/// <summary> /// The likelihood ratio test of the overall model, also called the model chi-square test. /// </summary> /// /// <param name="input">A set of input data.</param> /// <param name="time">The time-to-event before the output occurs.</param> /// <param name="output">The corresponding output data.</param> /// /// <remarks> /// <para> /// The Chi-square test, also called the likelihood ratio test or the log-likelihood test /// is based on the deviance of the model (-2*log-likelihood). The log-likelihood ratio test /// indicates whether there is evidence of the need to move from a simpler model to a more /// complicated one (where the simpler model is nested within the complicated one).</para> /// <para> /// The difference between the log-likelihood ratios for the researcher's model and a /// simpler model is often called the "model chi-square".</para> /// </remarks> /// public ChiSquareTest ChiSquare(double[][] input, double[] time, SurvivalOutcome[] output) { ProportionalHazards regression = new ProportionalHazards(Inputs); double ratio = GetLogLikelihoodRatio(input, time, output, regression); return(new ChiSquareTest(ratio, Coefficients.Length)); }
/// <summary> /// Creates a new Cox's Proportional Hazards that is a copy of the current instance. /// </summary> /// public object Clone() { var regression = new ProportionalHazards(Coefficients.Length); regression.Coefficients = (double[])this.Coefficients.Clone(); regression.StandardErrors = (double[])this.StandardErrors.Clone(); regression.Offsets = (double[])this.Offsets.Clone(); return(regression); }
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 }, }; var regression = new ProportionalHazards(1); double[][] inputs = data.GetColumn(0).ToArray(); double[] time = data.GetColumn(1); SurvivalOutcome[] output = data.GetColumn(2).To<SurvivalOutcome[]>(); var target = new ProportionalHazardsNewtonRaphson(regression); double error = target.Run(inputs, time, output); double log = -2 * regression.GetPartialLogLikelihood(inputs, time, output); // Tested against http://www.sph.emory.edu/~cdckms/CoxPH/prophaz2.html 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])); } }
/// <summary> /// Gets the Log-Likelihood Ratio between two models. /// </summary> /// /// <remarks> /// The Log-Likelihood ratio is defined as 2*(LL - LL0). /// </remarks> /// /// <param name="input">A set of input data.</param> /// <param name="time">The time-to-event before the output occurs.</param> /// <param name="output">The corresponding output data.</param> /// <param name="hazards">Another Cox Proportional Hazards model.</param> /// /// <returns>The Log-Likelihood ratio (a measure of performance /// between two models) calculated over the given data sets.</returns> /// public double GetLogLikelihoodRatio(double[][] input, double[] time, SurvivalOutcome[] output, ProportionalHazards hazards) { return(2.0 * (this.GetPartialLogLikelihood(input, time, output) - hazards.GetPartialLogLikelihood(input, time, output))); }
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).ToArray(); double[] time = data.GetColumn(1); int[] output = data.GetColumn(2).ToInt32(); { 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 PredictTest1() { // 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 }, }; double[] distHazards = { 0, 0.0351683340828711, 0.0267358118285064, 0, 0.0103643094219679, 0, 0, 0, 0, 0.000762266794052363, 0 }; double[] distTimes = { 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 }; ProportionalHazards regression = new ProportionalHazards(1, new EmpiricalHazardDistribution(distTimes, distHazards)); regression.Coefficients[0] = 0.37704239281494084; regression.StandardErrors[0] = 0.25415755113043753; regression.Offsets[0] = 51.181818; double[][] inputs = data.GetColumn(0).ToArray(); double[] time = data.GetColumn(1); int[] output = data.GetColumn(2).ToInt32(); double[] expected = { 0.000000000000, 0.919466527073, 0.000074105451, 0.000001707560, 0.657371730925, 0.046771996036, 0.000074105451, 0.006836271860, 0.000008042445, 0.339562971888, 2.029832541310 }; double[] actual = new double[inputs.Length]; for (int i = 0; i < inputs.Length; i++) actual[i] = regression.Compute(inputs[i], time[i]); for (int i = 0; i < actual.Length; i++) { Assert.AreEqual(expected[i], actual[i], 1e-6); 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 }, }; var regression = new ProportionalHazards(2); double[][] inputs = data.Submatrix(null, 0, 1).ToArray(); double[] time = data.GetColumn(2); SurvivalOutcome[] output = data.GetColumn(3).To<SurvivalOutcome[]>(); var 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])); }
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> /// The likelihood ratio test of the overall model, also called the model chi-square test. /// </summary> /// /// <param name="input">A set of input data.</param> /// <param name="time">The time-to-event before the output occurs.</param> /// <param name="output">The corresponding output data.</param> /// /// <remarks> /// <para> /// The Chi-square test, also called the likelihood ratio test or the log-likelihood test /// is based on the deviance of the model (-2*log-likelihood). The log-likelihood ratio test /// indicates whether there is evidence of the need to move from a simpler model to a more /// complicated one (where the simpler model is nested within the complicated one).</para> /// <para> /// The difference between the log-likelihood ratios for the researcher's model and a /// simpler model is often called the "model chi-square".</para> /// </remarks> /// public ChiSquareTest ChiSquare(double[][] input, double[] time, int[] output) { ProportionalHazards regression = new ProportionalHazards(Inputs); double ratio = GetLogLikelihoodRatio(input, time, output, regression); return new ChiSquareTest(ratio, Coefficients.Length); }
public void BaselineHazardTest() { double[,] data = { // t c in { 8, 0, -1.2372626521865966 }, { 4, 1, 0.22623087329625477 }, { 12, 0, -0.8288458543774289 }, { 6, 0, 0.49850873850236665 }, { 10, 0, -0.38639432341749696 }, { 8, 1, 1.0430644689145904 }, { 5, 0, -1.6797141831465285 }, { 5, 0, 1.0770992020653544 }, { 3, 1, 1.0770992020653544 }, { 14, 1, -0.38639432341749696 }, { 8, 0, -0.8969153206789568 }, { 11, 0, 1.6897243987791061 }, { 7, 0, -1.2712973853373605 }, { 7, 0, -0.38639432341749696 }, { 7, 1, -0.45446378971902495 }, { 12, 0, 0.4644740053516027 }, { 8, 0, 1.4514812667237584 }, }; double[] time = data.GetColumn(0); SurvivalOutcome[] censor = data.GetColumn(1).To<SurvivalOutcome[]>(); double[][] inputs = data.GetColumn(2).ToArray(); var regression = new ProportionalHazards(1); var target = new ProportionalHazardsNewtonRaphson(regression); target.Normalize = false; target.Lambda = 0; regression.Coefficients[0] = 0.47983261821350764; double error = target.Run(inputs, time, censor); /* Tested against http://statpages.org/prophaz2.html 13, 8, 0 56, 4, 1 25, 12, 0 64, 6, 0 38, 10, 0 80, 8, 1 0 , 5, 0 81, 5, 0 81, 3, 1 38, 14, 1 23, 8, 0 99, 11, 0 12, 7, 0 38, 7, 0 36, 7, 1 63, 12, 0 92, 8, 0 */ double[] baseline = { regression.Survival(3), // 0.9465 regression.Survival(4), // 0.8919 regression.Survival(7), // 0.8231 regression.Survival(8), // 0.7436 regression.Survival(12), // 0.7436 regression.Survival(14), // 0.0000 }; Assert.AreEqual(0.9465, baseline[0], 1e-4); Assert.AreEqual(0.8919, baseline[1], 1e-4); Assert.AreEqual(0.8231, baseline[2], 1e-4); Assert.AreEqual(0.7436, baseline[3], 1e-4); Assert.AreEqual(0.7436, baseline[4], 1e-4); Assert.AreEqual(0.0000, baseline[5], 1e-4); // The value of the baseline must be exact the same if it was computed // after the Newton-Raphson or in a standalone EmpiricalHazard computation double[] outputs = inputs.Apply(regression.Compute); var empirical = EmpiricalHazardDistribution.Estimate(time, censor, outputs); baseline = new[] { empirical.ComplementaryDistributionFunction(3), // 0.9465 empirical.ComplementaryDistributionFunction(4), // 0.8919 empirical.ComplementaryDistributionFunction(7), // 0.8231 empirical.ComplementaryDistributionFunction(8), // 0.7436 empirical.ComplementaryDistributionFunction(12), // 0.7436 empirical.ComplementaryDistributionFunction(14), // 0.0000 }; Assert.AreEqual(0.9465, baseline[0], 1e-4); Assert.AreEqual(0.8919, baseline[1], 1e-4); Assert.AreEqual(0.8231, baseline[2], 1e-4); Assert.AreEqual(0.7436, baseline[3], 1e-4); Assert.AreEqual(0.7436, baseline[4], 1e-4); Assert.AreEqual(0.0000, baseline[5], 1e-4); }
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).ToArray(); 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(regression.Compute); /* 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); }
public void PredictTest1() { // Data from: http://statpages.org/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 }, }; var regression = new ProportionalHazards(1); double[][] inputs = data.GetColumn(0).ToArray(); double[] time = data.GetColumn(1); int[] censor = data.GetColumn(2).ToInt32(); var target = new ProportionalHazardsNewtonRaphson(regression); double error = target.Run(inputs, time, censor); // Tested against http://statpages.org/prophaz2.html Assert.AreEqual(0.3770, regression.Coefficients[0], 1e-4); Assert.AreEqual(0.2542, regression.StandardErrors[0], 1e-4); Assert.AreEqual(51.18181818181818, regression.Offsets[0]); double mean = regression.Offsets[0]; // Baseline survivor function at predictor means double[] baseline = { regression.Survival(2), regression.Survival(7), regression.Survival(9), regression.Survival(10), }; // Tested against http://statpages.org/prophaz2.html Assert.AreEqual(0.9979, baseline[0], 1e-4); Assert.AreEqual(0.9820, baseline[1], 1e-4); Assert.AreEqual(0.9525, baseline[2], 1e-4); Assert.AreEqual(0.8310, baseline[3], 1e-4); double[] expected = { 0, 2.51908236823927, 0.000203028311170645, 4.67823782106946E-06, 1.07100164957025, 0.118590728553659, 0.000203028311170645, 0.0187294821517496, 1.31028937819308E-05, 0.436716853556834, 5.14665484304978 }; double[] actual = new double[inputs.Length]; for (int i = 0; i < inputs.Length; i++) { double a = actual[i] = regression.Compute(inputs[i], time[i]); double e = expected[i]; Assert.AreEqual(e, a, 1e-3); } // string exStr = actual.ToString(CSharpArrayFormatProvider.InvariantCulture); }
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, 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ProportionalHazards(2); var 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])); }
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 }, }; var regression = new ProportionalHazards(1); double[][] inputs = data.GetColumn(0).ToArray(); double[] time = data.GetColumn(1); int[] output = data.GetColumn(2).ToInt32(); var 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 KaplanMeierTest2() { int[][] data = Groups.Expand( new[] { 1, 2, 3, 4, 5, 6 }, // years new[] { 3, 3, 3, 3, 3, 0 }, // died new[] { 5, 10, 15, 20, 25, 10 } // censored ); double[] time = data.GetColumn(0).ToDouble(); int[] censor = data.GetColumn(1); ProportionalHazards kp; ProportionalHazards km; double errkm, errp; { km = new ProportionalHazards(inputs: 0); var target = new ProportionalHazardsNewtonRaphson(km) { Estimator = HazardEstimator.KaplanMeier }; errkm = target.Run(time, censor); Assert.AreEqual(-63.734599918211551, errkm); } { kp = new ProportionalHazards(inputs: 0); var target = new ProportionalHazardsNewtonRaphson(kp) { Estimator = HazardEstimator.BreslowNelsonAalen }; errp = target.Run(time, censor); Assert.AreEqual(errkm, errp); } }
/// <summary> /// Gets the Log-Likelihood Ratio between two models. /// </summary> /// /// <remarks> /// The Log-Likelihood ratio is defined as 2*(LL - LL0). /// </remarks> /// /// <param name="input">A set of input data.</param> /// <param name="time">The time-to-event before the output occurs.</param> /// <param name="output">The corresponding output data.</param> /// <param name="hazards">Another Cox Proportional Hazards model.</param> /// /// <returns>The Log-Likelihood ratio (a measure of performance /// between two models) calculated over the given data sets.</returns> /// public double GetLogLikelihoodRatio(double[][] input, double[] time, int[] output, ProportionalHazards hazards) { return 2.0 * (this.GetPartialLogLikelihood(input, time, output) - hazards.GetPartialLogLikelihood(input, time, output)); }
public void KaplanMeierTest() { double[,] data = { // time censor { 1, 0 }, // died at time 1 { 2, 1 }, // lost at time 2 { 3, 0 }, // died at time 3 { 5, 0 }, // died at time 5 { 7, 1 }, // lost at time 7 { 11, 0 }, // ... { 4, 0 }, { 6, 0 }, { 8, 0 }, { 9, 1 }, { 10, 1 }, }; double[] time = data.GetColumn(0); int[] censor = data.GetColumn(1).ToInt32(); var regression = new ProportionalHazards(inputs: 0); var target = new ProportionalHazardsNewtonRaphson(regression) { Estimator = HazardEstimator.KaplanMeier }; double error = target.Run(time, censor); Assert.AreEqual(-5.7037824746562009, error); }
/// <summary> /// Creates a new Cox's Proportional Hazards that is a copy of the current instance. /// </summary> /// public object Clone() { var regression = new ProportionalHazards(Coefficients.Length); regression.Coefficients = (double[])this.Coefficients.Clone(); regression.StandardErrors = (double[])this.StandardErrors.Clone(); regression.Offsets = (double[])this.Offsets.Clone(); return regression; }
public void PredictTest1() { // 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[] expected = { 0.000000000000, 0.919466527073, 0.000074105451, 0.000001707560, 0.657371730925, 0.046771996036, 0.000074105451, 0.006836271860, 0.000008042445, 0.339562971888, 2.029832541310 }; double[] actual = new double[inputs.Length]; for (int i = 0; i < inputs.Length; i++) actual[i] = regression.Compute(inputs[i], time[i]); for (int i = 0; i < actual.Length; i++) { Assert.AreEqual(expected[i], actual[i], 1e-6); Assert.IsFalse(Double.IsNaN(actual[i])); } }