public static TestResult RunPairedTest(List <double> x, List <double> y, PairedTest pt)
{
BivariateSample s = new BivariateSample();
s.Add(x, y);
switch (pt)
{
case PairedTest.KendallTau:
return(s.KendallTauTest());
case PairedTest.PearsonR:
return(s.PearsonRTest());
case PairedTest.SpearmanRho:
return(s.SpearmanRhoTest());
case PairedTest.StudentT:
return(s.PairedStudentTTest());
default:
return(null);
}
}
public void KendallNullDistributionTest()
{
// pick independent distributions for x and y, which needn't be normal and needn't be related
Distribution xDistrubtion = new LogisticDistribution();
Distribution yDistribution = new ExponentialDistribution();
Random rng = new Random(314159265);
// generate bivariate samples of various sizes
//int n = 64; {
foreach (int n in TestUtilities.GenerateIntegerValues(4, 64, 8))
{
Sample testStatistics = new Sample();
Distribution testDistribution = null;
for (int i = 0; i < 128; i++)
{
BivariateSample sample = new BivariateSample();
for (int j = 0; j < n; j++)
{
sample.Add(xDistrubtion.GetRandomValue(rng), yDistribution.GetRandomValue(rng));
}
TestResult result = sample.KendallTauTest();
testStatistics.Add(result.Statistic);
testDistribution = result.Distribution;
}
//TestResult r2 = testStatistics.KolmogorovSmirnovTest(testDistribution);
//Console.WriteLine("n={0} P={1}", n, r2.LeftProbability);
//Assert.IsTrue(r2.RightProbability > 0.05);
Console.WriteLine("{0} {1}", testStatistics.PopulationVariance, testDistribution.Variance);
Assert.IsTrue(testStatistics.PopulationMean.ConfidenceInterval(0.95).ClosedContains(testDistribution.Mean));
Assert.IsTrue(testStatistics.PopulationVariance.ConfidenceInterval(0.95).ClosedContains(testDistribution.Variance));
}
}