* </p>
*
* @param observed1 array of observed frequency counts of the first data set
* @param observed2 array of observed frequency counts of the second data set
* @return chiSquare test statistic
* @throws MathIllegalArgumentException the the length of the arrays does not match
* @throws MathIllegalArgumentException if any entries in <code>observed1</code> or
* <code>observed2</code> are negative
* @throws MathIllegalArgumentException if either all counts of <code>observed1</code> or
* <code>observed2</code> are zero, or if the count at some index is zero
* for both arrays
*/
public double chiSquareDataSetsComparison(long[] observed1, long[] observed2)
throws MathIllegalArgumentException {
// Make sure lengths are same
if (observed1.length < 2) {
throw new MathIllegalArgumentException(LocalizedCoreFormats.DIMENSIONS_MISMATCH,
observed1.length, 2);
}
MathUtils.checkDimension(observed1.length, observed2.length);
// Ensure non-negative counts
MathArrays.checkNonNegative(observed1);
MathArrays.checkNonNegative(observed2);
// Compute and compare count sums
long countSum1 = 0;
long countSum2 = 0;
for (int i = 0; i < observed1.length; i++) {
countSum1 += observed1[i];
countSum2 += observed2[i];
}
// Ensure neither sample is uniformly 0
if (countSum1 == 0 || countSum2 == 0) {
throw new MathIllegalArgumentException(LocalizedCoreFormats.ZERO_NOT_ALLOWED);
}
// Compare and compute weight only if different
double weight = 0.0;
boolean unequalCounts = countSum1 != countSum2;
if (unequalCounts) {
weight = FastMath.sqrt((double) countSum1 / (double) countSum2);
}
// Compute ChiSquare statistic
double sumSq = 0.0d;
for (int i = 0; i < observed1.length; i++) {
if (observed1[i] == 0 && observed2[i] == 0) {
throw new MathIllegalArgumentException(LocalizedCoreFormats.OBSERVED_COUNTS_BOTTH_ZERO_FOR_ENTRY, i);
} else {
final double obs1 = observed1[i];
final double obs2 = observed2[i];
final double dev;
if (unequalCounts) { // apply weights
dev = obs1/weight - obs2 * weight;
} else {
dev = obs1 - obs2;
}
sumSq += (dev * dev) / (obs1 + obs2);
}
}
return sumSq;
}
/**
* 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 Chi-Square two sample test comparing
* bin frequency counts in <code>observed1</code> and
* <code>observed2</code>.
* <p>
* The number returned is the smallest significance level at which one
* can reject the null hypothesis that the observed counts conform to the
* same distribution.
* <p>
* See {@link #chiSquareDataSetsComparison(long[], long[])} for details
* on the formula used to compute the test statistic. The degrees of
* of freedom used to perform the test is one less than the common length
* of the input observed count arrays.
* <p>
* <strong>Preconditions</strong>:
* <ul>
* <li>Observed counts must be non-negative.</li>
* <li>Observed counts for a specific bin must not both be zero.</li>
* <li>Observed counts for a specific sample must not all be 0.</li>
* <li>The arrays <code>observed1</code> and <code>observed2</code> 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 observed1 array of observed frequency counts of the first data set
* @param observed2 array of observed frequency counts of the second data set
* @return p-value
* @throws MathIllegalArgumentException the the length of the arrays does not match
* @throws MathIllegalArgumentException if any entries in <code>observed1</code> or
* <code>observed2</code> are negative
* @throws MathIllegalArgumentException if either all counts of <code>observed1</code> or
* <code>observed2</code> are zero, or if the count at the same index is zero
* for both arrays
* @throws MathIllegalStateException if an error occurs computing the p-value
*/
public double chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)
throws MathIllegalArgumentException,
MathIllegalStateException {
final ChiSquaredDistribution distribution =
new ChiSquaredDistribution((double) observed1.length - 1);
return 1 - distribution.cumulativeProbability(
chiSquareDataSetsComparison(observed1, observed2));
}
/**
* Performs a Chi-Square two sample test comparing two binned data
* sets. The test evaluates the null hypothesis that the two lists of
* observed counts conform to the same frequency distribution, with
* significance level <code>alpha</code>. Returns true iff the null
* hypothesis can be rejected with 100 * (1 - alpha) percent confidence.
* <p>
* See {@link #chiSquareDataSetsComparison(long[], long[])} for
* details on the formula used to compute the Chisquare statistic used
* in the test. The degrees of of freedom used to perform the test is
* one less than the common length of the input observed count arrays.
* <p>
* <strong>Preconditions</strong>:
* <ul>
* <li>Observed counts must be non-negative.</li>
* <li>Observed counts for a specific bin must not both be zero.</li>
* <li>Observed counts for a specific sample must not all be 0.</li>
* <li>The arrays <code>observed1</code> and <code>observed2</code> must
* have the same length and their common length must be at least 2.</li>
* <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 observed1 array of observed frequency counts of the first data set
* @param observed2 array of observed frequency counts of the second data set
* @param alpha significance level of the test
* @return true iff null hypothesis can be rejected with confidence
* 1 - alpha
* @throws MathIllegalArgumentException the the length of the arrays does not match
* @throws MathIllegalArgumentException if any entries in <code>observed1</code> or
* <code>observed2</code> are negative
* @throws MathIllegalArgumentException if either all counts of <code>observed1</code> or
* <code>observed2</code> are zero, or if the count at the same index is zero
* for both arrays
* @throws MathIllegalArgumentException if <code>alpha</code> is not in the range (0, 0.5]
* @throws MathIllegalStateException if an error occurs performing the test
*/
public boolean chiSquareTestDataSetsComparison(final long[] observed1,
final long[] observed2,
final double alpha)
throws MathIllegalArgumentException, MathIllegalStateException {
if (alpha <= 0 ||
alpha > 0.5) {
throw new MathIllegalArgumentException(LocalizedStatFormats.OUT_OF_BOUND_SIGNIFICANCE_LEVEL,
alpha, 0, 0.5);
}
return chiSquareTestDataSetsComparison(observed1, observed2) < 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.
*
* @param in input 2-way table to check
* @throws NullArgumentException if the array is null
* @throws MathIllegalArgumentException if the array is not valid
* @throws MathIllegalArgumentException if the array contains any negative entries
*/
private void checkArray(final long[][] in)
throws MathIllegalArgumentException, NullArgumentException {
if (in.length < 2) {
throw new MathIllegalArgumentException(LocalizedCoreFormats.DIMENSIONS_MISMATCH,
in.length, 2);
}
if (in[0].length < 2) {
throw new MathIllegalArgumentException(LocalizedCoreFormats.DIMENSIONS_MISMATCH,
in[0].length, 2);
}
MathArrays.checkRectangular(in);
MathArrays.checkNonNegative(in);
}
}