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));
}
}
public void BivariateLinearRegression()
{
// do a set of logistic regression fits
// make sure not only that the fit parameters are what they should be, but that their variances/covariances are as returned
Random rng = new Random(314159);
// define logistic parameters
double a0 = 2.0; double b0 = -1.0;
// keep track of sample of returned a and b fit parameters
BivariateSample ps = new BivariateSample();
// also keep track of returned covariance estimates
// since these vary slightly from fit to fit, we will average them
double caa = 0.0;
double cbb = 0.0;
double cab = 0.0;
// also keep track of test statistics
Sample fs = new Sample();
// do 100 fits
for (int k = 0; k < 100; k++) {
// we should be able to draw x's from any distribution; noise should be drawn from a normal distribution
Distribution xd = new LogisticDistribution();
Distribution nd = new NormalDistribution(0.0, 2.0);
// generate a synthetic data set
BivariateSample s = new BivariateSample();
for (int i = 0; i < 25; i++) {
double x = xd.GetRandomValue(rng);
double y = a0 + b0 * x + nd.GetRandomValue(rng);
s.Add(x, y);
}
// do the regression
FitResult r = s.LinearRegression();
// record best fit parameters
double a = r.Parameter(0).Value;
double b = r.Parameter(1).Value;
ps.Add(a, b);
// record estimated covariances
caa += r.Covariance(0, 0);
cbb += r.Covariance(1, 1);
cab += r.Covariance(0, 1);
// record the fit statistic
fs.Add(r.GoodnessOfFit.Statistic);
Console.WriteLine("F={0}", r.GoodnessOfFit.Statistic);
}
caa /= ps.Count;
cbb /= ps.Count;
cab /= ps.Count;
// check that mean parameter estimates are what they should be: the underlying population parameters
Assert.IsTrue(ps.X.PopulationMean.ConfidenceInterval(0.95).ClosedContains(a0));
Assert.IsTrue(ps.Y.PopulationMean.ConfidenceInterval(0.95).ClosedContains(b0));
Console.WriteLine("{0} {1}", caa, ps.X.PopulationVariance);
Console.WriteLine("{0} {1}", cbb, ps.Y.PopulationVariance);
// check that parameter covarainces are what they should be: the reported covariance estimates
Assert.IsTrue(ps.X.PopulationVariance.ConfidenceInterval(0.95).ClosedContains(caa));
Assert.IsTrue(ps.Y.PopulationVariance.ConfidenceInterval(0.95).ClosedContains(cbb));
Assert.IsTrue(ps.PopulationCovariance.ConfidenceInterval(0.95).ClosedContains(cab));
// check that F is distributed as it should be
Console.WriteLine(fs.KolmogorovSmirnovTest(new FisherDistribution(2, 48)).LeftProbability);
}
public void SampleMaximumLikelihoodFit()
{
// normal distriubtion
Console.WriteLine("normal");
double mu = -1.0;
double sigma = 2.0;
Distribution nd = new NormalDistribution(mu, sigma);
Sample ns = CreateSample(nd, 500);
//FitResult nr = ns.MaximumLikelihoodFit(new NormalDistribution(mu + 1.0, sigma + 1.0));
FitResult nr = ns.MaximumLikelihoodFit((IList<double> p) => new NormalDistribution(p[0], p[1]), new double[] { mu + 1.0, sigma + 1.0 });
Console.WriteLine(nr.Parameter(0));
Console.WriteLine(nr.Parameter(1));
Assert.IsTrue(nr.Dimension == 2);
Assert.IsTrue(nr.Parameter(0).ConfidenceInterval(0.95).ClosedContains(mu));
Assert.IsTrue(nr.Parameter(1).ConfidenceInterval(0.95).ClosedContains(sigma));
FitResult nr2 = NormalDistribution.FitToSample(ns);
Console.WriteLine(nr.Covariance(0,1));
// test analytic expression
Assert.IsTrue(TestUtilities.IsNearlyEqual(nr.Parameter(0).Value, ns.Mean, Math.Sqrt(TestUtilities.TargetPrecision)));
// we don't expect to be able to test sigma against analytic expression because ML value has known bias for finite sample size
// exponential distribution
Console.WriteLine("exponential");
double em = 3.0;
Distribution ed = new ExponentialDistribution(em);
Sample es = CreateSample(ed, 100);
//FitResult er = es.MaximumLikelihoodFit(new ExponentialDistribution(em + 1.0));
FitResult er = es.MaximumLikelihoodFit((IList<double> p) => new ExponentialDistribution(p[0]), new double[] { em + 1.0 });
Console.WriteLine(er.Parameter(0));
Assert.IsTrue(er.Dimension == 1);
Assert.IsTrue(er.Parameter(0).ConfidenceInterval(0.95).ClosedContains(em));
// test against analytic expression
Assert.IsTrue(TestUtilities.IsNearlyEqual(er.Parameter(0).Value, es.Mean, Math.Sqrt(TestUtilities.TargetPrecision)));
Assert.IsTrue(TestUtilities.IsNearlyEqual(er.Parameter(0).Uncertainty, es.Mean / Math.Sqrt(es.Count), Math.Sqrt(Math.Sqrt(TestUtilities.TargetPrecision))));
// lognormal distribution
Console.WriteLine("lognormal");
double l1 = -4.0;
double l2 = 5.0;
Distribution ld = new LognormalDistribution(l1, l2);
Sample ls = CreateSample(ld, 100);
//FitResult lr = ls.MaximumLikelihoodFit(new LognormalDistribution(l1 + 1.0, l2 + 1.0));
FitResult lr = ls.MaximumLikelihoodFit((IList<double> p) => new LognormalDistribution(p[0], p[1]), new double[] { l1 + 1.0, l2 + 1.0 });
Console.WriteLine(lr.Parameter(0));
Console.WriteLine(lr.Parameter(1));
Console.WriteLine(lr.Covariance(0, 1));
Assert.IsTrue(lr.Dimension == 2);
Assert.IsTrue(lr.Parameter(0).ConfidenceInterval(0.99).ClosedContains(l1));
Assert.IsTrue(lr.Parameter(1).ConfidenceInterval(0.99).ClosedContains(l2));
// weibull distribution
Console.WriteLine("weibull");
double w_scale = 4.0;
double w_shape = 2.0;
WeibullDistribution w_d = new WeibullDistribution(w_scale, w_shape);
Sample w_s = CreateSample(w_d, 20);
//FitResult w_r = w_s.MaximumLikelihoodFit(new WeibullDistribution(1.0, 0.5));
FitResult w_r = w_s.MaximumLikelihoodFit((IList<double> p) => new WeibullDistribution(p[0], p[1]), new double[] { 2.0, 2.0 });
Console.WriteLine(w_r.Parameter(0));
Console.WriteLine(w_r.Parameter(1));
Console.WriteLine(w_r.Covariance(0, 1));
Assert.IsTrue(w_r.Parameter(0).ConfidenceInterval(0.95).ClosedContains(w_d.ScaleParameter));
Assert.IsTrue(w_r.Parameter(1).ConfidenceInterval(0.95).ClosedContains(w_d.ShapeParameter));
// logistic distribution
Console.WriteLine("logistic");
double logistic_m = -3.0;
double logistic_s = 2.0;
Distribution logistic_distribution = new LogisticDistribution(logistic_m, logistic_s);
Sample logistic_sample = CreateSample(logistic_distribution, 100);
//FitResult logistic_result = logistic_sample.MaximumLikelihoodFit(new LogisticDistribution());
FitResult logistic_result = logistic_sample.MaximumLikelihoodFit((IList<double> p) => new LogisticDistribution(p[0], p[1]), new double[] { 2.0, 3.0 });
Console.WriteLine(logistic_result.Parameter(0));
Console.WriteLine(logistic_result.Parameter(1));
Assert.IsTrue(logistic_result.Dimension == 2);
Assert.IsTrue(logistic_result.Parameter(0).ConfidenceInterval(0.95).ClosedContains(logistic_m));
Assert.IsTrue(logistic_result.Parameter(1).ConfidenceInterval(0.95).ClosedContains(logistic_s));
// beta distribution
// not yet!
/*
double beta_alpha = 0.5;
double beta_beta = 2.0;
Distribution beta_distribution = new BetaDistribution(beta_alpha, beta_beta);
Sample beta_sample = CreateSample(beta_distribution, 100);
FitResult beta_result = beta_sample.MaximumLikelihoodFit(new BetaDistribution(1.0, 1.0));
Console.WriteLine("Beta:");
Console.WriteLine(beta_result.Parameter(0));
Console.WriteLine(beta_result.Parameter(1));
Assert.IsTrue(beta_result.Dimension == 2);
Assert.IsTrue(beta_result.Parameter(0).ConfidenceInterval(0.95).ClosedContains(beta_alpha));
Assert.IsTrue(beta_result.Parameter(1).ConfidenceInterval(0.95).ClosedContains(beta_beta));
*/
}
