public void ComputeTest1()
{
// Consider the following example data, adapted from John C. Pezzullo's
// example for his great Cox's proportional hazards model example in
// JavaScript (http://statpages.org/prophaz2.html).
// In this data, we have three columns. The first column denotes the
// input variables for the problem. The second column, the survival
// times. And the last one is the output of the experiment (if the
// subject has died [1] or has survived [0]).
double[,] example =
{
// input time censor
{ 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 },
};
// First we will extract the input, times and outputs
double[,] inputs = example.GetColumns(new[] { 0 });
double[] times = example.GetColumn(1);
SurvivalOutcome[] output = example.GetColumn(2).To<SurvivalOutcome[]>();
// Now we can proceed and create the analysis
var cox = new ProportionalHazardsAnalysis(inputs, times, output);
cox.Compute(); // compute the analysis
// Now we can show an analysis summary
// Accord.Controls.DataGridBox.Show(cox.Coefficients);
// We can also investigate all parameters individually. For
// example the coefficients values will be available at
double[] coef = cox.CoefficientValues;
double[] stde = cox.StandardErrors;
// We can also obtain the hazards ratios
double[] ratios = cox.HazardRatios;
// And other information such as the partial
// likelihood, the deviance and also make
// hypothesis tests on the parameters
double partial = cox.LogLikelihood;
double deviance = cox.Deviance;
// Chi-Square for whole model
ChiSquareTest chi = cox.ChiSquare;
// Wald tests for individual parameters
WaldTest wald = cox.Coefficients[0].Wald;
// Finally, we can also use the model to predict
// scores for new observations (without considering time)
double y1 = cox.Regression.Compute(new double[] { 63 });
double y2 = cox.Regression.Compute(new double[] { 32 });
// Those scores can be interpreted by comparing then
// to 1. If they are greater than one, the odds are
// the patient will not survive. If the value is less
// than one, the patient is likely to survive.
// The first value, y1, gives approximately 86.138,
// while the second value, y2, gives about 0.00072.
// We can also consider instant estimates for a given time:
double p1 = cox.Regression.Compute(new double[] { 63 }, 2);
double p2 = cox.Regression.Compute(new double[] { 63 }, 10);
// Here, p1 is the score after 2 time instants, with a
// value of 0.0656. The second value, p2, is the time
// after 10 time instants, with a value of 6.2907.
Assert.AreEqual(86.138421225296526, y1);
Assert.AreEqual(0.00072281400325299814, y2);
Assert.AreEqual(0.17989138010770425, p1);
Assert.AreEqual(15.950244161356357, p2);
Assert.AreEqual(1, coef.Length);
Assert.AreEqual(0.37704239281490765, coef[0]);
Assert.AreEqual(0.25415746361167235, stde[0]);
Assert.AreEqual(1.4579661153488215, ratios[0]);
Assert.AreEqual(-2.0252666205735466, partial, 1e-6);
Assert.AreEqual(4.0505332411470931, deviance, 1e-6);
Assert.AreEqual(0.13794183001851756, wald.PValue, 1e-4);
Assert.AreEqual(1, chi.DegreesOfFreedom);
Assert.AreEqual(7.3570, chi.Statistic, 1e-4);
Assert.AreEqual(0.0067, chi.PValue, 1e-3);
}
