scipy.stats.mstats.

theilslopes#

scipy.stats.mstats.theilslopes(y, x=None, alpha=0.95, method='separate')[source]#

Computes the Theil-Sen estimator for a set of points (x, y).

theilslopes implements a method for robust linear regression. It computes the slope as the median of all slopes between paired values.

Deprecated since version 2.0.0: scipy.stats.mstats.theilslopes is deprecated as of SciPy 2.0.0 and will be removed, along with the scipy.stats.mstats namespace, in SciPy 2.4.0. For similar functionality, use scipy.stats.theilslopes with MArray(s) instead of NumPy masked array(s).

Parameters:

yarray_like

Dependent variable.

xarray_like or None, optional

Independent variable. If None, use arange(len(y)) instead.

alphafloat, optional

Confidence degree between 0 and 1. Default is 95% confidence. Note that alpha is symmetric around 0.5, i.e. both 0.1 and 0.9 are interpreted as “find the 90% confidence interval”.

method{‘joint’, ‘separate’}, optional

Method to be used for computing estimate for intercept. Following methods are supported,

‘joint’: Uses np.median(y - slope * x) as intercept.
‘separate’: Uses np.median(y) - slope * np.median(x)
as intercept.

The default is ‘separate’.

Added in version 1.8.0.

Returns:

resultTheilslopesResult instance

The return value is an object with the following attributes:

slopefloat: Theil slope.
interceptfloat: Intercept of the Theil line.
low_slopefloat: Lower bound of the confidence interval on slope.
high_slopefloat: Upper bound of the confidence interval on slope.