private void computeInformation(double[][] inputData, double[] outputData, double[] weights)
{
// Store model information
#pragma warning disable 612, 618
this.result = regression.Compute(inputData);
#pragma warning restore 612, 618
this.NumberOfSamples = inputData.Length;
if (weights == null)
{
this.deviance = regression.GetDeviance(inputData, outputData);
this.logLikelihood = regression.GetLogLikelihood(inputData, outputData);
this.chiSquare = regression.ChiSquare(inputData, outputData);
}
else
{
this.deviance = regression.GetDeviance(inputData, outputData, weights);
this.logLikelihood = regression.GetLogLikelihood(inputData, outputData, weights);
this.chiSquare = regression.ChiSquare(inputData, outputData, weights);
}
// Store coefficient information
for (int i = 0; i < regression.NumberOfParameters; i++)
{
this.standardErrors[i] = regression.StandardErrors[i];
this.waldTests[i] = regression.GetWaldTest(i);
this.coefficients[i] = regression.GetCoefficient(i);
this.confidences[i] = regression.GetConfidenceInterval(i);
this.oddsRatios[i] = regression.GetOddsRatio(i);
}
}
/// <summary>
/// Constructs a new Logistic regression model.
/// </summary>
///
internal StepwiseLogisticRegressionModel(StepwiseLogisticRegressionAnalysis analysis, LogisticRegression regression,
int[] variables, ChiSquareTest chiSquare, ChiSquareTest[] tests)
{
this.Analysis = analysis;
this.Regression = regression;
int coefficientCount = regression.NumberOfInputs + 1;
this.Inputs = analysis.Inputs.Get(variables);
this.ChiSquare = chiSquare;
this.LikelihoodRatioTests = tests;
this.Variables = variables;
this.StandardErrors = new double[coefficientCount];
this.WaldTests = new WaldTest[coefficientCount];
this.CoefficientValues = new double[coefficientCount];
this.Confidences = new DoubleRange[coefficientCount];
this.OddsRatios = new double[coefficientCount];
// Store coefficient information
for (int i = 0; i < regression.NumberOfInputs + 1; i++)
{
this.StandardErrors[i] = regression.StandardErrors[i];
this.WaldTests[i] = regression.GetWaldTest(i);
this.CoefficientValues[i] = regression.GetCoefficient(i);
this.Confidences[i] = regression.GetConfidenceInterval(i);
this.OddsRatios[i] = regression.GetOddsRatio(i);
}
StringBuilder sb = new StringBuilder();
for (int i = 0; i < Inputs.Length; i++)
{
sb.Append(Inputs[i]);
if (i < Inputs.Length - 1)
{
sb.Append(", ");
}
}
this.Names = sb.ToString();
var logCoefs = new List <NestedLogisticCoefficient>(coefficientCount);
for (int i = 0; i < coefficientCount; i++)
{
logCoefs.Add(new NestedLogisticCoefficient(this, i));
}
this.Coefficients = new NestedLogisticCoefficientCollection(logCoefs);
}