/// <summary>
/// Creates a new GeneralizedLinearRegression that is a copy of the current instance.
/// </summary>
///
public object Clone()
{
ILinkFunction function = (ILinkFunction)linkFunction.Clone();
var regression = new GeneralizedLinearRegression(function, coefficients.Length);
regression.coefficients = (double[])this.coefficients.Clone();
regression.standardErrors = (double[])this.standardErrors.Clone();
return(regression);
}
/// <summary>
/// The likelihood ratio test of the overall model, also called the model chi-square test.
/// </summary>
///
/// <param name="input">A set of input data.</param>
/// <param name="output">A set of output data.</param>
///
/// <remarks>
/// <para>
/// The Chi-square test, also called the likelihood ratio test or the log-likelihood test
/// is based on the deviance of the model (-2*log-likelihood). The log-likelihood ratio test
/// indicates whether there is evidence of the need to move from a simpler model to a more
/// complicated one (where the simpler model is nested within the complicated one).</para>
/// <para>
/// The difference between the log-likelihood ratios for the researcher's model and a
/// simpler model is often called the "model chi-square".</para>
/// </remarks>
///
public ChiSquareTest ChiSquare(double[][] input, double[] output)
{
double y0 = 0;
double y1 = 0;
for (int i = 0; i < output.Length; i++)
{
y0 += 1.0 - output[i];
y1 += output[i];
}
var intercept = Math.Log(y1 / y0);
var regression = new GeneralizedLinearRegression(linkFunction, Inputs, intercept);
double ratio = GetLogLikelihoodRatio(input, output, regression);
return(new ChiSquareTest(ratio, coefficients.Length - 1));
}
/// <summary>
/// Gets the Log-Likelihood Ratio between two models.
/// </summary>
///
/// <remarks>
/// The Log-Likelihood ratio is defined as 2*(LL - LL0).
/// </remarks>
///
/// <param name="input">A set of input data.</param>
/// <param name="output">A set of output data.</param>
/// <param name="weights">The weights associated with each input vector.</param>
/// <param name="regression">Another Logistic Regression model.</param>
///
/// <returns>The Log-Likelihood ratio (a measure of performance
/// between two models) calculated over the given data sets.</returns>
///
public double GetLogLikelihoodRatio(double[][] input, double[] output, double[] weights, GeneralizedLinearRegression regression)
{
return(2.0 * (this.GetLogLikelihood(input, output, weights) - regression.GetLogLikelihood(input, output, weights)));
}