public void KappaTestConstructorTest1()
{
// Example from Ientilucci, Emmett (2006). "On Using and Computing the Kappa Statistic".
// Available on: http://www.cis.rit.edu/~ejipci/Reports/On_Using_and_Computing_the_Kappa_Statistic.pdf
// This paper uses the delta method approximation
// for computing the kappa variance.
int[,] matrix1 =
{
{ 317, 23, 0, 0 },
{ 61, 120, 0, 0 },
{ 2, 4, 60, 0 },
{ 35, 29, 0, 8 },
};
int[,] matrix2 =
{
{ 377, 79, 0, 0 },
{ 2, 72, 0, 0 },
{ 33, 5, 60, 0 },
{ 3, 20, 0, 8 },
};
GeneralConfusionMatrix a = new GeneralConfusionMatrix(matrix1);
GeneralConfusionMatrix b = new GeneralConfusionMatrix(matrix2);
Assert.AreEqual(0.7663, a.OverallAgreement, 1e-4);
Assert.AreEqual(0.7845, b.OverallAgreement, 1e-4);
Assert.AreEqual(0.4087, a.ChanceAgreement, 1e-4);
Assert.AreEqual(0.47986, b.ChanceAgreement, 1e-4);
double kA = a.Kappa;
double kB = b.Kappa;
double varA = KappaTest.DeltaMethodKappaVariance(a);
double varB = KappaTest.DeltaMethodKappaVariance(b);
TwoMatrixKappaTest target = new TwoMatrixKappaTest(kA, varA, kB, varB);
Assert.AreEqual(TwoSampleHypothesis.ValuesAreDifferent, target.Hypothesis);
Assert.AreEqual(DistributionTail.TwoTail, target.Tail);
// Compare Kappas
Assert.AreEqual(0.605, target.EstimatedValue1, 1e-3);
Assert.IsFalse(double.IsNaN(a.Kappa));
Assert.AreEqual(0.586, target.EstimatedValue2, 1e-3);
Assert.IsFalse(double.IsNaN(b.Kappa));
// Compare variances:
Assert.AreEqual(0.00073735, target.Variance1, 1e-7);
Assert.IsFalse(double.IsNaN(a.Variance));
Assert.AreEqual(0.00087457, target.Variance2, 1e-7);
Assert.IsFalse(double.IsNaN(b.Variance));
Assert.AreEqual(0.475, target.Statistic, 1e-3);
Assert.IsFalse(double.IsNaN(target.Statistic));
Assert.IsFalse(target.Significant);
}
private void buildModel()
{
if(mctable==null)getMatrix();
cofM = new ConfusionMatrix(mctable);
gcofM1 = new GeneralConfusionMatrix(g1table);
gcofM2 = new GeneralConfusionMatrix(g2table);
gcofM3 = new GeneralConfusionMatrix(g3table);
mct = new McNemarTest(cofM);
akt = new TwoMatrixKappaTest(gcofM1, gcofM2);
bkt = new BowkerTest(gcofM3);
bht = new BhapkarTest(gcofM3);
}
public void KappaTestConstructorTest4()
{
{
double k1 = 0.819223955119253;
double v1 = 0.00296025931609249;
double k2 = 0.833170126346748;
double v2 = 0.00278659995785188;
TwoMatrixKappaTest target = new TwoMatrixKappaTest(k1, v1, k2, v2);
Assert.AreEqual(TwoSampleHypothesis.ValuesAreDifferent, target.Hypothesis);
Assert.AreEqual(DistributionTail.TwoTail, target.Tail);
Assert.AreEqual(v1 + v2, target.OverallVariance);
Assert.AreEqual(0.1839669091631167, target.Statistic, 1e-16);
Assert.IsFalse(double.IsNaN(target.Statistic));
Assert.IsFalse(target.Significant);
}
{
double k1 = 0.946859215964404;
double v1 = 0.000111244462937448;
double k2 = 0.98368298182233;
double v2 = 0.0000353910186138505;
TwoMatrixKappaTest target = new TwoMatrixKappaTest(k1, v1, k2, v2);
Assert.AreEqual(TwoSampleHypothesis.ValuesAreDifferent, target.Hypothesis);
Assert.AreEqual(DistributionTail.TwoTail, target.Tail);
Assert.AreEqual(v1 + v2, target.OverallVariance);
Assert.AreEqual(3.0409457018033272, target.Statistic);
Assert.IsTrue(target.Significant);
}
}
public void KappaTestConstructorTest3()
{
double k1 = 0.95;
double v1 = 6.10e-6;
double k2 = 0.9241;
double v2 = 9.02e-6;
TwoMatrixKappaTest target = new TwoMatrixKappaTest(k1, v1, k2, v2);
Assert.AreEqual(TwoSampleHypothesis.ValuesAreDifferent, target.Hypothesis);
Assert.AreEqual(DistributionTail.TwoTail, target.Tail);
Assert.AreEqual(v1 + v2, target.OverallVariance);
Assert.AreEqual(6.6607612733636143, target.Statistic);
Assert.IsTrue(target.Significant);
}
public void KappaTestConstructorTest2()
{
// Example from Congalton
int[,] matrix1 = // pg 108
{
{ 65, 4, 22, 24 },
{ 6, 81, 5, 8 },
{ 0, 11, 85, 19 },
{ 4, 7, 3, 90 },
};
GeneralConfusionMatrix a = new GeneralConfusionMatrix(matrix1);
Assert.AreEqual(115, a.RowTotals[0]);
Assert.AreEqual(100, a.RowTotals[1]);
Assert.AreEqual(115, a.RowTotals[2]);
Assert.AreEqual(104, a.RowTotals[3]);
Assert.AreEqual(75, a.ColumnTotals[0]);
Assert.AreEqual(103, a.ColumnTotals[1]);
Assert.AreEqual(115, a.ColumnTotals[2]);
Assert.AreEqual(141, a.ColumnTotals[3]);
int[,] matrix2 = // pg 109
{
{ 45, 4, 12, 24 },
{ 6, 91, 5, 8 },
{ 0, 8, 55, 9 },
{ 4, 7, 3, 55 },
};
GeneralConfusionMatrix b = new GeneralConfusionMatrix(matrix2);
Assert.AreEqual(85, b.RowTotals[0]);
Assert.AreEqual(110, b.RowTotals[1]);
Assert.AreEqual(72, b.RowTotals[2]);
Assert.AreEqual(69, b.RowTotals[3]);
Assert.AreEqual(55, b.ColumnTotals[0]);
Assert.AreEqual(110, b.ColumnTotals[1]);
Assert.AreEqual(75, b.ColumnTotals[2]);
Assert.AreEqual(96, b.ColumnTotals[3]);
// Check overall accuracy
Assert.AreEqual(0.74, a.OverallAgreement, 0.005);
Assert.AreEqual(0.73, b.OverallAgreement, 0.005);
double kA = a.Kappa;
double kB = b.Kappa;
double varA = KappaTest.DeltaMethodKappaVariance(a);
double varB = KappaTest.DeltaMethodKappaVariance(b);
// Create the test
TwoMatrixKappaTest target = new TwoMatrixKappaTest(kA, varA, kB, varB);
Assert.AreEqual(TwoSampleHypothesis.ValuesAreDifferent, target.Hypothesis);
Assert.AreEqual(DistributionTail.TwoTail, target.Tail);
// Compare Kappas (pg 109)
Assert.AreEqual(0.65, target.EstimatedValue1, 0.05);
Assert.IsFalse(double.IsNaN(a.Kappa));
Assert.AreEqual(0.64, target.EstimatedValue2, 0.05);
Assert.IsFalse(double.IsNaN(b.Kappa));
// Compare variances:
Assert.AreEqual(0.0007778, target.Variance1, 1e-7);
Assert.IsFalse(double.IsNaN(a.Variance));
Assert.AreEqual(0.0010233, target.Variance2, 1e-7);
Assert.IsFalse(double.IsNaN(b.Variance));
Assert.AreEqual(0.3087, target.Statistic, 1e-5);
Assert.IsFalse(double.IsNaN(target.Statistic));
Assert.IsFalse(target.Significant);
}
public void KappaTestConstructorTest2()
{
int[,] matrix1 =
{
{ 317, 23, 0, 0 },
{ 61, 120, 0, 0 },
{ 2, 4, 60, 0 },
{ 35, 29, 0, 8 },
};
int[,] matrix2 =
{
{ 377, 79, 0, 0 },
{ 2, 72, 0, 0 },
{ 33, 5, 60, 0 },
{ 3, 20, 0, 8 },
};
GeneralConfusionMatrix a = new GeneralConfusionMatrix(matrix1);
GeneralConfusionMatrix b = new GeneralConfusionMatrix(matrix2);
TwoMatrixKappaTest target = new TwoMatrixKappaTest(a, b);
Assert.AreEqual(0.605, a.Kappa, 1e-3);
Assert.IsFalse(double.IsNaN(a.Kappa));
Assert.AreEqual(0.00073735, a.Variance, 1e-5);
Assert.IsFalse(double.IsNaN(a.Variance));
Assert.AreEqual(0.586, b.Kappa, 1e-3);
Assert.IsFalse(double.IsNaN(b.Kappa));
Assert.AreEqual(0.00087457, b.Variance, 1e-5);
Assert.IsFalse(double.IsNaN(b.Variance));
Assert.AreEqual(0.475, target.Statistic, 1e-3);
Assert.IsFalse(double.IsNaN(target.Statistic));
Assert.IsFalse(target.Significant);
}
public void KappaTestConstructorTest3()
{
double k1 = 0.95;
double v1 = 6.10e-6;
double k2 = 0.9241;
double v2 = 9.02e-6;
TwoMatrixKappaTest target = new TwoMatrixKappaTest(k1, v1, k2, v2);
Assert.AreEqual(Hypothesis.TwoTail, target.Hypothesis);
Assert.AreEqual(v1 + v2, target.Variance);
Assert.AreEqual(6.6607612733636143, target.Statistic);
Assert.IsTrue(target.Significant);
}