public void BivariateNullAssociation()
{
Random rng = new Random(314159265);
// Create sample sets for our three test statisics
Sample PS = new Sample();
Sample SS = new Sample();
Sample KS = new Sample();
// variables to hold the claimed distribution of teach test statistic
Distribution PD = null;
Distribution SD = null;
Distribution KD = null;
// generate a large number of bivariate samples and conduct our three tests on each
for (int j = 0; j < 100; j++) {
BivariateSample S = new BivariateSample();
// sample size should be large so that asymptotic assumptions are justified
for (int i = 0; i < 100; i++) {
double x = rng.NextDouble();
double y = rng.NextDouble();
S.Add(x, y);
}
TestResult PR = S.PearsonRTest();
PS.Add(PR.Statistic);
PD = PR.Distribution;
TestResult SR = S.SpearmanRhoTest();
SS.Add(SR.Statistic);
SD = SR.Distribution;
TestResult KR = S.KendallTauTest();
KS.Add(KR.Statistic);
KD = KR.Distribution;
}
// do KS to test whether the samples follow the claimed distributions
//Console.WriteLine(PS.KolmogorovSmirnovTest(PD).LeftProbability);
//Console.WriteLine(SS.KolmogorovSmirnovTest(SD).LeftProbability);
//Console.WriteLine(KS.KolmogorovSmirnovTest(KD).LeftProbability);
Assert.IsTrue(PS.KolmogorovSmirnovTest(PD).LeftProbability < 0.95);
Assert.IsTrue(SS.KolmogorovSmirnovTest(SD).LeftProbability < 0.95);
Assert.IsTrue(KS.KolmogorovSmirnovTest(KD).LeftProbability < 0.95);
}
public void SpearmanNullDistributionTest()
{
// pick independent distributions for x and y, which needn't be normal and needn't be related
Distribution xDistrubtion = new UniformDistribution();
Distribution yDistribution = new CauchyDistribution();
Random rng = new Random(1);
// generate bivariate samples of various sizes
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.SpearmanRhoTest();
testStatistics.Add(result.Statistic);
testDistribution = result.Distribution;
}
TestResult r2 = testStatistics.KuiperTest(testDistribution);
Console.WriteLine("n={0} P={1}", n, r2.LeftProbability);
Assert.IsTrue(r2.RightProbability > 0.05);
Assert.IsTrue(testStatistics.PopulationMean.ConfidenceInterval(0.99).ClosedContains(testDistribution.Mean));
Assert.IsTrue(testStatistics.PopulationVariance.ConfidenceInterval(0.99).ClosedContains(testDistribution.Variance));
}
}