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.

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’.

New 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.