Computes the Spearman's rank correlation coefficient between two sequences.
The first input sequence.
The second input sequence.
Error conditions that occur before this node runs. The node responds to this input according to standard error behavior.
Default: No error
Correlation coefficient between the two input sequences.
Understanding the Values of This Output
correlation coefficient r always is in the interval [-1, 1]. The following table explains the meaning of different correlation coefficient r values:
|correlation coefficient r||Explanation|
x and y have a complete positive correlation. The data points from x and y lie on a perfectly straight, positively-sloped line.
x and y have a complete negative correlation. The data points from x and y lie on a perfectly straight, negatively-sloped line.
x and y have no correlation.
Square of the correlation coefficient.
Error information. The node produces this output according to standard error behavior.
The Spearman's rank correlation coefficient is a non-parametric measure of monotone association between two data sets and is defined as the linear correlation coefficient applied to the rank-transformations of x and y. Use the Spearman's rank correlation coefficient when the distribution of the data makes the Pearson's correlation coefficient undesirable or misleading.
Where This Node Can Run:
Desktop OS: Windows
FPGA: Not supported