scipy.stats.mstats.trimmed_stde#
- scipy.stats.mstats.trimmed_stde(a, limits=(0.1, 0.1), inclusive=(1, 1), axis=None)[source]#
Returns the standard error of the trimmed mean along the given axis.
- Parameters
- asequence
Input array
- limits{(0.1,0.1), tuple of float}, optional
tuple (lower percentage, upper percentage) to cut on each side of the array, with respect to the number of unmasked data.
If n is the number of unmasked data before trimming, the values smaller than
n * limits[0]
and the values larger thann * `limits[1]
are masked, and the total number of unmasked data after trimming isn * (1.-sum(limits))
. In each case, the value of one limit can be set to None to indicate an open interval. If limits is None, no trimming is performed.- inclusive{(bool, bool) tuple} optional
Tuple indicating whether the number of data being masked on each side should be rounded (True) or truncated (False).
- axisint, optional
Axis along which to trim.
- Returns
- trimmed_stdescalar or ndarray