private void init(int n1, int n2, double[] ranks, bool?exact)
{
int n = n1 + n2;
this.n1 = n1;
this.n2 = n2;
// From https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test: For large samples, U
// is approximately normally distributed. In that case, the standardized value is given
// by z = (U - mean) / stdDev where:
double mean = (n1 * n2) / 2.0;
double stdDev = Math.Sqrt((n1 * n2 * (n + 1)) / 12.0);
bool hasVectors = ranks != null;
// For small samples (< 30) the distribution can be exact
this.exact = hasVectors && (n1 <= 30 && n2 <= 30);
if (exact.HasValue)
{
if (exact.Value && !hasVectors)
{
throw new ArgumentException("exact", "Cannot use exact method if rank vectors are not specified.");
}
this.exact = exact.Value; // force
}
if (hasVectors)
{
// Apply correction to the variance
double correction = MannWhitneyDistribution.correction(ranks);
stdDev = Math.Sqrt((n1 * n2 * ((n + 1.0) - correction)) / 12.0);
if (this.exact)
{
initExactMethod(ranks);
}
}
this.approximation = new NormalDistribution(mean, stdDev);
}
private void init(int n1, int n2, double[] ranks, bool?exact)
{
if (n1 <= 0)
{
throw new ArgumentOutOfRangeException("n1", "The number of observations in the first sample (n1) must be higher than zero.");
}
if (n2 <= 0)
{
throw new ArgumentOutOfRangeException("n2", "The number of observations in the second sample (n2) must be higher than zero.");
}
if (n1 > n2)
{
Trace.TraceWarning("Creating a MannWhitneyDistribution where the first sample is larger than the second sample. If possible, please re-organize your samples such that the first sample is smaller than the second sample.");
}
if (ranks != null)
{
if (ranks.Length <= 1)
{
throw new ArgumentOutOfRangeException("ranks", "The rank vector must contain a minimum of 2 elements.");
}
for (int i = 0; i < ranks.Length; i++)
{
if (ranks[i] < 0)
{
throw new ArgumentOutOfRangeException("The rank values cannot be negative.");
}
}
}
int n = n1 + n2;
this.n1 = n1;
this.n2 = n2;
// From https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test: For large samples, U
// is approximately normally distributed. In that case, the standardized value is given
// by z = (U - mean) / stdDev where:
double mean = ((double)n1 * n2) / 2.0;
double stdDev = Math.Sqrt(((double)n1 * n2 * (n + 1)) / 12.0);
bool hasVectors = ranks != null;
// For small samples (< 30) the distribution can be exact
this.exact = hasVectors && (n1 <= 30 && n2 <= 30);
if (exact.HasValue)
{
if (exact.Value && !hasVectors)
{
throw new ArgumentException("exact", "Cannot use exact method if rank vectors are not specified.");
}
this.exact = exact.Value; // force
}
if (hasVectors)
{
// Apply correction to the variance
double correction = MannWhitneyDistribution.correction(ranks);
double a = ((double)n1 * n2) / 12.0;
double b = (n + 1.0) - correction;
stdDev = Math.Sqrt(a * b);
if (this.exact)
{
initExactMethod(ranks);
}
}
this.approximation = new NormalDistribution(mean, stdDev);
}
