java.lang.Object

org.apache.commons.math4.stat.inference.InferenceTestUtils

public class InferenceTestUtils
extends java.lang.Object

A collection of static methods to create inference test instances or to perform inference tests.

Since:: 1.1

Method Summary

Modifier and Type	Method	Description
`static double`	`approximateP(double d, int n, int m)`
`static double`	`chiSquare(double[] expected, long[] observed)`
`static double`	`chiSquare(long[][] counts)`
`static double`	`chiSquareDataSetsComparison(long[] observed1, long[] observed2)`
`static double`	`chiSquareTest(double[] expected, long[] observed)`
`static boolean`	`chiSquareTest(double[] expected, long[] observed, double alpha)`
`static double`	`chiSquareTest(long[][] counts)`
`static boolean`	`chiSquareTest(long[][] counts, double alpha)`
`static double`	`chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)`
`static boolean`	`chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)`
`static double`	`exactP(double d, int m, int n, boolean strict)`
`static double`	`g(double[] expected, long[] observed)`
`static double`	`gDataSetsComparison(long[] observed1, long[] observed2)`
`static double`	`gTest(double[] expected, long[] observed)`
`static boolean`	`gTest(double[] expected, long[] observed, double alpha)`
`static double`	`gTestDataSetsComparison(long[] observed1, long[] observed2)`
`static boolean`	`gTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)`
`static double`	`gTestIntrinsic(double[] expected, long[] observed)`
`static double`	`homoscedasticT(double[] sample1, double[] sample2)`
`static double`	`homoscedasticT(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)`
`static double`	`homoscedasticTTest(double[] sample1, double[] sample2)`
`static boolean`	`homoscedasticTTest(double[] sample1, double[] sample2, double alpha)`
`static double`	`homoscedasticTTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)`
`static double`	`kolmogorovSmirnovStatistic(double[] x, double[] y)`
`static double`	`kolmogorovSmirnovStatistic(ContinuousDistribution dist, double[] data)`
`static double`	`kolmogorovSmirnovTest(double[] x, double[] y)`
`static double`	`kolmogorovSmirnovTest(double[] x, double[] y, boolean strict)`
`static double`	`kolmogorovSmirnovTest(ContinuousDistribution dist, double[] data)`
`static double`	`kolmogorovSmirnovTest(ContinuousDistribution dist, double[] data, boolean strict)`
`static boolean`	`kolmogorovSmirnovTest(ContinuousDistribution dist, double[] data, double alpha)`
`static double`	`monteCarloP(double d, int n, int m, boolean strict, int iterations, UniformRandomProvider rng)`
`static double`	`oneWayAnovaFValue(java.util.Collection<double[]> categoryData)`
`static double`	`oneWayAnovaPValue(java.util.Collection<double[]> categoryData)`
`static boolean`	`oneWayAnovaTest(java.util.Collection<double[]> categoryData, double alpha)`
`static double`	`pairedT(double[] sample1, double[] sample2)`
`static double`	`pairedTTest(double[] sample1, double[] sample2)`
`static boolean`	`pairedTTest(double[] sample1, double[] sample2, double alpha)`
`static double`	`rootLogLikelihoodRatio(long k11, long k12, long k21, long k22)`
`static double`	`t(double[] sample1, double[] sample2)`
`static double`	`t(double mu, double[] observed)`
`static double`	`t(double mu, StatisticalSummary sampleStats)`
`static double`	`t(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)`
`static double`	`tTest(double[] sample1, double[] sample2)`
`static boolean`	`tTest(double[] sample1, double[] sample2, double alpha)`
`static double`	`tTest(double mu, double[] sample)`
`static boolean`	`tTest(double mu, double[] sample, double alpha)`
`static double`	`tTest(double mu, StatisticalSummary sampleStats)`
`static boolean`	`tTest(double mu, StatisticalSummary sampleStats, double alpha)`
`static double`	`tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)`
`static boolean`	`tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2, double alpha)`

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

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

Class InferenceTestUtils

Method Summary

Methods inherited from class java.lang.Object

Method Details