ComplementaryDistributionFunction() public méthode

Gets the complementary cumulative distribution function (ccdf) for this distribution evaluated at point x. This function is also known as the Survival function.
The Complementary Cumulative Distribution Function (CCDF) is the complement of the Cumulative Distribution Function, or 1 minus the CDF.
public ComplementaryDistributionFunction ( double x ) : double
x double A single point in the distribution range.
Résultat double
ShapiroWilkDistribution Class Documentation
Exemple #1
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        public void ConstructorTest()
        {
            // Example from http://www.nag.com/numeric/cl/nagdoc_cl23/pdf/G01/g01ddc.pdf

            double[] a = 
            { 
                0.11, 7.87, 4.61, 10.14, 7.95, 3.14, 0.46, 4.43, 
                0.21, 4.75, 0.71, 1.52, 3.24, 0.93, 0.42, 4.97,
                9.53, 4.55, 0.47, 6.66 
            };

            var sw = new ShapiroWilkDistribution(a.Length);

            double expected = 0.0421;
            double actual = sw.ComplementaryDistributionFunction(0.9005);
            Assert.AreEqual(expected, actual, 1e-4);
        }
Exemple #2
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        public void ConstructorTest2()
        {
            // Example from http://www.nag.com/numeric/cl/nagdoc_cl23/pdf/G01/g01ddc.pdf

            double[] b =
            {
                1.36, 1.14, 2.92, 2.55, 1.46, 1.06, 5.27, 1.11, 3.48,
                1.10, 0.88, 0.51, 1.46, 0.52, 6.20, 1.69, 0.08, 3.67,
                2.81, 3.49
            };

            var sw = new ShapiroWilkDistribution(b.Length);

            double expected = 0.5246;
            double actual = sw.ComplementaryDistributionFunction(0.9590);
            Assert.AreEqual(expected, actual, 1e-3);
        }
Exemple #3
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        public void ConstructorTest3()
        {
            var sw = new ShapiroWilkDistribution(samples: 12);

            double mean = sw.Mean;     // 0.940148636841248
            double median = sw.Median; // 0.940148636841248
            double mode = sw.Mode;     // 0.940148636841248

            double cdf = sw.DistributionFunction(x: 0.42); // 0.999995183174473
            double pdf = sw.ProbabilityDensityFunction(x: 0.42); // 0.000043477460596194137
            double lpdf = sw.LogProbabilityDensityFunction(x: 0.42); // -10.043267901368219

            double ccdf = sw.ComplementaryDistributionFunction(x: 0.42); // 0.0000048168255270011394
            double icdf = sw.InverseDistributionFunction(p: cdf); // 0.42000002275671627

            double hf = sw.HazardFunction(x: 0.42); // 9.0261647121070521
            double chf = sw.CumulativeHazardFunction(x: 0.42); // 12.243395451233496

            string str = sw.ToString(CultureInfo.InvariantCulture); // W(x; n = 12)

            Assert.AreEqual(0.940148636841248, mean);
            Assert.AreEqual(0.940148636841248, mode);
            Assert.AreEqual(0.940148636841248, median, 1e-8);
            Assert.AreEqual(12.243395451233496, chf);
            Assert.AreEqual(0.999995183174473, cdf);
            Assert.AreEqual(0.000043477460596194137, pdf);
            Assert.AreEqual(-10.043267901368219, lpdf);
            Assert.AreEqual(9.0261647121070521, hf);
            Assert.AreEqual(0.0000048168255270011394, ccdf);
            Assert.AreEqual(0.42000002275671627, icdf, 1e-8);
            Assert.AreEqual("W(x; n = 12)", str);

            var range1 = sw.GetRange(0.95);
            Assert.AreEqual(0.8607805197002204, range1.Min);
            Assert.AreEqual(0.97426955790462533, range1.Max);

            var range2 = sw.GetRange(0.99);
            Assert.AreEqual(0.80248479750351542, range2.Min);
            Assert.AreEqual(0.98186388183806661, range2.Max);

            var range3 = sw.GetRange(0.01);
            Assert.AreEqual(0.80248479750351542, range3.Min);
            Assert.AreEqual(0.98186388183806661, range3.Max);
        }
Exemple #4
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        public void ConstructorTest4()
        {
            // Create a new Shapiro-Wilk's W for 5 samples
            var sw = new ShapiroWilkDistribution(samples: 5);

            double mean = sw.Mean;     // 0.81248567196628929
            double median = sw.Median; // 0.81248567196628929
            double mode = sw.Mode;     // 0.81248567196628929

            double cdf = sw.DistributionFunction(x: 0.84); // 0.83507812080728383
            double pdf = sw.ProbabilityDensityFunction(x: 0.84); // 0.82021062372326459
            double lpdf = sw.LogProbabilityDensityFunction(x: 0.84); // -0.1981941135071546

            double ccdf = sw.ComplementaryDistributionFunction(x: 0.84); // 0.16492187919271617
            double icdf = sw.InverseDistributionFunction(p: cdf); // 0.84000000194587177

            double hf = sw.HazardFunction(x: 0.84); // 4.9733281462602292
            double chf = sw.CumulativeHazardFunction(x: 0.84); // 1.8022833766369502

            string str = sw.ToString(CultureInfo.InvariantCulture); // W(x; n = 12)

            Assert.AreEqual(0.81248567196628929, mean);
            Assert.AreEqual(0.81248567196628929, mode);
            Assert.AreEqual(0.81248567196628929, median, 1e-8);
            Assert.AreEqual(1.8022833766369502, chf);
            Assert.AreEqual(0.83507812080728383, cdf);
            Assert.AreEqual(0.82021062372326459, pdf);
            Assert.AreEqual(-0.1981941135071546, lpdf);
            Assert.AreEqual(4.9733281462602292, hf);
            Assert.AreEqual(0.16492187919271617, ccdf);
            Assert.AreEqual(0.84000000194587177, icdf, 1e-8);
            Assert.AreEqual("W(x; n = 5)", str);

            var range1 = sw.GetRange(0.95);
            Assert.AreEqual(0.77509977845943778, range1.Min);
            Assert.AreEqual(0.98299906816568339, range1.Max);

            var range2 = sw.GetRange(0.99);
            Assert.AreEqual(0.70180031139628618, range2.Min);
            Assert.AreEqual(0.99334588234528642, range2.Max);

            var range3 = sw.GetRange(0.01);
            Assert.AreEqual(0.70180031139628618, range3.Min);
            Assert.AreEqual(0.99334588234528642, range3.Max);
        }