public void ComputeTest4()
{
// 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[][] inputs =
{
// Age Smoking
new double[] { 55, 0 },
new double[] { 28, 0 },
new double[] { 65, 1 },
new double[] { 46, 0 },
new double[] { 86, 1 },
new double[] { 56, 1 },
new double[] { 85, 0 },
new double[] { 33, 0 },
new double[] { 21, 1 },
new double[] { 42, 1 },
};
// Additionally, we also have information about whether
// or not they those patients had lung cancer. The array
// below gives 0 for those who did not, and 1 for those
// who did.
double[] output =
{
0, 0, 0, 1, 1, 1, 0, 0, 0, 1
};
// Create a Logistic Regression analysis
var regression = new LogisticRegressionAnalysis(inputs, output);
regression.Regularization = 0;
regression.Compute(); // compute the analysis.
// Now we can show a summary of the analysis
// Accord.Controls.DataGridBox.Show(regression.Coefficients);
// We can also investigate all parameters individually. For
// example the coefficients values will be available at the
// vector
double[] coef = regression.CoefficientValues;
// The first value refers to the model's intercept term. We
// can also retrieve the odds ratios and standard errors:
double[] odds = regression.OddsRatios;
double[] stde = regression.StandardErrors;
// Finally, we can also use the analysis to classify a new patient
double y = regression.Regression.Compute(new double[] { 87, 1 });
// For those inputs, the answer probability is approximately 75%.
Assert.AreEqual(0.085627701183146374, odds[0], 1e-8);
Assert.AreEqual(1.0208597029292648, odds[1], 1e-8);
Assert.AreEqual(5.8584748981777919, odds[2], 1e-8);
Assert.AreEqual(2.1590686019473897, stde[0], 1e-8);
Assert.AreEqual(0.033790422321041035, stde[1], 1e-8);
Assert.AreEqual(1.4729903935788211, stde[2], 1e-8);
Assert.AreEqual(0.75143272858389798, y, 1e-8);
}
