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Java example source code file (ChiSquareTest.java)
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The ChiSquareTest.java Java example source code
/*
* 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
*
* 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.math3.stat.inference;
import org.apache.commons.math3.distribution.ChiSquaredDistribution;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.MaxCountExceededException;
import org.apache.commons.math3.exception.NotPositiveException;
import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.exception.OutOfRangeException;
import org.apache.commons.math3.exception.ZeroException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.MathArrays;
/**
* 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 a priori
* but provided by one sample, or when the hypothesis under test is that the two
* samples come from the same underlying distribution.</p>
*
*/
public class ChiSquareTest {
/**
* Construct a ChiSquareTest
*/
public ChiSquareTest() {
super();
}
/**
* Computes the <a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm">
* Chi-Square statistic</a> comparing observed and expected
* 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>
* <p>
* <strong>Preconditions:
* <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>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException is thrown.
* <p> Note: This implementation rescales the
* <code>expected array if necessary to ensure that the sum of the
* expected and observed counts are equal.</p>
*
* @param observed array of observed frequency counts
* @param expected array of expected frequency counts
* @return chiSquare test statistic
* @throws NotPositiveException if <code>observed has negative entries
* @throws NotStrictlyPositiveException if <code>expected has entries that are
* not strictly positive
* @throws DimensionMismatchException if the arrays length is less than 2
*/
public double chiSquare(final double[] expected, final long[] observed)
throws NotPositiveException, NotStrictlyPositiveException,
DimensionMismatchException {
if (expected.length < 2) {
throw new DimensionMismatchException(expected.length, 2);
}
if (expected.length != observed.length) {
throw new DimensionMismatchException(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, or observed
* frequency counts to those in the <code>expected 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>
* <p>
* <strong>Preconditions:
* <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>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException is thrown.
* <p>Note: This implementation rescales the
* <code>expected array if necessary to ensure that the sum of the
* expected and observed counts are equal.</p>
*
* @param observed array of observed frequency counts
* @param expected array of expected frequency counts
* @return p-value
* @throws NotPositiveException if <code>observed has negative entries
* @throws NotStrictlyPositiveException if <code>expected has entries that are
* not strictly positive
* @throws DimensionMismatchException if the arrays length is less than 2
* @throws MaxCountExceededException if an error occurs computing the p-value
*/
public double chiSquareTest(final double[] expected, final long[] observed)
throws NotPositiveException, NotStrictlyPositiveException,
DimensionMismatchException, MaxCountExceededException {
// pass a null rng to avoid unneeded overhead as we will not sample from this distribution
final ChiSquaredDistribution distribution =
new ChiSquaredDistribution(null, 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. Returns true iff the null
* hypothesis can be rejected with 100 * (1 - alpha) percent confidence.
* <p>
* <strong>Example:
* To test the hypothesis that <code>observed follows
* <code>expected at the 99% level, use
* <code>chiSquareTest(expected, observed, 0.01)
* <p>
* <strong>Preconditions:
* <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> 0 < alpha < 0.5
* </li>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException is thrown.
* <p>Note: This implementation rescales the
* <code>expected array if necessary to ensure that the sum of the
* expected and observed counts are equal.</p>
*
* @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 NotPositiveException if <code>observed has negative entries
* @throws NotStrictlyPositiveException if <code>expected has entries that are
* not strictly positive
* @throws DimensionMismatchException if the arrays length is less than 2
* @throws OutOfRangeException if <code>alpha is not in the range (0, 0.5]
* @throws MaxCountExceededException if an error occurs computing the p-value
*/
public boolean chiSquareTest(final double[] expected, final long[] observed,
final double alpha)
throws NotPositiveException, NotStrictlyPositiveException,
DimensionMismatchException, OutOfRangeException, MaxCountExceededException {
if ((alpha <= 0) || (alpha > 0.5)) {
throw new OutOfRangeException(LocalizedFormats.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 counts
* array, viewed as a two-way table.
* <p>
* The rows of the 2-way table are
* <code>count[0], ... , count[count.length - 1]
* <p>
* <strong>Preconditions:
* <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 counts must have at
* least 2 columns and at least 2 rows.
* </li>
* </li>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException is thrown.
*
* @param counts array representation of 2-way table
* @return chiSquare test statistic
* @throws NullArgumentException if the array is null
* @throws DimensionMismatchException if the array is not rectangular
* @throws NotPositiveException if {@code counts} has negative entries
*/
public double chiSquare(final long[][] counts)
throws NullArgumentException, NotPositiveException,
DimensionMismatchException {
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;
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 += ((counts[row][col] - expected) *
(counts[row][col] - expected)) / expected;
}
}
return sumSq;
}
/**
* Returns the <i>observed significance level, or counts
* array, viewed as a two-way table.
* <p>
* The rows of the 2-way table are
* <code>count[0], ... , count[count.length - 1]
* <p>
* <strong>Preconditions:
* <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 counts must have at least 2
* columns and at least 2 rows.
* </li>
* </li>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException is thrown.
*
* @param counts array representation of 2-way table
* @return p-value
* @throws NullArgumentException if the array is null
* @throws DimensionMismatchException if the array is not rectangular
* @throws NotPositiveException if {@code counts} has negative entries
* @throws MaxCountExceededException if an error occurs computing the p-value
*/
public double chiSquareTest(final long[][] counts)
throws NullArgumentException, DimensionMismatchException,
NotPositiveException, MaxCountExceededException {
checkArray(counts);
double df = ((double) counts.length -1) * ((double) counts[0].length - 1);
// pass a null rng to avoid unneeded overhead as we will not sample from this distribution
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.
* 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]
* <p>
* <strong>Example:
* To test the null hypothesis that the counts in
* <code>count[0], ... , count[count.length - 1]
* all correspond to the same underlying probability distribution at the 99% level, use</p>
* <p>chiSquareTest(counts, 0.01)
* <p>
* <strong>Preconditions:
* <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 counts must have at least 2 columns and
* at least 2 rows.</li>
* </li>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException 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 DimensionMismatchException if the array is not rectangular
* @throws NotPositiveException if {@code counts} has any negative entries
* @throws OutOfRangeException if <code>alpha is not in the range (0, 0.5]
* @throws MaxCountExceededException if an error occurs computing the p-value
*/
public boolean chiSquareTest(final long[][] counts, final double alpha)
throws NullArgumentException, DimensionMismatchException,
NotPositiveException, OutOfRangeException, MaxCountExceededException {
if ((alpha <= 0) || (alpha > 0.5)) {
throw new OutOfRangeException(LocalizedFormats.OUT_OF_BOUND_SIGNIFICANCE_LEVEL,
alpha, 0, 0.5);
}
return chiSquareTest(counts) < alpha;
}
/**
* <p>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 and observed2. 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 / (observed1[i] + observed2[i])]
* </code> where
* <br/>K = &sqrt;[&sum(observed2 / ?(observed1)]
* </p>
* <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:
* <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 observed1 and observed2 must have
* the same length and their common length must be at least 2.
* </li>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException 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 chiSquare test statistic
* @throws DimensionMismatchException the the length of the arrays does not match
* @throws NotPositiveException if any entries in <code>observed1 or
* <code>observed2 are negative
* @throws ZeroException if either all counts of <code>observed1 or
* <code>observed2 are zero, or if the count at some index is zero
* for both arrays
* @since 1.2
*/
public double chiSquareDataSetsComparison(long[] observed1, long[] observed2)
throws DimensionMismatchException, NotPositiveException, ZeroException {
// Make sure lengths are same
if (observed1.length < 2) {
throw new DimensionMismatchException(observed1.length, 2);
}
if (observed1.length != observed2.length) {
throw new DimensionMismatchException(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;
boolean unequalCounts = false;
double weight = 0.0;
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 ZeroException();
}
// Compare and compute weight only if different
unequalCounts = countSum1 != countSum2;
if (unequalCounts) {
weight = FastMath.sqrt((double) countSum1 / (double) countSum2);
}
// Compute ChiSquare statistic
double sumSq = 0.0d;
double dev = 0.0d;
double obs1 = 0.0d;
double obs2 = 0.0d;
for (int i = 0; i < observed1.length; i++) {
if (observed1[i] == 0 && observed2[i] == 0) {
throw new ZeroException(LocalizedFormats.OBSERVED_COUNTS_BOTTH_ZERO_FOR_ENTRY, i);
} else {
obs1 = observed1[i];
obs2 = observed2[i];
if (unequalCounts) { // apply weights
dev = obs1/weight - obs2 * weight;
} else {
dev = obs1 - obs2;
}
sumSq += (dev * dev) / (obs1 + obs2);
}
}
return sumSq;
}
/**
* <p>Returns the observed significance level, or and
* <code>observed2.
* </p>
* <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>
* <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:
* <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 observed1 and observed2 must
* have the same length and
* their common length must be at least 2.
* </li>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException 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 DimensionMismatchException the the length of the arrays does not match
* @throws NotPositiveException if any entries in <code>observed1 or
* <code>observed2 are negative
* @throws ZeroException if either all counts of <code>observed1 or
* <code>observed2 are zero, or if the count at the same index is zero
* for both arrays
* @throws MaxCountExceededException if an error occurs computing the p-value
* @since 1.2
*/
public double chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)
throws DimensionMismatchException, NotPositiveException, ZeroException,
MaxCountExceededException {
// pass a null rng to avoid unneeded overhead as we will not sample from this distribution
final ChiSquaredDistribution distribution =
new ChiSquaredDistribution(null, (double) observed1.length - 1);
return 1 - distribution.cumulativeProbability(
chiSquareDataSetsComparison(observed1, observed2));
}
/**
* <p>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. Returns true iff the null
* hypothesis can be rejected with 100 * (1 - alpha) percent confidence.
* </p>
* <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:
* <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 observed1 and observed2 must
* have the same length and their common length must be at least 2.
* </li>
* <li> 0 < alpha < 0.5
* </li>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException 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 DimensionMismatchException the the length of the arrays does not match
* @throws NotPositiveException if any entries in <code>observed1 or
* <code>observed2 are negative
* @throws ZeroException if either all counts of <code>observed1 or
* <code>observed2 are zero, or if the count at the same index is zero
* for both arrays
* @throws OutOfRangeException if <code>alpha is not in the range (0, 0.5]
* @throws MaxCountExceededException if an error occurs performing the test
* @since 1.2
*/
public boolean chiSquareTestDataSetsComparison(final long[] observed1,
final long[] observed2,
final double alpha)
throws DimensionMismatchException, NotPositiveException,
ZeroException, OutOfRangeException, MaxCountExceededException {
if (alpha <= 0 ||
alpha > 0.5) {
throw new OutOfRangeException(LocalizedFormats.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 DimensionMismatchException if the array is not valid
* @throws NotPositiveException if the array contains any negative entries
*/
private void checkArray(final long[][] in)
throws NullArgumentException, DimensionMismatchException,
NotPositiveException {
if (in.length < 2) {
throw new DimensionMismatchException(in.length, 2);
}
if (in[0].length < 2) {
throw new DimensionMismatchException(in[0].length, 2);
}
MathArrays.checkRectangular(in);
MathArrays.checkNonNegative(in);
}
}
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