scipy.stats.mstats.trimmed_mean_ci(data, limits=(0.2, 0.2), inclusive=(True, True), alpha=0.05, axis=None)[source]

Selected confidence interval of the trimmed mean along the given axis.


data : array_like

Input data.

limits : {None, tuple}, optional

None or a two item tuple. Tuple of the percentages to cut on each side of the array, with respect to the number of unmasked data, as floats between 0. and 1. If n is the number of unmasked data before trimming, then (n * limits[0])th smallest data and (n * limits[1])th largest data are masked. The total number of unmasked data after trimming is n * (1. - sum(limits)). The value of one limit can be set to None to indicate an open interval.

Defaults to (0.2, 0.2).

inclusive : (2,) tuple of boolean, optional

If relative==False, tuple indicating whether values exactly equal to the absolute limits are allowed. If relative==True, tuple indicating whether the number of data being masked on each side should be rounded (True) or truncated (False).

Defaults to (True, True).

alpha : float, optional

Confidence level of the intervals.

Defaults to 0.05.

axis : int, optional

Axis along which to cut. If None, uses a flattened version of data.

Defaults to None.


trimmed_mean_ci : (2,) ndarray

The lower and upper confidence intervals of the trimmed data.