public void FromSummary_new_method()
{
#region doc_learn_summary
// Suppose we have a (fictitious) data set about patients who
// underwent cardiac surgery. The first column gives the number
// of arterial bypasses performed during the surgery. The second
// column gives the number of patients whose surgery went well,
// while the third column gives the number of patients who had
// at least one complication during the surgery.
//
int[,] data =
{
// # of stents success complications
{ 1, 140, 45 },
{ 2, 130, 60 },
{ 3, 150, 31 },
{ 4, 96, 65 }
};
// Get input variable and number of positives and negatives
double[][] inputs = data.GetColumn(0).ToDouble().ToJagged();
int[] positive = data.GetColumn(1);
int[] negative = data.GetColumn(2);
// Create a new Logistic Regression Analysis from the summary data
var lra = new LogisticRegressionAnalysis();
// compute the analysis
LogisticRegression regression = lra.Learn(inputs, positive, negative);
// 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 = lra.CoefficientValues;
// The first value refers to the model's intercept term. We
// can also retrieve the odds ratios and standard errors:
double[] odds = lra.OddsRatios;
double[] stde = lra.StandardErrors;
// Finally, we can use it to estimate risk for a new patient
double y = lra.Regression.Score(new double[] { 4 }); // 67.0
#endregion
Assert.AreEqual(3.7586367581050162, odds[0], 1e-8);
Assert.AreEqual(0.85772731075090014, odds[1], 1e-8);
Assert.AreEqual(0.20884336554629004, stde[0], 1e-6);
Assert.AreEqual(0.075837785246620285, stde[1], 1e-6);
Assert.AreEqual(0.67044096045332713, y, 1e-8);
LogisticRegressionAnalysis expected;
{
int[] qtr = data.GetColumn(0);
var expanded = Accord.Statistics.Tools.Expand(qtr, positive, negative);
double[][] inp = expanded.GetColumn(0).ToDouble().ToJagged();
double[] outputs = expanded.GetColumn(1).ToDouble();
expected = new LogisticRegressionAnalysis();
expected.Learn(inp, outputs);
double slope = expected.Coefficients[1].Value; // should return -0.153
double inter = expected.Coefficients[0].Value;
double value = expected.ChiSquare.PValue; // should return 0.042
Assert.AreEqual(-0.15346904821339602, slope, 1e-8);
Assert.AreEqual(1.324056323049271, inter, 1e-8);
Assert.AreEqual(0.042491262992507946, value, 1e-8);
}
var actual = lra;
Assert.AreEqual(expected.Coefficients[0].Value, actual.Coefficients[0].Value, 1e-8);
Assert.AreEqual(expected.Coefficients[1].Value, actual.Coefficients[1].Value, 1e-8);
Assert.AreEqual(expected.ChiSquare.PValue, actual.ChiSquare.PValue, 1e-8);
Assert.AreEqual(expected.WaldTests[0].PValue, actual.WaldTests[0].PValue, 1e-8);
Assert.AreEqual(expected.WaldTests[1].PValue, actual.WaldTests[1].PValue, 1e-8);
Assert.AreEqual(expected.Confidences[0].Max, actual.Confidences[0].Max, 1e-6);
Assert.AreEqual(expected.Confidences[0].Min, actual.Confidences[0].Min, 1e-6);
Assert.AreEqual(expected.Confidences[1].Max, actual.Confidences[1].Max, 1e-6);
Assert.AreEqual(expected.Confidences[1].Min, actual.Confidences[1].Min, 1e-6);
}
