private void computeInner()
{
if (inputCount <= 2)
{
return;
}
// Perform likelihood-ratio tests against diminished nested models
ProportionalHazards innerModel = new ProportionalHazards(inputCount - 1);
ProportionalHazardsNewtonRaphson learning = new ProportionalHazardsNewtonRaphson(innerModel);
for (int i = 0; i < inputCount; i++)
{
// Create a diminished inner model without the current variable
double[][] data = inputData.RemoveColumn(i);
System.Diagnostics.Trace.Assert(data[0].Length > 0);
Array.Clear(innerModel.Coefficients, 0, inputCount - 1);
learning.Iterations = Iterations;
learning.Tolerance = Tolerance;
learning.Run(data, timeData, censorData);
double ratio = 2.0 * (logLikelihood - innerModel.GetPartialLogLikelihood(data, timeData, censorData));
ratioTests[i] = new ChiSquareTest(ratio, 1);
}
innerComputed = true;
}
public void RunTest()
{
// Data from: http://www.sph.emory.edu/~cdckms/CoxPH/prophaz2.html
double[,] data =
{
{ 50, 1, 0 },
{ 70, 2, 1 },
{ 45, 3, 0 },
{ 35, 5, 0 },
{ 62, 7, 1 },
{ 50, 11, 0 },
{ 45, 4, 0 },
{ 57, 6, 0 },
{ 32, 8, 0 },
{ 57, 9, 1 },
{ 60, 10, 1 },
};
ProportionalHazards regression = new ProportionalHazards(1);
double[][] inputs = data.GetColumn(0).ToArray();
double[] time = data.GetColumn(1);
int[] output = data.GetColumn(2).ToInt32();
ProportionalHazardsNewtonRaphson target = new ProportionalHazardsNewtonRaphson(regression);
double error = target.Run(inputs, time, output);
double log = -2 * regression.GetPartialLogLikelihood(inputs, time, output);
Assert.AreEqual(0.3770, regression.Coefficients[0], 1e-4);
Assert.IsFalse(Double.IsNaN(regression.Coefficients[0]));
Assert.AreEqual(0.2542, regression.StandardErrors[0], 1e-4);
Assert.IsFalse(Double.IsNaN(regression.StandardErrors[0]));
double[] actual = new double[inputs.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = regression.Compute(inputs[i]);
}
double[] expected =
{
// Computed using R's predict(fit,type="risk")
0.640442743, 1206.226657448, 0.097217211, 0.002240107,
59.081223025, 0.640442743, 0.097217211, 8.968345353,
0.000722814, 8.968345353, 27.794227993
};
for (int i = 0; i < actual.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 1e-3);
Assert.IsFalse(Double.IsNaN(actual[i]));
}
}
public void RunTest2()
{
// Data from: http://www.sph.emory.edu/~cdckms/CoxPH/prophaz2.html
double[,] data =
{
{ 50, 30, 1, 0 },
{ 70, 22, 2, 1 },
{ 45, 12, 3, 0 },
{ 35, 22, 5, 0 },
{ 62, 54, 7, 1 },
{ 50, 12, 11, 0 },
{ 45, 11, 4, 0 },
{ 57, 62, 6, 0 },
{ 32, 16, 8, 0 },
{ 57, 14, 9, 1 },
{ 60, 12, 10, 1 },
};
ProportionalHazards regression = new ProportionalHazards(2);
double[][] inputs = data.Submatrix(null, 0, 1).ToArray();
double[] time = data.GetColumn(2);
int[] output = data.GetColumn(3).ToInt32();
ProportionalHazardsNewtonRaphson target = new ProportionalHazardsNewtonRaphson(regression);
double error = target.Run(inputs, time, output);
double log = -2 * regression.GetPartialLogLikelihood(inputs, time, output);
Assert.AreEqual(3.4261, log, 1e-4);
Assert.IsFalse(Double.IsNaN(log));
double actual = regression.Coefficients[0];
Assert.AreEqual(0.3909, regression.Coefficients[0], 1e-4);
Assert.IsFalse(Double.IsNaN(regression.Coefficients[0]));
Assert.AreEqual(0.0424, regression.Coefficients[1], 1e-4);
Assert.IsFalse(Double.IsNaN(regression.Coefficients[1]));
Assert.AreEqual(0.2536, regression.StandardErrors[0], 1e-4);
Assert.IsFalse(Double.IsNaN(regression.StandardErrors[0]));
Assert.AreEqual(0.0624, regression.StandardErrors[1], 1e-4);
Assert.IsFalse(Double.IsNaN(regression.StandardErrors[1]));
}
private void computeInformation()
{
// Store model information
this.result = regression.Compute(inputData, timeData);
this.deviance = regression.GetDeviance(inputData, timeData, censorData);
this.logLikelihood = regression.GetPartialLogLikelihood(inputData, timeData, censorData);
this.chiSquare = regression.ChiSquare(inputData, timeData, censorData);
// Store coefficient information
for (int i = 0; i < regression.Coefficients.Length; i++)
{
this.standardErrors[i] = regression.StandardErrors[i];
this.waldTests[i] = regression.GetWaldTest(i);
this.coefficients[i] = regression.Coefficients[i];
this.confidences[i] = regression.GetConfidenceInterval(i);
this.hazardRatios[i] = regression.GetHazardRatio(i);
}
}
public void RunTest4()
{
// Data from: http://www.sph.emory.edu/~cdckms/CoxPH/prophaz2.html
// with added tied times
double[,] data =
{
{ 50, 1, 1 },
{ 60, 1, 1 },
{ 40, 1, 1 },
{ 51, 1, 1 },
{ 70, 2, 1 },
{ 45, 3, 0 },
{ 35, 5, 0 },
{ 62, 7, 1 },
{ 50, 11, 0 },
{ 45, 4, 0 },
{ 57, 6, 0 },
{ 32, 8, 0 },
{ 57, 9, 1 },
{ 60, 10, 1 },
};
ProportionalHazards regression = new ProportionalHazards(1);
double[][] inputs = data.GetColumn(0).ToArray();
double[] time = data.GetColumn(1);
int[] output = data.GetColumn(2).ToInt32();
ProportionalHazardsNewtonRaphson target = new ProportionalHazardsNewtonRaphson(regression);
double error = target.Run(inputs, time, output);
double log = -2 * regression.GetPartialLogLikelihood(inputs, time, output);
Assert.AreEqual(0.04863, regression.Coefficients[0], 1e-4);
Assert.IsFalse(Double.IsNaN(regression.Coefficients[0]));
Assert.AreEqual(0.04186, regression.StandardErrors[0], 1e-4);
Assert.IsFalse(Double.IsNaN(regression.StandardErrors[0]));
}
public void RunTest5()
{
double[,] inputs =
{
{ 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1 },
{ 0.9, 1.3, 1.5, 1, 2.4, 1.1, 1.3, 1.1, 0.8, 0.7, 1.1, 0.8, 1, 1, 1.4, 1.4, 1.2, 1.1, 1, 1.4, 2.5, 1.6, 1.2, 0.7, 0.8, 0.7, 0.7, 1.3, 1, 1.7, 1, 1.1, 1.3, 0.9, 1, 1.1, 0.7, 0.9, 1.3, 1.3, 0.9, 1.3, 0.9, 0.6, 1.2, 1.1, 1.1, 0.9, 1.1, 1.1, 1.9, 1.1, 0.9, 1.3, 1.1, 1.2, 0.8, 0.9, 1.7, 1.2, 0.7, 1.2, 1.6, 2.9, 1, 0.9, 0.8, 1, 1.5, 0.8, 0.7, 1.1, 1.2, 0.5, 0.9, 0.9, 0.9, 1.2, 1, 0.9, 1.3, 0.6, 1.2, 0.6, 1.7, 0.8, 1.1, 1, 1.1, 1.2, 0.6, 1.3, 1.7, 1.2, 2.7, 0.8, 1, 1, 1, 1.6, 1.2, 1.2, 2.4, 0.7, 0.8, 0.7, 1.3, 0.9, 1.1, 1.2, 1.2, 1.7, 1, 1.1, 2.1, 1.8, 2.2, 0.5, 1, 1.6, 1.2, 1.5, 1.1, 1, 1, 1.4, 1.2, 1.2, 1.1, 1.5, 0.9, 0.9, 1.2, 1.4, 1.2, 1, 0.9, 1.9, 1.7, 1.3, 1.2, 1.4, 1.7, 0.9, 1.6, 0.7, 0.9, 1, 1.1, 1.4, 1, 1.1, 1.4, 1.3, 1.1, 1.8, 1.6, 1.2, 1, 0.8, 1.1, 0.7, 1.4, 1.1, 1.8, 0.7, 1.7, 1, 1.2, 1.5, 1.1, 1.6, 1.7, 1.3, 0.8, 1.7, 1.5, 0.8, 1.4, 0.9, 4.7, 1, 0.7, 0.9, 0.9, 1, 2.6, 1, 1.1, 1.4, 1, 0.9, 1.3, 1.6, 1.8, 0.9, 1.6, 0.9, 1.2, 1, 1.2, 0.7, 1.4, 1.6, 1.2, 1.1, 1.3, 0.9, 1.3, 0.7, 1.2, 1.1, 1, 1, 0.9, 0.8, 1, 1, 1.5, 1.3, 1.2, 1, 1.2, 1.1, 1.6, 1.7, 1.1, 0.9, 1, 0.9, 1.3, 0.8, 0.8, 1, 0.9, 1, 1.2, 1.4, 0.8, 1, 1, 1.2, 1.3, 1.1, 1, 1.6, 1.2, 0.8, 2.1, 1, 1.5, 1, 1.5, 1.2, 1.1, 1.3, 0.8, 1, 1.1, 1.5, 0.8, 1.2, 1.1, 0.8, 0.9, 1.4, 1.5, 1, 1.3, 1.1, 1, 1.2, 2.2, 1.2, 1.3, 1.3, 0.7, 1, 1.1, 1.1, 1.7, 1.3, 1, 1.1, 1, 1.8, 1.2, 1.2, 1.3, 1.1, 1.6, 4.1, 1, 0.9, 1.5, 1.5, 1.4, 1, 0.5, 0.9, 0.8, 0.6, 1.3, 0.7, 0.7, 1.1, 1.4, 0.9, 1, 4.2, 1, 0.9, 1.2, 2.1, 2.4, 1.2, 2.5, 1, 1.2, 1.3, 0.9, 1.1, 1.6, 1, 1.5, 1.1, 1, 2.6, 0.9, 1.3, 1.4, 0.8, 1.4, 2.7, 1.1, 0.8, 1.2, 1.4, 1.2, 1, 1.1, 1.2, 1.2, 1.3, 1.2, 1.2, 1.2, 1.1, 1.3, 0.9, 1, 1.9, 1.4, 1.2, 1.1, 0.7, 0.9, 1.1, 1.1, 1.4, 1, 1.2, 1.4, 1, 2.8, 1.1, 0.7, 0.7, 1.2, 0.7, 1, 1.5, 1.5, 1.7, 0.9, 1.6, 0.9, 1.7, 0.8, 1, 0.8, 1.1, 1.3, 1.2, 0.9, 1.1, 1.1, 0.9, 0.9, 0.7, 1.1, 1.8, 0.8, 0.9, 1.4, 0.9, 1.2, 1.4, 1, 0.8, 1.4, 1.4, 1, 0.7, 0.6, 0.8, 1.4, 1.4, 0.8, 1.9, 1.3, 1, 1.3, 0.9, 0.9, 1.5, 1.5, 0.8, 1.4, 1.1, 0.8, 0.8, 1.4, 1.3, 1.1, 1, 2.2, 1.6, 1.7, 1.2, 0.9, 1.1, 1, 2.6, 1.6, 0.9, 0.9, 0.6, 1.6, 1.1, 1.3, 1.7, 1.2, 1.2, 1.6, 0.9, 0.9, 1.3, 2.2, 1.3, 1, 0.8, 1.5, 1.3, 1, 1.2, 1.4, 1.3, 1.3, 1.4, 1.3, 1.4, 1.2, 1, 1.2, 0.8, 1.1, 1.2, 1.1, 1.2, 1.8, 1.1, 1.6, 1.1, 0.8, 1.2, 1, 1.2, 0.7, 1.9, 1.6, 1.5, 0.9, 1.1, 0.8, 1.3, 1.1, 0.7, 1, 2, 1.2, 1.1, 1.2, 0.9, 0.9, 1, 1.2, 1, 1.2, 1.1, 1.1, 1, 1.4, 0.8, 1.3, 0.8, 1, 1.4, 1.2, 1.1, 1.3, 0.7, 1.2, 0.6, 1.2, 1.4, 1.5, 1.7, 0.9, 1, 0.6, 0.9, 1.4, 1.5, 1.1, 1.3, 0.9, 1.3, 2.1, 1.2, 1.3, 1.1, 1.4, 0.9, 1.4, 1.5, 1, 1.5, 1, 1.3, 0.9, 1.2, 1.1, 0.9, 1.1, 1.4, 1.4, 0.8, 1, 1.2, 1.1, 0.8, 1.2, 4.1, 0.7, 2, 0.8, 1.2, 3.8, 1.2, 1.4, 1, 1, 1.7, 1, 1, 0.9, 1.6, 0.7, 1.2, 1.1, 1, 1, 1, 0.7, 1.5, 1.1, 1, 0.9, 0.8, 0.9, 1.9, 1, 1.4, 1.3, 1.5, 0.9, 1.1, 1.2, 0.8, 1, 1.4, 4.2, 2.5, 1.2, 1.6, 0.9, 1.1, 1, 0.9, 0.6, 1, 1.1, 1.2, 1.2, 1.2, 0.9, 1.2, 1.1, 1.2, 0.8, 1, 1.6, 0.9, 1.2, 1.4, 0.7, 1.6, 1.1, 1.5, 0.8, 1.4, 0.9, 0.9, 1.5, 1.1, 9.1, 1.6, 1.3, 0.8, 4, 0.7, 1.4, 0.8, 3.6, 1.3, 1.3, 1, 1.1, 0.8, 1.2, 0.8, 0.9, 1.5, 1, 0.7, 6.6, 1.2, 1.8, 1, 1.3, 1, 1.3, 1.1, 1.1, 1, 0.9, 0.9, 1.1, 1.1, 0.9, 1.5, 4.5, 1.1, 0.8, 0.7, 0.9, 0.8, 1.6, 0.8, 0.8, 2, 2, 1.5, 1.2, 1.2, 0.9, 1.7, 1.4, 0.8, 1.5, 1, 1.1, 1.1, 0.7, 1.1, 1.2, 0.9, 1.5, 0.7, 1.5, 1.3, 0.8, 0.7, 1.1, 1, 1, 1.2, 0.8, 0.9, 0.8, 1.6, 2.4, 1.1, 2, 0.9, 1.4, 1.2, 1.1, 1.6, 0.9, 0.9, 1.3, 2, 1, 1.4, 1.3, 1.3, 0.7, 0.9, 1.1, 1.7, 1.1, 1.2, 0.9, 0.9, 1.1, 0.9, 5.2, 1.3, 0.7, 1.4, 1.4, 0.8, 1.2, 1.4, 0.9, 1.1, 1.1, 1.3, 1.7, 0.8, 1, 1.2, 1.9, 1.1, 1.3, 1.8, 0.6, 0.8, 1.4, 0.7, 0.9, 0.9, 1.2, 1.5, 0.7, 4.2, 1.1, 1.1, 1, 1.3, 0.8, 1.3, 1.1, 1.1, 0.8, 1.1, 1.5, 1.2, 1.2, 1.1, 1.7, 1, 1, 0.9, 0.9, 0.8, 1.2, 1.1, 0.7, 0.8, 1.1, 0.9, 1.4, 1, 1.1, 1.4, 0.9, 1, 1.7, 0.9, 1.3, 1.3, 0.8, 2.1, 1, 0.9, 1.2, 0.9, 1.1, 1.1, 1.2, 1.2, 0.9, 1.4, 1.2, 0.8, 1.1, 1.3, 1.1, 1.1, 0.8, 0.9, 0.9, 1.2, 1.1, 1.7, 1.3, 1.1, 1.7, 0.8, 0.9, 1.5, 1.1, 1.4, 1.4, 1.5, 1.1, 1.3, 0.9, 1.1, 1.1, 0.7, 1, 1.1, 0.6, 1, 1.2, 1.4, 1.1, 0.8, 1, 1.3, 0.9, 1, 1, 0.9, 1.5, 1.1, 1.5, 6.3, 1.4, 1.1, 5.2, 1.6, 1, 1.2, 1.3, 0.6, 1.1, 1.2, 1.1, 1.2, 1, 1.1, 1.9, 1.1, 1.2, 1.1, 0.7, 1.4, 2.2, 1.1, 1.5, 0.9, 1.2, 1.1, 0.8, 1.3, 1.1, 1.3, 1.8, 1.1, 1.1, 1.1, 1, 0.8, 1.7, 1.2, 1.4, 1.1, 1.4, 1.1, 0.8, 1, 1.1, 1.2, 1.4, 1, 1.3, 1.1, 2.3, 0.7, 1.3, 0.7, 0.9, 0.9, 1.2, 2, 0.7, 1.2, 1.6, 1.3, 1.4, 2.7, 1.5, 1, 1, 1.5, 1 }
};
double[,] outputs =
{
{ 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 9, 30, 30, 30, 30, 30, 2, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 2, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 13, 30, 30, 30, 30, 30, 30, 0, 30, 13, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 1, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 16, 30, 30, 30, 30, 30, 30, 0, 30, 30, 30, 30, 30, 30, 30, 30, 5, 30, 30, 30, 30, 30, 30, 30, 30, 1, 30, 21, 30, 30, 30, 30, 30, 30, 30, 30, 1, 30, 30, 30, 16, 30, 30, 30, 30, 30, 30, 30, 30, 2, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 16, 30, 30, 30, 30, 30, 3, 30, 18, 30, 30, 30, 30, 30, 28, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 0, 30, 30, 30, 30, 30, 1, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 17, 30, 1, 30, 30, 1, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 9, 30, 30, 30, 30, 1, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 9, 30, 30, 30, 27, 30, 30, 30, 30, 30, 30, 30, 30, 30, 0, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 16, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 20, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 1, 30, 30, 4, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 26, 30, 30, 30, 30, 12, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 21, 30, 1, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 9, 30, 30, 30, 30, 30, 30, 30, 1, 30, 1, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 0, 30, 25, 30, 30, 12, 30, 30, 30, 30, 30, 30, 10, 30, 30, 30, 30, 3, 30, 11, 30, 30, 30, 30, 30, 11, 30, 30, 30, 30, 30, 30, 4, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 1, 30, 30, 30, 30, 30, 1, 0, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 11, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 2, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 1, 30, 10, 30, 30, 30, 0, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 3, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 19, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 29, 30, 30, 30, 30, 30, 30, 2, 30, 30, 30, 30, 15, 30, 30, 30, 30, 30, 30, 30, 30, 3, 30, 30, 0, 30, 30, 30, 2, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 9, 30, 30, 30, 2, 30, 30, 30, 2, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 8, 30, 30, 30, 30, 30, 30, 0, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 14, 30, 30, 30, 9, 30, 30, 30, 30, 30, 13, 30, 30, 30, 4, 30, 30, 30, 30, 1, 30, 30, 30, 30, 30, 30, 10, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 1, 30, 30, 2, 30, 30, 30, 30, 30 },
{ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0 }
};
double[][] covariates = inputs.Transpose().ToArray();
double[] time = outputs.GetRow(0);
int[] censor = outputs.GetRow(1).ToInt32();
string inputStr = inputs.Transpose().ToString(Accord.Math.DefaultMatrixFormatProvider.InvariantCulture);
string outputStr = outputs.Transpose().ToString(Accord.Math.DefaultMatrixFormatProvider.InvariantCulture);
ProportionalHazards regression = new ProportionalHazards(2);
ProportionalHazardsNewtonRaphson target = new ProportionalHazardsNewtonRaphson(regression);
double error = target.Run(covariates, time, censor);
double log = -2 * regression.GetPartialLogLikelihood(covariates, time, censor);
Assert.AreEqual(-0.270, regression.Coefficients[0], 1e-4);
Assert.AreEqual(0.463, regression.Coefficients[1], 1e-2);
Assert.IsFalse(Double.IsNaN(regression.Coefficients[0]));
Assert.IsFalse(Double.IsNaN(regression.Coefficients[1]));
Assert.AreEqual(0.2454, regression.StandardErrors[0], 1e-4);
Assert.AreEqual(0.0671, regression.StandardErrors[1], 1e-4);
Assert.IsFalse(Double.IsNaN(regression.StandardErrors[0]));
Assert.IsFalse(Double.IsNaN(regression.StandardErrors[1]));
}
private void computeInner()
{
if (inputCount <= 2)
{
return;
}
// Perform likelihood-ratio tests against diminished nested models
var innerModel = new ProportionalHazards(inputCount - 1);
var learning = createLearner(innerModel);
for (int i = 0; i < inputCount; i++)
{
// Create a diminished inner model without the current variable
double[][] data = inputData.RemoveColumn(i);
#if DEBUG
if (data[0].Length == 0)
{
throw new Exception();
}
#endif
Array.Clear(innerModel.Coefficients, 0, inputCount - 1);
learning.MaxIterations = Iterations;
learning.Tolerance = Tolerance;
learning.Learn(data, timeData, censorData);
double ratio = 2.0 * (logLikelihood - innerModel.GetPartialLogLikelihood(data, timeData, censorData));
ratioTests[i] = new ChiSquareTest(ratio, 1);
}
innerComputed = true;
}
public void RunTest()
{
// Data from: http://www.sph.emory.edu/~cdckms/CoxPH/prophaz2.html
double[,] data =
{
{ 50, 1, 0 },
{ 70, 2, 1 },
{ 45, 3, 0 },
{ 35, 5, 0 },
{ 62, 7, 1 },
{ 50, 11, 0 },
{ 45, 4, 0 },
{ 57, 6, 0 },
{ 32, 8, 0 },
{ 57, 9, 1 },
{ 60, 10, 1 },
};
ProportionalHazards regression = new ProportionalHazards(1);
regression.Coefficients[0] = 0.37704239281494084;
regression.StandardErrors[0] = 0.25415755113043753;
double[][] inputs = data.GetColumn(0).ToJagged();
double[] time = data.GetColumn(1);
SurvivalOutcome[] output = data.GetColumn(2).To <SurvivalOutcome[]>();
{
double actual = -2 * regression.GetPartialLogLikelihood(inputs, time, output);
double expected = 4.0505;
Assert.AreEqual(expected, actual, 1e-4);
Assert.IsFalse(Double.IsNaN(actual));
}
{
var test = regression.GetWaldTest(0);
Assert.AreEqual(0.1379, test.PValue, 1e-4);
}
{
var ci = regression.GetConfidenceInterval(0);
Assert.AreEqual(0.8859, ci.Min, 1e-4);
Assert.AreEqual(2.3993, ci.Max, 1e-4);
}
{
double actual = regression.GetHazardRatio(0);
double expected = 1.4580;
Assert.AreEqual(expected, actual, 1e-4);
}
{
var chi = regression.ChiSquare(inputs, time, output);
Assert.AreEqual(7.3570, chi.Statistic, 1e-4);
Assert.AreEqual(1, chi.DegreesOfFreedom);
Assert.AreEqual(0.0067, chi.PValue, 1e-3);
}
}
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);
}
}
/// <summary>
/// Runs the Newton-Raphson update for Cox's hazards learning until convergence.
/// </summary>
///
/// <param name="inputs">The input data.</param>
/// <param name="censor">The output (event) associated with each input vector.</param>
/// <param name="time">The time-to-event for the non-censored training samples.</param>
///
/// <returns>The maximum relative change in the parameters after the iteration.</returns>
///
public double Run(double[][] inputs, double[] time, SurvivalOutcome[] censor)
{
if (inputs.Length != time.Length || time.Length != censor.Length)
{
throw new DimensionMismatchException("time",
"The inputs, time and output vector must have the same length.");
}
// Sort data by time to accelerate performance
EmpiricalHazardDistribution.Sort(ref time, ref censor, ref inputs);
double[] means = new double[parameterCount];
double[] sdev = new double[parameterCount];
for (int i = 0; i < sdev.Length; i++)
{
sdev[i] = 1;
}
if (normalize)
{
// Store means as regression centers
means = inputs.Mean();
for (int i = 0; i < means.Length; i++)
{
regression.Offsets[i] = means[i];
}
// Convert to unit scores for increased accuracy
sdev = BestCS.Statistics.Tools.StandardDeviation(inputs);
inputs = inputs.Subtract(means, 0).ElementwiseDivide(sdev, 0, inPlace: true);
for (int i = 0; i < regression.Coefficients.Length; i++)
{
regression.Coefficients[i] *= sdev[i];
}
}
// Compute actual outputs
double[] output = new double[inputs.Length];
for (int i = 0; i < output.Length; i++)
{
double sum = 0;
for (int j = 0; j < regression.Coefficients.Length; j++)
{
sum += regression.Coefficients[j] * inputs[i][j];
}
output[i] = Math.Exp(sum);
}
// Compute ties
int[] ties = new int[inputs.Length];
for (int i = 0; i < inputs.Length; i++)
{
for (int j = 0; j < time.Length; j++)
{
if (time[j] == time[i])
{
ties[i]++;
}
}
}
if (parameterCount == 0)
{
createBaseline(time, censor, output);
return(regression.GetPartialLogLikelihood(inputs, time, censor));
}
CurrentIteration = 0;
double smooth = Lambda;
do
{
// learning iterations until convergence
// or maximum number of iterations reached
CurrentIteration++;
// Reset Hessian matrix and gradient
Array.Clear(gradient, 0, gradient.Length);
Array.Clear(hessian, 0, hessian.Length);
// For each observation instance
for (int i = 0; i < inputs.Length; i++)
{
// Check if we should censor
if (censor[i] == SurvivalOutcome.Censored)
{
continue;
}
// Compute partials
double den = 0;
Array.Clear(partialGradient, 0, partialGradient.Length);
Array.Clear(partialHessian, 0, partialHessian.Length);
for (int j = 0; j < inputs.Length; j++)
{
if (time[j] >= time[i])
{
den += output[j];
}
}
for (int j = 0; j < inputs.Length; j++)
{
if (time[j] >= time[i])
{
// Compute partial gradient
for (int k = 0; k < partialGradient.Length; k++)
{
partialGradient[k] += inputs[j][k] * output[j] / den;
}
// Compute partial Hessian
for (int ii = 0; ii < inputs[j].Length; ii++)
{
for (int jj = 0; jj < inputs[j].Length; jj++)
{
partialHessian[ii, jj] += inputs[j][ii] * inputs[j][jj] * output[j] / den;
}
}
}
}
// Compute gradient vector
for (int j = 0; j < gradient.Length; j++)
{
gradient[j] += inputs[i][j] - partialGradient[j];
}
// Compute Hessian matrix
for (int j = 0; j < partialGradient.Length; j++)
{
for (int k = 0; k < partialGradient.Length; k++)
{
hessian[j, k] -= partialHessian[j, k] - partialGradient[j] * partialGradient[k];
}
}
}
// Decompose to solve the linear system. Usually the Hessian will
// be invertible and LU will succeed. However, sometimes the Hessian
// may be singular and a Singular Value Decomposition may be needed.
// The SVD is very stable, but is quite expensive, being on average
// about 10-15 times more expensive than LU decomposition. There are
// other ways to avoid a singular Hessian. For a very interesting
// reading on the subject, please see:
//
// - Jeff Gill & Gary King, "What to Do When Your Hessian Is Not Invertible",
// Sociological Methods & Research, Vol 33, No. 1, August 2004, 54-87.
// Available in: http://gking.harvard.edu/files/help.pdf
//
decomposition = new SingularValueDecomposition(hessian);
double[] deltas = decomposition.Solve(gradient);
if (convergence.Iterations > 0 || convergence.Tolerance > 0)
{
// Update coefficients using the calculated deltas
for (int i = 0; i < regression.Coefficients.Length; i++)
{
regression.Coefficients[i] -= smooth * deltas[i];
}
}
smooth += Lambda;
if (smooth > 1)
{
smooth = 1;
}
// Check relative maximum parameter change
convergence.NewValues = regression.Coefficients;
if (convergence.HasDiverged)
{
// Restore previous coefficients
for (int i = 0; i < regression.Coefficients.Length; i++)
{
regression.Coefficients[i] = convergence.OldValues[i];
}
}
// Recompute current outputs
for (int i = 0; i < output.Length; i++)
{
double sum = 0;
for (int j = 0; j < regression.Coefficients.Length; j++)
{
sum += regression.Coefficients[j] * inputs[i][j];
}
output[i] = Math.Exp(sum);
}
} while (!convergence.HasConverged);
for (int i = 0; i < regression.Coefficients.Length; i++)
{
regression.Coefficients[i] /= sdev[i];
}
if (computeStandardErrors)
{
// Grab the regression information matrix
double[,] inverse = decomposition.Inverse();
// Calculate coefficients' standard errors
double[] standardErrors = regression.StandardErrors;
for (int i = 0; i < standardErrors.Length; i++)
{
standardErrors[i] = Math.Sqrt(Math.Abs(inverse[i, i])) / sdev[i];
}
}
if (computeBaselineFunction)
{
createBaseline(time, censor, output);
}
return(regression.GetPartialLogLikelihood(inputs, time, censor));
}
public double Run(double[][] inputs, double[] time, SurvivalOutcome[] censor)
{
Learn(inputs, time, censor, null);
return(regression.GetPartialLogLikelihood(inputs, time, censor));
}
public void BaselineHazardTestR()
{
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);
SurvivalOutcome[] censor = data.GetColumn(1).To <SurvivalOutcome[]>();
double[][] inputs = data.GetColumn(2).ToJagged();
var regression = new ProportionalHazards(1);
var target = new ProportionalHazardsNewtonRaphson(regression);
double error = target.Run(inputs, time, censor);
// Assert.AreEqual(-10.257417973830666, error, 1e-8);
/*
* library('survival')
* options(digits=17)
* time <- c(8, 4, 12, 6, 10, 8, 5, 5, 3, 14, 8, 11, 7, 7, 12, 8, 7)
* x <- c(13, 56, 25, 64, 38, 80, 0, 81, 81, 38, 23, 99, 12, 36, 63, 92, 38)
* c <- c(0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0)
*
* fit <- coxph(Surv(time, c) ~ x, ties="breslow")
*
* predict(fit,type="risk")
*
* fit$loglik
*
* coef exp(coef) se(coef) z p
* x 0.01633097532122 1.016465054586 0.01711960930183 0.9539338797573 0.340117112635
*
* Likelihood ratio test=0.94 on 1 df, p=0.332836850925 n= 17, number of events= 5
*/
// Tested against GNU R
Assert.AreEqual(49.352941176470587, regression.Offsets[0]);
Assert.AreEqual(0.01633097532122, regression.Coefficients[0], 1e-10);
Assert.AreEqual(0.01711960930183, regression.StandardErrors[0], 1e-10);
Assert.AreEqual(0.340117112635, regression.GetWaldTest(0).PValue, 1e-5);
Assert.AreEqual(-10.2879332934202168, regression.GetPartialLogLikelihood(time, censor));
Assert.AreEqual(-9.8190189050165948, regression.GetPartialLogLikelihood(inputs, time, censor));
double[] actual = inputs.Apply(x => regression.Compute(x));
/*
* predict(r,type="risk")
* [1] 0.55229166964915244 1.11466393245000361 0.67185866444081555 1.27023351821156782 0.83076808526813917 1.64953983529334769 0.44664925161695829 1.67669959872327912
* [9] 1.67669959872327912 0.83076808526813917 0.65026895029003673 2.24967304521214029 0.54334545703992021 0.80407192663266613 1.24965783376477391 2.00665280971219540
* [17] 0.83076808526813917
*/
double[] expected =
{
0.55229166964915244, 1.11466393245000361, 0.67185866444081555, 1.27023351821156782,
0.83076808526813917, 1.64953983529334769, 0.44664925161695829, 1.67669959872327912,
1.67669959872327912, 0.83076808526813917, 0.65026895029003673, 2.24967304521214029,
0.54334545703992021, 0.80407192663266613, 1.24965783376477391, 2.00665280971219540,
0.83076808526813917
};
for (int i = 0; i < actual.Length; i++)
{
Assert.AreEqual(expected[i], actual[i], 0.025);
}
}
