private void computeInformation(double[][] inputData, double[] outputData, double[] weights)
{
// Store model information
#pragma warning disable 612, 618
result = regression.Compute(inputData);
#pragma warning restore 612, 618
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.Coefficients.Length; i++)
{
this.standardErrors[i] = regression.StandardErrors[i];
this.waldTests[i] = regression.GetWaldTest(i);
this.coefficients[i] = regression.Coefficients[i];
this.confidences[i] = regression.GetConfidenceInterval(i);
this.oddsRatios[i] = regression.GetOddsRatio(i);
}
}
/// <summary>
/// Computes the Logistic Regression Analysis.
/// </summary>
/// <remarks>The likelihood surface for the
/// logistic regression learning is convex, so there will be only one
/// peak. Any local maxima will be also a global maxima.
/// </remarks>
/// <param name="limit">
/// The difference between two iterations of the regression algorithm
/// when the algorithm should stop. If not specified, the value of
/// 10e-4 will be used. The difference is calculated based on the largest
/// absolute parameter change of the regression.
/// </param>
/// <param name="maxIterations">
/// The maximum number of iterations to be performed by the regression
/// algorithm.
/// </param>
/// <returns>
/// True if the model converged, false otherwise.
/// </returns>
///
public bool Compute(double limit, int maxIterations)
{
double delta;
int iteration = 0;
do // learning iterations until convergence
{
delta = regression.Regress(inputData, outputData);
iteration++;
} while (delta > limit && iteration < maxIterations);
// Check if the full model has converged
bool converged = iteration <= maxIterations;
// Store model information
this.result = regression.Compute(inputData);
this.deviance = regression.GetDeviance(inputData, outputData);
this.logLikelihood = regression.GetLogLikelihood(inputData, outputData);
this.chiSquare = regression.ChiSquare(inputData, outputData);
// Store coefficient information
for (int i = 0; i < regression.Coefficients.Length; i++)
{
this.waldTests[i] = regression.GetWaldTest(i);
this.standardErrors[i] = regression.GetStandardError(i);
this.coefficients[i] = regression.Coefficients[i];
this.confidences[i] = regression.GetConfidenceInterval(i);
this.oddsRatios[i] = regression.GetOddsRatio(i);
}
// Perform likelihood-ratio tests against diminished nested models
for (int i = 0; i < inputCount; i++)
{
// Create a diminished inner model without the current variable
double[][] data = inputData.RemoveColumn(i);
LogisticRegression inner = new LogisticRegression(inputCount - 1);
iteration = 0;
do // learning iterations until convergence
{
delta = inner.Regress(data, outputData);
iteration++;
} while (delta > limit && iteration < maxIterations);
double ratio = 2.0 * (logLikelihood - inner.GetLogLikelihood(data, outputData));
ratioTests[i + 1] = new ChiSquareTest(ratio, 1);
}
// Returns true if the full model has converged, false otherwise.
return(converged);
}
/// <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);
}
private void computeInformation()
{
// Store model information
this.result = regression.Compute(inputData);
this.deviance = regression.GetDeviance(inputData, outputData);
this.logLikelihood = regression.GetLogLikelihood(inputData, outputData);
this.chiSquare = regression.ChiSquare(inputData, outputData);
// Store coefficient information
for (int i = 0; i < regression.Coefficients.Length; i++)
{
this.standardErrors[i] = regression.StandardErrors[i];
this.waldTests[i] = regression.GetWaldTest(i);
this.coefficients[i] = regression.Coefficients[i];
this.confidences[i] = regression.GetConfidenceInterval(i);
this.oddsRatios[i] = regression.GetOddsRatio(i);
}
}
