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 DistributionFunctionTest()
{
double[] values =
{
1.0000000000000000, 0.8724284533876597, 0.9698946958777951,
1.0000000000000000, 0.9840887140861863, 1.0000000000000000,
1.0000000000000000, 1.0000000000000000, 1.0000000000000000,
0.9979137773216293, 1.0000000000000000
};
double[] times =
{
11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1
};
EmpiricalHazardDistribution target = EmpiricalHazardDistribution
.FromSurvivalValues(times, values);
// Data from: http://www.sph.emory.edu/~cdckms/CoxPH/prophaz2.html
double[] expectedBaselineSurvivalFunction =
{
1.0000, 0.9979, 0.9979, 0.9979,
0.9979, 0.9979, 0.9820,
0.9820, 0.9525, 0.8310, 0.8310,
};
double[] hazardFunction = new double[expectedBaselineSurvivalFunction.Length];
double[] survivalFunction = new double[expectedBaselineSurvivalFunction.Length];
for (int i = 0; i < 11; i++)
{
hazardFunction[i] = target.CumulativeHazardFunction(i + 1);
}
for (int i = 0; i < 11; i++)
{
survivalFunction[i] = target.ComplementaryDistributionFunction(i + 1);
}
for (int i = 0; i < expectedBaselineSurvivalFunction.Length; i++)
{
Assert.AreEqual(expectedBaselineSurvivalFunction[i], survivalFunction[i], 0.01);
// Ho = -log(So)
Assert.AreEqual(hazardFunction[i], -Math.Log(survivalFunction[i]), 0.01);
// So = exp(-Ho)
Assert.AreEqual(survivalFunction[i], Math.Exp(-hazardFunction[i]), 0.01);
}
}
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 DistributionFunctionTest2_KaplanMeier()
{
double[] values =
{
0.0000000000000000, 0.0351683340828711, 0.0267358118285064,
0.0000000000000000, 0.0103643094219679, 0.0000000000000000,
0.0000000000000000, 0.0000000000000000, 0.0000000000000000,
0.000762266794052363, 0.000000000000000
};
double[] times =
{
11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1
};
var target = new EmpiricalHazardDistribution(times, values, SurvivalEstimator.KaplanMeier);
double[] expected =
{
1.000000000000000,
0.999238023657475,
0.999238023657475,
0.999238023657475,
0.999238023657475,
0.999238023657475,
0.98893509519066469,
0.98893509519066469,
0.96284543081744489,
0.92957227114936058,
0.92957227114936058,
};
double[] hazardFunction = new double[expected.Length];
double[] survivalFunction = new double[expected.Length];
for (int i = 0; i < 11; i++)
{
hazardFunction[i] = target.CumulativeHazardFunction(i + 1);
}
for (int i = 0; i < 11; i++)
{
survivalFunction[i] = target.ComplementaryDistributionFunction(i + 1);
}
for (int i = 0; i < expected.Length; i++)
{
Assert.AreEqual(expected[i], survivalFunction[i], 1e-3);
// Ho = -log(So)
Assert.AreEqual(hazardFunction[i], -Math.Log(survivalFunction[i]), 1e-5);
// So = exp(-Ho)
Assert.AreEqual(survivalFunction[i], Math.Exp(-hazardFunction[i]), 1e-5);
}
Assert.AreEqual(1, target.ComplementaryDistributionFunction(0));
Assert.AreEqual(0, target.ComplementaryDistributionFunction(Double.PositiveInfinity));
}
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 BaselineHazardTest()
{
double[,] data =
{
// t c in
{ 8, 0, 13 },
{ 4, 1, 56 },
{ 12, 0, 25 },
{ 6, 0, 64 },
{ 10, 0, 38 },
{ 8, 1, 80 },
{ 5, 0, 0 },
{ 5, 0, 81 },
{ 3, 1, 81 },
{ 14, 1, 38 },
{ 8, 0, 23 },
{ 11, 0, 99 },
{ 7, 0, 12 },
{ 7, 1, 36 },
{ 12, 0, 63 },
{ 8, 0, 92 },
{ 7, 0, 38 },
};
double[] time = data.GetColumn(0);
int[] censor = data.GetColumn(1).ToInt32();
double[][] inputs = data.GetColumn(2).ToArray();
ProportionalHazards regression = new ProportionalHazards(1);
ProportionalHazardsNewtonRaphson target = new ProportionalHazardsNewtonRaphson(regression);
target.Normalize = false;
double error = target.Run(inputs, time, censor);
double log = -2 * regression.GetPartialLogLikelihood(inputs, time, censor);
EmpiricalHazardDistribution baseline = regression.BaselineHazard as EmpiricalHazardDistribution;
double[] actual = new double[(int)baseline.Support.Max];
for (int i = (int)baseline.Support.Min; i < baseline.Support.Max; i++)
{
actual[i] = baseline.CumulativeHazardFunction(i);
}
Assert.AreEqual(14, actual.Length);
double[] expected =
{
0, 0, 0,
0.025000345517572315, 0.052363663484639708, 0.052363663484639708, 0.052363663484639708,
0.16317880290786446,
0.34217461190678861, 0.34217461190678861, 0.34217461190678861,
0.34217461190678861, 0.34217461190678861, 0.34217461190678861
};
for (int i = 0; i < actual.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 0.025);
}
}
public void DistributionFunctionTest2()
{
double[] values =
{
0.0000000000000000, 0.0351683340828711, 0.0267358118285064,
0.0000000000000000, 0.0103643094219679, 0.0000000000000000,
0.0000000000000000, 0.0000000000000000, 0.0000000000000000,
0.000762266794052363, 0.000000000000000
};
double[] times =
{
11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1
};
EmpiricalHazardDistribution target =
new EmpiricalHazardDistribution(times, values);
double[] expected =
{
1.000000000000000,
0.999238023657475,
0.999238023657475,
0.999238023657475,
0.999238023657475,
0.999238023657475,
0.98893509519066469,
0.98893509519066469,
0.96284543081744489,
0.92957227114936058,
0.92957227114936058,
};
double[] hazardFunction = new double[expected.Length];
double[] survivalFunction = new double[expected.Length];
double[] complementaryDistribution = new double[expected.Length];
for (int i = 0; i < 11; i++)
hazardFunction[i] = target.CumulativeHazardFunction(i + 1);
for (int i = 0; i < 11; i++)
survivalFunction[i] = target.ComplementaryDistributionFunction(i + 1);
for (int i = 0; i < expected.Length; i++)
{
Assert.AreEqual(expected[i], survivalFunction[i], 1e-5);
// Ho = -log(So)
Assert.AreEqual(hazardFunction[i], -Math.Log(survivalFunction[i]), 1e-5);
// So = exp(-Ho)
Assert.AreEqual(survivalFunction[i], Math.Exp(-hazardFunction[i]), 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 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]));
}
