Accord.Tests.Statistics.NewtonRaphsonCoxLearningTest.BaselineHazardTestR C# (CSharp) Method

BaselineHazardTestR() private method

private BaselineHazardTestR ( ) : void
return void
        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);

        }