eelbrain.test.MannWhitneyU¶

class eelbrain.test.MannWhitneyU(y: Union[eelbrain._data_obj.Var, str], x: Union[eelbrain._data_obj.Factor, eelbrain._data_obj.Interaction, eelbrain._data_obj.NestedEffect, str, eelbrain._data_obj.Var], c1: Union[str, Tuple[str, ...]] = None, c0: Union[str, Tuple[str, ...]] = None, match: Union[eelbrain._data_obj.Factor, eelbrain._data_obj.Interaction, eelbrain._data_obj.NestedEffect, str] = None, sub: Union[eelbrain._data_obj.Var, numpy.ndarray, str] = None, ds: eelbrain._data_obj.Dataset = None, tail: int = 0, continuity: bool = True)¶

Mann-Whitney U-test (non-parametric independent measures test)

The test data can be specified in two forms:

In long form, with y supplying the data, x specifying condition for each case.

With y and x supplying data for the two conditions.

Parameters:

y : Var: Dependent variable. Alternatively, the first of two variables that are compared.
x : categorial: Model containing the cells which should be compared. Alternatively, the second of two varaibles that are compared.
c1 : str | tuple | None: Test condition (cell of x). c1 and c0 can be omitted if x only contains two cells, in which case cells will be used in alphabetical order.
c0 : str | tuple | None: Control condition (cell of x).
match : categorial: Units within which measurements are related (e.g. ‘subject’ in a within-subject comparison). If match is unspecified, it is assumed that y and x are two measurements with matched cases.
sub : None | index-array: Perform the test with a subset of the data.
ds : None | Dataset: If a Dataset is specified, all data-objects can be specified as names of Dataset variables.
tail : 0 | 1 | -1: Which tail of the t-distribution to consider: 0: both (two-tailed, default); 1: upper tail (one-tailed); -1: lower tail (one-tailed).
continuity : bool: Continuity correction (default True).

eelbrain.test.MannWhitneyU¶

Methods¶

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