GetOddsRatio() public method

Gets the Odds Ratio for a given coefficient.
The odds ratio can be computed raising Euler's number (e ~~ 2.71) to the power of the associated coefficient.
public GetOddsRatio ( int index ) : double
index int /// The coefficient's index. The first value /// (at zero index) is the intercept value. ///
return double
        public void ComputeTest()
        {
            // Suppose we have the following data about some patients.
            // The first variable is continuous and represent patient
            // age. The second variable is dichotomic and give whether
            // they smoke or not (This is completely fictional data).
            double[][] input =
            {
                new double[] { 55, 0 }, // 0 - no cancer
                new double[] { 28, 0 }, // 0
                new double[] { 65, 1 }, // 0
                new double[] { 46, 0 }, // 1 - have cancer
                new double[] { 86, 1 }, // 1
                new double[] { 56, 1 }, // 1
                new double[] { 85, 0 }, // 0
                new double[] { 33, 0 }, // 0
                new double[] { 21, 1 }, // 0
                new double[] { 42, 1 }, // 1
            };

            // We also know if they have had lung cancer or not, and 
            // we would like to know whether smoking has any connection
            // with lung cancer (This is completely fictional data).
            double[] output =
            {
                0, 0, 0, 1, 1, 1, 0, 0, 0, 1
            };


            // To verify this hypothesis, we are going to create a logistic
            // regression model for those two inputs (age and smoking).
            LogisticRegression regression = new LogisticRegression(inputs: 2);

            // Next, we are going to estimate this model. For this, we
            // will use the Iteratively Reweighted Least Squares method.
            var teacher = new IterativeReweightedLeastSquares(regression);

            teacher.Regularization = 0;

            // Now, we will iteratively estimate our model. The Run method returns
            // the maximum relative change in the model parameters and we will use
            // it as the convergence criteria.

            double delta = 0;
            do
            {
                // Perform an iteration
                delta = teacher.Run(input, output);

            } while (delta > 0.001);

            // At this point, we can compute the odds ratio of our variables.
            // In the model, the variable at 0 is always the intercept term, 
            // with the other following in the sequence. Index 1 is the age
            // and index 2 is whether the patient smokes or not.

            // For the age variable, we have that individuals with
            //   higher age have 1.021 greater odds of getting lung
            //   cancer controlling for cigarette smoking.
            double ageOdds = regression.GetOddsRatio(1); // 1.0208597028836701

            // For the smoking/non smoking category variable, however, we
            //   have that individuals who smoke have 5.858 greater odds
            //   of developing lung cancer compared to those who do not 
            //   smoke, controlling for age (remember, this is completely
            //   fictional and for demonstration purposes only).
            double smokeOdds = regression.GetOddsRatio(2); // 5.8584748789881331

            double[] actual = new double[output.Length];
            for (int i = 0; i < input.Length; i++)
                actual[i] = regression.Compute(input[i]);

            double[] expected = 
            {
                0.21044171560168326,
                0.13242527535212373,
                0.65747803433771812,
                0.18122484822324372,
                0.74755661773156912,
                0.61450041841477232,
                0.33116705418194975,
                0.14474110902457912,
                0.43627109657399382,
                0.54419383282533118
            };

            for (int i = 0; i < actual.Length; i++)
                Assert.AreEqual(expected[i], actual[i]);

            Assert.AreEqual(1.0208597028836701, ageOdds, 1e-10);
            Assert.AreEqual(5.8584748789881331, smokeOdds, 1e-8);
            Assert.IsFalse(double.IsNaN(ageOdds));
            Assert.IsFalse(double.IsNaN(smokeOdds));

            Assert.AreEqual(-2.4577464307294092, regression.Intercept, 1e-8);
            Assert.AreEqual(-2.4577464307294092, regression.Coefficients[0], 1e-8);
            Assert.AreEqual(0.020645118265359252, regression.Coefficients[1], 1e-10);
            Assert.AreEqual(1.7678893101571855, regression.Coefficients[2], 1e-8);
        }
Esempio n. 2
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        public void ComputeTest()
        {
            // Suppose we have the following data about some patients.
            // The first variable is continuous and represent patient
            // age. The second variable is dicotomic and give whether
            // they smoke or not (This is completely fictional data).
            double[][] input =
            {
                new double[] { 55, 0 }, // 0 - no cancer
                new double[] { 28, 0 }, // 0
                new double[] { 65, 1 }, // 0
                new double[] { 46, 0 }, // 1 - have cancer
                new double[] { 86, 1 }, // 1
                new double[] { 56, 1 }, // 1
                new double[] { 85, 0 }, // 0
                new double[] { 33, 0 }, // 0
                new double[] { 21, 1 }, // 0
                new double[] { 42, 1 }, // 1
            };

            // We also know if they have had lung cancer or not, and 
            // we would like to know whether smoking has any connection
            // with lung cancer (This is completely fictional data).
            double[] output =
            {
                0, 0, 0, 1, 1, 1, 0, 0, 0, 1
            };


            // To verify this hypothesis, we are going to create a logistic
            // regression model for those two inputs (age and smoking).
            LogisticRegression regression = new LogisticRegression(inputs: 2);

            // Next, we are going to estimate this model. For this, we
            // will use the Iteravely reweighted least squares method.
            var teacher = new IterativeReweightedLeastSquares(regression);

            // Now, we will iteratively estimate our model. The Run method returns
            // the maximum relative change in the model parameters and we will use
            // it as the convergence criteria.

            double delta = 0;
            do
            {
                // Perform an iteration
                delta = teacher.Run(input, output);

            } while (delta > 0.001);

            // At this point, we can compute the odds ratio of our variables.
            // In the model, the variable at 0 is always the intercept term, 
            // with the other following in the sequence. Index 1 is the age
            // and index 2 is whether the patient smokes or not.

            // For the age variable, we have that individuals with
            //   higher age have 1.021 greater odds of getting lung
            //   cancer controlling for cigarrete smoking.
            double ageOdds = regression.GetOddsRatio(1); // 1.0208597028836701

            // For the smoking/non smoking category variable, however, we
            //   have that individuals who smoke have 5.858 greater odds
            //   of developing lung cancer compared to those who do not 
            //   smoke, controlling for age (remember, this is completely
            //   fictional and for demonstration purposes only).
            double smokeOdds = regression.GetOddsRatio(2); // 5.8584748789881331


            Assert.AreEqual(1.0208597028836701, ageOdds);
            Assert.AreEqual(5.8584748789881331, smokeOdds);
        }
        public void ComputeTest3()
        {
            double[][] input =
            {
                new double[] { 55, 0 }, // 0 - no cancer
                new double[] { 28, 0 }, // 0
                new double[] { 65, 1 }, // 0
                new double[] { 46, 0 }, // 1 - have cancer

                new double[] { 86, 1 }, // 1
                new double[] { 86, 1 }, // 1
                new double[] { 56, 1 }, // 1
                new double[] { 85, 0 }, // 0

                new double[] { 33, 0 }, // 0
                new double[] { 21, 1 }, // 0
                new double[] { 42, 1 }, // 1
            };

            double[] output =
            {
                0, 0, 0, 1,
                1, 1, 1, 0, 
                0, 0, 1
            };

            double[] weights =
            {
                1.0, 1.0, 1.0, 1.0, 
                0.5, 0.5, 1.0, 1.0,
                1.0, 1.0, 1.0
            };


            LogisticRegression regression = new LogisticRegression(inputs: 2);

            var teacher = new IterativeReweightedLeastSquares(regression);

            teacher.Regularization = 0;


            double delta = 0;
            do
            {
                delta = teacher.Run(input, output, weights);

            } while (delta > 0.001);


            double ageOdds = regression.GetOddsRatio(1);
            double smokeOdds = regression.GetOddsRatio(2);

            Assert.AreEqual(1.0208597028836701, ageOdds, 1e-10);
            Assert.AreEqual(5.8584748789881331, smokeOdds, 1e-8);
            Assert.IsFalse(double.IsNaN(ageOdds));
            Assert.IsFalse(double.IsNaN(smokeOdds));


            Assert.AreEqual(-2.4577464307294092, regression.Intercept, 1e-8);
            Assert.AreEqual(-2.4577464307294092, regression.Coefficients[0], 1e-8);
            Assert.AreEqual(0.020645118265359252, regression.Coefficients[1], 1e-8);
            Assert.AreEqual(1.7678893101571855, regression.Coefficients[2], 1e-8);
        }