private void computeInformation()
{
// Store model information
this.results = regression.Compute(inputData);
this.deviance = regression.GetDeviance(inputData, outputData);
this.logLikelihood = regression.GetLogLikelihood(inputData, outputData);
this.chiSquare = regression.ChiSquare(inputData, outputData);
this.coefficients = regression.Coefficients;
this.standardErrors = regression.StandardErrors;
// Store coefficient information
for (int i = 0; i < waldTests.Length; i++)
{
this.waldTests[i] = regression.GetWaldTest(i);
this.confidences[i] = regression.GetConfidenceInterval(i);
this.oddsRatios[i] = regression.GetOddsRatio(i);
}
}
private MultinomialLogisticRegression buildModel()
{
if (independent == null)
{
formatData();
}
mlr = new MultinomialLogisticRegression(nvars, ncat);
LowerBoundNewtonRaphson lbn = new LowerBoundNewtonRaphson(mlr);
do
{
delta = lbn.Run(independent, dependent);
iteration++;
} while (iteration < totit && delta > converg);
coefficients = mlr.Coefficients;
standarderror = new double[ncat - 1][];
waldstat = new double[ncat - 1][];
waldpvalue = new double[ncat - 1][];
for (int i = 0; i < coefficients.Length; i++)
{
double[] steArr = new double[nvars + 1];
double[] waldStatArr = new double[nvars + 1];
double[] waldPvalueArr = new double[nvars + 1];
for (int j = 0; j < nvars + 1; j++)
{
Accord.Statistics.Testing.WaldTest wt = mlr.GetWaldTest(i, j);
steArr[j] = wt.StandardError;
waldStatArr[j] = wt.Statistic;
waldPvalueArr[j] = wt.PValue;
}
waldstat[i] = waldStatArr;
waldpvalue[i] = waldPvalueArr;
standarderror[i] = steArr;
}
loglikelihood = mlr.GetLogLikelihood(independent, dependent);
deviance = mlr.GetDeviance(independent, dependent);
x2 = mlr.ChiSquare(independent, dependent).Statistic;
pv = mlr.ChiSquare(independent, dependent).PValue;
return(mlr);
}
