| 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);
}
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);
}