org.apache.commons.math3.stat.inference
Class WilcoxonSignedRankTest

java.lang.Object
  extended by org.apache.commons.math3.stat.inference.WilcoxonSignedRankTest

public class WilcoxonSignedRankTest
extends Object

An implementation of the Wilcoxon signed-rank test.

Version:
$Id: WilcoxonSignedRankTest.java 1416643 2012-12-03 19:37:14Z tn $

Constructor Summary
WilcoxonSignedRankTest()
          Create a test instance where NaN's are left in place and ties get the average of applicable ranks.
WilcoxonSignedRankTest(NaNStrategy nanStrategy, TiesStrategy tiesStrategy)
          Create a test instance using the given strategies for NaN's and ties.
 
Method Summary
 double wilcoxonSignedRank(double[] x, double[] y)
          Computes the Wilcoxon signed ranked statistic comparing mean for two related samples or repeated measurements on a single sample.
 double wilcoxonSignedRankTest(double[] x, double[] y, boolean exactPValue)
          Returns the observed significance level, or p-value, associated with a Wilcoxon signed ranked statistic comparing mean for two related samples or repeated measurements on a single sample.
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Constructor Detail

WilcoxonSignedRankTest

public WilcoxonSignedRankTest()
Create a test instance where NaN's are left in place and ties get the average of applicable ranks. Use this unless you are very sure of what you are doing.


WilcoxonSignedRankTest

public WilcoxonSignedRankTest(NaNStrategy nanStrategy,
                              TiesStrategy tiesStrategy)
Create a test instance using the given strategies for NaN's and ties. Only use this if you are sure of what you are doing.

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

wilcoxonSignedRank

public double wilcoxonSignedRank(double[] x,
                                 double[] y)
                          throws NullArgumentException,
                                 NoDataException,
                                 DimensionMismatchException
Computes the Wilcoxon signed ranked statistic comparing mean for two related samples or repeated measurements on a single sample.

This statistic can be used to perform a Wilcoxon signed ranked test evaluating the null hypothesis that the two related samples or repeated measurements on a single sample has equal mean.

Let Xi denote the i'th individual of the first sample and Yi the related i'th individual in the second sample. Let Zi = Yi - Xi.

Preconditions:

Parameters:
x - the first sample
y - the second sample
Returns:
wilcoxonSignedRank statistic (the larger of W+ and W-)
Throws:
NullArgumentException - if x or y are null.
NoDataException - if x or y are zero-length.
DimensionMismatchException - if x and y do not have the same length.

wilcoxonSignedRankTest

public double wilcoxonSignedRankTest(double[] x,
                                     double[] y,
                                     boolean exactPValue)
                              throws NullArgumentException,
                                     NoDataException,
                                     DimensionMismatchException,
                                     NumberIsTooLargeException,
                                     ConvergenceException,
                                     MaxCountExceededException
Returns the observed significance level, or p-value, associated with a Wilcoxon signed ranked statistic comparing mean for two related samples or repeated measurements on a single sample.

Let Xi denote the i'th individual of the first sample and Yi the related i'th individual in the second sample. Let Zi = Yi - Xi.

Preconditions:

Parameters:
x - the first sample
y - the second sample
exactPValue - if the exact p-value is wanted (only works for x.length <= 30, if true and x.length > 30, this is ignored because calculations may take too long)
Returns:
p-value
Throws:
NullArgumentException - if x or y are null.
NoDataException - if x or y are zero-length.
DimensionMismatchException - if x and y do not have the same length.
NumberIsTooLargeException - if exactPValue is true and x.length > 30
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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