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Commons Math example source code file (UnknownDistributionChiSquareTest.java)

This example Commons Math source code file (UnknownDistributionChiSquareTest.java) is included in the DevDaily.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Java - Commons Math tags/keywords

chisquaretest, chisquaretest, illegalargumentexception, illegalargumentexception, mathexception, mathexception, unknowndistributionchisquaretest, unknowndistributionchisquaretest

The Commons Math UnknownDistributionChiSquareTest.java 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.math.stat.inference;

import org.apache.commons.math.MathException;

/**
 * An interface for Chi-Square tests for unknown distributions.
 * <p>Two samples tests are used when the distribution is unknown a priori
 * but provided by one sample. We compare the second sample against the first.</p>
 *
 * @version $Revision: 811685 $ $Date: 2009-09-05 13:36:48 -0400 (Sat, 05 Sep 2009) $
 * @since 1.2
 */
public interface UnknownDistributionChiSquareTest extends ChiSquareTest {

    /**
     * <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 statistic * @throws IllegalArgumentException if preconditions are not met */ double chiSquareDataSetsComparison(long[] observed1, long[] observed2) throws IllegalArgumentException; /** * <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 IllegalArgumentException if preconditions are not met * @throws MathException if an error occurs computing the p-value */ double chiSquareTestDataSetsComparison(long[] observed1, long[] observed2) throws IllegalArgumentException, MathException; /** * <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 IllegalArgumentException if preconditions are not met * @throws MathException if an error occurs performing the test */ boolean chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha) throws IllegalArgumentException, MathException; }
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