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Module « scipy.stats »

Fonction tsem - module scipy.stats

Signature de la fonction tsem

def tsem(a, limits=None, inclusive=(True, True), axis=0, ddof=1, *, nan_policy='propagate', keepdims=False) 

Description

help(scipy.stats.tsem)

    


Compute the trimmed standard error of the mean.

This function finds the standard error of the mean for given
values, ignoring values outside the given `limits`.

Parameters
----------
a : array_like
    Array of values.
limits : None or (lower limit, upper limit), optional
    Values in the input array less than the lower limit or greater than the
    upper limit will be ignored. When limits is None, then all values are
    used. Either of the limit values in the tuple can also be None
    representing a half-open interval.  The default value is None.
inclusive : (bool, bool), optional
    A tuple consisting of the (lower flag, upper flag).  These flags
    determine whether values exactly equal to the lower or upper limits
    are included.  The default value is (True, True).
axis : int or None, default: 0
    If an int, the axis of the input along which to compute the statistic.
    The statistic of each axis-slice (e.g. row) of the input will appear in a
    corresponding element of the output.
    If ``None``, the input will be raveled before computing the statistic.
ddof : int, optional
    Delta degrees of freedom.  Default is 1.
nan_policy : {'propagate', 'omit', 'raise'}
    Defines how to handle input NaNs.
    
    - ``propagate``: if a NaN is present in the axis slice (e.g. row) along
      which the  statistic is computed, the corresponding entry of the output
      will be NaN.
    - ``omit``: NaNs will be omitted when performing the calculation.
      If insufficient data remains in the axis slice along which the
      statistic is computed, the corresponding entry of the output will be
      NaN.
    - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
keepdims : bool, default: False
    If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the input array.

Returns
-------
tsem : float
    Trimmed standard error of the mean.

Notes
-----
`tsem` uses unbiased sample standard deviation, i.e. it uses a
correction factor ``n / (n - 1)``.

Beginning in SciPy 1.9, ``np.matrix`` inputs (not recommended for new
code) are converted to ``np.ndarray`` before the calculation is performed. In
this case, the output will be a scalar or ``np.ndarray`` of appropriate shape
rather than a 2D ``np.matrix``. Similarly, while masked elements of masked
arrays are ignored, the output will be a scalar or ``np.ndarray`` rather than a
masked array with ``mask=False``.

Examples
--------
>>> import numpy as np
>>> from scipy import stats
>>> x = np.arange(20)
>>> stats.tsem(x)
1.3228756555322954
>>> stats.tsem(x, (3,17))
1.1547005383792515

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