Accord.Tests.Statistics.EmpiricalHazardDistributionTest.DocumentationExample_KaplanMeier C# (CSharp) Method

        public void DocumentationExample_KaplanMeier()
        {
            // Consider the following hazard rates, occurring at the given time steps
            double[] times = { 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 17, 20, 21 };

            double[] hazards = 
            { 
                0, 0.111111111111111, 0.0625, 0.0714285714285714, 0.0769230769230769,
                0, 0.0909090909090909, 0, 0.111111111111111, 0.125, 0, 
                0.166666666666667, 0.2, 0, 0.5, 0 
            };


            // Create a new distribution given the observations and event times
            var distribution = new EmpiricalHazardDistribution(times, hazards, SurvivalEstimator.KaplanMeier);

            // Common measures
            double mean = distribution.Mean;     // 5.49198237428757
            double median = distribution.Median; // 11.999999704601453
            double var = distribution.Variance;  // 39.83481657555663

            // Cumulative distribution functions
            double cdf = distribution.DistributionFunction(x: 4);               //  0.275274821017619
            double ccdf = distribution.ComplementaryDistributionFunction(x: 4); //  0.018754904264376961
            double icdf = distribution.InverseDistributionFunction(p: cdf);     //  4.4588994137113307

            // Probability density functions
            double pdf = distribution.ProbabilityDensityFunction(x: 4);         //  0.055748090690952365
            double lpdf = distribution.LogProbabilityDensityFunction(x: 4);     // -2.8869121169242962

            // Hazard (failure rate) functions
            double hf = distribution.HazardFunction(x: 4);                      //  0.0769230769230769
            double chf = distribution.CumulativeHazardFunction(x: 4);           //  0.32196275946275932

            string str = distribution.ToString(); // H(x; v, t)

            try { double mode = distribution.Mode; Assert.Fail(); }
            catch { }

            Assert.AreEqual(SurvivalEstimator.KaplanMeier, distribution.Estimator);
            Assert.AreEqual(1, distribution.ComplementaryDistributionFunction(0));
            Assert.AreEqual(0, distribution.ComplementaryDistributionFunction(Double.PositiveInfinity));

            Assert.AreEqual(5.49198237428757, mean);
            Assert.AreEqual(11.999999704601453, median, 1e-6);
            Assert.AreEqual(39.83481657555663, var);
            Assert.AreEqual(0.33647223662121273, chf);
            Assert.AreEqual(0.28571428571428559, cdf);
            Assert.AreEqual(0.054945054945054937, pdf);
            Assert.AreEqual(-2.9014215940827497, lpdf);
            Assert.AreEqual(0.0769230769230769, hf);
            Assert.AreEqual(0.71428571428571441, ccdf);
            Assert.AreEqual(5.8785425101214548, icdf, 1e-8);
            Assert.AreEqual("H(x; v, t)", str);

            var range1 = distribution.GetRange(0.95);
            var range2 = distribution.GetRange(0.99);
            var range3 = distribution.GetRange(0.01);

            Assert.AreEqual(1, range1.Min, 1e-3);
            Assert.AreEqual(20.562, range1.Max, 1e-3);
            Assert.AreEqual(1, range2.Min, 1e-3);
            Assert.AreEqual(20.562, range2.Max, 1e-3);
            Assert.AreEqual(1, range3.Min, 1e-3);
            Assert.AreEqual(20.562, range3.Max, 1e-3);

            for (int i = 0; i < hazards.Length; i++)
                Assert.AreEqual(hazards[i], distribution.HazardFunction(times[i]));
        }