/*
* 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.math4.stat.inference;
import java.util.Collection;
import org.apache.commons.rng.UniformRandomProvider;
import org.apache.commons.math4.distribution.RealDistribution;
import org.apache.commons.math4.exception.ConvergenceException;
import org.apache.commons.math4.exception.DimensionMismatchException;
import org.apache.commons.math4.exception.InsufficientDataException;
import org.apache.commons.math4.exception.MaxCountExceededException;
import org.apache.commons.math4.exception.NoDataException;
import org.apache.commons.math4.exception.NotPositiveException;
import org.apache.commons.math4.exception.NotStrictlyPositiveException;
import org.apache.commons.math4.exception.NullArgumentException;
import org.apache.commons.math4.exception.NumberIsTooSmallException;
import org.apache.commons.math4.exception.OutOfRangeException;
import org.apache.commons.math4.exception.ZeroException;
import org.apache.commons.math4.stat.descriptive.StatisticalSummary;
/**
* A collection of static methods to create inference test instances or to
* perform inference tests.
*
* @since 1.1
*/
public class InferenceTestUtils {
/** Singleton TTest instance. */
private static final TTest T_TEST = new TTest();
/** Singleton ChiSquareTest instance. */
private static final ChiSquareTest CHI_SQUARE_TEST = new ChiSquareTest();
/** Singleton OneWayAnova instance. */
private static final OneWayAnova ONE_WAY_ANANOVA = new OneWayAnova();
/** Singleton G-Test instance. */
private static final GTest G_TEST = new GTest();
/** Singleton K-S test instance */
private static final KolmogorovSmirnovTest KS_TEST = new KolmogorovSmirnovTest();
/**
* Prevent instantiation.
*/
private InferenceTestUtils() {
super();
}
// CHECKSTYLE: stop JavadocMethodCheck
/**
* @param sample1 array of sample data values
* @param sample2 array of sample data values
* @return t statistic
* @see org.apache.commons.math4.stat.inference.TTest#homoscedasticT(double[], double[])
*/
public static double homoscedasticT(final double[] sample1, final double[] sample2)
throws NullArgumentException, NumberIsTooSmallException {
return T_TEST.homoscedasticT(sample1, sample2);
}
/**
* @param sampleStats1 StatisticalSummary describing data from the first sample
* @param sampleStats2 StatisticalSummary describing data from the second sample
* @return t statistic
* @see org.apache.commons.math4.stat.inference.TTest#homoscedasticT(org.apache.commons.math4.stat.descriptive.StatisticalSummary, org.apache.commons.math4.stat.descriptive.StatisticalSummary)
*/
public static double homoscedasticT(final StatisticalSummary sampleStats1,
final StatisticalSummary sampleStats2)
throws NullArgumentException, NumberIsTooSmallException {
return T_TEST.homoscedasticT(sampleStats1, sampleStats2);
}
/**
* @param sample1 array of sample data values
* @param sample2 array of sample data values
* @param alpha significance level of the test
* @return true if the null hypothesis can be rejected with
* confidence 1 - alpha
* @see org.apache.commons.math4.stat.inference.TTest#homoscedasticTTest(double[], double[], double)
*/
public static boolean homoscedasticTTest(final double[] sample1, final double[] sample2,
final double alpha)
throws NullArgumentException, NumberIsTooSmallException,
OutOfRangeException, MaxCountExceededException {
return T_TEST.homoscedasticTTest(sample1, sample2, alpha);
}
/**
* @param sample1 array of sample data values
* @param sample2 array of sample data values
* @return p-value for t-test
* @see org.apache.commons.math4.stat.inference.TTest#homoscedasticTTest(double[], double[])
*/
public static double homoscedasticTTest(final double[] sample1, final double[] sample2)
throws NullArgumentException, NumberIsTooSmallException, MaxCountExceededException {
return T_TEST.homoscedasticTTest(sample1, sample2);
}
/**
* @param sampleStats1 StatisticalSummary describing data from the first sample
* @param sampleStats2 StatisticalSummary describing data from the second sample
* @return p-value for t-test
* @see org.apache.commons.math4.stat.inference.TTest#homoscedasticTTest(org.apache.commons.math4.stat.descriptive.StatisticalSummary, org.apache.commons.math4.stat.descriptive.StatisticalSummary)
*/
public static double homoscedasticTTest(final StatisticalSummary sampleStats1,
final StatisticalSummary sampleStats2)
throws NullArgumentException, NumberIsTooSmallException, MaxCountExceededException {
return T_TEST.homoscedasticTTest(sampleStats1, sampleStats2);
}
/**
* @param sample1 array of sample data values
* @param sample2 array of sample data values
* @return t statistic
* @see org.apache.commons.math4.stat.inference.TTest#pairedT(double[], double[])
*/
public static double pairedT(final double[] sample1, final double[] sample2)
throws NullArgumentException, NoDataException,
DimensionMismatchException, NumberIsTooSmallException {
return T_TEST.pairedT(sample1, sample2);
}
/**
* @param sample1 array of sample data values
* @param sample2 array of sample data values
* @param alpha significance level of the test
* @return true if the null hypothesis can be rejected with
* confidence 1 - alpha
* @see org.apache.commons.math4.stat.inference.TTest#pairedTTest(double[], double[], double)
*/
public static boolean pairedTTest(final double[] sample1, final double[] sample2,
final double alpha)
throws NullArgumentException, NoDataException, DimensionMismatchException,
NumberIsTooSmallException, OutOfRangeException, MaxCountExceededException {
return T_TEST.pairedTTest(sample1, sample2, alpha);
}
/**
* @param sample1 array of sample data values
* @param sample2 array of sample data values
* @return p-value for t-test
* @see org.apache.commons.math4.stat.inference.TTest#pairedTTest(double[], double[])
*/
public static double pairedTTest(final double[] sample1, final double[] sample2)
throws NullArgumentException, NoDataException, DimensionMismatchException,
NumberIsTooSmallException, MaxCountExceededException {
return T_TEST.pairedTTest(sample1, sample2);
}
/**
* @param mu comparison constant
* @param observed array of values
* @return t statistic
* @see org.apache.commons.math4.stat.inference.TTest#t(double, double[])
*/
public static double t(final double mu, final double[] observed)
throws NullArgumentException, NumberIsTooSmallException {
return T_TEST.t(mu, observed);
}
/**
* @param mu comparison constant
* @param sampleStats DescriptiveStatistics holding sample summary statitstics
* @return t statistic
* @see org.apache.commons.math4.stat.inference.TTest#t(double, org.apache.commons.math4.stat.descriptive.StatisticalSummary)
*/
public static double t(final double mu, final StatisticalSummary sampleStats)
throws NullArgumentException, NumberIsTooSmallException {
return T_TEST.t(mu, sampleStats);
}
/**
* @param sample1 array of sample data values
* @param sample2 array of sample data values
* @return t statistic
* @see org.apache.commons.math4.stat.inference.TTest#t(double[], double[])
*/
public static double t(final double[] sample1, final double[] sample2)
throws NullArgumentException, NumberIsTooSmallException {
return T_TEST.t(sample1, sample2);
}
/**
* @param sampleStats1 StatisticalSummary describing data from the first sample
* @param sampleStats2 StatisticalSummary describing data from the second sample
* @return t statistic
* @see org.apache.commons.math4.stat.inference.TTest#t(org.apache.commons.math4.stat.descriptive.StatisticalSummary, org.apache.commons.math4.stat.descriptive.StatisticalSummary)
*/
public static double t(final StatisticalSummary sampleStats1,
final StatisticalSummary sampleStats2)
throws NullArgumentException, NumberIsTooSmallException {
return T_TEST.t(sampleStats1, sampleStats2);
}
/**
* @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
* @see org.apache.commons.math4.stat.inference.TTest#tTest(double, double[], double)
*/
public static boolean tTest(final double mu, final double[] sample, final double alpha)
throws NullArgumentException, NumberIsTooSmallException,
OutOfRangeException, MaxCountExceededException {
return T_TEST.tTest(mu, sample, alpha);
}
/**
* @param mu constant value to compare sample mean against
* @param sample array of sample data values
* @return p-value
* @see org.apache.commons.math4.stat.inference.TTest#tTest(double, double[])
*/
public static double tTest(final double mu, final double[] sample)
throws NullArgumentException, NumberIsTooSmallException,
MaxCountExceededException {
return T_TEST.tTest(mu, sample);
}
/**
* @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
* @see org.apache.commons.math4.stat.inference.TTest#tTest(double, org.apache.commons.math4.stat.descriptive.StatisticalSummary, double)
*/
public static boolean tTest(final double mu, final StatisticalSummary sampleStats,
final double alpha)
throws NullArgumentException, NumberIsTooSmallException,
OutOfRangeException, MaxCountExceededException {
return T_TEST.tTest(mu, sampleStats, alpha);
}
/**
* @param mu constant value to compare sample mean against
* @param sampleStats StatisticalSummary describing sample data
* @return p-value
* @see org.apache.commons.math4.stat.inference.TTest#tTest(double, org.apache.commons.math4.stat.descriptive.StatisticalSummary)
*/
public static double tTest(final double mu, final StatisticalSummary sampleStats)
throws NullArgumentException, NumberIsTooSmallException,
MaxCountExceededException {
return T_TEST.tTest(mu, sampleStats);
}
/**
* @param sample1 array of sample data values
* @param sample2 array of sample data values
* @param alpha significance level of the test
* @return true if the null hypothesis can be rejected with
* confidence 1 - alpha
* @see org.apache.commons.math4.stat.inference.TTest#tTest(double[], double[], double)
*/
public static boolean tTest(final double[] sample1, final double[] sample2,
final double alpha)
throws NullArgumentException, NumberIsTooSmallException,
OutOfRangeException, MaxCountExceededException {
return T_TEST.tTest(sample1, sample2, alpha);
}
/**
* @param sample1 array of sample data values
* @param sample2 array of sample data values
* @return p-value for t-test
* @see org.apache.commons.math4.stat.inference.TTest#tTest(double[], double[])
*/
public static double tTest(final double[] sample1, final double[] sample2)
throws NullArgumentException, NumberIsTooSmallException,
MaxCountExceededException {
return T_TEST.tTest(sample1, sample2);
}
/**
* @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
* @see org.apache.commons.math4.stat.inference.TTest#tTest(org.apache.commons.math4.stat.descriptive.StatisticalSummary, org.apache.commons.math4.stat.descriptive.StatisticalSummary, double)
*/
public static boolean tTest(final StatisticalSummary sampleStats1,
final StatisticalSummary sampleStats2,
final double alpha)
throws NullArgumentException, NumberIsTooSmallException,
OutOfRangeException, MaxCountExceededException {
return T_TEST.tTest(sampleStats1, sampleStats2, alpha);
}
/**
* @param sampleStats1 StatisticalSummary describing data from the first sample
* @param sampleStats2 StatisticalSummary describing data from the second sample
* @return p-value for t-test
* @see org.apache.commons.math4.stat.inference.TTest#tTest(org.apache.commons.math4.stat.descriptive.StatisticalSummary, org.apache.commons.math4.stat.descriptive.StatisticalSummary)
*/
public static double tTest(final StatisticalSummary sampleStats1,
final StatisticalSummary sampleStats2)
throws NullArgumentException, NumberIsTooSmallException,
MaxCountExceededException {
return T_TEST.tTest(sampleStats1, sampleStats2);
}
/**
* @param observed array of observed frequency counts
* @param expected array of expected frequency counts
* @return chiSquare test statistic
* @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquare(double[], long[])
*/
public static double chiSquare(final double[] expected, final long[] observed)
throws NotPositiveException, NotStrictlyPositiveException,
DimensionMismatchException {
return CHI_SQUARE_TEST.chiSquare(expected, observed);
}
/**
* @param counts array representation of 2-way table
* @return chiSquare test statistic
* @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquare(long[][])
*/
public static double chiSquare(final long[][] counts)
throws NullArgumentException, NotPositiveException,
DimensionMismatchException {
return CHI_SQUARE_TEST.chiSquare(counts);
}
/**
* @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
* @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareTest(double[], long[], double)
*/
public static boolean chiSquareTest(final double[] expected, final long[] observed,
final double alpha)
throws NotPositiveException, NotStrictlyPositiveException,
DimensionMismatchException, OutOfRangeException, MaxCountExceededException {
return CHI_SQUARE_TEST.chiSquareTest(expected, observed, alpha);
}
/**
* @param observed array of observed frequency counts
* @param expected array of expected frequency counts
* @return p-value
* @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareTest(double[], long[])
*/
public static double chiSquareTest(final double[] expected, final long[] observed)
throws NotPositiveException, NotStrictlyPositiveException,
DimensionMismatchException, MaxCountExceededException {
return CHI_SQUARE_TEST.chiSquareTest(expected, observed);
}
/**
* @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
* @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareTest(long[][], double)
*/
public static boolean chiSquareTest(final long[][] counts, final double alpha)
throws NullArgumentException, DimensionMismatchException,
NotPositiveException, OutOfRangeException, MaxCountExceededException {
return CHI_SQUARE_TEST.chiSquareTest(counts, alpha);
}
/**
* @param counts array representation of 2-way table
* @return p-value
* @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareTest(long[][])
*/
public static double chiSquareTest(final long[][] counts)
throws NullArgumentException, DimensionMismatchException,
NotPositiveException, MaxCountExceededException {
return CHI_SQUARE_TEST.chiSquareTest(counts);
}
/**
* @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
* @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareDataSetsComparison(long[], long[])
*
* @since 1.2
*/
public static double chiSquareDataSetsComparison(final long[] observed1,
final long[] observed2)
throws DimensionMismatchException, NotPositiveException, ZeroException {
return CHI_SQUARE_TEST.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
* @return p-value
* @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareTestDataSetsComparison(long[], long[])
*
* @since 1.2
*/
public static double chiSquareTestDataSetsComparison(final long[] observed1,
final long[] observed2)
throws DimensionMismatchException, NotPositiveException, ZeroException,
MaxCountExceededException {
return CHI_SQUARE_TEST.chiSquareTestDataSetsComparison(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
* @see org.apache.commons.math4.stat.inference.ChiSquareTest#chiSquareTestDataSetsComparison(long[], long[], double)
*
* @since 1.2
*/
public static boolean chiSquareTestDataSetsComparison(final long[] observed1,
final long[] observed2,
final double alpha)
throws DimensionMismatchException, NotPositiveException,
ZeroException, OutOfRangeException, MaxCountExceededException {
return CHI_SQUARE_TEST.chiSquareTestDataSetsComparison(observed1, observed2, alpha);
}
/**
* @param categoryData <code>Collection</code> of <code>double[]</code>
* arrays each containing data for one category
* @return Fvalue
* @see org.apache.commons.math4.stat.inference.OneWayAnova#anovaFValue(Collection)
*
* @since 1.2
*/
public static double oneWayAnovaFValue(final Collection<double[]> categoryData)
throws NullArgumentException, DimensionMismatchException {
return ONE_WAY_ANANOVA.anovaFValue(categoryData);
}
/**
* @param categoryData <code>Collection</code> of <code>double[]</code>
* arrays each containing data for one category
* @return Pvalue
* @see org.apache.commons.math4.stat.inference.OneWayAnova#anovaPValue(Collection)
*
* @since 1.2
*/
public static double oneWayAnovaPValue(final Collection<double[]> categoryData)
throws NullArgumentException, DimensionMismatchException,
ConvergenceException, MaxCountExceededException {
return ONE_WAY_ANANOVA.anovaPValue(categoryData);
}
/**
* @param categoryData <code>Collection</code> of <code>double[]</code>
* arrays each containing data for one category
* @param alpha significance level of the test
* @return true if the null hypothesis can be rejected with
* confidence 1 - alpha
* @see org.apache.commons.math4.stat.inference.OneWayAnova#anovaTest(Collection,double)
*
* @since 1.2
*/
public static boolean oneWayAnovaTest(final Collection<double[]> categoryData,
final double alpha)
throws NullArgumentException, DimensionMismatchException,
OutOfRangeException, ConvergenceException, MaxCountExceededException {
return ONE_WAY_ANANOVA.anovaTest(categoryData, alpha);
}
/**
* @param observed array of observed frequency counts
* @param expected array of expected frequency counts
* @return G-Test statistic
* @see org.apache.commons.math4.stat.inference.GTest#g(double[], long[])
* @since 3.1
*/
public static double g(final double[] expected, final long[] observed)
throws NotPositiveException, NotStrictlyPositiveException,
DimensionMismatchException {
return G_TEST.g(expected, observed);
}
/**
* @param observed array of observed frequency counts
* @param expected array of expected frequency counts
* @return p-value
* @see org.apache.commons.math4.stat.inference.GTest#gTest( double[], long[] )
* @since 3.1
*/
public static double gTest(final double[] expected, final long[] observed)
throws NotPositiveException, NotStrictlyPositiveException,
DimensionMismatchException, MaxCountExceededException {
return G_TEST.gTest(expected, observed);
}
/**
* @param observed array of observed frequency counts
* @param expected array of expected frequency counts
* @return p-value
* @see org.apache.commons.math4.stat.inference.GTest#gTestIntrinsic(double[], long[] )
* @since 3.1
*/
public static double gTestIntrinsic(final double[] expected, final long[] observed)
throws NotPositiveException, NotStrictlyPositiveException,
DimensionMismatchException, MaxCountExceededException {
return G_TEST.gTestIntrinsic(expected, observed);
}
/**
* @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
* @see org.apache.commons.math4.stat.inference.GTest#gTest( double[],long[],double)
* @since 3.1
*/
public static boolean gTest(final double[] expected, final long[] observed,
final double alpha)
throws NotPositiveException, NotStrictlyPositiveException,
DimensionMismatchException, OutOfRangeException, MaxCountExceededException {
return G_TEST.gTest(expected, observed, 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 G-Test statistic
* @see org.apache.commons.math4.stat.inference.GTest#gDataSetsComparison(long[], long[])
* @since 3.1
*/
public static double gDataSetsComparison(final long[] observed1,
final long[] observed2)
throws DimensionMismatchException, NotPositiveException, ZeroException {
return G_TEST.gDataSetsComparison(observed1, observed2);
}
/**
* @param k11 number of times the two events occurred together (AB)
* @param k12 number of times the second event occurred WITHOUT the
* first event (notA,B)
* @param k21 number of times the first event occurred WITHOUT the
* second event (A, notB)
* @param k22 number of times something else occurred (i.e. was neither
* of these events (notA, notB)
* @return root log-likelihood ratio
* @see org.apache.commons.math4.stat.inference.GTest#rootLogLikelihoodRatio(long, long, long, long)
* @since 3.1
*/
public static double rootLogLikelihoodRatio(final long k11, final long k12, final long k21, final long k22)
throws DimensionMismatchException, NotPositiveException, ZeroException {
return G_TEST.rootLogLikelihoodRatio(k11, k12, k21, k22);
}
/**
* @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
* @see org.apache.commons.math4.stat.inference.GTest#gTestDataSetsComparison(long[], long[])
* @since 3.1
*/
public static double gTestDataSetsComparison(final long[] observed1,
final long[] observed2)
throws DimensionMismatchException, NotPositiveException, ZeroException,
MaxCountExceededException {
return G_TEST.gTestDataSetsComparison(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
* @see org.apache.commons.math4.stat.inference.GTest#gTestDataSetsComparison(long[],long[],double)
* @since 3.1
*/
public static boolean gTestDataSetsComparison(final long[] observed1,
final long[] observed2,
final double alpha)
throws DimensionMismatchException, NotPositiveException,
ZeroException, OutOfRangeException, MaxCountExceededException {
return G_TEST.gTestDataSetsComparison(observed1, observed2, alpha);
}
/**
* @param dist reference distribution
* @param data sample being evaluated
* @return Kolmogorov-Smirnov statistic \(D_n\)
* @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovStatistic(RealDistribution, double[])
* @since 3.3
*/
public static double kolmogorovSmirnovStatistic(RealDistribution dist, double[] data)
throws InsufficientDataException, NullArgumentException {
return KS_TEST.kolmogorovSmirnovStatistic(dist, data);
}
/**
* @param dist reference distribution
* @param data sample being being evaluated
* @return the p-value associated with the null hypothesis that {@code data} is a sample from
* {@code distribution}
* @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovTest(RealDistribution, double[])
* @since 3.3
*/
public static double kolmogorovSmirnovTest(RealDistribution dist, double[] data)
throws InsufficientDataException, NullArgumentException {
return KS_TEST.kolmogorovSmirnovTest(dist, data);
}
/**
* @param dist reference distribution
* @param data sample being being evaluated
* @param strict whether or not to force exact computation of the p-value
* @return the p-value associated with the null hypothesis that {@code data} is a sample from
* {@code distribution}
* @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovTest(RealDistribution, double[], boolean)
* @since 3.3
*/
public static double kolmogorovSmirnovTest(RealDistribution dist, double[] data, boolean strict)
throws InsufficientDataException, NullArgumentException {
return KS_TEST.kolmogorovSmirnovTest(dist, data, strict);
}
/**
* @param dist reference distribution
* @param data sample being being evaluated
* @param alpha significance level of the test
* @return true iff the null hypothesis that {@code data} is a sample from {@code distribution}
* can be rejected with confidence 1 - {@code alpha}
* @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovTest(RealDistribution, double[], double)
* @since 3.3
*/
public static boolean kolmogorovSmirnovTest(RealDistribution dist, double[] data, double alpha)
throws InsufficientDataException, NullArgumentException {
return KS_TEST.kolmogorovSmirnovTest(dist, data, alpha);
}
/**
* @param x first sample
* @param y second sample
* @return test statistic \(D_{n,m}\) used to evaluate the null hypothesis that {@code x} and
* {@code y} represent samples from the same underlying distribution
* @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovStatistic(double[], double[])
* @since 3.3
*/
public static double kolmogorovSmirnovStatistic(double[] x, double[] y)
throws InsufficientDataException, NullArgumentException {
return KS_TEST.kolmogorovSmirnovStatistic(x, y);
}
/**
* @param x first sample dataset
* @param y second sample dataset
* @return p-value associated with the null hypothesis that {@code x} and {@code y} represent
* samples from the same distribution
* @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovTest(double[], double[])
* @since 3.3
*/
public static double kolmogorovSmirnovTest(double[] x, double[] y)
throws InsufficientDataException, NullArgumentException {
return KS_TEST.kolmogorovSmirnovTest(x, y);
}
/**
* @param x first sample dataset.
* @param y second sample dataset.
* @param strict whether or not the probability to compute is expressed as
* a strict inequality (ignored for large samples).
* @return p-value associated with the null hypothesis that {@code x} and
* {@code y} represent samples from the same distribution.
* @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#kolmogorovSmirnovTest(double[], double[], boolean)
* @since 3.3
*/
public static double kolmogorovSmirnovTest(double[] x, double[] y, boolean strict)
throws InsufficientDataException, NullArgumentException {
return KS_TEST.kolmogorovSmirnovTest(x, y, strict);
}
/**
* @param d D-statistic value
* @param n first sample size
* @param m second sample size
* @param strict whether or not the probability to compute is expressed as a strict inequality
* @return probability that a randomly selected m-n partition of m + n generates \(D_{n,m}\)
* greater than (resp. greater than or equal to) {@code d}
* @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#exactP(double, int, int, boolean)
* @since 3.3
*/
public static double exactP(double d, int m, int n, boolean strict) {
return KS_TEST.exactP(d, n, m, strict);
}
/**
* @param d D-statistic value
* @param n first sample size
* @param m second sample size
* @return approximate probability that a randomly selected m-n partition of m + n generates
* \(D_{n,m}\) greater than {@code d}
* @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#approximateP(double, int, int)
* @since 3.3
*/
public static double approximateP(double d, int n, int m) {
return KS_TEST.approximateP(d, n, m);
}
/**
* @param d D-statistic value
* @param n first sample size
* @param m second sample size
* @param iterations number of random partitions to generate
* @param strict whether or not the probability to compute is expressed as a strict inequality
* @param rng RNG used for generating the partitions.
* @return proportion of randomly generated m-n partitions of m + n that result in \(D_{n,m}\)
* greater than (resp. greater than or equal to) {@code d}
* @see org.apache.commons.math4.stat.inference.KolmogorovSmirnovTest#monteCarloP(double,int,int,boolean,int,UniformRandomProvider)
* @since 3.3
*/
public static double monteCarloP(double d, int n, int m, boolean strict, int iterations, UniformRandomProvider rng) {
return KS_TEST.monteCarloP(d, n, m, strict, iterations, rng);
}
// CHECKSTYLE: resume JavadocMethodCheck
}