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java.lang.Objectorg.apache.commons.math3.stat.inference.MannWhitneyUTest
public class MannWhitneyUTest
An implementation of the Mann-Whitney U test (also called Wilcoxon rank-sum test).
Field Summary | |
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private NaturalRanking |
naturalRanking
Ranking algorithm. |
Constructor Summary | |
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MannWhitneyUTest()
Create a test instance using where NaN's are left in place and ties get the average of applicable ranks. |
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MannWhitneyUTest(NaNStrategy nanStrategy,
TiesStrategy tiesStrategy)
Create a test instance using the given strategies for NaN's and ties. |
Method Summary | |
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private double |
calculateAsymptoticPValue(double Umin,
int n1,
int n2)
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private double[] |
concatenateSamples(double[] x,
double[] y)
Concatenate the samples into one array. |
private void |
ensureDataConformance(double[] x,
double[] y)
Ensures that the provided arrays fulfills the assumptions. |
double |
mannWhitneyU(double[] x,
double[] y)
Computes the Mann-Whitney U statistic comparing mean for two independent samples possibly of different length. |
double |
mannWhitneyUTest(double[] x,
double[] y)
Returns the asymptotic observed significance level, or p-value, associated with a Mann-Whitney U statistic comparing mean for two independent samples. |
Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Field Detail |
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private NaturalRanking naturalRanking
Constructor Detail |
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public MannWhitneyUTest()
public MannWhitneyUTest(NaNStrategy nanStrategy, TiesStrategy tiesStrategy)
nanStrategy
- specifies the strategy that should be used for Double.NaN'stiesStrategy
- specifies the strategy that should be used for tiesMethod Detail |
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private void ensureDataConformance(double[] x, double[] y) throws NullArgumentException, NoDataException
x
- first sampley
- second sample
NullArgumentException
- if x
or y
are null
.
NoDataException
- if x
or y
are zero-length.private double[] concatenateSamples(double[] x, double[] y)
x
- first sampley
- second sample
public double mannWhitneyU(double[] x, double[] y) throws NullArgumentException, NoDataException
This statistic can be used to perform a Mann-Whitney U test evaluating the null hypothesis that the two independent samples has equal mean.
Let Xi denote the i'th individual of the first sample and Yj the j'th individual in the second sample. Note that the samples would often have different length.
Preconditions:
x
- the first sampley
- the second sample
NullArgumentException
- if x
or y
are null
.
NoDataException
- if x
or y
are zero-length.private double calculateAsymptoticPValue(double Umin, int n1, int n2) throws ConvergenceException, MaxCountExceededException
Umin
- smallest Mann-Whitney U valuen1
- number of subjects in first samplen2
- number of subjects in second sample
ConvergenceException
- if the p-value can not be computed
due to a convergence error
MaxCountExceededException
- if the maximum number of
iterations is exceededpublic double mannWhitneyUTest(double[] x, double[] y) throws NullArgumentException, NoDataException, ConvergenceException, MaxCountExceededException
Let Xi denote the i'th individual of the first sample and Yj the j'th individual in the second sample. Note that the samples would often have different length.
Preconditions:
Ties give rise to biased variance at the moment. See e.g. http://mlsc.lboro.ac.uk/resources/statistics/Mannwhitney.pdf.
x
- the first sampley
- the second sample
NullArgumentException
- if x
or y
are null
.
NoDataException
- if x
or y
are zero-length.
ConvergenceException
- if the p-value can not be computed due to a
convergence error
MaxCountExceededException
- if the maximum number of iterations
is exceeded
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