| /* |
| * 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.math.stat.inference; |
| |
| import org.apache.commons.math.MathException; |
| import org.apache.commons.math.distribution.ChiSquaredDistribution; |
| import org.apache.commons.math.distribution.ChiSquaredDistributionImpl; |
| import org.apache.commons.math.distribution.DistributionFactory; |
| |
| /** |
| * Implements Chi-Square test statistics defined in the |
| * {@link UnknownDistributionChiSquareTest} interface. |
| * |
| * @version $Revision$ $Date$ |
| */ |
| public class ChiSquareTestImpl implements UnknownDistributionChiSquareTest { |
| |
| /** Distribution used to compute inference statistics. */ |
| private ChiSquaredDistribution distribution; |
| |
| /** |
| * Construct a ChiSquareTestImpl |
| */ |
| public ChiSquareTestImpl() { |
| this(new ChiSquaredDistributionImpl(1.0)); |
| } |
| |
| /** |
| * Create a test instance using the given distribution for computing |
| * inference statistics. |
| * @param x distribution used to compute inference statistics. |
| * @since 1.2 |
| */ |
| public ChiSquareTestImpl(ChiSquaredDistribution x) { |
| super(); |
| setDistribution(x); |
| } |
| /** |
| * {@inheritDoc} |
| * <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.</p> |
| * |
| * @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 { |
| 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"); |
| } |
| 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 (Math.abs(sumExpected - sumObserved) > 10E-6) { |
| ratio = sumObserved / sumExpected; |
| rescale = true; |
| } |
| double sumSq = 0.0d; |
| double dev = 0.0d; |
| for (int i = 0; i < observed.length; i++) { |
| if (rescale) { |
| dev = ((double) observed[i] - ratio * expected[i]); |
| sumSq += dev * dev / (ratio * expected[i]); |
| } else { |
| dev = ((double) observed[i] - expected[i]); |
| sumSq += dev * dev / expected[i]; |
| } |
| } |
| return sumSq; |
| } |
| |
| /** |
| * {@inheritDoc} |
| * <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.</p> |
| * |
| * @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 |
| */ |
| public double chiSquareTest(double[] expected, long[] observed) |
| throws IllegalArgumentException, MathException { |
| distribution.setDegreesOfFreedom(expected.length - 1.0); |
| return 1.0 - distribution.cumulativeProbability( |
| chiSquare(expected, observed)); |
| } |
| |
| /** |
| * {@inheritDoc} |
| * <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.</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 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 counts array representation of 2-way table |
| * @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 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 |
| */ |
| public double chiSquareTest(long[][] counts) |
| throws IllegalArgumentException, MathException { |
| checkArray(counts); |
| double df = ((double) counts.length -1) * ((double) counts[0].length - 1); |
| distribution.setDegreesOfFreedom(df); |
| return 1 - distribution.cumulativeProbability(chiSquare(counts)); |
| } |
| |
| /** |
| * @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 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); |
| } |
| |
| /** |
| * @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 chi-square test statistic |
| * @throws IllegalArgumentException if preconditions are not met |
| * @since 1.2 |
| */ |
| public double chiSquareDataSetsComparison(long[] observed1, long[] observed2) |
| throws IllegalArgumentException { |
| |
| // Make sure lengths are same |
| if ((observed1.length < 2) || (observed1.length != observed2.length)) { |
| throw new IllegalArgumentException( |
| "oberved1, observed2 array lengths incorrect"); |
| } |
| // Ensure non-negative counts |
| if (!isNonNegative(observed1) || !isNonNegative(observed2)) { |
| throw new IllegalArgumentException( |
| "observed counts must be non-negative"); |
| } |
| // 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 * countSum2 == 0) { |
| throw new IllegalArgumentException( |
| "observed counts cannot all be 0"); |
| } |
| // Compare and compute weight only if different |
| unequalCounts = (countSum1 != countSum2); |
| if (unequalCounts) { |
| weight = Math.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 IllegalArgumentException( |
| "observed counts must not both be zero"); |
| } else { |
| obs1 = (double) observed1[i]; |
| obs2 = (double) observed2[i]; |
| if (unequalCounts) { // apply weights |
| dev = obs1/weight - obs2 * weight; |
| } else { |
| dev = obs1 - obs2; |
| } |
| sumSq += (dev * dev) / (obs1 + obs2); |
| } |
| } |
| return sumSq; |
| } |
| |
| /** |
| * @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 IllegalArgumentException if preconditions are not met |
| * @throws MathException if an error occurs computing the p-value |
| * @since 1.2 |
| */ |
| public double chiSquareTestDataSetsComparison(long[] observed1, long[] observed2) |
| throws IllegalArgumentException, MathException { |
| distribution.setDegreesOfFreedom((double) observed1.length - 1); |
| return 1 - distribution.cumulativeProbability( |
| chiSquareDataSetsComparison(observed1, observed2)); |
| } |
| |
| /** |
| * @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 IllegalArgumentException if preconditions are not met |
| * @throws MathException if an error occurs performing the test |
| * @since 1.2 |
| */ |
| public boolean chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, |
| double alpha) throws IllegalArgumentException, MathException { |
| if ((alpha <= 0) || (alpha > 0.5)) { |
| throw new IllegalArgumentException( |
| "bad significance level: " + alpha); |
| } |
| 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, |
| * throwing IllegalArgumentException if any of these checks fail. |
| * |
| * @param in input 2-way table to check |
| * @throws IllegalArgumentException if the array is not valid |
| */ |
| 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"); |
| } |
| |
| } |
| |
| //--------------------- Protected methods --------------------------------- |
| /** |
| * Gets a DistributionFactory to use in creating ChiSquaredDistribution instances. |
| * @deprecated inject ChiSquaredDistribution instances directly instead of |
| * using a factory. |
| */ |
| protected DistributionFactory getDistributionFactory() { |
| return DistributionFactory.newInstance(); |
| } |
| |
| //--------------------- Private array methods -- should find a utility home for these |
| |
| /** |
| * Returns true iff input array is rectangular. |
| * |
| * @param in array to be tested |
| * @return true if the array is rectangular |
| * @throws NullPointerException if input array is null |
| * @throws ArrayIndexOutOfBoundsException if input array is empty |
| */ |
| 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. |
| * 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 |
| * @throws NullPointerException if input array is null |
| */ |
| 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. |
| * 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 |
| * @throws NullPointerException if input array is null |
| */ |
| 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. |
| * 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 |
| * @throws NullPointerException if input array is null |
| */ |
| 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; |
| } |
| |
| /** |
| * Modify the distribution used to compute inference statistics. |
| * |
| * @param value |
| * the new distribution |
| * @since 1.2 |
| */ |
| public void setDistribution(ChiSquaredDistribution value) { |
| distribution = value; |
| } |
| } |
