public void SpearmanNullDistributionTest()
{
// Pick independent distributions for x and y, which needn't be normal and needn't be related.
ContinuousDistribution xDistrubtion = new UniformDistribution();
ContinuousDistribution 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();
ContinuousDistribution 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.KolmogorovSmirnovTest(testDistribution);
Assert.IsTrue(r2.Probability > 0.05);
Assert.IsTrue(testStatistics.PopulationMean.ConfidenceInterval(0.99).ClosedContains(testDistribution.Mean));
Assert.IsTrue(testStatistics.PopulationVariance.ConfidenceInterval(0.99).ClosedContains(testDistribution.Variance));
}
}
//Helpmer method for PerformMoleculeCovarianceNetworkAnalysis(). Takes paired fold changes for two molecules and calculates a spearman's rho coefficient and associated p-value.
//These data are returned in a Corrleation data object.
public static Correlation GetSpearmanCorrelation(List <double> molecule_a_fold_changes, List <double> molecule_b_fold_changes, int molecule_a_identifier, int molecule_b_identifier)
{
Correlation new_correlation = new Correlation();
new_correlation.mol_id_a = molecule_a_identifier;
new_correlation.mol_id_b = molecule_b_identifier;
new_correlation.data_points = molecule_a_fold_changes.Count();
var bs = new BivariateSample("dataOne", "dataTwo");
int count = 0;
for (int i = 0; i < molecule_a_fold_changes.Count; i++)
{
bs.Add(molecule_a_fold_changes[i], molecule_b_fold_changes[i]);
count++;
}
var res = bs.SpearmanRhoTest();
if (res.Statistic < 0)
{
new_correlation.correlation = res.Statistic;
new_correlation.p_value = (2 * res.LeftProbability); //2-tailed t-test
}
else
{
new_correlation.correlation = res.Statistic;
new_correlation.p_value = (2 * res.RightProbability); //2-tailed t-test
}
if (!double.IsNaN(new_correlation.correlation) && !double.IsNaN(new_correlation.p_value) && !double.IsInfinity(new_correlation.p_value) && !double.IsInfinity(new_correlation.correlation))
{
return(new_correlation);
}
return(null);
}
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
ContinuousDistribution PD = null;
ContinuousDistribution SD = null;
ContinuousDistribution 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 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);
}
}