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- biweight_midcorrelation

# biweight_midcorrelation

astropy.stats.biweight.biweight_midcorrelation(x, y, c=9.0, M=None, modify_sample_size=False)

  Compute the biweight midcorrelation between two variables.

  The biweight midcorrelation is a measure of similarity between samples. It is given by:

  \[r_{bicorr} = \frac{\zeta_{xy}}{\sqrt{\zeta_{xx} \ \zeta_{yy}}}\]

  where \(\zeta_{xx}\) is the biweight midvariance of \(x\), \(\zeta_{yy}\) is the biweight midvariance of \(y\), and \(\zeta_{xy}\) is the biweight midcovariance of \(x\) and \(y\).

  Parameters:
    x, y 1D numpy:array_like

      Input arrays for the two variables. `x` and `y` must be 1D arrays and have the same number of elements.

    c `python:float`, optional

      Tuning constant for the biweight estimator (default = 9.0). See `biweight_midcovariance` for more details.

    M `python:float` or numpy:array_like, optional

      The location estimate. If `M` is a scalar value, then its value will be used for the entire array (or along each `axis`, if specified). If `M` is an array, then its must be an array containing the location estimate along each `axis` of the input array. If `None` (default), then the median of the input array will be used (or along each `axis`, if specified). See `biweight_midcovariance` for more details.

    modify_sample_size bool, optional

      If `False` (default), then the sample size used is the total number of elements in the array (or along the input `axis`, if specified), which follows the standard definition of biweight midcovariance. If `True`, then the sample size is reduced to correct for any rejected values (i.e. the sample size used includes only the non-rejected values), which results in a value closer to the true midcovariance for small sample sizes or for a large number of rejected values. See `biweight_midcovariance` for more details.

  Returns:
    biweight_midcorrelation `python:float`

      The biweight midcorrelation between `x` and `y`.

  See also

  `biweight_scale`, `biweight_midvariance`, `biweight_midcovariance`, `biweight_location`

  References

  [1]

  https://en.wikipedia.org/wiki/Biweight_midcorrelation

  Examples

  Calculate the biweight midcorrelation between two variables:

      >>> import numpy as np
      >>> from astropy.stats import biweight_midcorrelation
      >>> rng = np.random.default_rng(12345)
      >>> x = rng.normal(0, 1, 200)
      >>> y = rng.normal(0, 3, 200)
      >>> # Introduce an obvious outlier
      >>> x[0] = 30.0
      >>> bicorr = biweight_midcorrelation(x, y)
      >>> print(bicorr)
      -0.09203238319481295