/// <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))); }