public void WeightedConfusionMatrixConstructorTest()
{
// Sample data from Fleiss, Cohen and Everitt (1968), Large sample standard errors
// of kappa and weighted kappa. Psychological Bulletin, Vol. 72, No. 5, 323-327
double[,] matrix =
{
{ 0.53, 0.05, 0.02 },
{ 0.11, 0.14, 0.05 },
{ 0.01, 0.06, 0.03 },
};
double[,] weights =
{
{ 1.0000, 0.0000, 0.4444 },
{ 0.0000, 1.0000, 0.6667 },
{ 0.4444, 0.6667, 1.0000 },
};
WeightedConfusionMatrix target = new WeightedConfusionMatrix(matrix, weights, samples: 200);
Assert.AreEqual(0.787, target.WeightedOverallAgreement, 1e-3);
Assert.AreEqual(0.567, target.WeightedChanceAgreement, 1e-3);
Assert.AreEqual(0.508, target.WeightedKappa, 1e-3);
Assert.AreEqual(0.00324823, target.WeightedVariance, 1e-5);
Assert.AreEqual(0.004270, target.WeightedVarianceUnderNull, 1e-3);
}
/// <summary>
/// Creates a new Kappa test.
/// </summary>
///
/// <param name="matrix">The contingency table to test.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test. If the
/// hypothesized kappa is left unspecified, a one-tailed test will be used. Otherwise, the
/// default is to use a two-sided test.</param>
/// <param name="hypothesizedWeightedKappa">The hypothesized value for the Kappa statistic. If the test
/// is being used to assert independency between two raters (i.e. testing the null hypothesis
/// that the underlying Kappa is zero), then the <see cref="AsymptoticKappaVariance(GeneralConfusionMatrix)">
/// standard error will be computed with the null hypothesis parameter set to true</see>.</param>
///
public KappaTest(WeightedConfusionMatrix matrix, double hypothesizedWeightedKappa,
OneSampleHypothesis alternate = OneSampleHypothesis.ValueIsDifferentFromHypothesis)
{
if (hypothesizedWeightedKappa == 0)
{
// Use the null hypothesis variance
Compute(matrix.WeightedKappa, hypothesizedWeightedKappa, matrix.WeightedStandardErrorUnderNull, alternate);
Variance = matrix.WeightedVarianceUnderNull;
}
else
{
// Use the default variance
Compute(matrix.WeightedKappa, hypothesizedWeightedKappa, matrix.WeightedStandardError, alternate);
Variance = matrix.WeightedVariance;
}
}
public void WeightedConfusionMatrixConstructorTest2()
{
double[,] matrix =
{
{ 0.53, 0.05, 0.02 },
{ 0.11, 0.14, 0.05 },
{ 0.01, 0.06, 0.03 },
};
double[,] weights =
{
{ 1.00, 0.50, 0.44 },
{ 0.90, 1.00, 0.56 },
{ 0.75, 0.66, 1.00 },
};
WeightedConfusionMatrix target = new WeightedConfusionMatrix(matrix, weights, 200);
Assert.AreEqual(0.4453478, target.WeightedKappa, 1e-5);
Assert.AreEqual(0.005535633, target.WeightedVariance, 1e-5);
}
/// <summary>
/// Computes the asymptotic variance for Fleiss's Kappa variance using the formulae
/// by (Fleiss et al, 1969). If <paramref name="nullHypothesis"/> is set to true, the
/// method will return the variance under the null hypothesis.
/// </summary>
///
/// <param name="matrix">A <see cref="GeneralConfusionMatrix"/> representing the ratings.</param>
/// <param name="stdDev">Kappa's standard deviation.</param>
/// <param name="nullHypothesis">True to compute Kappa's variance when the null hypothesis
/// is true (i.e. that the underlying kappa is zer). False otherwise. Default is false.</param>
///
/// <returns>Kappa's variance.</returns>
///
public static double AsymptoticKappaVariance(WeightedConfusionMatrix matrix, out double stdDev,
bool nullHypothesis = false)
{
double n = matrix.Samples;
double k = matrix.Kappa;
double[,] p = matrix.ProportionMatrix;
double[,] w = matrix.Weights;
double[] pj = matrix.ColumnProportions;
double[] pi = matrix.RowProportions;
double[] wi = matrix.WeightedColumnProportions;
double[] wj = matrix.WeightedRowProportions;
double Po = matrix.WeightedOverallAgreement;
double Pc = matrix.WeightedChanceAgreement;
double variance;
if (!nullHypothesis)
{
// References: Statistical Methods for Rates and Proportions, pg 610.
double a = 0;
for (int i = 0; i < pi.Length; i++)
{
for (int j = 0; j < pj.Length; j++)
{
double t = w[i, j] * (1.0 - Pc) - (wi[i] + wj[j]) * (1.0 - Po);
a += p[i, j] * (t * t);
}
}
double b = (Po * Pc - 2 * Pc + Po) * (Po * Pc - 2 * Pc + Po);
double c = (1.0 - Pc) * (1.0 - Pc);
stdDev = (a - b) / (c * Math.Sqrt(n));
variance = (a - b) / (c * c * n);
}
else
{
double a = 0;
for (int i = 0; i < pi.Length; i++)
{
for (int j = 0; j < pj.Length; j++)
{
double t = w[i, j] - (wj[i] + wi[j]);
a += pj[i] * pi[j] * t * t;
}
}
double b = (Pc * Pc);
double c = (1.0 - Pc);
stdDev = (a - b) / (c * Math.Sqrt(n));
variance = (a - b) / (c * c * n);
}
return(variance);
}
