public void EmpiricalHazardConstructorTest3() { double[] times = { 11, 10, 9, 8, 6, 5, 4, 2 }; double[] values = { 0.22, 0.67, 1.00, 0.18, 1.00, 1.00, 1.00, 0.55 }; EmpiricalHazardDistribution distribution = new EmpiricalHazardDistribution(times, values); double mean = distribution.Mean; // 0.93696461879063664 double median = distribution.Median; // 3.9999999151458066 double var = distribution.Variance; // 2.0441627748096289 double chf = distribution.CumulativeHazardFunction(x: 4.2); // 1.55 double cdf = distribution.DistributionFunction(x: 4.2); // 0.7877520261732569 double pdf = distribution.ProbabilityDensityFunction(x: 4.2); // 0.046694554241883471 double lpdf = distribution.LogProbabilityDensityFunction(x: 4.2); // -3.0641277326297756 double hf = distribution.HazardFunction(x: 4.2); // 0.22 double ccdf = distribution.ComplementaryDistributionFunction(x: 4.2); // 0.21224797382674304 double icdf = distribution.InverseDistributionFunction(p: cdf); // 4.3483975243778978 string str = distribution.ToString(); // H(x; v, t) Assert.AreEqual(0.93696461879063664, mean); Assert.AreEqual(3.9999999151458066, median, 1e-6); Assert.AreEqual(2.0441627748096289, var); Assert.AreEqual(1.55, chf); Assert.AreEqual(0.7877520261732569, cdf); Assert.AreEqual(0.046694554241883471, pdf); Assert.AreEqual(-3.0641277326297756, lpdf); Assert.AreEqual(0.22, hf); Assert.AreEqual(0.21224797382674304, ccdf); Assert.AreEqual(4.3483975243778978, icdf, 1e-8); Assert.AreEqual("H(x; v, t)", str); }
public void EmpiricalHazardConstructorTest3() { double[] times = { 11, 10, 9, 8, 6, 5, 4, 2 }; double[] values = { 0.22, 0.67, 1.00, 0.18, 1.00, 1.00, 1.00, 0.55 }; EmpiricalHazardDistribution distribution = new EmpiricalHazardDistribution(times, values); double mean = distribution.Mean; // 0.93696461879063664 double median = distribution.Median; // 3.9999999151458066 double var = distribution.Variance; // 2.0441627748096289 double chf = distribution.CumulativeHazardFunction(x: 4.2); // 1.55 double cdf = distribution.DistributionFunction(x: 4.2); // 0.7877520261732569 double pdf = distribution.ProbabilityDensityFunction(x: 4.2); // 0.046694554241883471 double lpdf = distribution.LogProbabilityDensityFunction(x: 4.2); // -3.0641277326297756 double hf = distribution.HazardFunction(x: 4.2); // 0.22 double ccdf = distribution.ComplementaryDistributionFunction(x: 4.2); // 0.21224797382674304 double icdf = distribution.InverseDistributionFunction(p: cdf); // 4.3483975243778978 string str = distribution.ToString(); // H(x; v, t) Assert.AreEqual(0.93696461879063664, mean); Assert.AreEqual(3.9999999151458066, median, 1e-6); Assert.AreEqual(2.0441627748096289, var); Assert.AreEqual(1.55, chf); Assert.AreEqual(0.7877520261732569, cdf); Assert.AreEqual(0.046694554241883471, pdf); Assert.AreEqual(-3.0641277326297756, lpdf); Assert.AreEqual(0.22, hf); Assert.AreEqual(0.21224797382674304, ccdf); Assert.AreEqual(4.3483975243778978, icdf, 1e-8); Assert.AreEqual("H(x; v, t)", str); }
public void ConstructorTest1() { double[] times; SurvivalOutcome[] censor; CreateExample1(out times, out censor); var distribution = EmpiricalHazardDistribution.Estimate(times, censor, SurvivalEstimator.FlemingHarrington, HazardEstimator.BreslowNelsonAalen); double[] t = distribution.Times; double[] s = distribution.Survivals; double[] h = distribution.Hazards; double[] nt = distribution.Times.Distinct(); double[] nh = nt.Apply(distribution.HazardFunction); var target = new EmpiricalHazardDistribution(nt, nh, SurvivalEstimator.FlemingHarrington); for (int i = 0; i < times.Length; i++) { double expected = distribution.HazardFunction(times[i]); double actual = target.HazardFunction(times[i]); Assert.AreEqual(expected, actual); } for (int i = 0; i < times.Length; i++) { double expected = distribution.CumulativeHazardFunction(times[i]); double actual = target.CumulativeHazardFunction(times[i]); Assert.AreEqual(expected, actual, 1e-5); } for (int i = 0; i < times.Length; i++) { double expected = distribution.ProbabilityDensityFunction(times[i]); double actual = target.ProbabilityDensityFunction(times[i]); Assert.AreEqual(expected, actual, 1e-5); } }
public void MeasuresTest_KaplanMeier() { double[] values = { 0.0000000000000000, 0.0351683340828711, 0.0267358118285064, 0.0000000000000000, 0.0103643094219679, 0.9000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.000762266794052363, 0.000000000000000 }; double[] times = { 11, 1, 9, 8, 7, 3, 6, 5, 4, 2, 10 }; var target = new EmpiricalHazardDistribution(times, values, SurvivalEstimator.KaplanMeier); var general = new GeneralContinuousDistribution(target); //Assert.AreEqual(general.Mean, target.Mean); //Assert.AreEqual(general.Variance, target.Variance); Assert.AreEqual(general.Median, target.Median); for (int i = -10; i < 10; i++) { double x = i; double expected = general.CumulativeHazardFunction(x); double actual = target.CumulativeHazardFunction(x); Assert.AreEqual(expected, actual, 1e-4); } for (int i = -10; i < 10; i++) { double x = i; double expected = general.HazardFunction(x); double actual = target.HazardFunction(x); Assert.AreEqual(expected, actual, 1e-5); } }
public void DocumentationExample_Aalen() { // 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); // Common measures double mean = distribution.Mean; // 6.1658527179584119 double median = distribution.Median; // 11.999999704601453 double var = distribution.Variance; // 44.101147497430993 // Cumulative distribution functions double cdf = distribution.DistributionFunction(x: 4); // 0.275274821017619 double ccdf = distribution.ComplementaryDistributionFunction(x: 4); // 0.724725178982381 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.FlemingHarrington, distribution.Estimator); Assert.AreEqual(1, distribution.ComplementaryDistributionFunction(0)); Assert.AreEqual(0, distribution.ComplementaryDistributionFunction(Double.PositiveInfinity)); Assert.AreEqual(6.1658527179584119, mean); Assert.AreEqual(11.999999704601453, median, 1e-6); Assert.AreEqual(44.101147497430993, var); Assert.AreEqual(0.32196275946275932, chf); Assert.AreEqual(0.275274821017619, cdf); Assert.AreEqual(0.055748090690952365, pdf); Assert.AreEqual(-2.8869121169242962, lpdf); Assert.AreEqual(0.0769230769230769, hf); Assert.AreEqual(0.724725178982381, ccdf); Assert.AreEqual(4.4588994137113307, 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])); } }
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])); } }
public void MeasuresTest_KaplanMeier() { double[] values = { 0.0000000000000000, 0.0351683340828711, 0.0267358118285064, 0.0000000000000000, 0.0103643094219679, 0.9000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.000762266794052363, 0.000000000000000 }; double[] times = { 11, 1, 9, 8, 7, 3, 6, 5, 4, 2, 10 }; var target = new EmpiricalHazardDistribution(times, values, SurvivalEstimator.KaplanMeier); var general = new GeneralContinuousDistribution(target); //Assert.AreEqual(general.Mean, target.Mean); //Assert.AreEqual(general.Variance, target.Variance); Assert.AreEqual(general.Median, target.Median); for (int i = -10; i < 10; i++) { double x = i; double expected = general.CumulativeHazardFunction(x); double actual = target.CumulativeHazardFunction(x); Assert.AreEqual(expected, actual, 1e-4); } for (int i = -10; i < 10; i++) { double x = i; double expected = general.HazardFunction(x); double actual = target.HazardFunction(x); Assert.AreEqual(expected, actual, 1e-5); } }
public void ConstructorTest1() { double[] times; SurvivalOutcome[] censor; CreateExample1(out times, out censor); var distribution = EmpiricalHazardDistribution.Estimate(times, censor, SurvivalEstimator.FlemingHarrington, HazardEstimator.BreslowNelsonAalen); double[] t = distribution.Times; double[] s = distribution.Survivals; double[] h = distribution.Hazards; double[] nt = distribution.Times.Distinct(); double[] nh = nt.Apply(distribution.HazardFunction); var target = new EmpiricalHazardDistribution(nt, nh, SurvivalEstimator.FlemingHarrington); for (int i = 0; i < times.Length; i++) { double expected = distribution.HazardFunction(times[i]); double actual = target.HazardFunction(times[i]); Assert.AreEqual(expected, actual); } for (int i = 0; i < times.Length; i++) { double expected = distribution.CumulativeHazardFunction(times[i]); double actual = target.CumulativeHazardFunction(times[i]); Assert.AreEqual(expected, actual, 1e-5); } for (int i = 0; i < times.Length; i++) { double expected = distribution.ProbabilityDensityFunction(times[i]); double actual = target.ProbabilityDensityFunction(times[i]); Assert.AreEqual(expected, actual, 1e-5); } }
public void DocumentationExample_Aalen() { // 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); // Common measures double mean = distribution.Mean; // 6.1658527179584119 double median = distribution.Median; // 11.999999704601453 double var = distribution.Variance; // 44.101147497430993 // Cumulative distribution functions double cdf = distribution.DistributionFunction(x: 4); // 0.275274821017619 double ccdf = distribution.ComplementaryDistributionFunction(x: 4); // 0.724725178982381 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.FlemingHarrington, distribution.Estimator); Assert.AreEqual(1, distribution.ComplementaryDistributionFunction(0)); Assert.AreEqual(0, distribution.ComplementaryDistributionFunction(Double.PositiveInfinity)); Assert.AreEqual(6.1658527179584119, mean); Assert.AreEqual(11.999999704601453, median, 1e-6); Assert.AreEqual(44.101147497430993, var); Assert.AreEqual(0.32196275946275932, chf); Assert.AreEqual(0.275274821017619, cdf); Assert.AreEqual(0.055748090690952365, pdf); Assert.AreEqual(-2.8869121169242962, lpdf); Assert.AreEqual(0.0769230769230769, hf); Assert.AreEqual(0.724725178982381, ccdf); Assert.AreEqual(4.4588994137113307, 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])); }
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])); }