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Removed file to be replaced by TTestImpl, ChiSquareTestImpl. 

git-svn-id: https://svn.apache.org/repos/asf/jakarta/commons/proper/math/trunk@141203 13f79535-47bb-0310-9956-ffa450edef68
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Phil Steitz 2004-05-03 02:54:09 +00:00
parent 063bb6b880
commit 8490149ab0
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src/java/org/apache/commons/math/stat/inference/TestStatisticImpl.java
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/*
* Copyright 2003-2004 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.TDistribution;
import org.apache.commons.math.distribution.ChiSquaredDistribution;
import org.apache.commons.math.stat.StatUtils;
import org.apache.commons.math.stat.univariate.StatisticalSummary;
/**
* Implements test statistics defined in the TestStatistic interface.
*
* @version $Revision: 1.5 $ $Date: 2004/04/27 16:42:34 $
*/
public class TestStatisticImpl implements TestStatistic, Serializable {
/** Serializable version identifier */
static final long serialVersionUID = 3357444126133491679L;
/**
* @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, double[] 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 ((StatUtils.min(expected) <= 0) || (StatUtils.min(observed) < 0)) {
throw new IllegalArgumentException(
"observed counts must be non-negative expected counts must be postive");
}
for (int i = 0; i < observed.length; i++) {
dev = (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, double[] 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, double[] 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 mu comparison constant
* @param observed array of values
* @return t statistic
* @throws IllegalArgumentException if input array length is less than 5
*/
public double t(double mu, double[] observed)
throws IllegalArgumentException {
if ((observed == null) || (observed.length < 5)) {
throw new IllegalArgumentException("insufficient data for t statistic");
}
return t(StatUtils.mean(observed), mu, StatUtils.variance(observed), observed.length);
}
/**
* @param mu constant value to compare sample mean against
* @param sample array of sample data values
* @param alpha significance level of the test
* @return p-value
* @throws IllegalArgumentException if the precondition is not met
* @throws MathException if an error occurs computing the p-value
*/
public boolean tTest(double mu, double[] sample, double alpha)
throws IllegalArgumentException, MathException {
if ((alpha <= 0) || (alpha > 0.5)) {
throw new IllegalArgumentException("bad significance level: " + alpha);
}
return (tTest(mu, sample) < alpha);
}
/**
* @param sample1 array of sample data values
* @param sample2 array of sample data values
* @return t-statistic
* @throws IllegalArgumentException if the precondition is not met
*/
public double t(double[] sample1, double[] sample2)
throws IllegalArgumentException {
if ((sample1 == null) || (sample2 == null ||
Math.min(sample1.length, sample2.length) < 5)) {
throw new IllegalArgumentException("insufficient data for t statistic");
}
return t(StatUtils.mean(sample1), StatUtils.mean(sample2), StatUtils.variance(sample1),
StatUtils.variance(sample2), (double) sample1.length, (double) sample2.length);
}
/**
*
* @param sample1 array of sample data values
* @param sample2 array of sample data values
* @return tTest p-value
* @throws IllegalArgumentException if the precondition is not met
* @throws MathException if an error occurs computing the p-value
*/
public double tTest(double[] sample1, double[] sample2)
throws IllegalArgumentException, MathException {
if ((sample1 == null) || (sample2 == null ||
Math.min(sample1.length, sample2.length) < 5)) {
throw new IllegalArgumentException("insufficient data");
}
return tTest(StatUtils.mean(sample1), StatUtils.mean(sample2), StatUtils.variance(sample1),
StatUtils.variance(sample2), (double) sample1.length, (double) sample2.length);
}
/**
* @param sample1 array of sample data values
* @param sample2 array of sample data values
* @param alpha significance level
* @return true if the null hypothesis can be rejected with
* confidence 1 - alpha
* @throws IllegalArgumentException if the preconditions are not met
* @throws MathException if an error occurs performing the test
*/
public boolean tTest(double[] sample1, double[] sample2, double alpha)
throws IllegalArgumentException, MathException {
if ((alpha <= 0) || (alpha > 0.5)) {
throw new IllegalArgumentException("bad significance level: " + alpha);
}
return (tTest(sample1, sample2) < alpha);
}
/**
* @param mu constant value to compare sample mean against
* @param sample array of sample data values
* @return p-value
* @throws IllegalArgumentException if the precondition is not met
* @throws MathException if an error occurs computing the p-value
*/
public double tTest(double mu, double[] sample)
throws IllegalArgumentException, MathException {
if ((sample == null) || (sample.length < 5)) {
throw new IllegalArgumentException("insufficient data for t statistic");
}
return tTest( StatUtils.mean(sample), mu, StatUtils.variance(sample), sample.length);
}
/**
* @param mu comparison constant
* @param sampleStats StatisticalSummary holding sample summary statitstics
* @return t statistic
* @throws IllegalArgumentException if the precondition is not met
*/
public double t(double mu, StatisticalSummary sampleStats)
throws IllegalArgumentException {
if ((sampleStats == null) || (sampleStats.getN() < 5)) {
throw new IllegalArgumentException("insufficient data for t statistic");
}
return t(sampleStats.getMean(), mu, sampleStats.getVariance(), sampleStats.getN());
}
/**
* @param sampleStats1 StatisticalSummary describing data from the first sample
* @param sampleStats2 StatisticalSummary describing data from the second sample
* @return t statistic
* @throws IllegalArgumentException if the precondition is not met
*/
public double t(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
throws IllegalArgumentException {
if ((sampleStats1 == null) ||
(sampleStats2 == null ||
Math.min(sampleStats1.getN(), sampleStats2.getN()) < 5)) {
throw new IllegalArgumentException("insufficient data for t statistic");
}
return t(sampleStats1.getMean(), sampleStats2.getMean(), sampleStats1.getVariance(),
sampleStats2.getVariance(), (double) sampleStats1.getN(), (double) sampleStats2.getN());
}
/**
* @param sampleStats1 StatisticalSummary describing data from the first sample
* @param sampleStats2 StatisticalSummary describing data from the second sample
* @return p-value for t-test
* @throws IllegalArgumentException if the precondition is not met
* @throws MathException if an error occurs computing the p-value
*/
public double tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
throws IllegalArgumentException, MathException {
if ((sampleStats1 == null) || (sampleStats2 == null ||
Math.min(sampleStats1.getN(), sampleStats2.getN()) < 5)) {
throw new IllegalArgumentException("insufficient data for t statistic");
}
return tTest(sampleStats1.getMean(), sampleStats2.getMean(), sampleStats1.getVariance(),
sampleStats2.getVariance(), (double) sampleStats1.getN(), (double) sampleStats2.getN());
}
/**
* @param sampleStats1 StatisticalSummary describing sample data values
* @param sampleStats2 StatisticalSummary describing sample data values
* @param alpha significance level of the test
* @return true if the null hypothesis can be rejected with
* confidence 1 - alpha
* @throws IllegalArgumentException if the preconditions are not met
* @throws MathException if an error occurs performing the test
*/
public boolean tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2,
double alpha)
throws IllegalArgumentException, MathException {
if ((alpha <= 0) || (alpha > 0.5)) {
throw new IllegalArgumentException("bad significance level: " + alpha);
}
return (tTest(sampleStats1, sampleStats2) < alpha);
}
/**
* @param mu constant value to compare sample mean against
* @param sampleStats StatisticalSummary describing sample data values
* @param alpha significance level of the test
* @return p-value
* @throws IllegalArgumentException if the precondition is not met
* @throws MathException if an error occurs computing the p-value
*/
public boolean tTest( double mu, StatisticalSummary sampleStats,double alpha)
throws IllegalArgumentException, MathException {
if ((alpha <= 0) || (alpha > 0.5)) {
throw new IllegalArgumentException("bad significance level: " + alpha);
}
return (tTest(mu, sampleStats) < alpha);
}
/**
* @param mu constant value to compare sample mean against
* @param sampleStats StatisticalSummary describing sample data
* @return p-value
* @throws IllegalArgumentException if the precondition is not met
* @throws MathException if an error occurs computing the p-value
*/
public double tTest(double mu, StatisticalSummary sampleStats)
throws IllegalArgumentException, MathException {
if ((sampleStats == null) || (sampleStats.getN() < 5)) {
throw new IllegalArgumentException("insufficient data for t statistic");
}
return tTest(sampleStats.getMean(), mu, sampleStats.getVariance(), sampleStats.getN());
}
//----------------------------------------------- Private methods
/**
* Computes approximate degrees of freedom for 2-sample t-test.
*
* @param v1 first sample variance
* @param v2 second sample variance
* @param n1 first sample n
* @param n2 second sample n
* @return approximate degrees of freedom
*/
private double df(double v1, double v2, double n1, double n2) {
return (((v1 / n1) + (v2 / n2)) * ((v1 / n1) + (v2 / n2))) /
((v1 * v1) / (n1 * n1 * (n1 - 1d)) + (v2 * v2) /
(n2 * n2 * (n2 - 1d)));
}
/**
* Computes t test statistic for 2-sample t-test.
*
* @param m1 first sample mean
* @param m2 second sample mean
* @param v1 first sample variance
* @param v2 second sample variance
* @param n1 first sample n
* @param n2 second sample n
* @return t test statistic
*/
private double t(double m1, double m2, double v1, double v2, double n1,double n2) {
return (m1 - m2) / Math.sqrt((v1 / n1) + (v2 / n2));
}
/**
* Computes t test statistic for 1-sample t-test.
*
* @param m sample mean
* @param mu constant to test against
* @param v sample variance
* @param n sample n
* @return t test statistic
*/
private double t(double m, double mu, double v, double n) {
return (m - mu) / Math.sqrt(v / n);
}
/**
* Computes p-value for 2-sided, 2-sample t-test.
*
* @param m1 first sample mean
* @param m2 second sample mean
* @param v1 first sample variance
* @param v2 second sample variance
* @param n1 first sample n
* @param n2 second sample n
* @return p-value
* @throws MathException if an error occurs computing the p-value
*/
private double tTest(double m1, double m2, double v1, double v2, double n1, double n2)
throws MathException {
double t = Math.abs(t(m1, m2, v1, v2, n1, n2));
TDistribution tDistribution =
DistributionFactory.newInstance().createTDistribution(df(v1, v2, n1, n2));
return 1.0 - tDistribution.cumulativeProbability(-t, t);
}
/**
* Computes p-value for 2-sided, 1-sample t-test.
*
* @param m sample mean
* @param mu constant to test against
* @param v sample variance
* @param n sample n
* @return p-value
* @throws MathException if an error occurs computing the p-value
*/
private double tTest(double m, double mu, double v, double n)
throws MathException {
double t = Math.abs(t(m, mu, v, n));
TDistribution tDistribution =
DistributionFactory.newInstance().createTDistribution(n - 1);
return 1.0 - tDistribution.cumulativeProbability(-t, t);
}
}