scipy.stats.mstats.trimmed_mean_ci#
- 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.
- Parameters
- dataarray_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 isn * (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).
- alphafloat, optional
Confidence level of the intervals.
Defaults to 0.05.
- axisint, optional
Axis along which to cut. If None, uses a flattened version of data.
Defaults to None.
- Returns
- trimmed_mean_ci(2,) ndarray
The lower and upper confidence intervals of the trimmed data.