public void ConstructorTest2() { var distribution = new NoncentralTDistribution( degreesOfFreedom: 4, noncentrality: 2.42); double mean = distribution.Mean; // 3.0330202123035104 double median = distribution.Median; // 2.6034842414893795 double var = distribution.Variance; // 4.5135883917583683 double cdf = distribution.DistributionFunction(x: 1.4); // 0.15955740661144721 double pdf = distribution.ProbabilityDensityFunction(x: 1.4); // 0.23552141805184526 double lpdf = distribution.LogProbabilityDensityFunction(x: 1.4); // -1.4459534225195116 double ccdf = distribution.ComplementaryDistributionFunction(x: 1.4); // 0.84044259338855276 double icdf = distribution.InverseDistributionFunction(p: cdf); // 1.4000000000123853 double hf = distribution.HazardFunction(x: 1.4); // 0.28023498559521387 double chf = distribution.CumulativeHazardFunction(x: 1.4); // 0.17382662901507062 string str = distribution.ToString(CultureInfo.InvariantCulture); // T(x; df = 4, μ = 2.42) Assert.AreEqual(3.0330202123035104, mean); Assert.AreEqual(2.6034842414893795, median); Assert.AreEqual(4.5135883917583683, var); Assert.AreEqual(0.17382662901507062, chf); Assert.AreEqual(0.15955740661144721, cdf); Assert.AreEqual(0.23552141805184526, pdf); Assert.AreEqual(-1.4459534225195116, lpdf); Assert.AreEqual(0.28023498559521387, hf); Assert.AreEqual(0.84044259338855276, ccdf); Assert.AreEqual(1.4000000000123853, icdf); Assert.AreEqual("T(x; df = 4, μ = 2.42)", str); }
public void ConstructorTest2() { var distribution = new NoncentralTDistribution( degreesOfFreedom: 4, noncentrality: 2.42); double mean = distribution.Mean; // 3.0330202123035104 double median = distribution.Median; // 2.6034842414893795 double var = distribution.Variance; // 4.5135883917583683 double mode = distribution.Mode; // 2.0940683409246641 double cdf = distribution.DistributionFunction(x: 1.4); // 0.15955740661144721 double pdf = distribution.ProbabilityDensityFunction(x: 1.4); // 0.23552141805184526 double lpdf = distribution.LogProbabilityDensityFunction(x: 1.4); // -1.4459534225195116 double ccdf = distribution.ComplementaryDistributionFunction(x: 1.4); // 0.84044259338855276 double icdf = distribution.InverseDistributionFunction(p: cdf); // 1.4000000000123853 double hf = distribution.HazardFunction(x: 1.4); // 0.28023498559521387 double chf = distribution.CumulativeHazardFunction(x: 1.4); // 0.17382662901507062 string str = distribution.ToString(CultureInfo.InvariantCulture); // T(x; df = 4, μ = 2.42) Assert.AreEqual(3.0330202123035104, mean); Assert.AreEqual(2.6034842414893795, median); Assert.AreEqual(4.5135883917583683, var); Assert.AreEqual(2.0940683409246641, mode); Assert.AreEqual(0.17382662901507062, chf); Assert.AreEqual(0.15955740661144721, cdf); Assert.AreEqual(0.23552141805184526, pdf); Assert.AreEqual(-1.4459534225195116, lpdf); Assert.AreEqual(0.28023498559521387, hf); Assert.AreEqual(0.84044259338855276, ccdf); Assert.AreEqual(1.4000000000123853, icdf); Assert.AreEqual("T(x; df = 4, μ = 2.42)", str); var range1 = distribution.GetRange(0.95); var range2 = distribution.GetRange(0.99); var range3 = distribution.GetRange(0.01); Assert.AreEqual(0.7641009341279591, range1.Min); Assert.AreEqual(6.6668131011180742, range1.Max); Assert.AreEqual(0.098229727233034247, range2.Min); Assert.AreEqual(10.541194525031729, range2.Max); Assert.AreEqual(0.09822972723303551, range3.Min); Assert.AreEqual(10.541194525031729, range3.Max); }
public void ProbabilityFunctionTest() { double[,] table = { // x d df expected { 3.00, 0.0, 1, 0.03183098861 }, { 3.00, 0.0, 2, 0.02741012223 }, { 3.00, 0.0, 3, 0.02297203730 }, { 3.00, 0.5, 1, 0.05359565579 }, { 3.00, 0.5, 2, 0.05226515196 }, { 3.00, 0.5, 3, 0.04788249161 }, { 3.00, 7.0, 15, 0.0009236578208725 }, { 15.00, 7.0, 15, 0.0013850587855 }, { 15.00, 7.0, 25, 0.00018206084230 }, { 0.00, 7.0, 25, 0.0000000000090438 }, { 0.00, 2.0, 1, 0.0430785586036 }, { 0.00, 2.0, 2, 0.047848248255205 }, { 0.00, 2.0, 3, 0.0497428348122 }, { 0.00, 4.0, 1, 0.000106781070 }, { 0.00, 4.0, 2, 0.000118603949 }, }; for (int i = 0; i < table.GetLength(0); i++) { double x = table[i, 0]; double delta = table[i, 1]; double df = table[i, 2]; var target = new NoncentralTDistribution(df, delta); double expected = table[i, 3]; double actual = target.ProbabilityDensityFunction(x); Assert.AreEqual(expected, actual, 1e-10); } }