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Statistics: document NaN behavior in Correlation.Pearson/WeightedPearson/Spearman #1155

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Statistics: document NaN behavior in Correlation.Pearson/WeightedPearson/Spearman #1155

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6 changes: 3 additions & 3 deletions src/Numerics/Statistics/Correlation.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -161,7 +161,7 @@ static double[] AutoCorrelationFft(double[] x, int kLow, int kHigh)
/// </summary>
/// <param name="dataA">Sample data A.</param>
/// <param name="dataB">Sample data B.</param>
/// <returns>The Pearson product-moment correlation coefficient.</returns>
/// <returns>The Pearson product-moment correlation coefficient. Returns NaN if either series has zero variance (e.g. fewer than two elements, or all values equal).</returns>
public static double Pearson(IEnumerable<double> dataA, IEnumerable<double> dataB)
{
int n = 0;
Expand Down Expand Up @@ -217,7 +217,7 @@ public static double Pearson(IEnumerable<double> dataA, IEnumerable<double> data
/// <param name="dataA">Sample data A.</param>
/// <param name="dataB">Sample data B.</param>
/// <param name="weights">Corresponding weights of data.</param>
/// <returns>The Weighted Pearson product-moment correlation coefficient.</returns>
/// <returns>The Weighted Pearson product-moment correlation coefficient. Returns NaN if either series has zero weighted variance.</returns>
public static double WeightedPearson(IEnumerable<double> dataA, IEnumerable<double> dataB, IEnumerable<double> weights)
{
double meanA = 0;
Expand Down Expand Up @@ -310,7 +310,7 @@ public static Matrix<double> PearsonMatrix(IEnumerable<double[]> vectors)
/// </summary>
/// <param name="dataA">Sample data series A.</param>
/// <param name="dataB">Sample data series B.</param>
/// <returns>The Spearman ranked correlation coefficient.</returns>
/// <returns>The Spearman ranked correlation coefficient. Returns NaN if either series has zero variance after ranking (e.g. fewer than two elements, or all values tied).</returns>
public static double Spearman(IEnumerable<double> dataA, IEnumerable<double> dataB)
{
return Pearson(Rank(dataA), Rank(dataB));
Expand Down