public void FisherExactTestConstructorTest1()
{
// Example from http://rfd.uoregon.edu/files/rfd/StatisticalResources/lec_05a.txt
ConfusionMatrix matrix = new ConfusionMatrix
(
14, 10,
21, 3
);
{
var target = new FisherExactTest(matrix, OneSampleHypothesis.ValueIsSmallerThanHypothesis);
Assert.AreEqual(OneSampleHypothesis.ValueIsSmallerThanHypothesis, target.Hypothesis);
Assert.AreEqual(DistributionTail.OneLower, target.Tail);
Assert.AreEqual(0.02450, target.PValue, 1e-5);
}
{
var target = new FisherExactTest(matrix, OneSampleHypothesis.ValueIsDifferentFromHypothesis);
Assert.AreEqual(OneSampleHypothesis.ValueIsDifferentFromHypothesis, target.Hypothesis);
Assert.AreEqual(DistributionTail.TwoTail, target.Tail);
Assert.AreEqual(0.04899, target.PValue, 1e-4);
}
{
var target = new FisherExactTest(matrix, OneSampleHypothesis.ValueIsGreaterThanHypothesis);
Assert.AreEqual(OneSampleHypothesis.ValueIsGreaterThanHypothesis, target.Hypothesis);
Assert.AreEqual(DistributionTail.OneUpper, target.Tail);
Assert.AreEqual(0.99607, target.PValue, 1e-4);
}
}
public void FisherExactTestConstructorTest1()
{
// Example from http://rfd.uoregon.edu/files/rfd/StatisticalResources/lec_05a.txt
ConfusionMatrix matrix = new ConfusionMatrix
(
14, 10,
21, 3
);
{
var target = new FisherExactTest(matrix, OneSampleHypothesis.ValueIsSmallerThanHypothesis);
Assert.AreEqual(OneSampleHypothesis.ValueIsSmallerThanHypothesis, target.Hypothesis);
Assert.AreEqual(DistributionTail.OneLower, target.Tail);
Assert.AreEqual(0.02450, target.PValue, 1e-5);
}
{
var target = new FisherExactTest(matrix, OneSampleHypothesis.ValueIsDifferentFromHypothesis);
Assert.AreEqual(OneSampleHypothesis.ValueIsDifferentFromHypothesis, target.Hypothesis);
Assert.AreEqual(DistributionTail.TwoTail, target.Tail);
Assert.AreEqual(0.04899, target.PValue, 1e-4);
}
{
var target = new FisherExactTest(matrix, OneSampleHypothesis.ValueIsGreaterThanHypothesis);
Assert.AreEqual(OneSampleHypothesis.ValueIsGreaterThanHypothesis, target.Hypothesis);
Assert.AreEqual(DistributionTail.OneUpper, target.Tail);
Assert.AreEqual(0.99607, target.PValue, 1e-4);
}
}
protected override void EndProcessing()
{
var hypo = TestingHelper.GetOneSampleHypothesis(Alternate);
var mat = new ConfusionMatrix(_data.ToIntMatrix());
var test = new FisherExactTest(mat, hypo)
{
Size = Size
};
WriteObject(test);
}
/// <summary>
/// Checks if two variables can be eliminated.
/// </summary>
///
public static bool CanEliminate(bool[] actual, bool[] expected, double alpha)
{
var matrix = new ConfusionMatrix(actual, expected);
double maxExpectedFrequency = matrix.ExpectedValues.Max();
IHypothesisTest test;
if (maxExpectedFrequency > 10)
{
test = new ChiSquareTest(matrix, yatesCorrection: false)
{
Size = alpha
}
}
;
else if (maxExpectedFrequency >= 5)
{
test = new ChiSquareTest(matrix, yatesCorrection: true)
{
Size = alpha
}
}
;
else
{
test = new FisherExactTest(matrix)
{
Size = alpha
}
};
return(!test.Significant);
}
public void doc_test()
{
#region doc_test
// Example from https://en.wikipedia.org/wiki/Fisher%27s_exact_test
// This example comes directly from the Wikipedia page referenced above. In this example,
// a sample of teenagers might be divided into male and female on the one hand, and those
// that are and are not currently studying for a statistics exam on the other. We hypothesize,
// for example, that the proportion of studying individuals is higher among the women than
// among the men, and we want to test whether any difference of proportions that we observe
// is significant. The data might look like this:
var matrix = new ConfusionMatrix(new int[, ]
{
/* Men Women */
/* Studying */ { 1, 9 },
/* Not-studying*/ { 11, 3 },
});
// Let's check that the row totals and column
// totals match the ones reported in Wikipedia:
int[] rowTotals = matrix.RowTotals; // should be 10, 14
int[] colTotals = matrix.ColumnTotals; // should be 12, 12
int total = matrix.NumberOfSamples; // should be 24
// The question we ask about these data is: knowing that 10 of these 24 teenagers are studiers,
// and that 12 of the 24 are female, and assuming the null hypothesis that men and women are
// equally likely to study, what is the probability that these 10 studiers would be so unevenly
// distributed between the women and the men? If we were to choose 10 of the teenagers at random,
// what is the probability that 9 or more of them would be among the 12 women, and only 1 or fewer
// from among the 12 men?
var fet = new FisherExactTest(matrix, alternate: OneSampleHypothesis.ValueIsDifferentFromHypothesis);
double p = fet.PValue; // should be approximately 0.0027594561852200832
bool significant = fet.Significant; // should be true
// The same result can be verified in R using:
// fisher.test(matrix(c(1, 9, 11, 3), 2,2))
/*
* Fisher's Exact Test for Count Data
*
* data: matrix(c(1, 9, 11, 3), 2, 2)
* p - value = 0.002759
* alternative hypothesis: true odds ratio is not equal to 1
* 95 percent confidence interval:
* 0.0006438284 0.4258840381
* sample estimates:
* odds ratio
* 0.03723312
*/
#endregion
Assert.AreEqual(1, matrix.Matrix[0, 0]);
Assert.AreEqual(9, matrix.Matrix[0, 1]);
Assert.AreEqual(11, matrix.Matrix[1, 0]);
Assert.AreEqual(3, matrix.Matrix[1, 1]);
Assert.AreEqual(new[] { 10, 14 }, rowTotals);
Assert.AreEqual(new[] { 12, 12 }, colTotals);
Assert.AreEqual(24, total);
Assert.AreEqual(0.0027594561852200832, p);
Assert.IsTrue(significant);
}
/// <summary>
/// Checks if two variables can be eliminated.
/// </summary>
///
public static bool CanEliminate(bool[] actual, bool[] expected, double alpha)
{
var matrix = new ConfusionMatrix(actual, expected);
double maxExpectedFrequency = matrix.ExpectedValues.Max();
IHypothesisTest test;
if (maxExpectedFrequency > 10)
test = new ChiSquareTest(matrix, yatesCorrection: false) { Size = alpha };
else if (maxExpectedFrequency >= 5)
test = new ChiSquareTest(matrix, yatesCorrection: true) { Size = alpha };
else
test = new FisherExactTest(matrix) { Size = alpha };
return !test.Significant;
}
