org.hipparchus.stat.inference

Class MannWhitneyUTest

java.lang.Object

org.hipparchus.stat.inference.MannWhitneyUTest

public class MannWhitneyUTest
extends Object

An implementation of the Mann-Whitney U test.

The definitions and computing formulas used in this implementation follow those in the article, Mann-Whitney U Test

In general, results correspond to (and have been tested against) the R wilcox.test function, with exact meaning the same thing in both APIs and CORRECT uniformly true in this implementation. For example, wilcox.test(x, y, alternative = "two.sided", mu = 0, paired = FALSE, exact = FALSE correct = TRUE) will return the same p-value as mannWhitneyUTest(x, y, false). The minimum of the W value returned by R for wilcox.test(x, y...) and wilcox.test(y, x...) should equal mannWhitneyU(x, y...).

Constructor Summary

Constructors

Constructor and Description
MannWhitneyUTest() Create a test instance using where NaN's are left in place and ties get the average of applicable ranks.
MannWhitneyUTest(NaNStrategy nanStrategy, TiesStrategy tiesStrategy) Create a test instance using the given strategies for NaN's and ties.

Method Summary

Modifier and Type	Method and Description
double	mannWhitneyU(double[] x, double[] y) Computes the Mann-Whitney U statistic comparing means for two independent samples possibly of different lengths.
double	mannWhitneyUTest(double[] x, double[] y) Returns the asymptotic observed significance level, or p-value, associated with a Mann-Whitney U Test comparing means for two independent samples.
double	mannWhitneyUTest(double[] x, double[] y, boolean exact) Returns the asymptotic observed significance level, or p-value, associated with a Mann-Whitney U Test comparing means for two independent samples.

Methods inherited from class java.lang.Object

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

Constructor Detail

MannWhitneyUTest

public MannWhitneyUTest()

Create a test instance using where NaN's are left in place and ties get the average of applicable ranks.

MannWhitneyUTest

public MannWhitneyUTest(NaNStrategy nanStrategy,
                        TiesStrategy tiesStrategy)

Create a test instance using the given strategies for NaN's and ties.

Parameters:

nanStrategy - specifies the strategy that should be used for Double.NaN's

tiesStrategy - specifies the strategy that should be used for ties

Method Detail

mannWhitneyU

public double mannWhitneyU(double[] x,
                           double[] y)
                    throws MathIllegalArgumentException,
                           NullArgumentException

Computes the Mann-Whitney U statistic comparing means for two independent samples possibly of different lengths.

This statistic can be used to perform a Mann-Whitney U test evaluating the null hypothesis that the two independent samples have equal mean.

Let X_i denote the i'th individual of the first sample and Y_j the j'th individual in the second sample. Note that the samples can have different lengths.

Preconditions:

• All observations in the two samples are independent.
• The observations are at least ordinal (continuous are also ordinal).

Parameters:

x - the first sample

y - the second sample

Returns:

Mann-Whitney U statistic (minimum of U^x and U^y)

Throws:

NullArgumentException - if x or y are null.

MathIllegalArgumentException - if x or y are zero-length.

mannWhitneyUTest

public double mannWhitneyUTest(double[] x,
                               double[] y)
                        throws MathIllegalArgumentException,
                               NullArgumentException

Returns the asymptotic observed significance level, or p-value, associated with a Mann-Whitney U Test comparing means for two independent samples.

Let X_i denote the i'th individual of the first sample and Y_j the j'th individual in the second sample.

Preconditions:

• All observations in the two samples are independent.
• The observations are at least ordinal.

If there are no ties in the data and both samples are small (less than or equal to 50 values in the combined dataset), an exact test is performed; otherwise the test uses the normal approximation (with continuity correction).

If the combined dataset contains ties, the variance used in the normal approximation is bias-adjusted using the formula in the reference above.

Parameters:

x - the first sample

y - the second sample

Returns:

approximate 2-sized p-value

Throws:

NullArgumentException - if x or y are null.

MathIllegalArgumentException - if x or y are zero-length

mannWhitneyUTest

public double mannWhitneyUTest(double[] x,
                               double[] y,
                               boolean exact)
                        throws MathIllegalArgumentException,
                               NullArgumentException

Returns the asymptotic observed significance level, or p-value, associated with a Mann-Whitney U Test comparing means for two independent samples.

Let X_i denote the i'th individual of the first sample and Y_j the j'th individual in the second sample.

Preconditions:

• All observations in the two samples are independent.
• The observations are at least ordinal.

If exact is true, the p-value reported is exact, computed using the exact distribution of the U statistic. The computation in this case requires storage on the order of the product of the two sample sizes, so this should not be used for large samples.

If exact is false, the normal approximation is used to estimate the p-value.

If the combined dataset contains ties and exact is true, MathIllegalArgumentException is thrown. If exact is false and the ties are present, the variance used to compute the approximate p-value in the normal approximation is bias-adjusted using the formula in the reference above.

Parameters:

x - the first sample

y - the second sample

exact - true means compute the p-value exactly, false means use the normal approximation

Returns:

approximate 2-sided p-value

Throws:

NullArgumentException - if x or y are null.

MathIllegalArgumentException - if x or y are zero-length or if exact is true and ties are present in the data