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);
}
/// <summary>
/// Computes the power for a test with givens values of
/// <see cref="IPowerAnalysis.Effect">effect size</see> and <see cref="IPowerAnalysis.Samples">
/// number of samples</see> under <see cref="IPowerAnalysis.Size"/>.
/// </summary>
///
/// <returns>
/// The power for the test under the given conditions.
/// </returns>
///
public override void ComputePower()
{
double delta = Effect * Math.Sqrt(Samples);
double df = (Samples - 1);
TDistribution td = new TDistribution(df);
NoncentralTDistribution nt = new NoncentralTDistribution(df, delta);
switch (Tail)
{
case DistributionTail.TwoTail:
{
double Ta = td.InverseDistributionFunction(1.0 - Size / 2);
double pa = nt.ComplementaryDistributionFunction(+Ta);
double pb = nt.DistributionFunction(-Ta);
Power = pa + pb;
break;
}
case DistributionTail.OneLower:
{
double Ta = td.InverseDistributionFunction(Size);
Power = nt.DistributionFunction(Ta);
break;
}
case DistributionTail.OneUpper:
{
double Ta = td.InverseDistributionFunction(1.0 - Size);
Power = nt.ComplementaryDistributionFunction(Ta);
break;
}
default:
throw new InvalidOperationException();
}
}
