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Correlation Coefficient (Spearman) (G Dataflow)

Version:
    Last Modified: January 9, 2017

    Computes the Spearman's rank correlation coefficient between two sequences.

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    x

    The first input sequence.

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    y

    The second input sequence.

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    error in

    Error conditions that occur before this node runs. The node responds to this input according to standard error behavior.

    Default: No error

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    correlation coefficient r

    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
    1

    x and y have a complete positive correlation. The data points from x and y lie on a perfectly straight, positively-sloped line.

    -1

    x and y have a complete negative correlation. The data points from x and y lie on a perfectly straight, negatively-sloped line.

    0

    x and y have no correlation.

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    r^2

    Square of the correlation coefficient.

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    error out

    Error information. The node produces this output according to standard error behavior.

    Understanding the Spearman's Rank Correlation Coefficient

    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


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