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
- result
TheilslopesResult
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.
- result
See also
siegelslopes
a similar technique using repeated medians
Notes
For more details on
theilslopes
, seescipy.stats.theilslopes
.