biweight_location¶
-
astropy.stats.
biweight_location
(a, c=6.0, M=None, axis=None)[source] [edit on github]¶ Compute the biweight location.
The biweight location is a robust statistic for determining the central location of a distribution. It is given by:
C_{bl}= M+\frac{\Sigma_{\|u_i\|<1} (x_i-M)(1-u_i^2)^2} {\Sigma_{\|u_i\|<1} (1-u_i^2)^2}
where M is the sample median (or the input initial guess) and u_i is given by:
u_{i} = \frac{(x_i-M)}{c\ MAD}
where c is the tuning constant and MAD is the median absolute deviation.
For more details, see Beers, Flynn, and Gebhardt (1990); AJ 100, 32.
Parameters: a : array-like
Input array or object that can be converted to an array.
c : float, optional
Tuning constant for the biweight estimator. Default value is 6.0.
M : float or array-like, optional
Initial guess for the biweight location. An array can be input when using the
axis
keyword.axis : int, optional
Axis along which the biweight locations are computed. The default (
None
) is to compute the biweight location of the flattened array.Returns: biweight_location : float or
ndarray
The biweight location of the input data. If
axis
isNone
then a scalar will be returned, otherwise andarray
will be returned.See also
Examples
Generate random variates from a Gaussian distribution and return the biweight location of the distribution:
>>> import numpy as np >>> from astropy.stats import biweight_location >>> rand = np.random.RandomState(12345) >>> from numpy.random import randn >>> loc = biweight_location(rand.randn(1000)) >>> print(loc) -0.0175741540445