/// <summary>
/// Creates a new MultinomialLogisticRegression that is a copy of the current instance.
/// </summary>
///
public object Clone()
{
var mlr = new MultinomialLogisticRegression(Inputs, Categories);
for (int i = 0; i < coefficients.Length; i++)
{
for (int j = 0; j < coefficients[i].Length; j++)
{
mlr.coefficients[i][j] = coefficients[i][j];
mlr.standardErrors[i][j] = standardErrors[i][j];
}
}
return(mlr);
}
/// <summary>
/// The likelihood ratio test of the overall model, also called the model chi-square test.
/// </summary>
///
/// <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[] sums = output.Sum();
double[] intercept = new double[Categories - 1];
for (int i = 0; i < intercept.Length; i++)
{
intercept[i] = Math.Log(sums[i + 1] / sums[0]);
}
var regression = new MultinomialLogisticRegression(Inputs, Categories, intercept);
double ratio = GetLogLikelihoodRatio(input, output, regression);
return(new ChiSquareTest(ratio, (Inputs) * (Categories - 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="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, MultinomialLogisticRegression regression)
{
return(2.0 * (this.GetLogLikelihood(input, output) - regression.GetLogLikelihood(input, output)));
}
