biweight_midcorrelation¶
- astropy.stats.biweight.biweight_midcorrelation(x, y, c=9.0, M=None, modify_sample_size=False)[source]¶
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, y1D numpy:array_like
Input arrays for the two variables.
x
andy
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 eachaxis
, if specified). IfM
is an array, then its must be an array containing the location estimate along eachaxis
of the input array. IfNone
(default), then the median of the input array will be used (or along eachaxis
, if specified). Seebiweight_midcovariance
for more details.- modify_sample_sizebool, optional
If
False
(default), then the sample size used is the total number of elements in the array (or along the inputaxis
, if specified), which follows the standard definition of biweight midcovariance. IfTrue
, 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. Seebiweight_midcovariance
for more details.
- Returns:
- biweight_midcorrelation
python:float
The biweight midcorrelation between
x
andy
.
- biweight_midcorrelation
References
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