Calculates a Spearman rank-order correlation coefficient and the
p-value to test for non-correlation. The Spearman correlation is
a nonparametric measure of the linear relationship between two
datasets. Unlike the Pearson correlation, the Spearman
correlation does not assume that both datasets are normally
distributed. Like other correlation coefficients, this one
varies between -1 and +1 with 0 implying no correlation.
Correlations of -1 or +1 imply an exact linear relationship.
Positive correlations imply that as x increases, so does y.
Negative correlations imply that as x increases, y decreases.
Missing values are discarded pair-wise: if a value is missing in
x, the corresponding value in y is masked. The p-value roughly
indicates the probability of an uncorrelated system producing
datasets that have a Spearman correlation at least as extreme as
the one computed from these datasets. The p-values are not
entirely reliable but are probably reasonable for datasets
larger than 500 or so. x : 1D array (Spearma