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). - theilslopesimplements 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’. - Added in version 1.8.0. 
 
- Returns:
- resultTheilslopesResultinstance
- 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, see- scipy.stats.theilslopes.